Methodology for systematic literature review applied to engineering and education from Kitchenhan and Bacca

P. V. Torres-Carrión, C. S. González-González, S. Aciar and G. Rodríguez-Morales, "Methodology for systematic literature review applied to engineering and education," 2018 IEEE Global Engineering Education Conference (EDUCON), Santa Cruz de Tenerife, Spain, 2018, pp. 1364-1373, doi: 10.1109/EDUCON.2018.8363388.Volume 104,
2019,
Pages 333-339,
ISSN 0148-2963,
https://doi.org/10.1016/j.jbusres.2019.07.039.

("traffic signal control" OR "smart traffic light" OR "traffic light control") AND ("vehicle counting" OR "vehicle detection" OR sensors) AND ("image recognition system" OR "image processing" OR software OR algorithms)

Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment

Noh, Byeongjoon (57195287901); Lin, Tengfeng (57423452100); Lee, Sungju (57194190442); Jeong, Taikyeong (16312519300)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170342650&doi=10.3390%2fs23177504&partnerID=40&md5=921345a1bc1e94e1016331f5d222b262

This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances. This research contributes to the field of intelligent transportation by offering a practical and efficient approach to 3D bounding box estimation using monovision sensors. We validated feasibility and applicability are through real-world road images captured by CCTV cameras. © 2023 by the authors.

cuboid detection; deep learning; object detection; road geometry; road vehicle detection

Cost effectiveness; Deep learning; Geometry; Object recognition; Roads and streets; Vehicles; Cuboid detection; Deep learning; Flow vectors; Monovision; Objects detection; Road geometry; Road vehicle detection; Road vehicles; Sensor geometries; Vehicles detection; Object detection

2-s2.0-85170342650

A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting

Weng, Wenchao (58245787900); Fan, Jin (56542158600); Wu, Huifeng (55703741200); Hu, Yujie (58246493100); Tian, Hao (58245556700); Zhu, Fu (58246030000); Wu, Jia (23971568900)

58245787900; 56542158600; 55703741200; 58246493100; 58245556700; 58246030000; 23971568900

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159053072&doi=10.1016%2fj.patcog.2023.109670&partnerID=40&md5=d543abb8eaaf8c85fe0b4f6ca2c5a727

Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate predictions of traffic flow within a road network. Traffic signals used for forecasting are usually generated by sensors along roads, which can be represented as nodes on a graph. These sensors typically produce normal signals representing normal traffic flows and abnormal signals indicating unknown traffic disruptions. Graph convolution networks are widely used for traffic prediction due to their ability to capture correlations between network nodes. However, existing approaches use a predefined or adaptive adjacency matrix that does not accurately reflect real-world relationships between signals. To address this issue, we propose a decomposition dynamic graph convolutional recurrent network (DDGCRN) for traffic forecasting. DDGCRN combines a dynamic graph convolution recurrent network with an RNN-based model that generates dynamic graphs based on time-varying traffic signals, allowing for the extraction of both spatial and temporal features. Additionally, DDGCRN separates abnormal signals from normal traffic signals and models them using a data-driven approach to further improve predictions. Results from our analysis of six real-world datasets demonstrate the superiority of DDGCRN compared to the current state-of-the-art. The source codes are available at: https://github.com/wengwenchao123/DDGCRN. © 2023

Dynamic graph generation; Graph convolution network; Residual decomposition; Segmented learning; Traffic forecasting

Forecasting; Graph theory; Street traffic control; Traffic signals; Decomposition dynamic; Dynamic graph; Dynamic graph generation; Graph convolution network; Graph generation; Recurrent networks; Residual decomposition; Segmented learning; Traffic flow; Traffic Forecasting; Convolution

2-s2.0-85159053072

Research on the weaving area capacity of freeways under man–machine mixed traffic flow

Li, Xia (57206741849); Liu, Ziyi (58507427700); Li, Mingye (58506761100); Liu, Yimei (58506976300); Wang, Chunyang (58607518200); Ma, Xinwei (57194044466); Liang, Yaxin (58508316300)

57206741849; 58507427700; 58506761100; 58506976300; 58607518200; 57194044466; 58508316300

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165884140&doi=10.1016%2fj.physa.2023.129040&partnerID=40&md5=90c456dfd9f91a3741af2f13626fbb92

With the maturity and development of autonomous driving technology, mixed autonomous and manual vehicle traffic will become the main form of traffic flow in the future. First, conservative and radical lane change models of manual and autonomous vehicles in the weaving area are constructed to further study the freeway traffic capacity. Additionally, this study uses US-101 and Jinbao interchange trajectory data to fit the conservative-radical model switching point distribution function for manual vehicles. Moreover, simulation experiments determine the optimal switching point position of an autonomous vehicle lane-changing model to optimize traffic flow in the weaving area. Based on gap acceptance theory, the method considers the distribution of model switching points, estimates the ideal safe gap for each type of lane change, calculates the occurrence probability of different lane change types of vehicles, and determines the expected number of lane-changing vehicles in each weaving lane. Then, a capacity model of the weaving area considering man–machine mixed traffic flow is constructed using linear programming. Then, this study uses MATLAB to determine the optimal value of the linear programming model and compares the SUMO simulation results with the theoretical analysis results. The results show that when the autonomous vehicle penetration rate is 20%, 50% and 80%, the deviation rates of the optimal solution of the theoretical model and the simulation results are 4.7%, 9.1%, and 9.2%, respectively, verifying the reliability of the traffic capacity model. The proposed traffic capacity model of the weaving area under mixed traffic flow can provide technical support for the planning and design of the weaving area and improving its traffic operation state. © 2023 Elsevier B.V.

Capacity model; Man–machine mixed traffic flow; Model switching point; Simulation of urban mobility (SUMO); Weaving area

Distribution functions; Linear programming; MATLAB; Optimal systems; Capacity modeling; Man machines; Man–machine mixed traffic flow; Mixed traffic flow; Model switching; Model switching point; Simulation of urban mobility; Switching points; Urban mobility; Weaving area; Autonomous vehicles

2-s2.0-85165884140

Dutta, Pratik (57655322200); Khatua, Sunirmal (36675235100); Choudhury, Sankhayan (24479431700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171337711&doi=10.1016%2fj.vehcom.2023.100666&partnerID=40&md5=24467c63c9dd458ca8826f9d1f7eb507

Efficient and dynamic traffic management is an integral part of any smart city. Finding the shortest route considering congestion in real-time and managing traffic signals determine the efficiency of overall traffic flow. Route planning within a dynamic traffic environment remains an open challenge due to some essential unaddressed issues. Specially, considering priority vehicles to provide better traffic flow. For example, the routes of the vehicles need to be derived in such a way that higher priority vehicles like ambulances, police cars, etc. are provided efficient routes and green signals as per their given priority. To establish the solution we need to consider two factors. Vehicle rerouting to obtain shorter path and traffic signal optimization for lesser waiting time by a vehicle on a traffic signal. Existing works don't consider both factors in their work and tried to solve the issue reactively; upon request for clearance by a vehicle on a particular traffic signal. The proposed work seems to be a proactive approach that considers both factors. In this work, a dynamic real-time traffic environment with prioritized vehicles is modeled as a bi-level optimization problem. The solution ensures that the routes are offered to the vehicles in a way such that the unit travel time of a vehicle with higher priority must be less compared to vehicles with lesser priority. In addition, the traffic signal settings are optimized in a coordinated way to achieve the above-mentioned goal. Rigorous experimentation is done for validation of the proposed solution. Several traffic situations consisting of inherent dynamism are simulated through SUMO and the experimental findings substantiate our claims in terms of various traffic metrics. © 2023 Elsevier Inc.

ITS; Optimizing traffic signal settings; Prioritized vehicle; Traffic signal settings; VANET

2-s2.0-85171337711

Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function

Song, Hao-Ming (57580167200); Xing, Cheng (57214993841); Wang, Jie-Sheng (56575668800); Wang, Yu-Cai (58195912600); Liu, Yu (57710486300); Zhu, Jun-Hua (57828586800); Hou, Jia-Ning (57546267600)

57580167200; 57214993841; 56575668800; 58195912600; 57710486300; 57828586800; 57546267600

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153535568&doi=10.1007%2fs00500-023-08205-w&partnerID=40&md5=1893d8a4c52eb03c230cf76d4f43cb0c

Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Chaotic mapping; Elementary mathematical function; Engineering optimization; Function optimization; Pelican optimization algorithm

Benchmarking; Optimization; Chaotic mapping; Chaotics; Elementary mathematical function; Engineering optimization; Function Optimization; Interference factor; Mathematical functions; Optimization algorithms; Pelican optimization algorithm; Performance; Heuristic algorithms

2-s2.0-85153535568

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169101080&doi=10.1007%2fs42979-023-02013-7&partnerID=40&md5=1cc138de579135e1af8d5e12ee3d36aa

The exponential growth of the metropolitan cities of the country has generated and magnified urban sprawl into the problematic proportions. Lack of the efficient traffic control and management has many a times lead to loss of lives due to ambulances getting stuck in traffic jams. To overcome this problem, network routing model is required with new GPS technology along with optimized path. To improvise it, the need of developing new VANET model with IoT-based Traffic Signal control system is required. In this paper, we discuss on the reliability issues and pitfalls of the existing methods and used ARDUINO setup for experiments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

2-s2.0-85169101080

Shi, Xiaohui (57205248322); Ma, Shengli (56043015700); Shen, Yang (58595846100); Yang, Yankun (58191485300); Tan, Zexin (57217236028)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153103821&doi=10.1186%2fs13640-023-00604-1&partnerID=40&md5=fe319e622341278b9db8270e6cac10a6

Vehicle Logo Detection (VLD) is of great significance to Intelligent Transportation Systems (ITS). Although many methods have been proposed for VLD, it remains a challenging problem. To improve the VLD accuracy, an Intersection over Average (IoAverage) loss is proposed for enhancing the bounding box regression. The IoAverage loss accelerates the convergence of bounding box regression than using the Intersection over Union (IoU) loss. In the experiments, IoAverage loss has been incorporated into the state-of-the-art object detection framework YOLOV5s, namely YOLOV5s-IoAv in this paper. The advantages of the IoAverage loss are verified on the PASCAL VOC2007 datasets. The results of using the IoAverage loss show performance gains of + 15.27% mAP0.5 and + 30.87% mAP0.5:0.95 higher than that of the Complete IoU (CIoU) loss. The application of YOLOV5s-IoAv is implemented to VLD on dataset VLD100K-61. VLD100K-61 is a self-collected dataset containing 100,041 images supplied by traffic surveillance cameras in the real world from 61 categories. YOLOV5s-IoAv achieves performance gains as + 15.27% mAP0.5:0.95 for VLD than YOLOV5s-CIoU. The proposed method yields the mAP0.5 value of up to 0.992 on the dataset VLD100K-61, providing a promising solution to vehicle logo recognition applications. © 2023, The Author(s).

Intelligent systems; Intelligent vehicle highway systems; Object detection; Security systems; Average loss; Bounding-box; Detection accuracy; Intelligent transportation systems; Intersection over average loss; Logo detections; Performance Gain; Vehicle logo detection; VLD100K-61 dataset; YOLOV5-ioav; Vehicles

2-s2.0-85153103821

CAV-enabled data analytics for enhancing adaptive signal control safety environment

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170636716&doi=10.1016%2fj.aap.2023.107290&partnerID=40&md5=658ec3905b2fe4e037c02e2f6cf3cfab

Given the connected and autonomous vehicle (CAV) generated trajectories as a “floating sensor” data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision. © 2023 Elsevier Ltd

Data Analytics; Data fusion; Safety engineering; Street traffic control; Traffic signals; Adaptive signal control; Adaptive traffic signal control; Assessment models; Autonomous Vehicles; Collision risks; Connected and autonomous vehicle-generated data; Fusion model; Mobility datum; Safety assessments; Surrogate safety assessment model; article; autonomous vehicle; Ohio; probability; proof of concept; risk assessment; velocity; Risk assessment

2-s2.0-85170636716

Traffic flow modelling for uphill and downhill highways: Analysed by soft computing-based approach

Khan, Muhammad Fawad (57321283200); Alshammari, Fahad Sameer (56426929700); Laouini, Ghaylen (57217147812); Khalid, Majdi (57192190458)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169289711&doi=10.1016%2fj.compeleceng.2023.108922&partnerID=40&md5=3aad3bc4a038e463da4e1e1d511cb870

Researchers have made significant strides in understanding car-following behaviour and traffic flow, especially with the advent of intelligent and networked technologies. Diverse mathematical models analyse traffic flow, each with pros and cons. This study focuses on a sensitivity-based mathematical model for uphill and downhill highways, examining position, velocity, and acceleration profiles to predict traffic jam occurrence. The model employs an ordinary differential equation and a machine learning-based approach (machine learning procedure neural network) for numerical solutions, exhibiting high accuracy (10−8−10−10) compared to the reference Runge–Kutta method For accuracy, reliability and stability of the results are evaluated by various performance indicators and statistical terms. For multiple independent executions, mean absolute deviation, root mean square error and error in Nash–Sutcliffe efficiency are calculated. Their values are lies in range 10−8−10−14. Moreover, graphical analysis is established for better visualization of traffic flow and congestion. © 2023 Elsevier Ltd

Artificial intelligence; Downhill highway; Neural network; Soft-computing; Traffic congestion; Traffic control; Transportation; Uphill highway

Learning algorithms; Machine learning; Mean square error; Motor transportation; Numerical methods; Ordinary differential equations; Runge Kutta methods; Soft computing; Street traffic control; Car-following behavior; Downhill highway; Intelligent technology; Machine-learning; Networked technologies; Neural-networks; Soft-Computing; Traffic flow; Traffic flow modelling; Uphill highway; Traffic congestion

2-s2.0-85169289711

Deep reinforcement learning with domain randomization for overhead crane control with payload mass variations

Zhang, Jianfeng (57843958700); Zhao, Chunhui (56525667900); Ding, Jinliang (57798290400)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171439108&doi=10.1016%2fj.conengprac.2023.105689&partnerID=40&md5=7725ccac079c4ff310c32698fe38b145

Overhead cranes, as an important tool for loading and transporting, play an important role in modern industry. A key challenge in overhead crane control is payload mass variation: a policy learned to solve the overhead crane control in the fixed payload scenario often fails to solve the control task in the payload variation scenario. Therefore, from a practical perspective, this paper designs a novel deep reinforcement learning (DRL) control algorithm, domain randomization memory-augmented Beta proximal policy optimization (DR-MABPPO), which leverages the memory-augmented policy and incorporates the domain randomization (DR) training strategy to address the control problem of the overhead crane with payload masses variations. With the help of the DR training strategy and the memory-augmented policy, DR-MABPPO can learn a universal policy that is robust to the wide range of payload mass variations. As far as we know, this is the first time that the DRL technique is applied to solve the overhead crane control with payload mass variations. Simulation studies are conducted to demonstrate the effectiveness of the proposed method in the presence of payload mass variations, exhibiting satisfactory control performance when compared to PID and LQR. © 2023 Elsevier Ltd

Deep reinforcement learning; Domain randomization; Memory-augmented policy; Overhead cranes; Payload mass variations

Deep learning; Gantry cranes; Random processes; Crane control; Deep reinforcement learning; Domain randomization; Mass variations; Memory-augmented policy; Overhead crane; Payload mass; Payload mass variation; Randomisation; Reinforcement learnings; Reinforcement learning

2-s2.0-85171439108

Event-triggered secure control of nonlinear multi-agent systems under sensor attacks

Hu, Xiaoyan (57220960301); Li, Yuan-Xin (56530886200); Tong, Shaocheng (57218665805)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165225650&doi=10.1016%2fj.jfranklin.2023.06.043&partnerID=40&md5=809da13defb466f13ab546fd86867ef5

In this paper, a security consistent tracking control scheme with event-triggered strategy and sensor attacks is developed for a class of nonlinear multi-agent systems. For the sensor attacks on the system, a security measurement preselector and a state observer are introduced to combat the impact of the attacks and achieve secure state estimation. In addition, command filtering technology is introduced to overcome the “complexity explosion” caused by the use of the backstepping approach. Subsequently, a new dynamic event-triggered strategy is proposed, in which the triggering conditions are no longer constants but can be adjusted in real time according to the adaptive variables, so that the designed event-triggered mechanism has stronger online update ability. The measurement states are only transmitted through the network based on event-triggered conditions. The proposed adaptive backstepping algorithm not only ensures the security of the system under sensor attacks but also saves network resources and ensures the consistent tracking performance of multi-agent systems. The boundedness of all closed-loop signals is proved by Lyapunov stability analysis. Simulation examples show the effectiveness of the control scheme. © 2023 The Franklin Institute

Backstepping; Network security; State estimation; Back-stepping approaches; Condition; Control schemes; Dynamic events; Event-triggered; On-line updates; Real- time; Security measurement; States observer; Tracking controls; Multi agent systems

2-s2.0-85165225650

Transfer learning based graph convolutional network with self-attention mechanism for abnormal electricity consumption detection

Meng, Songping (57226446431); Li, Chengdong (16175211600); Tian, Chongyi (55371538400); Peng, Wei (57192101142); Tian, Chenlu (57202209239)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159217782&doi=10.1016%2fj.egyr.2023.05.006&partnerID=40&md5=8ee977b63030e8a2f2e7b281d477207a

In the case of limited energy, the waste of energy and economic loss caused by abnormal electricity consumption should not be underestimated and its detection plays an important role. However, abnormal electricity consumption detection also faces many challenges. On the one hand, labeled abnormal data are difficult to obtain. On the other hand, building different models for different users undoubtedly increases the demand for data and the burden of training. To tackle these challenges, in this paper, we propose a transfer learning based graph convolutional network with self-attention mechanism method to detect abnormal electricity consumption. With the help of transfer learning, we firstly pre-train the source domain network using the sufficient data. Then, a small amount of data in the target domain is utilized to fine-tune the pre-training model to get the final detection model. This can not only effectively alleviate the problem of insufficient data, but also reduce the training burden caused by building different models for different users. In addition, to improve the effect of feature extraction and enhance the performance of the network, we employ the self-attention mechanism to enhance the network's attention to different data information. Finally, we adopt the graph convolutional networks to discover the relationships of electricity consumption data among different moments and to classify the electricity consumption data. We have done detailed experiments to verify the effectiveness of the proposed method, and experimental results show that the proposed method is effective and robust. © 2023 The Author(s)

Abnormal electricity consumption detection; Graph convolutional networks; Self-attention mechanism; Transfer learning

Classification (of information); Convolution; Convolutional neural networks; Learning systems; Losses; Abnormal electricity consumption detection; Attention mechanisms; Convolutional networks; Economic loss; Electricity-consumption; Energy; Graph convolutional network; Limited energies; Self-attention mechanism; Transfer learning; Electric power utilization

2-s2.0-85159217782

A Comprehensive Review of Architecture, Communication, and Cybersecurity in Networked Microgrid Systems

Aghmadi, Ahmed (57204510802); Hussein, Hossam (57679877000); Polara, Ketulkumar Hitesh (58551793100); Mohammed, Osama (55395286900)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168861896&doi=10.3390%2finventions8040084&partnerID=40&md5=36186de0369acc2b1acad6e1d2aed955

Networked microgrids (NMGs) are developing as a viable approach for integrating an expanding number of distributed energy resources (DERs) while improving energy system performance. NMGs, as compared to typical power systems, are constructed of many linked microgrids that can function independently or as part of a more extensive network. This allows NMGs to be more flexible, dependable, and efficient. The present study comprehensively investigates architecture, communication, and cybersecurity issues in NMGs. This comprehensive study examines various aspects related to networked microgrids (NMGs). It explores the architecture of NMGs, including control techniques, protection, standards, and the challenges associated with their adoption. Additionally, it investigates communication in NMGs, focusing on the technologies, protocols, and the impact of communication on the functioning of these systems. Furthermore, this study addresses cybersecurity challenges specific to NMGs, such as diverse cyberattack types, detection and mitigation strategies, and the importance of awareness training. The findings of this study offer valuable insights for NMG researchers and practitioners, emphasizing critical aspects that must be considered to ensure the safe and efficient operation of these systems. © 2023 by the authors.

architecture and control methods; communication; cybersecurity; distributed energy resources (DERs); networked microgrids (NMGs); regulations; standards

2-s2.0-85168861896

A comprehensive survey on blockchain-based C-ITS applications: Classification, challenges, and open issues

BelMannoubi, Souad (57209615196); Touati, Haifa (25925508900); Hadded, Mohamed (56487944300); Toumi, Khalifa (35219380000); Shagdar, Oyunchimeg (8832576100); Kamoun, Farouk (55959731700)

57209615196; 25925508900; 56487944300; 35219380000; 8832576100; 55959731700

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161479884&doi=10.1016%2fj.vehcom.2023.100607&partnerID=40&md5=399550107dadb20bf6b106083bfc0bcc

Cooperative Intelligent Transport Systems (C-ITS) aim to improve road safety and provide comfort to both drivers and passengers by enabling vehicles, infrastructure, and other road users to exchange environmental and driving data. Therefore, the accuracy of the information exchanged as well as the authenticity of the entities, effectively affect the efficiency and performance of the system. Blockchain is a new distributed ledger technology that enables secure and transparent information storage and transmission without the control of a central entity. Its inherent features of trust, anonymity, immutability, and data availability offer great potential to solve current C-ITS issues. Recently, several research works are carried out to explore the potential of the blockchain technology in a vehicular environment. In this paper, we present a comprehensive review of the literature related to the application of the blockchain technology in C-ITS. We provide background knowledge on blockchain and the motivations for applying this technology in vehicular networks. Then, we discuss the major challenges that can bring the application of blockchains in a vehicular environment, such as mobility, latency, etc. Next, we review current research and classify it into four main categories. Moreover, a qualitative comparison of the discussed solutions is presented to identify the strengths and the weaknesses of each proposal. Finally, we highlight some open research issues that should be considered in future studies to improve the performance of blockchain-based vehicular systems. © 2023

Blockchain; C-ITS; Consensus; Data sharing; Privacy; Security; Smart contract; Trust; Vehicular networks

2-s2.0-85161479884

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149380630&doi=10.1111%2fmice.12985&partnerID=40&md5=494f62387f74a665742492efa531ab01

In this paper, a novel approach to train control systems based on virtual coupling is presented. Virtual coupling is a concept that has evolved from platooning of vehicles and allows to reduce the distance and headway between trains without constructing new lines while ensuring safe operation. With this objective in mind, we propose a decentralized robust model predictive control (MPC) framework for a virtually coupled train set based on a min–max approach. Unlike the nominal MPC, robust MPC is designed to consider external undetermined disturbances and errors to improve robustness in real-world applications. Therefore, in this study, we present the formulation of a robust MPC based on solving a finite-horizon optimization problem with bounded uncertainties. The bounds consider resistive modeling errors, positioning errors, communication delays, and a possible adhesion loss of up to 10%. We then performed four simulations to compare the behavior of the robust MPC with the equivalent nominal MPC. In these simulations, we simulated a metro line, main line, and high-speed line. The simulations also analyzed the behavior of the robust MPC under the considered perturbations and different communication delays. The results show that the robust MPC ensures safer operation than nominal MPC in subways, conventional lines, and high-speed lines. Future research can focus on centralized MPC and artificial intelligence. © 2023 The Authors. Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor.

Errors; Predictive control systems; Robust control; Subways; Communication delays; Control approach; Decentralized robust controls; High speed lines; Nominal models; Robust model predictive control; Safe operation; Train sets; Virtual coupling; adhesion; artificial intelligence; computer simulation; control system; error analysis; optimization; Model predictive control

2-s2.0-85149380630

Deep Reinforcement Learning Perspectives on Improving Reliable Transmissions in IoT Networks: Problem Formulation, Parameter Choices, Challenges, and Future Directions

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163979729&doi=10.1016%2fj.iot.2023.100846&partnerID=40&md5=656a97a2fd72497b2841ffd400dac352

The majority of communication protocols used in IoT networks for caching and congestion control techniques were rule-based which implies that these protocols are dependent on explicitly stated static models. To solve this issue, techniques are becoming more adaptive to changes in the network environment by incorporating a learning-based approach using Machine Learning (ML) and Deep Learning (DL). Recent surveys and review papers have covered topics on the use of ML and DL in either caching or congestion control techniques used in various types of networks. However, there is not an article in the literature dedicated to surveying the design of caching and congestion control mechanisms in IoT networks from the perspective of a Deep Reinforcement Learning (DRL) problem. Hence, this work aimed to survey the state-of-the-art DRL-based caching and congestion control techniques in IoT networks from 2019 to 2023. It also presented general frameworks for DRL-based caching and congestion control techniques based on surveyed works as a baseline for designing future protocols in IoT networks. Moreover, this paper classified the parameter choices of surveyed DRL-based techniques and identified the issues and challenges behind these techniques. Finally, a discussion of the possible future directions of this research domain was presented. © 2023 Elsevier B.V.

Artificial Intelligence; Caching; Congestion control; Deep Reinforcement Learning; Internet of Things; Machine Learning

2-s2.0-85163979729

CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor

Mukhtar, Hamza (57224001235); Afzal, Adil (57225755022); Alahmari, Sultan (58018358600); Yonbawi, Saud (57817797700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169905129&doi=10.1016%2fj.neunet.2023.07.027&partnerID=40&md5=70c2bbd1ad0b966a11aea73c86449423

Tackling traffic signal control through multi-agent reinforcement learning is a widely-employed approach. However, current state-of-the-art models have drawbacks: intersections optimize their own local rewards and cause traffic to waste time and fuel with a start-stop mode at each intersection. They also lack information sharing among intersections and their specialized policy hinders the ability to adapt to new traffic scenarios. To overcome these limitations, This work presents a centralized collaborative graph network (CCGN) with the core objective of a signal-free corridor once the traffic flows have waited at the entry intersection of the traffic intersection network on either side, the subsequent intersection gives the open signal as the traffic flows arrive. CCGN combines local policy networks (LPN) and global policy networks, where LPN employed at each intersection predicts actions based on Transformer and Graph Convolutional Network (GCN). In contrast, GPN is based on GCN and Q-network that receives the LPN states, traffic flow and road information to manage intersections to provide a signal-free corridor. We developed the Deep Graph Convolution Q-Network (DGCQ) by combining Deep Q-Network (DQN) and GCN to achieve a signal-free corridor. DGCQ leverages GCN's intersection collaboration and DQN's information aggregation for traffic control decisions Proposed CCGN model is trained on the robust synthetic traffic network and evaluated on the real-world traffic networks that outperform the other state-of-the-art models. © 2023 The Author(s)

Collaborative intersection signal control; Cooperative traffic signal control; Graph convolutional network; Intelligent transportation; Markov decision processes; Multi-agent centralized reinforcement learning

Accidents, Traffic; Convolution; Deep learning; Flow graphs; Information management; Markov processes; Multi agent systems; Centralised; Collaborative intersection signal control; Convolutional networks; Cooperative traffic signal control; Graph convolutional network; Intelligent transportation; Markov Decision Processes; Multi agent; Multi-agent centralized reinforcement learning; Reinforcement learnings; Signal control; Traffic signal control; article; learning; Markov decision process; reinforcement (psychology); traffic accident; Reinforcement learning

2-s2.0-85169905129

Al-Momin, Mohammed (55372661600); Alkhafaji, Mohammed K. (57216298535); Al-Musawi, Mrtdaa M. H. (58317361300)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162109440&doi=10.12928%2fTELKOMNIKA.v21i4.24706&partnerID=40&md5=274b3f962a4445bd14067eeb49dd16e5

Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising. © This is an open access article under the CC BY-SA license.

Image processing; Pattern recognition; Smart traffic light; Traffic flow management; Vehicle counting

All Open Access; Green Open Access; Hybrid Gold Open Access

2-s2.0-85162109440

Vojtov V.; Muzylyov D.; Karnaukh M.; Kravtcov A.; Goryayinov O.; Gorodetska T.; Ivanov V.; Pavlenko I.

Vojtov, Viktor (6603293469); Muzylyov, Dmitriy (57189388248); Karnaukh, Mykola (58554832500); Kravtcov, Andriy (58555104300); Goryayinov, Oleksiy (58554969500); Gorodetska, Tetiana (57189038553); Ivanov, Vitalii (55769747343); Pavlenko, Ivan (56435834300)

6603293469; 57189388248; 58554832500; 58555104300; 58554969500; 57189038553; 55769747343; 56435834300

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169105718&doi=10.3390%2fapp13169307&partnerID=40&md5=c268decc405172253df9b348788c77fc

Assessing the transport flow robustness is a significant aspect of a qualitative solution to traffic management problems. Therefore, management should be based on appropriate criteria, accounting for different factors characterizing traffic flow sustainability. That’s why it is crucial to establish the impact rate for each group of factors on the robustness criterion. Therefore, the current study aims to obtain the dependence of the criterion changes for traffic flow sustainability on the traffic jam occurrence when changing the gradients’ product of traffic flow density and its speed. The value of the robustness criterion allows for performing an impact rating for input factors on traffic flow sustainability. All factors affecting transport flow robustness are divided into three groups. Based on simulation results, factors rating that impact the robustness margin value of the traffic flow is presented. Length and weight of automobiles are at first place according to impact terms on the sustainability loss of the traffic flow. In second place of impact on sustainability loss are the temporary factors group and factors group that considers the roadway environment’s infrastructure. Hence, the results can be used to analyze sustainability traffic flows in controlled highway network stretches and develop measures to increase sustainability reserve. © 2023 by the authors.

density gradient; dynamic model; mathematical modelling; robustness traffic flow; stability criterion; sustainability traffic flow; sustainable supply chain; velocity gradient

2-s2.0-85169105718

Behravan, Kobra (58310481600); Farzaneh, Nazbanoo (36942364800); Jahanshahi, Mohsen (56114369300); Hosseini Seno, Seyed Amin (31767463800)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161606830&doi=10.1016%2fj.vehcom.2023.100604&partnerID=40&md5=5d3ccb847d5c234ae99587341ad7561b

With the advent of fog computing, its use to provide real-time services at the network's edge has increased. However, running computationally-demanding applications in smart vehicles is constrained by the limited storage and computation capacity and energy consumption of the vehicles. A suitable solution is to offload the applications demanding massive processing to a conventional centralized cloud which wastes huge bandwidth and increases the delay. The number of smart vehicles is expected to increase in the future; there is a need for new technology such as fog computing (FC) to meet unique requirements such as QoS, resource management, and heterogeneity in vehicular ad-hoc networks (VANET). FC brings computation and storage resources to the edge of vehicles enabling them to run the highly-demanding applications in the smart vehicles while meeting strict delay requirements. This survey focuses on different applications, architectures, technical issues, and significant performance metrics using FC in vehicular network. Finally, open issues and future directions are discussed. © 2023 Elsevier Inc.

2-s2.0-85161606830

Mode Search Optimization Algorithm for Traffic Prediction and Signal Controlling Using Bellman–Ford with TPFN Path Discovery Model Based on Deep LSTM Classifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170395756&doi=10.1007%2fs42979-023-02140-1&partnerID=40&md5=5df9555e33d1b6a1a6404f552c89aee5

Given the increasing population, urbanization, and industry, traffic congestion is particularly severe during peak hours, especially in cities. This leads to high fuel consumption, noise pollution, and a range of health problems. Consequently, traffic management is essential for managing the aforementioned concerns. This paper proposes a novel traffic prediction and control approach based on wireless sensor networks (WSNs). The proposed mode-search optimization is designed by blending the particular characteristics of squawks with the theoretical foundations of traffic prediction and control optimization. Initially, the sensors are used to collect velocity, acceleration, jitter, and priority data for the network’s vehicles, after which the mode-search optimization method is suggested based on the data to cluster the vehicles. Next, for traffic projections, the possible paths are identified using a multi-objective approach. The proposed mode-search based Deep Long Short-Term Memory is then used to forecast traffic once the Deep LSTM (Long Short-Term Memory) has been trained using the proposed mode-search optimization to reduce training loss. Additionally, the use of traffic signal control also improves traffic flow management. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Optimization process; Signal control; TPFN; Traffic forecast; VANET; Wireless sensor network

2-s2.0-85170395756

Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (CAM) system design based on general modeling network specification (GMNS)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163417466&doi=10.1016%2fj.trc.2023.104223&partnerID=40&md5=7b8be319a0f97e56e259c86357c697e7

This study presents a novel framework and open-source tools for simulating and managing connected and automated mobility (CAM) systems, taking into account their hierarchical nature and various levels of scheduling. The framework is based on a multi-layered network representation, which allows for efficient and accurate modeling of CAM systems at different levels of granularity, from macroscopic to microscopic. By employing this hierarchical approach, we achieve a balance between the level of detail in the representation and computational efficiency. Additionally, a spatial-discrete virtual track-based representation is introduced for precise vehicle dynamics modeling and for ensuring consistency with higher-level routing decisions. This facilitates individualized active traffic management for CAM applications. As part of our research, we have developed osm2gmns, an open-source package that allows users to effortlessly access and process transportation networks from OpenStreetMap in the General Modeling Network Specification (GMNS) format, facilitating data sharing and research collaboration. Furthermore, we explore traffic simulation, optimization, and operation methodologies for CAM systems, particularly focusing on the extent of scheduling capabilities. To support the research community, we further introduce an open-source package CAMLite for CAM system modeling. The effectiveness of our proposed methodologies and tools is demonstrated through a series of numerical experiments. © 2023 Elsevier Ltd

Connected and autonomous mobility; Layer decomposition; Open-source tools; Schedulability

Computational efficiency; Hierarchical systems; Network layers; Open systems; Autonomous mobilities; Connected and autonomous mobility; General model; Layer decomposition; Mobility systems; Model networks; Open source package; Open source tools; Schedulability; Virtual track; computer simulation; road traffic; routing; software; traffic management; unmanned vehicle; Specifications

2-s2.0-85163417466

A dynamic task assignment model for aviation emergency rescue based on multi-agent reinforcement learning

Shen, Yang (36995100800); Wang, Xianbing (58477300600); Wang, Huajun (58477397600); Guo, Yongchen (57095179100); Chen, Xiang (58477267400); Han, Jiaqi (58477336500)

36995100800; 58477300600; 58477397600; 57095179100; 58477267400; 58477336500

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164223640&doi=10.1016%2fj.jnlssr.2023.06.001&partnerID=40&md5=00b850c474df0696655cd0bb2fa0b7d4

China's natural disaster situation presents a complex and severe scenario, resulting in substantial human and material losses as a result of large-scale emergencies. Recognizing the significance of aviation emergency rescue, the state provides strong support for its development. However, China's current aviation emergency rescue system is still under construction and encounters various challenges; one such challenge is to match the dynamically changing multi-point rescue demands with the limited availability of aircraft dispatch. We propose a dynamic task assignment model and a trainable model framework for aviation emergency rescue based on multi-agent reinforcement learning. Combined with a targeted design, the scheduling matching problem is transformed into a stochastic game process from the rescue location perspective. Subsequently, an optimized strategy model with high robustness can be obtained by solving the training framework. Comparative experiments demonstrate that the proposed model is able to achieve higher assignment benefits by considering the dynamic nature of rescue demands and the limited availability of rescue helicopter crews. Additionally, the model is able to achieve higher task assignment rates and average time satisfaction by assigning tasks in a more efficient and timely manner. The results suggest that the proposed dynamic task assignment model is a promising approach for improving the efficiency of aviation emergency rescue. © 2023

Aviation emergency rescue; Benefit evaluation; Multi-agent reinforcement learning; Task assignment

Aircraft accidents; Disasters; Learning systems; Multi agent systems; Reinforcement learning; Training aircraft; Aviation emergency rescue; Benefit evaluation; Disaster situations; Dynamic tasks; Emergency rescue; Material loss; Multi-agent reinforcement learning; Natural disasters; Task assignment models; Tasks assignments; Stochastic systems

2-s2.0-85164223640

Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention

Mishra, Sumit (57191539399); Rajendran, Praveen Kumar (57376770000); Vecchietti, Luiz Felipe (57215033500); Har, Dongsoo (56971057700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159793289&doi=10.1109%2fTITS.2023.3271395&partnerID=40&md5=2041b7f3528b2ab9a90e2a255f538bdb

In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam. For this purpose, Google Street View images around accident hotspots (areas of dense accident occurrence) identified by a real-accident dataset are used to train a novel attention module to classify a given urban scene into an accident hotspot or a non-hotspot (area of sparse accident occurrence). The proposed module leverages channel, point, and spatial-wise attention learning on top of different CNN backbones. This leads to better classification results and more certain AP-features with better contextual knowledge when compared with CNN backbones alone. Our proposed module achieves up to 92% classification accuracy. The capability of detecting AP-features by the proposed model were analyzed by a comparative study of three different class activation map (CAM) methods, which are used to inspect specific AP-features causing the classification decision. The outputs of the CAM methods were processed by an image processing pipeline to extract only the AP-features that are explainable to drivers and notified using a visual notification system. Range of experiments was performed to prove the efficacy and AP-features of the system. Ablation of the AP-features taking 9.61%, on average, of the total area in each image sample increased the chance of a given area to be classified as a non-hotspot by up to 21.8%. © 2000-2011 IEEE.

accident hotspot; Accident prevention; accident prone feature; attentive driving system; head-up display; street view images

Accidents; Cams; Classification (of information); Feature extraction; Interactive computer systems; Real time systems; Traffic signs; Accident hotspot; Accident prone feature; Attentive driving system; Convolutional neural network; Driving systems; Features extraction; Head-UpDisplay; Heads-up-display; Hotspots; Real - Time system; Road; Street view image; Neural networks

2-s2.0-85159793289

Towards cyber security for low-carbon transportation: Overview, challenges and future directions

Cao, Yue (55470310200); Li, Sifan (58101678900); Lv, Chenchen (58179558200); Wang, Di (57956778200); Sun, Hongjian (8865475600); Jiang, Jing (57189332238); Meng, Fanlin (57213962702); Xu, Lexi (35207681600); Cheng, Xinzhou (36631926300)

55470310200; 58101678900; 58179558200; 57956778200; 8865475600; 57189332238; 57213962702; 35207681600; 36631926300

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162158699&doi=10.1016%2fj.rser.2023.113401&partnerID=40&md5=777a6f32dd22c651b27c15b710b79444

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned. © 2023 Elsevier Ltd

Blockchain; Denial of service attack; Edge computing; Information and communication technology; Internet of vehicles; Intrusion detection system; Low-carbon transportation; Sustainable city; Trust management; Vehicle to grid

Blockchain; Carbon; Cybersecurity; Denial-of-service attack; Edge computing; Information management; Intrusion detection; Network architecture; Vehicle to vehicle communications; Vehicles; Block-chain; Denialof- service attacks; Edge computing; Information and Communication Technologies; Internet of vehicle; Intrusion Detection Systems; Low carbon transportations; Sustainable cities; Trust management; Vehicle to grids; Network security

2-s2.0-85162158699

On studying active radio measurements estimating the mobile network quality of service for the Regulatory Authority's purposes

Mongay Batalla, Jordi (58295911200); Sujecki, Sławomir (6603917806); Kelner, Jan M. (25630670100); Śliwka, Piotr (15064537300); Zmysłowski, Dariusz (58062844100)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170230649&doi=10.1016%2fj.comnet.2023.109980&partnerID=40&md5=9a918327983fa2828e26b9c9fbe595f4

The Regulatory Authority monitors and regulates the telecom market at a national-wide range. One of its main tasks is to put spectrum at the disposal of the Mobile Network Operators (MNOs) for serving the increasing number of users and services. Another task of the Regulator is to demand a minimum quality of service to the network infrastructure. For this scope, the Regulator needs to have an independent estimation of the quality of service of the mobile network. This paper presents a methodology for Regulator-triggered monitoring of 5G radio resources and estimation of the maximum throughput offered to the end users. The requirements of the methodology are: (1) monitoring measurements must be external to the network, (2) all information needed for Quality of Service (QoS) estimation may be obtained only through active measurements in the network, and (3) the number of measurements should be as low as possible (for cost-saving) while offering statistical significance. The presented spot measurements validate the methodology and show the boundary use cases where the methodology would overestimate the transmission quality. © 2023 The Author(s)

Frequency band; Network measurements; Quality of Service; Regulator; Spot metering

5G mobile communication systems; Wireless networks; Main tasks; Network measurement; Network quality of services; Quality-of-service; Radio measurements; Regulator; Regulatory authorities; Spectra's; Spot metering; Telecom market; Quality of service

2-s2.0-85170230649

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Gao, Yuan (57197868611); Chen, Junfeng (57221421523); Chen, Xi (58193027500); Wang, Chongyang (57202807310); Hu, Junjie (57218798179); Deng, Fuqin (57305280400); Lam, Tin Lun (26659084400)

57197868611; 57221421523; 58193027500; 57202807310; 57218798179; 57305280400; 26659084400

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153403681&doi=10.1109%2fTRO.2023.3257541&partnerID=40&md5=a1f209dc840c473cff8ccdcef1893b66

Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS. © 2004-2012 IEEE.

Asymmetric self-play; catching systems; heterogeneous multirobot system (HMRS); reinforcement learning (RL)

Behavioral research; Curricula; Deep learning; Heuristic algorithms; Industrial robots; Intelligent robots; Job analysis; Multi agent systems; Multipurpose robots; Robot learning; Robot programming; Asymmetric self-play; Catching system; Heterogeneous multi-robot system; Heterogeneous multirobot system; Heuristics algorithm; Multi-robot systems; Reinforcement learning; Reinforcement learnings; Self-play; Task analysis; Reinforcement learning

2-s2.0-85153403681

Traffic Signal Optimization Based on Fuzzy Control and Differential Evolution Algorithm

Lin, Haifeng (36640121300); Han, Yehong (55489334900); Cai, Weiwei (57215898790); Jin, Bo (57218305846)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136883820&doi=10.1109%2fTITS.2022.3195221&partnerID=40&md5=1cadba21452026a75ef5d4823eff07a3

Urban traffic congestion is often concentrated at urban intersections. An urban road traffic signal control system is needed to prevent problems such as driving delays caused by frequent traffic congestions on trunk lines, exhaust emissions owing to frequent start and stop of vehicles, and fuel wastage due to long idling times. Maximizing the traffic capacity of an intersection and reducing the delay rate of vehicles has always been a problem for traffic control research. The coordinated control of urban traffic signals is regarded as a multi-objective optimization problem. A mathematical model for urban trunk traffic is studied herein. An average delay model, average queue length model, total delay calculation model for vehicles at intersections, and vehicle exhaust emission model are established to obtain an optimization model for a new traffic trunk coordinated control system. In addition, our study combines the fuzzy control theory with the adaptive sequencing mutation multi-objective differential evolution algorithm (FASM-MDEA). This new optimization method for traffic signal control at urban intersections is proposed as a solution for the traffic flow optimization model to solve the problem of traffic signal coordination and control of urban trunk lines. The simulation results demonstrate the effectiveness of the model optimization algorithm proposed in this study. © 2000-2011 IEEE.

adaptive sorting mutation; coordinated control; differential evolution algorithm (DEA); fuzzy control; multi-objective optimization; Urban traffic signal

Fuzzy control; Genetic algorithms; Heuristic algorithms; Street traffic control; Traffic congestion; Traffic signals; Vehicles; Adaptation models; Adaptive sorting; Adaptive sorting mutation; Co-ordinated control; Coordinated control; Delay; Differential evolution algorithm; Differential evolution algorithms; Heuristics algorithm; Multi-objectives optimization; Optimisations; Road; Urban traffic; Urban traffic signal; Multiobjective optimization

2-s2.0-85136883820

Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning

Hu, Cong (57204907479); Xu, Kai (56828524300); Zhu, Zhengqiu (57190276477); Qin, Long (55175523200); Yin, Quanjun (23096547400)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169100439&doi=10.3390%2fapp13169174&partnerID=40&md5=17bcec7f8644ebb12398e0f589840c0b

In this study, we propose an innovative approach to address a chronological planning problem involving the multiple agents required to complete tasks under precedence constraints. We model this problem as a stochastic game and solve it with multi-agent reinforcement learning algorithms. However, these algorithms necessitate relearning from scratch when confronted with changes in the chronological order of tasks, resulting in distinct stochastic games and consuming a substantial amount of time. To overcome this challenge, we present a novel framework that incorporates meta-learning into a multi-agent reinforcement learning algorithm. This approach enables the extraction of meta-parameters from past experiences, facilitating rapid adaptation to new tasks with altered chronological orders and circumventing the time-intensive nature of reinforcement learning. Then, the proposed framework is demonstrated through the implementation of a method named Reptile-MADDPG. The performance of the pre-trained model is evaluated using average rewards before and after fine-tuning. Our method, in two testing tasks, improves the average rewards from −44 to −37 through 10,000 steps of fine-tuning in two testing tasks, significantly surpassing the two baseline methods that only attained −51 and −44, respectively. The experimental results demonstrate the superior generalization capabilities of our method across various tasks, thus constituting a significant contribution towards the design of intelligent unmanned systems. © 2023 by the authors.

2-s2.0-85169100439

Sensor based traffic signal pre-emption for emergency vehicles using efficient short-range communication network

Kavitha, Yarra (58317882100); Satyanarayana, Penke (57216887489); Mirza, Shafi Shahsavar (57195977131)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162204699&doi=10.1016%2fj.measen.2023.100830&partnerID=40&md5=49c6a56061691b6769cc864c8e42a6ec

Roads are becoming increasingly congested due to urbanization and the increased use of private vehicles. As a result, emergency vehicles like ambulances, police cars, and fire trucks get stuck in traffic and take longer to get where they're going, which may cause risk, damage to property, and losing valuable lives. The acoustic-based pre-emption technology uses the EV's siren to alert oncoming cars. Therefore, acoustic reflection from a building or other large container vehicle might cause a fall. This paper implements a vehicle-to-infrastructure (V2I) communication system that anticipates traffic signals using Global Positioning System (GPS) and Dedicated Short Range Communication (DSRC). Line of sight (LOS) is not required when choosing the desired range of DSRC communication, and the traffic signal unit receives pre-emption messages anytime an emergency vehicle is nearby. Thus, these messages cause the traffic light to turn green for the emergency vehicle instead of operating normally. As a result, this type of system necessitates two hardware modules, one at each vehicle On-Board-Unit (OBU) and one at each intersection Road-Side-Unit (RSU), as well as the Decision Support System i.e., Traffic Management Controller (TMC). In the hardware OBU and RSU, the IMX-6 Processor, GPS Module, and DSRC transmitter and receivers are used. The Emergency Vehicle (EV) equipped with the OBU transmits requests to the intersection unit, which is fully autonomous and can be employed as a component of the Intelligent Transportation System (ITS), negating the need for a driver in this method. © 2023

And traffic management controller; Dedicated short-range communication; Intelligent transportation system; On-board-unit; Road-side-unit

Decision support systems; Emergency vehicles; Global positioning system; Intelligent systems; Intelligent vehicle highway systems; Roads and streets; Street traffic control; Traffic signals; Vehicle to vehicle communications; And traffic management controller; Communications networks; Intelligent transportation systems; Management controller; On-board units; Road sides; Road-side-unit; Short-range communication; Signal pre-emptions; Traffic management; Dedicated short range communications

2-s2.0-85162204699

IoVST: Internet of vehicles and smart traffic - Architecture, applications, and challenges

Rajamohan, Kavitha (58537898700); Rangasamy, Sangeetha (58511585100); Pinto, Nikhil Anthony (58537947700); Manoj, B.E. (58537784100); Mukherjee, Debanjana (58412273100); Shukla, Jimit (58537784200)

58537898700; 58511585100; 58537947700; 58537784100; 58412273100; 58537784200

Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168066871&doi=10.4018%2f978-1-6684-8785-3.ch015&partnerID=40&md5=a6169433eebffb100b71fdc63c0221fe

The internet of things (IoT) is the network of sensors, devices, processors, and software, enabling connection, communication, and data transfer between devices. IoT is able to collect and analyze large amounts of data which can then be used to automate daily tasks in various fields. IoT holds the potential to revolutionise and create many opportunities in multiple industries like smart cities, smart transport, etc. Autonomous vehicles are smart vehicles that are able to navigate and move around on their own on a well-planned road. © 2023, IGI Global.

2-s2.0-85168066871

A comprehensive operation and maintenance assessment for intelligent highways: A case study in Hong Kong-Zhuhai-Macao bridge

Wei, Sen (58147197700); Li, Yanping (58550094700); Yang, Hanqing (58549925500); Xie, Minghui (57765264000); Wang, Yuanqing (56098300200)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168806181&doi=10.1016%2fj.tranpol.2023.08.009&partnerID=40&md5=a5238bf626a7b2c95fd76f13b18d4354

The integration of business flow status updates creates a framework for dynamically evaluating operation and maintenance activities, and that assessment is an essential component of achieving intelligent management and control. Highway managers have relied on the operation and maintenance system to integrate business initiatives that improve performance at the organizational and individual levels recently. There are a few studies considering the actual accident or road infrastructure to improve the quality of the highway, but they ignore the power of operation and maintenance (O&M) management involving multiple parties. Furthermore, there is no comprehensive scale to measure the effect of implementing management measures. Therefore, this paper aims to develop a comprehensive multidimensional evaluation framework that considers the integration of the actual business and the objective environment from four aspects: worker, equipment, environment, and management measures. The proposed framework evaluates the current O&M performance with the improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model, which is used to interpret the comprehensive index results, and highlights the differences and variations of the factors that influence the evaluation results based on the deconstruction analysis. We utilize a mixed dataset of the Hong Kong-Zhuhai-Macao Bridge (HZMB) for two consecutive years to evaluate the performance of each month. The results show that the evaluation framework can effectively measure the O&M performance, and “Worker- Quality of daily inspection work” was identified as the most critical factor affecting the O&M performance. In particular, a comprehensive multiparty operational status assessment helps to review and revise current management strategies for targeted improvements that will enhance the safety and quality of management. © 2023 Elsevier Ltd

Composite index; Deconstruction contrast; Hong Kong-Zhuhai-Macao bridge; Improved TOPSIS; Operation and maintenance assessment

China; Guangdong; Hong Kong; Macau; Zhuhai; Bridges; Highway administration; Human resource management; Quality control; Composite index; Deconstruction contrast; Hong kong-zhuhai-macao bridge; Hong-kong; Ideal solutions; Improved technique for order preference by similarity to an ideal solution; Improved techniques; Maintenance assessment; Operation and maintenance assessment; Operations and maintenance; composite; intelligent transportation system; maintenance; road construction; transportation infrastructure; Maintenance

2-s2.0-85168806181

Model-Based Predictive Control and Reinforcement Learning for Planning Vehicle-Parking Trajectories for Vertical Parking Spaces

Shi, Junren (57207823424); Li, Kexin (58022394200); Piao, Changhao (8651863500); Gao, Jun (58020294700); Chen, Lizhi (58548959000)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168736825&doi=10.3390%2fs23167124&partnerID=40&md5=5a68c557706d7abd496cd250b8da51ab

This paper proposes a vehicle-parking trajectory planning method that addresses the issues of a long trajectory planning time and difficult training convergence during automatic parking. The process involves two stages: finding a parking space and parking planning. The first stage uses model predictive control (MPC) for trajectory tracking from the initial position of the vehicle to the starting point of the parking operation. The second stage employs the proximal policy optimization (PPO) algorithm to transform the parking behavior into a reinforcement learning process. A four-dimensional reward function is set to evaluate the strategy based on a formal reward, guiding the adjustment of neural network parameters and reducing the exploration of invalid actions. Finally, a simulation environment is built for the parking scene, and a network framework is designed. The proposed method is compared with the deep deterministic policy gradient and double-delay deep deterministic policy gradient algorithms in the same scene. Results confirm that the MPC controller accurately performs trajectory-tracking control with minimal steering wheel angle changes and smooth, continuous movement. The PPO-based reinforcement learning method achieves shorter learning times, totaling only 30% and 37.5% of the deep deterministic policy gradient (DDPG) and twin-delayed deep deterministic policy gradient (TD3), and the number of iterations to reach convergence for the PPO algorithm with the introduction of the four-dimensional evaluation metrics is 75% and 68% shorter compared to the DDPG and TD3 algorithms, respectively. This study demonstrates the effectiveness of the proposed method in addressing a slow convergence and long training times in parking trajectory planning, improving parking timeliness. © 2023 by the authors.

Learning algorithms; Learning systems; Predictive control systems; Reinforcement learning; Trajectories; Vehicles; Autoparking; Deterministics; Model-predictive control; Parking spaces; Policy gradient; Policy optimization; Proximal policy optimization; Reinforcement learnings; Trajectory Planning; Vehicle parking; adult; article; learning; model predictive control; reinforcement learning (machine learning); reward; simulation; timeliness; Model predictive control

2-s2.0-85168736825

Liu, Ya (58109007400); Zhou, Yingjie (35241533300); Yang, Kai (57198557163); Wang, Xin (57218519379)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149411999&doi=10.1109%2fJIOT.2023.3243391&partnerID=40&md5=8b98090faa3d4d767197cb29e1c5c7fc

Internet of Things (IoT) time-series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial-temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time-series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised DL for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public data sets, existing challenges, and future research directions in this area. © 2014 IEEE.

Anomaly detection; clustering; Internet of Things (IoT); time series; unsupervised deep learning (DL)

Anomaly detection; Deep learning; Extraction; Feature extraction; Harmonic analysis; Network security; Time series analysis; Anomaly detection; Clusterings; Deep learning; Features extraction; IoT; Time-series analysis; Times series; Unsupervised deep learning; Vehicle's dynamics; Internet of things

2-s2.0-85149411999

Using Radial Basis Function and Back Propagation to predicate fault in a railway dangerous goods transportation system considering the Markov Correction

Huang, Wencheng (57192083247); Sun, Luohao (58492548000); Yang, Zhenlong (58491578700); Yin, Yanhui (58181688500)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165027283&doi=10.1016%2fj.asoc.2023.110593&partnerID=40&md5=55026e6fabdf4424441f50618bd03a64

Nowadays, we can obtain a huge amount of information and data of railway dangerous goods transportation system through various technical documents, accident reports, monitoring technologies etc. For the space composed of massive historical information and data, a data-driven fault predication approach that can better grasp some important local information while ignoring the negative impact of other data in the global might be practical. However, we have the following challenges: (i) How to reduce the impact of unimportant information on prediction results in advance? (ii) What kind of local approximation data-driven approach should be applied, and (iii) How to correct the results and improve the reliability and accuracy after fault predication based on the local approximation data-driven approach? In this paper, two data-driven approaches including Back Propagation with Markov Correction and Radial Basis Function with Markov Correction are proposed to predicate the fault in a railway dangerous goods transportation system. In order to solve challenge (i), we decompose the whole transportation process into multiple sub-processes based on Work Breakdown Structure with clear duration boundary on the time axis, and use Risk Breakdown Structure to detect the possible faults in each sub-process. In order to solve challenge (ii), we use and compare Back Propagation/Radial Basis Function with global approximation/local approximation and nonlinear prediction ability to learn and predicate the fault based on the collected historical data. In order to solve challenge (iii), we correct the prediction results with fluctuating deviation by using Markov Correction due to its non-aftereffect. Finally, a case study is conducted based on the collected historical fault data of railway dangerous goods transportation system in China. The results show that, for the prediction results based on Back Propagation, the average error increases from 1.43 to 1.88 and the Mean Square Error increases from 3.27 to 5.60 after Markov Correction. For the prediction results based on Radial Basis Function, the average error decreases from 1.13 to 1.11 and the Mean Square Error decreases from 1.81 to 1.76 after Markov Correction. The non-aftereffect of Markov chain well corresponds to the local approximation of Radial Basis Function. When the prediction value is determined by some data of the series, Radial Basis Function with Markov Correction can be applied. When the prediction value is determined by all the data (small-scale) of the series, Back Propagation with Markov Correction is better. © 2023 Elsevier B.V.

Back Propagation-Markov Correction; Data-driven; Fault prediction; Radial Basis Function-Markov Correction; Railway dangerous goods transportation system

Backpropagation; Errors; Image segmentation; Markov processes; Mean square error; Radial basis function networks; Railroad accidents; Railroad transportation; Railroads; Back Propagation; Back propagation-markov correction; Base function; Dangerous goods transportations; Data driven; Fault prediction; Radial base function-markov correction; Radial basis; Railway dangerous good transportation system; Transportation system; Forecasting

2-s2.0-85165027283

Optimal Reactive Power Dispatch in ADNs using DRL and the Impact of Its Various Settings and Environmental Changes

Zamzam, Tassneem (57203139495); Shaban, Khaled (8938806700); Massoud, Ahmed (7006870160)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168726598&doi=10.3390%2fs23167216&partnerID=40&md5=062627228767dd1638ec50607ae127c6

Modern active distribution networks (ADNs) witness increasing complexities that require efforts in control practices, including optimal reactive power dispatch (ORPD). Deep reinforcement learning (DRL) is proposed to manage the network’s reactive power by coordinating different resources, including distributed energy resources, to enhance performance. However, there is a lack of studies examining DRL elements’ performance sensitivity. To this end, in this paper we examine the impact of various DRL reward representations and hyperparameters on the agent’s learning performance when solving the ORPD problem for ADNs. We assess the agent’s performance regarding accuracy and training time metrics, as well as critic estimate measures. Furthermore, different environmental changes are examined to study the DRL model’s scalability by including other resources. Results show that compared to other representations, the complementary reward function exhibits improved performance in terms of power loss minimization and convergence time by 10–15% and 14–18%, respectively. Also, adequate agent performance is observed to be neighboring the best-suited value of each hyperparameter for the studied problem. In addition, scalability analysis depicts that increasing the number of possible action combinations in the action space by approximately nine times results in 1.7 times increase in the training time. © 2023 by the authors.

active distribution network; deep reinforcement learning; hyperparameters; neural network; optimal reactive power dispatch; power loss; reactive power; reward functions

Deep learning; Electric load dispatching; Electric power distribution; Energy resources; Reactive power; Scalability; Active distribution network; Active distributions; Deep reinforcement learning; Hyper-parameter; Neural-networks; Optimal reactive power dispatch; Performance; Powerloss; Reinforcement learnings; Reward function; article; environmental change; human; learning; reinforcement (psychology); reward; Reinforcement learning

2-s2.0-85168726598

Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction

Xu, Mengyun (57210175716); Qiu, Tony Z. (36548928300); Fang, Jie (55595692300); He, Hangyu (58080167100); Chen, Hongting (58080019600)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159207131&doi=10.1016%2fj.eswa.2023.120393&partnerID=40&md5=12a2ca18abf7d1a7dce4f6458dfede31

Forecasting the forthcoming intersection movement-based traffic volume enables adaptive traffic control systems to dynamically respond to the fluctuation of traffic demands. In this paper, a deep-learning based Signal-control Refined Dynamic Traffic Graph (ScR-DTG) Model is proposed for advancing the network-level movement-based traffic volume prediction task. The proposed model attempts to further improve the-state-of-art and practice algorithms in traffic prediction for arterial network adaptive signal control utilizing tradition traffic flow theory boosted deep-learning methodology. For precisely inferencing the movement-based demand at cycle-to-cycle level, the proposed model incorporates spatial graph convolution inferencing layer and temporal inferencing layer to explore both the intricate spatial temporal dependencies, respectively. A signal control refining module is contrived to deduce the controlled movement saturation flow and introduce the essential control inferences, which is of great significance but frequently neglected in the previous researches. Additionally, according to the real-time movement specified travel time, this paper creatively constructs an adjacent graph with dynamic order for more accurately capturing the ever-changing spatial relevancies. Field experiments with multiple signal schemes were conducted in the downtown area of Zhangzhou (China). The promising results demonstrated the state-of-the-art accuracy than other high-performance volume prediction algorithms. Implementing the proposed model enables to obtain accurate movement-based volume predictions, which would assist the traffic management agencies in adjusting signal timing adaptively and further improve the efficiency of signal intersection. © 2023 Elsevier Ltd

Deep learning; Dynamic order arterial graph; Movement-based traffic volume prediction; Signal control inferences

Adaptive control systems; Deep learning; Forecasting; Graph theory; Learning systems; Street traffic control; Deep learning; Dynamic order; Dynamic order arterial graph; Dynamic traffic; Graph model; Movement-based; Movement-based traffic volume prediction; Signal control; Signal control inference; Traffic volume prediction; Travel time

2-s2.0-85159207131

Traffic signal optimization control method based on adaptive weighted averaged double deep Q network

Chen, Youqing (57226845594); Zhang, Huizhen (35111996200); Liu, Minglei (57755375200); Ye, Ming (57393319500); Xie, Hui (57216614393); Pan, Yubiao (54399218100)

57226845594; 35111996200; 57755375200; 57393319500; 57216614393; 54399218100

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146955182&doi=10.1007%2fs10489-023-04469-9&partnerID=40&md5=4410ddda6f4b3ed3be65d4ad2c999b84

As a critical node and major bottleneck of the urban traffic networks, the control of traffic signals at road intersections has an essential impact on road traffic flow and congestion. Deep reinforcement learning algorithms have shown excellent control effects on traffic signal timing optimization. Still, the diversity of actual road control scenarios and real-time control requirements have put forward higher requirements on the adaptiveness of the algorithms. This paper proposes an Adaptive Weighted Averaged Double Deep Q Network (AWA-DDQN) based traffic signal optimal control method. Firstly, the formula is used to calculate the double estimator weight for updating the network model. Then, the mean value of the action evaluation is calculated by the network history parameters as the target value. Based on this, a certain number of adjacent action evaluation values are used to generate hyperparameters for weight calculation through the fully connected layer, and the number of action values for mean calculation is gradually reduced to enhance the stability of model training. Finally, simulation experiments were conducted using the traffic simulation software Vissim. The results show that the AWA-DDQN-based signal control method effectively reduces the average delay time, the average queue length and the average number of stops of vehicles compared with existing methods, and significantly improves traffic flow efficiency at intersections. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Deep learning; Double deep Q network; Intelligent transportation; Reinforcement learning; Traffic signal control

Adaptive control systems; Computer software; Deep learning; Learning algorithms; Learning systems; Real time control; Reinforcement learning; Roads and streets; Street traffic control; Traffic congestion; Control methods; Critical node; Deep learning; Double deep Q network; Intelligent transportation; Network-based; Optimization control; Reinforcement learnings; Traffic signal control; Traffic signal optimizations; Traffic signals

2-s2.0-85146955182

Explore deep reinforcement learning for efficient task processing based on federated optimization in big data

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166232482&doi=10.1016%2fj.future.2023.06.027&partnerID=40&md5=17d22f2695988aa7305128015631c207

In recent years, along with the extensive application of consumer electronics, the task execution with cloud computing for big data has become one of the research focuses. Nevertheless, the traditional theories and algorithms are still employed by existing research work to explore the feasible solutions, which takes a beating from low generalization performance, system load imbalance, more response delay, etc. To solve the matter, a task execution method called DROP (Deep Reinforcement network aided Optimization method aiming at task Processing) has been put forward, which is capable of completing task request allocation through virtual network embedding. The prominence of this method is explained by its effect in reducing load balancing degree, minimizing bandwidth resource overhead, and preserving electric energy as well as meeting customer demands. It makes use of Deep Deterministic Policy Gradient (DDPG) instead of depending on tons of iterations for better path selection schemes in previous methods, through continuous environment interaction and trial-and-error evaluation to get better strategy selection for virtual link embedding. To realize the virtual node embedding in the federated optimization based system architecture, the intentional deep feature learning network is applied. Compared with the cutting edge approaches, the performance benefits of DROP can be verified by the experimental results in terms of bringing down the extra cost on resources and energy of the substrate network during the task execution for big data. © 2023 Elsevier B.V.

Big data; Consumer electronics; Deep reinforcement learning; Federated optimization; Virtual network embedding

Big data; Computation theory; Deep learning; Drops; E-learning; Network embeddings; Cloud-computing; Deep reinforcement learning; Embeddings; Federated optimization; Optimisations; Reinforcement learnings; Research focus; Task executions; Task-processing; Virtual network embedding; Reinforcement learning

2-s2.0-85166232482

Adaptive traffic lights based on traffic flow prediction using machine learning models

Moumen, Idriss (58479985500); Abouchabaka, Jaafar (58571621700); Rafalia, Najat (9278139100)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164331257&doi=10.11591%2fijece.v13i5.pp5813-5823&partnerID=40&md5=961944946dfb7167c8068a685e47e8f5

Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Adaptive traffic light system; Intelligent transport systems; Machine learning; Traffic prediction; Traffic simulation

2-s2.0-85164331257

Prakash, Ashwini Bukanakere (57207662480); Sumathi, Ranganathaiah (24529229000); Sudhira, Honnudike Satyanarayana (6504411069)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167369521&doi=10.11591%2fijai.v12.i4.pp1755-1764&partnerID=40&md5=5012acbad429dd575d534aa6e3ca5ee1

A reliable transit service can motivate commuters to switch their traveling mode from private to public. Providing necessary information to passengers will reduce the uncertainties encountered during their travel and improve service reliability. This article addresses the challenge of predicting dynamic travel times in urban areas where real-time traffic flow information is unavailable. In this perspective, a hybrid travel time estimation model (HTTEM) is proposed to predict the dynamic travel time using the predicted travel times of the machine learning model and the preceding trip details. The proposed model is validated using the location data of public transit buses of, Tumakuru, India. From the numerical results through error metrics, it is found that HTTEM improves the prediction accuracy, finally, it is concluded that the proposed model is suitable for estimating travel time in urban areas with heterogeneous traffic and limited traffic infrastructure. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Bus travel time prediction; Dynamic model; Gradient boosting regression; Hybrid model; Machine learning; Passenger information system; trees

2-s2.0-85167369521

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162203993&doi=10.1016%2fj.physa.2023.128877&partnerID=40&md5=2a75222df5ae077265e1705b8f8fecd8

This article studies adaptive traffic signal control problem of single intersection in dynamic environment. A novel cycle-based signal timing method with traffic flow prediction (CycleRL) is proposed to improve the traffic efficiency under dynamic traffic flow. Firstly, the empirical mode decomposition is applied to denoise the flow data. Then a data-model hybrid driven traffic flow prediction strategy is designed to predict the traffic flow, which combines a model-based Kalman filter and an LSTM network-based predictor and adopts another Kalman filter to fuse both prediction results to improve the prediction precision. Besides, a robust signal cycle timing strategy based on human–machine collaboration is developed to deal with dynamic traffic flow, which firstly designs a rule-based signal cycle scheme according to the predicted flow data as the preliminary scheme, and then finetunes the preliminary scheme based on Soft Actor–Critic (SAC) algorithm according to the real-time traffic dynamics. The experiments in both synthetic scenario and real-world scenario show that the proposed data-model hybrid driven traffic flow prediction algorithm has better prediction performance and the proposed CycleRL method outperforms rule-based methods, flow-based allocation methods and traditional reinforcement learning method. Moreover, it is also shown that the proposed CycleRL method has better transferability to bridge the discrepancy across domains. © 2023 Elsevier B.V.

Data-model hybrid driven; Human–machine collaboration; Soft Actor–Critic; Traffic flow prediction; Traffic signal control

Empirical mode decomposition; Forecasting; Long short-term memory; Reinforcement learning; Street traffic control; Timing circuits; Traffic signals; Actor critic; Cycle based signals; Data-model hybrid driven; Dynamic environments; Human-machine collaboration; Hybrid-driven; Signal timing; Soft actor–critic; Traffic flow prediction; Traffic signal control; Kalman filters

2-s2.0-85162203993

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169146217&doi=10.3390%2fapp13169271&partnerID=40&md5=fcb9620e70b0b1033d883a8dc4812fb7

The issue of congestion on urban roads stems from an imbalance between transport demand and supply. It has become imperative to address the problem from the traffic demand side. While managing effective traffic demand relies on understanding the individual preferences of drivers, the current method for gathering preferences (i.e., through questionnaires) is both expensive and may not accurately capture the characteristics of respondents due to their varying interpretations of the options. To overcome these challenges, we proposed a path recommendation method that takes individual travel preferences into consideration by employing automatic license plate recognition (ALPR) data for the extraction of individual travel preferences. We initially identified key factors influencing the path selection behaviors of drivers, including path attributes, travel attributes, and individual attributes. Subsequently, we constructed a path satisfaction model based on individual preferences, employing an improved analytic hierarchy process (AHP). Furthermore, we utilized the pth percentile approach, rather than expert scores, in order to determine the relative importance of each indicator in the improved AHP. By applying the proposed model to the ALPR data from Xuancheng City, we successfully extracted the path selection preferences of drivers. We designed various scenarios to verify the reliability of the model, and the experimental results demonstrated that the proposed path satisfaction model can effectively capture the influence of underlying indicators on the path selection behavior of individuals with diverse travel preferences, considering different driver types and path attributes. Moreover, compared to the real trajectory, the recommended paths yielded an overall satisfaction improvement of over 10%, confirming the reliability and practicality of our proposed model. © 2023 by the authors.

analytic hierarchy process; automatic license plate recognition data; driving preference; intelligent transportation; path satisfaction

2-s2.0-85169146217

A survey on urban traffic control under mixed traffic environment with connected automated vehicles

Li, Jinjue (57223918230); Yu, Chunhui (56514352800); Shen, Zilin (58485599900); Su, Zicheng (57215352978); Ma, Wanjing (23097654300)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164720745&doi=10.1016%2fj.trc.2023.104258&partnerID=40&md5=265be9e172b728e2c6491e1afcbce568

Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. © 2023 Elsevier Ltd

Mixed traffic with connected automated vehicle; Signal control; Trajectory/route planning; Urban traffic control

Automation; Street traffic control; Traffic congestion; Traffic signals; Vehicle to vehicle communications; Vehicles; Automated vehicles; Mixed traffic; Mixed traffic with connected automated vehicle; Route planning; Signal control; Traffic environment; Traffic signal timings; Trajectory/route planning; Urban traffic control; Vehicle trajectories; traffic congestion; traffic management; trajectory; transportation development; transportation planning; transportation system; Trajectories

2-s2.0-85164720745

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Lazar, Razvan-Gabriel (57221006403); Pauca, Ovidiu (57200272519); Maxim, Anca (55553834600); Caruntu, Constantin-Florin (57198890773)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170351880&doi=10.3390%2fs23177576&partnerID=40&md5=c33af6d9c7d3c782287c09002f09eb85

A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today’s traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons. © 2023 by the authors.

Adaptive cruise control; Controllers; Motor transportation; Network architecture; Vehicle to vehicle communications; Vehicles; Connected vehicle platoon; Control architecture; Control functionality; Cooperative adaptive cruise control; Distributed model-based predictive control; Distributed models; Model based predictive control; Sensors data; V2X communication; Vehicle platoons; Vehicle to Everything

2-s2.0-85170351880

A Novel Traffic Characteristics Aware and Context Prediction Protocol for Intelligent Connected Vehicles

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151552940&doi=10.1109%2fTVT.2023.3259903&partnerID=40&md5=d9fa8a80633eb9ff7acf6123770013c2

Digital maps have been installed and attached to vehicles recently. They help with the GPS receivers to determine the relative locations of vehicles to other existing traffic and objects over the road network such as entrance/exit points, obstacles, road intersections, etc. This helps drivers or autonomous vehicles to decide the most appropriate reaction, in terms of speed, take-over, or stop operations ahead of time. Several daily traveling vehicles do not have digital maps. Besides, digital maps are vulnerable to being destroyed or inaccurate. They require regular updates due to the continuous construction and re-design of the road networks. These constructions aimed to enhance the road design and the traffic efficiency there. Moreover, accidents, broken vehicles, traffic congestion, or other ad-hoc obstacles appear unpredictably over the road network. In this article, we aim to introduce a prediction protocol that gathers and analyzes the traffic characteristics of vehicles over the investigated road scenario using wireless transceivers in vehicles. Then, it predicts the physical and traffic context based on the analyzed traffic data. This protocol can replace the absent or broken digital maps in vehicles. It also can be used to verify the correctness of the digital map in vehicles. From the experimental results, we can infer that the proposed protocol has succeeded in predicting the road context over highways and downtown scenarios. More accurate and better predictions are acquired by increasing the percentage of wireless transceiver-equipped vehicles. © 1967-2012 IEEE.

context prediction; difference speed; downtown; highways; relative distance; Traffic distribution

Accidents; Ad hoc networks; Forecasting; Global positioning system; Highway planning; Motor transportation; Radio transceivers; Roads and streets; Traffic congestion; Vehicle to vehicle communications; Ad-hoc networks; Context predictions; Difference speed; Downtown; Highway; Relative distances; Road; Road transportation; Traffic distributions; Wireless communications; Vehicles

2-s2.0-85151552940

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170565821&doi=10.1016%2fj.eswa.2023.121325&partnerID=40&md5=2ee095ad6b9e3b62a3c8663d16a0c2ed

Congestion is a bane of urban life that affects a large share of the population on a daily basis. Thus, congestion gets tremendous attention from city stakeholders, residents, and researchers. The key challenge to preventing congestion is to accurately predict the traffic status (e.g., speed) of a particular road segment which is greatly affected by many factors, such as spatial, temporal, and road conditions. Although several research studies have focused on preventing congestion, most prediction-based literature came short of accurate predictions regarding precision and time efficiency regarding large-scale datasets. This paper proposes a new hybrid approach called GRIZZLY. This approach utilizes an improved Sequence to Sequence Bi-directional Long Short Term Memory Neural Network model that integrates data pre-processing techniques such as normalization and embeddings to improve traffic prediction accuracy. Carried out experiments on two large-scale real-world datasets, namely PEMS-BAY and METR-LA, pinpointing that the proposed approach outperformed the pioneering competitors from time-series-based and hybrid neural network-based baselines in terms of the agreed-on evaluation criteria (precision and computation time). © 2023 Elsevier Ltd

Embedding; Hybrid Bi-directional LSTM neural network; Intelligent transportation system (ITS); Normalization; Sequence to Sequence; Time-series; Traffic speed prediction

Data handling; Forecasting; Intelligent systems; Intelligent vehicle highway systems; Large dataset; Long short-term memory; Roads and streets; Time series; Traffic congestion; Bi-directional; Embeddings; Hybrid bi-directional LSTM neural network; Intelligent transportation system; Intelligent transportation systems; Neural-networks; Normalisation; Sequence to sequence; Speed prediction; Times series; Traffic speed; Traffic speed prediction; Embeddings

2-s2.0-85170565821

Alapati, Sai Deepak (58361480000); Arunachalam, Muthukumar (58579720900); Chennamsetty, Chandana (58360858100); Dantam, Pujitha (58360220700); Dabbara, Anusha (58363368200)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171382469&doi=10.4018%2f978-1-6684-7110-4.ch008&partnerID=40&md5=69e8fb0922bc987915a8941ff4e4afb3

This chapter presents the application of YOLO, a deep learning-based object detection algorithm, for traffic monitoring. The algorithm was applied to real-time video streams from roadway cameras to detect and track vehicles. The results were compared with traditional computer vision methods and showed superior accuracy and processing speed. This study highlights the potential of YOLO for traffic monitoring and the significance of incorporating deep learning into intelligent transportation systems. YOLO V7 outperforms all other real-time object detectors on the GPU V100 in terms of speed and accuracy in the range of 5 to 160 frames per second and has the highest accuracy of 56.8% AP. YOLO V7 also introduces a new training methodology that improves the convergence rate and the generalization capabilities of the model. Experimental results show that YOLO V7 outperforms existing methods in terms of accuracy, speed, and efficiency, making it an attractive solution for real-world applications. © 2023, IGI Global. All rights reserved.

2-s2.0-85171382469

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Li, Min (57219136069); Tang, Jinjun (56044310100); Chen, Qun (35219750000); Liu, You (57911157900)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164220680&doi=10.1016%2fj.physa.2023.128995&partnerID=40&md5=584aa50634a8d236a4a7f1ae56977bbb

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications. © 2023 Elsevier B.V.

License plate recognition data; Link travel time; Signal cycle length; Traffic arrival pattern; Traffic flow; Urban intersection

Automatic vehicle identification; License plates (automobile); Optical character recognition; Parameter estimation; Poisson distribution; Travel time; Arrival patterns; Cycle length; License plate recognition data; Licenses plate recognition; Link travel time; Signal cycle; Signal cycle length; Traffic arrival pattern; Traffic flow; Urban intersections; Traffic signals

2-s2.0-85164220680

A parallel computing architecture based on cellular automata for hydraulic analysis of water distribution networks

Suvizi, Ali (58184732000); Farghadan, Azim (57193745469); Saheb Zamani, Morteza (57535461600)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152595864&doi=10.1016%2fj.jpdc.2023.03.009&partnerID=40&md5=b23a2801a55561ab082adb3dfc4b9efe

Water distribution networks (WDNs) are one of the largest infrastructures in society. Various methods for formulation and hydraulic analysis of water distribution networks, including numerical and non-numerical methods, have been previously proposed. Due to the complexity, the nonlinearity of the hydraulic equations of water distribution networks, and the need for multiple executions and uncertainties in parameters, solving the hydraulic model of water distribution networks has high time complexity. In this paper, a parallel computational architecture based on the concept of cellular automata is proposed to accelerate the numerical solution of the steady-state water distribution network model. Taylor series is proposed to solve hydraulic equations. The presented architecture was implemented as a parallel hardware platform on a field-programmable gate array. The performance of the proposed method was compared with EPANET software for networks with different complexities and topologies. The results show that the proposed parallel algorithm can accelerate the hydraulic analysis of regular water distribution networks up to 700 times and 250 times for small and large networks, respectively. © 2023 Elsevier Inc.

Cellular automata; Field programmable gate array; Steady-state hydraulic analysis; Taylor series; Water distribution network

Complex networks; Field programmable gate arrays (FPGA); Hydraulic models; Logic gates; Network architecture; Parallel architectures; Taylor series; Water distribution systems; Architecture-based; Cellular automatons; Field programmable gate array; Field programmables; Hydraulic analysis; Programmable gate array; Steady state; Steady-state hydraulic analyse; Taylor-series; Water distribution networks; Numerical methods

2-s2.0-85152595864

Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions

Ahmad, Shahnawaz (57216740438); Shakeel, Iman (58066778900); Mehfuz, Shabana (24465237900); Ahmad, Javed (57210531373)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162216339&doi=10.1016%2fj.cosrev.2023.100568&partnerID=40&md5=d321fb6c4e7bcdba56b88f5909b51ce3

In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area. © 2023 Elsevier Inc.

Cloud analytics; Data handling; Deep learning; Digital storage; Energy utilization; Fog; Fog computing; Green computing; Internet of things; Learning algorithms; Learning systems; Cloud-computing; Computing model; Computing paradigm; Deep learning; Edge computing; IoT; Learning models; Machine learning communities; Machine-learning; Network edges; Edge computing

2-s2.0-85162216339

Evolution of cooperation in vehicular cloud assisted networks for ITS services: A hunt game-based approach

Rajput, Nitin Singh (57197365834); Banerjee, Rahul (57189368284); Sanghi, Dheeraj (57205084505); Kalyansundaram, Chitra (58196637200)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153564886&doi=10.1016%2fj.future.2023.04.013&partnerID=40&md5=c56a7491cfe8c29a1465b28423711113

Modern-day vehicles that are generally equipped with advanced sensors, devices, and communication technologies and sophisticated cloud computing facilities paved the way for vehicular cloud-assisted networks. It has the potential to effectively offer a broad range of on-demand services to help intelligent transportation systems sustain current traffic loads. The system can be fully functional only when the vehicles remain available and cooperate to form such a system and provide services. However, the behavior of the nodes is highly dependent on the neighborhood environment and the needs of the owner. Hence, nodes may bail out from the system at random. Therefore, the stimulation of cooperation becomes a research challenge. In this work, we analyzed the behavior of nodes by modeling the event of intelligent transportation service provisioning in a vehicular cloud-assisted network as a cooperative hunt game to find a sufficient condition under which cooperation could be evolved. Subsequently, an incentive-based cooperation scheme is proposed that guarantees optimal incentives to the nodes depending upon their roles and the amount of participation in the network activities. To evaluate the scheme, we have created a static vehicular cloud testbed that provides Intelligent Transportation services like optimal route information to vehicles. Evaluation results show that our scheme brings about conditions where obeying network protocols and serving faithfully to the system becomes the most suitable and stable strategy for nodes. We have also analyzed various network parameters such as end-to-end delay, packet delivery ratio, normalized acceptance rate, normalized delivery rate, etc., that show the improvement in network performance as compared to other strategies. © 2023 Elsevier B.V.

Cooperation in vehicular networks; Cooperative game theory; Intelligent transportation system; Security; Service provisioning; Vehicle route optimization; Vehicular ad hoc networks; Vehicular cloud computing

Cloud computing; Computation theory; Computer games; Game theory; Intelligent systems; Intelligent vehicle highway systems; Network protocols; Vehicle to vehicle communications; Vehicular ad hoc networks; Cloud-computing; Cooperation in vehicular network; Cooperative game theory; Intelligent transportation systems; Security; Service provisioning; Vehicle route optimization; Vehicular Adhoc Networks (VANETs); Vehicular cloud computing; Vehicular clouds; Vehicular networks; Vehicles

2-s2.0-85153564886

Cyber-physical models for distributed CAV data intelligence in support of self-organized adaptive traffic signal coordination control

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152603213&doi=10.1016%2fj.eswa.2023.120035&partnerID=40&md5=00794a09194c44f9128a38488fa3c3b8

While more studies have been focused on adaptive traffic signal control (ATSC) algorithms to learn the control policy from interactions with the traffic environment by using connected and automated vehicle (CAV) data with no human intervention, less mechanism on streamlining the interactions between signal control regulations and CAV-data based traffic features has been integrated into the ATSC algorithms. Such underlying mechanism is of essence to make the CAV-data intelligent functions scalable to varied scales of ATSC system. The presented cyber-physical modeling methodology is attempted to fill in the gap through developing intelligent cell models embedded within the proposed roadside units data processors (RSU-DPs) (i.e., a V2X hub-signalized intersection), which can communicate with on-board units (OBU) mounted on vehicles through C-V2X technologies. The RSU-DPs can directly update the controller for dynamic update of vehicle status and arrivals at the concerned intersection approaches. The intelligent cell model is developed to connect the traffic flow status of the upstream and downstream intersections so as to dynamically adjust or ensure reasonable parameters for signal coordination control (such as offset and/or bandwidth). In this way, the naturalized CAV mobility data can be modeled as a “floating sensing mobility” data source from moving CAVs that will be potentially available ubiquitously on a cycle-to-cycle basis. The simulation-based experiments are designed to test the developed mechanism and models for producing core parameters for implementing the CAV-data-driven self-organized adaptive traffic signal coordination control and testing the functions for improving the efficiency of the corridor mobility, compared with traditional ATSC schemes. © 2023 Elsevier Ltd

CAV-enabled self-organized adaptive traffic signal coordination control; Cyber-physical integration modeling; Distributed CAV data intelligence; Roadside units data processors

Cyber Physical System; Roadsides; Street traffic control; Vehicle to Everything; Automated vehicles; Connected and automated vehicle-enabled self-organized adaptive traffic signal coordination control; Coordination control; Cybe-physical integration modeling; Cyber physicals; Data intelligence; Data processors; Distributed connected and automated vehicle data intelligence; Integration models; Roadside unit data processor; Roadside units; Self-organised; Traffic signal coordinations; Vehicles

2-s2.0-85152603213

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

Li, Tao (57213775664); Gong, Baoli (58074535800); Peng, Yong (56714313900); Nie, Jin (36935844200); Wang, Zheng (58567574000); Chen, Yiqi (58567940300); Xie, Guoquan (57226304440); Wang, Kui (55501601800); Zhang, Honghao (57190024208)

57213775664; 58074535800; 56714313900; 36935844200; 58567574000; 58567940300; 57226304440; 55501601800; 57190024208

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170278515&doi=10.3390%2fen16176235&partnerID=40&md5=ae8e71023e4be3fe665ec91f9e5b384f

With the development of the economy, urban road transportation has been continuously improved, and the number of motor vehicles has also increased significantly, leading to serious energy consumption issues. As critical nodes in the urban road transportation network, intersections have become a focal point of research on vehicle energy consumption. To investigate whether traffic signal lights affect fuel consumption and emissions, this study analyzed the operating characteristics, fuel consumption, and emissions of intersections with and without traffic signal lights using real-world vehicle data. The data from the INTERACTION dataset for both signalized intersection VA and unsignalized intersection MA are used in the study, with a time duration of 3200 s. The VT-micro energy consumption and emissions model was applied to calculate and comprehensively analyze the vehicle flow, fuel consumption, and emissions. Additionally, the study compared the fuel consumption and emissions for different driving scenarios, including straight through, left turn, right turn, and U-turn, within a single traffic signal cycle. The results revealed that at signalized intersections, the average fuel consumption per vehicle was 26.54 L/100 km, NOx emissions were 68.76 g/100 km, and CO2 emissions were 61.07 g/100 km. In contrast, at unsignalized intersections, the average fuel consumption per vehicle was 46.88 L/100 km, NOx emissions were 149.26 g/100 km, and CO2 emissions were 107.16 g/100 km. The study indicated that for traffic volumes between 50 and 103 vehicles per 100 s, signalized intersections demonstrated better fuel consumption and emission performance than unsignalized intersections. Signalized intersections could accommodate larger traffic volumes and provide enhanced traffic safety. In conclusion, the findings of this study are important for urban traffic planning and environmental policies. They provide a scientific basis for reducing fuel consumption and emissions and improving road traffic efficiency. Due to the advantages of signalized intersections in terms of energy consumption and emissions, future urban traffic planning should consider more signal light controls to achieve energy savings, emission reduction, and improved traffic operation efficiency. © 2023 by the authors.

emissions; energy consumption; fuel consumption; signalized intersection; traffic signal lights; unsignalized intersection

Carbon dioxide; Emission control; Energy conservation; Environmental protection; Fuel consumption; Motor transportation; Nitrogen oxides; Roads and streets; Street traffic control; Traffic signals; Urban transportation; Emission; Energy emissions; Energy-consumption; Fuel emissions; NO x emission; Signal light; Signalized intersection; Traffic signal light; Unsignalized intersections; Urban road; Energy utilization

2-s2.0-85170278515

IoT based real-time traffic monitoring system using images sensors by sparse deep learning algorithm

Barbosa, Rodrigo (57214186625); Ogobuchi, Okey Daniel (58092785400); Joy, Omole Oluwatoyin (58566189400); Saadi, Muhammad (55638443900); Rosa, Renata Lopes (36242302100); Otaibi, Sattam Al (57210173824); Rodríguez, Demóstenes Zegarra (36242276900)

57214186625; 58092785400; 58566189400; 55638443900; 36242302100; 57210173824; 36242276900

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170088303&doi=10.1016%2fj.comcom.2023.08.007&partnerID=40&md5=154b1a5e1c542b9f5068b52866a2281d

Intelligent traffic monitoring systems are necessary and useful tools due to the emerging technologies related to the Internet of Things (IoT) and Artificial Intelligence (AI). The integration of both technologies can facilitate better urban traffic management for fast networks, such as 5G, 5G+ and 6G environments. However, existing studies focus on complex and expensive solutions or present a latency for generating new training models with accuracy not superior to 98%. Thus, this paper proposes a vehicle identification using a sparse and soft variant of the CNN-based approach, the LightSpaN, which offers a fast training model, without using a complex solution. The effectiveness of the proposed solution is evaluated using the Simulation of Urban MObility (SUMO) tool and a real vehicle traffic implementation using IoT devices. The proposal identified the majority kind of vehicles in a short period of time, faster and more accurately than the related works. The results validated the proposed solution for a real-time traffic monitoring system, presenting an average accuracy of around 99.9% for emergency vehicles. Furthermore, a reduction of both total waiting time and total traveling time was reached by our proposal. © 2023

Artificial intelligence; CNN-based approach; Deep learning; Internet of things; Soft algorithms; Vehicle identification

5G mobile communication systems; Complex networks; Deep learning; Learning algorithms; Vehicles; CNN-based approach; Deep learning; Emerging technologies; Intelligent traffics; Realtime traffic; Soft algorithm; Traffic monitoring systems; Training model; Urban traffic management; Vehicle identification; Internet of things

2-s2.0-85170088303

Ensemble meta-heuristics and Q-learning for solving unmanned surface vessels scheduling problems

Gao, Minglong (58494824400); Gao, Kaizhou (36443489100); Ma, Zhenfang (58247545500); Tang, Weiyu (57222898446)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165233345&doi=10.1016%2fj.swevo.2023.101358&partnerID=40&md5=ea78def308bb0dc53d3f04c86acd14c3

This work addresses multiple unmanned surface vessel (USV) scheduling problems with minimizing maximum completion time. First, a mathematical model is developed with considering battery capacity and uncertain mapping time. Second, meta-heuristics and Q-learning are combined for solving the concerned problems. Based on the feature of USV scheduling problems, six heuristic rules are designed to obtain high-quality initializing solutions. According to the structure of solution space, six local search operators are designed. Q-learning is employed to select a premium local search operator in each iteration for improving the search efficiency of meta-heuristics. Finally, the performance of the proposed meta-heuristics with Q-learning based local search are verified by solving 10 cases with different scales. The experimental results and statistical tests demonstrate the competitiveness of the proposed algorithms for solving USVs scheduling problems. The particle swarm optimization with the first Q-learning strategy for local search selection is the best one among all compared algorithms. © 2023 Elsevier B.V.

Local search (optimization); Particle swarm optimization (PSO); Reinforcement learning; Unmanned surface vehicles; Battery capacity; Completion time; Heuristic learning; Local search; Local search operators; Metaheuristic; Q-learning; Scheduling; Scheduling problem; Unmanned surface vessels; Iterative methods

2-s2.0-85165233345

Shakya, Ashish Kumar (57527197000); Pillai, Gopinatha (7005839948); Chakrabarty, Sohom (56509092500)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164698132&doi=10.1016%2fj.eswa.2023.120495&partnerID=40&md5=62a4ffe6cb876468e82087ef5308126b

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential decision-making in complex problems. RL is inspired by trial-and-error based human/animal learning. It can learn an optimal policy autonomously with knowledge obtained by continuous interaction with a stochastic dynamical environment. Problems considered virtually impossible to solve, such as learning to play video games just from pixel information, are now successfully solved using deep reinforcement learning. Without human intervention, RL agents can surpass human performance in challenging tasks. This review gives a broad overview of RL, covering its fundamental principles, essential methods, and illustrative applications. The authors aim to develop an initial reference point for researchers commencing their research work in RL. In this review, the authors cover some fundamental model-free RL algorithms and pathbreaking function approximation-based deep RL (DRL) algorithms for complex uncertain tasks with continuous action and state spaces, making RL useful in various interdisciplinary fields. This article also provides a brief review of model-based and multi-agent RL approaches. Finally, some promising research directions for RL are briefly presented. © 2023 Elsevier Ltd

Deep Reinforcement Learning (DRL); Function approximation; Reinforcement learning; Stochastic optimal control

Approximation algorithms; Decision making; Deep learning; Learning algorithms; Learning systems; Multi agent systems; Stochastic control systems; Stochastic systems; Complex problems; Deep reinforcement learning; Functions approximations; Learn+; Machine learning techniques; Reinforcement learning algorithms; Reinforcement learnings; Sequential decision making; Stochastic optimal control; Reinforcement learning

2-s2.0-85164698132

Liu, Chunxia (58514306200); Lu, Kaihong (56082225000); Chen, Xiaojie (35228540400); Szolnoki, Attila (6701809628)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166292949&doi=10.1016%2fj.amc.2023.128251&partnerID=40&md5=bbad7a717b51dc3fbe39b96748f0c0ce

The distributed task allocation problem, as one of the most interesting distributed optimization challenges, has received considerable research attention recently. Previous works mainly focused on the task allocation problem in a population of individuals, where there are no constraints for affording task amounts. The latter condition, however, cannot always be hold. In this paper, we study the task allocation problem with constraints of task allocation in a game-theoretical framework. We assume that each individual can afford different amounts of task and the cost function is convex. To investigate the problem in the framework of population games, we construct a potential game and calculate the fitness function for each individual. We prove that when the Nash equilibrium point in the potential game is in the feasible solutions for the limited task allocation problem, the Nash equilibrium point is the unique globally optimal solution. Otherwise, we also derive analytically the unique globally optimal solution. In addition, in order to confirm our theoretical results, we consider the exponential and quadratic forms of cost function for each agent. Two algorithms with the mentioned representative cost functions are proposed to numerically seek the optimal solution to the limited task problems. We further perform Monte Carlo simulations which provide agreeing results with our analytical calculations. © 2023 Elsevier Inc.

Distributed optimization; Evolutionary game theory; Nash equilibrium; Population game; Replicator dynamics

Computation theory; Game theory; Intelligent systems; Monte Carlo methods; Number theory; Optimal systems; Allocation problems; Cost-function; Distributed optimization; Evolutionary game theory; Nash equilibria; Optimal solutions; Population games; Potential games; Replicator dynamics; Task allocation; Cost functions

2-s2.0-85166292949

Gallego-Madrid, Jorge (57210361966); Sanchez-Iborra, Ramon (55933950900); Ortiz, Jordi (55241420600); Santa, Jose (18038232200)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151432755&doi=10.1016%2fj.icte.2023.03.011&partnerID=40&md5=8f3e66ffb3693b3a91e3da67aae1bfcf

A great lack of 5G design is the traditional bottom-up development of network evolution, which has not effectively considered the requirements of applications and, particularly, vehicle to everything (V2X) applications. This paper provides a service-centric approach towards 6G V2X, with a concise overview of the upcoming hyper-connected vehicular ecosystem and its integration in the whole 6G fabric, analysing its particular infrastructure needs, as a way to reach key performance indicators (KPIs). We also present a 6G-oriented platform design able to manage the life-cycle of V2X applications across different domains by means of intelligent orchestration decisions. © 2023 The Author(s)

2-s2.0-85151432755

TL-Detector: Lightweight Based Real-Time Traffic Light Detection Model for Intelligent Vehicles

Yao, Zikai (57211075510); Liu, Qiang (57198641446); Xie, Qian (58118646000); Li, Qing (56384510800)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159702799&doi=10.1109%2fTITS.2023.3267430&partnerID=40&md5=b495004a2f9c0185552f67a784fce753

With the leap-forward development of intelligent driving, traffic light detection with high accuracy and high speed is important for intelligent transportation systems to ensure safety. However, existing methods encounter difficulties in balancing the detection speed and accuracy. This paper aims to present a flexible and robust lightweight model TL (Traffic Light)-Detector for real-time detection of traffic light. The model is composed of three parts. An enhanced backbone network combined with G-module is proposed to generate abundant information and reduce computational load, and the coordinate attention mechanism is introduced to focus on location features thereby strengthening the feature extract ability. The lightweight neck promotes multi-scale feature information aggregation for feature fusion to build a lightweight feature fusion network. The lightweight detection head adopts anchor-free mechanism to eliminate the hyperparameters related to the anchor. The dataset TL2022 of traffic light is built based on real traffic sceneries. Experimental results show that TL-Detector has the best comprehensive performance. TL-Detector achieves a detection speed of 277 FPS and a precision of 73.24% with only 0.72 GFLOPs. The experiments on LaRA (La Route Automatisee) public traffic light dataset show the excellent generalization ability. It indicates that TL-Detector can effectively achieve accurate and real-time detection for traffic lights. © 2000-2011 IEEE.

deep learning; Intelligent transportation system; lightweight network; traffic light detection; traffic safety

Deep learning; Feature extraction; Intelligent systems; Intelligent vehicle highway systems; Interactive computer systems; Signal detection; Computational modelling; Deep learning; Features extraction; Image color analysis; Intelligent transportation systems; Light detection; Lightweight network; Real - Time system; Traffic light; Traffic light detection; Traffic safety; Real time systems

2-s2.0-85159702799

Cooperative Cruise Control for Intelligent Connected Vehicles: A Bargaining Game Approach

Arevalo-Castiblanco, Miguel F. (57202155784); Pachon, Jaime (57886754500); Tellez-Castro, Duvan (57191867526); Mojica-Nava, Eduardo (26422453700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167922975&doi=10.3390%2fsu151511898&partnerID=40&md5=b2d8a21b2ab7160027e02cd3698d3bbe

Intelligent transportation systems (ITSs) are at the forefront of advancements in transportation, offering enhanced efficiency, safety, and environmental friendliness. To enable ITSs, autonomous systems play a pivotal role, contributing to the development of autonomous driving, data-driven modeling, and multiagent control strategies to establish sustainable and coordinated traffic management. The integration of networked and automated vehicles has garnered significant attention as a potential solution for alleviating traffic congestion and improving fuel economy, achieved through global route optimization and cooperative driving. This study focuses on a predictive control perspective to address the cooperative cruise control problem. Online decision making is employed during the driving process, utilizing information gathered from the network. By employing bargaining games to establish an operating agreement among vehicles, we formalize a synchronization approach based on predictive control theory. Ultimately, these findings are put to the test in an emulation environment within a hardware-in-the-loop system. The results revealed that the proposed cruise control successfully achieved convergence toward the desired reference signal. These results demonstrate the effectiveness of our approach in achieving synchronized platoon behavior and correct bargaining outcomes. These findings underscore the effectiveness and potential of DMPC with bargaining games in coordinating and optimizing vehicular networks. This paves the way for future research and development in this promising area. © 2023 by the authors.

bargaining; cooperative sector; decision making; intelligent transportation system; optimization; research and development; traffic congestion

2-s2.0-85167922975

Du, Wenlu (57991863500); Dash, Ankan (57224852283); Li, Jing (55392553100); Wei, Hua (57202789320); Wang, Guiling (55738657700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168995371&doi=10.3390%2fdesigns7040100&partnerID=40&md5=945ddf7f9fc9afea31bfc3b1bc38c93e

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions. © 2023 by the authors.

crash prediction; crash risk assessment; deep learning; machine learning; proactive safety methods; safety analysis; statistical analysis methods; survey; traffic safety

2-s2.0-85168995371

Traffic Flow Video Image Recognition and Analysis Based on Multi-Target Tracking Algorithm and Deep Learning

Zou, Songshang (57217196109); Chen, Hao (57188828991); Feng, Hui (58018099300); Xiao, Guangyi (26768413000); Qin, Zhen (57198995879); Cai, Weiwei (57215898790)

57217196109; 57188828991; 58018099300; 26768413000; 57198995879; 57215898790

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144072434&doi=10.1109%2fTITS.2022.3222608&partnerID=40&md5=faa215e7c77c7d6c8bfd0aa317771b9d

Traffic flow parameters are an important data support for the research and development of several technologies in the intelligent transportation system. Therefore, accurate and real-time estimation of traffic flow is particularly important for urban traffic. In this study, a real-time traffic flow detection system framework was constructed based on video image collection and analysis. According to the vehicle detection and tracking results, a traffic flow parameter estimation model and an improved LSTM network are proposed for spatiotemporal counting feature recognition. The results conclude that the developed framework can estimate the traffic flow density and count vehicles, as well as estimate the traffic flow velocity and traffic volume to estimate and optimize traffic flow, respectively. Additionally, the simulation results show that the proposed method can not only counts the two-way traffic vehicles quickly and accurately, but also avoids the use of the complex multi-target tracking method to spatiotemporal correlation of a single target, increases the speed and accuracy of the spatiotemporal information processing procedure, and has stronger scene adaptability. © 2000-2011 IEEE.

estimation model; feature recognition; LSTM; multi-target tracking; Traffic flow real-time detection

Clutter (information theory); Deep learning; Highway traffic control; Image analysis; Image recognition; Intelligent systems; Interactive computer systems; Parameter estimation; Signal detection; Street traffic control; Target tracking; Vehicles; Wireless sensor networks; Estimation models; Features recognition; LSTM; Multi-target-tracking; Real - Time system; Real-time detection; Road; Spatiotemporal phenomenon; Streaming medium; Traffic flow; Traffic flow real-time detection; Real time systems

2-s2.0-85144072434

Thahir, Adam Rizvi (57441026200); Coşkun, Mustafa (57189031203); Kılıç, Sultan Kübra (58485694100); Gungor, Vehbi Cagri (10739803300)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164686151&doi=10.1016%2fj.csi.2023.103771&partnerID=40&md5=4b5cb031c74fbef63216bd8ef66f53fe

In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations. © 2023 Elsevier B.V.

Deep learning; Flow graphs; Graphic methods; Traffic congestion; Traffic signs; Congestion; Edge flow; Flow prediction; Graph-based; Intelligent traffics; Light systems; Reinforcement learnings; Semi-supervised learning; Traffic flow; Traffic light; Reinforcement learning

2-s2.0-85164686151

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid A.J.M.; Kamarulzaman S.F.; Rahman M.A.; Murad S.A.; Kamal M.A.S.; Alenezi A.H.

Muzahid, Abu Jafar Md (57221741096); Kamarulzaman, Syafiq Fauzi (54393365400); Rahman, Md Arafatur (55457938100); Murad, Saydul Akbar (57218952421); Kamal, Md Abdus Samad (7202025866); Alenezi, Ali H (57221753256)

57221741096; 54393365400; 55457938100; 57218952421; 7202025866; 57221753256

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146195244&doi=10.1038%2fs41598-022-27026-9&partnerID=40&md5=b34b47dab2042b15d5c0a3aa68889234

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems. © 2023, The Author(s).

achievement; article; avoidance behavior; conceptual framework; decision making; human; taxonomy

2-s2.0-85146195244

Lee, Garyoung (58030259900); Ding, Zijian (57964454100); Laval, Jorge (7102083709)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165655343&doi=10.1016%2fj.trc.2023.104239&partnerID=40&md5=379c781424c8b91cda24462de8771d9f

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. We discovered that: (i) subsets of the MFD determined by the loop detector position can help estimate whether the loop detector MFD will have a bias; (ii) non-uniform distribution of loop detectors is more likely to cause a discrepancy in the position subsets of the MFD, particularly if detectors in the network are positioned more downstream with a greater variation; and (iii) a lower ratio of link length to green signal time increases the possibility of bias in loop detector MFD, while the impact of the aggregation interval was found to be negligible. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself. © 2023 Elsevier Ltd

Detector positions; Down-stream; Empirical data; Loop detector; Loop detector data; Macroscopic fundamental diagram; Non-uniform distribution; Traffic simulations; Traffic-state estimations; Uniform distribution; analytical method; detection method; diagram; empirical analysis; positioning; signal; traffic management; Traffic control

2-s2.0-85165655343

Khan, Zahid (57022980500); Koubaa, Anis (15923354900); Benjdira, Bilel (57193950453); Boulila, Wadii (37088273900)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162798488&doi=10.1016%2fj.compeleceng.2023.108825&partnerID=40&md5=3affb34c0855a9ec7f30db2f3cbe11e6

The rapid increase in population and transportation resources presents numerous challenges, including traffic congestion and accidents. This paper proposes a smart traffic management (STM) framework that combines the Internet of Vehicles (IoV) and game theory to manage traffic loads at road intersections. The intersection is considered a non-cooperative game, where traffic flow for each route is determined by the Nash Equilibrium (NE) to ensure that no individual can improve their performance by changing their strategy. In severe congestion, many players/vehicles significantly affect the strategy selection process. To address this, we adopt an agent-representative approach, using spectral clustering to group players with the same strategies and payoff. The proposed STM is compared with other schemes, i.e., the conventional approach without NE and SmartRoute. Our STM significantly outperformed existing protocols, reducing intersection traffic intensity by 30% and average waiting time by 40%, demonstrating its effectiveness in managing traffic loads at uncontrolled intersections. © 2023 Elsevier Ltd

Game theory; IoV; Nash equilibrium; Smart transportation; Traffic congestion; Traffic management

Clustering algorithms; Computation theory; Traffic congestion; Internet of vehicle; Management frameworks; Nash equilibria; Noncooperative game; Road intersections; Smart traffic; Smart transportation; Traffic loads; Traffic management; Transportation resources; Game theory

2-s2.0-85162798488

Applications of evolutionary game theory in urban road transport network: A state of the art review

Ahmad, Furkan (57195577677); Shah, Zubair (56428700200); Al-Fagih, Luluwah (56606211500)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168001204&doi=10.1016%2fj.scs.2023.104791&partnerID=40&md5=2775ff4f175f88573c00d2f466890610

A sustainable transport infrastructure is one of the pillars of a sustainable city. However, the literature indicates that urbanization, population growth, changes in population density, and motorization make it difficult for the current road transport system to meet mobility needs for a sustainable city. Traffic crashes and congestion on roads are common as a result of increasing travel times, fuel consumption, and carbon emissions, thereby reducing efficiency and sustainability of mobility systems. Managing these issues involves the interaction of multiple decision-makers, such as vehicles, pedestrians, traffic system operators, and authorities. Accordingly, these are well-suited to being analyzed under the guise of game theory. While classical game theory possesses multiple limitations, it can be argued that evolutionary game theory (EGT) models are more effective for real-world scenarios. This manuscript presents a state-of-the-art review on EGT applied to the road transportation network. The manuscript has divided the application of EGT in advancing the transportation network into multiple categories, i.e., choice-based analysis, traffic management, behavioral interactions, routing operation, and transport safety. This manuscript provides an in-depth analysis and a comparative criticism of the various proposed evolutionary game models. Finally, the manuscript discusses the challenges and provides recommendations for future research on evolutionary game models in transportation networks. These insights aim to facilitate targeted activities based on current research needs. © 2023 Elsevier Ltd

Sustainable road transport; Traffic control; Traffic routing, and accident prediction; Travel behavior

Accidents; Decision making; Motor transportation; Population statistics; Roads and streets; Traffic congestion; Travel time; Urban transportation; Accident prediction; Evolutionary game theory; Road transports; State-of-the art reviews; Sustainable cities; Sustainable road transport; Traffic routing; Traffic routing, and accident prediction; Transportation network; Travel behaviour; accident; evolutionary theory; flood routing; game theory; road transport; routing; traffic congestion; traffic management; transportation safety; travel behavior; urban transport; Game theory

2-s2.0-85168001204

Gaining insight for the design, development, deployment and distribution of assistive navigation systems for blind and visually impaired people through a detailed user requirements elicitation

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131937043&doi=10.1007%2fs10209-022-00885-9&partnerID=40&md5=870382a0d33a1263114b31383519a548

The autonomy, independence, productivity and, in general, quality of life of people with visual impairments often rely significantly on their ability to use new assistive technologies. In particular, their ability to navigate by foot, use means of transport and visit indoor spaces may be greatly enhanced by the use of assistive navigation systems. In this paper, a detailed analysis of user needs and requirements is presented concerning the design and development of assisting navigation systems for blind and visually impaired people (BVIs). To this end, the elicited user needs and requirements from interviews with the BVIs are processed and classified into seven main categories. Interestingly, one of the categories represents the requirements of the BVIs to be trained on the use of the mobile apps that would be included in an assistive navigation system. The need of the BVIs to be confident in their ability to safely use the apps revealed the requirement that training versions of the apps should be available. These versions would be able to simulate real-world conditions during the training process. The requirements elicitation and classification reported in this paper aim to offer insight into the design, development, deployment and distribution of assistive navigation systems for the BVIs. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Assistive navigation system; Blind and visually impaired people; Mobile apps design; Requirements’ elicitation

Requirements engineering; Assistive navigation system; Assistive navigations; Blind and visually impaired; Blind and visually impaired people; Design development; Mobile app; Mobile app design; Requirements elicitation; User requirements; Visually impaired people; Navigation systems

2-s2.0-85131937043

Active design of dynamic GP models for model predictive control using expected improvement

Sun, Pei (57217828446); Chen, Junghui (8507113100); Xie, Lei (7402590530); Su, Hongye (7401459370)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147492181&doi=10.1002%2fcjce.24822&partnerID=40&md5=e95e69c0a6d14c53826f3cd55bef0eef

Modelling is a basic and key requirement for model-based controlling, monitoring, or other process strategies. In non-linear model predictive control (NMPC), although data-driven models can be more easily established than first-principle ones, representative data may not be adequately included in advance to train a complete model, which is an attractive research topic. An actively improved Gaussian process (GP) model building strategy is developed, especially for incomplete models based on the idea of Bayesian optimization. The GP model can be used online as the internal model of model predictive control (MPC) directly. The model-building objective is based on the expected improvement strategy, which can exploit information gained from the currently gathered data as well as explore uncharted regions. The proposed method is a real-time design of experiments based on variance information of GP for efficient model building with insufficient initial training data for NMPC. Multi-step ahead prediction model is considered to give full play to predicting features of NPMC. Besides, a novel disturbance rejection strategy is also proposed based on GP outputs. Two simulation results, including comparisons with some traditional algorithms, are presented to demonstrate the effectiveness of the proposed method. © 2022 Canadian Society for Chemical Engineering.

Disturbance rejection; Gaussian distribution; Gaussian noise (electronic); Model buildings; Model predictive control; Predictive control systems; Structural design; Active improvement; Data-driven model; Expected improvements; First principles; Gaussian process models; Gaussian Processes; Model-based OPC; Model-predictive control; Nonlinear model predictive control; Process strategies; Design of experiments

2-s2.0-85147492181

Learning positioning policies for mobile manipulation operations with deep reinforcement learning

Iriondo, Ander (57205507262); Lazkano, Elena (8669272700); Ansuategi, Ander (37107056900); Rivera, Andoni (57909871200); Lluvia, Iker (57204806422); Tubío, Carlos (36919365100)

57205507262; 8669272700; 37107056900; 57909871200; 57204806422; 36919365100

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150052693&doi=10.1007%2fs13042-023-01815-8&partnerID=40&md5=bb1c1b258720dfaef1af7109bb11ae8a

This work focuses on the operation of picking an object on a table with a mobile manipulator. We use deep reinforcement learning (DRL) to learn a positioning policy for the robot’s base by considering the reachability constraints of the arm. This work extends our first proof-of-concept with the ultimate goal of validating the method on a real robot. Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to model the base controller, and is optimised using the feedback from the MoveIt! based arm planner. The idea is to encourage the base controller to position itself in areas where the arm reaches the object. Following a simulation-to-reality approach, first we create a realistic simulation of the robotic environment in Unity, and integrate it in Robot Operating System (ROS). The drivers for both the base and the arm are also implemented. The DRL-based agent is trained in simulation and, both the robot and target poses are randomised to make the learnt base controller robust to uncertainties. We propose a task-specific setup for TD3, which includes state/action spaces, reward function and neural architectures. We compare the proposed method with the baseline work and show that the combination of TD3 and the proposed setup leads to a 11 % higher success rate than with the baseline, with an overall success rate of 97 %. Finally, the learnt agent is deployed and validated in the real robotic system where we obtain a promising success rate of 75 %. © 2023, The Author(s).

Deep reinforcement learning; Mobile manipulation; Pick and place; Sim-to-real transfer

Controllers; Deep learning; Manipulators; Robot Operating System; Robots; Deep reinforcement learning; Learn+; Learning to learn; Mobile manipulation; Mobile manipulator; Pick and place; Proof of concept; Reachability constraints; Reinforcement learnings; Sim-to-real transfer; Reinforcement learning

2-s2.0-85150052693

Gholami, Farshad (57203091655); Khanmirza, Esmaeel (15071270500); Riahi, Mohammad (6701389655)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160641084&doi=10.1007%2fs11554-023-01314-7&partnerID=40&md5=5137555336ba5941624324f0f3fd9c4d

In this research, the accuracy and speed of obstacle detection in data fusion of ultrasonic and stereo vision have been improved. The smoothness assumption has been used in such a way that the responses are significantly improved without increasing calculation. In addition, with the development of the proposed method to run on the graphics card, the cross-checking process has been done without the need to change the reference image and without more calculation of the cost function. The results of this study show that the proposed method improved the quality of the responses compared to the previous study, and the obstacle detection rate in intelligent vehicles has increased to 41 pairs of frames per second. This processing rate is 477.40 times faster than the usual local stereo method and 33.77% faster than the previous study on the data fusion of ultrasonic and stereo vision. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Cost functions; Intelligent vehicle highway systems; Obstacle detectors; Stereo image processing; Stereo vision; Ultrasonic applications; Cost-function; Detection rates; Frames per seconds; Graphic cards; Obstacles detection; Processing rates; Real- time; Reference image; Stereo method; Data fusion

2-s2.0-85160641084

Event-triggered cooperative control for uncertain multi-agent systems and applications

Wang, Shihao (57299884700); Zheng, Shiqi (55446613400); Ahn, Choon Ki (8437989300); Shi, Peng (36748941200); Jiang, Xiaowei (55724181000)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158853815&doi=10.1002%2frnc.6752&partnerID=40&md5=7ad639b0ee5cbf4077fd97ccb9ebf9ad

This paper investigates the event-triggered cooperative output regulation problem for uncertain multi-agent systems, demonstrating that the output regulation error of the uncertain multi-agent system can converge to a small neighborhood of the origin. The proposed method has several notable features. Firstly, both the leader and follower systems contain unknown parameters, and follower systems can be heterogeneous. Secondly, the controller only requires the relative output of each agent, rather than the absolute output or state. Thirdly, the controller is fully distributed, which implies that it does not rely on any global information. In order to achieve the above purpose, a fully distributed reduced-order observer is proposed with new adaptive laws. Furthermore, new adaptive event-triggered mechanisms are designed on the basis of the neighborhood regulation error (NRE) and the observational regulation error (ORE). Finally, the proposed approach is validated through simulation and experiment on a multi-joint robot manipulator. © 2023 John Wiley & Sons Ltd.

event-triggered mechanism; fully distributed; multi-agent systems; output regulation problem

Controllers; Errors; Manipulators; Robot applications; Uncertainty analysis; Adaptive laws; Co-operative control; Event-triggered; Event-triggered mechanism; Fully distributed; Global informations; Neighbourhood; Output regulation; Output regulation problem; Reduced-order observers; Multi agent systems

2-s2.0-85158853815

A Cooperative Multiagent Reinforcement Learning Framework for Droplet Routing in Digital Microfluidic Biochips

Jiang, Chen (57850215700); Yang, Rongquan (57575397000); Xu, Qi (56564490800); Yao, Hailong (7401678194); Ho, Tsung-Yi (25227323400); Yuan, Bo (38860982900)

57850215700; 57575397000; 56564490800; 7401678194; 25227323400; 38860982900

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146254911&doi=10.1109%2fTCAD.2022.3233019&partnerID=40&md5=59423e675258164a72fb384ab878fe75

Digital microfluidic biochips (DMFBs) have shown great advantages in automatically executing biochemical protocols through manipulating discrete nano/picoliter droplets which are transported in parallel to achieve high-throughput outcomes. However, because of electrode degradations, the droplet transportation may fail, causing incorrect fluidic operations. To perform safety-critical bio-protocols, the reliability of droplet transportation becomes an utmost concern for DMFBs. It has been shown by the previous works that a reliable transportation policy can be learned using reinforcement learning (RL)-based methods by capturing the underlying health conditions of electrodes and making online decisions. However, previous RL methods may fail to accomplish routing tasks with multiple droplets, because there is a lack of cooperation among different agents (each agent represents one droplet). To deal with this problem and scale RL methods to many droplets, this article proposes a new cooperative centralized learning and distributed execution multiagent RL (MARL) framework for droplet routing in DMFBs using value-decomposition networks (VDNs). Moreover, to speed up the training and decision process as well as apply our method in large biochips, we use a partial observation space where agents can only observe environment in a limited field of view (FOV) centered around themselves. Compared with the state-of-the-art approach, the superior performance of the proposed approach is demonstrated on different DMFBs in terms of success rate and average completion time. We also validate our method on large biochips (e.g., 50 × 50 DMFBs) with more droplets than state-of-the-art approach (e.g., ten droplets). © 2022 IEEE.

Digital microfluidic biochips (DMFBs); droplet routing; field of view (FOV); multiagent reinforcement learning (RL); value-decomposition networks (VDNs)

Biochips; Drops; Fertilizers; Reinforcement learning; Safety engineering; Decomposition networks; Digital microfluidic biochips; Droplet routing; Field of views; Multi-agent reinforcement learning; Reinforcement learnings; Routings; Task analysis; Value decomposition; Value-decomposition network; Multi agent systems

2-s2.0-85146254911

Rathee, Geetanjali (56357928400); Garg, Sahil (55601765300); Kaddoum, Georges (24587843100); Choi, Bong Jun (35955497300); Benslimane, Abderrahim (7005984674); Hassan, Mohammad Mehedi (57201949986)

56357928400; 55601765300; 24587843100; 35955497300; 7005984674; 57201949986

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165968957&doi=10.1016%2fj.aej.2023.07.020&partnerID=40&md5=88cc8fdcb690ddffb4d9e3d382816eb4

To ensure better facilitation of vehicular services and improve driving safety in the Internet of Vehicles (IoV), context prediction among vehicles plays a very crucial role. However, as more malicious IoV devices get involved in the network, the context prediction accuracy shared among various servers may degrade severely. Existing schemes have used cryptographic mechanisms to securely and accurately identify malicious devices. However, time and the subsequent delay in identifying and rating the legitimate communicating IoV devices emerge as a crucial issue. Hence, to solve this critical problem, we put forth an efficient and reliable trust framework where trust and context prediction is achieved by Tidal Trust Mechanism (TTM) and Contract Theory (CT). TTM can successfully rate the degree of trust between the devices with a high level of accuracy, whereas CT can verify the context prediction reliably. The proposed mechanism based on TTM and CT ensures that trusted IoV devices are identified with high accuracy and verified reliably. The proposed framework is simulated over real-world data set in MATLAB for various performance metrics, such as altered records, accuracy prediction, response time, and utilities of IoV devices. Simulation results show that the proposed framework provides a significant improvement of approximately 87% in comparison to existing (baseline) approaches while analyzing the accuracy, record alteration, and resource utility among the devices in the network. © 2023 THE AUTHORS

Contract theory; Internet of vehicles; Secure context prediction; Tidal trust mechanism; Trust rate

Autonomous vehicles; MATLAB; Autonomous Vehicles; Context predictions; Contract Theory; Internet of vehicle; Secure context prediction; Secure contexts; Tidal trust mechanism; Tidal trusts; Trust mechanism; Trust rate; Forecasting

2-s2.0-85165968957

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

Ding, Lige (57207948158); Zhao, Dong (55322775300); Wang, Zhaofeng (57478280100); Wang, Guang (57214494811); Tan, Chang (57206482975); Fan, Lei (57211342673); Ma, Huadong (7403096223)

57207948158; 55322775300; 57478280100; 57214494811; 57206482975; 57211342673; 7403096223

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134241698&doi=10.1109%2fTMC.2022.3188344&partnerID=40&md5=f06415b03b55b3eff587d7d96a6396f7

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines. © 2002-2012 IEEE.

Cooperative vehicle-infrastructure system; deep reinforcement learning; emergency vehicles; route planning

Deep learning; Interactive computer systems; Iterative methods; Real time systems; Roads and streets; Scheduling; Traffic congestion; Traffic signals; Cooperative vehicle-infrastructure system; Cooperative vehicles; Deep reinforcement learning; Dynamic scheduling; Features extraction; Green products; Infrastructure systems; Real - Time system; Reinforcement learnings; Road; Route planning; Vehicle's dynamics; Reinforcement learning

2-s2.0-85134241698

Anand, Rohit (56937904300); Jain, Vipin (57208404480); Singh, Anushi (58107879200); Rahal, Disha (58455075400); Rastogi, Prachi (58452963400); Rajkumar, Avinash (57951223000); Gupta, Ankur (57215604870)

56937904300; 57208404480; 58107879200; 58455075400; 58452963400; 57951223000; 57215604870

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163967138&doi=10.1201%2f9781003319238-14&partnerID=40&md5=a5c16dc71894672352d12b1c3cb9ae58

2-s2.0-85163967138

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang, Wentao (57221431466); Chen, Ronghua (58204834100); Li, Jie (57215906067); Huang, Tianyin (37020052700); Wu, Bingdang (57215374780); Ma, Jun (57948644700); Wen, Qingqi (58205576700); Tan, Jie (57215903093); Huang, Wenguang (56576681600)

57221431466; 58204834100; 57215906067; 37020052700; 57215374780; 57948644700; 58205576700; 57215903093; 56576681600

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85154068487&doi=10.1007%2fs42773-023-00225-x&partnerID=40&md5=ff9f3c61785f16f85adb52c2c1d3d09d

Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract: [Figure not available: see fulltext.] © 2023, The Author(s).

Adsorption; Artificial intelligence; Biochar; Machine learning; Pollutant removal; Pyrolysis

2-s2.0-85154068487

Real-Time IoT-Based Connected Vehicle Infrastructure for Intelligent Transportation Safety

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153373728&doi=10.1109%2fTITS.2023.3263271&partnerID=40&md5=f6c754190051e2c4f7a78857ea5cea5b

The transportation sector faces severe consequences due to the incrementing population influx yielding congestions, fatalities and haphazard traffic scenarios. Advanced Driver Assistance Systems (ADAS) assists highly in such scenarios by eradicating probable accidents and ensures traffic safety. This paper presents intelligent transportation systems (ITS) approach through the connected vehicle technology infrastructure. YOLO v4 (You Only Look Once) inspired real-time computer vision capable of detecting vehicles, pedestrians and animals at high efficiency (0.9777 mean average precision) is deployed on the GPU (Graphics Processing Unit) which offered higher frame rate of detection (74.26 fps). The locations of animals and potholes were mapped through consistent survey and mobile app which relayed the detected locations to the cloud server forming a geospatial database. Clustered locations from the geospatial database on dense transportation network were utilized for constructing animal and pothole hotspot zones. A basic level of display warning was triggered when the vehicle approached animal and pothole areas. Furthermore, advanced alert comprising of display and sound alert was trigger when the vehicle approached hotspot zones. This was implemented using real-time Internet of things (IoT) and cloud infrastructure applications for continuous vehicle's location monitoring and triggering as per the hotspot geo-locations. The proposed system ensured traffic safety and assisted in avoiding probable crashes and accidents that generally led to congestions and fatalities. © 2000-2011 IEEE.

Accident prevention; Accidents; Advanced driver assistance systems; Automobile drivers; Autonomous vehicles; Computer graphics; Computer graphics equipment; Computer vision; Graphics processing unit; Internet of things; Location; Program processors; Traffic congestion; Geospatial database; Hotspots; Intelligent transportation; Intelligent transportation systems; Population influx; Real-time internet; Traffic safety; Transport safety; Transportation safety; Transportation sector; Intelligent systems

2-s2.0-85153373728

Combined extreme learning machine and max pressure algorithms for traffic signal control

Faqir, Nada (57221263501); Loqman, Chakir (25224542600); Boumhidi, Jaouad (16443952100)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165069740&doi=10.1016%2fj.iswa.2023.200255&partnerID=40&md5=2e42a93924c8725791377030eb74126f

Nowadays, rush-hour traffic congestion problems persist in most major cities around the world, resulting in increased pollution, noise, and stress for citizens. Therefore, an optimal traffic light strategy is needed. For this purpose, several models have been proposed. However, these models often overlook the non-stationarity of traffic, which occurs due to changing traffic conditions over time. Additionally, these models are steady-state process models, leading to a decrease in their predictive power over time. To address these issues, this paper proposes the combination of two algorithms: a passive Extreme Learning Machine with periodic mini-batch learning (PB-ELM) for predicting traffic flow and the Max Pressure control algorithm (MPA) for signal control. In the first step, the passive periodic Extreme Learning Machine (PB-ELM) adjusts quickly and regularly based on new data, overcoming traffic non-stationarity and improving long-term performance. In the second step, the MPA is preferred for signal control due to its simplicity and speed. The PB-ELM-MPA model is a combination of predictive algorithms that takes the current road network conditions as input and predicts the flow of vehicles at intersections. The model utilizes learned characteristics of the source and destination roads to estimate the number of vehicles in each movement. The PB-ELM outputs serve as the starting point for the max-pressure algorithm, which reduces congestion by considering only the vehicles on road segments closest to the intersection and selecting the highest pressure at each time interval. The proposed PB-ELM-MPA model is evaluated on an isolated intersection simulated with the SUMO micro-simulator, demonstrating a significant improvement in avoiding traffic jams. The total staying time of all vehicles present at the intersection is reduced by 65% compared to the fixed configuration of traffic lights. Additionally, CO2 emissions and fuel consumption are reduced by approximately 34% compared to the classic MPA and Deep Q-Network approaches. © 2023

Extreme learning machine; Max Pressure algorithm; Non-stationarity; Passive learning; Traffic signal control

Knowledge acquisition; Learning algorithms; Machine learning; Motor transportation; Roads and streets; Street traffic control; Traffic congestion; Traffic signals; Traffic signs; Algorithm model; Congestion problem; Extreme learning machine; Learning machines; Max pressure algorithm; Non-stationarities; Passive learning; Signal control; Traffic light; Traffic signal control; Vehicles

2-s2.0-85165069740

HARMONIC: Shapley values in market games for resource allocation in vehicular clouds

Ribeiro A., Jr.; da Costa J.B.D.; Filho G.P.R.; Villas L.A.; Guidoni D.L.; Sampaio S.; Meneguette R.I.

Ribeiro, Aguimar (58080419100); da Costa, Joahannes B.D. (57210285313); Filho, Geraldo P. Rocha (55990312500); Villas, Leandro A. (23567383600); Guidoni, Daniel L. (24070391000); Sampaio, Sandra (56382119200); Meneguette, Rodolfo I. (55549293300)

58080419100; 57210285313; 55990312500; 23567383600; 24070391000; 56382119200; 55549293300

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162189521&doi=10.1016%2fj.adhoc.2023.103224&partnerID=40&md5=88274a88fd9882e74b7178ecd3de40ca

Real-time allocation of resources to fulfill service requests from road vehicles is becoming increasingly complex, for two main reasons: the continuous increase in the number of Internet-connected vehicles on roads all over the world, and the emergence of complex and resource-greedy applications that require fast execution, often under limited availability of computational resources. While many resource allocation solutions to this problem have been proposed recently, these solutions rely on unrealistic scenarios and constraints that limit their practical use. This paper presents HARMONIC, a Game Theory-based coalition game that aims to maximize resource utilization and dynamically balance resource usage across multiple Vehicular Clouds (VCs). HARMONIC employs a Shapley value-based strategy to determine the order of task allocation to available resources. It is built upon our proposed Market Game model, specifically designed to address resource allocation challenges in dynamic VCs. We conduct a comparative analysis with existing literature solutions under various scenarios and resource constraints to evaluate HARMONIC's performance. Our simulation results demonstrate that HARMONIC achieves resource allocation in fewer rounds and with fewer failures. Furthermore, it effectively distributes tasks to more VCs, improving load balancing and overall system efficiency. © 2023 Elsevier B.V.

Game Theory; Load-balancing; Resource allocation; Shapley Value; VANET; Vehicular Clouds

Commerce; Computation theory; Harmonic analysis; Resource allocation; Load-Balancing; Market game; Real- time; Resources allocation; Road vehicles; Service requests; Shapley value; Time-allocation; VANET; Vehicular clouds; Game theory

2-s2.0-85162189521

An advanced control strategy for connected autonomous vehicles based on Micro simulation models at multiple intersections

Wang, Jie (58465261600); Cai, Zhiyu (58292994700); Chen, Yaohui (57954984600); Yang, Peng (58291997400); Chen, Bokui (47561038200)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160433413&doi=10.1016%2fj.physa.2023.128836&partnerID=40&md5=d50e01a458b2a184751ef1641b33a3b4

The high controllability and connectivity of connected automated vehicles (CAVs) have created opportunities to enhance the road network's performance. Considering the case that all vehicles in the road network are CAVs, we build an intersection model in which all CAVs follow a given route through the intersection. We obtain the basic evolution rules of vehicles based on the cellular automata model and design conflict judgment and speed adjustment rules to ensure the passage of CAVs within intersections without conflicts. Further, with the goal of minimizing traffic delays in the road network, we design a coordinated control strategy for multiple intersections by considering the density of downstream vehicles. Finally, we conduct simulation experiments in different traffic scenarios. The results show that as the vehicle arrival rates rise, CAVs’ average delays rise and their average speeds decrease. The growth rate of traffic flow through the intersection slows down when a certain percentage is reached. Meanwhile, the coordinated control strategy for multiple intersections can obtain a minor average delay of vehicles on the road network. © 2023

Cellular automaton; Conflict; Connected automated vehicles; Downstream density; Intersection

Autonomous vehicles; Growth rate; Motor transportation; Roads and streets; Advanced control strategy; Automated vehicles; Average delay; Cellular automatons; Conflict; Connected automated vehicle; Coordinated control strategy; Down-stream; Downstream density; Road network; Cellular automata

2-s2.0-85160433413

S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152371339&doi=10.1007%2fs11227-023-05216-0&partnerID=40&md5=7b9d2c2f2db56d45ae0791b3e16dcee9

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes’ longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Edge computing; Fuzzy inference system (FIS); Intelligent transportation system (ITS); Internet of things (IoT); Smart city; Traffic light controller (TLC); Traffic light scheduling

Adaptive control systems; Controllers; Edge computing; Fleet operations; Fog computing; Fuzzy inference; Fuzzy systems; Intelligent systems; Intelligent vehicle highway systems; Roads and streets; Smart city; Street traffic control; Urban transportation; Vehicles; Edge computing; Fuzzy inference system; Fuzzy inference systems; Intelligent transportation system; Intelligent transportation systems; Internet of thing; Traffic light control; Traffic light controller; Traffic light scheduling; Internet of things

2-s2.0-85152371339

Ullah, Waseem (57218579944); Min Ullah, Fath U (57221463575); Ahmad Khan, Zulfiqar (58314622900); Wook Baik, Sung (58314871700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162012232&doi=10.1016%2fj.eswa.2023.120599&partnerID=40&md5=d7bd152db934acac083f96b9e3d140a5

Surveillance cameras are installed across various sectors of a smart city in order to capture ongoing events for monitoring purposes. The analysis of these surveillance videos is an important research topic that involves activity recognition, object detection, anomaly recognition, and other problems. However, anomaly recognition is the most common task in a smart city, and has received significant attention with the aim of ensuring public safety and security. Many works have been published in this field, but these schemes have not been able to provide the desired detection outcomes. Mainstream anomaly recognition methods are heavily dependent on strong supervision to achieve satisfactory performance, which is time-consuming and impractical. With a particular focus on this problem, this article presents a deep convolution neural network (CNN)-based novel anomaly recognition model, in which deep features are extracted from surveillance video frames. These features are forwarded to the proposed temporal convolution network (TCN) that includes a multi-head attention module to enable it to recognise anomalies from these videos. The multi-head temporal attention mechanism enables the model to obtain more key temporal information about the complex surveillance environment. Experiments conducted on standard datasets and a comparison with state-of-the-art approaches demonstrate the effectiveness and superiority of the proposed framework, which achieves increases in accuracy of 0.9%, 1.9%, 0.65%, 0.27%, and 1.5% on the UCF-Crime2local, LAD-2000, RWF-2000, RLVS, and Crowd Violence datasets, respectively. These outcomes indicate the suitability of our method for deployment in real-time surveillance schemes. © 2023 Elsevier Ltd

Abnormal activity; Attention mechanism; Expert surveillance system; Intelligent video surveillance; Temporal learning mechanism; Video anomaly recognition

Anomaly detection; Convolution; Security systems; Smart city; Abnormal activity; Anomaly recognition; Attention mechanisms; Expert surveillance system; Intelligent video surveillance; Learning mechanism; Surveillance systems; Temporal learning; Temporal learning mechanism; Video anomaly recognition; Object detection

2-s2.0-85162012232

A multi-agent framework for collaborative geometric modeling in virtual environments

Conesa, J. (22733323100); Mula, F.J. (56786456400); Contero, M. (6603334082); Camba, J.D. (7801470334)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151799677&doi=10.1016%2fj.engappai.2023.106257&partnerID=40&md5=8a837bc6dec10b8d729d22d629d333a1

The use of collaborative applications in which several individuals interact to solve a problem is an important strategy both in educational settings as well as professional environments. Virtual Reality (VR) technology, in particular, has acquired a predominant role in many industries. However, as the level of immersion and realism of the VR experience increases, so does the demand for computational resources, as rendering processes become increasingly intensive and can negatively affect other communication processes that are critical in collaborative environments. In this paper, we present a software framework based on multi-agent systems that enables the separation of rendering processes from internal data management tasks and communication processes between users, which are prevalent in collaborative virtual reality applications. The results of our validation studies show that, compared to other techniques based on protocol or network enhancements, the proposed architecture can significantly improve collaborative processes in general, and virtual reality-based applications in particular. © 2023 The Author(s)

Application programs; Computer programming; Information management; Multi agent systems; Network architecture; Software agents; Agent-based systems; Collaborative application; Collaborative systems; Communication process; Educational settings; Geometric models; Multiagent framework; Professional environments; Rendering process; Virtual reality technology; Virtual reality

2-s2.0-85151799677

Research trends, themes, and insights on artificial neural networks for smart cities towards SDG-11

Jain, Akshat (57680353300); Gue, Ivan Henderson (57189057771); Jain, Prateek (57998446300)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159191082&doi=10.1016%2fj.jclepro.2023.137300&partnerID=40&md5=120b9533046d295655967784c4eaa7be

Smart Cities can promote economic growth, sustainable transport, environmental sustainability, and good governance among cities. These benefits can support cities in achieving the SDG-11 targets. Smart Cities entails the integration of smart technologies, including machine learning techniques, in cities. Among the machine learning techniques, Artificial Neural Network (ANN) is prominent. Literature revealed significant research interest on ANN for Smart Cities, resulting to several existing review works. Existing works revealed research interests on applications for structural monitoring, Internet of Things (IoT), transport systems, and cybersecurity among others. However there is a scarcity in understanding the implications of ANN for Smart Cities towards SDG-11. This work, therefore, reviews the research trends on ANN for Smart Cities towards SDG-11 through a systematic bibliometric methodology. This work utilizes a keyword-based search retrieving 743 documents for descriptive analysis and 131 documents for content analysis. The results reveal an exponential growth in research interest and cluster formation among pertinent themes. This work determined the prominent themes on Environmental Impact, on Transport Systems, and on Urbanization. This review highlights insights on research trends, on thematic prominence, and on specific SDG-11 themes. © 2023

Artificial Neural Network; Digital cities; Neuron; Sustainability; Sustainable development goals

Economics; Environmental impact; Internet of things; Learning algorithms; Machine learning; Smart city; Sustainable development; Economic growths; Environmental goods; Environmental sustainability; Good governances; Machine learning techniques; Research interests; Research trends; Sustainable development goal; Sustainable transport; Transport systems; Neural networks

2-s2.0-85159191082

AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning

Parvini, Mohammad (57224668025); Javan, Mohammad Reza (56890868700); Mokari, Nader (35574242600); Abbasi, Bijan (56482972300); Jorswieck, Eduard A. (7003871700)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151545935&doi=10.1109%2fTVT.2023.3259688&partnerID=40&md5=b53ee4a591341649569777b0abcde160

This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers while ensuring timely delivery of safety-critical messages to the Road-Side Unit (RSU). To lower the computational load at the RSU and cope with the challenges of dynamic channel conditions, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. Motivated by the existing literature in RL, we propose two novel MARL frameworks based on the multi-agent deep deterministic policy gradient (MADDPG), named Modified MADDPG, and Modified MADDPG with task decomposition. Both algorithms train two critics with the following goals: A global critic which estimates the global expected reward and motivates the agents toward a cooperating behavior and an exclusive local critic for each agent that estimates the local individual reward. Furthermore, based on the tasks each agent has to accomplish, in the second algorithm, the holistic individual reward of each agent is decomposed into multiple sub-reward functions where task-wise value functions are learned separately. Numerical results indicate our proposed algorithms' effectiveness compared with other contemporary RL frameworks, e.g., federated reinforcement learning (FRL) in terms of AoI performance and CAM message transmission probability. © 1967-2012 IEEE.

Cams; Cooperative communication; Fertilizers; Information management; Long Term Evolution (LTE); Multi agent systems; Natural resources management; Reinforcement learning; Safety engineering; Vehicle to Everything; Vehicle to vehicle communications; Vehicles; Age of information; Interference; Long-term evolution; Multi-agent reinforcement learning; Platoon cooperation; Resource management; Task analysis; V2X; Vehicle's dynamics; Wireless communications; Resource allocation

2-s2.0-85151545935

Applications of Reinforcement Learning for maintenance of engineering systems: A review

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162209496&doi=10.1016%2fj.advengsoft.2023.103487&partnerID=40&md5=ff5976356f4062ddab1a74363b595989

Nowadays, modern engineering systems require sophisticated maintenance strategies to ensure their correct performance. Maintenance has become one of the most important tasks of the systems lifecycle. This paper presents a literature review of the application of Reinforcement Learning algorithms for the maintenance of engineering systems. Reinforcement Learning-based maintenance has been classified regarding four types of system: transportation systems, manufacturing and production systems, civil infrastructures, power and energy systems, and other systems. Based on the literature review, this paper includes an overall analysis of the current state and a discussion of main limitations, challenges, and future trends in this field. A summary table is provided to present clearly the most important references. This research work demonstrates that Reinforcement Learning algorithms have a great potential for generating maintenance policies, outperforming most conventional strategies. © 2023 Elsevier Ltd

Engineering systems; Machine learning; Maintenance management; Reinforcement Learning; System reliability

Engineering education; Learning algorithms; Life cycle; Reinforcement learning; Engineering systems; Literature reviews; Machine-learning; Maintenance management; Maintenance strategies; Modern engineering; Performance; Reinforcement learning algorithms; Reinforcement learnings; System reliability; Maintenance

2-s2.0-85162209496

Zhang, Yulin (57679766500); Shang, Ke (57567292700); Cui, Zhiwei (58290603700); Zhang, Zihan (57215021294); Zhang, Feizhou (8321523000)

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167770649&doi=10.3390%2fs23156683&partnerID=40&md5=1b6c680fdef98426323b95089b530022

Traditional nonintelligent signal control systems are typically used in road traffic signal systems, which cannot provide optimal guidance and have low traffic efficiency during rush hour. This study proposes a traffic signal phase dynamic timing optimization strategy based on a time convolution network and attention mechanism to improve traffic efficiency at intersections. The corresponding optimization was performed after predicting traffic conditions with different impacts using the digital twinning technique. This method uses a time-convolution network to extract the cross-time nonlinear characteristics of traffic data at road intersections. An attention mechanism was introduced to capture the relationship between the importance distribution and duration of the historical time series to predict the traffic flow at an intersection. The interpretability and prediction accuracy of the model was effectively improved. The model was tested using traffic flow data from a signalized intersection in Shangrao, Jiangxi Province, China. The experimental results indicate that the model generated by training has a strong learning ability for the temporal characteristics of traffic flow. The model has high prediction accuracy, good optimization results, and wide application prospects in different scenarios. © 2023 by the authors.

attention mechanism; digital twin; time convolution network; traffic flow prediction; traffic saturation

Convolution; Efficiency; Forecasting; Roads and streets; Street traffic control; Attention mechanisms; Optimisations; Prediction accuracy; Signal control systems; Time convolution; Time convolution network; Traffic efficiency; Traffic flow; Traffic flow prediction; Traffic saturation; article; attention; China; digital twin; learning; prediction; time series analysis; Traffic signals

2-s2.0-85167770649