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1 | NATIONAL INSTITUTE FOR TRANSPORTATION AND COMMUNITIES (NITC) AT TRB 2023 | ||||||||||
2 | *Due to the evolving status of speaker attendance and TRB programming, please check your TRB schedule for the most current information in the event of changes.* | ||||||||||
3 | Date | Time (Eastern) | # | Type | Session | Presentation Title | Downloaded Materials | Presenters | Lead NITC University | Additional NITC Universities | Abstract |
4 | 1/8/2023 | 9:00 AM- 12:00 PM | 1002 | W | Micromobility, Bicycles, and the Future of Cities | Micromobility and E-bike Parking Policies - Best and Worst Practices | - | Nicholas Klein, Cornell University; Sharada Strasmore, District Department of Transportation; John MacArthur, Portland State University | Portland State University | This panel will provide a mix of practitioner and researcher perspectives on the best and worst practices related to understanding and addressing micromobility, bike share, and e-bike parking. | |
5 | 1/8/2023 | 9:00 AM- 12:00 PM | 1002 | W | Micromobility, Bicycles, and the Future of Cities | Micromobility and E-bike Equity - Best and Worst Practices | - | Olatunji Reed, Equiticity; Amanda Howell, University of Oregon; Celeste Chavis, Morgan State University | University of Oregon | This panel will provide a mix of practitioner and researcher perspectives on the best and worst practices related to understanding and addressing micromobility, bike share, and e-bike equity. | |
7 | 1/8/2023 | 9:00 AM- 12:00 PM | 1003 | W | Urban Roadway Design: Time for a New Paradigm? | Urban Design and AASHTO Pain Points | Presentation | Alyssa Ryan, University of Arizona | University of Arizona | Context-sensitive design has spliced a wide range of considerations into existing standards but is it time to consider urban street design as a distinct practice with different strategic goals and controlling conditions? Can we use the principles of social psychology and human neurology to shape street design like projectile physics was used to guide highway design? This workshop is intended to start a discussion about how codes and standards should grow to support urban multi-modal systems. | |
8 | 1/8/2023 | 9:00 AM- 12:00 PM | 1007 | W | Traffic Control Device Student Challenge 2023: Innovative Traffic Control Devices to Improve Vulnerable Road User Safety | Actuated Micromobility Users Presence Awareness System in Urban Arterials | - | Agustin Guerra, University of Florida; Liliana Salas*, University of Arizona | University of Arizona | Now in its 6th year of competition, the objective of the Traffic Control Device (TCD) Student Challenge is to promote innovation and stimulate ideas in the traffic control devices area with a goal to improve operations and safety. The challenge is sponsored by and conducted cooperatively by the Transportation Research Board (TRB) Standing Committee on Traffic Control Devices (ACP55) and the American Traffic Safety Services Association (ATSSA). The theme for the 2023 TCD Student Challenge is: Innovative Traffic Control Devices to Improve Vulnerable Road User Safety. Vulnerable road users include, but are not limited to, pedestrians, bicyclists, and micromobility users. | |
9 | 1/8/2023 | 4:00 PM- 5:15 PM | — | C | Technology Transfer Subcommittee | -Presiding- | Xiaoyue Cathy Liu, University of Utah | University of Utah | |||
10 | 1/8/2023 | 2:00 PM- 3:00 PM | — | C | Traffic Signal Systems Simulation Subcommittee, ACP25(5) | -Presiding- | Pengfei (Taylor) Li, University of Texas, Arlington | University of Texas at Arlington | |||
11 | 1/9/2023 | 8:00 AM- 9:45 AM | 2024 | L | Equitable Access to Essential Places and Services | -Presiding- | Aaron Golub, Portland State University | Portland State University | |||
12 | 1/9/2023 | 8:00 AM- 9:45 AM | — | C | Transportation History Subcommittee, AME00(1) | -Presiding- | John MacArthur, Portland State University | Portland State University | |||
13 | 1/9/2023 | 10:15 AM- 12:00 PM | — | C | Transit, Freight, and Logistics Modeling Subcommittee, AEP40(1) | -Presiding- | Avinash Unnikrishnan, Portland State University | Portland State University | |||
14 | 1/9/2023 | 10:15 AM- 12:00 PM | 2059 | L | Micromobility and Bicycle Safety: Recent Findings and Their Implications for Practice | Analyzing the Impacts of Intersection Treatments and Traffic Characteristics on Bicyclist Safety: Development of Data-Driven Guidance on the Application of Bike Boxes, Mixing Zones, and Bicycle Signals | Presentation | Brendan Russo, Northern Arizona University; Sirisha Kothuri, Portland State University; Edward Smaglik, Northern Arizona University; David Hurwitz, Oregon State University | Portland State University | Transportation modes converge at intersections resulting in conflicts between bicyclists and motor vehicles. A common conflict and crash type involving bicycles at intersections is the right-hook, where a right-turning vehicle collides with a through bicyclist. While various geometric and signal control treatments have been used in attempts to mitigate bicycle-vehicle crashes and conflicts, agencies often face questions regarding optimal treatment selection. To date, limited research has been conducted to analyze how certain treatments (bike boxes, mixing zones, and bicycle signals) along with traffic characteristics (e.g. vehicle, bicycle, and pedestrian volumes) impact the frequency of bicycle-vehicle conflicts, as well as the severity of such conflicts. To address this research need, this study provides an analysis of bicycle-vehicle conflicts reduced from field-collected videos at twelve intersections; three with bike boxes, three with mixing zones, three with bicycle signals, and three control sites with no specific bicycle treatment. From these videos, bicycle-vehicle conflicts were identified and measured using post-encroachment time (PET), along with conflict-involved road user speeds and hourly road user volumes. From these data, a series of Poisson regression models were estimated to assess hourly predicted conflicts as a function of bicycle and vehicle volumes across different treatment types. Additionally, conflict severity differences across treatments were analyzed, including through use of a novel measure which incorporates both PET and vehicle speed. Ultimately, several differences with respect to conflict frequency and severity were observed between the treatment types providing new data-driven guidance to assist practitioners in treatment selection. | |
15 | 1/9/2023 | 1:30 PM- 5:30 PM | — | C | Public Engagement and Communications Committee | Transportation Academies as Catalysts for Civic Engagement in Transportation Decision-making | Presentation | Nathan McNeil, Portland State University; Keith Bartholomew, University of Utah; Matthew Ryan, University of Utah | Portland State University | University of Utah | Citizen planning academies, which became popular in the 1990s, are increasingly being used in transportation planning and decision-making contexts. By making use of a longer-term, multi-week educational format, transportation academies have the potential to reduce barriers and enhance community capital leading to more meaningful and sustained government-community interaction. This paper tracks the rise of transportation academies in North America, and provides a detailed look at two academies: one in Portland, Oregon with a 30-year history, and another recently launched in the Salt Lake City, Utah region. Post-academy surveys of students provide data that illuminate whether the transportation academy model is effective in fostering greater, and longer-term community engagement. Using an evaluation framework developed for assessing citizen planning academies, the data indicate positive outcomes and provide a basis for further expansion of the use of academy-type engagement initiatives. |
16 | 1/9/2023 | 3:45 PM- 5:30 PM | — | C | Trucking Industry Research Committee | COVID-19 and the Impacts of Trucking Operations and Hours-Of-Service for Truck Drivers | - | Brianna Velasquez, Oregon State University; Salvador Hernandez, Oregon State University; Jason Anderson, Portland State University; Sarah Hernandez, University of Arkansas, Fayetteville | Portland State University | This paper studies the effects of the COVID-19 pandemic on the trucking industry to asses the impacts of the relaxation in hours-of-service rules on truck driver behavior. Due to the inherent growth in truck activity, it is imperative to understand these factors so that effect programs and policy initiatives related to large truck safety can be developed. A stated-preference survey distributed to truck drivers collected data regarding specific characteristics of the trucking industry. A total of 47 paired variables were generated from the driver survey responses. Their medians were tested for statistical differences through a rank-sum procedure. The results were 13 comparisons in which there was a statistical difference in the pre- and post-COVID period. | |
17 | 1/9/2023 | 3:45 PM- 5:30 PM | 2202 | P | Pedestrian Behaviors and Travel Patterns | -Presiding- | - | Sirisha Kothuri, Portland State University | Portland State University | ||
18 | 1/9/2023 | 3:45 PM- 5:30 PM | 2203 | P | Pedestrian Behaviors and Travel Patterns | Driver and Bicyclist Comprehension of Blue Light Detection Confirmation Systems | Poster | Douglas Cobb, Burgess & Niple; Hisham Jashami, Oregon State University; Christopher Monsere, Portland State University; Sirisha Kothuri, Portland State University; David Hurwitz, Oregon State University | Portland State University | This study analyzed motorist and bicyclist understanding and preference of positive confirmation of detection of a bicycle by the traffic signal infrastructure using a blue light detection confirmation (BLDC). The research analyzed results of an online survey of 1,123 respondents and intercept survey of 337 respondents. The study initially found that participants of the survey did not understand the meaning of the blue light itself, but comprehension of the system rose by 40% to 50% when supplemental signs were used. Respondents overwhelmingly indicated that they preferred the sign option that included symbols, text, and a representation of the blue light, in comparison to the sign options that only included symbol and text or text and blue dot. Additionally, respondents indicated that they “Strongly Agree” that the supplemental signage helped with understanding the purpose of the detection confirmation devices, that they would support the system at intersections, and that it made them feel better about waiting at an intersection with light. Including supplemental signage with the symbol, text, and blue dot is highly recommended as supplemental information BLDC. | |
19 | 1/9/2023 | 8:00 AM- 9:45 AM | 2002 | L | Measuring What Matters: Defining and Measuring Walkability | Subjective vs. Objective: The Divergence between Subjective Walkability and Walk Score during the Covid-19 Pandemic | Presentation | Sian Meng*, University of Oregon; Yizhao Yang, University of Oregon; Rebecca Lewis, University of Oregon | University of Oregon | Walk Score has been widely used for evaluating walking-based accessibility of a location to various meaningful destinations. Its validity remains contested because a Walk Score, often determined by objectively measured physical distances, might be inconsistent with people’s perceptions and irrelevant to destinationless leisure walking trips. This study examines the divergence between subjective walkability measures and objective Walk Scores using data collected by an online survey of residents in the Eugene-Springfield region, OR. This survey took place during the COVID-19 lockdown, a period witnessing a greater level of leisure walking, which has the potential to increase the divergence between subjective walkability measures and the walk scores, affording us greater analytical power to investigate the underlying factors. We conducted a bivariate local Moran’s I analysis to compare subjective walkability to Walk Score and used multi-level regression models to examine how individual- and neighborhood-level factors relate to the divergence between subjective walkability and Walk Score. Spatial analysis shows that respondents in certain areas tend to overestimate or underestimate walkability compared to Walk Score. Regression analysis reveals that population density, the number of grocery stores, and the proportion of commercial area, zero-car households and people below the poverty line are positively related to the divergence score, while income, housing value, median year structure built, and perception of neighborhood safety are negatively associated with the gap. Results suggest that planners and policymakers applying Walk Score should consider the spatial mismatch and develop other indicators to assess walkability comprehensively. | |
20 | 1/9/2023 | 10:15 AM- 12:00 PM | 2100 | P | Transportation Planning, Policies, and Processes Posters | When Minimum Parking Requirements Go Away, Does Housing Come to Stay? The Relationship Between Residential Development and the Elimination of Minimum Parking Requirements in San Francisco, CA. | Sadie Mae Palmatier, University of Oregon | University of Oregon | Previous literature has identified that minimum parking requirements (MPRs) drive up the cost of all housing types in multiple residential zones. This same research suggests two actions to decrease housing costs 1) unbundle parking requirements and rent for existing residential structures and 2) eliminate MPRs for new residential buildings. However, few researchers have traced the impact of eliminating MPRs on the residential development landscape. To fill this gap, this paper explores whether the elimination of parking minimums translates into increased residential development overall, and specifically affordable housing development. To answer this question, I examined residential development trends for three years prior to and following San Francisco, CA’s citywide elimination of parking minimums in December 2018. I used data from the City and County of San Francisco’s Building Permit Tracking system to determine 1) the total number of residential permits proposed in the three years post and pre-parking requirement elimination, 2) the total number of proposed residential units, 3) the share of the total proposed development that is affordable housing, and 4) the share of housing types (such as multifamily, Accessory Dwelling Units, etc.) proposed over the same period. I found that overall, the number of proposed permits and units for residential development increased post-MPR. Furthermore, the number of proposed multifamily and affordable housing units increased, suggesting that the elimination of the MPR may help densify residential areas. | ||
21 | 1/9/2023 | 8:00 AM- 9:45 AM | 2035 | P | Freeway Operation 2023 | Evaluating Traffic Incident Management System Performance in Post-Incident Response: Method to Homogenize Incident Duration and Spatial Autocorrelation Analysis | Zirui Huang*, University of Arizona; Xiaofeng Li, University of Arizona; Yi-Chang Chiu, Metropia Inc.; Yao-Jan Wu, University of Arizona | University of Arizona | Congestions brought on by incidents frequently prevent the efficient use of the road network. It is essential to design an intelligent Traffic Incident Management (TIM) system and develop measures to assess its post-incident response performance. The conventional measures do not account for the incident characteristics such as incident type and the number of involved vehicles. Thus, they are not appropriate as benchmarks for comparisons. This study introduced a measure termed Response Performance (RP) which homogenizes incident durations from incident characteristics and singles out TIM system response efficiency. Also, this study developed a method for transportation agencies and response providers to pinpoint problem areas for TIM system improvement. The method calculates the proposed measure RP using the Cox Proportional Hazard-Based model and performs spatial autocorrelation analysis using Getis-Ord Gi* local statistics. We tested the model using the incident data provided by TranStar Transportation Management Center (TMC) in Houston. We thoroughly analyzed zip code and longitudinal/latitudinal data granularity levels. The zip code level analysis found that the areas closer to the center of Houston have high-RP clusters. The analysis at the longitudinal/latitudinal level identifies the locations the response team spends relatively more time. Our study is one of the few studies evaluating the TIM system post-incident response performance using a measure that adjusts incident duration by reducing inhomogeneity in incident characteristics. The proposed method facilitates policymakers’ use of the findings for identifying the areas and locations for future improvement. | ||
22 | 1/9/2023 | 8:00 AM- 9:45 AM | 2052 | P | Through a Gender Lens: Travel Behavior and Workforce Development | Measuring Gender Equity: Best Practices Identified in Current Literature | Sushmita Bhandari*, University of Arizona; Melrose Pan*, University of Arizona; Alyssa Ryan, University of Arizona | University of Arizona | Access to a dependable, safer, and environmentally friendly transportation system is the first step in the transportation industry's shift. A person's ability to access healthcare, work, education, municipal services, and a vast array of social, civic, and cultural activities is frequently contingent on their access to safe, reliable, and comfortable transportation. Transportation planning and design are typically considered "gender-neutral." However, it is essential to recognize gender distinctions and treat them equitably. Consequently, this study presents the most effective methods for measuring gender equity in the transportation sector as identified by an in-depth review of current literature across the field. This study is ongoing and will be finalized in mid-November. It will provide an overview of what types of data and methodologies are required to quantify gender inequity in transportation planning and design projects. This methodology will aid transportation planners, engineers, policymakers, and decision-makers in comprehending gender concerns and eliminating gender disparities in order to create an equitable transportation system. | ||
23 | 1/9/2023 | 10:15 AM- 12:00 PM | 2082 | L | Gender and Non-Auto-Oriented Travel | -Presiding- | Alyssa Ryan, University of Arizona | University of Arizona | |||
24 | 1/9/2023 | 10:15 AM- 12:00 PM | 2095 | P | Doctoral Student Research in Transportation Operations and Traffic Control | Behavior Science Methods for Active Transportation Demand Management | Melrose Pan*, University of Arizona | University of Arizona | |||
25 | 1/9/2023 | 10:15 AM- 12:00 PM | 2098 | P | Safety Effects of Roadway Characteristics and Treatments | Safety Performance Evaluation of Flashing Yellow Arrow: Time-of-Day vs 24-hour Operations | Xi Zhang, University of Arizona; Xiaofeng Li, University of Arizona; Yao-Jan Wu, University of Arizona | University of Arizona | The flashing yellow arrow (FYA), as the permissive left-turn indication, has been widely implemented to mitigate driving confusion caused by the permissive steady green indication. The selection of an appropriate FYA operation type, however, remains an open-ended question as few studies have evaluated the safety effectiveness of FYA under different FYA operation types, i.e., 24-hour, time-of-day (TOD), and switching from 24-hour to TOD. Moreover, since FYA has been typically used at intersections with a single left-turn lane, its safety effectiveness at intersections with dual left-turn lanes has not been well investigated. To bridge these gaps, this paper focuses on evaluating the safety effectiveness of FYA at intersections with varying numbers of left-turn lanes under different operation types and examining whether the FYA operation type switch affects safety performance. Empirical Bayes (EB) before-after studies are conducted for 24-hour, TOD, and switching from 24-hour to TOD FYA operation types using multivariate adaptive regression splines (MARS) models in comparison with traditional negative binomial (NB) models. Safety performance functions (SPFs) are developed for different combinations of crash types and a different number of left-turn lanes. Results show that for intersections with either a single left-turn lane or dual left-turn lanes, 24-hour and TOD FYA operation types reduce crashes by 8.76% to 50%. However, intersections with dual left-turn lanes experience a 31.2% increase in total crashes when switching from 24-hour to TOD FYA operation type, while intersections with a single left-turn lane see a 60% decrease in rear-end crashes. | ||
26 | 1/9/2023 | 1:30 PM- 3:15 PM | 2149 | P | Innovations in Traffic Law Enforcement | Severity Analysis of Red-Light Running Behavior at Signalized Intersections | Pouya Jalali Khalilabadi*, University of Arizona; Abolfazl Karimpour, State University of New York (SUNY); Yao-Jan Wu, University of Arizona | University of Arizona | Red-Light Running (RLR) is one of the riskiest behaviors at signalized intersections. According to the report published by AAA Foundation for Traffic Safety, in 2019, more than two people were killed each day as a result of disregarding red signal indications. This study aims to utilize intersection and corridor-level characteristics to identify variables that impact the frequency and severity of RLR violations. The severity of RLR is defined based on the time when the violations happened. That is, less severe violations are the ones that happen within three seconds of the signal light turning red (RLR3), and more severe violations happen between three to ten seconds of the signal light turning red (RLR10). As RLR data are non-negative integers, a conventional parametric model, zero-inflated negative binomial, and a nonparametric model, random forest, were utilized to calibrate the violation models. Results of the ZINB model showed that an increase in intersection delay and split failure increases the frequency of both types of RLR severities. An increase in the yellow interval, cycle length, and the number of lanes, reduces the frequency of RLR3 but increases the frequency of RLR10. Furthermore, based on the factor importance analysis conducted through the random forest, it was shown split failure is one of the most predictive variables for RLR3 and RLR10. The founding of this research could help transportation agencies in different geographic jurisdictions utilize the calibrated models to understand the impact of different intersection and corridor-based characteristics on the frequency and severity of RLR. | ||
27 | 1/9/2023 | 1:30 PM- 3:15 PM | 2163 | P | Advances in Climate Change Resilient Design, Operations, and Decision Making | The Effect of Vehicular Waste Heat on Personal Heat Exposure | Ashley Avila*, University of Arizona; Nicole Iroz-Elardo, Willamette University; Ladd Keith, University of Arizona; Kristina Currans, University of Arizona | University of Arizona | Understanding the causes of heat in various microclimates in cities is vital to improving human thermal comfort and health in outdoor spaces. This research uses an experimental design to evaluate microclimate heat risk – including ambient air temperature (TA) and wet bulb globe temperature (WBGT) – and approximate personal heat exposure of people 5-10 feet away from idling vehicles. These measurements were taken at a University of Arizona covered parking garage in June 2022 to investigate personal heat exposure attributable to vehicular waste heat while also minimizing the effect of solar radiant heat and wind. We used Kestrel 5400 devices to document the waste heat effects of a fleet of six identical gasoline-powered vehicles on the surrounding microclimate by collecting TA, wind velocity, and WBGT. When compared to the control, as well as comparing a period when engines were not idling, we found a strong correlation between vehicle presence, TA, and WBGT. Specifically, ordinary least-mean square (OLS) modeling shows additional TA per minute increase of 0.019°F from idling vehicles for a total of 0.054°F increase per minute when the vehicles are on and idling versus 0.035°F per minute increase that occurs from expected background morning temperatures when the vehicle effect is removed. Until we transition to an electric fleet, these findings can help inform how transportation professionals design the built environment and manage traffic and transit during summer months to prevent excessive heat exposure for pedestrians, cyclists, transit riders, and other individuals near idling vehicles. | ||
28 | 1/9/2023 | 1:30 PM- 3:15 PM | 2163 | P | Advances in Climate Change Resilient Design, Operations, and Decision Making | Adaptive Transit Scheduling to Reduce Rider Vulnerability During Heatwaves | Lauren Heath*, University of Arizona; Nicole Iroz-Elardo, Willamette University; Ladd Keith, University of Arizona; Kristina Currans, University of Arizona | University of Arizona | Extreme heat events, induced by climate change, present a growing risk to transit passenger comfort and health. To reduce exposure, agencies may consider changes to schedules that reduce headways on heavily trafficked bus routes serving vulnerable populations. This paper develops a schedule optimization model to minimize heat exposure and applies it to local bus services in Phoenix, Arizona, using agent-based simulation to inform travel demand and rider characteristics. Rerouting as little as 10% of a fleet is found to reduce network-wide exposure by as much as 35% when operating at maximum fleet capacity. Outcome improvements are notably characterized by diminishing returns, owing to skewed ridership and the inverse relationship between fleet size and passenger wait time. Access to spare vehicles can also ensure significant reductions in exposure, especially under the most extreme temperatures. Rerouting, therefore, presents a low-cost, adaptable resilience strategy to protect riders from extreme heat exposure. | ||
29 | 1/9/2023 | 3:45 PM- 5:30 PM | 2211 | P | Analytical Frameworks for Employing Scenario Planning Practices | Current Practices in Scenario Planning: What Works in Transportation and Adjacent Sectors | Melrose Pan*, University of Arizona; Alyssa Ryan, University of Arizona | University of Arizona | Scenario planning practices have been practiced for decades; however, the use of scenario planning is still not widespread or practiced by all transportation agencies. This poster will discuss the current practices of scenario planning by those in the transportation sector, and beyond, describing the best implementation strategies that can be adopted by a variety of different transportation agencies for different goals. | ||
30 | 1/9/2023 | 8:00 AM- 9:45 AM | 2040 | P | Geographic Information Science Research | Delineating race-specific driving patterns for racial segregation | Yirong Zhou*, University of Utah; Tony Grubesic, University of Texas, Austin; Ran Wei, University of California, Riverside; Xiaoyue Cathy Liu, University of Utah | University of Utah | Since the late 20th century, transportation equity has become one of the major concerns of transportation planners. There are different ways to evaluate equity in Transportation planning, such as providing equitable transportation resources, favoring disadvantaged social parties, and dealing with racism, sexism, and social disparities. Different indices have been proposed to measure segregation in across socio-geographical spaces. However, most of the literature focuses on intergroup contact and exposure that happens in residential places. In this paper, we first delineate race-specific driving patterns by incorporating all publicly accessible data using Information Maximization (IM) model. Then, we extensively studied the connection between social demographic data and race-specific driving patterns. At last, we proposed a new exposure index that considers residents, workers, and commuters by utilizing race-specific driving patterns. We identified the most racially-segregated road segments, residential areas, and workplaces, as well as how they are formed based on the commuters’ information in San Diego County, California. This paper is the first to associate commuters with racial segregation analysis. | ||
31 | 1/9/2023 | 8:00 AM- 9:45 AM | 2046 | P | Research Supporting Advancements in Roadside Safety | An Automated FHWA Roadside Safety Rating System for Rural Roadways using Computer Vision | Ali Hassandokht Mashhadi*, University of Utah; Abbas Rashidi, University of Utah; Juan Medina, University of Utah; Nikola Marković, University of Utah | University of Utah | The high rate of run-off-road (ROR) crashes, especially in rural areas, make roadside features an important set of elements in safety analysis. To this end, FHWA defines a 7-point rating system to rank roadside safety based on four parameters: clear zone width, existence of guardrails, presence of rigid obstacles, and angle of the sideslope. Roadside conditions are ranked by human experts who examine images of different road segments and apply the 7-point rating, which is time-consuming and labor-intensive. To automate the process of safety ranking, this study proposed a computer vision-based approach to rank roadside conditions along a segment based on images. Images are classified using pre-trained models along with the XGBoost algorithm. VGG16, ResNet50, and Inception v3 were the three algorithms used for feature extraction. The proposed approach yielded 81% accuracy in classifying the images into 7 groups based on the roadside conditions. Keywords: Roadside Safety, Computer Vision, Deep Learning, Machine Learning | ||
32 | 1/9/2023 | 10:15 AM- 12:00 PM | 2111 | P | Recent Advances in Rail Infrastructure Design and Maintenance | Rail defect detection using ultrasonic A-scan data and deep autoencoder | Yuning Wu*, University of Utah; Xuan Zhu, University of Utah | University of Utah | |||
33 | 1/9/2023 | 1:30 PM- 3:15 PM | 2128 | L | Advances in Geospatial Data Acquisition | Vehicle Detection, Tracking, and Bounding Box Estimation from LiDAR Data under Severe Weather | Shanglian Zhou, University of Hawai'i, Manoa; Hao Xu, University of Nevada, Reno; Guohui Zhang, University of Hawai'i, Manoa; Tianwei Ma, Texas A&M University-Corpus Christi; Yin Yang, University of Utah | University of Utah | In the recent decade, Light Detection and Ranging (LiDAR) technology has emerged as one of the most advanced and popular technologies for transportation safety analysis, condition monitoring, and object recognition tasks. Despite its vigorous development, several real-world challenges still exist upon leveraging this type of technology for transportation-related applications. The influence from severe weather (e.g., snow and fog), for example, may drastically deteriorate the quality of LiDAR data and cause false detections in the object recognition process. Current methodologies usually adopt handcrafted data processing and filtering techniques for noise removal and data enhancement. However, such filtering techniques usually involve many parameters, which need to be carefully tuned based on the work scenario, user expertise, and prior knowledge. In this paper, we introduce a novel framework based on deep learning to detect and track vehicle objects from roadside LiDAR data collected under severe snow conditions. The proposed methodology consists of two stages: first, the YOLOv4 object detection network is employed for vehicle detection from LiDAR data under snow contamination, relying on the self-learning and adaptation of the deep learning model to tackle the issue of weather influence; then, a novel algorithm is proposed as a post-processing procedure for vehicle object tracking and bounding box estimation. The experimental results show that the proposed methodology is capable of addressing the issue of snow disturbance in LiDAR data and yield accurate and robust detection and tracking results. | ||
34 | 1/9/2023 | 3:45 PM- 5:30 PM | 2174 | L | Spotlight of Artificial Intelligence (AI) Research: Explainable AI | A Transfer Learning-based LSTM for Traffic Flow Prediction with Missing Data | Zhao Zhang, University of Utah; Xianfeng Yang, University of Maryland, College Park | University of Utah | Traffic flow prediction task plays important role in Intelligent Transportation Systems (ITS) on freeways. However, incomplete traffic information tends to be collected by traffic detectors, which is a major constraint for existing methods to get precise traffic predictions. To overcome this limitation, this study aims to propose and evaluate a new advanced model, named transfer learning-based long short-term memory (LSTM) model for traffic flow forecasting with incomplete traffic information, that adopts traffic information from similar locations for the target location to increase the data quality. More specifically, Dynamic Time Warping (DTW) is used to evaluate the similarity between the source and target domains and then transfer the most similar data to the target domain to generate a hybrid complete training sample for LSTM to improve the prediction performance. To evaluate the effectiveness of the transfer learning-based LSTM, this study implements empirical studies with a real-world dataset collected from a stretch of I-15 freeway in Utah. Experimental study results indicate that the transfer learning-based LSTM network could effectively predict the traffic flow conditions with a training sample with missing values. | ||
35 | 1/9/2023 | 6:00 PM- 7:30 PM | 2219 | L | Hot Topics in Transportation Safety Management Systems: A Lectern-Poster Session | Calibration of Segment-Level Risk Assessments from usRAP – A Methodology for Systemic Analysis Tools | Fahmid Hossain, McMahon Associates, Inc.; Juan Medina, University of Utah | University of Utah | The United States Road Assessment Program (usRAP) provides a systemic approach to estimate the risk of severe injury and fatal crashes along roadway segments based on expected safety performance dictated by roadway and roadside characteristics. As the adoption of usRAP grows in the U.S., calibration of the methodology and proposed risk assessments is of significant value, not only to identify strengths and limitations within the U.S. context, but also to consolidate usRAP as an additional tool available to roadway agencies. This paper demonstrates a methodology suitable to calibrate the usRAP risk assessment formulation when analyzing non-intersection crashes, including run-off road and head-on crashes. The analysis focuses on the interactions between contributing factors, which by default in usRAP are multiplicative factors with the same weight in the overall crash risk formulation. Relaxation of the functional form to allow for different interactions indicates potential improvements in the risk estimation to match long-term safety performance observed in the field. The methodology presented in this paper opens possibilities for calibration to local conditions beyond those offered by the original usRAP methodology, and is suitable to calibrate other systemic safety analysis tools. | ||
36 | 1/9/2023 | 6:00 PM- 7:30 PM | 2225 | P | Driving Behavior Research Methods, Models, and Measures | Inferencing Driver Characteristics using Microscopic Driving Behavior Analysis from Naturalistic Data on Freeway Segments | Juan Medina, University of Utah; Arman Malekloo*, University of Utah; Xiaoyue Cathy Liu, University of Utah | University of Utah | Driving behavior dictates and influences transportation planning, design, and operations. The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Studies (NDS) program offers new opportunities for research in driving behavior, with records of over 3,400 drivers and millions of driving miles. This study utilized 18 variables extracted from trajectories of over 13,000 trips on freeway segments from the NDS program to delineate people’s driving behavior and attempted to associate the heterogeneity with demographic attributes (e.g., age, gender, and driving experience). Driving behavior in car following condition in terms of different vehicle kinematics features (i.e., spacing, speed, acceleration, and jerk) is first analyzed using Principal Component Analysis (PCA) for data whitening and 2D visualizations. Using the rotating components of PCA, 𝑘𝑘-means clustering is employed to distinguish clusters characterized by the extracted features. Results showed significant differences in the two clusters in terms of demographic attributes across drivers, indicating distinctions in driving behavior. The study offers insights on appropriate selection and measurement of vehicle kinematics features as well as driving behavior associated with specific demographic groups to improve traffic state forecasting, car-following model calibration, and advanced driver-assistance system implementation. | ||
37 | 1/9/2023 | 8:00 AM- 9:45 AM | 2041 | P | Workforce Development as a Retention Tool | Effects of Outdoor Cold Temperatures on Construction Workers’ Health, Performance, and Safety | Sanjgna Karthick, University of Texas at Arlington; Sharareh Kermanshachi, University of Texas, Arlington; Apurva Pamidimukkala*, University of Texas at Arlington | University of Texas at Arlington | Extreme cold temperatures not only create hazardous conditions to work but also affects health and safety. Workers involved in outdoor occupational sectors are one of the most affected individuals in cold temperatures. Their mental health is impacted to great extend due to working in unfavorable weather coupled with manually laborious activities. Therefore, this study identifies and analyses the various physical and mental health challenges experienced by workers in construction industry. To achieve this objective, a questionnaire survey was developed and distributed to various professionals performing in construction industry. The survey comprised of 111 questions. The results of the survey were descriptively and quantitatively analyzed. The quantitative analysis was performed using Kruskal-Wallis test. The results of the analysis revealed that the health challenges differ based on workers’ clothing comfort and exhibit varied heart rate. Conditions like hypothermia and respiratory issues were experienced differently in workers with better clothing comfort, as clothing is considered as a primary factor is trapping heat in cold weather. Various mental health issues like lack of concentration, and distraction were found to be significant based on the workers acclimatization practices. Workers performing physically intense activities in cold weather need to be provided with appropriate clothing fit to entrap heat, and heating gloves can be helpful when working in cold weather. This study also outlines the various existing standards governing workers’ health in cold weather conditions. The findings of this study will help professionals in construction industry to take preventive actions in protecting the workers’ health. | ||
38 | 1/9/2023 | 8:00 AM- 9:45 AM | 2041 | P | Workforce Development as a Retention Tool | Transportation Curriculum with Culturally Responsive Teaching: Lesson Learned from Pre-Service Teachers and Future Transportation Workforce | Farah Naz*, University of Texas, Arlington; Troyee Saha*, University of Texas, Arlington; Kate Hyun, University of Texas, Arlington | University of Texas at Arlington | With K-12 students from diverse social and cultural background in the classroom today, it is crucial to develop a more inclusive and socially diverse curriculum especially in STEM topics. Transportation, in particular, is a topic that can relate to people’s culture and can be a suitable medium to introduce diversity and inclusion in STEM field. Culturally Responsive Teaching (CRT) is a technique that can help connect students’ cultures, languages, and life experiences with their learning. Despite the known benefits of CRT, this strategy had not been adequately implemented in transportation education because of limited awareness and knowledge of educators. This research contributes to the literature by investigating the current state of knowledge, awareness, and resources present in transportation pedagogy and investigates the feasibility of transportation as a suitable topic to incorporate culturally responsive learning strategies through surveys and workshops with pre-service science teachers and future transportation workforce. Five key elements emerged from the analysis: i) Existing lack of diversity in STEM curriculum started to change to be inclusive, ii) Teacher’s awareness and preparation is crucial for creating quality educational materials, iii) Curriculum topics that can relate to cultural components in daily living enhance social diversity in transportation education, therefore iv) Transportation can be a good curriculum topic, and v) Barriers including difficulty of incorporating many cultural components, privacy, and legal issues, however, still exist. The identified the gaps and highlighted areas can contribute to the current state of knowledge and practice of CRT in STEM and transportation pedagogy. | ||
39 | 1/9/2023 | 1:30 PM- 3:15 PM | 2165 | P | Current Issues in Economic Development and Land Use | Toward a New Direction on Integrated Land Use Transportation Models: A Prototype Application of ELUENT as an Innovative Model | Ardeshir Anjomani, University of Texas at Arlington | University of Texas at Arlington | In this paper, I first briefly discuss a developed model, ELUENT, which attempts to overcome some of the prevalent deficiencies of the operational land use-transportation interaction models through its open structure and innovative approaches. Then, I present a prototype implementation of this model and the results, including a scenario application, from Austin, Texas (a mid-size U.S. metropolitan area). | ||
40 | 1/9/2023 | 3:45 PM- 5:30 PM | 2210 | P | Emerging Vehicle (Conventional, Autonomous Vehicle, and Electric Vehicle) Ownership Patterns | Exploring Factors Affecting Shared Autonomous Vehicles Adoption: A Structural Equation Modeling Analysis | Ronik Patel, University of Texas, Arlington; Roya Etminani-Ghasrodashti, University of Texas, Arlington; Sharareh Kermanshachi, University of Texas, Arlington; Jay Rosenberger, University of Texas, Arlington; Ann Foss, City of Arlington, TX | University of Texas at Arlington | Even though shared autonomous vehicles (SAVs) have been studied extensively in recent years, little is known about how passengers who have used SAVs will adapt to the emerging technology. Additionally, an understanding of SAV technology's efficacy when integrated with the existing transportation systems is necessary for its wider adoption. This study aims to determine the factors influencing willingness to ride SAVs in the future using the data gathered from users and nonusers of a self-driving shuttle. We used structural equation modeling to evaluate the relationship between factors like owning private vehicles, the frequency of using SAVs, availability of other modes of transportation, SAV service attributes, and sociodemographic characteristics on individuals' willingness to ride SAVs in the future. Young Asian individuals with no access to private vehicles were a majority of SAV users. We discovered that the frequency of using SAV services positively influences people's willingness to use SAVs. This study provides crucial insights into user behavior following the integration of SAVs into current transportation systems to guide policymakers to develop policies enhancing SAV operations. | ||
41 | 1/10/2023 | 10:15 AM- 12:00 PM | 3060 | L | Signs, Pavement Markings, and Traffic Signals | Evaluation of Red Colored Pavement Markings for Transit Lanes | Nathan McNeil, Portland State University; Christopher Monsere, Portland State University; Jennifer Dill, Portland State University | Portland State University | Many U.S. agencies have experimented with red colored pavement markings in transit lanes to enhance the message that they are restricted to transit vehicles. This study evaluates non-transit drivers’ comprehension of and compliance with red colored lane markings in transit priority lanes intended to communicate lane restrictions and appropriate turning and merging locations. Two complementary research methods were used: 1) an online survey of drivers’ comprehension of red colored pavement markings; and, 2) evaluation of video collected at locations pre and post installation of red colored pavement markings. In the survey, most drivers recognize the red pavement color as a restriction on use, but most do not associate it specifically with transit, or understand the intended meaning of the broken red bar patterns designed to designate a merge area. However, we observed relatively high compliance rates with the transit lane restrictions, and the red pavement coloring was generally associated with improved compliance. Of the 22,803 vehicles observed at these locations, 206 were observed driving in the transit lanes, accounting for a violation rate of 0.9%. For the locations with pre and post data, driving in transit lane violations were either reduced or there was no significant change. For locations with shared turn and transit lanes, between 62% and 76% of drivers merged as intended, and we observed an increase in appropriate merging behavior with the addition of red pavement color. | ||
42 | 1/10/2023 | 8:00 AM- 9:45 AM | 3043 | P | Information and Communication Technologies, Activity Participation, and Travel Choices | Chronicling Food Shopping During the COVID-19 Pandemic | Kelly Clifton, University of British Columbia; Amanda Howell, University of Oregon; Kristina Currans, University of Arizona | University of Oregon | University of Arizona | This paper presents high-level results from a repeated cross-sectional survey administered to households in five U.S. states chronicling food shopping behavior and preferences during the COVID-19 pandemic. Surveys were administered across four waves in September-October 2020, February-March 2021, June-September 2021, and September-November 2021 and were designed to capture information about household socio-demographics, composition, and resources; shopping preferences and behaviors, including frequency, locations, mode of transportation, and use of e-commerce and delivery; and barriers to access and impacts on household provisioning and wellbeing. One signifcant outcome of this research is the publicly available data, which can be analyzed at the individual household level. | |
43 | 1/10/2023 | 1:30 PM- 3:15 PM | 3112 | L | Recent Advances in Access Management | Dynamic Curbside Management: Optimizing Supply and Demand at the Curb with New Data and Technologies | Meghan Mitman, Fehr & Peers; Alex Rixey, Montgomery County Planning; Tory Gibler, Fehr & Peers; Amanda Howell, University of Oregon; Jay Primus, Primus Consulting; Melanie Flores, Fehr & Peers; Servet Lapardhaja, Fehr & Peers | University of Oregon | Curbside management is critically important and offers exciting new avenues for innovation as communities juggle competing and increasing demands for limited right-of-way at the curb. Dynamic curbside management is the data-driven understanding, allocation, and operation of the curb across space and time to optimally serve curb uses and users as determined by community values. To date, dynamic curbside management has largely been the purview of a limited number of cities through pilot programs. This paper, based on recently-completed research for NCHRP 20-102(26): Dynamic Curbside Management: Keeping Pace with New and Emerging Mobility and Technology in the Public Right of Way, presents strategies and tools that state, regional, and local transportation agencies can use to develop and implement dynamic curbside management. The paper explores opportunities to expand pilot efforts and create the policy framework and program capacity to move into more significant efforts. State Departments of Transportation (DOTs), Metropolitan Planning Organizations (MPOs), and other regional agencies can be important partners for local entities because, in many cases, roadways and other curb zone elements are part of the regional or state network. Additionally, state and regional agencies can fund, streamline, lay the groundwork for, pilot test, and evaluate dynamic curbside management policies and tools. At this phase of emerging practice, the opportunity to advance dynamic curbside management through engagement and leadership from state and regional agencies is significant. | ||
44 | 1/10/2023 | 8:00 AM- 9:45 AM | 3038 | P | Advanced Frameworks of Autonomous and Connected Vehicles Applications | Development of an Intelligent Signal Control System using a Deep Learning Traffic State Estimation Model in a Connected Vehicle Environment | Debashis Das*, University of Arizona; K. Larry Head, University of Arizona | University of Arizona | This paper presents an intelligent traffic signal control (I-Sig) system that minimizes the travel time delays for all vehicles at signalized intersections. A dynamic programming (DP) model was developed to consider estimated traffic state information based on connected vehicle data when the market penetration is low. In addition, special classes of connected vehicles, including transit vehicles and trucks, are provided preferential treatment, or priority. The traffic state information is estimated by a deep neural network-traffic state estimation (DNN-TSE) model. The DNN-TSE model estimates traffic state in real-time based on the connected vehicle trajectory data and connected intersection SPaT data. The I-Sig algorithm follows a rolling horizon approach to accommodate new traffic information that is collected over time. Simulation results indicate that the DNN-TSE model can learn about the traffic flow distribution and estimate the traffic state of the non-connected vehicles when the market penetration rate is low. Numerical experiments demonstrate that I-Sig can reduce the travel time and delays of regular vehicles while providing priority to the special types of vehicles. | ||
45 | 1/10/2023 | 10:15 AM- 12:00 PM | 3093 | P | Toward the Next Generation of Intelligent Transportation Systems | Real-Time Secondary Crash Prediction Algorithm | Henrick Haule, University of Arizona; Angela Kitali, University of Washington, Tacoma; Haifeng Wang, Florida International University; Priyanka Alluri, Florida International University; Thobias Sando, University of North Florida | University of Arizona | This research developed an application to predict the likelihood of secondary crashes in real time. The application uses incident data from SunGuide®, traffic data from HERE, rainfall data from the Next-Generation Radar Level-II network (NEXRAD), and roadway geometric characteristics data from the Florida Department of Transportation. The algorithm estimates the impact area of an incident and predicts the likelihood of secondary crashes in real time. It consists of an Internal Storage Database, which stores incident, speed, and rainfall data collected in real time. It also archives the secondary crash prediction results, historical databases, secondary crash prediction equation, and secondary crash likelihood parameters. The second part of the application is Backend Applications for collecting, parsing, and saving incident, traffic, and rainfall data in real time. One of the applications continuously accesses the SunGuide® database every two minutes and ping new incidents. Speed data are retrieved from the HERE real-time flow Extensible Markup Language feed every minute. Rainfall data are obtained from NEXRAD every 4-6 minutes. The information from the Internal Storage database and Real-time Data Backend Programs are combined using the Secondary Crash Prediction Application to predict the likelihood of secondary crashes every 15 minutes until the incident is cleared. | ||
46 | 1/10/2023 | 10:15 AM- 12:00 PM | 3097 | P | TRB Minority Student Fellows Poster Session | Invited Student Paper: Affordable Housing Provision and Transit-Oriented Development | Alonso Carrillo*, University of Arizona; Nicole Iroz-Elardo, Willamette University; Arlie Adkins, University of Arizona | University of Arizona | As housing and transportation rise and wages fail to keep up, the need for affordable housing in transit-accessible locations has never been more important. The development of affordable housing units with transit-oriented development (TOD) is one proven strategy for achieving this, but we have seen evidence that TOD is not always affordable and may contribute to displacement and gentrification in some circumstances. This paper reports on the findings from a scoping review of research and gray literature on the provision of affordable housing in TODs. Researchers reviewed over 600 article abstracts to identify twenty resources that included examples from twelve U.S. states and two Canadian provinces. Across these resources, we identified a variety of strategies and policies, including land banking and joint development; creative land ownership models like land trusts; zoning mechanisms (e.g., parking requirement reduction); density bonuses and other incentives; inclusionary zoning; requirements to pay into affordable housing funds; and prioritization of transit proximity in state Low-Income Housing Tax Credit allocation plans. In addition to these specific policies, we observed several important elements that seemed to be associated with successfully moving the needle, including cross-sector partnerships; local, regional, and state cooperation; support of a robust non-profit affordable housing eco-system; and targeting or amplifying affordable housing incentives to areas planned for TOD. | ||
47 | 1/10/2023 | 8:00 AM- 9:45 AM | 3022 | L | Leveraging Technology for Improved Maintenance Operations | Pavement Marking Conditions Assessment using Mobile Phone Based on YOLO and Similarity Detection | Biao Kuang*, University of Utah; Jianli Chen, University of Utah; Xianfeng Yang, University of Maryland, College Park; Sayantan Tarafdar, University of Maryland, College Park | University of Utah | Some explorations have been made on the application of deep learning to the detection of faded pavement markings, in order to facilitate automatic, efficient, and low-cost pavement marking inspection and promote safety for road users. However, current practices on pavement marking conditions assessment only considered some specific markings and ignored the degraded issues in other types of markings. Also, existing studies mainly focused on identifying faded markings rather than evaluating the degree of fading in a certain pavement section. Therefore, this study attempts not to distinguish the specific type of pavement markings but to classify them based on their colors. First, some videos were collected by an iPhone 12pro and then converted into images. Then, 1479 images were labeled into two classes (i.e., white faded markings and yellow faded markings) and trained by YOLOv5. Training results show that the precision and recall are 0.77 and 0.66, respectively. Our model performs well in detecting some types of faded markings, including lane markings, arrow markings, delineators, crosswalks, etc. Additionally, a new algorithm was developed based on similarity measurement to calculate fading severity in a pavement section. Test results show that its accuracy is around 80%. The methods proposed in this study can be a reference for decision making of pavement markings’ maintenance, which would further improve road users’ traffic safety. | ||
48 | 1/10/2023 | 8:00 AM- 9:45 AM | 3047 | P | Cracking Processes in Asphalt Mixtures | Equivalency of Intermediate Temperature Fracture Test to Predict Cracking Performance | Abdullah Al Mamun*, University of Utah; Carlos Hermoza*, University of Utah; Pedro Romero, University of Utah; Beatriz Paula Fieldkircher*, University of Utah | University of Utah | Different test methods have been used to predict the performance of asphalt pavement by addressing the cracking mechanism, and the two most prevalent tests are the Indirect Tensile Asphalt Cracking Test (IDEAL-CT) and the Illinois Flexibility Index Test (I-FIT). Both tests conform to the fracture energy based principle in defining the cracking potential of asphalt concrete at intermediate temperature, and therefore, for quality assurance, many agencies use these tests interchangeably following the convenience without comprehending the relationship between the tests. Therefore, the study aims to find an equivalency between the tests by evaluating the fracture behaviour and different measured parameters that are used to develop the cracking indices and subsequent performance. In this regard, eleven different mixes have been evaluated to predict the cracking potential following their significant properties. Although both tests consistently identify possibly poor mixes, there is no appreciable proportionality between them. Moreover, the tests exhibit different fracture energy and strength and show individual slopes in failure mechanism trends and, compared to fracture energy, the post-peak slope and the strength of the mixture seem to correlate better with the respective indices. In general, the tests are not interchangeable, and therefore, the selection of a mixture following the index value should be based on carefully observed field performance rather than just convenience. It is also recommended to incorporate the field performance of mixtures to find a better correlation when selecting the appropriate test performance criteria. | ||
49 | 1/10/2023 | 10:15 AM- 12:00 PM | 3108 | P | Current Issues in Alternative Fuels and Technologies | Enable Decision Making For Battery Electric Bus Deployment Using Robust High-Resolution Interdependent Visualization | Gabrielius Kudirka*, University of Utah; Xinyuan Yan*, University of Utah; Sarah Kunzler*, University of Utah; Yirong Zhou*, University of Utah; Bei Wang, University of Utah; Xiaoyue Cathy Liu, University of Utah | University of Utah | Encouraged by the advancement of battery technology, the transition from diesel or Compressed Natural Gas to fully zero-emission bus fleets has been the trend in the United States. Policymakers and transit agencies have set up goals to accelerate such transition yet various challenges that are by nature, institutional, technological and/or financial still present themselves. For example, in terms of institutional challenges, cities without a proper fleet management framework will have a hard time transiting directly to battery electric buses (BEBs). Also, BEBs will require a significantly larger upfront financial investment which could hinder the chance of deploying BEBs. From the technological perspective, successfully deploying BEBs requires a combined knowledge of transportation system, energy/power system, optimization, and risk assessment. To address the aforementioned challenges, we design a bi-objective optimization framework that takes cost and environmental equity into consideration. The flexible framework can also be applied to optimize any transit-related objectives. Built upon this framework, we develop a prototype of visualization tool, referred to as the BEBExplorer. Users are able to test, visualize, and explore deployment scenarios given all combinations of constraints on budget, bus schedule, bus routing, locations of charging stations, etc. | ||
50 | 1/10/2023 | 1:30 PM- 3:15 PM | 3149 | P | Transportation Safety Management Systems from Start to Finish | Investigating the Impact of COVID-19 on Traffic Safety: From “Lockdown” to the “New Normal” | Yaobang Gong, University of Utah; Pan Lu, North Dakota State University; Xianfeng Yang, University of Maryland, College Park | University of Utah | COVID-19 pandemic has placed pronounced and prolonged impacts on traffic safety. Many studies found the crash frequency reduced but the severity level increased during the earlier “Lockdown” period. However, there is a lack of studies investigating the pandemic’s impact on traffic safety during the later stage of the pandemic. Therefore, this study employs statistical methods to investigate whether the impact of COVID-19 on traffic safety differs during the different stages. Pairwise t-tests were conducted to compare the crash frequency and crash severity levels before, during the earlier stage, and the later stage of the pandemic. Negative binomial models and binary logit models were utilized to study the effects of the pandemic on the crash frequency and severity respectively while accounting for the exposure, environmental and human factors. The results show that the crash frequency is significantly less than that of the pre-pandemic during the whole course of the pandemic. However, it significantly increases during the later stage due to the relaxed restrictions and possibly drivers’ behavioral changes. Crash severity levels increased during the earlier pandemic due to the prevalence of risky driving behavior and increased presence of commercial vehicles, but it reduced to a level comparable to the pre-pandemic later. Statistical models show that the impacts of the pandemic on drivers’ behavior are decaying, leading to the insignificance of all pandemic quantifiers during the later stage of the pandemic when accounting for the exposure, weather, and economic factors. | ||
51 | 1/10/2023 | 3:45 PM- 5:30 PM | 3195 | P | Vehicle-Highway Automation 2023, Part 3 (Part 1, Session 2228; Part 2, Session 2229) | Connected Automated Vehicle Trajectory Optimization Along Signalized Arterial: A Decentralized Approach | Qinzheng Wang*, University of Utah; Yaobang Gong, University of Utah; Xianfeng Yang, University of Maryland, College Park | University of Utah | Trajectory optimization, as a key connected automated vehicles (CAVs) operation task, has the potential to mitigate traffic congestion, lower energy consumption, and increase the efficiency of traffic operation. This study proposes a decentralized approach to optimization CAV trajectories in both longitudinal and lateral dimensions along a signalized arterial under the mixed traffic environment, where human vehicles (HVs) and CAVs co-exist. More specifically, a 2-stage model is developed to optimize CAV trajectories based on traffic signal plans of downstream intersections and trajectory information of surrounding vehicles. The stage-1 is formulated to provide a rough estimate of the minimal travel time required for a single CAV traveling along this arterial with minimum stops. The stage-2 model is then designed to optimize the longitudinal and lateral behavior of CAVs with the objective of minimizing delay and lane-changing costs. This model is solved by a dynamic programming algorithm to satisfy the real-time optimization needs. A rolling horizon approach is adapted to dynamically implement the proposed model in light of changing traffic conditions. Numerical experiments have been conducted on a real-world arterial to evaluate the model performances. By comparing the optimized trajectories to the no optimization benchmark, the proposed model can reduce average stop delays of CAVs. Moreover, it can also reduce the stop delays of HVs and mixed traffic. | ||
52 | 1/10/2023 | 6:00 PM- 7:30 PM | 3216 | P | A Fresh Look at Crash Characteristics | Secondary Crashes Identification and Modeling along Highways in Utah | Zhao Zhang, University of Utah; Yaobang Gong, University of Utah; Pan Lu, North Dakota State University; Xianfeng Yang, University of Maryland, College Park | University of Utah | The occurrence of secondary crashes on highways would bring many adverse effects, such as traffic congestion, air pollution, leading to more crashes. Accurate identification of secondary crashes is the basis for identifying contributing factors and contributing factors are the cornerstones for incident management system to find effective strategies to reduce the risk of secondary crash. However, secondary crash records are often not recorded correctly. To tackle this issue, this research aims to propose a hybrid method to accurately identify primary and secondary crashes. Based on the identified primary and secondary crashes, this study developed a binary logit model to find contributing factors of secondary crashes and construct a HOPIT model to analyze the crash injury patterns in primary and secondary crashes with identified data of primary and secondary crashes, respectively. This study provides a better understanding of contributing factors as well as crash injury patterns of secondary crashes. | ||
53 | 1/10/2023 | 10:15 AM- 12:00 PM | 3079 | L | Improving Winter Maintenance | Operational Response of a Novel External Hydronic Heating Systems with Insulated Pipe Loop for Bridge Deck Deicing During a Record Snowstorm in Texas | Omid Habibzadeh-Bigdarvish, University of Texas at Arlington; Gang Lei, University of Texas, Arlington; Hussein Sanjani*, University of Texas at Arlington; Anand Puppala, Texas A&M University, College Station; Xinbao Yu, University of Texas, Arlington | University of Texas at Arlington | A geothermal-based external hydronic heating system (EHHS) has been developed as an effective solution to address the de-icing needs of in-service bridges with minimum negative impacts on the structure, traffic, and environment. This paper discusses the implementation and operational response of a new design of the EHHS in which rather than the whole bottom surface of the bridge deck, only the hydronic heating loops are covered with insulation material and provides the accessibility for visual inspection of the bridge deck. The first full-scale external hydronic heating system with an insulated loop (EHHS-IL) was installed on a mock-up bridge deck in north Texas and tested in a record snowstorm with a low ambient temperature of 19.5 ˚C. The system operated in three different stages and the inlet fluid temperature was adjusted according to the forecasted weather. Overall, during 10 days of operation, three ice and snow events and 209 hours of freezing ambient temperature were experienced. The heating system was able to maintain the heated bridge deck surface temperature above freezing except for 1.3 hours when the -19.5 ˚C low ambient temperature coincided with snowfall. The average surface heat flux during the test varied from 34.8 – 84 W/m2 and the average heating efficiency was estimated at 17.7%. Also, 422 kWh of electrical energy was consumed during 10 days of operation by the entire geothermal de-icing system. | ||
54 | 1/10/2023 | 10:15 AM- 12:00 PM | 3095 | P | Transportation Demand Management, Parking, and Congestion Pricing Showcase | What Factors Shape Transit Ridership Patterns in a University Community? | Muhammad Khan*, University of Texas, Arlington; Roya Etminani-Ghasrodashti, University of Texas, Arlington; Sharareh Kermanshachi, University of Texas, Arlington; Jay Rosenberger, University of Texas, Arlington; Greg Hladik*, University of Texas at Arlington; Qisheng Pan, University of Texas, Arlington | University of Texas at Arlington | Universities across the United States manage their own transit services to provide affordable and convenient mobility options to their students, staff, and faculty. Due to their subsidized rates, university people often prefer these transit services as compared to other mobility options available publicly to everyone. Ridership trends and determinant factors of public transit ridership have been extensively studied in the literature. However, there has not been much focus on how university communities use the dedicated transit options available to them and what are the influencing factors for the ridership of these services. This study is an attempt to bridge this gap by exploring the determinant factors of university transit service ridership by using data from a university campus. Results show that availability of a service at the time of need, accessibility provided by a service to the desired destination, customer service, safety, number of vehicles in the household, and immigration status of users are statistically significant factors associated with ridership of these services. Results also show that a large number of university community members do not use the transit options available to them because they do not know about the existence of these services. The findings of this study could help improve university transportation services to provide better mobility options to university communities. | ||
55 | 1/10/2023 | 10:15 AM- 12:00 PM | 3098 | P | Quality Assurance Management | Strengthening the Implementation of the Independent Quality Firm (IQF) Model for Design-Build Infrastructure Projects | Jung Hyun Lee, Roger Williams University; Jung Hyun Lee, Roger Williams University; Baabak Ashuri, Georgia Institute of Technology; Evan Mistur, University of Texas at Arlington | University of Texas at Arlington | Major projects delivered using alternative project delivery such as design-build (DB) require a new model to ensure effective quality management. Several state departments of transportation (DOTs) have adopted a new quality management system requiring the design-builder to hire an independent quality firm (IQF) that performs all required quality assurance (QA) tasks on behalf of the DOT. This transition can be challenging, creating a gap between public owners' expectations and the industry's understanding of the new QA model. Thus, it is imperative to identify how well the members of the DB transportation infrastructure industry understand important aspects of the IQF model. The overarching goal of this study is to identify understanding gaps in the implementation of the IQF model between public owners’ expectations and the industry’s perceptions. This study utilizes a mixed-method research methodology, analyzing data from a survey and semi-structured interviews of various professional groups, including public owners, owner’s representatives, general contractors, design consultants, and construction engineering and inspection (CEI) specialists. This study focuses on the level of stringency of quality acceptance decisions and the CEI roles and responsibilities under the IQF model compared to traditional QA. The results show that the traditional QA model is well understood, while there are conflicting perspectives on several CEI roles and responsibilities related to workmanship and contract compliance following the implementation of the IQF model. This study contributes to identifying the current QA practices and offering constructive guidance for adopting and strengthening the IQF model for future DB transportation infrastructure projects. | ||
56 | 1/10/2023 | 1:30 PM- 3:15 PM | 3125 | L | Demonstrating Moisture Influences and Mitigation Strategies | Investigation of Non-Equilibrium and Dynamic Behavior of Soil Water Characteristic Curve through Field Monitoring | Asif Ahmed, State University of New York; Md Jobair Bin Alam, Prairie View A&M University; Sachini Madanayake*, University of Texas, Arlington; Niloy Gupta*, University of Texas at Arlington | University of Texas at Arlington | In this paper, Soil Water Characteristics Curves (SWCC) were reconstructed through simultaneous field measurement of soil water content and suction. The objective of this study was to evaluate whether field-based monitoring can capture the different path of SWCC in various season. Moreover, field measurement of SWCC allowed to quantify the hysteresis phenomenon, analyze the effect of various temporal resolution on SWCC, and highlight the differences in SWCC reconstructed for the same soil during various weather conditions. Field data at one Texas site with expensive subgrade was collected through moisture and suction sensors over the monitoring period of 3 years. Analyzing the rainfall and evapotranspiration data, three distinct wetting and drying cycles were identified which allowed a correct discrimination of the cycles. Pattern of changes in SWCC was identified in the various wetting and drying cycles. While the field data was fitted through Van Genuchten equation, variation of the unsaturated soil parameters (α, n, m) were identified in various cycles. Air Entry Value (AEV) indicator parameters α ranged from 0.05 to 0.11 kPa-1 while the slope of the SWCC curve parameter n value varied from 1.1 to 1.45. Comparing with the previous studies, it was concluded that wetting and drying cycle parameters do not have any predefined relation. However, these unsaturated parameters required for numerical modeling are dependent on the climatic events happening around the year. | ||
57 | 1/10/2023 | 8:00 AM- 9:45 AM | 3050 | P | Explorations in Public Transportation Planning and Development | Impacts of Crime, Land Use, and Service Characteristics on Transit Ridership: Evidence from Five Metropolitan Cities in the Texas Triangle | Qian He, University of Maryland, College Park; Jianling Li, University of Texas, Arlington | University of Texas at Arlington | Transit ridership is an important factor in evaluating transit service performance and provides an essential source of revenue for transit authorities in the United States. Alongside internal factors such as the transit service characteristics, the environmental context of transit-adjacent areas has shown to affect ridership, such as the neighborhood crime rate. Although literature recognizes the negative impact of crime on transit ridership, however, little has been examined regarding the joint effect of crime, neighborhood environment, and the quality of transit service. Furthermore, there is little evidence across greater Metropolitan Statistical Areas (MSA) in the United States regarding the relationship between crime, land use, headway, and transit ridership. Using stop level ridership and service data from transit agencies in five metropolitan cities within the Texas Triangle (Austin, Dallas, Fort Worth, Houston, and San Antonio) in 2018 (n= 23,823), the result of the negative binomial regression model shows that although the crime rate is negatively associated with ridership, good quality of transit service (such as the number of trips per hour during the weekday as well as the average headway), mixed-use development (greater population density, greater employment density, and employment entropy) and better walkability are associated with more ridership during the workday, holding all else constant. Findings also show that block groups with households of lower income or with public assistance, as well as unemployed people, are associated with higher transit ridership. The discussion provides insights on building a transit-supportive environment through equitable communities and perspectives of criminal justice. | ||
58 | 1/10/2023 | 1:30 PM- 3:15 PM | 3121 | L | Bridge Load Rating via Diagnostic Load Testing: Case Studies | Evaluation and Load Rating of an Impact Damaged Steel Girder Bridge | Mohd Mezanur Rahman, Volkert, Inc.; Nur Yazdani, University of Texas, Arlington; Eyosias Beneberu, University of Texas, Arlington; Khadiza Binte Jalal, HNTB Corporation | University of Texas at Arlington | One of the most prevalent reasons for traffic disruptions and costly repairs or replacements of highway bridges is vehicle impact damage due to inadequate vertical clearance or over-height vehicles. The current study diagnosed and load rated an impact damaged and aging non-composite steel girder bridge on IH-30 in Dallas, TX, with extensive concrete deck delamination. Static load testing and Non-Destructive Evaluation (NDE) were used to evaluate the performance of the transversely symmetrical six span bridge. Span one was simply supported and 9.14 m long, while spans two and three were continuous and 18.29 and 21.34 m long, respectively. Impact Echo (IE) scan data of the deck surface revealed that approximately 40% of the deck concrete was severely delaminated. Ground Penetrating Radar (GPR) scans indicated that the concrete covers for the top layer reinforcements in the cast-in-place non-composite deck ranged from 38 to 64 mm for about 62% of the total deck area. Analysis of strain data from the load test showed that the girder neutral axes were in the web, demonstrating non-composite action between the deck and girders. An innovative approach was employed to load rate the concrete deck and the steel girders combining the results of NDE and load testing following AASHTO procedures. It was found that both the deck and the girders were unsafe for HS-20 loading, as required by the Texas Department of Transportation. Therefore, load posting and urgent rehabilitation of the bridge are recommended. | ||
59 | 1/10/2023 | 3:45 PM- 5:30 PM | 3197 | P | Applications and Innovations in Urban Travel Data | Incorporating Job competition and matching to an Indicator-based Transportation Equity Analysis for Auto & Transit in Dallas-Fort Worth | Soheil Sharifiasl*, University of Texas, Arlington; Subham Kharel*, University of Texas, Arlington; Qisheng Pan, University of Texas, Arlington | University of Texas at Arlington | Transportation equity is a hot but vague issue for planners and engineers in the U.S., where mobility-based transportation planning triggered the disproportionate distribution of benefits and burdens in transportation system. Previously, transportation equity studies have evaluated equity using different transportation service metrics and socio-demographic factors. Job accessibility is one principal metric for transportation quality as it directly relates to a communities’ economic prosperity. While most equity studies analyze aggregate accessibility for a single mode, our study measures blue-collar and white-collar job accessibilities for auto and transit by incorporating job proximity, competition, and matching. Using Lorenz Curve, Gini Coefficient, and Atkinson measure, we performed horizontal and vertical equity (within and between groups) studies and compared various population groups by disaggregating the population based on the two most cited Environmental Justice indicators, namely, income and race. Our findings suggest that (a) job accessibility by transit mode has higher inequalities compared to auto, (b) white-collar and blue-collar jobs have similar inequality levels, (c) low- and high-income populations experience somewhat the same inequality levels, and (d) inequality is highest among African-American communities, (e) blue-collar job accessibilities have higher inequality for Hispanic and Asian communities for both transportation modes and (f) higher disparities in job accessibility is observable among racial groups than income groups in Dallas-Fort Worth (DFW) Area. To address such disparities, planners must enhance transit availability through subsidizing transit costs and transit feeder services like on-demand shared services. | ||
60 | 1/10/2023 | 1:30 PM- 3:15 PM | 3117 | L | We Made It!: Time to Plan for Life After COVID-19 | Post-Pandemic Last-Mile and Shopping Center Trip Estimations Using Scenario Analysis | Mehrdad Arabi*, University of Texas, Arlington; Kate Hyun, University of Texas, Arlington; Stephen Mattingly, University of Texas, Arlington; Md Ashraful Imran*, University of Texas, Arlington | University of Texas at Arlington | The e-commerce activities have been continuously rising in the past few decades while the recent COVID-19 pandemic significantly accelerated the trend. At the same time, the retail sector has experienced challenges as people chose online purchase rather than making trips to shopping centers. These seemingly complementary activities affect the number of total trips on a network because of trip exchanges between shopping trips and last-mile deliveries. However, the impact of the COVID-19 shock and its influence on the future exchange and supplementary relationship between the last-mile and shopping trips remains uninvestigated; one critical research question concerns the potential for an increase in the total traffic. This study develops multiple scenarios that estimate the number of shopping and last-mile trips for post-pandemic in 2024 using their historical trends collected in 2019 and 2020 that represent pre- and during COVID-19, respectively. The scenarios incorporate uncertainties present in future trends because estimating the number of trips based on the existing trend cannot always lead to reliable results. The results show a considerable decline in number of trips to shopping malls after COVID-19, but no significant change in supermarkets and wholesale store trips post-pandemic. The pandemic significantly increases the last-mile deliveries in 2024 even though the growth rate highly varies by scenario. The research outcomes with scientific developments of scenario analytics will provide broader impacts in understanding consumer behaviors and their impacts on the freight transportation sector. | ||
61 | 1/10/2023 | 1:30 PM- 3:15 PM | 3139 | L | Freight Planning and Health Care Logistics During COVID-19 Pandemic | Equity Analysis of Last Mile Delivery Before and After COVID-19: A Case Study on Dallas, TX | Md Ashraful Imran*, University of Texas, Arlington; Kate Hyun, University of Texas, Arlington; Mehrdad Arabi*, University of Texas, Arlington | University of Texas at Arlington | COVID-19 has changed perceptions towards online shopping from luxury or occasional activities to essential needs. However, scant research has explored the equality or disparity in last-mile delivery traffic present to communities. Further, little is known about how the impacts changed during the pandemic even though discrimination prevailed before the pandemic. This study compared the last mile traffic among different communities before and after the pandemic by developing traffic impact measures. Link-level truck loading (volume) and OD trips between freight facilities and communities evaluated overall last-mile traffic for transportation disadvantaged communities. In particular, the Lorenz curve, a Z-score-based traffic concentration, and K-mean clustering were used to estimate and compare the last-mile traffic among different communities and evaluate last-mile impacts for Dallas, TX. The results indicate that the last-mile traffic was significantly increased in the northern part of the Dallas area resulting in 53% of more communities experiencing the increased last-mile traffic during the pandemic. The Environmental Justice (EJ) population, including youth (under 18 years old), older adults (65 years+), and those with low education, no internet or no motor vehicle, and non-white populations, were disproportionately impacted by last-mile traffic even before the pandemic. However, COVID-19 appears to worsen the overall inequality to these EJ populations due to the increased online shopping activities from affluent communities, which resulted in increased last-mile thru-traffic to EJ communities. | ||
62 | 1/10/2023 | 6:00 PM- 7:30 PM | 3211 | P | Advances in Data and Methods Related to Modeling Bicyclist and Micromobility User Behavior and Safety | Evaluation of Bicycle Network Connectivity Using Graph Theory and Level of Traffic Stress (LTS) | Md Mintu Miah, University of California, Berkeley; Stephen Mattingly, University of Texas, Arlington; Kate Hyun, University of Texas, Arlington | University of Texas at Arlington | The quality of the bicycle network determines ridership, safety, connectivity, equity, and livability. Very few past research studies investigate network connectivity for individual user types and identify network needs and barriers based on these rider types. This study measures the network connectivity for different rider types using Level of Traffic Stress (LTS) and graph theory concepts. As a symbolic representation of a road network and its connectivity, a graph represents the structural properties of networks and compares one measure over another by taking into account spatial features. In addition, this study defines a bicycle network for different types of riders using LTS metrics based on traffic speed, road geometry, and traffic volume. This study evaluates the city of Portland's OSM bicycle network as a case study. Three transit stations at the downtown, riverside and residential area were considered to assess the connectivity and barriers with a home at block level for last and first-mile coverage. The analysis shows that 29% of links in Portland need to be improved with more bicycle facilities to provide access to adult riders, while 33% of links improvement is required for children. The networks are well connected for ‘strong and fearless’ and ‘confident and enthused’ users but not well connected for adults and children in many neighborhoods with low alpha and GTP indices. The results indicate that planners and designers need to improve their network connectivity for all types of users to ensure equal active transportation opportunities beyond a particular portion of the network. | ||
63 | 1/10/2023 | 3:45 PM- 5:30 PM | 3201 | P | Innovative Freight Data Research and Applications | A Comparative Analysis of Intra-City and Inter-City Truck Travel Characteristics Based on GPS Trajectory Data | Wenwen Wang, Tongji University; Qisheng Pan, University of Texas, Arlington | University of Texas at Arlington | The lockdown of major cities in many countries during the COVID-19 pandemic era has stopped or severely impeded intra-city and inter-city truck movement, which significantly interrupted business and affects urban life. The characteristics of regional truck movement has attracted many interests in the field of freight transportation research. There are sparse existing studies using GPS trajectory data to examine urban or regional truck movement due to data availability. There are even few studies that compare the differences between intra-city and inter-city truck trips. To fill this gap, our paper conducts a comparative analysis on the characteristics of intra-city and inter-city truck trips in Hubei, a province in Central China, using GPS trajectory data of heavy-duty trucks in four months of a year. Our study yields several interesting findings: first, intra-city truck trips should be given more attention in terms of their trip volume, volatility, number of vehicles, and route choice. Second, truck travel cycles are influenced by traffic management policies, either by monthly or weekly cycles. Finally, no significant seasonal differences in trip frequency, distance, time, and speed are found in either intra-city or inter-city truck trips. | ||
64 | 1/10/2023 | 10:15 AM- 12:00 PM | 3096 | P | Dwight David Eisenhower Transportation Fellowship Program Poster (Session 2) | E-Bike Incentive Programs | Cameron Bennett*, Portland State University | Portland State University | |||
65 | 1/10/2023 | 1:30 PM- 3:15 PM | 3154 | P | Advancing Electric and Autonomous Bus System Planning, Design, and Operation | Capturing the Behavioral Determinant Behind the Use and Adoption of Shared Autonomous Vehicles | Roya Etminani-Ghasrodashti, University of Texas, Arlington; Sharareh Kermanshachi, University of Texas, Arlington; Jay Rosenberger, University of Texas, Arlington; Ann Foss, City of Arlington, TX | University of Texas at Arlington | Self-driving vehicles have the potential to reduce mobility barriers by providing affordable and flexible shared mobility options. However, integrating the benefits of self-driving vehicles into the current transit system depends highly on public acceptance and use of this new mobility mode when it is extensively available on the road. This paper seeks to advance self-driving technology diffusion by applying and testing a conceptual model designed to explore the possible determinants of using and adopting shared autonomous vehicles (SAVs). Accordingly, we utilized principles of socio-psychological theories of human behavior to develop the study framework and investigate two groups of people; a sample of people who used an available SAV on the road and a sample of non-users. We tested the validity of the research hypothesis by using the structural equation model (SEM) and examined the effects of motivations and restriction-related factors on SAV use and adoption. Results reveal that two behavioral factors of perceived usefulness and relatedness can increase respondents' motivations to use the available SAV more frequently. However, the future adoption of SAVs by both users and non-users is highly associated with individuals’ attitudes towards this technology. We also found that the perception of SAV risks can impede non-users from adopting the service in the future as well. The results of this study imply that the effective implementation of SAVs calls for a deep understanding of the behavioral motivations people experience while encountering mobility innovations. | ||
67 | 1/11/2023 | 8:00 AM- 9:45 AM | 4002 | L | Reproducible Research in Traffic Flow Theory | Explore Regional Variation in the Effects of Built Environment on Driving With High Resolution U.S. Nation-Wide Data | Liming Wang, Portland State University | Portland State University | There have now been numerous studies on the relationship between travel behavior and built environment over the last a few decades. Prior studies have mostly focused on producing point estimates of model coefficients and ended up with a wide range of estimates for the built environment elasticity of travel behavior, including household VMT. With few exceptions, previous studies use data from a single region or a small number of regions, and thus are not able to sufficiently investigate the regional variation in built environment elasticity. A few papers have addressed the heterogeneity of elasticity among different population groups and neighborhood types [for example, @salon_heterogeneity_2015; @voulgaris_synergistic_2016], but so far have paid little attention to regional variation of elasticity. In this paper, I use the 2009 U.S. National Household Travel Survey and high resolution built environment measures in the Smart Location Database to investigate the regional variation in the effect of built environment. | ||
68 | 1/11/2023 | 8:00 AM- 9:45 AM | 4003 | L | Advancements in Processing, Understanding, and Using Bicycle and Pedestrian Data | Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes | Joseph Broach, Portland State University; Sirisha Kothuri, Portland State University; Md Mintu Miah, University of California, Berkeley; Kate Hyun, University of Texas, Arlington (UTA); Nathan McNeil, Portland State University; Stephen Mattingly, University of Texas, Arlington (UTA); Krista Nordback, UNC Highway Safety Research Center; Frank Proulx, Frank Proulx Consulting | Portland State University | University of Texas at Arlington | This research integrated and evaluated emerging user data sources (Strava Metro, StreetLight, and hybrid docked/dockless bike share) of bicycle activity data with conventional “static” demand determinants (land use, built environment, sociodemographics) and measures (permanent and short-duration counts) to estimate annual average daily bicycle traffic (AADBT). We selected six locations (Boulder, Charlotte, Dallas, Portland, Bend, and Eugene) covering varied urban and suburban contexts and specified three sets of Poisson Regression models – all city pooled, Oregon pooled, and city-specific. Static variables, Strava, and Streetlight appeared to complement one another; that is, adding any two data sources together tended to outperform each data source on its own. Low-volume sites proved challenging to predict, with the best-performing models still demonstrating considerable error. City-specific models generally displayed better model fit and prediction performance. Using Strava or StreetLight alone to predict AADBT without static adjustment variables increased expected prediction error by a factor of about 1.4. That rule of thumb figure of 1.4 times was only slightly lower when combining Strava plus StreetLight without static variables (1.3x). Tests of transferability showed that transferring the model specifications from one year to the next without re-estimating the model parameters resulted in a 10-50% increase in error rate across models, so such transfer is not recommended. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, old “small” data sources will likely be very important for big data sources like Strava and StreetLight to achieve their potential for predicting AADBT. | |
69 | 1/11/2023 | 8:00 AM- 9:45 AM | 4017 | L | Equity in Past and Present Practice | -Presiding- | Aaron Golub, Portland State University | Portland State University | |||
70 | 1/11/2023 | 10:15 AM- 12:00 PM | 4058 | P | Automation, Technology, and Pedestrian Interactions | Exploring Pedestrian Crossing Behavior using LiDAR Sensors at Signalized Intersections | Sirisha Kothuri, Portland State University; Pengfei (Taylor) Li, University of Texas, Arlington (UTA) | Portland State University | University of Texas at Arlington | Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. Approximately 15% of pedestrian fatalities occurred at signalized intersections, where a variety of modes converge leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Commonly used detection systems for pedestrians at signalized intersections consist primarily of pushbuttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected especially with older devices and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. LiDAR sensors have been used in automated vehicles to identify surrounding vehicles and pedestrians. They used to be prohibitively expensive, but the price has dramatically reduced to a comparable range with the widely adopted video or radar detectors. The LiDAR sensors outperform video or radar sensors in many aspects such as slow object tracking and being able to work in adverse weather conditions. In this study, LiDAR sensors were deployed and tested at two signalized intersections to understand pedestrian crossing behavior. Pedestrian volume, crossing time, delay and modified perception-reaction times were observed. The findings reveal that LiDAR sensors can be successfully deployed at intersection for detecting crossing pedestrians and ADA-compliant pedestrian push buttons can significantly reduce pedestrians’ perception-reaction time to WALK signal. | |
71 | 1/11/2023 | 10:15 AM- 12:00 PM | 4059 | P | Investigating Pedestrian Safety and Accessibility | Midblock Pedestrian Signal Safety Effectiveness | Seth LaJeunesse, University of North Carolina; Wesley Kumfer, University of North Carolina, Chapel Hill; Sirisha Kothuri, Portland State University; Krista Nordback, UNC Highway Safety Research Center; Nathan McNeil, Portland State University | Portland State University | Unsignalized midblock crossing treatments can improve pedestrian safety, yet their quality of service for pedestrians remains understudied. The present study explores whether and how pedestrians’ satisfaction with crossing unsignalized midblock crossings varies according to the type of crossing treatment used, i.e., the Rectangular Rapid-Flash Beacon (RRFB) with median island, median island alone, marked crosswalk, and unmarked crosswalk. The research team collected intercept survey and video observation data from 358 pedestrians across a total of 40 sites in two different cities. Structural equation models illustrate how pedestrians’ crossing-oriented satisfaction is shaped by their positive perceptions of safety and low levels of delay in the act of crossing the street. Crossing satisfaction also varied by unsignalized crossing treatment type. Pedestrians perceived RRFBs with medians and median islands by themselves as comparably safer, more time efficient, and thus more satisfying than marked and unmarked crosswalks. | ||
72 | 1/11/2023 | 10:15 AM- 12:00 PM | 4071 | P | Equitable Access and Travel Costs | -Presiding- | Aaron Golub, Portland State University | Portland State University | |||
73 | 1/11/2023 | 10:15 AM- 12:00 PM | 4072 | P | Current Research in Transportation Equity | -Presiding- | Aaron Golub, Portland State University | Portland State University | |||
74 | 1/11/2023 | 10:15 AM- 12:00 PM | 4061 | P | Traffic Signal Control and Progression | Real-time Cycle-based Queue Length Estimation for Signalized Intersections using Single-channel Advance Detector Data | - | Pramesh Pudasaini*, University of Arizona; Abolfazl Karimpour, State University of New York (SUNY); Yao-Jan Wu, University of Arizona | University of Arizona | Queue length is one of the most important metrics required for the performance assessment of signalized intersections. However, the current methodology of estimating queue length in the literature suffers from several drawbacks, including unstable estimation and the requirement of multiple data sources. Moreover, for single-channel advance detection, which is a common detection scheme for signal control in many US cities, manual parameter calibration is required. To bridge these gaps, this study proposes a cycle-based maximum queue length estimation method based on: a) the empirical observations of breakpoints in the time gap between successive actuations; and b) the identification of queue status for all detector actuations in a cycle. Maximum queue length for cycles with long queues is estimated based on the saturation flow rate and the trajectory of the last vehicle in the queue. The proposed methodology was implemented on two study intersections in Tucson, Arizona. Results showed that using the proposed method queue length can be estimated with mean absolute percentage errors of 14.77% and 15.1% and MAE of 25 ft and 42.5 ft. The results showed significant improvements in queue length estimation from single-channel detection data when compared to similar methods in the literature. The proposed method can help transportation agencies accurately estimate queue length on intersections with single-channel advance detection without the need for manual field data collection and without installing lane-by-lane detection. | |
75 | 1/11/2023 | 3:45 PM- 5:30 PM | 4080 | P | Artificial Intelligence Applications in Transportation Planning | Data-driven Transfer Learning Framework for Estimating On-ramp and Off-ramp Traffic Flows | - | Xiaobo Ma*, University of Arizona; Abolfazl Karimpour, State University of New York (SUNY); Yao-Jan Wu, University of Arizona | University of Arizona | To develop the most appropriate control strategy and monitor, maintain, and evaluate the traffic performance of the freeway weaving areas, state and local Departments of Transportation need to have access to traffic flows at each pair of on-ramp and off-ramp. However, ramp flows are not always readily available to transportation agencies and little effort has been made to estimate these missing flows in locations where no physical sensors are installed. To bridge this research gap, a data-driven framework is proposed that can accurately estimate the missing ramp flows by solely using data collected from loop detectors on freeway mainlines. The proposed framework employs a transfer learning model. The transfer learning model relaxes the assumption that the underlying data distributions of the source and target domains must be the same. Therefore, the proposed framework can guarantee high accuracy estimation of on-ramp and off-ramp flows on freeways with different traffic patterns, distributions, and characteristics. Based on the experimental results, the flow estimation mean absolute errors range between 23.90 veh/h to 40.85 veh/h for on-ramps, and 31.58 veh/h to 45.31 veh/h for off-ramps; the flow estimation root mean square errors range between 34.55 veh/h to 57.77 veh/h for on-ramps, and 41.75 veh/h to 58.80 veh/h for off-ramps. Further, the comparison analysis shows that the proposed framework outperforms other conventional machine learning models. The estimated ramp flows based on the proposed method can help transportation agencies to enhance the operations of their ramp control strategies for locations where physical sensors are not installed. | |
76 | 1/11/2023 | 3:45 PM- 5:30 PM | 4080 | P | Artificial Intelligence Applications in Transportation Planning | VEMLAN: Imputing Missing Data for Failed Freeway Traffic Sensors | - | Adrian Cottam*, University of Arizona; Xiaofeng Li, University of Arizona; Yao-Jan Wu, University of Arizona | University of Arizona | Traffic volume is essential for traffic professionals to maintain the performance of freeways and highways. Freeway volume data is typically collected by traffic sensors. The most common traffic sensor used is the inductive loop detector. However, loop detectors can be prone to failures, either with the loop card itself, or due to communication loss. Sometimes these failures are intermittent, but in some cases the detector will fail altogether. In the case of a complete traffic sensor failure, it would be beneficial to use a method to impute or estimate missing volume data until the sensor can be repaired. Most studies focus on imputing intermittent missing volume data, but few studies evaluate imputation for a failed sensor. To address this research gap, this study introduces a Volume Estimation Machine Learning Agent Network (VEMLAN) algorithm to estimate volume data from failed traffic sensors using nearby traffic sensor data and crowdsourced data. The VEMLAN algorithm is an online, spatiotemporal volume estimation algorithm that can be used with several different machine learning methods and traffic sensors. Furthermore, it is modular, allowing it to adapt to several different failure network topologies. The VEMLAN algorithm is evaluated in conjunction with six different machine learning models for five different failure network topologies, and a sensitivity analysis of volume data aggregation levels is performed. When using VEMLAN with a dense neural network at an aggregation level of five minutes, a MAPE of 10.4% is achieved. Furthermore, it is observed that crowdsourced data can improve the estimation accuracy. | |
77 | 1/11/2023 | 10:15 AM- 12:00 PM | 4064 | P | Emerging Sensor Technologies for Critical Transportation Data Needs | Roadway Snow Estimation using Dual-Spectrum Camera Images and Computer Vision | - | Xiangdong He*, University of Utah; Yuning Wu*, University of Utah; Keping Zhang, University of Utah*; Xuan Zhu, University of Utah; Xianfeng Yang, University of Maryland, College Park | University of Utah | Safety is the principal concern of highway transportation, and slippery roads can pose high risks of traffic collisions in snowy regions, which cover about 70 percent of road networks and the population in the United States. We aim to develop a convenient tool to perform multi-lane road slippery condition evaluation in winter seasons. In this work, field data collection using a dual-spectrum camera is first performed at a field site in the State of Utah, US. We analyze optical and infrared images covering a field of view over three lanes through two snowstorms. Image processing techniques, including image registration and segmentation, are implemented on both types of images collected under different illumination and temperature conditions. Moreover, the ratio of snow-covered pixels is computed to quantify the snow coverage rate of individual lanes. Finally, we verify the system performance by comparing our estimation with the ground truth via confusion matrix. The high accuracy, precision, true positive rate, and true negative rate suggest that the developed approach can support satisfactory performance for roadway snow detection. The developed technique offers the potential to facilitate local agencies’ decision-making on snow-plowing resource planning and performance evaluation and support winter safety for connected vehicles. | |
78 | 1/11/2023 | 3:45 PM- 5:30 PM | 4079 | L | Light Rail Safety and Accessibility | A GIS-Based Accessibility Analysis on Transit Equity in Salt Lake County | Presentation | Faria Afrin Zinia*, University of Utah; Justice Prosper Tuffour,* University of Utah; Pukar Bhandari*, University of Utah; Andy Hong, University of Utah | University of Utah | The gap between the demand for and supply of transportation infrastructure is a major problem, which could result in the inequitable distribution of resources. In this study, we examined social equity dimensions of transportation in terms of the distribution of accessibility to light rail transit (LRT) stations in Salt Lake County, US. This research employed two novel methods seldom used in transportation research. First, we used the Two-Step Floating Catchment Area (2SFCA) method to consider both demand and supply aspects of public transit accessibility. Second, we developed spatial models to account for spatial autocorrelation issues in our data. Results showed little evidence of inequitable access to light rail transit in Salt Lake County. The accessibility to LRT stations was generally higher in the downtown and west side of Salt Lake City, where there is a concentration of low-income ethnic minority populations. We also found evidence of higher transit accessibility associated with households without a home and car ownership. Findings suggest that the light rail transit investments in Salt Lake Valley adequately addressed social equity issues. | |
79 | 1/11/2023 | 3:45 PM- 5:30 PM | 4080 | P | Artificial Intelligence Applications in Transportation Planning | Physics Constrained Gaussian Process for Traffic State Estimation | - | Yun Yuan, University of Utah; Xianfeng Yang, University of Maryland, College Park | University of Utah | Despite the success of model-based and data-driven approaches in traffic state estimation, those approaches either require great efforts in parameter calibrations or lack theoretical interpretations. As a hybrid approach, Physics Constrained Gaussian Process (PCGP) is proposed to encode physics models, i.e., classical traffic flow models, into an energy functional in the Gaussian Process (GP) architecture. This paper proves the existence of the numerically differentiable energy functional structure on a novel theoretical basis. Then, based on the derived approximate posterior objective function, an efficient alternating stochastic optimization algorithm is derived. To show the effectiveness of the proposed model, this paper conducts empirical studies on a real-world dataset which is collected from a stretch of I-15 freeway, Utah. Results show the enhanced PRGP model can outperform the previous compatible methods, such as calibrated physics models and pure machine learning methods, in estimation accuracy and robustness. | |
80 | 1/11/2023 | 3:45 PM- 5:30 PM | 4080 | P | Artificial Intelligence Applications in Transportation Planning | Freeway Traffic Flow Forecasting Using Physics-Guided LSTM with Flawed Data | - | Zhao Zhang, University of Utah; Qinzheng Wang, University of Utah; Hao Yang, McMaster University; Xianfeng Yang, University of Maryland, College Park | University of Utah | Having an accurate traffic flow predictions play an important role in intelligent transportation systems. Recently, long short-term memory (LSTM) model is prevailing in utilizing big data for traffic state prediction. The performance of LSTM model heavily depends on high-quality data due to their data-driven nature. However, historical traffic data usually contains flaws (e.g., incomplete, or incorrect information) since the malfunctioning of traffic sensors. To tackle this issue, this study aims to propose and evaluate a new advanced model, named as physics-guided LSTM, that adopt traffic physical knowledge into a modified loss function to guide the training process of LSTM. More specifically, PG-LSTM network could bring the physical knowledge from upstream traffic flow to overcome the data flaw problem downstream for traffic flow forecasting. To illustrate the effectiveness of the PG-LSTM, this study implements empirical studies with a real-world dataset collected from a stretch of I-15 freeway in Utah. Experimental study results show that the proposed PG-LSTM model could outperform the other compatible methods. | |
81 | 1/11/2023 | 8:00 AM- 9:45 AM | 4011 | L | Evaluation of Innovative Methods of Site Characterization and Material Testing | Electrical Resistivity Imaging for Identifying Critical Sulfate Concentration Zones along Highways | - | Mina Zamanian*, University of Texas, Arlington; Yatindra Thorat*, University of Texas at Arlington; Natnael Asfaw, Texas Department of Transportation; Prakash Chavda, Texas Department of Transportation; Mohsen Shahandashti, University of Texas, Arlington | University of Texas at Arlington | Assessing sulfate concentration levels and their distributions within road alignments is crucial in the design phase of highway projects. However, a reasonable assessment of the extent and levels of sulfate concentration using current practices such as conventional laboratory-based methods is still challenging due to the spatial heterogeneity of sulfate minerals in soils and their seasonal fluctuations. This study aims to assess the application of electrical resistivity imaging (ERI) to determine the levels and distributions of sulfate concentration in soils. A finite element and leastsquares optimization were used to process the data and generate inverted resistivity profiles of the subsurface. Fourteen electrical resistivity imaging surveys were carried out for two sites with a potentially high risk of sulfate-induced heaving to help determine the extent of critical sulfate concentration zones. Several laboratory tests (sulfate and moisture content tests) were conducted on ten samples collected from the fields to validate the ERI findings. The results showed that the electrical resistivities of critical sulfate concentration zones are significantly lower than typical ranges of electrical resistivity for earth materials due to the abundance of salt ions in the pore water, which facilitates the flow of electric current. The findings of this study were consistent with laboratory test results in determining the levels of sulfate concentration. This study showed that ERI successfully provides a rapid and continuous assessment of critical sulfate concentration zones within highway alignments. The findings of this study will help materials and pavement engineers determine where alternative material and pavement designs are needed. | |
82 | 1/11/2023 | 8:00 AM- 9:45 AM | 4012 | L | Quantifying Moisture for Resilient Roadway Design and Construction | Predicting Subgrade Resilience Modulus and Soil–Water Characteristic Curve Coefficients Using Artificial Neural Network (ANN) Model | - | Rami Khalifah, University of Texas, Tyler; Mena Souliman, University of Texas, Tyler; Gokhan Saygili, University of Texas, Tyler; Karthikeyan Loganathan, University of Texas at Arlington | University of Texas at Arlington | Resilient modulus (MR), a measure of subgrade soil stiffness, is a required parameter during pavement design using AASHOT 1993 or Pavement ME software. Similarly, Soil–Water Characteristic Curve (SWCC) coefficients are equally important parameters to characterize the degree of saturation of subgrade layers. While the MR and SWCC coefficients can be determined by lengthy and expensive laboratory testing, this paper showcases a research study that developed soft computing models to predict the resilient modulus of subgrade as well as the SWCC coefficients for the state of Texas. An efficient and powerful statistical analysis technique, Artificial Neural Network (ANN), is utilized with one hidden layer and multiple neurons. The basic soil index properties such as the percent passing sieves #4, #10, #40, #200, 0.002 mm, liquid limit, and plasticity index were utilized as significant predictors. As a result of multiple regression, strong correlation was observed between MR, SWCC coefficients and above-mentioned independent variables. In addition, the Monte Carlo simulation accounting for the variability of input parameters of the resilient modulus showed that resilient modulus obtained from predictive models can be under-predicted because uncertainties of individual input variables are not incorporated in predictive model predictions. The predictors in developed models can be readily measured in soil laboratories with simple equipment test setups compared to estimation of MR and SWCC coefficients involving extensive tests. The developed ANN models will allow transportation engineers to predict MR and SWCC coefficients, which will have a positive impact on producing an optimal structural pavement design. | |
83 | 1/11/2023 | 8:00 AM- 9:45 AM | 4029 | P | Management of Infrastructure Construction: Conception Through Delivery | Electronic Material Delivery in Highway Construction: A Semi-Structured Interview | - | Apurva Pamidimukkala*, University of Texas at Arlington; Sharareh Kermanshachi, University of Texas, Arlington; Karthik Subramanya, University of Texas at Arlington | University of Texas at Arlington | Electronic Construction (e-Construction) is widely being utilized to decrease paperwork and automate the work inherent to the Construction of roadway infrastructures. E-Ticketing is a part of e-construction that facilitates the electronic transmission of tickets for resources, which constitute half the construction costs; although the technology has been pilot tested by numerous states, it was disbanded for various reasons. This study aims to identify the barriers to deploying the e-Ticketing technology and assess the benefits of an e-Ticketing platform. The results of semi-structured interviews with individuals working for state departments of transportation (DOT), contractors, material vendors, and software providers are discussed, together with the literature findings relating to its utilization and implementation. The transcripts of the interviews were analyzed using the inductive thematic analysis method. The study's findings reveal the causes of delays and misconceptions regarding the e-Ticketing platform's implementation. The authors have also determined the strategies that will help in redefining the technology and assist DOT decision-makers and engineers in developing a standard e-Ticketing platform, implementing rules and guidelines, lowering project costs, providing initial funding, carrying out pilot testing, enhancing inspector safety, and finishing projects quickly and effectively. | |
84 | 1/11/2023 | 10:15 AM- 12:00 PM | 4046 | L | Site Assessment Using Remote Methods | Field Pullout Tests of a Percussion Driven Earth Anchor (PDEA) | - | Natnael Asfaw, University of Texas at Arlington; Mehran Azizian*, University of Texas at Arlington; Gang Lei, University of Texas, Arlington; Arjan Poudel*, University of Texas at Arlington; Laureano Hoyos, University of Texas, Arlington; Xinbao Yu, University of Texas, Arlington | University of Texas at Arlington | Percussion Driven Earth Anchors (PDEA) are driven into soils with percussion force using installation steel hammer rod. Texas Department of Transportation (TxDOT) planned to use these anchors for slope stability measure along the clear fork trinity river at Interstate Highway 20 (IH-20) in Benbrook, Texas. However, there isn’t clear design and construction guidelines for these systems. In this study, three PDEA, Duckbill model 138 II (DB-138 II) were installed and tested in the field on the proposed finished west channel bank slope to find their ultimate pull-out capacity. The anchors were embedded to an average depth of 10 feet into the slope bank that consisted predominantly of sandy lean clay (CL) soil. The slope was graded at an average 2:1 to 2.5:1 configuration. After installation, the anchors were subjected to an upward pullout force using a hydraulic jack system to measure their pull-out capacity. Their pullout load, displacement, and strains were continuously recorded with a load cell, LVDT and strain gauge, respectively. Pullout load versus displacement curves were produced and analyzed to determine the behavior of the anchors. The results were compared with calculated results using existing empirical pullout capacity estimation method by DAS 1980 (1). The comparison showed that the empirical method with in-situ TCP data resulted in reasonably good estimations. The field experiment results helped understand the relationship between the calculated and actual field pullout resistance when used in clayey soil slopes. | |
85 | 1/11/2023 | 3:45 PM- 5:30 PM | 4080 | P | Artificial Intelligence Applications in Transportation Planning | A New Framework for Regional Traffic Volumes Estimation Driven by Large-scale Connected Vehicle Data and Deep Neural Networks | Poster | Swastik Khadka*, University of Texas at Arlington; Peirong (Slade) Wang*, University of Texas, Arlington; Pengfei (Taylor) Li, University of Texas, Arlington; Francisco Torres, North Central Texas Council of Governments | University of Texas at Arlington | Connected vehicle (CV) data in this paper refer to the trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet-of-Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed for 3rd-party applications. Compared to other probe vehicle data, the CV data has a much higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the historical regional travel demand driven by the connected vehicle data and deep neural network (DNN) model. The training data sets for the DNN model are generated using the connected vehicle counts aggregated where infrastructure sensors are installed (the input) and the reported link volumes by the infrastructure sensors (the output). Then the trained DNN model is used to estimate all other roadway links according to the input of ubiquitous CV counts. We apply the proposed model to the Dallas-Fort-Worth (DFW) area, Texas, as a case study. The results are promising and can be expanded to large-scale applications. | |
86 | *Names with asterisks indicate that the presenter is a student or recent alumni. | ||||||||||