Philadelphia Smart Loading Zones: Analysis and Demand Prediction
Samriddhi Khare, Michael Dunst, Tiffany Luo, Ling Chen, Shengqian Wang
In October 2022, the City of Philadelphia introduced a pilot program that tested 20 paid, curbside loading spaces for delivery drivers in Center City, known as “smart loading zones.”
Delivery companies were able to reserve spaces and times through a smartphone app.
Img Source: https://www.phila.gov/programs/smartcityphl/smart-loading-zones/
The data collected from this pilot program offers interesting insight into reservation patterns that can be used for potential expansion of the program, answering the question:
Where should smart loading zones be located?
Add interesting viz here?
Img Source: https://whyy.org/articles/philly-to-test-new-tech-in-bid-to-stop-delivery-drivers-from-parking-illegally/
Challenge:
Without a strategically located smart loading zones, drivers have no choice but to double park, or spend too much time looking for vacant spots around the city.
Solution:
Our application will serve as a back-end tool that will allow users to predict demand for curbside loading spaces at any location.
Clients
Miriam Cherayil
Smart Infrastructure Project Manager
Akshay Malik
Smart Cities Director
Chris Shelley
Smart Mobility Coordinator
DATA
MODEL
APP
Study Area &
Pilot Site
Study Area &
Pilot Site
Study Area &
Pilot Site
Client Data from the Pilot
Challenge: Differing Hours of Operation
| Early morning | Morning | Midday | Evening | Late night |
Walnut St | | | | | |
Sansom St | | | | | |
Chestnut St | | | | | |
Challenge: Differing Hours of Operation
| Early morning | Morning | Midday | Evening | Late night |
Walnut St | | | | | |
Sansom St | | | | | |
Chestnut St | | | | | |
Model Data
Client Data from the Pilot
Average Bookings per Day of the Week
Walnut
Sansom
Chestnut
Understanding Demand for Curb Space
What is the process behind a vehicle choosing to stop alongside a curb?
Understanding Demand for Curb Space
What is the process behind a vehicle choosing to stop alongside a curb?
What might a driver need to stop for?
Distance to the nearest location of a variety of land uses: OpenStreetMap
Understanding Demand for Curb Space
What is the process behind a vehicle choosing to stop alongside a curb?
What might a driver need to stop for?
Distance to the nearest location of a variety of land uses: OpenStreetMap
How many vehicles use the road and why?
Official road classifications
Understanding Demand for Curb Space
What is the process behind a vehicle choosing to stop alongside a curb?
What might a driver need to stop for?
Distance to the nearest location of a variety of land uses: OpenStreetMap
How many vehicles use the road and why?
Official road classifications
What is the time of day?
Separating bookings into times of day
Correlations
Strongest relationships
Strength of Variable Relationships
Initial Data Exploration
External Data
OpenStreetMap Data, Bike and Road Network Data
Client Data
Booking Data, Curb Use Data, Vehicle types
Final Model
Based on correlations, the final variable list included information from a variety of sources: time series information, distance from amenities
�
DATA
MODEL
APP
1: CREATING NEW VARIABLES
Filtering the data, finding the best variables and creating predictive fixed effect and KNN.
5. CHECKING STATISTICAL ERRORS
Processing the results to see if there are spatial or statistical error clustering,.
4. CROSS VALIDATION
Checking the generalizability of the model using cross validation.
2. IDENTIFYING PREDICTIVE FACTORS
Running further analysis to see booking trends,
MODEL PROCESS
3. MODELLING THE REGRESSION
Running a random forest regression to predict and visualize loading zone demand.
Random Forest Model
Random forests is based on decision trees, where each tree is grown using a random subset of the data set.
mtry=17, ntree = 1000, nodesize = 22, maxnodes = 20
Reduced Overfitting Risk
Improved Model Accuracy
Model Choices and Results
Booking Events by Week and Day
Dependant Variable
Independent Variables (with highest performances)
Civic Center (Nearest Neighbor)
Road Class and Distance to Each Class of the Roads
Time
Top 20 Variables
Major
Arterials
Minor Arterials
Model Accuracy Evaluation: Spatial & Temporal Comparison (MAE)
Spatial
Temporal
Model Results
Performance of a Random Forest model used for predicting bookings on a weekly basis
Model Evaluation for Occupancy Prediction
The model correctly identifies 79.35% of actual 'occupied' cases
Sensitivity
The model correctly identifies 61.94% of actual 'unoccupied' cases
Specificity
Around 74.52% of the model's predictions are correct.
Accuracy
Generalization
Generalization
Generalization
Generalization
Generalization
DATA
MODEL
APP
Link to Application
Link to Walkthrough
Recommendations for Refining the Pilot Program
Either throughout the entire day or during consistent peak periods, instead of varying hours for different zones
Standardize the operating hours for all curb zones
Distribute curb zones more broadly across the city
Strategically place zones in a variety of neighborhoods, beyond just the center city area
Collect More Data Spatially, Temporally, and Scalably for More Generalizable Results
Problem
Solution
No Consistent Operating Hours
No Data in Diverse Neighborhoods of the City
Recommendations for Refining the Pilot Program
Evaluate the effectiveness across different seasons
Extend the duration of the pilot program
Enhance the visibility of the pilot app
Collaborate with widely-used applications for integration or advertisement.
Avoid the limitations of promoting a standalone new app
Collect More Data Spatially, Temporally, and Scalably for More Generalizable Results
Problem
Solution
Limited temporal data (October to April Only)
Limited App Usage
Conclusion: Takeaways & Next Steps
Thanks!
Samriddhi Khare, Michael Dunst, Tiffany Luo, Ling Chen, Shengqian Wang