Drivers’ Attitudes Toward Rerouting: Impacts on Network Congestion
This project aims to answer the following questions: (1) What machine learning (ML) approaches are useful to help people make rerouting decisions in congestion? (2) What properties of congested urban networks influence the ML result? (3) How do the rerouting decisions influence the bifurcation phenomena in macroscopic fundamental diagrams (MFDs)? Deep reinforcement learning (DRL), one of the advanced RL methods, is identified to analyze rerouting behavior. In congested urban networks, some factors are found to have a huge impact on the DRL result. First, the density of the network influences the performance of DRL significantly. Especially when the network is close to jam density, DRL cannot learn in such situations. Second, the DRL result is affected by the driver’s rerouting probabilities, or rerouting intention. Third, the blocking area distribution is an important value for the demand matrix because it is the reward in each iteration. The goal of this project is to distribute the blocking area evenly across the network. Because the project’s focus is on the impact of adaptive driving behavior, a more realistic network is built in SUMO to perform simulations. This helps assess how each of the above factors affects the results and whether there are any other factors that influence the results.
Language
- English
Project
- Status: Completed
- Funding: $194821
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Contract Numbers:
69A3551747116
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Center for Teaching Old Models New Tricks (TOMNET)
Arizona State University
Tempe, AZ United States 85287 -
Project Managers:
Pendyala, Ram
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Performing Organizations:
Georgia Institute of Technology
505 Tenth Street, NW
Atlanta, GA United States 30332-0420 -
Principal Investigators:
Laval, Jorge
- Start Date: 20210801
- Expected Completion Date: 20220731
- Actual Completion Date: 20220731
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Attitudes; Behavior; Drivers; Machine learning; Microscopic traffic flow; Routing; Simulation; Traffic congestion
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01868141
- Record Type: Research project
- Source Agency: Center for Teaching Old Models New Tricks (TOMNET)
- Contract Numbers: 69A3551747116
- Files: UTC, RIP
- Created Date: Dec 21 2022 11:21AM