Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network
- University of Tennessee at Chattanooga,Department of Computer Science and Engineering,USA
- Oak Ridge National Laboratory,Buildings and Transportation Sciences Division,USA
- School of Civil and Environmental Engineering, Georgia Institute of Technology,USA
Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.
- Research Organization:
- University of Tennessee at Chattanooga
- Sponsoring Organization:
- US Dept. of Energy’s Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- EE0009208
- OSTI ID:
- 2557494
- Journal Information:
- 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Journal Name: 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)
- Country of Publication:
- United States
- Language:
- English
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