Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins
- University of Tennessee at Chattanooga,Department of Computer Science and Engineering,USA; University of Tennessee at Chattanooga
- Oak Ridge National Laboratory,Applied Research for Mobility Systems,USA
- University of Tennessee at Chattanooga,Department of Computer Science and Engineering,USA
- Georgia Institute of Technology,School of Civil and Environmental Engineering,USA
Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.
- Research Organization:
- University of Tennessee at Chattanooga
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- EE0009208
- OSTI ID:
- 2439938
- Journal Information:
- 2023 IEEE Smart World Congress (SWC), Journal Name: 2023 IEEE Smart World Congress (SWC)
- Country of Publication:
- United States
- Language:
- English
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