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Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and alleviate traffic congestion. Recently, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion problems. However, performance gaps still exist when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulators and the real-world environments. In this work, we leverage the knowledge of Large Language Models (LLMs) to understand and profile the system dynamics by a prompt-based grounded action transformation to bridge the performance gap. Specifically, this paper exploits the pre-trained LLM's inference ability to understand how traffic dynamics change with weather conditions, traffic states, and road types. Being aware of the changes, the policies' action is taken and grounded based on realistic dynamics, thus helping the agent learn a more realistic policy. We conduct experiments on four different scenarios to show the effectiveness of the proposed PromptGAT's ability to mitigate the performance gap of reinforcement learning from simulation to reality (sim-to-real).
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Performance Evaluation of Modified Cyclic Max-Pressure Controlled Intersections in Realistic Corridors
Max-pressure (MP) signal timing is an actuated decentralized signal control policy. Rigorous mathematical studies have proven the stability properties and established the benefits of different MP policies. However, theoretical studies make assumptions about traffic properties that may not represent reality and whose effects are not explored much in the literature under realistic traffic conditions. This study focuses on examining how different variations of MP perform in realistic scenarios and on finding the most practical policy among those for implementation in real roads. Microsimulation models of seven intersections from two corridors, County Road (CR) 30 and CR 109 from Hennepin County, Minnesota were created. Real-life demand and current signal timing data provided by Hennepin County were used to make the simulations as close to reality as possible. In this paper, we compare the performance of current actuated-coordinated signal control with an acyclic MP and two variations of cyclic MP policies. We present the performance of different control policies as delay, throughput, worst lane delay, and number of phase changes. We also present how different parameters affect the performance of the MP policies. We found that better performance can be achieved with a cyclic MP policy by allowing phase skipping when no vehicles are waiting. Our findings also suggest that most of the claimed performance benefits can still be achieved in real-life traffic conditions even with the simplified assumptions made in the theoretical models. In most cases, MP control policies outperformed current signal control.
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- Award ID(s):
- 1935514
- PAR ID:
- 10375403
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2676
- Issue:
- 6
- ISSN:
- 0361-1981
- Format(s):
- Medium: X Size: p. 110-128
- Size(s):
- p. 110-128
- Sponsoring Org:
- National Science Foundation
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