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Reinforcement learning for traffic signal control : comparison with commercial systems
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Cabrejas-Egea, Alvaro, Zhang, Raymond and Walton, Neil (2021) Reinforcement learning for traffic signal control : comparison with commercial systems. Transportation Research Procedia, 58 . pp. 638-645. doi:10.1016/j.trpro.2021.11.084
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WRAP-reinforcement-learning-traffic-signal-control-comparison-commercial-systems-Cabrejas-Egea-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1012Kb) | Preview |
Official URL: https://doi.org/10.1016/j.trpro.2021.11.084
Abstract
Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems. Among this group, Reinforcement Learning (RL) approaches have performed particularly well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks comparison against commercially available systems and straightforward formulations of how the agents operate. Here we attempt to bridge that gap. We propose three different architectures for TSC RL agents and compare them against the currently used commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The agents use variations of Deep Q-Learning and Actor Critic, using states and rewards based on queue lengths. Their performance is compared in across different map scenarios with variable demand, assessing them in terms of the global delay and average queue length. We find that the RL-based systems can significantly and consistently achieve lower delays when compared with existing commercial systems.
| Item Type: | Journal Article | ||||||
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| Subjects: | T Technology > TE Highway engineering. Roads and pavements | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Traffic engineering -- Data processing, Traffic signs and signals, Vehicular ad hoc networks (Computer networks), Traffic flow -- Mathematical models, Urban transportation, Reinforcement learning -- Mathematical models | ||||||
| Journal or Publication Title: | Transportation Research Procedia | ||||||
| Publisher: | Elsevier | ||||||
| Official Date: | 8 December 2021 | ||||||
| Dates: |
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| Volume: | 58 | ||||||
| Page Range: | pp. 638-645 | ||||||
| DOI: | 10.1016/j.trpro.2021.11.084 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 10 February 2022 | ||||||
| Date of first compliant Open Access: | 11 February 2022 | ||||||
| RIOXX Funder/Project Grant: |
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