Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty (2404.19547v1)
Abstract: This paper develops an adaptive traffic control policy inspired by Maximum Pressure (MP) while imposing coordination across intersections. The proposed Coordinated Maximum Pressure-plus-Penalty (CMPP) control policy features a local objective for each intersection that consists of the total pressure within the neighborhood and a penalty accounting for the queue capacities and continuous green time for certain movements. The corresponding control task is reformulated as a distributed optimization problem and solved via two customized algorithms: one based on the alternating direction method of multipliers (ADMM) and the other follows a greedy heuristic augmented with a majority vote. CMPP not only provides a theoretical guarantee of queuing network stability but also outperforms several benchmark controllers in simulations on a large-scale real traffic network with lower average travel and waiting time per vehicle, as well as less network congestion. Furthermore, CPMM with the greedy algorithm enjoys comparable computational efficiency as fully decentralized controllers without significantly compromising the control performance, which highlights its great potential for real-world deployment.
- M. Czepkiewicz, J. Heinonen, and J. Ottelin, “Why do urbanites travel more than do others? A review of associations between urban form and long-distance leisure travel,” Environmental Research Letters, vol. 13, Jul. 2018.
- Gov.uk, “National road traffic projections 2022,” Dec. 2022.
- S. Jayasooriya and Y. Bandara, “Measuring the Economic costs of traffic congestion,” in 2017 Moratuwa Engineering Research Conference (MERCon), May 2017, pp. 141–146.
- S. Barman and M. W. Levin, “Performance Evaluation of Modified Cyclic Max-Pressure Controlled Intersections in Realistic Corridors,” Transportation Research Record, vol. 2676, no. 6, pp. 110–128, Jun. 2022.
- P. B. Hunt, D. I. Robertson, R. D. Bretherton, and M. C. Royle, “The SCOOT on-line traffic signal optimisation technique,” Traffic Engineering & Control, vol. 23, no. 4, Apr. 1982.
- P. Lowrie, “SCATS: Sydney Co-Ordinated Adaptive Traffic System: A traffic responsive method of controlling urban traffic,” in SCATS, Sydney Co-Ordinated Adaptive Traffic System. Transport for NSW, 1990.
- J. J. Henry, J. L. Farges, and J. Tuffal, “The PRODYN Real Time Traffic Algorithm,” IFAC Proceedings Volumes, vol. 16, no. 4, pp. 305–310, Apr. 1983.
- P. Varaiya, “Max pressure control of a network of signalized intersections,” Transportation Research Part C: Emerging Technologies, vol. 36, pp. 177–195, Nov. 2013.
- X. Sun and Y. Yin, “A Simulation Study on Max Pressure Control of Signalized Intersections,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2672, no. 18, pp. 117–127, 2018.
- J. Lioris, A. Kurzhanskiy, and P. Varaiya, “Adaptive Max Pressure Control of Network of Signalized Intersections,” IFAC-PapersOnLine, vol. 49, no. 22, pp. 19–24, Jan. 2016.
- J. Gregoire, X. Qian, E. Frazzoli, A. de La Fortelle, and T. Wongpiromsarn, “Capacity-Aware Backpressure Traffic Signal Control,” IEEE Transactions on Control of Network Systems, vol. 2, no. 2, pp. 164–173, Jun. 2015.
- N. Xiao, E. Frazzoli, Y. Li, Y. Wang, and D. Wang, “Pressure releasing policy in traffic signal control with finite queue capacities,” in 53rd IEEE Conference on Decision and Control, Dec. 2014, pp. 6492–6497.
- T. Le, P. Kovács, N. Walton, H. L. Vu, L. L. Andrew, and S. S. Hoogendoorn, “Decentralized signal control for urban road networks,” Transportation Research Part C: Emerging Technologies, vol. 58, pp. 431–450, 2015.
- M. W. Levin, J. Hu, and M. Odell, “Max-pressure signal control with cyclical phase structure,” Transportation Research Part C: Emerging Technologies, vol. 120, p. 102828, Nov. 2020.
- L. Bracciale and P. Loreti, “Lyapunov Drift-Plus-Penalty Optimization for Queues With Finite Capacity,” IEEE Communications Letters, vol. 24, no. 11, pp. 2555–2558, Nov. 2020.
- S. Hao, L. Yang, Y. Shi, and Y. Guo, “Backpressure based traffic signal control considering capacity of downstream links,” Transport, vol. 35, no. 4, pp. 347–356, Sep. 2020.
- T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson, “A survey of distributed optimization,” Annual Reviews in Control, vol. 47, no. 1, May 2019.
- B. Ji, C. Joo, and N. B. Shroff, “Delay-Based Back-Pressure Scheduling in Multihop Wireless Networks,” IEEE/ACM Transactions on Networking, vol. 21, no. 5, pp. 1539–1552, Oct. 2013.
- S. Boyd, Parikh Neal, Chu, Eric, Peleato, Borja, and Eckstein, Jonathan, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends® in Machine Learning, vol. 3, no. 1, pp. 1–122, 2010.
- H. Zhang, S. Feng, C. Liu, Y. Ding, Y. Zhu, Z. Zhou, W. Zhang, Y. Yu, H. Jin, and Z. Li, “CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario,” in The World Wide Web Conference, May 2019, pp. 3620–3624.