Adaptive network traffic control with approximate dynamic programming based on a non-homogeneous Poisson demand model

References

• Ahmed, Muaid Abdulkareem Alnazir, Hooi Ling Khoo, and Oon-Ee Ng. 2023. “Discharge Control Policy Based on Density and Speed for Deep Q-Learning Adaptive Traffic Signal.” Transportmetrica B: Transport Dynamics 11 (1): 1707–1726.
   Web of Science ®Google Scholar
• Baldi, Simone, Zichen Zhang, and Di Liu. 2022. “Eligibility Traces and Forgetting Factor in Recursive Least-Squares-Based Temporal Difference.” International Journal of Adaptive Control and Signal Processing 36 (2): 334–353. https://doi.org/10.1002/acs.v36.2.
   Web of Science ®Google Scholar
• Barman, Simanta, and Michael W. Levin. 2023. “Throughput Properties and Optimal Locations for Limited Deployment of Max-Pressure Controls.” Transportation Research Part C: Emerging Technologies 150:104105. https://doi.org/10.1016/j.trc.2023.104105.
   Web of Science ®Google Scholar
• Bowman, Casey N., and John A. Miller. 2018. “Modeling Traffic Flow using Simulation and Big Data Analytics.” Paper presented at the Winter Simulation Conference, Washington, DC, USA, December 2016.
   Google Scholar
• Boyan, Justin A. 2002. “Technical Update: Least-Squares Temporal Difference Learning.” Machine Learning 49 (2): 233–246. https://doi.org/10.1023/A:1017936530646.
   Web of Science ®Google Scholar
• Cai, Chen, Chi Kwong Wong, and Benjamin G. Heydecker. 2009. “Adaptive Traffic Signal Control Using Approximate Dynamic Programming.” Transportation Research Part C: Emerging Technologies 17 (5): 456–474. https://doi.org/10.1016/j.trc.2009.04.005.
   Web of Science ®Google Scholar
• Chen, Huifen, and Bruce W. Schmeiser. 2013. “I-SMOOTH: Iteratively Smoothing Mean-Constrained and Nonnegative Piecewise-Constant Functions.” Informs Journal on Computing 25 (3): 432–445. https://doi.org/10.1287/ijoc.1120.0512.
   Web of Science ®Google Scholar
• Chen, Huifen, and Bruce W. Schmeiser. 2017. “MNO-PQRS: Max Nonnegativity Ordering–Piecewise-Quadratic Rate Smoothing.” ACM Transactions on Modeling and Computer Simulation (TOMACS) 27 (3): 1–19. https://doi.org/10.1145/3067663.
   Web of Science ®Google Scholar
• Chen, Huifen, and Bruce W. Schmeiser. 2018. “MISE-Optimal Grouping of Point-Process Data with a Constant Dispersion Ratio.” Paper presented at the Winter Simulation Conference, Gothenburg, Sweden, December 2018.
   Google Scholar
• Chen, Huifen, and Bruce W. Schmeiser. 2019. “MISE-Optimal Intervals for MNO–PQRS Estimators of Poisson Rate Functions.” Paper presented at the Winter Simulation Conference, National Harbor, MD, USA, December 2019.
   Google Scholar
• Chow, Andy H. F. 2015. “Optimisation of Dynamic Motorway Traffic Via a Parsimonious and Decentralised Approach.” Transportation Research Part C: Emerging Technologies 55:69–84. https://doi.org/10.1016/j.trc.2015.01.009.
   Web of Science ®Google Scholar
• Daganzo, Carlos F. 1994. “The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory.” Transportation Research Part B: Methodological 28 (4): 269–287. https://doi.org/10.1016/0191-2615(94)90002-7.
   Web of Science ®Google Scholar
• Gregoire, Jean, Xiangjun Qian, Emilio Frazzoli, Arnaud De La Fortelle, and Tichakorn Wongpiromsarn. 2014. “Capacity-Aware Backpressure Traffic Signal Control.” IEEE Transactions on Control of Network Systems 2 (2): 164–173. https://doi.org/10.1109/TCNS.2014.2378871.
   Google Scholar
• Henderson, Shane G. 2003. “Estimation for Nonhomogeneous Poisson Processes From Aggregated Data.” Operations Research Letters 31 (5): 375–382. https://doi.org/10.1016/S0167-6377(03)00027-0.
   Web of Science ®Google Scholar
• Huang, Wei, Jing Hu, Guoyu Huang, and Hong K. Lo. 2023. “A Three-Layer Hierarchical Model-Based Approach for Network-Wide Traffic Signal Control.” Transportmetrica B: Transport Dynamics 11 (1): 1912–1942.
   Web of Science ®Google Scholar
• Hunt, P. B., D. I. Robertson, and R. D. Bretherton. 1982. “The SCOOT On-line Traffic Signal Optimisation Technique.” Traffic Engineering and Control 23 (4): 190–192.
   Google Scholar
• Jabari, Saif Eddin, and Henry X. Liu. 2012. “A Stochastic Model of Traffic Flow: Theoretical Foundations.” Transportation Research Part B: Methodological 46 (1): 156–174. https://doi.org/10.1016/j.trb.2011.09.006.
   Web of Science ®Google Scholar
• Kouvelas, Anastasios, Konstantinos Aboudolas, Markos Papageorgiou, and Elias B. Kosmatopoulos. 2011. “A Hybrid Strategy for Real-Time Traffic Signal Control of Urban Road Networks.” IEEE Transactions on Intelligent Transportation Systems 12 (3): 884–894. https://doi.org/10.1109/TITS.2011.2116156.
   Web of Science ®Google Scholar
• Kouvelas, Anastasios, Jennie Lioris, S. Alireza Fayazi, and Pravin Varaiya. 2014. “Maximum Pressure Controller for Stabilizing Queues in Signalized Arterial Networks.” Transportation Research Record2421 (1): 133–141. https://doi.org/10.3141/2421-15.
   Web of Science ®Google Scholar
• Kurzhanskiy, Alex A., and Pravin Varaiya. 2010. “Active Traffic Management on Road Networks: a Macroscopic Approach.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368 (1928): 4607–4626. https://doi.org/10.1098/rsta.2010.0185.
   PubMed Web of Science ®Google Scholar
• Lee, Jinwoo, Baher Abdulhai, Amer Shalaby, and Eui-Hwan Chung. 2005. “Real-Time Optimization for Adaptive Traffic Signal Control Using Genetic Algorithms.” Journal of Intelligent Transportation Systems 9 (3): 111–122. https://doi.org/10.1080/15472450500183649.
   Google Scholar
• Leemis, Larry. 2003. “Estimating and Simulating Nonhomogeneous Poisson Processes.” Paper presented at the International Conference in Reliability and Survival Analysis (ICRSA), South Carolina, USA, May 2003.
   Google Scholar
• Leemis, Lawrence M. 2004. “Nonparametric Estimation and Variate Generation for a Nonhomogeneous Poisson Process From Event Count Data.” IIE Transactions 36 (12): 1155–1160. https://doi.org/10.1080/07408170490507693.
   Web of Science ®Google Scholar
• Lewis, P. A. W., and G. S. Shedler. 1976. “Simulation of Nonhomogeneous Poisson Processes with Log Linear Rate Function.” Oxford University Press 63 (3): 501–505.
   Google Scholar
• Lin, WeiHua, and Chenghong Wang. 2004. “An Enhanced 0-1 Mixed-Integer LP Formulation for Traffic Signal Control.” IEEE Transactions on Intelligent Transportation Systems 5 (4): 238–245. https://doi.org/10.1109/TITS.2004.838217.
   Web of Science ®Google Scholar
• Liu, Hao, and Vikash V. Gayah. 2022. “A Novel Max Pressure Algorithm Based on Traffic Delay.” Transportation Research Part C: Emerging Technologies143:103803. https://doi.org/10.1016/j.trc.2022.103803.
   Web of Science ®Google Scholar
• Liu, Kaiwei, Xing Lü, Fei Gao, and Jiang Zhang. 2023. “Expectation-Maximizing Network Reconstruction and Most Applicable Network Types Based on Binary Time Series Data.” Physica D: Nonlinear Phenomena 454:133834. https://doi.org/10.1016/j.physd.2023.133834.
   Web of Science ®Google Scholar
• Liu, Di, Wenwu Yu, Simone Baldi, Jinde Cao, and Wei Huang. 2019. “A Switching-Based Adaptive Dynamic Programming Method to Optimal Traffic Signaling.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (11): 4160–4170. https://doi.org/10.1109/TSMC.6221021.
   Web of Science ®Google Scholar
• Logghe, S., and L. H. Immers. 2008. “Multi-Class Kinematic Wave Theory of Traffic Flow.” Transportation Research Part B: Methodological 42 (6): 523–541. https://doi.org/10.1016/j.trb.2007.11.001.
   Web of Science ®Google Scholar
• Luk, J. Y. 1984. “Two Traffic Responsive Area Traffic Control Methods: SCAT and SCOOT.” Traffic Engineering and Control 25 (1): 14–22.
   Google Scholar
• Ma, Dongfang, Jiawang Xiao, Xiang Song, Xiaolong Ma, and Sheng Jin. 2020. “A Back-Pressure-Based Model with Fixed Phase Sequences for Traffic Signal Optimization Under Oversaturated Networks.” IEEE Transactions on Intelligent Transportation Systems 22 (9): 5577–5588. https://doi.org/10.1109/TITS.2020.2987917.
   Web of Science ®Google Scholar
• Mao, Feng, Zhiheng Li, Yilun Lin, and Li Li. 2022. “Mastering Arterial Traffic Signal Control with Multi-Agent Attention-Based Soft Actor-Critic Model.” IEEE Transactions on Intelligent Transportation Systems 24 (3): 3129–3144. https://doi.org/10.1109/TITS.2022.3229477.
   Web of Science ®Google Scholar
• Meng, Qiang, and Hooi Ling Khoo. 2009. “Self-Similar Characteristics of Vehicle Arrival Pattern on Highways.” Journal of Transportation Engineering 135 (11): 864–872. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:11(864).
   Google Scholar
• Mercader, Pedro, Wasim Uwayid, and Jack Haddad. 2020. “Max-Pressure Traffic Controller Based on Travel Times: An Experimental Analysis.” Transportation Research Part C: Emerging Technologies110:275–290. https://doi.org/10.1016/j.trc.2019.10.002.
   Web of Science ®Google Scholar
• Morgan, Lucy E., Barry L. Nelson, Andrew C. Titman, and David J. Worthington. 2019. “A Spline-Based Method for Modelling and Generating a Nonhomogeneous Poisson Process.” Paper presented at the Winter Simulation Conference, National Harbor, MD, USA, December 2019.
   Google Scholar
• Nguyen, Cuong H. P., Nam H. Hoang, and Hai L. Vu. 2023. “A Joint Trajectory Planning and Signal Control Framework for a Network of Connected and Autonomous Vehicles.” IEEE Transactions on Intelligent Transportation Systems 24 (5): 5052–5068. https://doi.org/10.1109/TITS.2023.3241281.
   Web of Science ®Google Scholar
• Nicol, David M., and Lawrence M. Leemis. 2014. “A Continuous Piecewise-Linear NHPP Intensity Function Estimator.” Paper presented at the Winter Simulation Conference, Savannah, GA, USA, December 2014.
   Google Scholar
• Reyad, Passant, and Tarek Sayed. 2023. “Real-Time Multi-Objective Optimization of Safety and Mobility At Signalized Intersections.” Transportmetrica B: Transport Dynamics 11 (1): 847–868.
   Web of Science ®Google Scholar
• Su, Z. C., Andy H. F. Chow, C. L. Fang, E. M. Liang, and R. X. Zhong. 2023. “Hierarchical Control for Stochastic Network Traffic with Reinforcement Learning.” Transportation Research Part B: Methodological 167:196–216. https://doi.org/10.1016/j.trb.2022.12.001.
   Web of Science ®Google Scholar
• Su, Z. C., Andy H. F. Chow, and R. X. Zhong. 2021. “Adaptive Network Traffic Control with An Integrated Model-Based and Data-Driven Approach and a Decentralised Solution Method.” Transportation Research Part C: Emerging Technologies 128:103154. https://doi.org/10.1016/j.trc.2021.103154.
   Web of Science ®Google Scholar
• Tsitsiklis, J. N., and B. Van Roy. 1997. “An Analysis of Temporal-Difference Learning with Function Approximation.” IEEE Transactions on Automatic Control 42 (5): 674–690. https://doi.org/10.1109/9.580874.
   Web of Science ®Google Scholar
• Tsitsokas, Dimitrios, Anastasios Kouvelas, and Nikolas Geroliminis. 2021. “Efficient Max-Pressure Traffic Signal Control for Large-Scale Congested Urban Networks.” Paper presented at the 21st Swiss Transport Research Conference (STRC), Monte Verità, Ascona, Switzerland, September 2021.
   Google Scholar
• Varaiya, Pravin. 2013. “Max Pressure Control of a Network of Signalized Intersections.” Transportation Research Part C: Emerging Technologies 36:177–195. https://doi.org/10.1016/j.trc.2013.08.014.
   Web of Science ®Google Scholar
• Wang, Xingmin, Yafeng Yin, Yiheng Feng, and Henry X. Liu. 2022. “Learning the Max Pressure Control for Urban Traffic Networks Considering the Phase Switching Loss.” Transportation Research Part C: Emerging Technologies 140:103670. https://doi.org/10.1016/j.trc.2022.103670.
   Web of Science ®Google Scholar
• Wei, Hua, Chacha Chen, Guanjie Zheng, Kan Wu, Vikash Gayah, Kai Xu, and Zhenhui Li. 2019. “Presslight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network.” Paper presented at the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Monte Verità, Anchorage, AK, USA, August 2019.
   Google Scholar
• Xu, Te, Simanta Barman, Michael W. Levin, Rongsheng Chen, and Tianyi Li. 2022. “Integrating Public Transit Signal Priority Into Max-Pressure Signal Control: Methodology and Simulation Study on a Downtown Network.” Transportation Research Part C: Emerging Technologies 138:103614. https://doi.org/10.1016/j.trc.2022.103614.
   Web of Science ®Google Scholar
• Xu, Y., D. Li, and Y. Xi. 2018. “Stochastic Traffic Modeling and Decentralized Signal Control Based on a State Transition Probability Model.” IET Intelligent Transport Systems 12 (8): 909–920. https://doi.org/10.1049/itr2.v12.8.
   Web of Science ®Google Scholar
• Yin, Yu-Hang, Xing Lü, Rui Jiang, Bin Jia, and Ziyou Gao. 2024. “Kinetic Analysis and Numerical Tests of An Adaptive Car-Following Model for Real-Time Traffic in ITS.” Physica A: Statistical Mechanics and Its Applications 635:129494. https://doi.org/10.1016/j.physa.2024.129494.
   Web of Science ®Google Scholar
• Yu, X. H., and W. W. Recker. 2006. “Stochastic Adaptive Control Model for Traffic Signal Systems.” Transportation Research Part C: Emerging Technologies 14 (4): 263–282. https://doi.org/10.1016/j.trc.2006.08.002.
   Web of Science ®Google Scholar
• Zheng, Zeyu, and Peter W. Glynn. 2017. “Fitting Continuous Piecewise Linear Poisson Intensities via Maximum Likelihood and Least Squares.” Paper presented at the Winter Simulation Conference, Las Vegas, NV, USA, December 2017.
   Google Scholar
• Zhou, Dongqin, and Vikash V. Gayah. 2021. “Model-Free Perimeter Metering Control for Two-Region Urban Networks Using Deep Reinforcement Learning.” Transportation Research Part C: Emerging Technologies 124:102949. https://doi.org/10.1016/j.trc.2020.102949.
   Web of Science ®Google Scholar