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Volume 6 Issue 3
May  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Bao-Lin Ye, Weimin Wu, Keyu Ruan, Lingxi Li, Tehuan Chen, Huimin Gao and Yaobin Chen, "A Survey of Model Predictive Control Methods for Traffic Signal Control," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 623-640, May 2019. doi: 10.1109/JAS.2019.1911471
Citation: Bao-Lin Ye, Weimin Wu, Keyu Ruan, Lingxi Li, Tehuan Chen, Huimin Gao and Yaobin Chen, "A Survey of Model Predictive Control Methods for Traffic Signal Control," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 623-640, May 2019. doi: 10.1109/JAS.2019.1911471

A Survey of Model Predictive Control Methods for Traffic Signal Control

doi: 10.1109/JAS.2019.1911471
Funds:  This work was supported in part by the National Natural Science Foundation of China (61603154, 61773343, 61621002, 61703217), the Natural Science Foundation of Zhejiang Province (LY15F030021, LY19F030014), Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT1800407)
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  • Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control (MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

     

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