Paper Info
Title
A survey of model predictive control methods for traffic signal control
Abstract
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.
Year
DOI
Venue
2019
10.1109/JAS.2019.1911471
IEEE/CAA Journal of Automatica Sinica
Keywords
Field
DocType
Autonomous vehicles,coordination control,mixed integer programming,model predictive control,system decomposition,traffic flow models,traffic signal control
Traffic signal,Road networks,Complex dynamic systems,Model predictive control,Control engineering,Vehicle dynamics,Traffic efficiency,Linear programming,Mathematics,Traffic congestion
Journal
Volume
Issue
ISSN
6
3
2329-9266
Citations 
PageRank 
References 
4
0.39
0
Authors
7
Name
Order
Citations
PageRank
Bao-Lin Ye161.85
Weimin Wu223643.97
Keyu Ruan341.06
Lingxi Li416528.49
Tehuan Chen5124.40
Huimin Gao640.39
Yaobin Chen730737.90