Citation

Lin S, Dai J, Li R. Transp. Res. C Emerg. Technol. 2023; 147: e104007.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2022.104007

PMID

unavailable

Abstract

This paper presents a framework for signalized road network predictive optimization using real-time routing information from connected vehicles (CVs). An important feature of the real-time routing information is the ability of CVs to broadcast the target routes they expect to travel through to the infrastructure in real time while assuming that a majority of the CVs can provide their target routes. A fully movement-level network representation model is proposed to easily describe the traffic state and demand of the signalized network. The problem is formulated as a mixed integer linear programming model to predict the movement-level network dynamics, which is solved in real time to optimize phase-free movement signal timings. The objective is to maximize the network throughput within the prediction horizon while avoiding queue spillbacks. A decentralized solution algorithm is developed to decompose the network-level problem into intersection-level subproblems, thereby reducing computational complexity. Simulation experiments validate the advantages of the proposed framework over TRANSYT schemes and max pressure-based control strategy in various scenarios. Sensitivity analysis shows the control performance under different traffic demand levels and penetration rates of the target route information. Comparisons in prediction accuracy, control performance, and computational efficiency between centralized and decentralized solutions of the proposed model are also conducted. This study explores the application of real-time routing information as a potential type of data for the network-level predictive signal optimization in the future CV environment.

Language: en

Keywords

Connected vehicle; Network coordinated optimization; Predictive control; Real-time routing information; Traffic signal control