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Network-level Signal Predictive Control with Real-time Routing Information by Shichao Lin; Jingchen Dai; Ruimin Li is a Engineering article available to read on EtoBox.

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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

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Author

Shichao Lin; Jingchen Dai; Ruimin Li

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)