Effects of Dynamic User-optimal Routing on Network-level Signal Predictive Control.

This paper presents a network-level signal predictive control strategy considering dynamic user-optimal routing. In the connected vehicle (CV) environment, drivers can access real-time network traffic conditions and signal control schemes, and actively choose their best routes. With the real-time position and target route information provided by CV s, a network-level signal predictive optimization model is established. The model is formulated as a mixed integer linear programming problem and solved using a decentralized solution framework that decomposes it into intersection-level sub-problems. Simulation experiments validate the effectiveness of the proposed strategy, demonstrating that dynamic routing can effectively improve network capacity during congestion and reduce congestion duration. This study provides insights for traffic management and the construction of intelligent transportation systems in the future CV environment.