A novel game approach to integrating traffic assignment and signal control for enhanced efficiency and environmental performance in mixed networks

Abstract

The combined traffic assignment and signal control (CAC) has proven to be effective in enhancing the wholistic performance of mixed networks, which includes both expressways and surface streets. This study focuses on addressing the limitations of traditional Stackelberg game-based CAC models, particularly the rigid leader–follower dynamic. We propose an integrated Level-Change-MPC (Model Predictive Control)-VT-Meso-Emission Model (LCMVTM), which incorporates a dynamic role-change function triggered by the compliance rate of users to variable message signs (VMS). This role-change mechanism offers greater flexibility under varying road conditions and simplifies the authority’s task of predicting user routing behavior. Additionally, a Q-learning-based algorithm is developed to balance travel costs and emissions by managing the rate of emission accumulation across control horizons. The results demonstrate a reduction in total travel costs by 11% to over 30%, while emissions decrease by 16.98% to approximately 40% compared to the other different combination of control strategies and MAS/non MAS structured network. The emission accumulation rate also drops by 20% to 43.69%. LCMVTM outperformed the other benchmarks by reducing the number of stop&go per vehicle, resulting in improved efficiency and environmental outcomes.