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A Multi-Objective Model for Traffic Signal Coordination Control With Queue Profile Estimation
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AI
DOI：10.1109/TITS.2025.3616119.png)
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Abstract
En 中文
The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.
Keywords:
Traffic signal control
arterial signal coordination
minimum stops
over-saturated phases
queue profile estimation
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