A Heuristic Approach for Multi-Path Signal Progression Considering Traffic Flow Uncertainty

Abstract

The multi-path progression of an arterial signal model, generally, is applied to arterial traffic scenarios with large turning flows. However, existing methods generally fail to capture traffic flow uncertainty, which leads to high sensitivity to fluctuations in traffic flow. To bridge this gap, in this study, a heuristic approach for multi-path signal progression is proposed to deal with the uncertainties of flow fluctuation by using distributionally flow scenarios. The model varies the phase sequence and the offsets of each intersection to achieve optimal progression with weighting of efficiency and stability. The preference degree of the efficiency and stability of the model is selected by adjusting the efficiency stability coefficient and solved by using a genetic algorithm. A case study and comparison experiment with benchmark models is presented and analyzed to prove the advantages of the proposed model. The results show that the standard deviation of the proposed model decreases by 45% as compared with conventional methods. It indicates that the model proposed in this paper can reduce congestion due to uncertainties, and can significantly improve stability, on the premise of ensuring that the efficiency index maintains a better value.