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Abstract
Traffic congestion has become a significant issue in urban road networks. There have been massive works about traffic signal optimization to improve the efficiency of traffic flow operation, and the so-called back-pressure control policy has proven to be excellent for oversaturated conditions. Most of the existing works with back-pressure are based on an adaptive phase sequence, and research with cyclic phase sequence is based on calculating the splits for different phases using the traffic flow data at the beginning of each cycle, which is unfair for the non-initial phases. In this paper, we propose a decentralized model predictive signal control method with fixed phase sequence using back-pressure policy. The main idea of the new method is to form a control loop using the model predictive control, enabling the system to obtain real-time feedback from the traffic network and dynamically adjusting signal timing plans at the beginning of each phase. As links within a certain area have various lengths, the same queue length can imply different traffic conditions, so a method to normalize queue lengths is proposed. The normalized queue length decreases drastically when the actual length approaches link capacity, thus avoiding spillover. The proposed method was tested in a virtual road network. Numerical results suggest that the new method improves performance under congested conditions in terms of throughput, Gini coefficient and comprehensive transportation efficiency.