Congestion Prediction Based on Dissipative Structure Theory: A Case Study of Chengdu, China

With the continuous growth of traffic demand and the mismatch of urban transportation facilities, urban traffic congestion has been caused, leading to various related problems, such as environmental pollution, traffic accidents, and slow economic development. Many cities have implemented relevant measures to improve traffic congestion, but fewer are ideal. This study used the hidden Markov model combined with the dissipative structure theory and entropy theory to predict the congestion more accurately. The temporal and spatial distributions of the online ride-hailing Didi data in Chengdu were analyzed. There are morning peaks, noon peaks, and evening peaks during workdays. During the noon peak and evening peak, travel demand in the city’s central area is relatively stable. It is found that the prediction model has a higher accuracy after combining the dissipative structure theory and entropy theory, which could be used to propose methods to prevent congestion.