Research on Congestion Path Analysis Algorithm Based on Spatiotemporal Correlation of Traffic State of Urban Road Networks

Aiming at the random and non-linear features of short-term traffic flows in urban road networks, the research on spatiotemporal correlations of short-term traffic flows can demonstrate the entire evolution process of traffic congestion life cycles. Thus, the congestion path analysis algorithm is proposed to analyze spatiotemporal correlation of traffic state in urban road networks. Firstly, the existing Pearson correlation analysis method is improved by separating floating car data and extracting the trends and detail vectors of traffic flows, and the generalized least squares method makes the above method closer to the evolution of road traffic conditions. Secondly, based on the characteristics of traffic state classification, the Kendall coefficient is introduced to study the evolution of road traffic flow states. And finally, a high-correlation path search method based on fusion coefficients is proposed to provide method supports for traffic congestion predictions.