Traffic congestion prediction of urban trunk roads based on bus floating vehicle data

Abstract

Traffic congestion prediction is the premise to solve the problem of traffic congestion. Aiming at the problem that traffic volume was rarely considered in traffic congestion prediction based on spatio-temporal characteristics, this paper added two features of bus flow and time occupancy on the basis of temporal correlation and spatial correlation analysis of speed based on floating bus data. A BP neural network speed prediction model optimized by whale optimization algorithm (WOA) considering the temporal and spatial characteristics of bus flow was proposed, and the traffic state was divided into three levels by fuzzy theory. The results show that the speed prediction method based on temporal and spatial characteristics and bus flow characteristics proposed in this paper has good performance. Compared with the traditional BP neural network prediction results, the root mean square error and mean absolute error of WOA-BP neural network prediction model are reduced by 11.7% and 11.2% respectively, and the determination coefficient reaches 93.7%. The prediction accuracy of traffic congestion based on fuzzy theory is 90.06%, and the prediction accuracy of the model is higher.