Traffic state prediction based on stochastic repetitive hill climbing method
Abstract A reasonable structure of traffic state network is a prerequisite for traffic state prediction. In order to overcome the shortcomings of the hill climbing method, a traffic state prediction method based on the random repetitive hill climbing method is proposed. A multi-network structure is obtained by iteratively running the hill-climbing method on the randomly generated directed acyclic graph; the node and directed edge selection criteria in the optimal Bayesian network structure are determined by defining the confidence degree of directed edges and calculating the confidence threshold; using the optimal Bayesian network structure, four traffic states, such as smooth, smooth, congested and blocked, are predicted and evaluated comprehensively. The analysis results show that the overall accuracy of the method for traffic state prediction exceeds 85\% when only two variables such as time of day and holiday are selected, which can provide effective methods and data support for highway operation state monitoring and early warning and decision analysis.