Overall Traffic Mode Prediction by VOMM Approach and AR Mining Algorithm With Large-Scale Data

Abstract

Traffic state prediction has been a popular topic, since traffic congestion occurs in most cities and creates inconvenience to human daily life. In this paper, we propose a predicting method for a city’s overall traffic state, in order to help people avoid possible future congestion. Based on the variable-order Markov model theory and probability suffix tree, the proposed method makes use of the association rules to improve forecasting performance. Since the association rules are extracted from the historical traffic data and describe the traffic state relations among different regions, the proposed method can improve the predictive accuracy. The traffic system in Shanghai is considered as our experimental case because of its complicated and gigantic coupling transport network. The experimental results indicate more accuracy compared with other methods in long-term traffic status prediction.