Dynamic Hidden Markov Model for Metropolitan Traffic Flow Prediction

Traffic flow prediction is one of the core technologies in Intelligent Transportation System (ITS) to improve traffic management. However, in metropolitan circumstances, the complex traffic road networks and numerous unpredictable traffic anomalies are still tough problems, which bring challenges of leveraging topological and anomalies information to accurate traffic flow prediction. In this paper, we propose a Dynamic Hidden Markov Model (DHMM) based on global PageRank algorithm to overcome these challenges. The global PageRank algorithm is more applicable than traditional algorithm for traffic scenarios, through which the PageRank metric is calculated to measure the accumulation of traffic anomalies at intersections. By incorporating the PageRank metric, DHMM leverages topological and anomalies information to dynamically model the traffic variations. Experiments on real-world dataset demonstrate that the PageRank metric can describe the degree of traffic anomalies intuitively, and the proposed model has superior traffic flow prediction performance both under normal and abnormal traffic conditions.