关键词: 智能交通, 交通状态预测, 随机重复爬山法, 贝叶斯网络, 数据挖掘

Abstract:

Construct of reasonable network structure which influencing traffic status is the prerequisite of realizing traffic status prediction. In order to improve Hill-climbing algorithm, which may trap into the local optimum instead of the global optimum, a new traffic status prediction method is proposed based on Random Restart Hill-climbing. Proposed multi-network structures are obtained by executing Hill-climbing algorithm iteratively, to create directed acyclic graphs randomly. Furthermore, selection criterion for nodes and directed edges in the optimal Bayesian network structure is determined by the definition of directed edges-confidence and the calculation of confidence- threshold. The intelligent predictions and comprehensive evaluations of four kinds of traffic status including free, smooth, congestion and jam are achieved by using optimal Bayesian network structure. Results indicate that the prediction results are satisfactory with a high accuracyrate of more than 85% only selecting two variables such as hour and holiday. Therefore, the proposed method provides an effective way and experimental proof for monitoring, warning and decision analysis of expressway operation status.

Key words: intelligent transportation, traffic status prediction, Random Restart Hill- climbing, Bayesian network, data mining