中文摘要：

ETC收费数据中蕴含着大量的信息，如何利用数据挖掘技术解决运营管理中的问题成为高速公路管理部门的迫切需求．本文选取ETC历史数据构建路径序列事务数据库，针对基本Markov路径预测模型预测准确率低、覆盖率低的缺点，提出了一种基于混合MarkOV路径预测模型预测高速公路车辆路径的方法，利用该方法实现了高速公路ETC车辆未来通行状态的预测，同时检测出异常的路径序列．实验结果表明，该方法检测结果可靠，总体预测准确率达到83％以上，能够为高速公路管理部门开展收费稽查、提高ETC管理水平提供理论依据和决策参考．．

英文摘要：

ETC tolling data contains a vast amount of information, the dala mining to improve management efficiency is an urgent problem to the expressway administrations. In this paper, ETC raw data are used to construct the route sequences transactional database. Against the shortcomings of low accuracy and coverage rate with basic Markov route prediction model, a new method based on hybrid Markov route prediction model is proposed to predict vehicle route on the expressway. ETC vehicles＇ future driving states are predicted and unusual route sequences are detected using this method. The experimental results show that the detecting result is reliable, and the overall prediction accuracy rate is above 83%. It may provide theoretical foundation and decision support for expressway administrations to develop charge checking and improve ETC management level.