依据历史轨迹构建城市出租车移动概率模型

The moving probability model of urban cabs based on history trajectory

针对无法在线实时获取移动出租车实时状态信息的条件下,根据对历史轨迹信息的处理分析,提出将隐马尔科夫理论应用到城市出租车移动轨迹模型中,通过实际数据的分析建立出租车运动模型,通过对模型的计算来预测节点的位置分布概率,并在此模型上针对不同的用户需求进行查询处理,为用户提供搭车路线决策支持。通过利用真实数据集的实验证明,模型能够较好的模拟出出租车节点的运动状态,用户也能够从模型中获取较高精度的位置状态信息。

The model of urban cabs moving is one of the key issues for model building.The model needs reflecting moving state information of cabs.More importantly,users can quickly query the moving cabs.Under the condition of real time information of cabs which is hard to obtain,we should model the moving points and forecast the state information according to the moving history.A method which applies Hidden Markov theory to model of the moving trajectory is proposed.Through an analysis of real trajectory data of San Francisco,the caps moving model which is used to query the caps by users was constructed.Experiment with real datasets shows that the method proposed can simulate the moving state of caps.Users can also quickly obtain the useful location information from the model.