基于轨迹数据的出租车交接班时空分布识别方法

Time-space distribution identification method of taxi shift based on trajectory data

针对目前出租车交接班行为识别不够精准的问题,提出了一种基于轨迹数据挖掘的出租车交接班行为精准识别的方法。首先,分析出租车停留状态的数据特性后,提出了一种出租车非运营状态停留点检测方法;然后,对停留点进行聚类,从而得出了潜在的出租车交接班地点;最后,基于出租车交接班事件的判断指标与出租车交接班时间的核密度估计,有效地识别出出租车交接班地点和时间。以福州市4416辆出租车的轨迹数据为实验样本,共识别出了5639个交接班地点,这些交接班地点在市民主要工作区域、交通枢纽、商圈以及风景名胜。而识别出的交接班时间主要在凌晨4:00—6:00与傍晚16:00—18:00,与福州市民众出行规律相吻合。实验结果表明,该方法能有效地检测出出租车交接班的时空分布,能为城市的交通资源规划与管理提供合理建议,且使公众打车出行更加便捷,提高了出租车的运行效率,为城市加油站、充电站等汽车相关设施的选址优化提供了参考。

Concerning the problem of inaccurate identification of taxi shift behaviors,an accurate identification method of taxi shift behaviors based on trajectory data mining was proposed.Firstly,after analyzing the characteristics of taxi parking state data,a method for detecting taxi parking points in non-operating state was proposed.Secondly,by clustering the parking points,the potential taxi shift locations were obtained.Finally,based on the judgment indices of taxi shift event and the kernel density estimation of the taxi shift time,the locations and times of the taxi shift were identified effectively.Taking the trajectory data of 4416 taxis in Fuzhou as the experimental samples,a total of 5639 taxi shift locations were identified.These taxi shift locations are in the main working areas of citizens,transportation hubs,business districts and scenic spots.And the identified taxi shift time is mainly from 4:00 to 6:00 in the morning and from 16:00 to 18:00 in the evening,which is consistent with the travel patterns of Fuzhou citizens.Experimental results show that,the proposed method can effectively detect the time-space distribution of taxi shift,and provide reasonable suggestions for the planning and management of urban traffic resources.The proposed method can also help the people to take a taxi more conveniently,improve the operating efficiency of taxis,and provide references for the site selection optimization of urban gas stations,charging stations and other car related facilities.