城市公交GPS数据与IC卡数据时空特性融合算法

针对部分城市公交GPS数据和IC卡数据无直接联系，且2个系统存在不规律时间偏差，很难关联获取乘客上车数据的问题，进行了时空特性快速匹配数据融合分析。根据公交GPS数据和线路站点位置匹配获得公交运行时刻表，利用运行时刻表与时间修正后的IC卡数据进行遍历计算，采用时间相似度曲线寻找二者对应关系，利用时间平均偏差曲线进行关系验证，并获得2个系统之间的时间修正值。对西安市5条线路总计195辆车3d的相关数据进行试算，其中，191辆车具有明显的识别特征; 通过南宁16条线路已知对应关的344辆车进行算法验证，获得了336辆车的确切对应关系，平均时间修正误差为16.5 s。结果表明:该算法匹配率达97.67%，对于广泛存在的公交GPS数据和IC数据属于不同系统，难以判断刷卡上下车站点的情况，提供了快速高效的方法，扩大了原本不完善公交数据的应用范围，为公共交通出行中个体微观出行行为分析奠定了基础。 

A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation

Since there is no direct connection between the GPS data and IC card data of some urban buses, it is difficult to correlate and obtain the passenger boarding data. The situation becomes more difficult when the two sets of data have irregular time deviations. The paper analyzes the fast matching data fusion of spatiotemporal characteristics, containing the following steps. Firstly, the bus timetable is obtained according to the bus GPS data and stop location matching. Then, the time similarity curve is drawn between the timetable and time-corrected IC card data through tra versal calculation. The corresponding relationship is found and verified by the curve of time-average deviation. Finally, the time correction value between the two systems is determined. The relevant three-day data is calculated on 195 buses in 5 routes in Xi'an city, where 191 vehicles have obvious identification characteristics. Besides, the algorithm is verified through 344 vehicles with known correspondences in 16 routes in Nanning City. The exact correspondence between 336 vehicles is obtained, with an average time corrected error of 16.5 s. The results show that the matching rate of the algorithm is 97.67%. For the widely existing bus GPS data and IC data belonging to different systems, it is difficult to judge the situation of bus stops by swiping the card. The proposed method expands the application scope of the original imperfect bus data and lays a foundation for analyzing individual micro travel behaviors in public transportation.