A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation
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摘要: 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。Abstract: 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.
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Key words:
- traffic big data /
- GPS data /
- IC data /
- data fusion /
- time error
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图 5 刷卡时间修正变化与停车靠站时间相似度曲线——匹配关系
Figure 5. Similarity curve between the correction time of swiping cards and the parking time: matching relationship
图 6 刷卡时间修正变化与停车靠站时间相似度曲线——非匹配关系
Figure 6. Similarity curve between the correction time of swiping cards and parking time: mismatched relationship
图 7 刷卡时间修正变化与停车靠站时间平均偏差曲线——匹配关系
Figure 7. Average deviation curve between the correction time of swiping cards and the parking time: matching relationship
图 8 刷卡时间修正变化与停车靠站时间平均偏差曲线——非匹配关系
Figure 8. Average deviation curve between the correction time of swiping cards and the parking time: mismatched relationship
表 1 基本数据结构
Table 1. Basic data structure
GPS数据(Data_A) IC卡数据(Data_B) 站点坐标数据(Data_C) 轨迹时间Bus_t 卡号Card_n 线路名称line 轨迹线路编号Bus_m 刷卡时间Card_t 站点名称Station 轨迹车辆编号Bus_i 刷卡线路编号Card_m 站点经度ZD_Lon 轨迹经度Lon 刷卡车辆编号Card_i 站点纬度ZD_Lat 轨迹纬度Lat 方向编号S_m 站点编号Station_i 
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表 2 GPS轨迹数据与公交线路匹配统计
Table 2. Matching statistics of the GPS track data and bus routes
轨迹线路编号 轨迹数量/个 车辆数/辆 匹配数/辆 线路名 计算耗时/s 1142 93 505 44 42 5路 58 11154 60 923 40 39 217路 60 5945 74 807 40 39 308路 56 104 129 679 26 26 313路 61 122 245 022 45 45 700路 87
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表 3 公交运行时刻表判断结果统计
Table 3. Result statistics of judging bus operation timetable
轨迹线路编号 轨迹数量/个 匹配数/辆 简化后轨迹量/个 停车次数/次 耗时/s 1142 93 505 42 60 947 8 050 1.1 11154 60 923 39 35 695 6 209 0.8 5945 74 807 39 46 412 5 166 0.95 104 129 679 26 76 472 6 569 1.1 122 245 022 45 153 755 12 989 2.3
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表 4 GPS轨迹数据与IC卡数据匹配结果统计
Table 4. Statistics of matching results between GPS track data and IC data
轨迹线路编号 刷卡线路编号 车辆数/辆 匹配数/辆 刷卡时间修正/s 1142 0005 44 42 20 11154 0217 40 39 110 5945 0308 40 39 124 104 0313 26 26 -170 122 0700 45 45 -165
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