智能交通刷卡记录中的公交站点恢复方法

doi:10.3969/j.issn.1000-5641.2017.05.018

华东师范大学学报(自然科学版) ›› 2017, Vol. 2017 ›› Issue (5): 201-212.doi: 10.3969/j.issn.1000-5641.2017.05.018

智能交通刷卡记录中的公交站点恢复方法

王艺霖, 章志刚, 金澈清

华东师范大学数据科学与工程学院, 上海 200062

收稿日期:2017-06-30 出版日期:2017-09-25 发布日期:2017-09-25
通讯作者: 金澈清,男,教授,博士生导师,研究方向为基于位置的服务.E-mail:cqjin@sei.ecnu.edu.cn E-mail:cqjin@sei.ecnu.edu.cn
作者简介:王艺霖,女,硕士研究生,研究方向为基于位置的服务.E-mail:ylwang@stu.ecnu.edu.cn
基金资助:
国家重点研发计划重点专项（973）（2016YFB1000905）；国家自然科学基金（61370101，61532021，U1501252，U1401256，61402180）

Individual station estimation from smart card transactions

WANG Yi-lin, ZHANG Zhi-gang, JIN Che-qing

School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received:2017-06-30 Online:2017-09-25 Published:2017-09-25

摘要/Abstract

摘要： 随着城市公共交通网络的快速发展以及智能交通卡的普及，智能交通卡中隐藏着越来越丰富的个人及群体移动行为信息.但当前很多城市智能公交卡主要用于收费功能，并未包含乘客确切的上下车时间及站点信息，这给分析挖掘交通卡刷卡数据、提供基于精确位置的服务带来了阻碍.本文针对上海市不含公交上下车站点的刷卡数据集，借助于确定的地铁站点刷卡信息，分析个人的整体刷卡历史记录，提出一个基础的基于时空邻近性的恢复算法（STA，Space-Time Adjacencyalgorithm）和一个改进的基于历史的恢复算法（HTB，Historical Trip Basedalgorithm）.具体地，STA算法根据刷卡记录线路的时空邻近关系进行恢复，在此基础上，HTB算法将刷卡记录集合根据时间和空间属性进行切分，获得有明确出行意义的出行记录，再利用历史记录集合，提取乘坐线路以及频繁换乘线路，根据线路间的空间关系生成线路带权候选站点列表，再次进行站点恢复.实验证明本文算法可以较好地缩小线路的候选上下车站点范围，且时间效率较高.

关键词: 智能交通卡, 缺失数据, 刷卡数据挖掘, 站点推测

Abstract: With the fast development of public transportation network and widespread use of smart card, more and more rich semantic information about human mobility behaviors are hidden in smart card transaction data. However, a great number of current smart cards are initially designed for charging and do not record any detailed information about where and when a passenger gets on or gets off a bus, which brings out great difficulties for analyzing, mining transaction data and providing more precise location-based services. This paper presents Space-Time Adjacency algorithm (STA) and Historical Trip Based algorithm (HTB) to estimate the bus station of each card's transaction records with the aid of integral historical data including complete subway transaction data. Specifically, STA does the initial reconstruction work according to the space-time proximity of adjacent transaction records. Then HTB first cuts the collection of records to form trips that contain explicit trip purposes, then extracts taken lines and transfer lines using historical data, next generates candidate stations for each taken line, and finally uses them to recover the transaction records again. Experiments show that the proposed algorithms work well and narrow the range of candidate stations for bus lines, and have good time efficiency.

Key words: smart card, incomplete data, card mining, station estimation

中图分类号:

TP391

王艺霖, 章志刚, 金澈清. 智能交通刷卡记录中的公交站点恢复方法[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 201-212.

WANG Yi-lin, ZHANG Zhi-gang, JIN Che-qing. Individual station estimation from smart card transactions[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 201-212.

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智能交通刷卡记录中的公交站点恢复方法

Individual station estimation from smart card transactions

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