智能交通刷卡记录中的公交站点恢复方法
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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 Adjacency algorithm)和一个改进的基于历史的恢复算法(HTB,Historical Trip Based algorithm).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.
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.
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.
引文
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[21]胡继华,邓俊,黄泽.结合出行链的公交IC卡乘客下车站点判断概率模型[J].交通运输系统工程与信息,2014,14(2):62-67.
[22]上海市政府数据服务网.[DB/OL].[2017-05-20].http://www.datashanghai.gov.cn.
[2]LATHIA N,FROEHLICH J,CAPRA L.Mining public transport usage for personalised intelligent transport systems[C]//2010 IEEE 10th International Conference on Data Mining.IEEE,2010:887-892.
[3]BAGCHI M,WHITE P R.The potential of public transport smart card data[J].Transport Policy,2005,12(5):464-474.
[4]PELLETIER M P,TREPANIER M,MORENCY C.Smart card data use in public transit:A literature review[J].Transportation Research Part C Emerging Technologies,2011,19(4):557-568.
[5]ZHANG F,YUAN N J,WANG Y,et al.Reconstructing individual mobility from smart card transactions:A collaborative space alignment approach[J].Knowledge and Information Systems,2015,44(2):299-323.
[6]TREPANIER M,TRANCHANT N,CHAPLEAU R.Individual trip destination estimation in a transit smart card automated fare collection system[J].Journal of Intelligent Transportation Systems Technology Planning&Operations,2007,11(1):1-14.
[7]WANG W,ATTANUCCI J P,WILSON N H M.Bus passenger origin-destination estimation and related analyses using automated data collection systems[J].Journal of Public Transportation,2010,14(4):131-150.
[8]BARRY J,NEWHOUSER R,RAHBEE A,et al.Origin and destination estimation in New York City with automated fare system data[J].Transportation Research Record,2002,1817:183-187.
[9]SONG C,QU Z,BLUMM N,et al.Limits of predictability in human mobility[J].Science,2010,327:1018-1021.
[10]GIANNOTTI F,NANNI M,PEDRESCHI D,et al.Unveiling the complexity of human mobility by querying and mining massive trajectory data[J].The VLDB Journal,2011,20(5):695-719.
[11]LI Z,DING B,HAN J,et al.Mining periodic behaviors for moving objects[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2010:1099-1108.
[12]WANG Y,YUAN N J,LIAN D,et al.Regularity and conformity:Location prediction using heterogeneous mobility data[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:1275-1284.
[13]BALAN R K,NGUYEN K X,JIANG L.Real-time trip information service for a large taxi fleet[C]//Proceedings of the 9th International Conference on Mobile Systems,Applications,and Services.ACM,2011:99-112.
[14]DASH M,KOO K K,HOLLECZEK T,et al.From mobile phone data to transport network-gaining insight about human mobility[C]//IEEE International Conference on Mobile Data Management.IEEE,2015:243-250.
[15]DAI J,YANG B,GUO C,et al.Personalized route recommendation using big trajectory data[C]//IEEE 31st International Conference on Data Engineering.IEEE,2015:543-554.
[16]龙瀛,孙立君,陶遂.基于公共交通智能卡数据的城市研究综述[J].城市规划学刊,2015,3:70-77.
[17]LONG Y,THILL J C.Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing[J].Computers Environment&Urban Systems,2015,53:19-35.
[18]EL-GENEIDY A,GRIMSRUD M,WASFI R,et al.New evidence on walking distances to transit stops:Identifying redundancies and gaps using variable service areas[J].Transportation,2014,41(1):193-210.
[19]DANIELS R,MULLEY C.Explaining walking distance to public transport:The dominance of public transport supply[J].Journal of Transport&Land Use,2011,6(2):5-20.
[20]CUI A.Bus passenger origin-destination matrix estimation using automated data collection systems[D].Cambridge,MA:Massachusetts Institute of Technology,2006.
[21]胡继华,邓俊,黄泽.结合出行链的公交IC卡乘客下车站点判断概率模型[J].交通运输系统工程与信息,2014,14(2):62-67.
[22]上海市政府数据服务网.[DB/OL].[2017-05-20].http://www.datashanghai.gov.cn.
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