站点位置缺失的公交客流OD估计方法及应用
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摘要
利用公交自动收费(AFC)和自动车辆定位(AVL)系统产生的数据估计公交客流OD,可用于优化公交系统的规划设计和运营管理。针对公交站点位置缺失的情景,提出公交站点识别和上下车站点推断方法。首先通过时间匹配获得刷卡记录的位置信息,进而结合时间聚类方法和空间聚类方法DBSCAN识别公交站点并获得上车站点,接着依次采用个人公交出行链规则、个人公交出行规律性规则和公交线路客流分布规律性规则估计下车站点。应用上述方法估计深圳市公交客流OD,并开发包含线路、站点、客流和可达性4个模块的公交规划支持系统,用以支持公交系统的优化调整。
The bus passenger flow OD estimated from the data of auto fare collection(AFC) and auto vehicle location(AVL) systems can be used to optimize the planning, design, and operation management of the urban bus system. The paper proposes a method to identify the bus stops and to infer the boarding and alighting stops when the locations of bus stops are missing. First, the positions of AFC records are obtained through time matching. The temporal clustering method and the spatial clustering method DBSCAN are then used to identify the locations of bus stops and to obtain the boarding stops. Finally, the alighting stops are estimated with the individual bus trip-chain rule, the individual bus trip regularity rule, and the bus route flow distribution regularity rule sebsequently. The method is used to estimate the bus passenger flow OD of Shenzhen. A bus planning support system is developed to support bus system optimization, which includes four modules(route, stop, passenger flow and accessibility).
The bus passenger flow OD estimated from the data of auto fare collection(AFC) and auto vehicle location(AVL) systems can be used to optimize the planning, design, and operation management of the urban bus system. The paper proposes a method to identify the bus stops and to infer the boarding and alighting stops when the locations of bus stops are missing. First, the positions of AFC records are obtained through time matching. The temporal clustering method and the spatial clustering method DBSCAN are then used to identify the locations of bus stops and to obtain the boarding stops. Finally, the alighting stops are estimated with the individual bus trip-chain rule, the individual bus trip regularity rule, and the bus route flow distribution regularity rule sebsequently. The method is used to estimate the bus passenger flow OD of Shenzhen. A bus planning support system is developed to support bus system optimization, which includes four modules(route, stop, passenger flow and accessibility).
引文
[1] KUSAKABE T,ASAKURA Y.Behavioural data mining of transit smart card data:a data fusion approach[J].Transportation research part C:emerging technologies,2014,46:179-191.
[2] TAO S,ROHDE D,CORCORAN J.Examining the spatial-temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap[J].Journal of transport geography,2014,41:21-36.
[3] MA X,WU Y,WANG Y,et al.Mining smart card data for transit riders' travel patterns[J].Transportation research part C:emerging technologies,2013,36:1-12.
[4] JANG W.Travel time and transfer analysis using transit smart card data[J].Transportation research record:journal of the transportation research board,2010(2144):142-149.
[5] TRéPANIER M,TRANCHANT N,CHAPLEAU R.Individual trip destination estimation in a transit smart card automated fare collection system[J].Journal of intelligent transportation systems,2007,11(1):1-14.
[6] 陈学武,戴霄,陈茜.公交IC卡信息采集、分析与应用研究[J].土木工程学报,2004,37(2):105-110.
[7] CUI A.Bus passenger origin-destination matrix estimation using automated data collection systems[D].Boston:Massachusetts Institute of Technology,2006.
[8] 马晓磊,刘从从,刘剑锋,等.基于公交IC卡数据的上车站点推算研究[J]交通运输系统工程与信息,2015,15(4):78-84.
[9] 宋晓晴,方志祥,尹凌,等.基于IC卡综合换乘信息的公交乘客上车站点推算[J].地球信息科学学报,2016,18(8):1060-1068.
[10] 张颂,陈学武,陈峥嵘.基于公交IC卡数据的公交站点OD矩阵推导方法[J].武汉理工大学学报(交通科学与工程版),2014(2):333-337.
[11] ESTER M,KRIEGEL H,SANDER J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C].Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).Menlo Park:AAAI Press,1996:226-231.
[12] 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:journal of the transportation research board,2002(1817):183-187.
[2] TAO S,ROHDE D,CORCORAN J.Examining the spatial-temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap[J].Journal of transport geography,2014,41:21-36.
[3] MA X,WU Y,WANG Y,et al.Mining smart card data for transit riders' travel patterns[J].Transportation research part C:emerging technologies,2013,36:1-12.
[4] JANG W.Travel time and transfer analysis using transit smart card data[J].Transportation research record:journal of the transportation research board,2010(2144):142-149.
[5] TRéPANIER M,TRANCHANT N,CHAPLEAU R.Individual trip destination estimation in a transit smart card automated fare collection system[J].Journal of intelligent transportation systems,2007,11(1):1-14.
[6] 陈学武,戴霄,陈茜.公交IC卡信息采集、分析与应用研究[J].土木工程学报,2004,37(2):105-110.
[7] CUI A.Bus passenger origin-destination matrix estimation using automated data collection systems[D].Boston:Massachusetts Institute of Technology,2006.
[8] 马晓磊,刘从从,刘剑锋,等.基于公交IC卡数据的上车站点推算研究[J]交通运输系统工程与信息,2015,15(4):78-84.
[9] 宋晓晴,方志祥,尹凌,等.基于IC卡综合换乘信息的公交乘客上车站点推算[J].地球信息科学学报,2016,18(8):1060-1068.
[10] 张颂,陈学武,陈峥嵘.基于公交IC卡数据的公交站点OD矩阵推导方法[J].武汉理工大学学报(交通科学与工程版),2014(2):333-337.
[11] ESTER M,KRIEGEL H,SANDER J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C].Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).Menlo Park:AAAI Press,1996:226-231.
[12] 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:journal of the transportation research board,2002(1817):183-187.