利用GPS轨迹的转向级交通拥堵精细分析
|
摘要
目前,基于GPS轨迹探测城市交通状态的研究缺乏对不同行驶方向的交通拥堵精细感知。针对此问题,本文提出一种基于出租车GPS轨迹的转向级交通拥堵事件探测方法。该方法首先在分析出租车运营行为特征的基础上,采用特征聚类方法滤选出能够反映真实交通状态的有效轨迹段;然后基于滤选后轨迹分析当前道路交通运行状态,探测城市路网中轻度、中度、重度3种不同强度的交通拥堵事件;最后基于拥堵事件轨迹分析交叉口不同转向的拥堵时间、拥堵强度和拥堵距离等转向级精细交通拥堵状态。试验结果表明,本文方法不仅能有效探测路网中不同强度的拥堵事件,而且能实现交叉口转向级拥堵事件的精细分析。
For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion,this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level.Firstly,this approach analyzed taxis' operating patterns and filtered valid traces.Then this approach detected traffic congestion traces of three different intensities:mild congestion,moderate congestion and serious congestion,based on analyzing traffic conditions from the filtered valid trace segments.Finally,traffic flow speed,congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level.The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level.
For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion,this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level.Firstly,this approach analyzed taxis' operating patterns and filtered valid traces.Then this approach detected traffic congestion traces of three different intensities:mild congestion,moderate congestion and serious congestion,based on analyzing traffic conditions from the filtered valid trace segments.Finally,traffic flow speed,congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level.The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level.
引文
[1]中国安防展览网.交通拥堵造成经济损失---ITS快速成长方向明确[EB/OL].(2014-09-11)[2017-05-22].http:∥security.asmag.com.cn/news/201409/72772.html.AFZHAN.Traffic congestion has led to economic losses:the tendecy of ITS’s growth is deffinite[EB/OL].(2014-09-11)[2017-05-22].http:∥security.asmag.com.cn/news/201409/72772.html.
[2]刘有军,田聪.不同路网形态下城市交通拥堵特性分析[J].中国公路学报,2013,26(1):163-169,190.LIU Youjun,TIAN Cong.Analysis of dynamic characteristics of urban traffic congestion based on specificity of network configuration[J].China Journal of Highway and Transport,2013,26(1):163-169,190.
[3]TSEKERIS T,GEROLIMINIS N.City size,network structure and traffic congestion[J].Journal of Urban Economics,2013,76(1):1-14.
[4]KRAUSE B,VON ALTROCK C,POZYBILL M.Intelligent highway by fuzzy logic:congestion detection and traffic control on multi-lane roads with variable road signs[C]∥Proceedings of IEEE 5th International Conference on Fuzzy Systems.New Orleans,LA,USA:IEEE,1996:1832-1837.
[5]COIFMAN B.Identifying the onset of congestion rapidly with existing traffic detectors[J].Transportation Research Part A:Policy and Practice,2003,37(3):277-291.
[6]CHO Y,RICE J.Estimating velocity fields on a freeway from low-resolution videos[J].IEEE Transactions on Intelligent Transportation Systems,2006,7(4):463-469.
[7]BAUZA R,GOZALVEZ J,SANCHEZ-SORIANO J,et al.Road traffic congestion detection through cooperative vehicle-to-vehicle communications[C]∥Proceedings of IEEE Local Computer Networks Conference.Denver,CO,USA:IEEE,2010:606-612.
[8]仇培元,张恒才,余丽,等.微博客蕴含交通事件信息抽取的自动标注方法[J].中文信息学报,2017,31(2):107-116.QIU Peiyuan,ZHANG Hengcai,YU Li,et al.Automatic event labeling for traffic information extraction from microblogs[J].Journal of Chinese Information Processing,2017,31(2):107-116.
[9]王晓蒙,彭玲,池天河.基于稀疏浮动车数据的城市路网交通流速度估计[J].测绘学报,2016,45(7):866-873.DOI:10.11947/j.AGCS.2016.20150472.WANG Xiaomeng,PENG Ling,CHI Tianhe.A method of urban traffic flow speed estimation using sparse floating car data[J].Acta Geodaetica et Cartographica Sinica,2016,45(7):866-873.DOI:10.11947/j.AGCS.2016.20150472.
[10]XU Lin,YUE Yang,LI Qingquan.Identifying urban traffic congestion pattern from historical floating car data[J].Procedia-Social and Behavioral Sciences,2013,96(11):2084-2095.
[11]D’ANDREA E,MARCELLONI F.Detection of traffic congestion and incidents from GPS trace analysis[J].Expert Systems with Applications,2017,73(1):43-56.
[12]温慧敏,扈中伟,郭继孚,等.奥运期间北京市道路网运行分析[J].交通运输系统工程与信息,2008,8(6):32-37.WEN Huimin,HU Zhongwei,GUO Jifu,et al.Operational analysis on Beijing road network during the Olympic Games[J].Journal of Transportation Systems Engineering and Information Technology,2008,8(6):32-37.
[13]朱琳,于雷,宋国华,等.基于浮动车数据的交通拥堵时间维度特征[J].北京交通大学学报,2011,35(6):7-12.ZHU Lin,YU Lei,SONG Guohua,et al.Temporal characteristics of traffic congestion duration based on floating car data[J].Journal of Beijing Jiaotong University,2011,35(6):7-12.
[14]LIU Chengkun,QIN Kun,KANG Chaogui.Exploring timedependent traffic congestion patterns from taxi trajectory data[C]∥Proceedings of the 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM).Fuzhou:IEEE,2015:39-44.
[15]段滢滢,陆锋.基于道路结构特征识别的城市交通状态空间自相关分析[J].地球信息科学学报,2013,14(6):768-774.DUAN Yingying,LU Feng.Spatial autocorrelation of urban road traffic based on road network characterization[J].Journal of Geo-information Science,2013,14(6):768-774.
[16]WANG Zuchao,LU Min,YUAN Xiaoru,et al.Visual traffic jam analysis based on trajectory data[J].IEEETransactions on Visualization and Computer Graphics,2013,19(12):2159-2168.
[17]林树宽,于伶姿,乔建忠,等.基于GPS轨迹数据的拥堵路段预测[J].东北大学学报(自然科学版),2015,36(11):1530-1534.LIN Shukuan,YU Lingzi,QIAO Jianzhong,et al.The congestion road segment prediction based on GPStrajectory data[J].Journal of Northeastern University(Natural Science),2015,36(11):1530-1534.
[18]CASTRO P S,ZHANG Daqing,LI Shijian.Urban traffic modelling and prediction using large scale taxi GPS traces[C]∥Proceedings of the 10th International Conference on Pervasive Computing.Newcastle:Springer,2012:57-72.
[19]付子圣,李秋萍,柳林,等.利用GPS轨迹二次聚类方法进行道路拥堵精细化识别[J].武汉大学学报(信息科学版),2017,42(9):1264-1270.FU Zisheng,LI Qiuping,LIU Lin,et al.Identification of urban network congested segments using GPS trajectories double-clustering method[J].Geomatics and Information Science of Wuhan University,2017,42(9):1264-1270.
[20]MOHAN P,PADMANABHAN V N,RAMJEE R.Nericell:rich monitoring of road and traffic conditions using mobile smartphones[C]∥Proceedings of the 6th ACMConference on Embedded Network Sensor Systems.Raleigh,NC,USA:ACM,2009:323-336.
[21]HERRERA J C,WORK D B,HERRING R,et al.Evaluation of traffic data obtained via GPS-enabled mobile phones:the mobile century field experiment[J].Transportation Research Part C:Emerging Technologies,2010,18(4):568-583.
[22]NOLLE T,SCHWEIZER I,JANSSEN F.Data-driven detection of congestion-affected roads[R].(2014-11-25)[2017-02-14].http:∥www.ke.tu-darmstadt.de/publications/reports/TUD-KE-2014-02.pdf.
[23]TERROSO-SAENZ F,VALDES-VELA M,SOTOMAYOR-MARTINEZ C,et al.A cooperative approach to traffic congestion detection with complex event processing and VANET[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(2):914-929.
[24]CHEN Biyu,YUAN Hui,LI Qingquan,et al.Map-matching algorithm for large-scale low-frequency floating car data[J].International Journal of Geographical Information Science,2014,28(1):22-38.
[25]LIKAS A,VLASSIS N,VERBEEK J J.The global k-means clustering algorithm[J].Pattern Recognition,2003,36(2):451-461.
[26]北京市质量技术监督局.城市道路交通运行评价指标体系:DB11/T 785-2011[S].北京:北京市质量技术监督局,2011.Beijing Municipal Bureau of Quality and Technical Supervision.Urban road traffic performance index:DB11/T 785-2011[S].Beijing:Beijing Municipal Bureau of Quality and Technical Supervision,2011.
[27]武汉市交通规划网.武汉市交通运行周报[EB/OL].(2015-07-27)[2017-03-15].http:∥www.whtpi.com/News/11-9.html Website of Wuhan Transportation Planning.Weekly reports of traffic performance in Wuhan[EB/OL].(2015-07-27)[2017-03-15].http:∥www.whtpi.com/News/11-9.html.
[2]刘有军,田聪.不同路网形态下城市交通拥堵特性分析[J].中国公路学报,2013,26(1):163-169,190.LIU Youjun,TIAN Cong.Analysis of dynamic characteristics of urban traffic congestion based on specificity of network configuration[J].China Journal of Highway and Transport,2013,26(1):163-169,190.
[3]TSEKERIS T,GEROLIMINIS N.City size,network structure and traffic congestion[J].Journal of Urban Economics,2013,76(1):1-14.
[4]KRAUSE B,VON ALTROCK C,POZYBILL M.Intelligent highway by fuzzy logic:congestion detection and traffic control on multi-lane roads with variable road signs[C]∥Proceedings of IEEE 5th International Conference on Fuzzy Systems.New Orleans,LA,USA:IEEE,1996:1832-1837.
[5]COIFMAN B.Identifying the onset of congestion rapidly with existing traffic detectors[J].Transportation Research Part A:Policy and Practice,2003,37(3):277-291.
[6]CHO Y,RICE J.Estimating velocity fields on a freeway from low-resolution videos[J].IEEE Transactions on Intelligent Transportation Systems,2006,7(4):463-469.
[7]BAUZA R,GOZALVEZ J,SANCHEZ-SORIANO J,et al.Road traffic congestion detection through cooperative vehicle-to-vehicle communications[C]∥Proceedings of IEEE Local Computer Networks Conference.Denver,CO,USA:IEEE,2010:606-612.
[8]仇培元,张恒才,余丽,等.微博客蕴含交通事件信息抽取的自动标注方法[J].中文信息学报,2017,31(2):107-116.QIU Peiyuan,ZHANG Hengcai,YU Li,et al.Automatic event labeling for traffic information extraction from microblogs[J].Journal of Chinese Information Processing,2017,31(2):107-116.
[9]王晓蒙,彭玲,池天河.基于稀疏浮动车数据的城市路网交通流速度估计[J].测绘学报,2016,45(7):866-873.DOI:10.11947/j.AGCS.2016.20150472.WANG Xiaomeng,PENG Ling,CHI Tianhe.A method of urban traffic flow speed estimation using sparse floating car data[J].Acta Geodaetica et Cartographica Sinica,2016,45(7):866-873.DOI:10.11947/j.AGCS.2016.20150472.
[10]XU Lin,YUE Yang,LI Qingquan.Identifying urban traffic congestion pattern from historical floating car data[J].Procedia-Social and Behavioral Sciences,2013,96(11):2084-2095.
[11]D’ANDREA E,MARCELLONI F.Detection of traffic congestion and incidents from GPS trace analysis[J].Expert Systems with Applications,2017,73(1):43-56.
[12]温慧敏,扈中伟,郭继孚,等.奥运期间北京市道路网运行分析[J].交通运输系统工程与信息,2008,8(6):32-37.WEN Huimin,HU Zhongwei,GUO Jifu,et al.Operational analysis on Beijing road network during the Olympic Games[J].Journal of Transportation Systems Engineering and Information Technology,2008,8(6):32-37.
[13]朱琳,于雷,宋国华,等.基于浮动车数据的交通拥堵时间维度特征[J].北京交通大学学报,2011,35(6):7-12.ZHU Lin,YU Lei,SONG Guohua,et al.Temporal characteristics of traffic congestion duration based on floating car data[J].Journal of Beijing Jiaotong University,2011,35(6):7-12.
[14]LIU Chengkun,QIN Kun,KANG Chaogui.Exploring timedependent traffic congestion patterns from taxi trajectory data[C]∥Proceedings of the 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM).Fuzhou:IEEE,2015:39-44.
[15]段滢滢,陆锋.基于道路结构特征识别的城市交通状态空间自相关分析[J].地球信息科学学报,2013,14(6):768-774.DUAN Yingying,LU Feng.Spatial autocorrelation of urban road traffic based on road network characterization[J].Journal of Geo-information Science,2013,14(6):768-774.
[16]WANG Zuchao,LU Min,YUAN Xiaoru,et al.Visual traffic jam analysis based on trajectory data[J].IEEETransactions on Visualization and Computer Graphics,2013,19(12):2159-2168.
[17]林树宽,于伶姿,乔建忠,等.基于GPS轨迹数据的拥堵路段预测[J].东北大学学报(自然科学版),2015,36(11):1530-1534.LIN Shukuan,YU Lingzi,QIAO Jianzhong,et al.The congestion road segment prediction based on GPStrajectory data[J].Journal of Northeastern University(Natural Science),2015,36(11):1530-1534.
[18]CASTRO P S,ZHANG Daqing,LI Shijian.Urban traffic modelling and prediction using large scale taxi GPS traces[C]∥Proceedings of the 10th International Conference on Pervasive Computing.Newcastle:Springer,2012:57-72.
[19]付子圣,李秋萍,柳林,等.利用GPS轨迹二次聚类方法进行道路拥堵精细化识别[J].武汉大学学报(信息科学版),2017,42(9):1264-1270.FU Zisheng,LI Qiuping,LIU Lin,et al.Identification of urban network congested segments using GPS trajectories double-clustering method[J].Geomatics and Information Science of Wuhan University,2017,42(9):1264-1270.
[20]MOHAN P,PADMANABHAN V N,RAMJEE R.Nericell:rich monitoring of road and traffic conditions using mobile smartphones[C]∥Proceedings of the 6th ACMConference on Embedded Network Sensor Systems.Raleigh,NC,USA:ACM,2009:323-336.
[21]HERRERA J C,WORK D B,HERRING R,et al.Evaluation of traffic data obtained via GPS-enabled mobile phones:the mobile century field experiment[J].Transportation Research Part C:Emerging Technologies,2010,18(4):568-583.
[22]NOLLE T,SCHWEIZER I,JANSSEN F.Data-driven detection of congestion-affected roads[R].(2014-11-25)[2017-02-14].http:∥www.ke.tu-darmstadt.de/publications/reports/TUD-KE-2014-02.pdf.
[23]TERROSO-SAENZ F,VALDES-VELA M,SOTOMAYOR-MARTINEZ C,et al.A cooperative approach to traffic congestion detection with complex event processing and VANET[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(2):914-929.
[24]CHEN Biyu,YUAN Hui,LI Qingquan,et al.Map-matching algorithm for large-scale low-frequency floating car data[J].International Journal of Geographical Information Science,2014,28(1):22-38.
[25]LIKAS A,VLASSIS N,VERBEEK J J.The global k-means clustering algorithm[J].Pattern Recognition,2003,36(2):451-461.
[26]北京市质量技术监督局.城市道路交通运行评价指标体系:DB11/T 785-2011[S].北京:北京市质量技术监督局,2011.Beijing Municipal Bureau of Quality and Technical Supervision.Urban road traffic performance index:DB11/T 785-2011[S].Beijing:Beijing Municipal Bureau of Quality and Technical Supervision,2011.
[27]武汉市交通规划网.武汉市交通运行周报[EB/OL].(2015-07-27)[2017-03-15].http:∥www.whtpi.com/News/11-9.html Website of Wuhan Transportation Planning.Weekly reports of traffic performance in Wuhan[EB/OL].(2015-07-27)[2017-03-15].http:∥www.whtpi.com/News/11-9.html.