基于南京地铁AFC数据的大客流识别方法（英文）
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摘要
为了缓解城市轨道交通车站的交通拥堵问题,提出了一种基于自动售检票设备刷卡数据的大客流识别方法.首先,通过观察每小时进站乘客的时间分布和断面客流量的空间分布,分析乘客出行特征.其次,提出了大客流的识别方法.根据高峰时段的五分钟客流量进行频率直方图分析,通过Matlab拟合的概率密度函数计算阈值,根据获得的阈值划分预警等级.最后,以新街口站为例,证明了该识别方法的有效性和实用性.结论表明,与传统方法相比,所提出的综合方法消除了效率和延迟等缺陷.此外,所提方法也可适用于其他配备了自动售检票系统的地铁公司.
To relieve traffic congestion in urban rail transit stations, a new identification method of crowded passenger flow based on automatic fare collection data is proposed. First, passenger travel characteristics are analyzed by observing the temporal distribution of inflow passengers each hour and the spatial distribution concerning cross-section passenger flow. Secondly, the identification method of crowded passenger flow is proposed to calculate the threshold via the probability density function fitted by Matlab and classify the early-warning situation based on the threshold obtained. Finally, a case study of Xinjiekou station is conducted to prove the validity and practicability of the proposed method. Compared to the traditional methods, the proposed comprehensive method can remove defects such as efficiency and delay. Furthermore, the proposed method is suitable for other rail transit companies equipped with automatic fare collection systems.
To relieve traffic congestion in urban rail transit stations, a new identification method of crowded passenger flow based on automatic fare collection data is proposed. First, passenger travel characteristics are analyzed by observing the temporal distribution of inflow passengers each hour and the spatial distribution concerning cross-section passenger flow. Secondly, the identification method of crowded passenger flow is proposed to calculate the threshold via the probability density function fitted by Matlab and classify the early-warning situation based on the threshold obtained. Finally, a case study of Xinjiekou station is conducted to prove the validity and practicability of the proposed method. Compared to the traditional methods, the proposed comprehensive method can remove defects such as efficiency and delay. Furthermore, the proposed method is suitable for other rail transit companies equipped with automatic fare collection systems.
引文
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[13] Xu R H,Ye J M,Pan H C,et al.Method for early warning of heavy passenger flow at transfer station of urban rail transit network under train delay[J].China Railway Science,2014,35(5):127-133.DOI:10.3969/j.issn.1001-4632.2014.05.18.(in Chinese)
[14] Davidich M,Geiss F,Mayer H G,et al.Waiting zones for realistic modelling of pedestrian dynamics:A case study using two major German railway stations as examples[J].Transportation Research Part C:Emerging Technologies,2013,37:210-222.DOI:10.1016/j.trc.2013.02.016.
[15] Seriani S,Fernández R.Planning guidelines for metro-bus interchanges by means of a pedestrian microsimulation model[J].Transportation Planning and Technology,2015,38(5):569-583.DOI:10.1080/03081060.2015.1039235.
[16] Fernández R,Valencia A,Seriani S.On passenger saturation flow in public transport doors [J].Transportation Research Part A:Policy and Practice,2015,78:102-112.DOI:10.1016/j.tra.2015.05.001.
[2] Zhao J H,Rahbee A,Wilson N H M.Estimating a rail passenger trip origin-destination matrix using automatic data collection systems[J].Computer-Aided Civil and Infrastructure Engineering,2007,22(5):376-387.DOI:10.1111/j.1467-8667.2007.00494.x.
[3] Chan J.Rail transit OD matrix estimation and journey time reliability metrics using automated fare data [D].Cambridge,MA,USA:Massachusetts Institute of Technology,2007.
[4] Rao H.Real-time estimation and prediction of OD matrix for public passenger flow based on AFC data [D].Nanjing:Southeast University,2014.(in Chinese)
[5] Yao X M,Zhao P,Yu D D.Real-time origin-destination matrices estimation for urban rail transit network based on structural state-space model[J].Journal of Central South University,2015,22(11):4498-4506.DOI:10.1007/s11771-015-2998-4.
[6] Nagy V.Theoretical method for building OD matrix from AFC data[J].Transportation Research Procedia,2016,14:1802-1808.DOI:10.1016/j.trpro.2016.05.146.
[7] Hasan S,Schneider C M,Ukkusuri S V,et al.Spatiotemporal patterns of urban human mobility[J].Journal of Statistical Physics,2013,151(1/2):304-318.DOI:10.1007/s10955-012-0645-0.
[8] Sun Y S,Shi J G,Schonfeld P M.Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data:A case study of Shanghai Metro[J].Public Transport,2016,8(3):341-363.DOI:10.1007/s12469-016-0137-8.
[9] Ma L.Analysis and evaluation of passenger flow operation state in urban railway transit hub [D].Beijing:Beijing Jiaotong University,2009.(in Chinese)
[10] Huang H C.Research on safety evaluation of passenger flow of railway passenger integrated transport hub [D].Nanjing:Southeast University,2011.(in Chinese)
[11] Xu X,Ma Y N,Li T,et al.Risk early-warning study of passenger flow in business district [C]//2010 IEEE International Conference on Emergency Management and Management Sciences (ICEMMS).Beijing,China,2010:310-313.
[12] Li T,Jin L Z,Ma Y N,et al.Study on method for monitoring and early-warning of passenger flow in large-scale activities[J].Journal of Safety Science and Technology,2012,8(4):75-80.DOI:10.3969/j.issn.1673-193X.2012.04.014.(in Chinese)
[13] Xu R H,Ye J M,Pan H C,et al.Method for early warning of heavy passenger flow at transfer station of urban rail transit network under train delay[J].China Railway Science,2014,35(5):127-133.DOI:10.3969/j.issn.1001-4632.2014.05.18.(in Chinese)
[14] Davidich M,Geiss F,Mayer H G,et al.Waiting zones for realistic modelling of pedestrian dynamics:A case study using two major German railway stations as examples[J].Transportation Research Part C:Emerging Technologies,2013,37:210-222.DOI:10.1016/j.trc.2013.02.016.
[15] Seriani S,Fernández R.Planning guidelines for metro-bus interchanges by means of a pedestrian microsimulation model[J].Transportation Planning and Technology,2015,38(5):569-583.DOI:10.1080/03081060.2015.1039235.
[16] Fernández R,Valencia A,Seriani S.On passenger saturation flow in public transport doors [J].Transportation Research Part A:Policy and Practice,2015,78:102-112.DOI:10.1016/j.tra.2015.05.001.