Short-term Origin-destination Estimation for Urban Rail Transit Based on Multiple Temporal Scales

Abstract

Abstract:

To estimate the destinations of the entrance passenger flows at urban rail transit stations in each 15 minutes at rush hours, based on state-space models, a multi-model composition approach of short-term origin- destination estimation is proposed. Firstly, the split rates of the entrance passenger flows under different temporal scales are used as the state variables for the composition model. Then the split rates of the commuter entrance passenger flows are estimated based on the historical data. Secondly, the results of the estimated split rates under different temporal scales are weightily fused by the interacting multiple model algorithm. The research of the Beijing subway demonstrates that the average and maximum estimation errors of the split rates at morning rush hours are 16.4% and 21.8% respectively. And those of evening rush hours are 22.7% and 24.6% respectively. The result reduces the estimation errors by nearly one half in comparison with those in present literatures. The results of this paper can provide more accurate input data for the realtime prediction of network traffic distribution to assist the management agency in implementing the early warning and emergency response system on mass passenger flows.

Key words: traffic engineering, short-time OD estimation, multiple temporal scales, urban rail transit, state transition

摘要：

基于状态空间模型构建了城市轨道交通短时OD估计的多模型组合方法，估计早晚高峰期间15 min 内进站客流的去向目的站.组合模型以不同时间尺度下的进站客流分流率为状态变量，并利用历史数据预估其中通勤客流的分流率，然后通过交互多模型算法加权融合不同时间尺度下的分流率估计结果.以北京地铁为案例，研究表明：早高峰期间的15 min 分流率估计误差的平均值和最大值分别为16.4%和21.8%，晚高峰期间分别为22.7%和24.6%，比既有文献的估计误差减小了约一半.本文的研究成果可为实时的线网客流分布预测提供更准确的输入数据，以辅助运营管理部门实现客流预警和应急响应.

关键词: 交通工程, 短时OD估计, 多时间尺度, 城市轨道交通, 状态转移

CLC Number:

U239.5

CHEN Zhi-jie，MAO Bao-hua，BAI Yun，XU Qi，ZHANG Tong. Short-term Origin-destination Estimation for Urban Rail Transit Based on Multiple Temporal Scales[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(5): 166-172.

陈志杰，毛保华，柏赟，许奇，张桐. 基于多时间尺度的城市轨道交通短时OD 估计[J]. 交通运输系统工程与信息, 2017, 17(5): 166-172.

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URL: http://www.tseit.org.cn/EN/abstract/abstract19499.shtml

http://www.tseit.org.cn/EN/Y2017/V17/I5/166

References

[1] 李得伟, 张琦, 韩宝明. 城市轨道交通大客流风险动态控制理论与方法[M]. 北京：人民交通出版社, 2015. [LI D W, ZHANG Q, HAN B M. Dynamic control of passenger crowd risk in urban rail transit: theory and methods[M]. Beijing：China Communications Press, 2015. ]

[2] BERA S, RAO K V K. Estimation of origin-destination matrix from traffic counts: the state of the art[J]. European Transport, 2011(49): 2-23.

[3] ZHOU X, MAHMASSANI H S. A structural state space model for real- time traffic origin- destination demandestimation and prediction in a day-to-day learning framework[J]. Transportation Research Part B: Methodological, 2007, 41(8): 823-840.

[4] LIN P W, CHANG G L. A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix[J]. Transportation Research Part B: Methodological, 2007, 41(5): 554-572.

[5] 李得伟, 尹浩东. 基于物联网设备检测数据的城市轨道交通车站站台多维度实时客流密集度指数预测方法研究[J]. 铁道学报, 2014, 36(3): 9-13. [LI D W, YIN H D. Real-time forecast of passenger crowd index on urban rail transit station platform based on test data of internet of things[J]. Journal of the China Railway Society, 2014, 36(3): 9-13.]

[6] 唐小勇, 周涛, 陆百川,等. 一种基于手机信令的通勤 OD训练方法[J]. 交通运输系统工程与信息, 2016, 16 (5): 64-70. [TANG X Y, ZHOU T, LU B C, et al. A commuting OD matrix training method based on mobile phone data[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(5): 64-70.]

[7] 徐瑞华, 徐永实. 城市轨道交通线路客流分布的实时预测方法[J]. 同济大学学报(自然科学版), 2011, 39(6): 857- 861. [XU R H, XU Y S. Real- time forecast of passenger flow distribution on urban rail transit line[J]. Journal of Tongji University (Natural Science), 2011, 39 (6): 857-861.]

[8] 姚向明, 赵鹏, 禹丹丹. 城市轨道交通网络短时客流 OD估计模型[J]. 交通运输系统工程与信息, 2015, 15 (2):149- 162. [YAO X M, ZHAO P, YU D D. Shorttime passenger flow origin-destination estimation model for urban rail transit network[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(2): 149-162.]

[9] 姚向明, 赵鹏, 禹丹丹. 基于平均策略的城市轨道交通动态O-D矩阵估计[J]. 吉林大学学报(工学版), 2016, 46(1): 92-99. [YAO X M, ZHAO P, YU D D. Dynamic origin-destination matrix estimation for urban rail transit based on averaging strategy[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(1): 92-99.]

[10] MA X L, LIU C C, WEN H M, et al. Understanding commuting patterns using transit smart card data[J]. Journal of Transport Geography, 2017(58): 135-145.

[11] MA Z, XING J, MESBAH M, et al. Predicting shortterm bus passenger demand using a pattern hybrid approach[J]. Transportation Research Part C: Emerging Technologies, 2014(39): 148-163.

[12] XUE R, SUN D, CHEN S. Short-term bus passenger demand prediction based on time series model and interactive multiple model approach[J]. Discrete Dynamics in Nature & Society, 2015(682390): 1-11.

[13] CHHI C K, CHEN C R. Kalman filtering with realtime applications (fourth edition) [M]. Springer, 2009.

[14] RUBNER Y, TOMASI C, GUIBAS J. The earth mover’s distance as a metric for image retrieval[J]. International Journal of Computer Vision, 2000, 40(2): 99-121.

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