Forecasting Passenger Flow Distribution between Urban Rail Transit Stations Based on Behavior Analysis under Emergent Events

被引：0
|
作者：
Liu S. [1 ]
Yao E. [1 ]
Li B. [1 ]
Tang Y. [2 ]
机构：
[1] MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing
[2] Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou
来源：
Yao, Enjian (enjyao@bjtu.edu.cn) | 2018年 / Science Press卷 / 40期
关键词：
Behavior analysis; Emergent events; Passenger flow distribution; Urban rail transit;
D O I：
10.3969/j.issn.1001-8360.2018.09.004
中图分类号：
学科分类号：
摘要：
An approach was proposed to forecast passenger flow distribution between urban rail transit (URT) stations based on behavior analysis under emergent events, designed to provide decision support to contingency plan making and operational management measure establishment. First, an algorithm was proposed to define the URT passenger flow affected by an emergent event in an URT system. Second, using disaggregate theory, a travel choice model was developed to capture travel preference of passengers who are influenced by emergent events. Then, a multimode alternative travel plan set under emergent events was established and passenger spatiotemporal distribution was forecasted. Finally, based on the historical passenger flow data in an URT system under an emergent event, the proposed approach was validated. The results show that this approach can reflect the travel preference of URT passengers under emergent events, with the mean absolute error of forecasted origin-destination (OD) passenger flow of 2.05 persons, indicating good forecasting performance. © 2018, Department of Journal of the China Railway Society. All right reserved.
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页码：22 / 29
页数：7
相关论文
共 11 条
  • [1] Zeng L., Liu J., Qin Y., Et al., Optimal Control Method for Passenger Flow Limitation in Urban Rail Transit Network Based on Network Controllability, Journal of the China Railway Society, 40, 4, pp. 1-8, (2018)
  • [2] Preston J., Wall G., Batley R., Et al., Impact of Delays on Passenger Train Services: Evidence from Great Britain, Transportation Research Record: Journal of the Transportation Research Board, 2117, 2117, pp. 14-23, (2009)
  • [3] Barron A., Melo P., Cohen J., Et al., A Passenger-focused Management Approach to the Measurement of Train Delay Impacts, Transportation Research Record: Journal of the Transportation Research Board, 2351, 2351, pp. 46-53, (2013)
  • [4] Hong L., Gao J., Xu R., Calculation Method of Emergency Passenger Flow in Urban Rail Network, Journal of Tongji University(Natural Science), 39, 10, pp. 1485-1489, (2011)
  • [5] Sun H., Wu J., Wu L., Et al., Estimating the Influence of Common Disruptions on Urban Rail Transit Networks, Transportation Research Part A: Policy and Practice, 94, pp. 62-75, (2016)
  • [6] Zhang Z., Wu Q., Xu R., Simulation on the Dynamic Distribution of Passenger Flow in Urban Rail Transit, Urban Mass Transit, 9, 4, pp. 52-55, (2006)
  • [7] Pnevmatikou A.M., Karlaftis M.G., Kepaptsoglou K., Metro Service Disruptions: How do People Choose to Travel?, Transportation, 42, 6, pp. 933-949, (2015)
  • [8] Xu R., Ye J., Pan H., Method for Early Warning of Heavy Passenger Flow at Transfer Station of Urban Rail Transit Network under Train Delay, China Railway Science, 35, 5, pp. 127-133, (2014)
  • [9] Li W., Xu R., Simulation of Passenger Travel Behaviors on Urban Rail Transit in Emergency, Journal of East China Jiaotong University, 2, pp. 46-53, (2015)
  • [10] Ben-Akiva M., Lerman S.R., Discrete Choice Analysis: Theory and Application to Travel Demand, pp. 100-129, (1997)
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