Passenger flow prediction at entrance and exit of rail transit stations：A case study of Beijing

被引：0
作者：
Ma, Jie [1 ,2 ]
Liu, Zhi-Li [1 ]
Wang, Shu-Ling [2 ]
Dong, Hao [3 ]
机构：
[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing
[2] Beijing Transport Institute, Beijing
[3] Beijing Public Transport Tram Corporation, Beijing
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2024年 / 54卷 / 08期
关键词：
CRITIC method; nonlinear regression; rail transit; two-level passenger flow prediction model;
D O I：
10.13229/j.cnki.jdxbgxb.20221387
中图分类号：
学科分类号：
摘要：
Regarding the accurate prediction of passenger flow at the entrance and exit of rail transit stations，Considering the land use around the station，Rail transit connection conditions，station attributes and attraction，a two-level passenger flow prediction model is constructed in this paper，station attributes and attraction，a two-level passenger flow prediction model is constructed in this paper，including the passenger flow prediction at the traffic district level based on multiple nonlinear regression and the passenger flow prediction at the entrance and exit level based on the CRITIC method. On the basis of the available data，the total passenger flow of all entrances and exits in the traffic district is predicted，and then be allocated to each entrance and exit. Different types of rail transit stations are randomly selected to verify the effectiveness of the model. The results show that the error between the predicted value of the model and the actual value of the daily entry volume is within 30%，and the average error is 20%，which has a high prediction accuracy. © 2024 Editorial Board of Jilin University. All rights reserved.
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收藏
页码：2197 / 2205
页数：8
相关论文
共 17 条
  • [1] Santos G, Maoh H, Potoglou D, Et al., Factors influencing modal split of commuting journeys in medium-size European cities, Journal of Transport Geography, 30, pp. 127-137, (2013)
  • [2] Wu J Y, Sun H J, Gao Z Y, Et al., Risk-based stochastic equilibrium assignment model in augmented urban railway network, Journal of Advanced Transportation, 48, 4, pp. 332-347, (2014)
  • [3] Wang J, Peeta S, He X, Et al., Combined multinomial logit modal split and paired combinatorial logit traffic assignment model, Transportmetrica A: Transport Science, 14, 9, pp. 737-760, (2018)
  • [4] Wang Shu-wei, Sun Li-shan, Hao Si-yuan, Et al., Station level transit ridership direct estimation model based on precise land use, Journal of Transportation Systems Engineering and Information Technology, 15, 3, pp. 37-43, (2015)
  • [5] Qi Chang-lu, Hu Hao, Research on ridership forecast of urban rail transit based on mixed geographic weighted regression, Journal of Railway Science and Engineering, 18, 7, pp. 1903-1909, (2021)
  • [6] He Zheng-qiang, Study of prediction methods of passenger flow in different directions of urban rail transit station, Transportation Science & Technology, 2012, 2, pp. 119-122, (2012)
  • [7] Zhang Zhe-ning, Cheng Yuan, Ma Yi-lin, Et al., The forecast method of urban rail transit passenger volume in the preliminary design phase, Journal of Transportation Engineering, 17, 3, pp. 8-13, (2017)
  • [8] Fu Bo-feng, Wu Jiao-rong, Hua Chen-rui, Estimation method of distribution coefficient of passenger flow of subway station entrance, Journal of Tongji University(Natural Science), 40, 11, pp. 1660-1665, (2012)
  • [9] Cervero R, Kockelman K., Travel demand and the 3Ds: density, diversity, and design, Transportation Research Part D: Transport and Environment, 2, 3, pp. 199-219, (1997)
  • [10] Li Guo-qiang, Yang Min, Wu Yun-teng, Et al., Research on influencing factors of passenger flow in rail transit stations based on AFC and POI data, Innovation-driven and Intelligent Development - Proceedings of the 2018 Annual Conference on Urban Transportation Planning in China, pp. 1-11, (2018)
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