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2019 | OriginalPaper | Chapter

Urban Rail Transit Demand Analysis and Prediction: A Review of Recent Studies

Authors : Zhiyan Fang, Qixiu Cheng, Ruo Jia, Zhiyuan Liu

Published in: Intelligent Interactive Multimedia Systems and Services

Publisher: Springer International Publishing

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Abstract

Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.

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Literature
1.
Milenkovic, M., Nebojsa, B.: Railway Demand Forecasting (2016)
2.
Los vehículos, aeronaves. Urban Rail Transit. Betascript Publishing, London (2011)
3.
Xiao, J.: Demand Forecasting Method of Inter-city Rail Transit, Urban Mass Transit (2006)
4.
Wang, Z., Li, X., Chen, F.: Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data. Transp. Res. Part A Policy Pract. 77, 213–224 (2015)CrossRef
5.
Huang, D., Liu, Z., Liu, P., Chen, J.: Optimal transit fare and service frequency of a nonlinear origin-destination based fare structure. Transp. Res. Part E Logistics Transp. Rev. 96, 1–19 (2016)CrossRef
6.
Börjesson, M.: Forecasting demand for high speed rail. Transp. Res. Part A Policy Pract. 70, 81–92 (2014)CrossRef
7.
Zhu, J., Hu, L., Jiang, Y., Khattak, A.: Circulation network design for urban rail transit station using a PH(n)/PH(n)/C/C queuing network model. Eur. J. Oper. Res. 260, 1043–1068 (2017)MathSciNetCrossRef
8.
Liu, Z., Wang, S., Chen, W., Zheng, Y.: Willingness to board: A novel concept for modeling queuing up passengers. Transp. Res. Part B Methodological 90, 70–82 (2016)CrossRef
9.
Shao, C., Xia, J.C., Lin, T.G., Goulias, K.G., Chen, C.: Logistic regression models for the nearest train station choice: a comparison of captive and non-captive stations. Case Stud. Transp. Policy 3, 382–391 (2015)CrossRef
10.
Wang, S., Qu, X.: Station choice for Australian commuter rail lines: equilibrium and optimal fare design. Eur. J. Oper. Res. 258, 144–154 (2017)MathSciNetCrossRef
11.
Gong, X., Currie, G., Liu, Z., Guo, X.: A disaggregate study of urban rail transit feeder transfer penalties including weather effects. In: Transportation (2017)
12.
Puello, L.C.L.P., Geurs, K.T.: Modelling observed and unobserved factors in cycling to railway stations: application to transit-oriented-developments in the Netherlands. Eur. J. Trans. Infrastruct. Res. 15, 27–50 (2015)
13.
Liu, Z., Yan, Y., Qu, X., Zhang, Y.: Bus stop-skipping scheme with random travel time. Transp. Res. Part C Emerg. Technol. 35, 46–56 (2013)CrossRef
14.
Yan, Y., Liu, Z., Meng, Q., Jiang, Y.: Robust optimization model of bus transit network design with stochastic travel time. J. Transp. Eng. 139, 625–634 (2013)CrossRef
15.
Yan, Y., Liu, Z., Bie, Y.: Performance evaluation of bus routes using automatic vehicle location data. J. Transp. Eng. 142, 04016029 (2016)CrossRef
16.
Qu, X., Zhang, J., Wang, S.: On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications. Transp. Res. Part B Methodological 104, 256–271 (2017)CrossRef
17.
Bai, L.: Urban rail transit normal and abnormal short-term passenger flow forecasting method. J. Transp. Syst. Eng. Inf. Technol. 17, 127–135 (2016)
18.
Guo, J., Liu, X.: An Analysis of Forecast of the Passenger Flow of Urban Rail Transit, Shanxi Science & Technology (2017)
19.
20.
Zhou, J.Z., Zhang, D.Y.: Direct ridership forecast model of urban rail transit stations based on spatial weighted LS-SVM. J. Chin. Railway Soc. 36, 1–7 (2014)
21.
Zhang, Z.N., Cheng, Y., Yi-Lin, M.A., Sun, F.L.: The forecast method of urban rail transit passenger volume in the preliminary design phase. J. Transp. Eng. (2017)
22.
Kato, H., Kaneko, Y.: Choice of Travel Demand Forecast Models: Comparative Analysis in Urban Rail Route Choice (2007)
23.
Zhou, C.: Research on demand forecast of passenger transfer of rail transit and intercity railway-taking Beijing as an example. In: Presented at the International Conference on Mechatronics, Materials, Chemistry and Computer Engineering, August 2017
24.
Li, X., Liu, Y., Gao, Z., Liu, D.: Decision tree based station-level rail transit ridership forecasting. J. Urban Plann. Dev. 142, 04016011 (2016)CrossRef
25.
He, Z., Huang, J., Du, Y., Wang, B., Yu, H.: The prediction of passenger flow distribution for urban rail transit based on multi-factor model. In: IEEE International Conference on Intelligent Transportation Engineering (2016)
26.
Wang, Z.: Passenger flow prediction model of the newly constructed urban rail transit line. In: International Conference of Logistics Engineering and Management, pp. 1301–1306 (2014)
27.
Yang, R., Wu, B.: Short-term passenger flow forecast of urban rail transit based on BP neural network. In: Intelligent Control and Automation, pp. 4574–4577 (2010)
28.
Hensher, D.A., Greene, W.H.: The mixed logit model: the state of practice. Transportation 30(2), 133–176 (2003)CrossRef
29.
Li, R., Rushton, L., Jones, M.: Peak spreading forecast in urban rail transit demand. In: Presented at the Australasian Transport Research Forum (ATRF), 38th, 2016, Melbourne, Victoria, Australia November (2016)
30.
Shang, B., Zhang, X.N.: Passengers flow forecasting model of urban rail transit based on the macro-factors. Adv. Eng. Forum 6–7, 688–693 (2012)CrossRef
31.
Liu, M., Jiao, P., Sun, T.: On Short-term Forecasting Model of Passenger Flow in Urban Rail Transit, Urban Mass Transit (2015)
32.
Li, B.: Research on the computer algorithm application in urban rail transit holiday passenger flow prediction. In: International Conference on Network and Information Systems for Computers (2017)
33.
Liu, Z., Wang, S., Meng, Q.: Optimal joint distance and time toll for cordon-based congestion pricing. Transp. Res. Part B Methodological 69, 81–97 (2014)CrossRef
34.
Liu, Z., Wang, S., Meng, Q.: Toll pricing framework under logit-based stochastic user equilibrium constraints. J. Adv. Transp. 48, 1121–1137 (2014)CrossRef
35.
Liu, Z., Wang, S., Zhou, B., Cheng, Q.: Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics. Transp. Res. Part C Emerg. Technol. 79, 58–72 (2017)CrossRef
36.
Zheng, Z., Liu, Z., Liu, C., Shiwakoti, N.: Understanding public response to a congestion charge: a random-effects ordered logit approach. Transp. Res. Part A Policy Pract. 70, 117–13 (2014)CrossRef
37.
Wu, L., Yang, Y.: Research of multilevel models for demand forecast of urban rail transit. In: International Conference on Electric Technology and Civil Engineering, pp. 1444–1447 (2011)
38.
He, Z., Wang, B., Huang, J., Du, Y.: Station passenger flow forecast for urban rail transit based on station attributes. In: IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 410–414 (2015)
39.
Zhang, L., Jia, Y., Yin, X., Niu, Z.H.: The Arrival Passenger Flow Short-Term Forecasting of Urban Rail Transit Based on the Fractal Theory. Springer, Heidelberg (2014)CrossRef
40.
41.
Li, Z., Zhang, Q., Wang, L.: Flow prediction research of urban rail transit based on support vector machine. In: International Conference on Transportation Information and Safety, pp. 2276–2282 (2011)
42.
Li, J., Ye, X., Ma, J.: Forecasting method of urban rail transit ridership at station-level on the basis of back propagation neural network. In: Transportation Research Board Annual Meeting (2015)
43.
Hou, Y., Dong, H., Jia, L.: A study on the forecast method of urban rail transit. In: Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation, pp. 365–372. Springer, Heidelberg (2016)CrossRef
44.
Li, Q., Qin, Y., Wang, Z., Zhan, M., Liu, Y., Zhao, Z., Li, Z.: The research of urban rail transit sectional passenger flow prediction method. J. Intell. Learn. Syst. Appl. 5(4), 227–231 (2013)
45.
Li, J., Cheng, J.H., Shi, J.Y., Huang, F.: Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement, vol. 169, pp. 553–558 (2012)
46.
Wang, L., Zeng, Y., Chen, T.: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42, 855–863 (2015)CrossRef
47.
Li, Q., Qin, Y., Wang, Z.Y., Zhao, Z.X., Zhan, M.H., Liu, Y.: Prediction of urban rail transit sectional passenger flow based on elman neural network. Appl. Mech. Mater. 505–506, 1023–1027 (2014)
48.
Yue, X., Zheng, Y., Lin, J.: Urban rail transit passenger flow prediction based on improved WNN. In: Computer Engineering & Applications (2016)
49.
Alexandridis, A.K., Zapranis, A.D.: Wavelet neural networks: a practical guide. Neural Netw. 42, 1–27 (2013)CrossRef
50.
Zhu, G., Yang, C., Huang, D., Zhang, P.: A Combined Forecasting Model of Urban Rail Transit Peak-Hour Cross-Section Passenger Volume (2015)
51.
Li, S.W.: Passenger flow forecast algorithm for urban rail transit. Telkomnika Indonesian J. Electr. Eng. 12 (2013)
52.
Zhou, M., Qu, X., Li, X.: A recurrent neural network based microscopic car following model to predict traffic oscillation. Transp. Res. Part C 84, 245–264 (2017)CrossRef
Metadata
Title
Urban Rail Transit Demand Analysis and Prediction: A Review of Recent Studies
Authors
Zhiyan Fang
Qixiu Cheng
Ruo Jia
Zhiyuan Liu
Copyright Year
2019
Book
Intelligent Interactive Multimedia Systems and Services

Print ISBN: 978-3-319-92230-0

Electronic ISBN: 978-3-319-92231-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-92231-7_31

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