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

  • Zhiyan Fang
  • Qixiu ChengEmail author
  • Ruo Jia
  • Zhiyuan Liu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)


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.



This study is supported by the General Projects (No. 71771050) and Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603).


  1. 1.
    Milenkovic, M., Nebojsa, B.: Railway Demand Forecasting (2016)Google Scholar
  2. 2.
    Los vehículos, aeronaves. Urban Rail Transit. Betascript Publishing, London (2011)Google Scholar
  3. 3.
    Xiao, J.: Demand Forecasting Method of Inter-city Rail Transit, Urban Mass Transit (2006)Google Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Börjesson, M.: Forecasting demand for high speed rail. Transp. Res. Part A Policy Pract. 70, 81–92 (2014)CrossRefGoogle Scholar
  7. 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)MathSciNetCrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)MathSciNetCrossRefGoogle Scholar
  11. 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)Google Scholar
  12. 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)Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 15.
    Yan, Y., Liu, Z., Bie, Y.: Performance evaluation of bus routes using automatic vehicle location data. J. Transp. Eng. 142, 04016029 (2016)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)Google Scholar
  18. 18.
    Guo, J., Liu, X.: An Analysis of Forecast of the Passenger Flow of Urban Rail Transit, Shanxi Science & Technology (2017)Google Scholar
  19. 19.
    Baidubaike. Four-Stage Model. Accessed Jan 2018
  20. 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)Google Scholar
  21. 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)Google Scholar
  22. 22.
    Kato, H., Kaneko, Y.: Choice of Travel Demand Forecast Models: Comparative Analysis in Urban Rail Route Choice (2007)Google Scholar
  23. 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 2017Google Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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)Google Scholar
  26. 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)Google Scholar
  27. 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)Google Scholar
  28. 28.
    Hensher, D.A., Greene, W.H.: The mixed logit model: the state of practice. Transportation 30(2), 133–176 (2003)CrossRefGoogle Scholar
  29. 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)Google Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Liu, M., Jiao, P., Sun, T.: On Short-term Forecasting Model of Passenger Flow in Urban Rail Transit, Urban Mass Transit (2015)Google Scholar
  32. 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)Google Scholar
  33. 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)CrossRefGoogle Scholar
  34. 34.
    Liu, Z., Wang, S., Meng, Q.: Toll pricing framework under logit-based stochastic user equilibrium constraints. J. Adv. Transp. 48, 1121–1137 (2014)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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)CrossRefGoogle Scholar
  37. 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)Google Scholar
  38. 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)Google Scholar
  39. 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)CrossRefGoogle Scholar
  40. 40.
    Wikipedia. Artificial Neural Network. Accessed Jan 2018
  41. 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)Google Scholar
  42. 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)Google Scholar
  43. 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)CrossRefGoogle Scholar
  44. 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)Google Scholar
  45. 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)Google Scholar
  46. 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)CrossRefGoogle Scholar
  47. 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)Google Scholar
  48. 48.
    Yue, X., Zheng, Y., Lin, J.: Urban rail transit passenger flow prediction based on improved WNN. In: Computer Engineering & Applications (2016)Google Scholar
  49. 49.
    Alexandridis, A.K., Zapranis, A.D.: Wavelet neural networks: a practical guide. Neural Netw. 42, 1–27 (2013)CrossRefGoogle Scholar
  50. 50.
    Zhu, G., Yang, C., Huang, D., Zhang, P.: A Combined Forecasting Model of Urban Rail Transit Peak-Hour Cross-Section Passenger Volume (2015)Google Scholar
  51. 51.
    Li, S.W.: Passenger flow forecast algorithm for urban rail transit. Telkomnika Indonesian J. Electr. Eng. 12 (2013)Google Scholar
  52. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Zhiyan Fang
    • 1
  • Qixiu Cheng
    • 1
    Email author
  • Ruo Jia
    • 1
  • Zhiyuan Liu
    • 1
  1. 1.Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of TransportationSoutheast UniversityNanjingChina

Personalised recommendations