Modeling Demand for Passenger Transfers in the Bounds of Public Transport Network

  • Vitalii NaumovEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 879)


The proposed model is based on the developed approach to perform the routing assignment stage in the classical four-stage urban planning procedure. Demand for trips is generated for each stop of a public transport system on the grounds of stochastic variable of the time interval between passengers arrival to the respective stop. After defining of the destination stop, the route for the passenger’s trip is determined with the use of Dijkstra’s algorithm within the frame of a public transport network which is presented as a graph model with stops for vertices and route segments for edges. Transfer nodes are defined in the model as such graph vertices which are common for at least two lines of the public transport system. The author presents a class library implemented with the use of the Python programming language. On the basis of this library, the model for simulations of demand for transfers within the given public transport system was developed. The proposed approach to the demand modeling and the developed software were used for simulations of demand for transfers within the bounds of the public transport system of Bochnia (Poland).


Public transportation Transfer demand Trip simulations 


  1. 1.
    Bruno, G., Improta, G., Sgalambro, A.: Models for the schedule optimisation problem at a public transit terminal. OR Spectr. 31(3), 465–481 (2009)CrossRefGoogle Scholar
  2. 2.
    Wu, Y., Yang, H., Tang, J., Yu, Y.: Multi-objective re-synchronizing of bus timetable: model, complexity and solution. Transp. Res. Part C 67, 149–168 (2016)CrossRefGoogle Scholar
  3. 3.
    Wihartiko, F.D., Buono, A., Silalahi, B.P.: Integer programming model for optimizing bus timetable using genetic algorithm. IOP Conf. Ser. Mater. Sci. Eng. 166, 012016 (2017)CrossRefGoogle Scholar
  4. 4.
    Liu, T., Ceder, A.: Synchronisation of public transport timetabling with multiple vehicle types. Transp. Res. Rec. 2539, 84–93 (2016)CrossRefGoogle Scholar
  5. 5.
    Ibarra-Rojas, O.J., López-Irarragorri, F., Rios-Solis, Y.A.: Multiperiod bus timetabling. Transp. Sci. 50(3), 805–822 (2016)CrossRefGoogle Scholar
  6. 6.
    Shen, Y., Wang, S.: An adaptive differential evolution approach for the maximal synchronisation problem of feeder buses to metro. J. Comput. Theor. Nanosci. 13(6), 3548–3555 (2016)CrossRefGoogle Scholar
  7. 7.
    Teodorović, D., Lučić, P.: Schedule synchronisation in public transit using the fuzzy ant system. Transp. Plan. Technol. 28(1), 47–76 (2005)CrossRefGoogle Scholar
  8. 8.
    Hassannayebi, E., Zegordi, S.H., Yaghini, M., Amin-Naseri, M.R.: Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals. Transp. Plan. Technol. 40(3), 278–304 (2017)CrossRefGoogle Scholar
  9. 9.
    Frei, C., Hyland, M., Mahmassani, H.S.: Flexing service schedules: assessing the potential for demand-adaptive hybrid transit via a stated preference approach. Transp. Res. Part C Emerg. Technol. 76, 71–89 (2017)CrossRefGoogle Scholar
  10. 10.
    Sirmatel, I.I., Geroliminis, N.: Dynamical modeling and predictive control of bus transport systems: a hybrid systems approach. IFAC-PapersOnLine 50(1), 7499–7504 (2017)CrossRefGoogle Scholar
  11. 11.
    Ronald, N., Thompson, R., Winter, S.: Simulating ad-hoc demand-responsive transportation: a comparison of three approaches. Transp. Plan. Technol. 40(3), 340–358 (2017)CrossRefGoogle Scholar
  12. 12.
    Kujala, R., Weckström, C., Mladenović, M.N., Saramäki, J.: Travel times and transfers in public transport: comprehensive accessibility analysis based on Pareto-optimal journeys. Comput. Environ. Urban Syst. 67, 41–54 (2018)CrossRefGoogle Scholar
  13. 13.
    Naumov, V.: Optimizing the number of vehicles for a public bus line on the grounds of computer simulations. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, pp. 176–181 (2017)Google Scholar
  14. 14.
    Class library for simulations of technological processes in a public transport network. Accessed 15 Feb 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Cracow University of TechnologyKrakowPoland

Personalised recommendations