Synchronization of public passenger transport subsystems using stochastic simulation: the case of lines with longer headway times

  • Josef Bulíček ,
  • Pavel Drdla ,
  • Jaroslav Matuška 
  • a,b,c University of Pardubice, Faculty of Transport Engineering, Studentská 95, Pardubice, 
    CZ-532 10, Czech Republic
Cite as
Bulíček J., Drdla P., Matuška J. (2020). Synchronization of public passenger transport subsystems using stochastic simulation: the case of lines with longer headway times. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 248-253. DOI: https://doi.org/10.46354/i3m.2020.emss.035

Abstract

This paper is focused on timetable synchronization of urban, regional and long-distance public passenger transport subsystems in stochastic conditions. Proposed method is based on a mesoscopic simulation model. Model is applied within a process (method) leading to a robust timetable in the point of view of passengers changing e.g. from trains to buses of urban public transport. Solution is focused on specific condition – the case of relative long time headways between individual services of public transport like 30, 60 or 120 minutes, so that possible time loss can be serious. There are a number of such cases in reality. It is typical for small and medium sized cities or municipalities. Some lines with similar headway time can be found in large cities as well. Solution is illustrated by the case study focused on interchange node located in Žďár nad Sázavou (Czech Republic). Modified approach to generating of train input delays allowing to take overall operational situation on railway infrastructure into account (e.g. influence of construction works) is presented as well. This is compared to the way based on generation of delays in an individual way for individual trains.

References

  1. Abdolmaleki, M., Masoud, N., and Yin, Y. (2019). Transit timetable synchronization for transfer time minimization. Transportation Research Part B, 131 (2020) 143-159.
  2. Altazin, E., Dauzère-Pérès, S., Ramond, F., and Tréfond, S. A multi-objective optimization simulation approach for real time rescheduling in dense railway systems. European Journal of
    Operation Research, 286 (2020) 662-672.
  3. Blanco, V., Conde, E., Hinojosa, Y., and Puerto, J. (2020). An optimization model for line planning and timetabling in automated metro subway networks. A case study. Omega, 92 April 2020, 102165.
  4. Bulíček, J. (2015). Integrace systémů osobní dopravy – technologie provozu přestupních uzlů. (Integration of passenger transport systems – Technology of operation of interchange nodes). Thesis (in Czech). University of Pardubice, 2015.
  5. Bulíček, J. (2018). Timetable synchronization: Urban public transport in busy hubs of long-distance transport. MATEC Web of Conferences, 239: article number 02001.
  6. Ceder, A., Golany, B., and Tal, O. (2001). Transportation Research Part A, 35 (2001) 913-928.
  7. Hernandez, S., and Monzon, A. (2016). Key factors for defining an efficient urban transport interchange: Users' perceptions. Cities, 50, 158-167
  8. Högdahl, J., Bohlin, N., and Fröidh, O. (2019). A combined simulation-optimization approach for minimizing travel time and delays in railway timetables. Transportation Research Part B:
    Methodological, 126 (2019), 192-212.
  9. Chen, Y., Mao, B., Bai, Y., Ho, T.K., and Li, Z. (2019). Timetable synchronization of last trains for urban rail network with maximum accessibility. Transportation Research Part C, 99 (2019) 110-129.
  10. IDOS (2020). Online timetable information system IDOS. portal.idos.cz
  11. Kleprlík, J., and Matuška, J. (2017). The demand for public transport and modelling decision-making process of passengers. Transport Means 2017 (21), 197-202.
  12. Monzon, A., Alonzo, A., and Lopez-Lambas, M. (2017). Joint analysis of intermodal long distance-last mile trips using urban interchanges in EU cities. Transportation Research Procedia; 27 (2017) 1074-1079.
  13. Naumov, V. (2020). Genetic-based algorithm of the public transport lines synchronization in a transfer node. Transportation Research Procedia, 47 (2020) 315-322.
  14. Oostendorp, R., and Gebhardt, L. (2018). Combining means of transport as a users' strategy to optimize traveling in an urban context: empirical results on intermodal travel behavior from a survey in Berlin. Journal of Transport Geography, 71, 72-83.
  15. Popovic, Z., Puzavac, L., and Lazarevic, L. (2012). Improving the accessibility of passenger railways in the Republic of Serbia. RTR-Railway Technical Review-English Edition, (2), 25.
  16. Sever, D., Lutar, R., and Toplak, S. (2018). Assessment of Possibilities of On-Line Response Dynamic Traffic Management System Development in Medium Size Urban Areas. Tehnički vjesnik, 25, no. 5 (2018) 1478-1484.
  17. Široký, J., Šrámek, P., Magdechová, K., Tischer, E., and Hlavsová, P. (2019). Timetable performance evaluation. In Transport Means: proceedings of the international scientific conference. Kaunas: Kaunas University of Technology (2019) 1427-1432.
  18. Wang, D. (2019, August). Research on Optimization of Passenger Flow Organization in Passenger Transport Stations during Peak Period. In 1 st International Symposium on Innovation and Education, Law and Social Sciences (IELSS 2019). Atlantis Press.
  19. Xue, Q., Yang, X., Wu, J., Sun, H., Yin, H., and Qu, Y. (2019). Urban Rail Timetable Optimization to
    Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming
    Model. Sustainability, 2019, 11(13), 3701.
  20. Yizhen, W., Dewei, L., and Zhichao, C. (2020). Integrated timetable synchronization optimization
    with capacity constraint under time-dependent demand for a rail transit network. Computers &
    Industrial Engineering, 142 (2020) 106374.