Document Open Access Logo

Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport

Authors Julian Patzner, Ralf Rückert , Matthias Müller-Hannemann

PDF
Thumbnail PDF

File

OASIcs.ATMOS.2022.13.pdf
  • Filesize: 0.93 MB
  • 18 pages

Document Identifiers

Author Details

Julian Patzner
  • Martin-Luther-Universität Halle-Wittenberg, Germany
Ralf Rückert
  • Martin-Luther-Universität Halle-Wittenberg, Germany
Matthias Müller-Hannemann
  • Martin-Luther-Universität Halle-Wittenberg, Germany

Funding

This work has been partially supported by DFG under grant MU 1482/7-2.

Cite As Get BibTex

Julian Patzner, Ralf Rückert, and Matthias Müller-Hannemann. Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport. In 22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022). Open Access Series in Informatics (OASIcs), Volume 106, pp. 13:1-13:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/OASIcs.ATMOS.2022.13

Abstract

Delays and disruptions are commonplace in public transportation. An important tool to limit the impact of severely delayed vehicles is the use of short turns, where a planned trip is shortened in order to be able to resume the following trip in the opposite direction as close to the schedule as possible. Short turns have different effects on passengers: some suffer additional delays and have to reschedule their route, while others can benefit from them. Dispatchers therefore need decision support in order to use short turns only if the overall delay of all affected passengers is positively influenced. In this paper, we study the planning of short turns based on passenger flows. We propose a simulation framework which can be used to decide upon single short turns in real time. An experimental study with a scientific model (LinTim) of an entire public transport system for the German city of Stuttgart including busses, trams, and local trains shows that we can solve these problems on average within few milliseconds. Based on features of the current delay scenario and the passenger flow we use machine learning to classify cases where short turns are beneficial. Depending on how many features are used, we reach a correct classification rate of more than 93% (full feature set) and 90% (partial feature set) using random forests. Since precise passenger flows are often not available in urban public transportation, our machine learning approach has the great advantage of working with significantly less detailed passenger information.

Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
Keywords
  • Public Transportation
  • Delays
  • Real-time Dispatching
  • Passenger Flows

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. C. E. Cortés, S. Jara-Díaz, and A. Tirachini. Integrating short turning and deadheading in the optimization of transit services. Transportation Research Part A: Policy and Practice, 45(5):419-434, 2011. URL: https://doi.org/10.1016/j.tra.2011.02.002.
  2. J. Dibbelt, T. Pajor, B. Strasser, and D. Wagner. Connection scan algorithm. Journal of Experimental Algorithmics, 23, March 2017. URL: https://doi.org/10.1145/3274661.
  3. Collection of open source public transport networks by DFG Research Unit "FOR 2083: Integrated Planning For Public Transportation", 2018. URL: https://github.com/FOR2083/PublicTransportNetworks.
  4. L. Ge, S. Voß, and L. Xie. Robustness and disturbances in public transport. Public Transport, 2022. URL: https://doi.org/10.1007/s12469-022-00301-8.
  5. K. Gkiotsalitis, O. Cats, and T. Liu. A review of public transport transfer synchronisation at the real-time control phase. Transport Reviews, 2022. URL: https://doi.org/10.1080/01441647.2022.2035014.
  6. M. J. Kang, S. Ataeian, and S. M. Mahdi Amiripour. A procedure for public transit OD matrix generation using smart card transaction data. Public Transport, 13:81-100, 2021. URL: https://doi.org/10.1007/s12469-020-00257-7.
  7. M. Lemnian, M. Müller-Hannemann, and R. Rückert. Sensitivity analysis and coupled decisions in passenger flow-based train dispatching. In M. Goerigk and R. Werneck, editors, 16th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2016), volume 54 of OpenAccess Series in Informatics (OASIcs), pages 2:1-2:15, Dagstuhl, Germany, 2016. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. URL: https://doi.org/10.4230/OASIcs.ATMOS.2016.2.
  8. C. Liebchen, S. Dutsch, S. Jin, N. Tomii, and Y. Wang. The ring never relieves – response rules for metro circle lines. Journal of Rail Transport Planning & Management, 23:100331, 2022. URL: https://doi.org/10.1016/j.jrtpm.2022.100331.
  9. L. Liu and R.-C. Chen. A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies, 84:74-91, 2017. URL: https://doi.org/10.1016/j.trc.2017.08.001.
  10. R. Liu, H. Yu, P. Wang, and H. Yan. A short-turn dispatching strategy to improve the reliability of bus operation. Journal of Advanced Transportation, Article ID 5947802, 2020. URL: https://doi.org/10.1155/2020/5947802.
  11. D. Luo, D. Zhao, Q. Ke, X. You, L. Liu, D. Zhang, H. Ma, and X. Zuo. Fine-grained service-level passenger flow prediction for bus transit systems based on multitask deep learning. Trans. Intell. Transport. Sys., 22(11):7184-7199, 2021. URL: https://doi.org/10.1109/TITS.2020.3002772.
  12. S. Moon, S.-H. Cho, and D.-K. Kim. Designing multiple short-turn routes to mitigate the crowding on a bus network. Transportation Research Record, 2675(11):23-33, 2021. URL: https://doi.org/10.1177/03611981211003899.
  13. M. Müller-Hannemann, R. Rückert, and S. S. Schmidt. Vehicle capacity-aware rerouting of passengers in delay management. In V. Cacchiani and A. Marchetti-Spaccamela, editors, 19th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2019), volume 75 of OpenAccess Series in Informatics (OASIcs), pages 7:1-7:14, Dagstuhl, Germany, 2019. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. URL: https://doi.org/10.4230/OASIcs.ATMOS.2019.7.
  14. M. Müller-Hannemann and R. Rückert. Dynamic event-activity networks in public transportation - timetable information and delay management. Datenbank-Spektrum, 17:131-137, 2017. URL: https://doi.org/10.1007/s13222-017-0252-y.
  15. N. Nagaraj, H. L. Gururaj, B. H. Swathi, and Y.-C. Hu. Passenger flow prediction in bus transportation system using deep learning. Multimedia Tools Appl., 81(9):12519-12542, 2022. URL: https://doi.org/10.1007/s11042-022-12306-3.
  16. M. M. Nesheli, A. Ceder, and T. Liu. A robust, tactic-based, real-time framework for public- transport transfer synchronization. Transportation Research Procedia, 9:246-268, 2015. Papers selected for Poster Sessions at the 21st International Symposium on Transportation and Traffic Theory Kobe, Japan, 5-7 August, 2015. URL: https://doi.org/10.1016/j.trpro.2015.07.014.
  17. T. Oliphant. Numpy - the fundamental package for scientific computing with python, 2016. URL: https://numpy.org/.
  18. J. Parbo, O. Nielsen, and C. Prato. Passenger perspectives in railway timetabling: a literature review. Transport Reviews, 36(4):500-526, 2016. Google Scholar
  19. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011. Google Scholar
  20. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Classifier comparison, 2022. URL: https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html.
  21. R. Rückert, M. Lemnian, C. Blendinger, S. Rechner, and M. Müller-Hannemann. PANDA: a software tool for improved train dispatching with focus on passenger flows. Public Transport, 9(1):307-324, 2017. Google Scholar
  22. A. Schiewe, S. Albert, P. Schiewe, A. Schöbel, and F. Spühler. LinTim - Integrated Optimization in Public Transportation. Homepage. https://lintim.net, 2020.
  23. A. Schiewe, S. Albert, P. Schiewe, A. Schöbel, and F. Spühler. LinTim: An integrated environment for mathematical public transport optimization. Documentation for version 2020.12. Technical report, TU Kaiserslautern, 2020. URL: https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-62025.
  24. P. Serafini and W. Ukovich. A mathematical model for periodic scheduling problems. SIAM Journal on Discrete Mathematic, 2:550-581, 1989. Google Scholar
  25. S. Shen and N.H.M. Wilson. An Optimal Integrated Real-time Disruption Control Model for Rail Transit Systems, pages 335-363. Springer Berlin Heidelberg, Berlin, Heidelberg, 2001. URL: https://doi.org/10.1007/978-3-642-56423-9_19.
  26. Y. Wang, M. Zhang, S. Su, T. Tang, B. Ning, and L. Chen. An operation level based train regulation model for a metro line. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pages 2920-2925, 2019. URL: https://doi.org/10.1109/ITSC.2019.8916956.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact