Abstract
Unexpected transit service disruptions degrade the quality of service provided to the public. Bus bridging is considered a key response strategy used to handle metro service interruptions, where buses are retracted from scheduled services and deployed to offer shuttle services along disrupted segments. Most transit agencies rely on ad-hoc approaches (based on experience) to determine which buses should be dispatched from the scheduled services, with little (or no) consideration of the impacts on users’ delays. This paper presents a practical tool to estimate the total users’ delay associated with a user-specified bus bridging plan. The tool is based on deterministic queueing theory. The total delay is composed of two components; direct delays of affected metro passengers along the disrupted segment and indirect delays of bus riders on the routes from which shuttle buses are dispatched. The tool utilizes several input data, including travel times, train load information, boarding and alighting passenger counts, bus frequencies, and routes’ cycle times. It provides transit practitioners and operational managers with a valuable instrument for evaluating different bus bridging scenarios. A case study of the transit network in Toronto is used to illustrate the tool’s functionality.
Similar content being viewed by others
Notes
Shuttle buses can be initially dispatched to other non-end stations, which will be considered in a future phase of the research.
References
Bates J, Polak J, Jones P, Cook A (2001) The valuation of reliability for personal travel. Transp Res Part E Logist Transport Rev 37(2–3):191–229. https://doi.org/10.1016/S1366-5545(00)00011-9
Cats O (2016) The robustness value of public transport development plans. J Transp Geogr 51:236–246. https://doi.org/10.1016/j.jtrangeo.2016.01.011
Cats O, Jenelius E (2015) Planning for the unexpected: The value of reserve capacity for public transport network robustness. Transp Res Part A Policy Pract 81:47–61. https://doi.org/10.1016/j.tra.2015.02.013
Codina E, Marin A (2010) A design model for the bus bridging problem. Paper presented at the World Conference on Transport Research Society, Lyons, France.
Diab E, Shalaby A (2018) Subway service down again? Assessing the effects of subway service interruptions on local surface transit performance. Transp Res Record J Transp Res Board 2672:443–454. https://doi.org/10.1177/0361198118791665
Diab E, Badami M, El-Geneidy A (2015) Bus transit service reliability and improvement strategies: integrating the perspectives of passengers and transit agencies in North America. Trans Rev 23(3):292–328. https://doi.org/10.1080/01441647.2015.1005034
Diab E, Feng G, Shalaby A (2018) Breaking into emergency shuttle service: aspects and impacts of retracting buses from existing scheduled bus services. Can J Civ Eng 45:647–658. https://doi.org/10.1139/cjce-2017-0294
Itani A (2019) Bus bridging decision-support toolkit: optimization framework and policy analysis, MSc Thesis, University of Toronto. https://hdl.handle.net/1807/98101
Itani A, Aboudina A, Diab E, Srikukenthiran S, Shalaby A (2019) Managing unplanned rail disruptions: policy implications and guidelines towards an effective bus bridging strategy. Paper presented at the 98th annual meeting of the Transportation Research Board, Washington, D.C., USA.
Jin JG, Teo KM, Odoni AR (2016) Optimizing bus bridging services in response to disruptions of urban transit rail networks. Transp Sci 50(3):790–804
Kepaptsoglou K, Karlaftis M (2009) The bus bridging problem in metro operations: conceptual framework, models and algorithms. Public Transp 1(4):275–297
Lin T, Srikukenthiran S, Miller E, Shalaby A (2018) Econometric analysis of subway user mode choice in response to unplanned subway disruptions. Paper presented at the 97th annual meeting of the Transportation Research Board, Washington, D.C., USA.
Nam D, Park D, Khamkongkhun A (2005) Estimation of value of travel time reliability. J Adv Transp 39(1):39–61. https://doi.org/10.1002/atr.5670390105
National Academies of Sciences, Engineering and Medicine (2013) Transit capacity and quality of service manual, 3rd edn. The National Academies Press, Washington
Newell C (1982) Applications of queueing theory (Vol. 4). Springer Science and Business Media, Berlin
Noland R, Polak J (2002) Travel time variability: a review of theoretical and empirical issues. Transp Rev 22(1):39–54
Shelat S, Cats O (2017) Measuring spill-over effects of disruptions in public transport networks. Proceeding of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017—Proceedings 8005613, pp. 756–761. https://doi.org/10.1109/MTITS.2017.8005613
Srikukenthiran S, Shalaby A (2017) Enabling large-scale transit microsimulation for disruption response support using the Nexus platform. Public Transp 9(1):411–435. https://doi.org/10.1007/s12469-017-0158-y
TTC (2016) Daily customer service report. https://www.ttc.ca/Customer_Service/Daily_Customer_Service_Report/index.jsp. Accessed 1 Dec 2018
TTC (2016) Operating budget briefing notes. https://www.toronto.ca/legdocs/mmis/2016/ex/bgrd/backgroundfile-89230.pdf. Accessed 1 Dec 2018
TTC (2016) TTC operating statistics 2016. https://www.ttc.ca/About_the_TTC/Operating_Statistics/2016/section_one.jsp. Accessed 24 May 2018
Wang Y, Guo J, Currie G, Dong W, Pender B (2014) Bus bridging disruption in rail services with frustrated and impatient passengers. IEEE Trans Intell Transp Syst 15(5):2014–2023. https://doi.org/10.1109/TITS.2014.2307859
Wen BW, Srikukenthiran S, Shalaby A (2018) Data-driven mesoscopic simulation models of large-scale surface transit networks. Transportation Research Board 97th annual meeting (No. 18-02859).
Acknowledgements
The authors would like to acknowledge the following entities for their financial and computing support: Trapeze Group Ontario Centres of Excellence, Natural Sciences and Engineering Research Council and SOSCIP. The authors also wish to thank Junbo Liang for helping in extracting the bus service scheduled data and Islam Kamel for helping in collecting the travel time data from Google API.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Aboudina, A., Itani, A., Diab, E. et al. Evaluation of bus bridging scenarios for railway service disruption management: a users’ delay modelling tool. Public Transp 13, 457–481 (2021). https://doi.org/10.1007/s12469-020-00238-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12469-020-00238-w