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Enabling large-scale transit microsimulation for disruption response support using the Nexus platform

Author

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  • Siva Srikukenthiran

    (University of Toronto)

  • Amer Shalaby

    (University of Toronto)

Abstract

In many large cities, public transit systems have been carrying an ever-increasing burden of commuters. In such systems, service disruptions can negatively impact system performance and transit users well after they are resolved. Currently, transit agencies handle these disruption episodes in an ad-hoc fashion, largely due to the lack of adequate analytical tools to aid in analyzing and selecting appropriate response strategies. This paper presents a proof-of-concept case study of the Greater Toronto transit network using Nexus, a new crowd dynamics and transit network simulation platform. Nexus enables detailed simulation of all transit system actors using a novel method of linking together established simulators of surface transit, fully separated rail transit, and stations. Transit users, as agents in the model, move between the different simulators and have their routes determined by an external dynamic routing module. The case study focuses on interfacing Nexus with a commercial pedestrian simulator, MassMotion, to allow for detailed crowd simulation at key stations, and illustrating how the platform could be used for disruption management. To this end, the impact of disruptions of various lengths was analyzed, and a simple response strategy was implemented to provide an example of how the system could be used to test mitigating strategies.

Suggested Citation

  • Siva Srikukenthiran & Amer Shalaby, 2017. "Enabling large-scale transit microsimulation for disruption response support using the Nexus platform," Public Transport, Springer, vol. 9(1), pages 411-435, July.
    • Handle: RePEc:spr:pubtra:v:9:y:2017:i:1:d:10.1007_s12469-017-0158-y
    DOI: 10.1007/s12469-017-0158-y
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    References listed on IDEAS

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    1. Lee, Keumsook & Jung, Woo-Sung & Park, Jong Soo & Choi, M.Y., 2008. "Statistical analysis of the Metropolitan Seoul Subway System: Network structure and passenger flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6231-6234.
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    3. Mohamed Wahba & Amer Shalaby, 2011. "Large-scale application of MILATRAS: case study of the Toronto transit network," Transportation, Springer, vol. 38(6), pages 889-908, November.
    4. Anthony Chen & Chao Yang & Sirisak Kongsomsaksakul & Ming Lee, 2007. "Network-based Accessibility Measures for Vulnerability Analysis of Degradable Transportation Networks," Networks and Spatial Economics, Springer, vol. 7(3), pages 241-256, September.
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    Cited by:

    1. Aya Aboudina & Alaa Itani & Ehab Diab & Siva Srikukenthiran & Amer Shalaby, 2021. "Evaluation of bus bridging scenarios for railway service disruption management: a users’ delay modelling tool," Public Transport, Springer, vol. 13(3), pages 457-481, October.
    2. Ansarilari, Zahra & Bodur, Merve & Shalaby, Amer, 2024. "A novel model for transfer synchronization in transit networks and a Lagrangian-based heuristic solution method," European Journal of Operational Research, Elsevier, vol. 317(1), pages 76-91.
    3. Wen Hua & Ghim Ping Ong, 2018. "Effect of information contagion during train service disruption for an integrated rail-bus transit system," Public Transport, Springer, vol. 10(3), pages 571-594, December.
    4. Mahmood Mahmoodi Nesheli & Siva Srikukenthiran & Amer Shalaby, 2022. "An optimization model for planning limited-stop transit operations," Public Transport, Springer, vol. 14(1), pages 63-83, March.

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