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Why Does Public Transport Not Arrive on Time? The Pervasiveness of Equal Headway Instability

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  • Carlos Gershenson
  • Luis A Pineda

Abstract

Background: The equal headway instability phenomenon is pervasive in public transport systems. This instability is characterized by an aggregation of vehicles that causes inefficient service. While equal headway instability is common, it has not been studied independently of a particular scenario. However, the phenomenon is apparent in many transport systems and can be modeled and rectified in abstraction. Methodology: We present a multi-agent simulation where a default method with no restrictions always leads to unstable headways. We discuss two methods that attempt to achieve equal headways, called minimum and maximum. Since one parameter of the methods depends on the passenger density, adaptive versions—where the relevant parameter is adjusted automatically—are also put forward. Our results show that the adaptive maximum method improves significantly over the default method. The model and simulation give insights of the interplay between transport design and passenger behavior. Finally, we provide technological and social suggestions for engineers and passengers to help achieve equal headways and thus reduce delays. Conclusions: The equal headway instability phenomenon can be avoided with the suggested technological and social measures.

Suggested Citation

  • Carlos Gershenson & Luis A Pineda, 2009. "Why Does Public Transport Not Arrive on Time? The Pervasiveness of Equal Headway Instability," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-15, October.
  • Handle: RePEc:plo:pone00:0007292
    DOI: 10.1371/journal.pone.0007292
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    References listed on IDEAS

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    1. Nagatani, Takashi, 2004. "Dynamical transitions in peak elevator traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 441-452.
    2. Newell, G. F., 1998. "Strategies for Serving Peak Elevator Traffic," Transportation Research Part B: Methodological, Elsevier, vol. 32(8), pages 583-588, November.
    3. Nagatani, Takashi, 2003. "Complex behavior of elevators in peak traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 326(3), pages 556-566.
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    Cited by:

    1. Seyed Mohammad Hossein Moosavi & Amiruddin Ismail & Choon Wah Yuen, 2020. "Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-26, May.
    2. Dailisan, Damian N. & Lim, May T., 2020. "Crossover transitions in a bus–car mixed-traffic cellular automata model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    4. Vee-Liem Saw & Lock Yue Chew, 2020. "No-boarding buses: Synchronisation for efficiency," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-34, March.
    5. Andres, Matthias & Nair, Rahul, 2017. "A predictive-control framework to address bus bunching," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 123-148.
    6. Ramli, Muhamad Azfar & Jayaraman, Vasundhara & Kwek, Hyen Chee & Tan, Kian Heong & Lee Kee Khoon, Gary & Monterola, Christopher, 2018. "Improved estimation of commuter waiting times using headway and commuter boarding information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 217-226.
    7. Alejandro Sánchez-Atondo & Leonel García & Julio Calderón-Ramírez & José Manuel Gutiérrez-Moreno & Alejandro Mungaray-Moctezuma, 2020. "Understanding Public Transport Ridership in Developing Countries to Promote Sustainable Urban Mobility: A Case Study of Mexicali, Mexico," Sustainability, MDPI, vol. 12(8), pages 1-21, April.

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