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Big Data Analysis for Travel Time Characterization in Public Transportation Systems

Author

Listed:
  • Sergio Nesmachnow

    (Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Renzo Massobrio

    (Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay
    Departamento de Ingeniería Informática, Universidad de Cádiz, 11519 Puerto Real, Spain
    Transport & Planning Department, Delft University of Technology, 2628 CN Delft, The Netherlands)

  • Santiago Guridi

    (Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Santiago Olmedo

    (Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay)

  • Andrei Tchernykh

    (CICESE Research Center, Ensenada 22860, Baja California, Mexico)

Abstract

In this article, we introduces a model based on big data analysis to characterize the travel times of buses in public transportation systems. Travel time is a critical factor in evaluating the accessibility of opportunities and the overall quality of service of public transportation systems. The methodology applies data analysis to compute estimations of the travel time of public transportation buses by leveraging both open-source and private information sources. The approach is evaluated for the public transportation system in Montevideo, Uruguay using information about bus stop locations, bus routes, vehicle locations, ticket sales, and timetables. The estimated travel times from the proposed methodology are compared with the scheduled timetables, and relevant indicators are computed based on the findings. The most relevant quantitative results indicate a reasonably good level of punctuality in the public transportation system. Delays were between 10.5% and 13.9% during rush hours and between 8.5% and 13.7% during non-peak hours. Delays were similarly distributed for working days and weekends. In terms of speed, the results show that the average operational speed is close to 18 km/h, with short local lines exhibiting greater variability in their speed.

Suggested Citation

  • Sergio Nesmachnow & Renzo Massobrio & Santiago Guridi & Santiago Olmedo & Andrei Tchernykh, 2023. "Big Data Analysis for Travel Time Characterization in Public Transportation Systems," Sustainability, MDPI, vol. 15(19), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14561-:d:1255199
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    References listed on IDEAS

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