Big Data Analysis for Travel Time Characterization in Public Transportation Systems

Sergio Nesmachnow (), Renzo Massobrio (), Santiago Guridi, Santiago Olmedo and Andrei Tchernykh
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Sergio Nesmachnow: Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay
Renzo Massobrio: Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay
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

Sustainability, 2023, vol. 15, issue 19, 1-26

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.

Keywords: intelligent transportation systems; travel time characterization; public transportation; urban data analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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