Publication
Title
Big data analysis for travel time characterization in public transportation systems
Author
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.
Language
English
Source (journal)
Sustainability
Publication
2023
ISSN
2071-1050
DOI
10.3390/SU151914561
Volume/pages
15 :19 (2023)
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Record
Identifier
Creation 26.03.2024
Last edited 26.03.2024
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