Smart Mobility for Public Transportation Systems: Improved Bus Timetabling for Synchronizing Transfers

Risso, Claudio; Nesmachnow, Sergio; Rossit, Diego Gabriel

doi:10.1007/978-3-031-28454-0_11

Cited by 2 publications

(2 citation statements)

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“…To improve the data analysis process, future work could consider data from different periods and process larger amounts of historical data to identify patterns. The application of estimated travel times and metrics presents several areas for future work, such as synchronization of timetables and transfers [33,46,47], bus network analysis and redesign [48,49], developing plans for sustainable mobility plans [50,51], multi-objective optimization of transportation processes [52,53], public transportation and health issues [54], and computing accessibility indicators for opportunities in the city using public transportation [55]. Overall, our future work around the proposed model will aim to enhance its usefulness in solving relevant issues in public transportation while improving its accuracy and efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…These specific thresholds (POSITIVE_SIGNIFICANT_BUFFER and NEGATIVE_SIGNIFICANT_BUFFER, respectively) are defined as the parameters for the calculation algorithm. The selected values are derived from the analysis of mean wait times for passengers [33,46]. The values state that wait time limits that are considered unacceptable if a public transportation system is to operate efficiently.…”

Section: On-time Arrival Ratementioning

confidence: 99%

See 1 more Smart Citation

Big Data Analysis for Travel Time Characterization in Public Transportation Systems

Nesmachnow,

Massobrio,

Guridi

et al. 2023

Sustainability

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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.

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