Data mining on open public transit data for transportation analytics during pre-COVID-19 era and COVID-19 era

Carson K. Leung, Yubo Chen, Siyuan Shang, Yan Wen, Connor C.J. Hryhoruk, Denis L. Levesque, Nicholas A. Braun, Nitya Seth, Prakhar Jain

Research output: Contribution to journalArticle

Abstract

© Springer Nature Switzerland AG 2021. As the urbanization of the world continues and the population of cities rise, the issue of how to effectively move all these people around the city becomes much more important. In order to use the limited space in a city most efficiently, many cities and their residents are increasingly looking towards public transportation as the solution. In this paper, we focus on the public bus system as the primary form of public transit. In particular, we examine open public transit data for the Canadian city of Winnipeg. We mine and conduct transportation analytics on data prior to the coronavirus disease 2019 (COVID-19) situation and during the COVID-19 situation. By discovering how often and when buses were reported to be too full to take on new passengers at bus stops, analysts can get an insight of which routes and destinations are the busiest. This information would help decision makers make appropriate actions (e.g., add extra bus for those busiest routines). This results in a better and more convenient transit system towards a smart city. Moreover, during the COVID-19 era, it leads to additional benefits of contributing to safer buses services and bus waiting experiences while maintaining social distancing.
Original languageEnglish
JournalAdvances in Intelligent Systems and Computing
Volume1263 AISC
DOIs
StatePublished - 1 Jan 2021

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