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Smart city article details
| Title | A Big Data Science And Engineering Solution For Transit Performance Analytics |
|---|---|
| ID_Doc | 456 |
| Authors | Ngoc Pham N.M.; Wu Y.; Leung C.K.; Munshi M.V.; Patel V.K.; Hryhoruk C.C.J. |
| Year | 2023 |
| Published | Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023 |
| DOI | http://dx.doi.org/10.1109/TrustCom60117.2023.00324 |
| Abstract | Public transit transportation plays a crucial role in the daily lives of many individuals, offering an affordable and convenient means of commuting to work, school, and various destinations. For instance, it serves as a vital mode of transportation to access their workplace, educational institutions or other activities. Any disruptions in bus schedules can lead to significant consequences, including missing meetings and other essential commitments for city residents. Thus, this paper presents a big data science and engineering solution for transit performance analytics in the area of transportation analysis. The insights derived from this data analysis are instrumental in enhancing the performance of public transportation, ultimately leading to an improved commuter experience in the city and contributing to the development of a smart city. To elaborate, our solution employs frequent pattern mining to identify variations in transit performance across different neighborhoods. By uncovering significant patterns, we establish connections that help us pinpoint the factors contributing to bus delays in specific areas. Improving the accuracy of bus arrival and departure times can significantly enhance the overall usability and appeal of public transit for commuters, as people are more likely to rely on buses when they are punctual and ensure timely arrivals at their destinations. Furthermore, our solution equips users with tools to visualize the insights gained from the analysis of bus departure times in various neighborhoods at different times of the day. The practicality of our big data science and engineering solution was demonstrated through an evaluation using real-life public transit data from a Canadian city, underscoring its potential to contribute to the development of a smart city. © 2023 IEEE. |
| Author Keywords | Association rule mining; Big data engineering; Big data science; Bus; Data mining; Frequent pattern mining; Neighborhoods; Public transit; Relational database; Transit performance analysis; Transportation analytics |
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