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Average Speed of Public Transport Vehicles Based on Smartcard Data

Average Speed of Public Transport Vehicles Based on Smartcard Data

Vera Costa, José Luís Borges, Teresa Galvão Dias
Copyright: © 2020 |Pages: 22
ISBN13: 9781799821120|ISBN10: 1799821129|ISBN13 Softcover: 9781799821137|EISBN13: 9781799821144
DOI: 10.4018/978-1-7998-2112-0.ch007
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MLA

Costa, Vera, et al. "Average Speed of Public Transport Vehicles Based on Smartcard Data." Smart Systems Design, Applications, and Challenges, edited by João M.F. Rodrigues, et al., IGI Global, 2020, pp. 123-144. https://doi.org/10.4018/978-1-7998-2112-0.ch007

APA

Costa, V., Borges, J. L., & Dias, T. G. (2020). Average Speed of Public Transport Vehicles Based on Smartcard Data. In J. Rodrigues, P. Cardoso, J. Monteiro, & C. Ramos (Eds.), Smart Systems Design, Applications, and Challenges (pp. 123-144). IGI Global. https://doi.org/10.4018/978-1-7998-2112-0.ch007

Chicago

Costa, Vera, José Luís Borges, and Teresa Galvão Dias. "Average Speed of Public Transport Vehicles Based on Smartcard Data." In Smart Systems Design, Applications, and Challenges, edited by João M.F. Rodrigues, et al., 123-144. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2112-0.ch007

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Abstract

In public transport, traveler dissatisfaction is widespread, due to long waits and travel time, or the low frequency of the service provided. Public transport providers are increasingly concerned about improving the service provided. To improve public transport, detailed knowledge of the network and its weaknesses is necessary. An easy and cheap way to achieve this information is to extract knowledge from the data daily collected in a public transport network. Thus, this chapter focuses on data analysis resulting from the smartcard-based ticketing system. The main objective is to detect patterns of average speed for all days of the week and times of the day, along with pairs of consecutive stops. To perform the analyses, the average speed was deduced from ticketing data, and clustering methods were applied. The results show that it is possible to find segments with similar patterns and identify days and times with similar patterns.

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