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Article

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

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Group Biometry, Biosignals, Security, and Smart Mobility, Departamento de Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain
2
Grupo de Investigación en Planificación del Transporte, Transport Research Centre (TRANSyT), Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4315; https://doi.org/10.3390/s20154315
Received: 9 July 2020 / Revised: 29 July 2020 / Accepted: 30 July 2020 / Published: 2 August 2020
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement. View Full-Text
Keywords: bicycle sharing systems; public transport systems; data-driven classification of trips; BSS underlying network; trip index bicycle sharing systems; public transport systems; data-driven classification of trips; BSS underlying network; trip index
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MDPI and ACS Style

Wilby, M.R.; Vinagre Díaz, J.J.; Fernández Pozo, R.; Rodríguez González, A.B.; Vassallo, J.M.; Sánchez Ávila, C. Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification. Sensors 2020, 20, 4315. https://doi.org/10.3390/s20154315

AMA Style

Wilby MR, Vinagre Díaz JJ, Fernández Pozo R, Rodríguez González AB, Vassallo JM, Sánchez Ávila C. Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification. Sensors. 2020; 20(15):4315. https://doi.org/10.3390/s20154315

Chicago/Turabian Style

Wilby, Mark R., Juan J. Vinagre Díaz, Rubén Fernández Pozo, Ana B. Rodríguez González, José M. Vassallo, and Carmen Sánchez Ávila. 2020. "Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification" Sensors 20, no. 15: 4315. https://doi.org/10.3390/s20154315

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