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Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas

Author

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  • Cristina Pronello

    (Département Génie des Systèmes Urbains (GSU) & EA 7284 AVENUES, Sorbonne Universités—Université de Technologie de Compiègne, 60200 Compiègne, France
    Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio, Politecnico di Torino, 10125 Torino, Italy)

  • Davide Longhi

    (Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio, Politecnico di Torino, 10125 Torino, Italy)

  • Jean-Baptiste Gaborieau

    (Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio, Politecnico di Torino, 10125 Torino, Italy)

Abstract

This paper aims to define an algorithm capable of building the origin-destination matrix from check-in data collected in the extra-urban area of Torino, Italy, where thousands of people commute every day, using smart cards to validate their travel documents while boarding. To this end, the methodological approach relied on a survey over three months to record smart-card validations. Peak and off-peak periods have been defined according to validation frequency. Then, the origin-destination matrix has been estimated using the time interval between two validations to outline the different legs of the journey. Finally, transport demand has been matched with existing bus services, showing which areas were not adequately covered by public transport. The results of this research could assist public transport operators and local authorities in the design of a more suitable transport supply and mobility services in accordance with user needs. Indeed, tailoring public transport to user needs attracts both more customers and latent demand, reducing reliance on cars and making transport more sustainable.

Suggested Citation

  • Cristina Pronello & Davide Longhi & Jean-Baptiste Gaborieau, 2018. "Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3489-:d:172746
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    References listed on IDEAS

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    1. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    2. Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
    3. Gschwender, Antonio & Munizaga, Marcela & Simonetti, Carolina, 2016. "Using smart card and GPS data for policy and planning: The case of Transantiago," Research in Transportation Economics, Elsevier, vol. 59(C), pages 242-249.
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    1. Jadrić Mario & Pašalić Ivana Ninčević & Ćukušić Maja, 2020. "Process Mining Contributions to Discrete-event Simulation Modelling," Business Systems Research, Sciendo, vol. 11(2), pages 51-72, October.

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