IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3489-d172746.html
   My bibliography  Save this article

Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3489/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/10/3489/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yap, Menno & Munizaga, Marcela, 2018. "Workshop 8 report: Big data in the digital age and how it can benefit public transport users," Research in Transportation Economics, Elsevier, vol. 69(C), pages 615-620.
    2. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    3. Cen Zhang & Jan-Dirk Schmöcker & Martin Trépanier, 2022. "Latent stage model for carsharing usage frequency estimation with Montréal case study," Transportation, Springer, vol. 49(1), pages 185-211, February.
    4. Cortés, Cristián E. & Donoso, Pedro & Gutiérrez, Leonel & Herl, Daniel & Muñoz, Diego, 2023. "A recursive stochastic transit equilibrium model estimated using passive data from Santiago, Chile," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    5. Pezoa, Raúl & Basso, Franco & Quilodrán, Paulina & Varas, Mauricio, 2023. "Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile," Journal of Transport Geography, Elsevier, vol. 109(C).
    6. Wenping Liu & Chenlu Dong & Weijuan Chen, 2017. "Mapping and Quantifying Spatial and Temporal Dynamics and Bundles of Travel Flows of Residents Visiting Urban Parks," Sustainability, MDPI, vol. 9(8), pages 1-15, July.
    7. Yilei Tao & Ying Wang & Xinyu Wang & Guohang Tian & Shumei Zhang, 2022. "Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China," Land, MDPI, vol. 11(3), pages 1-19, March.
    8. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    9. Munizaga, Marcela A. & Gschwender, Antonio & Gallegos, Nestor, 2020. "Fare evasion correction for smartcard-based origin-destination matrices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 307-322.
    10. Jungmin Kim & Juyong Park & Wonjae Lee, 2018. "Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    11. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
    12. Amaya, Margarita & Cruzat, Ramón & Munizaga, Marcela A., 2018. "Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis," Journal of Transport Geography, Elsevier, vol. 66(C), pages 330-339.
    13. Shi, Linchang & Yang, Jiayu & Lee, Jaeyoung Jay & Bai, Jun & Ryu, Ingon & Choi, Keechoo, 2024. "Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation," Journal of Transport Geography, Elsevier, vol. 117(C).
    14. Yunjiao Zhou, 2020. "Spatial-temporal Dynamics of Population Aggregation during the Spring Festival based on Baidu Heat Map in Central Area of Chengdu City, China," Modern Applied Science, Canadian Center of Science and Education, vol. 14(4), pages 1-44, April.
    15. Jacqueline Arriagada & Claudio Mena & Marcela Munizaga & Daniel Schwartz, 2023. "The effect of economic incentives and cooperation messages on user participation in crowdsourced public transport technologies," Transportation, Springer, vol. 50(5), pages 1585-1612, October.
    16. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    17. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.
    18. Yuhui Guo & Zhiwei Tang & Jie Guo, 2020. "Could a Smart City Ameliorate Urban Traffic Congestion? A Quasi-Natural Experiment Based on a Smart City Pilot Program in China," Sustainability, MDPI, vol. 12(6), pages 1-19, March.
    19. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    20. Seo, Toru & Kusakabe, Takahiko & Gotoh, Hiroto & Asakura, Yasuo, 2019. "Interactive online machine learning approach for activity-travel survey," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 362-373.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3489-:d:172746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.