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Using smart card and GPS data for policy and planning: The case of Transantiago

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  • Gschwender, Antonio
  • Munizaga, Marcela
  • Simonetti, Carolina

Abstract

The introduction in 2007 of a new public transport system in Santiago, Chile, brought to us an unexpected gift: the availability of Big Data; massive amounts of passive data obtained from technological devices installed to control the operation of buses and to administer the fare collection process. Many other cities in the world have experienced the same, and sooner or later, this is likely to happen everywhere. Seeing this opportunity, many researchers have developed tools to obtain valuable information from the available data. However, the case of Transantiago is particularly advantageous because all buses have GPS devices and the smart card presents an overall 97% penetration rate.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:retrec:v:59:y:2016:i:c:p:242-249
    DOI: 10.1016/j.retrec.2016.05.004
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    References listed on IDEAS

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    1. Itf, 2015. "Big Data and Transport: Understanding and Assessing Options," International Transport Forum Policy Papers 8, OECD Publishing.
    2. Bass, Pablo & Donoso, Pedro & Munizaga, Marcela, 2011. "A model to assess public transport demand stability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 755-764, October.
    3. Gibson, Jaime & Munizaga, Marcela A. & Schneider, Camila & Tirachini, Alejandro, 2016. "Estimating the bus user time benefits of implementing a median busway: Methodology and case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 72-82.
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    Citations

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    Cited by:

    1. Reinhart Buenk & Sara S (Saartjie) Grobbelaar & Isabel Meyer, 2019. "A Framework for the Sustainability Assessment of (Micro)transit Systems," Sustainability, MDPI, vol. 11(21), pages 1-24, October.
    2. 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.
    3. Mauricio Herrera & Alex Godoy-Faúndez, 2021. "Exploring the Roles of Local Mobility Patterns, Socioeconomic Conditions, and Lockdown Policies in Shaping the Patterns of COVID-19 Spread," Future Internet, MDPI, vol. 13(5), pages 1-24, April.
    4. 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.
    5. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
    6. Fangye Du & Jiaoe Wang & Yu Liu & Zihao Zhou & Haitao Jin, 2022. "Equity in Health-Seeking Behavior of Groups Using Different Transportations," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    7. 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.
    8. 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).
    9. Herrera, Fernanda & López, Sergio I., 2022. "Bus drivers in competition: A directed location approach," Research in Transportation Economics, Elsevier, vol. 95(C).
    10. 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.
    11. 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.
    12. Zhao, Pengjun & Cao, Yushu, 2020. "Commuting inequity and its determinants in Shanghai: New findings from big-data analytics," Transport Policy, Elsevier, vol. 92(C), pages 20-37.
    13. 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.
    14. 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).
    15. Rathachai Chawuthai & Agachai Sumalee & Thanunchai Threepak, 2023. "GPS Data Analytics for the Assessment of Public City Bus Transportation Service Quality in Bangkok," Sustainability, MDPI, vol. 15(7), pages 1-23, March.
    16. Gramsch, Benjamin & Guevara, C. Angelo & Munizaga, Marcela & Schwartz, Daniel & Tirachini, Alejandro, 2022. "The effect of dynamic lockdowns on public transport demand in times of COVID-19: Evidence from smartcard data," Transport Policy, Elsevier, vol. 126(C), pages 136-150.

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    More about this item

    Keywords

    Public transport; Passive data; Automatic vehicle location; Automatic fare collection;
    All these keywords.

    JEL classification:

    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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