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Fare evasion correction for smartcard-based origin-destination matrices

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  • Munizaga, Marcela A.
  • Gschwender, Antonio
  • Gallegos, Nestor

Abstract

Origin-Destination matrices obtained from smartcard data are very valuable because they contain vast amounts of information and can be obtained at a very low cost. However, they can only account for trips paid by smartcard. Trips paid by other means, as well as non-paid trips, must be incorporated using additional information. This paper discusses the biases that are introduced due to fare evasion and presents a sequential method to estimate correction factors due to partial evasion in some trip stages, as well as total fare evasion in all trip stages, using external information regarding trips not registered in the smartcard database. We apply this method to the case of Santiago, Chile, where partial evasion (during a bus trip stage prior to a Metro trip stage) and total evasion (during all bus-only trip stages) are relevant fare evasion situations. Information from the Santiago Metro Origin-Destination survey and from external fare evasion measurements is used. The results indicate a 5% partial evasion rate for bus trip stages prior to Metro trip stages, and a 37% total fare evasion rate for bus-only trips. This paper is a contribution towards establishing new methods to feasibly obtain OD matrices through the adequate merging of automatically-collected data with complementary traditional measurements and survey instruments.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:141:y:2020:i:c:p:307-322
    DOI: 10.1016/j.tra.2020.09.008
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    References listed on IDEAS

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    5. Delbosc, Alexa & Currie, Graham, 2016. "Cluster analysis of fare evasion behaviours in Melbourne, Australia," Transport Policy, Elsevier, vol. 50(C), pages 29-36.
    6. Barabino, Benedetto & Salis, Sara & Useli, Bruno, 2015. "What are the determinants in making people free riders in proof-of-payment transit systems? Evidence from Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 184-196.
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    8. 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.
    9. Guarda, Pablo & Galilea, Patricia & Paget-Seekins, Laurel & Ortúzar, Juan de Dios, 2016. "What is behind fare evasion in urban bus systems? An econometric approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 55-71.
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    2. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    3. 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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    5. Basnak, Paul & Giesen, Ricardo & Muñoz, Juan Carlos, 2022. "Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 140-156.

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