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Moving Towards a More Accurate Level of Inspection Against Fare Evasion in Proof-of-Payment Transit Systems

Author

Listed:
  • Benedetto Barabino

    (University of Cagliari
    CTM SpA)

  • Sara Salis

    (CTM SpA)

Abstract

This paper proposes an accurate economic framework to determine the optimum inspection level—the number of ticket inspectors—in a long time window, in order to maximize the system-wide profit when fare evasion occurs. This is the first framework that introduces: i) a refined characterization of the passenger demand, ii) a profit function with new constraints, iii) an alternative estimation of the percentage of passengers who choose to evade, and iv) a new formulation accounting for inspectors who cannot fine every passenger caught evading. The implementation of this framework is illustrated by using six years of data gathered from an Italian public transport company. Based on 57,256 stop-level inspections and 21,827 on-board personal interviews, the optimum inspection rate maximizing the profit is in the range of 3.4%-4.0%. This outcome provides more accurate results, which are discussed and compared to previous research. Finally, the framework is flexible, and it may be applied to any urban context in which proof-of-payment systems are adopted.

Suggested Citation

  • Benedetto Barabino & Sara Salis, 2019. "Moving Towards a More Accurate Level of Inspection Against Fare Evasion in Proof-of-Payment Transit Systems," Networks and Spatial Economics, Springer, vol. 19(4), pages 1319-1346, December.
    • Handle: RePEc:kap:netspa:v:19:y:2019:i:4:d:10.1007_s11067-019-09468-3
    DOI: 10.1007/s11067-019-09468-3
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    References listed on IDEAS

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    1. Zhi-Chun Li & William Lam & S. Wong, 2009. "The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network," Networks and Spatial Economics, Springer, vol. 9(2), pages 191-216, June.
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    4. Barabino, Benedetto & Salis, Sara & Useli, Bruno, 2014. "Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 1-17.
    5. Sasaki, Yasuo, 2014. "Optimal choices of fare collection systems for public transportations: Barrier versus barrier-free," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 107-114.
    6. Delbosc, Alexa & Currie, Graham, 2016. "Cluster analysis of fare evasion behaviours in Melbourne, Australia," Transport Policy, Elsevier, vol. 50(C), pages 29-36.
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    9. 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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    Cited by:

    1. Ramos, Raúl & Silva, Hugo E., 2023. "Fare evasion in public transport: How does it affect the optimal design and pricing?," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    2. Celse, Jérémy & Grolleau, Gilles, 2023. "Fare evasion and information provision: What information should be provided to reduce fare-evasion?," Transport Policy, Elsevier, vol. 138(C), pages 119-128.
    3. Guzman, Luis A. & Arellana, Julian & Camargo, José Pablo, 2021. "A hybrid discrete choice model to understand the effect of public policy on fare evasion discouragement in Bogotá's Bus Rapid Transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 140-153.
    4. Oscar Egu & Patrick Bonnel, 2020. "Can we estimate accurately fare evasion without a survey? Results from a data comparison approach in Lyon using fare collection data, fare inspection data and counting data," Public Transport, Springer, vol. 12(1), pages 1-26, March.
    5. Brotcorne, L. & Escalona, P. & Fortz, B. & Labbé, M., 2021. "Fare inspection patrols scheduling in transit systems using a Stackelberg game approach," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 1-20.
    6. Boyd, Colin, 2020. "Revisiting the foundations of fare evasion research," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 313-324.
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    8. Evgeny Alexandrovich Uvarov, 2020. "The Scale of the Shadow Economy in the Sphere of Urban and Suburban Passenger Transportation in Regions of Russia," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 2, pages 124-141.
    9. Benedetto Barabino & Sara Salis, 2023. "Segmenting fare-evaders by tandem clustering and logistic regression models," Public Transport, Springer, vol. 15(1), pages 61-96, March.
    10. Porath, Keiko & Galilea, Patricia, 2020. "Temporal analysis of fare evasion in Transantiago: A socio-political view," Research in Transportation Economics, Elsevier, vol. 83(C).

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