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A modified model to curb fare evasion and enforce compliance: Empirical evidence and implications

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  • Barabino, Benedetto
  • Salis, Sara
  • Useli, Bruno

Abstract

Fare evasion is a major problem for transit companies due to lost fare revenues and damage to their corporate images. Therefore, the establishment and proper management of ticket inspection teams deployed to tackle fare dodgers is highly important and represents a severe challenge. In this paper, an existent profit maximization model for estimating the optimum level of inspection has been extended, calibrated, and tested in a real case, using data available from an Italian transit operator, resulting from 98days of checks and 3659 completed on-board interviews. Given the current network-wide inspection level per single verifier, and considering the level of fines currently applied, the optimal value of the total inspection rate is found to amount to 4.5%. The model provides empirical evidence towards understanding the fare evasion problem, besides highlighting the need for collaboration with the managers of the transit company. An overview of the manipulation of some control variables related to risk perception and the main implications of the findings are presented to transport companies using “honour” ticketing systems.

Suggested Citation

  • Barabino, Benedetto & Salis, Sara & Useli, Bruno, 2013. "A modified model to curb fare evasion and enforce compliance: Empirical evidence and implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 58(C), pages 29-39.
    • Handle: RePEc:eee:transa:v:58:y:2013:i:c:p:29-39
    DOI: 10.1016/j.tra.2013.10.007
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    References listed on IDEAS

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    1. Polinsky, Mitchell & Shavell, Steven, 1979. "The Optimal Tradeoff between the Probability and Magnitude of Fines," American Economic Review, American Economic Association, vol. 69(5), pages 880-891, December.
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    1. 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.
    2. 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).
    3. 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.
    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. 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.
    6. 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.
    7. Elmar Wilhelm M. Fürst & David M. Herold, 2018. "Fare Evasion and Ticket Forgery in Public Transport: Insights from Germany, Austria and Switzerland," Societies, MDPI, vol. 8(4), pages 1-16, October.
    8. Troncoso, Rodrigo & de Grange, Louis, 2017. "Fare evasion in public transport: A time series approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 311-318.
    9. 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.
    10. Boyd, Colin, 2020. "Revisiting the foundations of fare evasion research," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 313-324.
    11. Felipe González & Carolina Busco & Katheryn Codocedo, 2019. "Fare Evasion in Public Transport: Grouping Transantiago Users’ Behavior," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    12. Mehdizadeh, Milad & Shariat-Mohaymany, Afshin, 2020. "Who are more likely to break the rule of congestion charging? Evidence from an active scheme with no referendum voting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 63-79.
    13. 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.
    14. Delbosc, Alexa & Currie, Graham, 2016. "Cluster analysis of fare evasion behaviours in Melbourne, Australia," Transport Policy, Elsevier, vol. 50(C), pages 29-36.
    15. Porath, Keiko & Galilea, Patricia, 2020. "Temporal analysis of fare evasion in Transantiago: A socio-political view," Research in Transportation Economics, Elsevier, vol. 83(C).
    16. Benedetto Barabino & Cristian Lai & Alessandro Olivo, 2020. "Fare evasion in public transport systems: a review of the literature," Public Transport, Springer, vol. 12(1), pages 27-88, March.

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