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Cluster analysis of fare evasion behaviours in Melbourne, Australia

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  • Delbosc, Alexa
  • Currie, Graham

Abstract

Fare evasion on transit can reduce revenue by millions of dollars, undermining financial viability. Research has examined how design solutions, such as ticket barriers and ticket inspections, can reduce fare evasion. However little research examines how transit users think about fare evasion or attempts to understand why people fare evade. This research uses a quantitative cluster analysis to segment fare evasion behaviours into three categories which show distinct personality and behavioural characteristics. A web-based survey of was administered to residents of Melbourne, Australia with a total sample size of 1561. The questionnaire was introduced as a survey about transit travel and ticketing but included questions about various aspects of fare evasion behavior. Notably, three broad types of fare evasion were explored: ‘accidental’ fare evasion (e.g. meant to pay but machines were not working), ‘unintentional’ fare evasion (e.g. meant to validate but I was in a hurry or I forgot) and ‘deliberate’ fare evasion (e.g. decided not to pay because I was only going a few stops). A two-step cluster analysis was conducted using a range of categorical and continuous variables including fare evasion behavior, predicted likelihood of continuing to fare evade, age and frequency of transit use. Three clusters of fare evaders emerged: deliberate evaders, unintentional evaders and never-evaders. Deliberate evaders were the smallest cluster but the most frequent transit users. In contrast, unintentional evaders were more common but only fare evaded infrequently. The clusters also had distinct personality differences; deliberate evaders were more likely to be sensation-seekers and believed it was acceptable to bend the rules to save money. Implications for transit policy and practice are discussed.

Suggested Citation

  • Delbosc, Alexa & Currie, Graham, 2016. "Cluster analysis of fare evasion behaviours in Melbourne, Australia," Transport Policy, Elsevier, vol. 50(C), pages 29-36.
  • Handle: RePEc:eee:trapol:v:50:y:2016:i:c:p:29-36
    DOI: 10.1016/j.tranpol.2016.05.015
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    References listed on IDEAS

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    Cited by:

    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. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "On evasion behaviour in public transport: Dissatisfaction or contagion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 626-651.
    4. 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.
    5. 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.
    6. Currie, Graham & Delbosc, Alexa, 2017. "An empirical model for the psychology of deliberate and unintentional fare evasion," Transport Policy, Elsevier, vol. 54(C), pages 21-29.
    7. Martina Manfre' & Viola Angelini, 2018. "Does The Financial Situation affect Cheating Behavior? An Investigation through Financial Literacy," Working Papers 06/2018, University of Verona, Department of Economics.
    8. Meng, Meng & Rau, Andreas & Mahardhika, Hita, 2018. "Public transport travel time perception: Effects of socioeconomic characteristics, trip characteristics and facility usage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 24-37.
    9. 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.
    10. 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.
    11. Ayal, Shahar & Celse, Jérémy & Hochman, Guy, 2021. "Crafting messages to fight dishonesty: A field investigation of the effects of social norms and watching eye cues on fare evasion," Organizational Behavior and Human Decision Processes, Elsevier, vol. 166(C), pages 9-19.
    12. Åse Jevinger & Jan A. Persson, 2019. "Exploring the potential of using real-time traveler data in public transport disturbance management," Public Transport, Springer, vol. 11(2), pages 413-441, August.
    13. 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.
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    15. Chung, Yi-Shih & Chiou, Yu-Chiun, 2017. "Willingness-to-pay for a bus fare reform: A contingent valuation approach with multiple bound dichotomous choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 289-304.

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