Skip to main content
SpringerLink
Log in

Segmenting fare-evaders by tandem clustering and logistic regression models

  • Original Research
  • Published:
Public Transport Aims and scope Submit manuscript

Abstract

In this study, a tandem clustering is applied on data collected by an Italian public transport company. Three clusters of evader passengers are discovered. Next, for each cluster, the influence of significant determinants in evaluating the chance of being a frequent fare evader is shown by logistic regression models. Members of Cluster 1 are a small segment of choice-workers, who seldom evade fares significantly. Members of Cluster 2 represent a big segment of captive students, who often evade the fare. Members of Cluster 3 are a medium segment of captive unemployed, who always evade the fare. The logistic regression models show that attributes related to the situational factors are significant, and honesty is the common variable that significantly affects the chance of being a frequent fare evader among segments. These outcomes are relevant and useful for both research and practice. Indeed, this paper contributes to the empirical understanding of the determinants of being a frequent fare evader for segments a posteriori selected. Moreover, it helps PTCs to better understand how some segments differ from each other.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. http://eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html (accessed on September 2019).

Abbreviations

PTC:

Public transport companies

HAC:

Hierarchical agglomerative clustering

References

  • Abeler J, Becker A, Falk A (2014) Representative evidence on lying costs. J Public Econ 113:96–104

    Article  Google Scholar 

  • Abrate G, Fraquelli G, Meko E, Rodia G (2008) L’Evasione Tariffaria nel Trasporto Pubblico Locale: un’Analisi Empirica. In: Conferenza Società Italiana di Economia Pubblica, XX Riunione Scientifica. Pavia, 25–26 Settembre 2008

  • Avenhaus R (2004) Applications of inspection games. Math Model Anal 9(3):179–192

    Article  Google Scholar 

  • Barabino B, Salis S (2019) Moving towards a more accurate level of inspection against fare evasion in proof-of-payment transit systems. Netw Spat Econ 19(4):1319–1346

    Article  Google Scholar 

  • Barabino B, Salis S (2020) Do students, workers, and unemployed passengers respond differently to the intention to evade fares? An empirical research. Transport Res Interdisciplin Perspect 7:100215

    Article  Google Scholar 

  • Barabino B, Salis S, Useli B (2013a) A modified model to curb fare evasion and enforce compliance: empirical evidence and implications. Transport Res Part A 58:29–39

    Google Scholar 

  • Barabino B, Deiana E, Mozzoni S (2013b) The quality of public transport service: the 13816 standard and a methodological approach to an Italian case. Ingegneria Ferroviaria 68(5):475–499

    Google Scholar 

  • Barabino B, Salis S, Useli B (2014) Fare evasion in proof-of-payment transit systems: deriving the optimum inspection level. Transport Res Part B 70:1–17

    Article  Google Scholar 

  • Barabino B, Salis S, Useli B (2015) What are the determinants in making people free riders in proof-of-payment transit systems? Evidence from Italy. Transport Res Part A 80:184–196

    Google Scholar 

  • Barabino B, Lai C, Olivo A (2020) Fare evasion in public transport systems: a review of the literature. Public Transport 12:27–88

    Article  Google Scholar 

  • Barabino B, Salis S, Useli B (2022) Assessing the Intention to evade fares for demographic segments of passengers: empirical research in italy for building smart (er) cities. J Urban Plann Dev 148(1):05021066

    Article  Google Scholar 

  • Bijleveld C (2007) Fare dodging and the strong arm of the law. J Exp Criminol 3(2):183–199

    Article  Google Scholar 

  • Bonfanti G, Wagenknecht T (2010) Human factors reduce aggression and fare evasion. Public Transport Int 59(1):28–32

    Google Scholar 

  • Borndörfer R, Omont B, Sagnol G, Swarat E (2012) A Stackelberg game to optimize the distribution of controls in transportation networks. Game theory for networks. Springer, Berlin Heidelberg, pp 224–235

    Chapter  Google Scholar 

  • Boyd C, Martini C, Rickard J, Russell A (1989) Fare evasion and non-compliance: a simple model. JTEP 23(2):189–197

    Google Scholar 

  • Bratton WJ (1999) Great expectations: how higher expectations for police departments can lead to a decrease in crime. In: Measuring what matters: proceedings from the policing research institute meetings. National Institute of Justice, Washington, pp 11–26

    Google Scholar 

  • Brotcorne L, Escalona P, Fortz B, Labbé M (2021) Fare inspection patrols scheduling in transit systems using a Stackelberg game approach. Transport Res Part B 154:1–20

    Article  Google Scholar 

  • Bucciol A, Landini F, Piovesan M (2013) Unethical behaviour in the field: demographic characteristics and beliefs of the cheater. J Econ Behav Organ 93:248–257

    Article  Google Scholar 

  • Busco C, González F, Jaqueih Y, Jiménez F, Alonso B (2021) Understanding Transantiago users’ motivations for paying or evading payment of bus fares. J Public Transportat. https://doi.org/10.5038/2375-0901.23.2.2

    Article  Google Scholar 

  • Clarke RV, Contre S, Petrossian G (2010) Deterrence and fare evasion: results of a natural experiment. Secur J 23(1):5–17

    Article  Google Scholar 

  • Cools M, Fabbro Y, Bellemans T (2018) Identification of the determinants of fare evasion. Case Stud Transp Policy 6(3):348–352

    Article  Google Scholar 

  • Correa, J. R., Harks, T., Kreuzen, V. J., & Matuschke, J. 2014. Fare Evasion in Transit Networks. arXiv preprint arXiv:1405.2826.

  • CTM (2015a) Passenger transport surveys on the CTM bus network. Passengers Count. Internal Technical Report (In Italian)

  • CTM (2015b) Passenger transport surveys on the CTM bus network. Customer Satisfaction. Internal Technical Report (In Italian)

  • CTM (2017) Carta della mobilità 2018. http://www.ctmcagliari.it/

  • Currie G, Delbosc A (2017) An empirical model for the psychology of deliberate and unintentional fare evasion. Transp Policy 54:21–29

    Article  Google Scholar 

  • Dai Z, Galeotti F, Villeval MC (2018) Cheating in the lab predicts fraud in the field: an experiment in public transportation. Manage Sci 64(3):1081–1100

    Article  Google Scholar 

  • Delbosc A, Currie G (2016a) Four types of fare evasion: a qualitative study from Melbourne, Australia. Transp Res Part F 43:254–264

    Article  Google Scholar 

  • Delbosc A, Currie G (2016b) Cluster analysis of fare evasion behaviours in Melbourne, Australia. Transp Policy 50:29–36

    Article  Google Scholar 

  • Diebel LE (1981) The introduction of European fare collection techniques in the United States. J Adv Transp 15(1):55–60

    Google Scholar 

  • Dreber A, Johannesson M (2008) Gender difference in deception. Econ Lett 99(1):197–199

    Article  Google Scholar 

  • Eddy D (2010) Fare evasion – is it a youth issue? Transit Australia– Australia’s Urban Passenger Transp J 65(12):1–7

    Google Scholar 

  • Egu O, Bonnel P (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 12(1):1–26

    Article  Google Scholar 

  • Gonzalez F, Busco C, Codocedo K (2019) Fare evasion in public transport: grouping Transantiago users’ behavior. Sustainability 11(23):6543

    Article  Google Scholar 

  • Green PE, Krieger AM (1995) A comparison of alternative approaches to cluster-based market segmentation. Market Res Soc J 37(3):1–19

    Article  Google Scholar 

  • Guarda P, Galilea P, Paget-Seekins L, de Dios OJ (2016a) What is behind fare evasion in urban bus systems? An econometric approach. Transport Res Part A 84:55–71

    Google Scholar 

  • Guarda P, Galilea P, Handy S, Muñoz JC, de Dios Ortúzar J (2016b) Decreasing fare evasion without fines? A microeconomic analysis. Res Transp Econ 59:151–158

    Article  Google Scholar 

  • Hauber AR (1993) Fare evasion in a European perspective. Stud Crime Crime Prevent 2:122–141

    Google Scholar 

  • Killias M, Scheidegger D, Nordenson P (2009) The effects of increasing the certainty of punishment: a field experiment on public transportation. Eur J Criminol 6(5):387–400

    Article  Google Scholar 

  • Kooreman P (1993) Fare evasion as a result of expected utility maximisation. Some empirical support. J Transp Econ Policy 27(1):69–74

    Google Scholar 

  • Kotler P (1997) Marketing management: analysis, planning, implementation, and control. Prentice Hall International Inc, Upper Saddle River

    Google Scholar 

  • Lee J (2011) Uncovering San Francisco, California, muni’s proof-of-payment patterns to help reduce fare evasion. Transp Res Rec 2216:75–84

    Article  Google Scholar 

  • Louviere JJ, Hensher DA, Swait JD (2000) Stated choice methods: analysis and applications. Cambridge University Press

    Book  Google Scholar 

  • Maskin ES (1977) Nash Equilibrium and Welfare Optimality. MIT Discussion Paper.

  • Matsushima H (2008) Role of honesty in full implementation. J Econ Theory 139(1):353–359

    Article  Google Scholar 

  • McCarron E, Clarke S, Forell S (2008) Fine but not fair: fines and disadvantage. Justice Issues 3:1

    Google Scholar 

  • McFadden D (1977) Quantitative methods for analyzing travel behaviour of individuals: Some recent developments (Cowles Foundation Discussion Papers No. 474). Cowles Foundation for Research in Economics, Yale University

  • Mehlkop G, Becker R, Imhof R (2007) Extending the rational action theory of crime: success, norms and risk-taking. An empirical re-assessment of the theory of subjective expected utility. In: Proceedings of the International Conference on Rational Choice and Social Institutions, Zurich, 6–8 September

  • Multisystem, Inc., Mundle & Associates, Inc., and Parsons Transportation Group, Inc., (2002) A toolkit for self-service, barrier-free fare collection. Transit Cooperative Research Program, Report 80. TRB, Washington

  • Pourmonet H, Bassetto S, Trépanier M (2015) Vers la maîtrise de l’évasion tarifaire dans un réseau de transport collectif. In: 11e Congrès International De Génie Industriel (Québec, Canada)

  • Reddy AV, Kuhls J, Lu A (2011) Measuring and controlling subway fare evasion. Transp Res Rec 2216:85–99

    Article  Google Scholar 

  • Salis S, Barabino B, Useli B (2017) Segmenting fare evader groups by factor and cluster analysis. WIT Trans Built Environ 176:503–515

    Article  Google Scholar 

  • Sasaki Y (2014) Optimal choices of fare collection systems for public transportations: barrier versus barrier-free. Transport Res Part B 60:107–114

    Article  Google Scholar 

  • Schwerdtfeger S (2016) Fare Evasion: the last resort of social exclusion or simply a way to save money? RGS-IBG Ann Int Conf

  • Smith MJ, Clarke RV (2000) Crime and public transport. In: Tonry M (ed) Crime and justice. A review of research, 27. University of Chicago Press, Chicago, pp 169–233

    Google Scholar 

  • Suquet JB (2010) Drawing the line: how inspectors enact deviant behaviors. J Service Market 24(6):468–475

    Article  Google Scholar 

  • Transit Cooperative Research Program (TCRP), Synthesis 63 (2005) On board and intercept transit survey techniques—a synthesis of transit practice. Transportation Research Board, Washington

    Google Scholar 

  • Transit Cooperative Research Program (TCRP) (2019) https://www.trb.org/TCRP/TCRP.aspx. Accessed 03 June 2019

  • Traxler C (2010) Social norms and conditional cooperative taxpayers. Eur J Polit Econ 26(1):89–103

    Article  Google Scholar 

  • Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244

    Article  Google Scholar 

  • Weidner RR (1996) Target-hardening at a New York City subway station: Decreased fare evasion–at what price? Crime Prevent Stud 6:117–132

    Google Scholar 

  • Yin Z, Jiang AX, Johnson M, Tambe M, Kiekintveld C, Leyton-Brown K, Sandholm T, Sullivan J (2012a) TRUSTS: scheduling randomized patrols for fare inspection in transit systems. In: Proceedings of the Twenty-Fourth AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI). Menlo Park: AAAI Press

  • Yin Z, Jiang AX, Johnson M, Tambe M, Kiekintveld C, Leyton-Brown K, Sandholm T, Sullivan J (2012b) TRUSTS: scheduling randomized patrols for fare inspection in transit systems using game theory. AI Mag 33(4):59–72

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to some anonymous reviewers, who helped them write a better paper.

Author information

Authors and Affiliations

  1. Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, Brescia, Italy

    Benedetto Barabino

  2. Department of Business Development of CTM SpA, viale Trieste 159/3, 09123, Cagliari, Italy

    Sara Salis

Authors
  1. Benedetto Barabino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sara Salis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sara Salis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barabino, B., Salis, S. Segmenting fare-evaders by tandem clustering and logistic regression models. Public Transp 15, 61–96 (2023). https://doi.org/10.1007/s12469-022-00297-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12469-022-00297-1

Keywords

Search

Navigation