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

Barabino, Benedetto; Salis, Sara; Useli, Bruno

doi:10.1016/j.tra.2013.10.007

Cited by 19 publications

(27 citation statements)

References 4 publications

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“…On the one hand, to date, it is no surprise that a plethora of studies on fare evasion have focused attention on how to protect PTCs from monetary losses (Economic studies). This was achieved by evaluating changes to: ticketing systems infrastructure and management e.g., [4], [5]; policy and enforcement issues e.g., [6], [7], [8] and operational issues e.g., [9]. Besides, fare evasion can be associated with criminal behaviour of people.…”

Section: Introductionmentioning

confidence: 99%

“…The outcomes of this paper will provide researchers with a better refinement of the main characteristics, which identify specific evader groups, thanks to the application of a robust two-step methodological framework. Moreover, due to the common problem of unpaid fines [6], [7], [11], [18], this paper could help in the identification of specific groups who will never pay the fine from the ones who might pay. Furthermore, according to [18], [19], knowing the characteristics which are most affect specific groups can help in understanding whether they are statistically significant in affecting the costs of the PTC as well.…”

Section: Introductionmentioning

confidence: 99%

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Segmenting Fare Evader Groups by Factor and Cluster Analysis

Salis¹,

Barabino²,

Useli³

2017

WIT Transactions on the Built Environment

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In proof-of-payment transit systems, fare evasion represents a crucial topic and undermines financial viability for public transport companies (PTCs). Two studies segmented all transit passengers by using qualitative research and explorative two-step cluster analysis as well as web-survey data. However, as far as the authors know, no study segmented exclusively the fare evader passengers, with the aim to know deeply characteristics of distinct groups, and to use data gathered from an intercept survey. Moreover, the combined use of a factorial and a cluster analysis was not adopted to solve this issue. By using 850 on-board personal interviews, gathered from an Italian PTC, a Multiple Correspondence Analysis and a Hierarchical Cluster Analysis have been performed to group the fare evader passengers. Three clusters of evaders' passengers are derived: 1) the chronic, 2) the calculated-risk and 3) the accidental fare evader. The chronic cluster includes passengers who always evade the fare, and represents a medium numerous groups of fare evaders. The calculated-risk cluster includes passengers who usually evade the fare and represents the most numerous group of evaders. The accidental cluster includes passengers who rarely evades the fare and represents the less numerous groups. These outcomes are very relevant and useful for both research and practice. Indeed, this paper contributes to the empirical understanding of the determinants of being a fare evader for a selected group. Moreover, it helps PTCs understand how evaders' groups differ each other, adopt suitable strategies for each group and evaluate their effects.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmenting Fare Evader Groups by Factor and Cluster Analysis

Salis¹,

Barabino²,

Useli³

2017

WIT Transactions on the Built Environment

Self Cite

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“…To conclude, it can be observed that despite growing literature about fare evasion and ticket forgery in public transport [4,42,44,[49][50][51][52], there has been rather little research so far on this topic with practical relevance, particularly from an economic perspective [7].…”

Section: Fare Evasion and Ticket Forgery As Threats To The Financial mentioning

confidence: 97%

“…The problems of fare evasion (free-riding, fare dodging or toll fraud; i.e., people using a transport service without paying for it) and ticket forgery (ticket fraud, ticket falsification; i.e., the production of an illegal ticket facsimile) are, of course, not new to transport industries [5,6]. Due to technological advances (on the part of suppliers as well as the passengers), these phenomena seem to be developing as well [2,7].…”

Section: Introductionmentioning

confidence: 99%

Fare Evasion and Ticket Forgery in Public Transport: Insights from Germany, Austria and Switzerland

Fürst

Herold

2018

Societies

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Local public transport companies provide important mobility services to the general public. Although these services are usually subsidised, companies rely on revenues generated by ticket sales. Therefore, fare evasion (i.e., people using a transport service without paying for it) and ticket forgery (the production of an illegal ticket facsimile) have considerable influence on the companies’ economic sustainability. As existing research regarding the economic perspective is limited, this paper presents a Delphi study that investigates the phenomena with a survey of experts in public transport companies and transport associations in Germany, Austria, and Switzerland. The findings of the survey provide insights into the overall perception and discuss relevant aspects of both fare evasion and ticket forgery, thereby not only highlighting practical implications, but also helping policy makers shape adequate policies for public transport in societies.

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“…2) They typically underestimate passenger volumes owing to possible fare evaders who do not buy tickets or have invalid tickets [3]. Therefore, ticket-dependent methods are not very accurate, if passenger data are used to rearrange service planning and operations.…”

Section: A Measuring Passenger Volumesmentioning

confidence: 99%

An Offline Framework for Handling Automatic Passenger Counting Raw Data

Barabino

Francesco

Mozzoni³

2014

IEEE Trans. Intell. Transport. Syst.

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Knowledge of ridership data on bus routes is pivotal for the quality and efficient operational planning of public transport companies. Automatic passenger counting (APC) can represent a powerful resource for supporting this activity, because it can provide a databank of accurate counts. However, relevant challenges, such as the matching of data to the bus stop, data validation, tackling anomalies, and building intelligible performance reports, must be faced in order to make APC data a mainstream source of information. This paper proposes an offline framework for addressing these challenges. In order to illustrate a possible application of the framework, its use for setting bus frequencies is investigated. The results are represented by easy-to-read control dashboards composed of tables and graphs. The methodology is experimentally tested with data records provided by the bus operator CTM in Cagliari, Italy. Finally, we discuss the implications on service rearrangement.

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

Cited by 19 publications

References 4 publications

Segmenting Fare Evader Groups by Factor and Cluster Analysis

Segmenting Fare Evader Groups by Factor and Cluster Analysis

Fare Evasion and Ticket Forgery in Public Transport: Insights from Germany, Austria and Switzerland

An Offline Framework for Handling Automatic Passenger Counting Raw Data

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