Segmenting fare-evaders by tandem clustering and logistic regression models

Barabino, Benedetto; Salis, Sara

doi:10.1007/s12469-022-00297-1

Cited by 6 publications

(11 citation statements)

References 36 publications

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“…As for the intermediate socio-demographic factors, males practice 1.482 times more severe evasion than females. Although we investigated the severity as opposed to the likelihood or the intention to evade the fare, this result is consistent with past research (e.g., Bucciol et al, 2013; Barabino et al, 2015;Barabino and Salis, 2020;Barabino et al, 2022;Barabino and Salis, 2023;Cools et al 2018;Cantillo et al, 2022). The severity of fare evasion is a crucial issue in those younger than 26 years of age.…”

Section: Severity Prediction Modelsupporting

confidence: 86%

“…Passengers fill out a questionnaire and/or are asked about their self-reported fare evasion frequency or their intent to evade fares. However, surveys suffer from limited spatial-temporal coverage, and, although anonymous surveys are performed, direct questions might not cover people's fears (e.g., Reddy et al, 2011;Egu and Bonnel, 2020;Barabino and Salis, 2023).…”

Section: Data Collectionmentioning

confidence: 99%

“…In (ii), other studies applied quantitative methods to understand the characteristics and patterns of fare evaders using intercept/on-board and web-based surveys. Data were mainly processed by descriptive statistics (Delbosc and Currie 2016b;Gonzalez et al, 2019;Busco et al, 2022), inferential statistics (Currie and Delbosc, 2017;Barabino and Salis, 2020;Barabino et al, 2022) and mixed statistics (Barabino and Salis, 2023).…”

Section: Determinants Of Fare Evasion Probabilitymentioning

confidence: 99%

“…Nevertheless, personality traits can moderate the effect of satisfaction on fare evasion. Salis et al (2017) and Barabino and Salis (2023) discovered a minor portion of choice workers who rarely evade fares, a large number of captive students who frequently evade the fare and a medium segment of captive and chronic unemployed evaders. Finally, honesty is the common factor that considerably influences the likelihood to be a frequent fare evader among all clusters.…”

Section: Determinants Of Fare Evasion Probabilitymentioning

confidence: 99%

“…Specifically, surveyors ask passengers for their self-reported fare evasion as follows: 'How many times did you travel without a valid ticket in the last four months? 4 ' Note that a fourmonth period was felt to be adequate for this purpose (Barabino and Salis, 2023). Answers were clustered into five groups ranging from never to always.…”

Section: Data Type and Sourcesmentioning

confidence: 99%

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Evaluating Fare Evasion Risk in Bus Transit Networks

Barabino¹,

Francesco²,

Ventura³

2022

SSRN Journal

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Section: Severity Prediction Modelsupporting

confidence: 86%

Section: Data Collectionmentioning

confidence: 99%

Section: Determinants Of Fare Evasion Probabilitymentioning

confidence: 99%

Section: Determinants Of Fare Evasion Probabilitymentioning

confidence: 99%

Section: Data Type and Sourcesmentioning

confidence: 99%

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Evaluating Fare Evasion Risk in Bus Transit Networks

Barabino¹,

Francesco²,

Ventura³

2022

SSRN Journal

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Fare inspection patrolling under in-station selective inspection policy

Escalona,

Brotcorne,

Fortz

et al. 2023

Ann Oper Res

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A patrolling strategy that defines fare inspection frequencies on a proofof-payment transportation system is operationally useful to the transit authority when there is a mechanism for its practical implementation. This study addresses the operational implementation of a fare inspection patrolling strategy under an in-station selective inspection policy using an unpredictable patrolling schedule, where the transit authority select a patrolling schedule each day with some probability to induce uncertainty. The challenge is to determine the set of patrolling schedules and their respective probabilities of being selected whose systematic day-to-day application matches the inspection frequencies that inhibit the action of opportunistic passengers in the medium term. We use a Stackelberg game approach to represent the hierarchical decision making process between the transit authority and opportunistic passengers, whose decision on whether to evade the fare depends on the inspection frequencies set by Fare inspection patrolling under in-station selective inspection policy the transit authority. Numerical experiments show that a joint strategyschedule approach provides good-quality unpredictable patrolling schedules with respect to the optimality gap for large-scale networks.

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