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A spatial statistics methodology for inspector allocation against fare evasion
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Autores
Orientador(es)
Resumo(s)
This article discusses public transport fare evasion from the point of view of the relations
between inspection actions and detected evasion, with the aim of improving the efficacy of
the former. By applying spatial statistics methods to a large dataset from Lisbon, Portugal,
namely, entropy-based local bivariate relationships (LBR) and geographically weighted
regression (GWR), it is shown that the two variables are associated in a widespread manner
throughout the city, mostly in a linear way. Mapping out marginal gains from inspection
actions then shows where they detect the most evaders, allowing transport companies to
relocate their inspector teams in a more effective manner. Results for Lisbon show that
gains in effectiveness of circa 50% can be obtained, mostly by moving some inspector teams
from the centre of the city to the periphery during daytime. The methodology requires
only inspection/detection databases, which transport companies usually have, making it a
valuable, practical tool to combat fare evasion.
Descrição
Palavras-chave
Fare evasion Public transport Local bivariate relationships Geographically weighted regression
Contexto Educativo
Citação
Freiria S, Sousa N (2026). A Spatial Statistics Methodology for Inspector Allocation Against Fare Evasion. ISPRS International Journal of Geo-Information, 15(2):53
Editora
MDPI