Research Centre for Territory, Transports and Environment

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Spatial analysis of the impact of transport accessibility on regional performance: a study for Europe

Publication . Freiria, Susana; Sousa, Nuno; Calvo-Poyo, Francisco

Several works have addressed the impact of transport accessibility on regional development. Nevertheless, it is not consensual that new levels of accessibility lead to the improvement of regional development. This article proposes a methodological approach to assess whether significant relationship exist between these two variables. The methodology uses Data Envelopment Analysis to evaluate regional performance and spatial statistics methods (local bivariate relationships) to evaluate the type of relationships between accessibility and performance for each region, along with its significance. The approach was applied to 186 NUTS that cover 19 European countries, extending previous works which focus on global results, in the sense that only one (usually large) area is considered. Results indicate that approximately half the regions have a significant relationship between transport accessibility and regional performance. Logit regression analyses show that central regions are more likely to exhibit significance, as well as regions with high (low) levels of performance surrounded by regions with high (low) levels of accessibility, hinting at a synergy between the two variables. An important policy implication is that regions of high performance surrounded by regions of low accessibility may benefit from investments in the transport infrastructure.

2022-06Journal article

Open access

Determinants of fare evasion in urban bus lines: case study of a large database considering spatial components

Publication . Freiria, Susana; Sousa, Nuno

This article presents a large case study of fare evasion on bus lines in the city of Lisbon, Portugal, a common problem in dense urban areas. Focus is put on geographic factors, and an analysis is carried out using a generalized spatial two-step least-squares regression (GS2SLS). The large database, spanning one year of fare evasion reports, made it possible to segregate the analysis according to type of day (workday or weekend) and time period (rush hours, nighttime, etc.). Results show that indeed the type of day and time period lead to considerable differences between the seven models analyzed. It was found that the number of inspection actions is the strongest predictor of evasion, with geographic factors also playing a role in predicting fare evasion. Consideration of this spatial component made it possible to find moderate evidence for dissuasive effects of inspection actions in some models and of pockets of evasive tendencies in other models, which appear in the statistical error term. Interestingly, and contrary to other studies, age was found to have almost no influence on the propensity to evade fares. From a managerial point of view, this study highlights the importance of running inspection actions systematically and closely monitoring their outcomes. From a more theoretical standpoint, it suggests further research on geographic factors is needed to fully understand spatial patterns of evasion.

2025-06-18Journal article

Open access