A review of spatial approaches in road safety. (February 2020)

• Record Type: Journal Article
• Authors: Ziakopoulos, Apostolos; Yannis, George
• Is Part Of: Accident analysis and prevention. Volume 135(2020)
• Publication Date: 2020-02
• Subjects: Road safety -- spatial analysis -- crash analysis -- study characteristics -- areal units; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2019.105323
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: This paper reviews spatial analyses in road safety research Design characteristics of 132 spatial road safety studies are summarized on tables The various area units, problems, configurations and spatial models are examined Vulnerable Road User particularities are mentioned in a spatial context as well Future research suggestions are provided based on unexplored research directions Abstract: Spatial analyses of crashes have been adopted in road safety for decades in order to determine how crashes are affected by neighboring locations, how the influence of parameters varies spatially and which locations warrant interventions more urgently. The aim of the present research is to critically review the existing literature on different spatial approaches through which researchers handle the dimension of space in its various aspects in their studies and analyses. Specifically, the use of different areal unit levels in spatial road safety studies is investigated, different modelling approaches are discussed, and the corresponding study design characteristics are summarized in respective tables including traffic, road environment and area parameters and spatial aggregation approaches. Developments in famous issues in spatial analysis such as the boundary problem, the modifiable areal unit problem and spatial proximity structures are also discussed. Studies focusing on spatially analyzing vulnerable road users are reviewed as well. Regarding spatial models, the application, advantages and disadvantages of various functional/econometric approaches, Bayesian models and machine learning methods are discussed. Based on the reviewed studies, present challenges and future research directions are determined.