Road safety studies at micro, meso, and macroscopic levels: A systematic review

Abstract

Traditionally, road safety studies have been conducted independently, either at microscopic or macroscopic levels. This study synthesizes existing literature on road safety research conducted at microscopic, macroscopic, and mesoscopic levels using a Systematic Literature Review (SLR). The objective of this research is to examine the advancement in crash prediction methodologies, crash analysis, and the integration of microscopic, macroscopic, and mesoscopic studies over the past two decades to understand the multiscale dynamics of crash occurrence. In addition, bibliometric analysis helps to map social, conceptual, and intellectual collaborations among sources, authors, and institutions. The comprehensive review of the existing literature shows that some analytical advancements in statistical approaches, as well as Machine Learning (ML) and Deep Learning (DL) approaches, have facilitated them to address data complexity issues. In the latter decade, researchers have started to integrate microscopic and macroscopic approaches to have a nuanced and cohesive understanding of the intrinsic relationships among crash contributing factors and to assess the impact of an integrated approach on the model's predictive performance. The bibliometric analysis of published literature revealed distinct clusters, each providing a unique perspective on road safety. The major gaps observed in the systematic review of studies are the lack of consideration of behavioural aspects of road users, the transferability of models between two independent frameworks, as well as across the integrated modelling methodologies. Another significant gap is the lack of a scale of adjacent street networks in mesoscopic studies. Overall, this review provided critical insights into safety studies that focus on distinct resolutions, analytical advancements in modelling methodologies, mapping of scientific collaborations and identifications of research gaps.