Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review

Abstract

In this systematic literature review (SLR), we use a series of quantitative bibliometric analyses to (1) identify the main papers, journals, and authors of the publications that make use of statistical analysis (SA) and machine learning (ML) tools as well as technological elements of smart cities (TESC) and Geographic Information Systems to predict road traffic accidents (RTAs); (2) determine the extent to which the identified methods are used for the analysis of RTAs and current trends regarding their use; (3) establish the relationship between the set of variables analyzed and the frequency and severity of RTAs; and (4) identify gaps in method use to highlight potential areas for future research. A total of 3888 papers published between January 2000 and June 2021, distributed in four clusters—RTA + HA + SA (SA, n = 399); RTA + HA + ML (ML, n = 858); RTA + HA + SC (TESC, n = 2327); and RTA + HA + GIS (GIS, n = 304)—were analyzed. We identified Accident Analysis and Prevention as the most important journal, Fred Mannering as the main author, and The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives as the most cited publication. Although the negative binomial regression method was used for several years, we noticed that other regression models as well as methods based on deep learning, convolutional neural networks, transfer learning, 5G technology, Internet of Things, and intelligent transport systems have recently emerged as suitable alternatives for RTA analysis. By introducing a new approach based on computational algorithms and data visualization, this SLR fills a gap in the area of RTA analysis and provides a clear picture of the current scientific production in the field. This information is crucial for projecting further research on RTA analysis and developing computational and data visualization tools oriented to the automation of RTA predictions based on intelligent systems.