A systematic review on GIS-based road traffic accidents analysis and road safety audit

Road traffic accidents (RTAs) cause approximately 1.35 million deaths and 50 million injuries annually, disproportionately affecting people aged 5–29 years. The objective of this review was to synthesize how Geographic Information Systems (GIS) support RTA analysis and road safety audits. Relevant articles were searched in different electronic databases such as Scopus, Web of Science, PubMed, and Google Scholar using predefined terms; after screening and eligibility checks, 75 peer‑reviewed studies were included. Dominant techniques included Kernel Density Estimation (KDE), Getis–Ord Gi* clustering, crash rate analysis, and Empirical Bayes (EB) analyses, as well as machine-learning clustering. Across contexts, GIS consistently identified spatial blackspots, supported spatiotemporal trend analysis, and informed targeted countermeasures; key limitations were heterogeneous data quality, inconsistent methodological choices, and the integration of real‑time and behavioral data. GIS is effective for blackspot detection and decision support in road safety. Future work should prioritize standardizing methods, incorporating real‑time IoT streams and deep learning, and integrating behavioral and exposure data to improve prediction and intervention design.