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

Destaw Kifile Endashaw; Kidanemariam Alula Habtegiorgis; Baqer Muhammad Al-Ramadan; Hassan Musaed Al-Ahmadi; Basha Fayissa Deressa

doi:10.1007/s43762-025-00221-w

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

Destaw Kifile Endashaw^*, Kidanemariam Alula Habtegiorgis, Baqer Muhammad Al-Ramadan, Hassan Musaed Al-Ahmadi, Basha Fayissa Deressa

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

Abstract

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.

Original language	English
Article number	53
Journal	Computational Urban Science
Volume	5
Issue number	1
DOIs	https://doi.org/10.1007/s43762-025-00221-w
State	Published - Dec 2025

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

Keywords

Accidents
Cluster analyses
GIS
Road traffic
Safety
Spatial analysis

ASJC Scopus subject areas

Environmental Science (miscellaneous)
Urban Studies
Computer Science Applications
Artificial Intelligence

Access to Document

10.1007/s43762-025-00221-w

Cite this

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title = "A systematic review on GIS-based road traffic accidents analysis and road safety audit",

abstract = "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.",

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author = "Endashaw, \{Destaw Kifile\} and Habtegiorgis, \{Kidanemariam Alula\} and Al-Ramadan, \{Baqer Muhammad\} and Al-Ahmadi, \{Hassan Musaed\} and Deressa, \{Basha Fayissa\}",

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AU - Habtegiorgis, Kidanemariam Alula

AU - Al-Ramadan, Baqer Muhammad

AU - Al-Ahmadi, Hassan Musaed

AU - Deressa, Basha Fayissa

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N2 - 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.

AB - 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.

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