An Adaptable Framework for Identifying and Prioritising Road Traffic Accident Hotspots

This study introduces a novel, adaptable framework for identifying and prioritising road traffic accident hotspots using the Getis Ord Gi* spatial autocorrelation tool. The framework classifies regions as hotspots or coldspots based on accident severity and frequency. A unique weighting system is developed to compute the Crash Severity Index (CSI), considering the severity of crashes in terms of fatalities and injuries. The identified hotspots are prioritised using the CSI, providing policymakers with a structured approach to allocate resources for crash remedial measures. The main contribution of this work is the development of a flexible framework applicable to various cities, states or countries to improve road safety. The framework’s effectiveness is demonstrated through a case study in Punjab, India, revealing that Sangrur, Hoshiarpur and Police Commissionerate Ludhiana are the top three hotspots. The study also offers a detailed analysis of crash statistics in Punjab, emphasising the severity of pedestrian crashes. This approach addresses the current lack of structured hotspot identification and prioritization strategies, marking a significant advancement in road safety management.

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Publication Details

Journal
PROMET - Traffic&Transportation
Published
2025-03-13
DOI
https://doi.org/10.7307/ptt.v37i2.751
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
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article

An Adaptable Framework for Identifying and Prioritising Road Traffic Accident Hotspots

Kaliprasana Muduli, Deorishabh Sahu, Indrajit Ghosh
PROMET - Traffic&Transportation
Traffic Prediction and Management Techniques
article

An Adaptable Framework for Identifying and Prioritising Road Traffic Accident Hotspots

Kaliprasana Muduli, Deorishabh Sahu, Indrajit Ghosh
article en

Abstract

This study introduces a novel, adaptable framework for identifying and prioritising road traffic accident hotspots using the Getis Ord Gi* spatial autocorrelation tool. The framework classifies regions as hotspots or coldspots based on accident severity and frequency. A unique weighting system is developed to compute the Crash Severity Index (CSI), considering the severity of crashes in terms of fatalities and injuries. The identified hotspots are prioritised using the CSI, providing policymakers with a structured approach to allocate resources for crash remedial measures. The main contribution of this work is the development of a flexible framework applicable to various cities, states or countries to improve road safety. The framework’s effectiveness is demonstrated through a case study in Punjab, India, revealing that Sangrur, Hoshiarpur and Police Commissionerate Ludhiana are the top three hotspots. The study also offers a detailed analysis of crash statistics in Punjab, emphasising the severity of pedestrian crashes. This approach addresses the current lack of structured hotspot identification and prioritization strategies, marking a significant advancement in road safety management.

PROMET - Traffic&TransportationVol. 37(2)
Indian Institute of Technology Roorkee (IN)
Openalex Percentile: Top 96%
Traffic Prediction and Management Techniques
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