Citation

Koramati S, Bandhu Majumdar B, Pani A, Sahu PK. Safety Sci. 2022; 153: e105805.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2022.105805

PMID

unavailable

Abstract

This study presents a comprehensive methodological approach for the identification and segmentation of the key risk factors associated with fatal road crashes. Hyderabad, an Indian metropolis with significant annual fatal crashes, is selected as the case study city. Data containing the date, time, and location of the crash, number of injuries and fatalities, accused and victim vehicle details are collected from Hyderabad Traffic Police, and a comprehensive registry-based crash database is developed. Based on the database, a Cross-sectional study is conducted, and risk ratio (RR) is used as a measure to test the association between the risk factors and fatal outcomes. Logistic regression, log-binomial regression, and robust Poisson regression models were also used to understand the association of fatal crash outcomes with different attributes. RR and associated confidence intervals are further used to classify the factors into three groups: significant, insignificant, and non-risk factors. Subsequently, the Apriori algorithm is used to determine the interrelationship/association between the risk factors leading to a fatal crash outcome. Using the Apriori algorithm, a set of association rules involving three factors leading to a significant number of fatal crashes are identified. Finally, the derived results are combined to segment the factors associated with fatal crashes into six specific segments: Very high, High, Moderate, Low, Very low, and Extremely Low-risk factors. Such identification and segmentation of potential risk factors associated with fatal crashes would help the planning agencies to formulate mitigation measures for a low and medium-income country (LMIC) like India.

Language: en

Keywords

Apriori Algorithm; Cross-sectional Study; LMIC; Risk Factors; Risk Ratio (RR); Segmentation