A registry-based investigation of road traffic fatality risk factors using police data: A case study of Hyderabad, India

•This study has identified and segmented key risk factors associated with fatal road crashes in Hyderabad, India.•Based on Police records, a comprehensive road crash database is developed for Hyderabad.•Cross-sectional study is used to test the association between the risk factors and fatal crashes using Risk-Ratio(RR)•Apriori algorithm was applied to identify association rules involving two or more risk factors leading to fatal crashes.•Risk factors were classified into six specific segments: Very high, High, Moderate, Low, Very low, and Extremely Low-risk factors.•Pedestrian as a victim, Heavy Motor Vehicles (HMV) as accused, were identified as very high-risk factors leading to fatal crash outcomes. 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.