Identification and Analysis of fatal Road Crash Black spot Clusters in an Urban Setting in South Coastal India

Abstract

ABSTRACT Introduction: Deaths from road crashes form the leading cause of mortality in India. Streamlining road crash data systems are essential for building robust prevention strategies. This study explores objectivisation of fatal road crash data by spatiotemporal analysis (geographical information system [GIS] technology) in an urban setting in South India. Aim: To identify clusters of fatal road crash black spot clusters in an urban setting and to analyze crash-related variables in clusters. Settings and Design: Secondary data analysis of fatal road crashes in Puducherry. Methods: Fatal road crash data from 2016 to 2018 were collected from South Traffic Police records. Spatiotemporal analysis was done using GIS to map high-density locations (black spots); these were further grouped into clusters. Crash-related variables in each cluster were studied to identify profiles of crash victims, alleged offenders, and risk factors. Results: Raw data accessed in descriptive format were converted to analyzable objective format using a self-developed data extraction template. A total of 154 fatal road crashes occurred in Puducherry South during the study period. Total 11 black spots and 3 clusters were mapped. One particular stretch of National Highway witnessed maximum (59%) black spots. Clusters differed from each other for variables such as age (of both victims and causing persons), time of the crash, and causing vehicle type. Intercluster similarities were observed in the preponderance of males (82.3%), youth (mean age: 28.9 years), vulnerable road users (92.6%), rainy season (43.4%), and weekends (46.7%) witnessing most fatal crashes. Conclusion: Standardized, objective format for data capturing, and seamless mechanism for data processing are essential. The crash location is a key index variable for data systems, with the feasibility to superimpose other data layers.