Road traffic accidents continue to be a major concern for sustainable urban safety and effective transportation management. Identifying crash hotspots with precision is essential for implementing focused safety interventions. In this study, a severity-weighted system was applied to evaluate crash hazard levels. A Geographic Information System (GIS)-based approach was adopted to analyze spatial patterns of road accidents in Hyderabad, with the primary objective of identifying high-risk locations. Using secondary data from 2021 to 2024, the research employed methods such as Kernel Density Estimation (KDE), Crash Hazard Level (CHL), and Predictive Accuracy Index (PAI) to examine accident frequency, severity, and spatial distribution. A total of 8,576 accident cases were analysed, classified according to factors such as time, location, accident type, and severity. KDE enabled the visualization of accident-prone areas, while CHL and PAI provided a quantitative framework for ranking hazardous zones and validating hotspot predictions. The analysis revealed an increasing trend in non-fatal accidents and highlighted traffic congestion as a major challenge for urban safety. Based on these findings, the study recommends targeted measures, including intersection redesign, enhanced road lighting, pedestrian safety improvements, awareness programs, and better emergency response systems. Overall, the GIS-based approach delivers valuable insights to support urban planners and policymakers in formulating data-driven strategies aimed at improving road safety in Hyderabad.
Singiti Chinnanarsaiah (Mon,) studied this question.