Weather-driven risk assessment model for two-wheeler road crashes in Uttar Pradesh, India

Abstract

Abstract This study investigates the relationship between weather conditions and two-wheeler road crashes in Uttar Pradesh, India, which experiences diverse climatic conditions. A novel framework, the Weather-Influenced Clustering and Random Sampling (WICRS) model, is proposed for Relative Accident (crash) Risk (RAR) analysis. Initially, a preliminary analysis of crash data based on location, human, and environmental factors provides insights into contributing factors. Building on these findings, the WICRS model categorizes weather patterns using highly randomized sampling-based clustering, a departure from traditional matched pair analysis (MPA). The study also conducts a stratified RAR analysis, considering variables such as gender, road type, and time of day. The effectiveness of the WICRS model is validated by comparing its impact with MPA, specifically examining risk analysis for wet and non-wet days. The dataset includes over 954,000 two-wheeler crash incidents, combined with historical weather data over six years. The findings highlight the significance of weather conditions in two-wheeler crashes and support the use of the WICRS model for detailed RAR analysis and road safety policy formulation.

Keywords

• Weather and road crashes
• Clustering techniques
• Road safety
• Two-wheeler road crashes
• Crash data analysis