Highway Crash Risk Assessment and Predictive Modelling in Madhya Pradesh: An Integrated Data-Driven Approach (2017–2024)
Keywords:
Road Accidents, Highways, Black-Spots, Traffic Safety, Predictive Models, Madhya PradeshAbstract
Highway accidents remain a critical safety and economic issue in India, accounting for nearly 65% of total road fatalities each year. Madhya Pradesh, located at the heart of the country, is a key transport corridor with heavy interstate freight and passenger traffic. This study investigates accident trends and risk factors across major highways in Madhya Pradesh between 2017 and 2024 using a combination of secondary crash records and primary field surveys. Data from over 11,200 reported crashes were analyzed using Geographic Information System (GIS) mapping and Poisson regression modeling. The findings reveal that over-speeding (47.2%), lane indiscipline (22.6%), and driver fatigue (15.4%) are dominant contributors to crashes. Black-spot analysis identifies NH-46 (Bhopal–Indore corridor) and SH-22 (Guna–Ashoknagar section) as high-risk segments, with crash densities exceeding 5.1 accidents/km/year. Predictive models demonstrate that a 10% rise in traffic volume correlates with a 5.3% increase in accident risk, while wet pavement conditions increase crash probability by 28%. Policy recommendations include AI-assisted enforcement, dynamic speed management, road geometry corrections, and community-based awareness programs. The study contributes a replicable data framework for state-level road safety planning in India.
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