Data-Driven Analysis and Prediction of Road Traffic Accidents Using ML Algorithms

Road traffic collisions pose a major societal problem, resulting in considerable human casualties and financial losses globally. This research introduces a novel internet-based vehicular crash pattern extraction system utilizing the ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal) methodology to uncover concealed trends within collision databases. The developed framework examines diverse causal elements such as velocity restrictions, meteorological circumstances, road elevation features, and crash categories to produce correlation principles that inform prevention strategies. The system design implements a hierarchical structure featuring distinct portals for system administrators, transportation authority personnel, and general public access. Test outcomes showcase the platform's ability to detect crucial collision trends through adjustable reliability parameters, empowering transportation agencies to deploy focused safety measures. The framework accomplished pattern identification with processing durations measured in milliseconds and revealed primary accident-inducing elements including intoxicated operation, vehicular impacts, and excessive velocity as leading causes of transportation incidents