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

Abstract

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.

Introduction

Road traffic accidents are a leading global cause of death and injury, involving complex factors like environment, infrastructure, vehicles, and human behavior. Traditional statistical methods often fail to uncover the intricate relationships in accident data. Data mining, especially association rule mining, has emerged as a powerful tool to discover hidden patterns in large crash datasets.

The ECLAT algorithm, known for its efficient frequent pattern mining through a depth-first search and transaction ID intersection, outperforms classical algorithms like Apriori and FP-Growth, particularly for the sparse, categorical data typical in traffic accident records.

This research develops a comprehensive web-based system utilizing ECLAT to analyze traffic accident data, supporting multiple user roles with tailored interfaces. The system’s three-tier architecture enables scalable, real-time pattern extraction and visualization, aiding traffic authorities in data-driven decision-making for accident prevention and road safety.

The literature review highlights the evolution from traditional statistical crash analysis to advanced data mining techniques, including correlation rule extraction, hybrid methods, and regional adaptations to account for geographic and cultural differences. Web-based architectures improve accessibility and collaboration, while optimization techniques enhance algorithm efficiency in handling large datasets.

Conclusion

This research presents a comprehensive traffic accident pattern mining system that successfully applies the ECLAT algorithm to discover meaningful relationships in accident datasets. The web-based architecture provides practical tools for traffic management authorities while ensuring accessibility across different user categories. The system demonstrates significant potential for enhancing road safety management through data-driven decision making. By identifying critical accident patterns and their contributing factors, traffic authorities can implement targeted interventions that address specific risk scenarios rather than applying generic safety measures. Experimental results validate the system\'s efficiency in pattern discovery while maintaining high-quality rule generation with strong confidence measures. The identified patterns provide valuable insights into accident causation mechanisms, enabling proactive safety management strategies. The modular design and scalable architecture position the system for broader deployment across diverse geographical regions and traffic management contexts. Integration capabilities ensure compatibility with existing infrastructure while providing enhanced analytical capabilities for modern traffic safety management. In the future, the system can be improved by incorporating public notification features to keep citizens informed in real time. A dedicated query module can also be added to facilitate direct interaction between administrators and the public regarding traffic-related concerns. Additionally, the system can be enhanced with predictive capabilities to analyze and identify potential reasons for accidents, enabling traffic departments to take proactive precautionary measures.

References

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Copyright

Copyright © 2025 Punith Kumar K B, Kavyashree G J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.