Machine Learning Techniques for Predicting Road Accidents Using Traffic and Weather Data - A Detailed Report

Abstract

Road accidents continue to be recognized as a critical public?health challenge that causes substantial human and economic losses worldwide. The recent proliferation of real time traffic sensors and meteorological observation systems has produced extensive multimodal data streams that can be leveraged by machine learning algorithms. This survey reviews and synthesizes contemporary research on data driven accident prediction models that exploit historical traffic and weather data. The review covers supervised learning approaches ranging from decision tree ensembles to graph and transformer networks, and it emphasizes recent advances published in 2025. Key data processing practices, feature engineering strategies, evaluation protocols, and deployment issues are examined. Gaps in scalability, interpretability, and cross regional generalizability are identified, and research directions are proposed. The findings are intended to guide both scholars and practitioners who aim to design proactive accident mitigation systems in intelligent transportation contexts.

Introduction

The paper surveys recent advancements in machine learning (ML)-based road traffic crash prediction, emphasizing the integration of traffic and weather data. Traditional reactive safety measures are inadequate, and the availability of high-resolution traffic and meteorological data now enables more proactive, data-driven approaches.

The literature review highlights key contributions from 2005 to 2025:

• Early studies (e.g., Abdel Aty & Radwan, 2005) demonstrated that crash risk could be predicted from traffic sensors and simple weather indicators using statistical models.

• Later research incorporated ensemble methods (e.g., Random Forests, GBM, LightGBM), deep learning (DNNs, CNNs), and sequence models (GRUs, Transformers) to capture complex interactions and temporal patterns.

• Recent innovations include graph neural networks for spatial dependencies, multimodal fusion networks (combining LiDAR, weather, and vehicle telemetry), reinforcement learning for adaptive traffic signals, transfer learning for cross-regional model adaptation, and federated learning for privacy-preserving collaboration across agencies.

• Practical deployments leverage edge computing for low-latency predictions and cloud-native architectures for real-time driver alerts.

• Explainable AI (XAI) techniques are increasingly used to enhance model transparency and stakeholder trust.

The problem statement identifies ongoing challenges:

• Data scarcity for rare crash events.

• Difficulty ensuring model generalizability across regions.

• Real-time inference latency constraints.

• Lack of interpretability in complex models.

• Privacy concerns when integrating heterogeneous data.

Addressing these issues is crucial to advancing the practical deployment and impact of ML-based crash prediction systems.

Conclusion

The surveyed literature demonstrates that machine learning methods can provide timely and accurate accident risk assessments by integrating traffic and weather data. Recent 2025 studies have concentrated on privacy preservation, explainability, and deployment at the edge, thereby extending earlier algorithmic advances. Future research is expected to pursue standardized benchmark datasets, unified evaluation metrics, and hybrid architectures that combine symbolic reasoning with deep representation learning.

References

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Copyright

Copyright © 2025 Dr. R. Hemalatha, Shima A N. 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.