mlsanityData to 13 Oct 2026

Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques

arXiv 2406.13968 ↗First posted 20 Jun 2024cs.LGcited by 4

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Abstract (arXiv)

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road safety by conducting a comprehensive review of recent advancements in applying machine learning (ML) techniques to traffic accident analysis and prediction. It examines 191 studies from the last five years, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data. To our knowledge, this study is the first to provide such a comprehensive review, covering the state-of-the-art across a wide range of domains related to accident analysis and prediction. The review highlights the effectiveness of integrating diverse data sources and advanced ML techniques to improve prediction accuracy and handle the complexities of traffic data. By mapping the current landscape and identifying gaps in the literature, this study aims to guide future research towards significantly reducing traffic-related deaths and injuries by 2030, aligning with the World Health Organization (WHO) targets.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 8 of its 193 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

8 of 193 references.

References · 8

  1. 2020Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learningcited by 91
  2. 2020RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Frameworkcited by 33
  3. 2019Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insightscited by 24
  4. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
  5. 2024CrashFormer: A Multimodal Architecture to Predict the Risk of Crashcited by 5
  6. 2023inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Datacited by 3
  7. 2019High-Resolution Road Vehicle Collision Prediction for the City of Montrealcited by 2
  8. 2022Reinforcement Learning for Predicting Traffic Accidentscited by 2

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniquescited by 2
  2. 2025Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods, Datasets, and Future Directionscited by 1
  3. 2025Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysiscited by 1
  4. 2025Predicting and Explaining Traffic Crash Severity Through Crash Feature Selectioncited by 0