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

arXiv: https://arxiv.org/abs/2406.13968 · HTML: https://mlsanity.com/paper/2406.13968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 193 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework](https://mlsanity.com/paper/2003.00819/) (2020)
- [Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights](https://mlsanity.com/paper/1909.09638/) (2019)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [CrashFormer: A Multimodal Architecture to Predict the Risk of Crash](https://mlsanity.com/paper/2402.05151/) (2024)
- [inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data](https://mlsanity.com/paper/2307.03854/) (2023)
- [High-Resolution Road Vehicle Collision Prediction for the City of Montreal](https://mlsanity.com/paper/1905.08770/) (2019)
- [Reinforcement Learning for Predicting Traffic Accidents](https://mlsanity.com/paper/2212.04677/) (2022)

## Built on by (most cited first, 4 of 4)

- [Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques](https://mlsanity.com/paper/2509.07845/) (2025)
- [Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods, Datasets, and Future Directions](https://mlsanity.com/paper/2505.07611/) (2025)
- [Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysis](https://mlsanity.com/paper/2506.16186/) (2025)
- [Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection](https://mlsanity.com/paper/2508.11504/) (2025)

## 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.