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

Noushin Behboudi, Sobhan Moosavi, Rajiv Ramnath

TL;DR

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 techniques to traffic accident analysis and prediction, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data.

Abstract

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.

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

TL;DR

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 techniques to traffic accident analysis and prediction, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data.

Abstract

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.
Paper Structure (19 sections, 6 figures, 9 tables)

This paper contains 19 sections, 6 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Research Method and Preliminaries
  3. Concepts
  4. Research Methodology
  5. Overview of The Collected Papers
  6. Accident Risk Prediction
  7. Risk Prediction Using Traditional Machine Learning Models
  8. Risk Prediction Using Deep Neural Network Models
  9. Accident Frequency Prediction
  10. Frequency Prediction Using Traditional Machine Learning Models
  11. Frequency Prediction Using Deep Neural Network Models
  12. Accident Severity Prediction
  13. Severity Prediction Using Traditional Machine Learning Models
  14. Severity Prediction Using Deep Neural Network Models
  15. Accident Duration Prediction
  16. ...and 4 more sections

Figures (6)

  • Figure 1: An overview of global road traffic accident fatalities (2010 - 2023) according to the WHO reports (WHO2010WHO2013WHO2015WHO2018WHO2021WHO2023)
  • Figure 2: Our Process to Collect and Summarize Relevant Research Articles
  • Figure 3: Yearly distribution of reviewed research papers
  • Figure 4: Distribution of different research categories that are reviewed in this study
  • Figure 5: Distribution of major topics in the reviewed papers
  • ...and 1 more figures

Theorems & Definitions (3)

  • Definition 2.1: Traditional Models
  • Definition 2.2: Deep Neural Network Models
  • Definition 2.3: Statistical Modeling and Analysis