Factors, Prediction, and Explainability of Vehicle Accident Risk Due to Driving Behavior through Machine Learning - A Systematic Literature Review, 2013-2023

Aug 5, 2026

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Road accidents are rising worldwide, causing 1.35 million deaths per year, motivating solutions that can improve safety. Autonomous vehicles are promising, yet fully automated driving remains distant, so research emphasizes predicting and explaining accident risk using real-time telematics. This systematic review analyzes factors, machine learning algorithms, and explainability methods used to assess vehicle accident risk based on driving behavior. Studies from 2013 to July 2023 are reviewed and categorized into five factor domains, reporting 115 factors and the most common 22 base algorithms and six explanation methods.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

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Introduction

Motivation and background

Evolution of vehicle accident risk prediction research

Driving behavior definition and categorization

computation Review Factors, Prediction, and Explainability of Vehicle Accident Risk Due to Driving Behavior through Machine Learning: A Systematic Literature Review, 2013–2023 Javier Lacherre 1, *, José Luis Castillo-Sequera 2 and David Mauricio 1 Citation: Lacherre, J.; Castillo-Sequera, J.L.; Mauricio, D. Factors, Prediction, and Explainability of Vehicle Accident Risk Due to Driving Behavior through Machine Learning: A Systematic Literature Review, 2013–2023 . Computation 2024, 12, 131. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)computation12070131 Academic Editor: Rafael Lahoz-Beltra Received: 25 April 2024 Revised: 20 June 2024 Accepted: 26 June 2024 Published: 28 June 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Faculty of Systems Engineering and Informatics, National University of San Marcos, Lima 15081, Peru; [dmauricios@unmsm.edu.pe](dmauricios@unmsm.edu.pe) 2 Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcala de Henares, Spain; [jluis.castillo@uah.es](jluis.castillo@uah.es) * [Correspondence: javier.lacherre@unmsm.edu.pe](Correspondence: javier.lacherre@unmsm.edu.pe) Abstract: Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, efforts have focused on predicting and explaining the risk of accidents using real-time telematics data. This study aims to analyze the factors, machine learning algorithms, and explainability methods most used to assess the risk of vehicle accidents based on driving behavior. A systematic review of the literature produced between 2013 and July 2023 on factors, prediction algorithms, and explainability methods to predict the risk of traffic accidents was carried out. Factors were categorized into five domains, and the most commonly used predictive algorithms and explainability methods were determined. We selected 80 articles from journals indexed in the Web of Science and Scopus databases, identifying 115 factors within the domains of environment, traffic, vehicle, driver, and management, with speed and acceleration being the most extensively examined. Regarding machine learning advancements in accident risk prediction, we identified 22 base algorithms, with convolutional neural network and gradient boosting being the most commonly used. For explainability, we discovered six methods, with random forest being the predominant choice, particularly for feature importance analysis. This study categorizes the factors affecting road accident risk, presents key prediction algorithms, and outlines methods to explain the risk assessment based on driving behavior, taking vehicle weight into consideration. Keywords: machine learning; prediction algorithms; risk assessment; road accident 1. Introduction There are around 1.35 million deaths worldwide per year due to vehicle accidents [1]; in Europe, 60% of such deaths occur on two-lane roads [2] . In this regard, the United Nations Organization has proposed 17 sustainable development goals (SDGs) for the year 2030, where SDG-3,“Good health and well-being”, aims to reduce deaths and injuries resulting from traffic incidents by 50% worldwide [3] . One potential option is the implementation of autonomous vehicles. Nevertheless, complete automation in driving is still a considerable distance away, making it unlikely in the foreseeable future [4]; furthermore, extensive research is still needed, especially in

FAQs

What does the systematic review analyze about vehicle accident risk?

It analyzes the factors, machine learning algorithms, and explainability methods used to assess vehicle accident risk based on driving behavior.

How were the factors organized in the reviewed studies?

Factors were categorized into five domains: environment, traffic, vehicle, driver, and management.

Which machine learning and explainability approaches were most commonly identified?

Convolutional neural networks and gradient boosting were most used for prediction, while random forest was the predominant explainability method, especially for feature importance.

Aug 5, 2026

Upload by Hazel