Leveraging Machine Learning for Road Accident Analysis - Machine learning models for traffic safety

Aug 2, 2026

Road accidents impose significant human and economic costs worldwide, creating urgent demand for smarter evidence-based safety planning. This paper explores how advanced machine learning supports deeper analysis of road accident data to extract actionable patterns and insights. Proposed approaches include hybrid neural network architectures optimized via nature-inspired algorithms and interpretable rule-based tree ensemble techniques. Models are trained and evaluated on road-related feature datasets, with results comparing strengths and weaknesses through standard performance metrics. Findings support proactive, data-driven traffic safety interventions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Machine learning for road accident analysis

Methodologies

Artificial Neural Networks

Cluster-augmented ANN

Multi-objective ANN optimization

Leveraging Machine Learning for Road Accident Analysis Janardhan Reddy Guntaka 1 , Ram Prakash Yallavula2 , Velangi Joseph Karunakar Reddy Gade3 , Dr. P.Vidya Sagar4 , Dr. A. Dinesh Kumar5 1 Department of CSE, Bachelor of Scholars, Koneru Lakshmaiah Educational Foundations, Green Fields, Vaddesawram, Guntur, [India. ](India. Klucse2000030351@gmail.com)[Klucse2000030351@gmail.com](India. Klucse2000030351@gmail.com) 2 Department of CSE, Bachelor of Scholars, Koneru Lakshmaiah Educational Foundations, Green Fields, Vaddesawram, Guntur, [India. ](India. Klucse2000031119@gmail.com)[Klucse2000031119@gmail.com](India. Klucse2000031119@gmail.com) 3 Department of CSE, Bachelor of Scholars, Koneru Lakshmaiah Educational Foundations, Green Fields, Vaddesawram, Guntur, [India. ](India. Klucse2000031154@gmail.com)[Klucse2000031154@gmail.com](India. Klucse2000031154@gmail.com) 4 Department of CSE, Assiosiate Professor, Koneru Lakshmaiah Educational Foundations, Green Fields, Vaddesawram, Guntur, [India. ](India. pvsagar20@gmail.com)[pvsagar20@gmail.com](India. pvsagar20@gmail.com) 5 Department of CSE, Assiosiate Professor, Koneru Lakshmaiah Educational Foundations, Green Fields, Vaddesawram, Guntur, India. adinesh@kluniversity.in Abstract: Road accidents result in high human and economic costs globally. This paper examines how advanced machine learning techniques can support enhanced analysis of road accident data to uncover patterns and insights to guide traffic safety interventions. Novel machine learning methods proposed include hybrid neural network architectures optimized using nature-inspired algorithms and interpretable rule-based tree ensemble techniques. Our investigation commences with the training and evaluation of each model on a diverse dataset comprising various roadrelated features. The performance metrics, including accuracy predictive capabilities. The results reveal nuanced strengths and weaknesses in each approach. Keywords: Machine learning, road Accident, Traffic, Safety. 1. INTRODUCTION Over 1 million people die worldwide every year from road traffic crashes, with millions more sustaining injuries and disabilities [1] . These accidents impose heavy financial burdens accounting for 2-5% of GDP in many nations [2] . Developing effective strategies to reduce this public health burden requires a multi-pronged approach targeting safer road infrastructure, vehicle standards, road user behaviors, trauma care and integrated traffic management. A key input to evidence-based road safety planning is in-depth understanding of the myriad factors contributing to accidents. Road accident data collected from police reports, hospitals, insurance claims and other sources provides valuable information on the circumstances and conditions associated with crashes. However, manually analyzing this high-dimensional heterogeneous data is challenging. Advanced analytical techniques are essential to derive meaningful insights from road accident datasets. Machine learning offers promising automated approaches to uncover complex relationships in multivariate traffic accident data. This paper reviews novel techniques proposed for applying machine learning to identify patterns in crash conditions, model injury severity outcomes, predict accident hotspots and support data-driven safety interventions. The specific methods examined include artificial neural networks, evolutionary optimized architectures, interpretable tree-based ensemble models, causal analysis, and transfer learning approaches. The paper is organized into sections describing each technique, its application for road accident analysis, benefits and limitations. Overall, the studies highlight the potential of machine learning to move beyond reactive statistics-based approaches towards proactive data-driven traffic safety management powered by deeper insights. Figure 1.Overview of a machine learning approach for road accident analysis. This diagram illustra

FAQs

Why is road accident data analysis important for traffic safety planning?

Road accident data from police reports, hospitals, insurance claims, and related sources captures crash circumstances and contributing factors. Using it helps derive meaningful insights that support evidence-based safety interventions.

What machine learning approaches does the paper examine for road accident analysis?

The paper reviews artificial neural networks, evolutionarily optimized architectures, interpretable tree-based ensemble models, causal analysis, and transfer learning approaches for tasks such as injury severity modeling and hotspot prediction.

What are key strengths and limitations of artificial neural networks in this context?

ANN can capture complex non-linear relationships and interactions among factors affecting severity. Limitations include overfitting risk and limited model interpretability, which motivates approaches like clustering and optimization.

Aug 2, 2026

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