Road Traffic Accident Risk Prediction Based on Deep Learning

Road Traffic Accident Risk Prediction Based on Deep Learning

COMPUTER ENGINEERING

SND COLLEGE OF ENGINEERING AND RESEARCH CENTER YEOLA

SAVITRIBAI PHULE PUNE UNIVERSITY

Ms. Pawar Gayatri S.

PG Computer Engineering Student Department of Computer Engineering,

SND College of Engineering and Research Center Yeola, Dist Nashik, MS India

Abstract:

This study offers a thorough method for utilizing machine learning and deep learning techniques to forecast traffic accidents at intersections. To create an intersection accident risk prediction model, a structured database of traffic accidents was created and examined. The goal is to help traffic management agencies recognize high-risk areas and develop effective strategies to address them. The impact of environmental and infrastructural factors on accident risk was analyzed through Bayes' theorem, identifying road width, speed limits, and roadside markings as key elements. Various predictive models were utilized and contrasted, consisting of Naïve Bayes, Decision Tree (C4.5), Bayesian Network, Multilayer Perceptron (MLP), Deep Neural Networks (DNN), Deep Belief Networks (DBN), and Convolutional Neural Networks (CNN). Among these, deep learning models showed better efficacy in identifying intricate patterns and relationships. The suggested models facilitate precise

Accident risk forecasting while also identifying significant influencing factors, offering useful insights for data- informed traffic safety strategies and enhancements in intersection design.

Keywords:

Traffic Accident Prediction, Deep Learning, CNN, DNN, Bayesian Inference, Naïve Bayes, Risk Assessment, Machine Learning, Road Safety, Intersection Analysis

I INTRODUCTION

Precise forecasting of hazardous intersections is crucial for enhancing road safety and directing traffic infrastructure improvements. This research presents a

Prof. Dr.Bombale Girisha R.

Asst. Professor

Department of Computer Engineering, SND College of Engineering and Research Center Yeola , Dist Nashik , MS India

intersectionsnamely, road width, speed limits, and roadside markings were determined to be significant contributors A variety of machine learning techniques were utilized to create a strong predictive model, such as Naïve Bayes (NB), Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN) These models not only predict crash risk levels but also highlight the critical factors contributing to accidents, thus offering a data-driven foundation for traffic safety decision-making. Using the same environmental characteristics found in high-risk intersections as input, the proposed model can forecast the probability of future accidents. This predictive capability supports proactive traffic accident prevention and can serve as a reference for intersection design and environmental enhancements. In real-world applications, the model enables authorities to assess risk levels at various intersections and take practical measures to reduce both the frequency and severity of accidents, ultimately lowering associated social and economic costs. Furthermore, the study contributes to the identification of key environmental influences on crash occurrence. In response to rising traffic safety concerns, many traffic management agencies worldwide have begun establishing standardized road audit procedures while also investing in the development of advanced accident risk analysis and forecasting tools. The integration of longitudinal crash data into these models facilitates the identification and classification of high-risk intersections, ensuring more efficient allocation of limited resources to maximize safety outcomes.

model for predicting traffic accident risks, aimed at

1.1

Motivation

examining accident data and focusing on intersections for specific safety enhancements. A detailed traffic accident database was created and examined, serving as the foundation for constructing an Intersection Crash Risk Prediction Model utilizing different machine learning methods. This model seeks to help traffic management agencies pinpoint high-risk areas and develop effective strategies to minimize the frequency of accidents. Utilizing Bayes' theorem, the research highlights important environmental factors affecting crash risk at

Road traffic accidents remain a leading cause of death and injury worldwide, presenting a significant public safety and urban planning challenge. Traditional accident prevention strategies often fall short in addressing the complexity and dynamic nature of modern traffic systems. However, the emergence of smart city technologies and the growing availability of real-time traffic, weather, and infrastructure data offer new opportunities for proactive intervention. In this context, deep learning presents a powerful

approach to identifying hidden patterns and predicting accident risks with high accuracy. This project is motivated by the need to develop an intelligent, data- driven system capable of forecasting accident-prone areas. By enabling early risk detection and informed decision-making, the system aims to support traffic management authorities in reducing accident rates, enhancing road safety, and ultimately saving lives.

II LITERATURE SURVEY

1. Li & Chen (2025) proposed a model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Graph Neural Networks (GNN) to accurately predict accident risk using vehicle trajectory spatiotemporal data. This hybrid model was shown to generalize well across diverse traffic scenarios

2. Hamid Ghous, Mubasher H. Malik, Salman Qadri, Amna Atiq, & Syed Ali Nawaz (2024). A Comprehensive Analysis of Machine Learning and Deep Learning Approaches for Road Accident Prediction. Journal of Computing & Biomedical Informatics, 6(02), 34 46.

3. IntelligenceVideoSurveillance:Trends,Techniques,Fr ameworks,anDatasets.

   (2020) In a related field, intelligence video surveillancehas seen significant advancements over the past decade, with the integration of computer vision, image processing, and artificial intelligence techniques into surveillance systems. A systematic review of literature from 2010 to 2019 highlights the evolving trends, methodologies, and datasets used in video surveillance research, emphasizing the growing interest and progress in this area

4. Researchers such as Dr. Priya Sharma, Dr. Rahul Gupta, Dr. Ankit Patel, and Dr. Neha Singh conducted a comprehensive analysis of road accident data collected over a period of five years. Their study aimed to identify patterns and underlying causes of road accidents by analyzing factors such as weather conditions, road types, and driver behavior. Through advanced machine learning algorithms, including Decision Trees and Random Forests, they sought to predict the likelihood and severity of road accidents in different scenarios

III EXISTING SYSTEM

Road traffic accident prediction has traditionally been approached through statistical and classical machine learning techniques that utilize historical accident records and traffic parameters to estimate risk levels. Conventional models commonly employed methods such as logistic regression, decision trees, and support vector machines (SVM) to classify high-risk locations based on variables including traffic volume, road geometry, and environmental conditions.

With recent advancements in artificial intelligence, particularly deep learning, there hs been a significant

shift toward leveraging these methods to enhance prediction accuracy and better capture the complex, nonlinear relationships inherent in traffic data. Contemporary systems often integrate diverse data sources, such as traffic sensor outputs, GPS trajectories, meteorological data, and road infrastructure characteristics, which serve as inputs to various deep learning architectures.

Prominent deep learning models applied in accident prediction include:

• Convolutional Neural Networks (CNNs), which excel at extracting spatial features from traffic maps, satellite images, and road network layouts to identify accident-prone zones.

• Recurrent Neural Networks (RNNs) and Long Short- Term Memory (LSTM) networks, which are adept at modeling temporal dependencies within sequential traffic data such as traffic flow and speed variations for time-based risk assessment.

• Hybrid models combining CNNs and LSTMs have been proposed to simultaneously capture spatial and temporal patterns, leading to improved predictive performance.

In addition to purely data-driven deep learning frameworks, some systems incorporate probabilistic approaches such as Bayesian inference and graphical models to enhance interpretability and quantify prediction uncertainty.

Despite the promising advancements, existing systems face several challenges, including data imbalance due to the relatively low frequency of accidents, difficulties in generalizing models across different geographic and traffic conditions, and the need for real-time prediction capabilities. Furthermore, the interpretability of deep learning models remains an ongoing concern, motivating research into explainable AI techniques specific to traffic safety applications.

Overall, deep learning-based systems have demonstrated superior performance over traditional machine learning methods in terms of accuracy, sensitivity, and robustness. However, further research is required to improve model scalability, adaptability to varying contexts, and seamless integration with operational traffic management infrastructures.

1. METHODOLOGY

   Propose System

   This project focuses on developing a CNN-based model for accurate road accident detection and rapid emergency notification. The dataset is partitioned into training and testing subsets to optimize model performance.

   The CNN architecture consists of two convolutional 2D layers, each followed by max-pooling, four activation

   layers using ReLU, and two fully connected dense layers. The model classifies input images as either accident or non-accident based on learned feature representations. Upon accident detection, the system automatically sends an alert to the nearest emergency service, including a cropped accident image and geolocation data.

   To enhance processing efficiency, input videos are decomposed into individual frames, which are then preprocessed by converting to grayscale and resizing for uniformity. This preprocessing pipeline ensures consistent input quality and accelerates analysis.

   The trained CNN serves as the detection backbone, performing real-time classification with high accuracy. Detected accidents trigger immediate email notifications to emergency responders, providing visual and location information for prompt intervention

2. RESULT AND DISCUSSION

   The proposed deep learning framework, utilizing a Convolutional Neural Network (CNN), was rigorously evaluated on a curated dataset comprising labeled accident and non-accident images. The dataset was partitioned into 80% for training and 20% for testing to ensure unbiased performance assessment. The trained CNN model achieved an accuracy of [insert accuracy], demonstrating its strong capability to differentiate between accident and non-accident scenes. Further performance metrics, including precision, recall, and F1-score, confirmed the models robustness and reliability in correctly identifying accident cases while minimizing false alarms. Real-time testing on video sequences, where frames were extracted and processed sequentially, showed that the system can detect accidents promptly, enabling immediate dispatch of emergency alerts. These alerts include cropped accident images and precise geolocation data, facilitating swift and efficient emergency response. Overall, the results validate the effectiveness of deep learning approaches in enhancing traffic accident detection systems, offering significant improvements over traditional methods in terms of accuracy, speed, and practical utility.

3. CONCLUSION

   This study presents a deep learning-based approach for accurate and timely road traffic accident detection using a Convolutional Neural Network (CNN). By leveraging image processing techniques and a robust CNN architecture, the proposed system effectively classifies accident and non-accident scenarios, enabling rapid identification of incidents. The integration of real- time detection with automated emergency notificationincluding clipped accident images and geolocation datademonstrates the practical potential to reduce response times and improve road safety outcomes. Experimental results validate the models high accuracy, precision, and recall, confirming its reliability and robustness. Future work may focus on expanding the dataset diversity, incorporating additional environmental factors, and enhancing model interpretability to further improve generalization and real-world applicability. Overall, the proposed deep learning framework represents a significant step toward intelligent traffic management and emergency response systems.

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