Road Accident Prediction Model Using Data Mining Techniques

Abstract

In recent decades, the rise in road traffic accidents poses a significant challenge to public health and infrastructure, especially in swiftly urbanizing regions such as India. Traditional reactive strategies for accident prevention have proven insufficient in proactively mitigating such occurrences. This research emphasizes the crucial need for intelligent predictive systems by introducing a data-driven framework for predicting road accidents using ML and data mining techniques. The approach involves preprocessing historical accident data to eliminate noise and missing values, followed by the training of ranking models like Decision Trees, Random Forest, and Support Vector Machines. Evaluation of model performance includes standards such as correctness, precision, recollect, and F1-score. Through integration with a Streamlit-based application, the model allows for real-time forecasting and visualization of high-risk accident areas. Findings reveal that Random Forest achieved the highest accuracy at 88%, underscoring the potential of the framework to support urban planners, traffic authorities, and emergency responders in implementing preventive measures. This investigation lays the groundwork for scalable, intelligent solutions to enhance transportation safety.

Introduction

• Road traffic accidents are a leading cause of injury and death globally, especially in developing countries with fast urbanization and increasing vehicle numbers.

• According to WHO, millions die yearly in traffic incidents.

• In India, NCRB consistently reports alarming stats on traffic deaths and injuries.

• Traditional accident analysis methods are reactive, based on past incidents and manual data review, offering limited prevention capability.

2. Need for Predictive Solutions

• There's a pressing need for predictive models that:

  • Analyze historical accident data.

  • Detect high-risk patterns.

  • Enable proactive traffic management and early intervention.

• This study proposes a data-driven, AI-enabled system that leverages machine learning (ML) to forecast road accidents and identify accident-prone zones.

3. Objective

• To build a reliable, real-time, and interactive accident prediction framework.

• Integrate it into a user-friendly dashboard using Streamlit for practical use by urban planners and traffic authorities.

4. Literature Review Highlights

• Several previous studies have applied ML to accident prediction:

  • Decision Trees and Naïve Bayes: For predicting based on weather, traffic, and road conditions.

  • Support Vector Machines (SVM): Effective in handling time-series and high-dimensional data.

  • Clustering (K-Means): Useful in identifying accident hotspots, though not predictive.

  • GIS + ML: Combines spatial and temporal data for better forecasting.

  • Ensemble Models: Random Forest and Gradient Boosting showed high recall.

  • LSTM Networks: Effective for sequential prediction but computationally intensive.

Many existing systems lacked real-time capabilities and user-friendly interfaces, which the current study aims to address.

5. Methodology Overview

A. Dataset Collection

• Multi-year accident data from open sources.

• Includes features like:

  • Location, time, weather, vehicle type, casualties, severity, and road type.

B. Data Preprocessing

• Handles missing values, categorical encoding, outlier removal, and feature scaling using Min-Max normalization.

C. Model Training

• Three supervised ML models tested:

  • Decision Tree

  • Random Forest

  • Support Vector Machine (SVM)

• Used:

  • 80/20 train-test split

  • GridSearchCV for hyperparameter tuning

  • 10-fold cross-validation for robustness

D. Performance Evaluation Metrics

• Accuracy

• Precision

• Recall (Sensitivity)

• F1-Score

• Confusion Matrix

E. Model Deployment

• Best-performing model (Random Forest) integrated into a web app (Streamlit).

• Users can input parameters (e.g., weather, road type) to receive real-time risk predictions.

• Visualizations include:

  • Historical trends

  • Hotspot mapping

6. Results & Findings

A. Model Performance

• Random Forest:

  • Accuracy: 88%

  • F1-score: 87%

  • Best at handling non-linear, complex data patterns

• SVM:

  • High precision, slightly lower recall

  • More conservative in flagging risks

• Decision Tree:

  • Interpretable but prone to overfitting

B. Key Insights

• Random Forest is most effective overall.

• Precision and recall are crucial for reducing false alarms and enhancing real-world safety.

• The model balances sensitivity and specificity, making it practical for deployment.

7. Contributions

• Predictive accident model using ML and historical data.

• Real-time dashboard interface for accessibility.

• Supports:

  • Proactive safety measures.

  • Smarter resource allocation.

  • Improved emergency response.

  • Lowering the socioeconomic impact of accidents.

???? Summary Points

• Traffic accidents are a serious and preventable global issue.

• Traditional analysis is not predictive—machine learning fills this gap.

• This study presents a full-stack solution: data preprocessing, ML modeling, evaluation, and deployment.

• Random Forest outperformed other models and was integrated into a Streamlit web app for practical use.

• The framework shows promise for real-world traffic safety systems.

Conclusion

This study introduces a reliable and scalable data-driven framework developed for forecasting road accidents utilizing advanced data mining and ML methods. The proposed system addresses the critical problem of increasing traffic-related incidents by utilizing historical accident data to forecast potential risk zones and periods. The methodology incorporated comprehensive pre-processing steps followed by model training using supervised learning classifiers Decision Tree, SVM, and Random Forest on curated and normalized datasets. The modular architecture of the framework enables accurate prediction, smooth integration with real-time traffic management systems, and offers transparency for policy development and urban safety planning. The outcomes of the experiment validated the dependability and efficacy of the proposed method. Random Forest, out of the models examined, exhibited superior performance by attaining an 88% overall accuracy and an 87% F1-score. This performance makes it the most suitable for practical implementation. alarms and maximize sensitivity to actual accident occu Precision and recall values were fine-tuned so that false results were minimized then. rrences. These findings support the adoption of the proposed system as an effective solution to the real-world challenge of accident prevention, significantly benefiting intelligent transportation and road safety management. As a component of upcoming projects, the system could be improved by integrating real-time information from traffic sensors, weather APIs, and live surveillance feeds to enhance dynamic forecasting. Furthermore, incorporating deep learning models and edge computing may enhance performance and scalability. The inclusion of multilingual interfaces and mobile-based alert systems could increase accessibility to a broader audience, thereby making the framework more inclusive and responsive in smart city environments.

References

[1] Y. Kumar and R. Toshniwal, \"Analysing Road accident data using a data mining framework,\" Journal of Big Data, vol. 2, no. 1, pp. 1–26, Dec. 2015. [2] S. Kumar and S. Toshniwal, \"Analysing Road accident data through machine learning paradigms: a Pune city case study,\" Procedia Computer Science, vol. 122, pp. 604–610, 2017. [3] R. B. Mishra and D. S. Pawar, \"Predicting Road accidents using the Random Forest Algorithm,\" International Journal of Engineering Research & Technology (IJERT), vol. 8, no. 5, May 2019. [4] T. N. S. Nair, R. R. Menon, and V. M. Nair, \"Road accident prediction using ML techniques,\" Procedia Computer Science, vol. 171, pp. 1049–1058, 2020. [5] L. Zhang and B. Ma, \"Prediction of road traffic accident severity through ensemble learning methods,\" Procedia Engineering, vol. 137, pp. 376–385, 2016. [6] S. Chien, K. Ding, and C. Wei, \"Dynamic artificial neural network-based bus arrival time prediction,\" Journal of Transportation Engineering, vol. 128, no. 5, pp. 429–438, 2002.

Copyright

Copyright © 2025 Dr. Girish Kumar D, Miss. Shravani Kamale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.