A Road Accident Prediction Model Using Data Mining Techniques

Abstract

The dangers and risks of the unpleasant phenomenon of road accidents, has been a major worrying issue throughout the whole the world that needs to be resolved quickly. The issue of traffic accident faq ' s has been of much focus of the society, secondary and road operators and organizations are likely to have on both social and economic aspects of the disaster. The most recent figures of the general record affecting accidents in Saudi Arabia showed that, in 2017, there were more than 32, 000 deaths due to road accidents. This is despite the fact that traffic rules and fined are heavily regulated and at relatively high levels. Often the ability of traditional methods used to predict and prevent accidents is not sufficient. Many of these methods lack the accuracy needed to produce a reliable model. The purpose of this research project is to create an advanced road accident to increase safety and to decrease the death rate. For this purpose, various data mining techniques have been used to develop road accident models. This project will also use various data mining techniques such as Decision Trees, Neural Networks and Ensemble methods to find solutions for this issue. Data from different sources will be collected in order to create a dependable model.

Country : India

1 Ch.Venkata Komali2 E. Vimalraj

  1. MCA student, Department of Computer Applications, Mohan Babu University, Tirupati, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India

IRJIET, Volume 9, Special Issue of INSPIRE’25 April 2025 pp. 371-376

doi.org/10.47001/IRJIET/2025.INSPIRE60

References

  1. Babu, S. N., & Tamilselvi, J. J. (2024). Map Reduce approach for road accident data analysis using data mining techniques. International Journal of Advanced Intelligence Paradigms, 27(1), 1-17.
  2. Dias, D., Silva, J. S., & Bernardino, A. (2023, January). The prediction of road-accident risk through data mining: A case study from Setubal, Portugal. In Informatics (Vol. 10, No. 1, p. 17). MDPI.
  3. Prajapati, G., Kumar, L., & Patil, S. R. S. (2023). Road Accident Prediction Using Machine Learning. Journal of Scientific Research and Technology, 48-59.
  4. Ahmed, S., Hossain, M. A., Ray, S. K., Bhuiyan, M.M. I., & Sabuj, S. R. (2023). A study on road accident prediction and contributing factors using explainable machine learning models: Analysis and performance. Transportation Research interdisciplinary perspectives, 19, 100814.
  5. Esenturk, E., Turley, D., Wallace, A., Khastgir, S., & Jennings, P. (2023). A data mining approach for traffic accidents, pattern extraction and test scenario generation for autonomous vehicles. International Journal of Transportation Science and Technology, 12(4), 955-972.
  6. Panda, C., Mishra, A. K., Dash, A. K., & Nawab, H. (2023). Predicting and explaining severity of road accident using artificial intelligence techniques, SHAP and feature analysis. International journal of 1crashworthiness, 28(2), 186-201.
  7. Alkheder, S., AlRukaibi, F., & Aiash, A. (2020). Risk analysis of traffic accidents’ severities: An application of three data mining models. ISA transactions, 106, 5213-220.
  8. Gutierrez-Osorio, C., & Pedraza, C. (2020). Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of traffic and transportation engineering (English edition), 7(4), 432-446.
  9. Chen, M. M., & Chen, M. C. (2020). Modeling road accident severity with comparisons of logistic regression decision tree and random forest. Information, 11(5), 270.
  10. Najafi Moghaddam Gilani, V., Hosseinian, S. M., Ghasedi, M., & Nikookar, M. (2021). Data-driven urban traffic accident analysis and prediction using logit and machine learning-based pattern recognition models. Mathematical problems in engineering, 2021, 1-11.
  11. Lin, Y., & Li, R. (2020). Real-time traffic accidents post-impact prediction: Based on crowd sourcing data. Accident Analysis & Prevention, 145, 105696.
  12. Labib, M. F., Rifat, A. S., Hossain, M. M., Das, A. K., & Nawrine, F. (2019, June). Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In 2019 7th international conference on smart computing & communications (ICSCC) (pp. 1-5). IEEE.