Open Access

Accident Risk Prediction Using Artificial Intelligence-Based Intelligent Transportation Systems

https://doi.org/10.55640/irjaet-v03i07-04

pdf

Dr. Puneet Garg ¹ ,

⁴ Associate Professor, Department of CSE, AIKIET Group of Institutions, Delhi NCR, Ghaziabad, India

Abstract

The necessity for intelligent accident risk prediction systems is highlighted by the fact that road accidents continue to be one of the top sources of deaths and financial losses globally. Within the scope of Intelligent Transportation Systems (ITS), this research presents a framework for accident risk prediction to enhance road safety via fast and accurate prediction. The suggested method makes use of the massive US Accidents dataset, which keeps track of millions of actual traffic accident records. For better data quality and predictive performance, extensive data preparation, feature engineering, and feature selection approaches are used. The CatBoost classifier is used due to its proficiency in handling categorical information, its capacity to lessen prediction bias, and its ability to minimize overfitting. The recommended model is evaluated using performance measures including Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix. Experimental results demonstrate that the proposed model obtains a ROC-AUC of 0.94, 92.6% accuracy (ACC), 92.3% precision (PRE), 92.1% recall (REC), and 92.8% F1-score (F1), putting it ahead of other existing machine learning models. Feature significance analysis goes a step further by determining which road and environmental features have the greatest impact on accident risk. Applications involving proactive road safety and intelligent traffic management might benefit from the suggested framework's dependable and efficient solution.

Keywords

Traffic Accident Prediction, Intelligent Transportation Systems (ITS), Machine Learning

References

W. Sun, L. N. Abdullah, P. Suhaiza Sulaiman, and F. Khalid, “Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence,” Vehicles, 2024, doi: 10.3390/vehicles6020034.

S. Singamsetty, “AI-powered Satellite Image Processing for Global Air Traffic Surveillance Techniques Using NCNN–EGSA Optimization Techniques,” in Machine Learning Based Air Traffic Surveillance System Using Image Processing, Emerald Publishing Limited, 2026, pp. 179–197. doi: 10.1108/978-1-80592-062-520251010.

Y. Muni, “Advanced Multi-Protocol Label Switching (MPLS)-Enabled Networks: A Survey of Emerging Approaches Via Traffic Engineering,” Eng. Technol. J., vol. 11, no. 01, Jan. 2026, doi: 10.47191/etj/v11i01.22.

S. Batita, A. Makni, and I. Amous, “Intelligent Transportation Systems: A Survey on Data Engineering,” in Proceedings of the 13th International Conference on Data Science, Technology and Applications, DATA 2024, 2024, pp. 169–179. doi: 10.5220/0012857300003756.

S. Singh, “Advancing Wireless Communications with Open Radio Access Network,” in 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), IEEE, Jul. 2025, pp. 682–687. doi: 10.1109/ICDICI66477.2025.11135206.

M. Akour, O. A. L. Qasem, and F. Hanandeh, “Traffic Accident Severity Prediction : A Comparison Study,” Int. J. Transp. Syst., vol. 7, pp. 24–28, 2022.

V. K. Sharma and A. K. S, “Hierarchical Cloud-IoT Architecture for AI-Powered Intelligent Disaster Response,” in 2025 7th International Conference on Innovative Data Communication Technologies and Application (ICIDCA), IEEE, Oct. 2025, pp. 603–610. doi: 10.1109/ICIDCA66325.2025.11280408.

Y. Wang, T. Liu, Y. Lu, H. Wan, P. Huang, and F. Deng, “Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion,” IEEE Access, vol. 12, pp. 18694–18702, 2024, doi: 10.1109/ACCESS.2024.3358453.

H. Li and L. Chen, “Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories,” PLoS One, 2025, doi: 10.1371/journal.pone.0320656.

R. Dandigam and C. Agrawal, “Comparative Study of Machine Learning Algorithms for Predictive Analytics in Web Applications,” in 2026 International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET), 2026, pp. 1–6. doi: 10.1109/ICAISET66439.2026.11542167.

H. K. Yadav, “Design and Implement Machine Learning Based Predictive Analytics for Road Safety and Accident Prevention,” in 2026 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IATMSI68868.2026.11465965.

J. Siswanto, A. S. N. Syaban, and H. Hariani, “Artificial Intelligence in Road Traffic Accident Prediction,” Jambura J. Informatics, vol. 5, no. 2, pp. 77–90, Nov. 2023, doi: 10.37905/jji.v5i2.22037.

Z. Tan, L. Cui, H. Xu, J. Xu, H. Feng, and P. Wang, “A hybrid machine learning approach for predicting traffic accident collision severity,” Int. J. Inj. Contr. Saf. Promot., vol. 33, no. 2, pp. 267–281, Apr. 2026, doi: 10.1080/17457300.2026.2640066.

M. A. Amiri, S. Afshari, and A. Soltani, “Machine learning approaches to traffic accident severity prediction: Addressing class imbalance,” Mach. Learn. with Appl., vol. 22, p. 100792, 2025, doi: https://doi.org/10.1016/j.mlwa.2025.100792.

D. F. El-Gezawi, “A data-driven framework for intelligent traffic risk prediction and accident prevention,” Appl. Oper. Anal., vol. 1, no. 1, pp. 1–15, Dec. 2025, doi: 10.1080/29966892.2025.2539184.

M. A. K. Rifat, A. Kabir, and A. Huq, “An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction,” Procedia Comput. Sci., vol. 246, no. C, pp. 1905–1914, 2024, doi: 10.1016/j.procs.2024.09.704.

I. C. Obasi and C. Benson, “Evaluating the effectiveness of machine learning techniques in forecasting the severity of traffic accidents,” Heliyon, vol. 9, no. 8, Aug. 2023, doi: 10.1016/j.heliyon.2023.e18812.

T. Augustine and S. Shukla, “Road Accident Prediction using Machine Learning Approaches,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 808–811. doi: 10.1109/ICACITE53722.2022.9823499.

R. D. Bhosale, “US_Accidents_March23.csv,” Kaggle, 2023, [Online]. Available: https://www.kaggle.com/datasets/rutuldbhosale/us-accidents-march23-csv

N. Adik, “The Rise of Open and Free Networks: A Community-Driven Paradigm for Decentralized Connectivity,” in 2025 IEEE First International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS), Coimbatore, India: IEEE, 2025, pp. 1–9, November. doi: 10.1109/ICINVENTS64613.2025.11402224.

A. P. Kumar and D. T. Santosh, “Road Accident Severity Prediction Using Machine Learning Algorithms,” Int. J. Comput. Eng. Res. Trends, vol. 9, no. 9, pp. 175–183, 2022.

G. M and D. V, “Deep Learning-Based Traffic Accident Prediction: An Investigative Study for Enhanced Road Safety,” EAI Endorsed Trans. Internet Things, vol. 10, pp. 1–10, Feb. 2024, doi: 10.4108/eetiot.5166.

M. Kushwaha and M.S. Abirami, “Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network,” Int. J. Comput. Commun. Control, vol. 18, no. 5, pp. 1–22, Aug. 2023, doi: 10.15837/ijccc.2023.5.5317.

O. I. Aboulola, E. A. Alabdulqader, A. A. AlArfaj, S. Alsubai, and T.-H. Kim, “An Automated Approach for Predicting Road Traffic Accident Severity Using Transformer Learning and Explainable AI Technique,” IEEE Access, vol. 12, pp. 61062–61072, 2024, doi: 10.1109/ACCESS.2024.3380895.

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>

International Research Journal of Advanced Engineering and Technology

Accident Risk Prediction Using Artificial Intelligence-Based Intelligent Transportation Systems

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles