Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.

Keywords: Road accidents, Logistic regression, Factors, Machine learning, Random forest.

[1] Y. Zou, B. Lin, X. Yang, L. Wu, M. M. Abid, and J. Tang (2021). Application of the Bayesian model averaging in analyzing freeway traffic incident clearance time for emergency management. J. Adv. Transp., Pages 1–9.

[2] J. Tang, L. Zheng, C. Han, W. Yin, Y. Zhang, Y. Zou, and H. Huang (2020). Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review. Anal. Methods Accident Res., 27.

[3] M. Umer, I. Ashraf, A. Mehmood, S. Ullah, and G.S. Choi (2021). Predicting numeric ratings for Google apps using text features and ensemble learning. ETRI J., 43(1): 95–108.

[4] M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G.S. Choi, and B.W. (2020). Fake news stance detection using deep learning architecture (CNNLSTM). IEEE Access, 8: 156695–156706.

[5] S. Sadiq, A. Mehmood, S. Ullah, M. Ahmad, G.S. Choi, and B.W. (2021). Aggression detection through deep neural model on Twitter. Future Gener. Comput. Syst., 114: 120–129.

[6] Z. Imtiaz, M. Umer, M. Ahmad, S. Ullah, G.S. Choi, and A. Mehmood (2020). Duplicate questions pair detection using Siamese MaLSTM. IEEE Access, 8: 21932–21942.

[7] M.I. Sameen and B. Pradhan (2017). Severity prediction of traffic accidents with recurrent neural networks. Appl. Sci., 7(6): 476.

[8] S. Seid and Pooja (2019). Road accident data analysis: Data preprocessing for better model building. J. Comput. Theor. Nanosci., 16(9): 4019–4027.

[9] S.K. Singh (2017). Road traffic accidents in India: Issues and challenges. Transp. Res. Proc., 25(5): 4708–4719.

[10] D. Delen, R. Sharda, and M. Bessonov (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Anal. Prevention, 38(3): 434–444.

[11] D.W. Kononen, C.A.C. Flannagan, and S.C. Wang (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident Anal. Prevention, 43(1): 112–122.

[12] P. Duan, Z. He, Y. He, F. Liu, A. Zhang, and D. Zhou (2020). Root cause analysis approach based on reverse cascading decomposition in QFD and fuzzy weight ARM for quality accidents. Comput. Ind. Eng., 147.

[13] H.M. Alnami, I. Mahgoub, and H. Al-Najada (2021). Highway accident severity prediction for optimal resource allocation of emergency vehicles and personnel. In Proc. IEEE 11th Annu. Comput. Commun. Workshop Conf. (CCWC), Pages 1231–1238.

[14] M. Umer, I. Ashraf, A. Mehmood, S. Kumari, S. Ullah, and G. S. Choi (2021). Sentiment analysis of tweets using a unified convolutional neural network-long short-term memory network model. Comput. Intell., 37(1): 409–434.

[15] S. Sadiq, M. Umer, S. Ullah, S. Mirjalili, V. Rupapara, and M. Nappi (2021). Discrepancy detection between actual user reviews and numeric ratings of Google app store using deep learning. Expert Syst. Appl., 181.

[16] P. Tiwari, S. Kumar, and D. Kalitin (2017). Road-user specific analysis of traffic accident using data mining techniques. In Proc. Int. Conf. Comput. Intell., Commun., Bus. Anal. New York, NY, USA, Pages 398–410.

[17] R.E. AlMamlook, K.M. Kwayu, M.R. Alkasisbeh, and A.A. Frefer (2019). Comparison of machine learning algorithms for predicting traffic accident severity. In Proc. IEEE Jordan Int. Joint Conf. Electr. Eng. Inf. Technol., Pages 272–276.

[18] T. Beshah and S. Hill (2010). Mining road traffic accident data to improve safety: Role of road-related factors on accident severity in Ethiopia. In Proc. AAAI Spring Symp., Artif. Intell. Develop., Volume 24, Princeton, NJ, USA: Citeseer, Pages 1173–1181.

[19] X. Ma, C. Ding, S. Luan, Y. Wang, and Y. Wang (2017). Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Trans. Intell. Transp. Syst., 18(9): 2303–2310.

[20] B. Yu, Y.T. Wang, J.B. Yao, and J.Y. Wang (2016). A comparison of the performance of ANN and SVM for the prediction of traffic accident duration. Neural Netw. World, 26(3): 271.

Source of Funding:

This study did not receive any grant from funding agencies in the public or not-for-profit sectors.

Competing Interests Statement:

The authors have declared no competing interests.

Consent for Publication:

The authors declare that they consented to the publication of this study.

Authors’ Contribution:

Both the authors took part in literature review, research, and manuscript writing equally.