Prediction of Severity of an Accident Based on the Extent of Injury using Machine Learning

© 2022 by IJCTT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Surendra Kumar Reddy Koduru
DOI :  10.14445/22312803/IJCTT-V70I9P106

How to Cite?

Surendra Kumar Reddy Koduru, "Prediction of Severity of an Accident Based on the Extent of Injury using Machine Learning," International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 43-49, 2022. Crossref,

Accidents are currently regarded as the most disturbing cause in many countries. Several deaths have been recorded for generating massive deaths throughout numerous countries just as a result of road accidents that predominantly occur during traffic. Vehicle accidents are the leading cause of deception, distress, and fatality. The majority of accidents occur only over a long period from various countries and are referred to as unsafe or dangerous conditions associated with large volumes of traffic, particularly vehicle traffic. Exploring the causes of these incidents can help identify the most important features in determining the accident`s severity.

Almost all the repercussions, such as light conditions, speed zones, part of the injury, climate, and so on, are also participating in and closely linked to the cause of traffic accidents, of which only a few are emphasized and addressed in accident criticality rules. The overall goal of this study is to measure the severity of traffic accidents that occur. The key directing vectors are the accident attributes, which include the part of the slight, car allocation on the highway, and ecologically responsible properties, all of which help the output results about the strong levels of the accident criticality classes.

Severity prediction, Machine learning, Accident prediction, Road accidents, Traffic system design.


[1] Pradhan and Sameen, “Predicting Injury Severity of Road Traffic Accidents using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach,” vol. 10, pp. 119-127, 2020.
[2] Xiaoyi and Pan Lu, “Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings using Random Forest Algorithm Compared with Decision Tree,” vol. 200, pp. 106931, 2020.
[3] Laura Ebolia and Carmen Forciniti, “Factors Influencing Accident Severity: An Analysis by Road Accident Type", vol. 47, pp. 449- 456, 2020.
[4] Md. Farhan and Ahmed, “Road Accident Analysis and Prediction of Accident Severity by using Machine Learning in Bangladesh,” pp. 1-5, 2019.
[5] Jaspreet and Gurvinder Singh, “Evaluation and Classification of Road Accidents using Machine Learning Techniques,” pp. 193-204, 2019.
[6] Lukuman and Haobin, “Evaluation and Classification of Road Accidents using Machine Learning Techniques,” pp. 1-8, 2019.
[7] Bulbula and Fengli, “Classification of Road Traffic Accident Data using Machine Learning Algorithms,” vol. 12, pp. 682-687, 2019.
[8] Rabia and Keneth, “Comparison of Machine Learning Algorithms for Predicting Traffic Accident Severity,” vol. 1, pp. 272-276, 2019.
[9] Mayura Yeole, Rakesh Kumar Jain, Radhika Menon, "Prediction of Road Accident using Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 151-161, 2022. Crossref,
[10] Mpho and Dr. Vukosi, “Predicting Road Traffic Accident Severity using Accident Report Data in South Africa,” pp. 11-17, 2019.
[11] S Vasavi, “Extracting Hidden Patterns within Road Accident Data using Machine Learning Techniques,” vol. 6, pp. 13-22, 2018.
[12] Cigdem, “Predicting the Severity of Motor Vehicle Accident Injuries in Adana-Turkey using Machine Learning Methods and Detailed Meteorological Data,” vol. 6, no. 1, pp. 72-79, 2018.
[13] Amirfarrokh and Aemal, “Comparison of Four Statistical and Machine Learning Methods for Crash Severity Prediction,” vol. 108, pp. 27-36, 2018.
[14] Sameen and Biswajeeth, “Severity Prediction of Traffic Accidents with Recurrent Neural Networks,” vol. 7, pp. 476-493, 2017.
[15] Bahar and Blanca, “The Identification of Patterns of Interurban Road Accident Frequency and Severity using Road Geometry and Traffic Indicators,” vol.14, pp. 4122-4129, 2016.
[16] Andrew, Joseph, “Prediction of In-hospital Mortality in Emergency Department Patients with Sepsis: A Local Big Data–Driven, Machine Learning Approach,” vol. 23, pp. 269-278, 2015.
[17] Akguuml A. P, & Do?an E, “An Application of Modified Smeed Adapted Andreassen and Artificial Neural Network Accident Models to Three Metropolitan Cities of Turkey,” Scientific Research and Essays, vol. 4, no. 9, pp. 906-913, 2009.
[18] Dr. Surendiran R, Dr. Thangamani M, Monisha S, Rajesh P, "Exploring the Cervical Cancer Prediction by Machine Learning and Deep Learning with Artificial Intelligence Approaches," International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 94- 107, 2022. Crossref,
[19] Delen D, Sharda R, & Bessonov M, “Identifying Significant Predictors of Injury Severity in Traffic Accidents using A Series of Artificial Neural Networks,” Accident Analysis and Prevention, vol. 38, no. 3, pp. 434–444, 2006.
[20] Polson N. G, & Sokolov V. O, “Deep Learning for Short-Term Traffic Flow Prediction,” Transportation Research Part C: Emerging Technologies, vol. 79, pp. 1–17, 2017.
[21] Abdel-Aty M. A, & Abdelwahab H. T, “Predicting Injury Severity Levels in Traffic Crashes a Modeling Comparison,” Journal of Transportation Engineering, vol. 130, no. 2, pp. 204–210, 2004.
[22] Crammer K, & Singer Y, “On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines,” Journal of Machine Learning Research, vol. 2, no. 12, pp. 265–292, 2001.
[23] Xie Y, Lord D, & Zhang Y, “Predicting Motor Vehicle Collisions using Bayesian Neural Network Models: An Empirical Analysis,” Accident Analysis and Prevention, vol. 39, no. 5, pp. 922–933, 2007.
[24] Chong M, Abraham A, & Paprzycki M, “Traffic Accident Analysis using Machine Learning Paradigms,” Informatica, vol. 29, no. 1, 2005.
[25] Kunt M. M, Aghayan I, & Noii N, “Prediction for Traffic Accident Severity: Comparing the Artificial Neural Network,” Genetic Algorithm Combined Genetic Algorithm and Pattern Search Methods, Transport, vol. 26, no. 4, pp. 353-366, 2011.
[26] Abellán J, López G, & De OñA J, “Analysis of Traffic Accident Severity using Decision Rules Via Decision Trees,” Expert Systems with Applications, vol. 40, no. 15, pp. 6047–6054, 2013.
[27] Friedman J. H, “Stochastic Gradient Boosting,” Computational Statistics & Data Analysis, vol. 38, no. 4, pp. 367-378, 2002.