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Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Saba Awan1, Zahid Mehmood2,*, Hassan Nazeer Chaudhry3, Usman Tariq4, Amjad Rehman5, Tanzila Saba5, Muhammad Rashid6
1 Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan
2 Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan
3 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20122, Italy
4 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
5 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
6 Department of Computer Engineering, Umm Al-Qura University, Makkah, 21421, Saudi Arabia
* Corresponding Author: Zahid Mehmood. Email:

Computers, Materials & Continua 2022, 71(3), 4609-4626. https://doi.org/10.32604/cmc.2022.019404

Received 12 April 2021; Accepted 25 October 2021; Issue published 14 January 2022

Abstract

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

Keywords

Prediction; hybrid framework; severity; class; casualty

Cite This Article

S. Awan, Z. Mehmood, H. Nazeer Chaudhry, U. Tariq, A. Rehman et al., "Profiling casualty severity levels of road accident using weighted majority voting," Computers, Materials & Continua, vol. 71, no.3, pp. 4609–4626, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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