Abstract
This study focused on exploiting machine learning algorithms for classifying and predicting injury severity of vehicle crashes in Yemen. The primary objective is to assess the contribution of the leading causes of injury severity. The selected machine learning algorithms compared with traditional statistical methods. The filtrated second data collected within two months (August-October 2015) from the two main hospitals included 156 injured patients of vehicle crashes reported from 128 locations. The data classified into three categories of injury severity: Severe, Serious, and Minor. It balanced using a synthetic minority oversampling technique (SMOTE). Multinomial logit model (MNL) compared with five machine learning classifiers: Naive Bayes (NB), J48 Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results showed that most of machine learning-based algorithms performed well in predicting and classifying the severity of the traffic injury. Out of five classifiers, RF is the best classifier with 94.84% of accuracy. The characteristics of road type, total injured person, crash type, road user, transport way to the emergency department (ED), and accident action were the most critical factors in the severity of the traffic injury. Enhancing strategies for using roadway facilities may improve the safety of road users and regulations.
Highlights
Vehicles are globally safety tools for transportation
Kappa statistic shows the goodness of the observed agreement ( ) in the classifier over the predicted agreement ( ) that is predicted by chance [42]
For checking the performance of Random Forest (RF) against the other classifiers based on weighted average F- Measure, paired ttest has proved reliable for comparing machine learning algorithms in related studies [43, 44], and it used for the same purpose in this study
Summary
Vehicles (cars, motorbikes, or bicycles) are globally safety tools for transportation. One of the negative results of its use is road traffic accidents (RTAs), which is one of the top ten leading causes of deaths. A global estimate of RTAs deaths is 1.35 million, and between 20 and 50 million suffer non-severe injuries. The most affected are the young pedestrians, cyclists, and motorcyclists [1, 2]. The behavior of riders, drivers, cyclists, and pedestrians are the crucial causes of accidents [3]. Driving/riding and using mobile phones or using drugs/alcohol simultaneously were reported as major causes of RTAs for young drivers. Cognitive, visual, and mobility injury are factors causing accidents for elderly drivers [4]. Due to the limited technology of vehicle control [5], it leads to the highest rate of RTAs in Africa and the Southern part of Asia [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
