Testing The Performance of Random Forest and Support Vector Machine Algorithms for Predicting Cyclist Casualty Severity
Journal Title: Firat University Journal of Experimental and Computational Engineering - Year 2023, Vol 2, Issue 3
Abstract
Traditional statistical regression models for predicting casualty severity have fundamental limitations. Machine learning algorithms for classifications have started to be applied in severity analysis in order to relax the assumptions and provide better accuracy in the models. However, the performances of highly advised classification algorithms for predicting cyclist casualty severity, which particularly occurred at roundabouts, have not been investigated comprehensively. Therefore, the study in this paper developed classification models for cyclist casualty severity prediction by applying the highest two advised algorithms in the literature namely Random Forest and Support Vector Machine. The dataset included 439 cyclist casualties which were recorded at give-way roundabouts in the North East of England. The predictive variables were sociodemographic information about cyclists, weather conditions, behavior-related contributory factors, speed limit, and roundabout geometrical parameters. 70% of the records were randomly selected for the training stage and 30% were used for the testing in both Random Forest and Support Vector Machine algorithms. After training the algorithm, the testing results showed that the Random Forest algorithm predicted the outcomes with 88.6% classification accuracy. On the other hand, Support Vector Machine algorithm predicted the testing values with 84.73% classification accuracy. The algorithms misestimated 18 and 20 of the casualties in Random Forest and Support Vector Machine, respectively. The outcomes suggested that both Random Forest and Support Vector Machine algorithms were applicable for cyclist casualty severity prediction models with high performance.
Authors and Affiliations
Nurten AKGÜN
Keywords
Related Articles
- EP ID EP727929
- DOI 10.5505/fujece.2023.57966
- Views 60
- Downloads 0
Investigation of some thermophysical properties of Asphodelus aestivus reinforced polyester composite
In this research, both environmentally friendly and economical composites have been produced by using biomass wastes in unsaturated polyester. The use of renewable biomass wastes as a filler in unsaturated polyester is r...
Comparison of equivalent earthquake load method for TEC-2007 and TBEC-2018: Adıyaman province example
In this study, in terms of their approach to the equivalent earthquake load method, a comparison of the Turkish building earthquake codes published in 2007 and 2018 was made. For the study a reinforced concrete residenti...
SKLBP14: A new textural environmental sound classification model based on a square-kernelled local binary pattern
Nowadays, the forward-forward (FF) algorithm is very popular in the machine learning society, and it uses a square-based activation function. In this research, we inspired the FF algorithm and presented a new kernel for...
Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images
One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to deat...
Behaviour of a strip footing adjacent to the existing supported excavation
This study investigates the results of the numerical analysis on effect of existing supported excavation on ultimate bearing capacity (qult) of strip footing adjacent to supported excavation in sandy soil. The influence...