The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms. (September 2020)

• Record Type: Journal Article
• Authors: Katanalp, Burak Yiğit; Eren, Ezgi
• Is Part Of: Accident analysis and prevention. Volume 144(2020)
• Publication Date: 2020-09
• Subjects: Fuzzy logic -- Decision tree -- Injury-severity -- Machine learning -- Cyclist safety; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105590
• Languages: English
• ISSNs: 0001-4575

Abstract

Graphical abstract: Highlights: Handling the classification of accident injury severity with a fuzzy approach can significantly improve model performance. A Hybrid model is created by combining different machine learning models can give better results than classical algorithms. As an alternative to ANN-FLs, DT-RFL model can be used to reduce or eliminate the expert opinion in the rule-creation task. Abstract: In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013–2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.