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Injury Severity Prediction of Traffic Collision by Applying a Series of Neural Networks: The City of London Case Study
Meisam Siamidoudaran, Ersun İşçioğlu
Keywords:road safety, traffic crash, injury severity prediction, contributory factors


This paper focuses on predicting injury severity of a driver or rider by applying multi-layer perceptron (MLP), support vector machine (SVM), and a hybrid MLP-SVM method. By correlating the injury severity results and the influences that support their creation, this study was able to determine the key influences affecting the injury severity. The result indicated that the vehicle type, vehicle manoeuvre, lack of necessary crossing facilities for cyclists, 1st point of impact, and junction actions had a greater effect on the likelihood of injury severity. Following this indication, by maximising the prediction accuracies, a comparison between the models was made through exerting the most sensitive predictors in order to evaluate the models’ performance against each other. The outcomes specified that the proposed hybrid model achieved a significant improvement in terms of prediction accuracy compared with other models.


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Siamidoudaran M, Iscioglu E, Siamidodaran M. Traffic injury severity prediction along with identification of contributory factors using learning vector quantization: A case study of the city of London. SN Applied Sciences. 2019 Oct;1(10): 1268. Available from: doi:10.1007/s42452-019-1314-6

Copyright (c) 2023 Meisam Siamidoudaran, Ersun İşçioğlu

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University of Zagreb, Faculty of Transport and Traffic Sciences
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