Injury Severity Prediction of Traffic Collision by Applying a Series of Neural Networks: The City of London Case Study

  • Meisam Siamidoudaran Eastern Mediterranean University, Cyprus & Voronezh State Technical University, Russia
  • Ersun İşçioğlu Eastern Mediterranean University, Cyprus
DOI: https://doi.org/10.7307/ptt.v31i6.3032
Keywords: road safety, traffic crash, injury severity prediction, contributory factors

Abstract

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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Meisam Siamidoudaran, Eastern Mediterranean University, Cyprus & Voronezh State Technical University, Russia

Meisam Siamidoudaran is a Ph.D. researcher at Eastern Mediterranean University as well as Voronezh State Technical University. His scientific field focuses on collision prevention and reduction techniques based on STATS19 road safety data. In the meantime, he is a government official in a local authority in the United Kingdom, where he has responsibility for the council casualty reduction targets along with delivery of many awarding schemes concerning road safety.

Ersun İşçioğlu, Eastern Mediterranean University, Cyprus

Assoc. Prof. Dr. Ersun İşçioğlu is chair of the Computer Education and Instructional Technologies at Eastern Mediterranean University, his research focuses on various artificial neural networks.

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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

Published
2019-12-16
How to Cite
1.
Siamidoudaran M, İşçioğlu E. Injury Severity Prediction of Traffic Collision by Applying a Series of Neural Networks: The City of London Case Study. Promet [Internet]. 2019Dec.16 [cited 2024Apr.24];31(6):643-54. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3032
Issue
Vol 31 No 6 (2019)
Section
Articles

Copyright (c) 2019 Meisam Siamidoudaran, Ersun Iscioglu

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