Deep neural network-based hybrid modelling for development of the cyclist infrastructure safety model

Faheem Malik*, Laurent Dala, Krishna Busawon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Downloads (Pure)

Abstract

This paper is concerned with modelling cyclist road safety by considering various factors including infrastructure, spatial, personal and environmental variables affecting cycling safety. Age is one of the personal attributes, reported to be a significant critical variable affecting safety. However, very few works in the literature deal with such a problem or undertaking modelling of this variable. In this work, we propose a hybrid approach by combining statistical and supervised deep learning with neural network classifier, and gradient descent backpropagation error function for road safety investigation. The study area of Tyne and Wear County in the north-east of England is used as a case study. An accurate dynamic road safety model is constructed, and an understanding of the key parameters affecting the cyclist safety is developed. It is hoped that this research will help in reducing the cyclist crash and contribute towards sustainable integrated cycling transportation system, by making use of cut above methodologies such as deep learning neural network.
Original languageEnglish
Pages (from-to)11603-11616
Number of pages14
JournalNeural Computing and Applications
Volume33
Issue number18
Early online date10 Mar 2021
DOIs
Publication statusPublished - 1 Sep 2021

Fingerprint

Dive into the research topics of 'Deep neural network-based hybrid modelling for development of the cyclist infrastructure safety model'. Together they form a unique fingerprint.

Cite this