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

Malik, Faheem, Dala, Laurent and Busawon, Krishna (2021) Deep neural network-based hybrid modelling for development of the cyclist infrastructure safety model. Neural Computing and Applications. ISSN 0941-0643 (In Press)

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Official URL: https://doi.org/10.1007/s00521-021-05857-3

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.

Item Type: Article
Uncontrolled Keywords: Cyclist safety, Deep learning neural network, Safety modelling Age, Infrastructure
Subjects: H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Elena Carlaw
Date Deposited: 12 Aug 2021 11:54
Last Modified: 12 Aug 2021 12:00
URI: http://nrl.northumbria.ac.uk/id/eprint/46901

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