Using Deep Learning to Construct a Real-Time Road Safety Model; Modelling the Personal Attributes for Cyclist

Malik, Faheem, Dala, Laurent and Busawon, Krishna (2020) Using Deep Learning to Construct a Real-Time Road Safety Model; Modelling the Personal Attributes for Cyclist. In: Intelligent Data Engineering and Automated Learning – IDEAL 2020. Lecture Notes in Computer Science, 12490 (2). Springer, Cham, pp. 610-619. ISBN 9783030623647, 9783030623654

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Abstract

This paper is concerned with the modelling of cyclist road traffic crashes by considering the personal attributes, i.e. gender and age of the cyclists. There are 21 different types of variables considered for each crash, which broadly fall into spatial, infrastructure, and environment categories. The study area of Tyne and Wear county in the north-east of England is selected for investigation. Six deep learning-based safety models are constructed using historic crash data. The effectiveness of deep learning methodology for road safety analysis is demonstrated, and it is found that spatial, infrastructural, and environmental conditions affect the safety interactions of a particular cyclist. These variables can be used for determining/predicting safety for a rider at a location. The model can predict age and gender of the rider, which is likely to be the most unsafe based upon the specific input variables. The significant accuracy is obtained for the constructed models with an overall accuracy of 84%. It is hoped that the proposed models can help in better designing of cyclist network, design, and planning, which will contribute to a sustainable transportation system.

Item Type:	Book Section
Uncontrolled Keywords:	cyclist safety, deep learning, road safety model, gender, age
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Mechanical and Construction Engineering
Related URLs:	Organisation
Depositing User:	John Coen
Date Deposited:	18 Nov 2020 15:57
Last Modified:	18 Nov 2020 15:57
URI:	http://nrl.northumbria.ac.uk/id/eprint/44791

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