Journal Article

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

Malik, Faheem Ahmed, Dala, Laurent, Busawon, Krishna

2021

Overview

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.

• Publisher: Springer London,Springer Nature B.V
• Subject: Artificial Intelligence / Artificial neural networks / Back propagation / Back propagation networks / Bicycling / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Data Mining and Knowledge Discovery / Deep learning / Error analysis / Error functions / Image Processing and Computer Vision / Infrastructure / Machine learning / Modelling / Neural networks / Original Article / Probability and Statistics in Computer Science / Traffic safety / Transportation networks / Transportation systems