Intelligent nanoscopic road safety model for cycling infrastructure

Malik, Faheem Ahmed, Dala, Laurent and Busawon, Krishna (2021) Intelligent nanoscopic road safety model for cycling infrastructure. In: 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, Piscataway, p. 9529298. ISBN 9781728189963, 9781728189956

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This paper is concerned with the development of intelligent safety modelling for cycling safety at the nanoscopic level. The present models are primarily focused on the motorists modelling at an aggregate level. In this work a framework for safety analysis is proposed consisting of a) Data collection unit, b) Data storage unit, and c) Knowledge processing unit. The predictive safety model is developed in the knowledge processing unit using supervised deep learning with neural network classifier, and gradient descent backpropagation error function. This framework is applied to a case study in Tyne and Wear county in England's northeast by using the crash database. An accurate safety model (88 accuracy) is developed with the output of the riskiest age and gender group, based upon the specific input variables. The most critical variables affecting the safety of an individual belonging to a particular age and gender groups, are the journey purpose, traffic flow regime and variable environmental conditions it is subjected to. It is hoped that the proposed framework can help in better understanding of cycling safety, aid the transportation professional for the design and planning of intelligent road infrastructure network for the cyclists

Item Type: Book Section
Additional Information: 7th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems MT-ITS 2021 ; Conference date: 16-06-2021 Through 17-06-2021
Uncontrolled Keywords: deep learning, infrastructure, road safety models, intelligent transportation system
Subjects: G500 Information Systems
H600 Electronic and Electrical Engineering
K400 Planning (Urban, Rural and Regional)
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 19 Aug 2021 14:09
Last Modified: 11 Oct 2021 13:03

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