Intelligent nanoscopic road safety model for cycling infrastructure

Research output: Contribution to conferencePaperpeer-review

1 Downloads (Pure)

Abstract

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
Original languageEnglish
Publication statusPublished - 17 Jun 2021
Event7th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems MT-ITS 2021 - Technical University of Munich (TUM) , München, Germany
Duration: 16 Jun 202117 Jun 2021
https://www.mt-its2021.tse.bgu.tum.de/

Conference

Conference7th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems MT-ITS 2021
CountryGermany
CityMünchen
Period16/06/2117/06/21
Internet address

Fingerprint

Dive into the research topics of 'Intelligent nanoscopic road safety model for cycling infrastructure'. Together they form a unique fingerprint.

Cite this