Real-Time Nanoscopic Rider Safety System for Smart and Green Mobility Based upon Varied Infrastructure Parameters
نویسندگان
چکیده
To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using combination of deep neural networks. The is used as case study in the Northumbria region England’s northeast. It made up three components: (a) input data unit, (b) knowledge processing and (c) output unit. demonstrated that various characteristics influence bikers’ interactions, which to estimate riskiest age gender rider groups. Two accurate prediction models are built, with male accuracy 88 per cent female 95 cent. findings concluded different infrastructures pose varying levels risk users ages genders. Certain aspects hazardous all bikers. However, cyclist’s determine level any feature presents. Following validation, built interoperable under scenarios, including current heterogeneous future semi-autonomous autonomous transportation systems. results contribute towards understanding variation types. study’s will help improve safety lead construction sustainable integrated cycling system.
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ژورنال
عنوان ژورنال: Future Internet
سال: 2021
ISSN: ['1999-5903']
DOI: https://doi.org/10.3390/fi14010009