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Semantic Management of Urban Traffic Congestion

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Title:Semantic Management of Urban Traffic Congestion
Authors:Shakil, Arshad
Juric, Radmila
Keywords:Decision Support for Smart Cities
owl
reasoning
swrl
traffic congestion
Date Issued:07 Jan 2020
Abstract:Urban traffic congestion is a problem which affects the world and is related to the massive urbanization and excessive number of cars on our streets. This causes a variety of problems, from economical/financial and health-related, to environmental warnings caused by high CO2 and NO2 emissions. This paper proposes a novel software engineering solution, which generates a software application aimed at individual drivers on urban roads, in order to help and ease overall congestion. The novelty is twofold. We target individual drivers in order to motivate them to re-think the purpose and goals of each journey they take. Consequently, the proposed software application enables reasoning upon various options an individual driver may have and helps in choosing the best possible solution for an individual. Our software application utilizes reasoning with SWRL enabled OWL ontologies, which can be hosted by any software application we run in our cars, ready to assist in driving, and implemented in Android / iOS environments.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63873
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.134
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Decision Support for Smart Cities


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