NDLI: Accident detection and action system using fuzzy logic theory

Content Provider	IEEE Xplore Digital Library
Author	Alkandari, A. Al-Shaikhli, I.F. Najaa, A. Aljandal, M.
Copyright Year	2013
Description	Author affiliation: Comput. Educ. Dept., Coll. of Basic Educ., Kuwait (Najaa, A.) \|\| Comput. Sci. Dept., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia (Alkandari, A.; Al-Shaikhli, I.F.) \|\| Electr. Dept., High Inst. of Energy, Kuwait (Aljandal, M.)
Abstract	Intelligent Traffic light systems primarily focus on solving the traffic congestion problems with ever evolving algorithms. The solutions cater only to the problems based on traffic flow and density and does not include if an accident is causing the delay. There are various solutions that exist for detecting the accidents close to traffic control systems but most lack the capability to take the necessary action after the detection. This paper discusses an accident detection system based on fuzzy logic to detect an accident near a traffic light system and also provide a mechanism to ease the traffic flow with help from the Action system. The accident detection systems is based on fuzzy logic; but the algorithm used for calculation the cycle time and other parameters is based on modification of Webster Method and is called Dynamic Webster with Dynamic Cycle time (DWDC). The paper focuses in the working principles of the Accident Detection system based on the linguistic variables, membership function and the rules sets as part of the Fuzzy Logic system.
Starting Page	385
Ending Page	390
File Size	1557653
Page Count	6
File Format	PDF
ISBN	9781479903863
DOI	10.1109/iFuzzy.2013.6825470
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-06
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Membership function Roads Cross ratio Accident detection Educational institutions Traffic light system Sensors Linguistic variables Artificial intelligence Accidents Pragmatics
Content Type	Text
Resource Type	Article

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