NDLI: Traffic congestion reduce mechanism by adaptive road routing recommendation in smart city

Content Provider	IEEE Xplore Digital Library
Author	Gwo-Jiun Horng Jian-Pan Li Sheng-Tzong Cheng
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Manage., Fortune Inst. of Technol., Kaohsiung, Taiwan (Gwo-Jiun Horng) \|\| Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan (Jian-Pan Li; Sheng-Tzong Cheng)
Abstract	Using fuzzy logic, we propose a model with a neural network for public transport, normal cars, and motorcycles. The model controls traffic-light systems to reduce traffic congestion and help vehicles with high priority pass through. A fuzzy neural network (FNN) calculates the traffic-light system and extends or terminates the green signal according to the traffic situation at the given junction while also computing from adjacent intersections. In the presence of public transports, the system decides which signal(s) should be red and how much of an extension should be given to green signals for the priority-based vehicle. The system also monitors the density of car flows and makes real-time decisions accordingly. In order to verify the proposed design algorithm, we adapted the simulations of sumo, ns2, and GLD to our model, and further results depict the performance of the proposed FNN in handling traffic congestion and priority-based traffic. The promising results present the efficiency and the scope of the proposed multi-module architecture for future development in traffic control.
Starting Page	714
Ending Page	717
File Size	275525
Page Count	4
File Format	PDF
DOI	10.1109/CECNet.2013.6703431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Adaptation models Traffic-signal control Intersection-delay prediction Roads Neural networks FNN Fuzzy neural networks Delays Traffic congestion Vehicles
Content Type	Text
Resource Type	Article

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