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Dynamic Traffic Light Control
Author(s) -
Shalini Priya,
S. Rajarajeshwari,
G. Indumathi,
P. Vinesha,
Athithya Janani. A
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8609.038620
Subject(s) - computer science , traffic flow (computer networking) , real time computing , convolutional neural network , traffic congestion , process (computing) , traffic bottleneck , intelligent transportation system , floating car data , traffic congestion reconstruction with kerner's three phase theory , control (management) , traffic signal , traffic optimization , simulation , transport engineering , artificial intelligence , computer network , engineering , operating system
Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

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