Paper Info
Title
Optimization of Traffic Lights Timing Based on Multiple Neural Networks
Abstract
This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-MNN Controller (Environment Observation Method based on Multiple Neural Networks Controller). Traffic congestion leads to problems like delays and higher fuel consumption. Consequently, alleviating congested situation is not only good to economy but also to environment. The problem of traffic light control is very challenging. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, modern artificial intelligent ways have gained more and more attentions. In this work, EOM is a very interesting mathematical method for determining traffic lights timing that was developed by Ejzenberg [4]. However, this method has some implications in which multiple neural networks were proposed to improve such problems. The solution was compared with the conventional method through scenario of simulation in microscopic traffic simulation software.
Year
DOI
Venue
2013
10.1109/ICTAI.2013.126
ICTAI
Keywords
Field
DocType
traffic light controller,traditional mathematical method,conventional method,multiple neural networks,traffic control,traffic lights timing,urban traffic road intersection,traffic congestion,microscopic traffic simulation software,interesting mathematical method,traffic light control
Computer science,Floating car data,Real-time computing,InSync adaptive traffic control system,Artificial intelligence,Traffic congestion reconstruction with Kerner's three-phase theory,Traffic congestion,Traffic generation model,Control theory,Road traffic control,Simulation,Traffic simulation,Machine learning
Conference
ISSN
Citations 
PageRank 
1082-3409
4
0.71
References 
Authors
2
2
Name
Order
Citations
PageRank
Michel B. W. De Oliveira140.71
Areolino de Almeida Neto252.84