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Title: | Adaptive Traffic Control System using Modified Round Robin and Genetic Algorithm |
Authors: | Nasir, Mohammed Sadiq Ojerinde, Oluwaseun Adeniyi Adepoju, Solomon Adelowo |
Keywords: | optimisation round robin component genetic algorithm signalised intersection |
Issue Date: | Apr-2018 |
Publisher: | i-manager's Journal on Digital signal processing |
Series/Report no.: | ;volume 6 number 2 |
Abstract: | Adaptive Traffic Control System (ATCS) serves as a main element in the constituents with which traffic control flow is achieved in fast developing, and developed urban areas. ATCS, however causes more delays on vehicles due to the fact that it is made up of intersecting points. Ensuring maximum efficiency at intersections has remained a challenge due to its dynamic nature of traffic. Additionally, a number of different methods that can be used to achieve higher performance at road traffic intersections have been recently proposed to engineers. In this study, a new and different method based on modified round robin scheduling algorithm through genetic algorithm technique to optimize the performance (in terms of timing) of a signalized intersection in one of the busiest and most crowded roads of Minna, Niger State - Nigeria (at Obasanjo shopping complex area). The technique uses an initial timing pattern to generate newer offspring (in terms of delay duration) to analyze cost function and to check if a global optimum is reached. This technique outweighs current techniques because the data upon which the nature of the system is built is relatively more phenomenal, as it puts into consideration the exact nature of the lane in many possible occurrences. In this work, a global optimum was reached at only a few number of iterations on the whole Genetic Algorithm process. |
URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/10710 |
ISSN: | 2321-7480 |
Appears in Collections: | Computer Science |
Files in This Item:
File | Description | Size | Format | |
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Adaptive traffic control_abs.pdf | 487.79 kB | Adobe PDF | View/Open |
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