Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/5978
Title: Application of queuing theory to vehicular traffic at signalized intersection in Kumasi-Ashanti Region, Ghana
Authors: Anokye, Martin
Abdul-Aziz, A.R.
Annin, Kwame
Oduro, Francis T.
Keywords: Queuing Theory
Traffic Congestion
Traffic Light System and Transportation
Issue Date: 2013
Publisher: University of Cape Coast
Abstract: Traffic congestion is a growing problem in many metropolitan areas as it increases travel time, air pollution, carbon dioxide (CO2) emissions and fuel use because cars cannot run efficiently. This paper seeks to model the vehicular traffic flow and explore how vehicular traffic could be minimized using queuing theory in order to reduce the delays on roads in the Kumasi metropolis of Ghana. The Oforikrom traffic intersection in the Kumasi metropolis of Ghana is currently operating with one service channel each from the various routes to the intersection. The results showed that traffic intensity, ρ<1 for all sessions, a condition that suggests a perfect traffic system. Consequently, smooth flow of traffic was shown since the server at each channel was able to serve more than the cars in waiting queue when servers resume work. Again, it was found that heavy traffic occurs in the evening. Stakeholders can task Motor Traffic Transport Unit (MTTU) to check that drivers desist from such practices so that there will be free flow of traffic in the evening and also promote the use of bikes, which apart from serving as a form of exercise also helps to reduce fuel consumption thereby saving money for the Government to tackle problem of other sectors of the economy. Finally, the government of Ghana could introduce a public transport system so that people do not travel with private cars to their places of work to reduce congestion on the roads, which in turn boosts productivity
Description: 7p:, ill.
URI: http://hdl.handle.net/123456789/5978
ISSN: 23105496
Appears in Collections:Department of Mathematics & Statistics

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