Intelligent Vehicular Traffic Control System Using Priority Longest Queue First Model

Intelligent Vehicular Traffic Control System Using Priority Longest Queue First Model

Dr. Samuel S. Udoh

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Samuel Sunday Udoh

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University of Uyo

Intelligent Vehicular Traffic Control System Using Priority Longest Queue First Model

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Abstract

Traffic congestion of vehicles at road intersections is a growing problem in many developing countries of the world, especially in large urban areas. This stems from a continuous increase in the human population, poor road networks and the proliferation of vehicles for transportation of humans and goods from one location to another towards the performance of civil, social and economic activities. These vehicles often meet at road intersections and desire the Right-of-Way (RoW) towards their destination. This situation always results in race competition, traffic jam and gridlock condition with its attendant effects on time, fuel wastages as well as accident and fire outbreak which often results to loss of lives and property. The conventional traffic light control system which employs a static time cycle for issuance of RoW to each lane at the intersection lacks human-like intelligence and traffic situational awareness.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Dr. Samuel S. Udoh. 2021. \u201cIntelligent Vehicular Traffic Control System Using Priority Longest Queue First Model\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 21 (GJCST Volume 21 Issue D1).

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GJCST Volume 21 Issue D1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-D Classification I.2.0

Submission ReceivedJune 10, 2021
Peer Review Double Blind
Handling Editor
Accepted July 3, 2021
Published July 15, 2021

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v1.2

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March 25, 2021

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