College of Engineering

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Browsing College of Engineering by Subject "Artificial Intelligence"

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    Intelligent traffic management for the Kumasi Metropolis
    (NOVEMBER, 2018) Appiah-Twum, Frederick Egyin
    The problem of vehicular traffic congestion is universal yet there has not been a long-term permanent solution to this problem, and it is increasingly worsening by the day all around the world with severe vehicular traffic taking its toll on all road users. With the upsurge in urban traffic jams, innovative control strategies are therefore essential to allow efficient flow of vehicular movement. It is thus not surprising that a myriad of novel control strategies has been developed over the past years in an attempt to manage the ever-growing urban gridlock. Many of the currently used traffic control strategies are based on the relatively inefficient fixed-time traffic systems, like in the case of Ghana, or on a central traffic-responsive control system, which is challenging to implement and even much more difficult to maintain. As a consequence of inefficiencies in traffic control, road users are saddled with regular and inconveniently long waiting times in queues. To mitigate this problem, a distributed artificial intelligence and multi-agent system is proposed as a viable approach to manage the traffic menace. The proposed system uses historical data for traffic management and was designed and implemented using Simulation of Urban Mobility (SUMO) software. Iterative learning control which is a technique for refining the momentary response performance of a system that functions repetitively over a fixed period of time is used to tune the phase splits of the traffic controller to obtain the optimal controller duty cycles with the least delay, resulting in frequent traffic flows, minimum waiting times and shorter queued vehicles. The result obtained in the comparison of the current fixed time-controlled system and designed system clearly indicated that the proposed system outperformed the fixed-time cycle controllers in every key performance index selected for evaluation.