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    Novel model for vehicles traffic monitoring using wireless sensor networksbetween major cities in South Africa

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    Date
    2013
    Author
    Munienge, Mbodila
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    Abstract
    With the growing number of vehicles and users, monitoring road and traffic within cities is becoming a huge research challenge. With urban scale enlargement coupled with the exponential growth in the number of vehicles, South Africa (SA) is not an exception. Consequently, congestion and pollution (i.e. noise and air) have become the order of the day. Road congestion and traffic-related pollution are well-known for huge negative socio-economic impact on several economies worldwide. For over a decade now, the number of cars on SA roads has increased tremendously and the road transport profile is characterized by its sizeable and total dependence on cars particularly in the highly developed urban areas alongside cycling, and other public transport. This has brought about increasing congestion in public roads which poses a serious problem not only for SA, but many countries of the world and has to be contained. Several solution methods have been proposed requiring dedicated hardware such as GPS devices and accelerometers in vehicles or camera on roadside and near traffic signals. Most other works in literature concentrated on lane systems and orderly traffic, which is common in the developing world and in some cases, the traffic is highly chaotic and unpredictable. The situation in SA cities like Johannesburg and Pretoria is not different. All these methods are costly and require much human effort. Therefore, in this dissertation, we present a novel model that is cost etfective, requires less human intervention, but uses wireless sensor networks, GPS and RFID scanner to monitor traffic in major SA cities. The novel model was developed and simulated using Visual Sense platform, the results obtained after simulation shows that the congestion level during busy hours was reduced and the traffic was managed.
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    http://hdl.handle.net/10394/15230
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    • Natural and Agricultural Sciences [2757]

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