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Real Time Adaptive Traffic Control System : A Hybrid Approach

机译:实时自适应交通控制系统:一种混合方法

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In present world, the problem of traffic congestion has become a major concern. It's not just restricted to megacities or metropolitans but even for small cities. Hence we require a smart or intelligent traffic control system. Our present traffic control system is not adaptive but is based on time and independent of the traffic density. This static nature makes it inefficient, chaotic and noisy. To tackle this issue of managing traffic roads a real time traffic information collection and monitoring system is proposed. The proposed model controls the clearance time of each lane in a sequential manner and is function of real time traffic density. The approach is rather hybrid - a blend of sensors networks and camera technology. United results of image processing and IOT ensures more accuracy and efficiency rather than native approach. Besides traffic congestion minimization, prioritization for emergency vehicle is also achieved through RFID. Other technology like stolen vehicle tracking/detection is also deployed. Simple GUI for use add an advantage to the system making it easy for control room to handle. The model could be used for analysis purpose and hence predict traffic jams and traffic status at different times. Hence proposed model is cost-effective and time efficient smart traffic control system.
机译:在当今世界,交通拥堵问题已经成为人们关注的主要问题。它不仅限于大城市或大城市,甚至还包括小城市。因此,我们需要一个智能或智能交通控制系统。我们当前的交通控制系统不是自适应的,而是基于时间并且与交通密度无关。这种静态性质使其效率低下,混乱且嘈杂。为了解决这个管理交通道路的问题,提出了一种实时交通信息收集和监视系统。所提出的模型以顺序的方式控制每个车道的通行时间,并且是实时交通密度的函数。这种方法相当混合-传感器网络和摄像头技术的融合。图像处理和物联网的统一结果可确保提供更高的准确性和效率,而不是原生方法。除了使交通拥堵最小化之外,还可以通过RFID实现对紧急车辆的优先处理。还采用了其他技术,例如被盗车辆跟踪/检测。简单的GUI使用为系统增加了一个优势,使控制室更易于处理。该模型可用于分析目的,因此可以预测不同时间的交通拥堵和交通状况。因此,提出的模型是具有成本效益和时间效率的智能交通控制系统。

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