Rapidly rising vehicle count is a significant indicator of a country’s cost-effective development. The increase in vehicle count results in increased traffic congestion. It leads to waste of electricity, time, and contamination of the atmosphere. Because traditional traffic lights have specific time intervals for transitioning from red to the green phase, they cannot meet the demands of an overgrowing city. This is a big problem that most cities are dealing with despite efforts to alleviate and eliminate it. In recent years, traffic congestion has emerged as one of the most significant problems for engineers, planners, and politicians worldwide, not just in urban areas. Several attempts have been made to regulate traffic lights based on the density of vehicles on the road using intelligent transportation systems (ITSs). Depending on the type of traffic, some researchers proposed using a variety of different techniques and computerized sensor frameworks to analyze traffic density and address the congestion issue. This paper compares and contrasts various sensor frameworks in terms of cost, reliability, accuracy, performance, and maintenance. The number of automobiles on the road has risen dramatically in recent years. Traffic congestion is a rising issue that everybody deals with daily. Manual traffic control by traffic cops has not proven to be successful. The signal’s fixed set time in all conditions (low and high traffic density) has not solved the issue. The use of the Internet of Things (IoT) is proposed as a model for effectively solving the problems described above. For Internet-based computing, we use the cloud, which provides various resources such as servers, storage, and application for traffic management. To track the number of vehicles and traffic congestion on a road, a network of sensors is used, and rerouting is done according to the traffic density on the lanes.


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    Title :

    IoT-Enabled Intelligent Traffic Management System


    Additional title:

    EAI/Springer Innovations in Communication and Computing




    Publication date :

    2022-10-01


    Size :

    23 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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