An important measurement for the cost-effective growth of any nation is rapidly increasing vehicle count. The effect of raise in vehicle count grows the traffic congestion. It results in wastage of energy, time and environmental pollution. To meet the demands of an overgrowing city the traditional traffic lights deployed in cities are not sufficient since these traffic lights have specific predetermined time intervals for changing from a red phase to green phase. This major issue, that most of the cities is facing in spite of measures being taken to palliate and reduce it. In recent years traffic congestion has become apparent as one of the major challenges for engineers, planners, and policymakers, not in all urban setting, but worldwide. In this regard with the help of Intelligent Transportation Systems (ITS), several attempts were made to automate the traffic lights based on the density of vehicles on the road. Some researchers suggested the use of various distinctive sorts of strategies and computerized sensor frameworks to examine traffic density and to tackle the congestion issue depending on the traffic nature. This paper reviews different sensor frameworks by analyzing the pros and cons of each in cost, reliability, accuracy, efficiency, and maintenance overhead.

Intelligent Transportation Systems; Computer Vision; Machine Learning; Wireless Sensors; Traffic Control

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