Transportation is the backbone for any economy. As the number of vehicles is increasing day by day, traffic jams or congestions are very common. Jams are not only frustrating, but also increases air pollution. It has adverse effect not just for our climate, but everybody's health too. Most ITS techniques are not capable to solve the problem of traffic congestions in developing regions due to high cost and assumptions of orderly traffic. This paper focuses on the techniques used for detecting the traffic jam and for avoiding congestion on roads. The survey is done to study the challenges faced by current vehicle detection techniques and the solution provided for reducing and avoiding congestion. In the end we conclude that a GPS based system can be a better alternative technique for traffic jam detection as it can monitor the whole road network and require low installation cost and can be incorporated with the strategies for congestion avoidance which will help to improve the traffic flow.
Traffic monitoring and parameters estimation from urban to non urban (battlefield environment) traffic is fast-emerging field based on acoustic signals. We present here a comprehensive review of the state-of-the-art acoustic signal for vehicular speed estimation, density estimation and classification, critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). In recent years video monitoring and surveillance systems has been widely used in traffic management and hence traffic parameters can be achieved using such systems, but installation, operational and maintenance cost associated with these approaches are relatively high compared to the use of acoustic signal which is having very low installation and maintenance cost. The classification process includes sensing unit, class definition, feature extraction, classifier application and system evaluation. The acoustic classification system is part of a multi sensor real time environment for traffic surveillance and monitoring. Classification accuracy achieved by various studied algorithms shows very good performance for the 'Heavy Weight' class of vehicles as compared to the other category "Light Weight". Also a slight performance degrades as vehicle speed increases. Vehicular speed estimation corresponds to average speed and traffic density measurement, and can be substantially used for traffic signal timings optimization.
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