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Mashhad, the second largest city in Iran, like many other big cities, is faced with increasing traffic congestion owing to rapidly increasing population and annual pilgrimage. In recent years, Mashhad traffic and transportation authorities have been challenged with how to manage the increasing congestion with limited budgets for major roadway construction projects. Mashhad has recognized the need to improve the existing system capacity to get the most out of their cur- rent transportation system infrastructures. Since most of the delay times occur at signalized intersections, using an intelligent control system with proper capabilities to overcome the growing traffic requirements is recommended. Following comprehensive studies carried out with the aim of developing the Mashhad traffic control center, the SCATS adaptive traffic control system was introduced as the selected intelligent control system for integrating signalized intersections. The first intersection was equipped with this system in 2005. This paper describes the results of a field evaluation in which fixed actuated-coordinated signal timings are compared with those dynamically computed by SCATS. The ef- fects of this system on optimizing fuel consumption as well as reducing air pollutants are fully discussed. It is found that SCATS consistently reduced travel times and the average delay per stopped or approaching vehicle. The positive impact of adaptive traffic control systems on fuel consumption and air pollution are also highlighted

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This paper proposes the application of 3 different kinds of feature extractors to recognize & classify 5 models of vehicles. These feature extractors are Fast Fourier transform, discrete wavelet transform & discrete curvelet transform. To justify the correct amount of each feature extractor, we perform each of the mentioned transforms to input images, precisely. The classifier used in this paper is called k nearest-neighbor. The results of this test show, that the right recognition rate of vehicle's model in this recognition system, at the time of using curvelet transform (Notice, all curvelet coefficients) is 100%. For decreasing the dimension of feature vectors more & choosing the best features we've used interclass variance criteria to infraclass variance criteria. As a result of this performance, the size of feature vectors will be extremely decreased. Then, we perform our final impact feature vectors (The best Curvelet coefficients or the best wavelet coefficients or the best Fourier coefficients) to the KNN Classifier. Also, the results of this test show, the right recognition rate of vehicle's model in this recognition system, at the time of using 0.1 of all curvelet coefficients is 100%.The comparison of the 3 proposed approaches for identifying the kind of vehicles showed that curvelet transform can extract better features among the proposed dataset.