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Various forecasting schemes have been proposed to manage urban road traffic data, which is collected by different sources such as, videos cameras, sensors and mobile phone services. However, these are not sufficient for the purpose because of their limited coverage and high costs of installation and maintenance. This paper describes urban road congestion as a resource assignment problem in urban areas, in which vehicles are assigned to available sections of road. In order to accomplish this and reduce road congestion, an estimation of the vehicle location is needed. Different strategies for estimating location have been proposed, such as the use of Wi-Fi and cellular systems, and GPS/GNSS. In this process, accuracy plays an important role. Therefore, to increase the accuracy of the primary GNSS system, an augmentation system is considered.
Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS) and global navigation satellite system (GNSS). Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA) model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.