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Real-time travel time estimation is a major requirement in many transportation related systems. One of the main challeges is to estimate the traffic speed and then forecast it for a short time. A valuable data source for this task is instant location of moving cars that is captured using global positioning system (GPS) and sent through internet in online manner. The main problem is that the resulting traffic data is severely sparse and also contains a lot of noise. Previous researchs on this type of data are mostly based on matrix or tensor factorization. In this work it is shown that despite the large fraction of missing value it is possible to use neural network for this problem with some...