Vehicle travel time estimation by sparse trajectories

With the development of economy as well as the quality of people's life, the cars' number has increased very fast, and the condition of transportation isalso becoming much more complex. Thus, it's necessary to come up with an efficient algorithm to estimate the travel time of cars. "Travel time estimation" isan important problem in intelligent transportation field, and is also pretty common in transportation monitoring and routing system. In this article, based on the work of Wang Y et al, we use data mining method to find a way to estimate the travel time of cars on any path efficiently. Firstly, considering the paths' condition, we use tensor decomposition to overcome the sparsity problem of trajectory data. Secondly we construct an evaluation function, and use dynamic programming with suffix tree optimization to choose the best combination of sub-paths. All the code are reproduced by Python language rather than Matlab, which is originally used in the work of Wang Y et al. The experiment suggests that this method can estimate travel time effectively. In this article, we investigate the study about this problem both domestically and internationally, and based on which improve the original algorithm by replacing SGD by Adam. We summarize the theory, provide detailed process of the algorithm and show the key code, after which we examine the effectiveness of it by conducting experiments. In the summary chapter, we analysis the pros and cons of the algorithm objectively, and come up with the direction of our study in the future.