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- W646532802 abstract "With the development of information technology, traffic states can be collected dynamically from floating car system with low-cost. But owing to the low penetration rate and random routes of floating cars, the traffic state of road network derived by the floating car system is usually incomplete and noised, which depresses its performance on the dynamic traffic management and related information applications. In this paper, the authors propose to estimate the traffic state of entire road network by combining the floating car system and weighted tensor completion method. Firstly, the authors form the network state into appropriate tensor model in which the observed noised entries represent the link information collected by floating car. Then the traffic state estimation problem is translated into a tensor recovery and completion problem. Leveraging the low intrinsic-dimensionality of traffic state and quality of state collected by floating car data, a weighted tensor completion method that weighting observed entries according to the quality of collected information was introduced to remove state noise and complete missing state. In the experiment, the simulated data are used to evaluate the strategy and results show that the proposed method tends to deliver high-quality solutions." @default.
- W646532802 created "2016-06-24" @default.
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- W646532802 date "2015-01-01" @default.
- W646532802 modified "2023-09-26" @default.
- W646532802 title "Weighted Tensor Completion-Based Traffic State Estimation Model" @default.
- W646532802 hasPublicationYear "2015" @default.
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