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• W2962917186 abstract "We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto." @default.
• W2962917186 created "2019-07-30" @default.
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• W2962917186 date "2013-07-01" @default.
• W2962917186 modified "2023-09-30" @default.
• W2962917186 title "Travel time estimation for urban road networks using low frequency probe vehicle data" @default.
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• W2962917186 doi "https://doi.org/10.1016/j.trb.2013.03.008" @default.
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