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- W824208268 abstract "The research presented in this paper develops a data-driven particle filter to predict travel times by sampling from historical data. In the proposed method, each particle corresponds to a travel time sequence from a database of historical data. The particle weight is calculated using a dissimilarity measure between measurement and particle sequences. A resampling method is developed in the data-driven particle filter to eliminate particles with low weights and re-select samples according to the probability of each track. Travel time predictions are computed by aggregating the weighted travel times of each particle. A freeway stretch from Newport News to Virginia Beach is selected to test the proposed algorithm using five-minute aggregated traffic data in 2010 provided by INRIX. The travel time prediction results during the summer season demonstrate that the proposed method outperforms two Kalman filter methods by reducing the prediction error by 30% and 57%." @default.
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- W824208268 date "2013-01-01" @default.
- W824208268 modified "2023-09-24" @default.
- W824208268 title "Data-Driven Particle Filter for Travel Time Prediction" @default.
- W824208268 hasPublicationYear "2013" @default.
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