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- W572111983 abstract "The space-time relationship is an essential aspect for the road traffic prediction in Intelligent Transport Systems. In this paper, we propose a new method to determine the spatial correlation on the transportation road network. Firstly, the traffic congestion indicator is presented for normalizing the traffic condition in a spatial area. Secondly, considering the different road connection types, we build the spatial correlation matrix by statistical learning method based on the historical traffic data. Thirdly, STARMA (Space-Time Auto Regressive Moving Average) is employed for the traffic prediction model building. We use the historical traffic data of 7 months for the practical experiment. The data of the first 6 months is adopted for prediction model building, and the data of the last month is used for the model evaluation. By the prediction accuracy comparison, the STARMA model based on this method is superior to AR (Auto Regressive) models for traffic prediction." @default.
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- W572111983 date "2010-01-01" @default.
- W572111983 modified "2023-09-23" @default.
- W572111983 title "A Spatial Correlation Determining Method for Space-Time Traffic Prediction" @default.
- W572111983 hasPublicationYear "2010" @default.
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