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• W2059128538 abstract "The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical." @default.
• W2059128538 created "2016-06-24" @default.
• W2059128538 creator A5010815570 @default.
• W2059128538 creator A5052807300 @default.
• W2059128538 creator A5064222887 @default.
• W2059128538 date "2014-10-01" @default.
• W2059128538 modified "2023-10-15" @default.
• W2059128538 title "Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections" @default.
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• W2059128538 doi "https://doi.org/10.1016/j.trc.2014.06.011" @default.
• W2059128538 hasPublicationYear "2014" @default.
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