Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Traffic flow time series prediction based on statistics learning theory
For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited training samples, which has a characteristic of rapid convergence and avoiding the local minimum. At the end of this paper, the simulation experiment for the traffic flow of one practice crossing proves the validity and efficiency and high application value in traffic flow prediction.
Traffic flow time series prediction based on statistics learning theory
For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited training samples, which has a characteristic of rapid convergence and avoiding the local minimum. At the end of this paper, the simulation experiment for the traffic flow of one practice crossing proves the validity and efficiency and high application value in traffic flow prediction.
Traffic flow time series prediction based on statistics learning theory
AiLing Ding, (Autor:in) / XiangMo Zhao, (Autor:in) / LiCheng Jiao, (Autor:in)
01.01.2002
213179 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
TRAFFIC FLOW TIME SERIES PREDICTION BASED ON STATISTICS LEARNING THEORY
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