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- W2366785822 abstract "City road traffic system is characterized as a system of nonlinearity,uncertainty and spatial-temporal correlation,which makes traffic system parameters description and knowledge extraction difficult,and results in current short-term traffic forecast methods can not obtain satisfactory accuracy.This paper presents a hybrid multiple-kernel support vector machine model(MSVM) for conducting short-term traffic forecast.With statistical analysis of large amounts of traffic condition data samples,the proposed model not only has a capacity of recognizing and dealing with different types of input data separately,but also takes advantages of global optimization,generalization and adaptability of support vector machine.Moreover,the parameters of the hybrid model is optimized with an improved particle swarm algorithm(PSO).Aiming at the linear correlation between real time and historical traffic condition,the nonlinear correlation between real time and previous time period,and also up and downstream traffic condition,the proposed model uses a linear kernel to extract the linear pattern of traffic flow and a nonlinear kernel to map the nonlinear pattern of residuals from the linear kernel.Both the historical regularity and time-variation characteristics of city road traffic are considered in the MSVM model so as to obtain the knowledge from the influential factors of real time traffic condition in order to improve forecast accuracy.The experimental results show that the proposed model behaves satisfactory performance and robustness,and has a good potential for applications." @default.
- W2366785822 created "2016-06-24" @default.
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- W2366785822 date "2010-01-01" @default.
- W2366785822 modified "2023-09-25" @default.
- W2366785822 title "Short-term urban traffic forecasting based on multi-kernel SVM model" @default.
- W2366785822 hasPublicationYear "2010" @default.
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