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- W2364804618 abstract "According to the characteristics of city road vehicle queuing system under emergency, considering the affect factors from the view of space-time, it establishes Support Vector Regression(SVR)model to predict vehicle queue length.Considering the sensitivity of parameters effecting on model performance, Particle Swarm Optimization(PSO)algorithm is proposed to select SVR parameters. Furthermore, the k-fold cross validation(k-CV) mean square error averaged is used as the fitness of PSO. The proposed PSO-SVR model compares with k-CV SVR model, GA-SVR model and BP network. The test results show that PSO-SVR model has higher prediction accuracy and generalization ability, and the model is effective to predict vehicle queue length." @default.
- W2364804618 created "2016-06-24" @default.
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- W2364804618 date "2016-01-01" @default.
- W2364804618 modified "2023-09-24" @default.
- W2364804618 title "Vehicle queue length prediction based on PSO-SVR dynamic model" @default.
- W2364804618 hasPublicationYear "2016" @default.
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