关键词: 支持向量回归, 粒子群算法, 参数优化, 车辆排队长度, 预测

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.

Key words: support vector regression, particle swarm algorithm, parameter optimization, vehicle queue length, prediction