基于免疫算法优化LSSVM的短时交通流预测

doi:10.13229/j.cnki.jdxbgxb20180540

Abstract

Abstract:

In order to intelligently solve the problems existing in urban road networks and improve the accuracy of short-term traffic flow prediction, a short-term traffic prediction model is established by using the least square support vector machine (LSSVM). Specifically, the immune algorithm is adopted to optimize the penalty factor and kernel parameters of the LSSVM, thus obtaining the optimal prediction model. The prediction simulation experiment takes the average speed and occupancy rate of vehicles as the input of the model to predict the traffic flow. The experimental results show that the prediction error of the optimized LSSVM model used in the simulation experiment is reduced, and the output result is closer to the real value.

Key words: engineering of communications and transportation system, traffic flow prediction, immune algorithm, least squares support vector machine, parameter optimization

CLC Number:

U491

Yuan-li GU, Yuan ZHANG, Xiao-ping RUI, Wen-qi LU, Meng LI, Shuo WANG. Short⁃term traffic flow prediction based on LSSVMoptimized by immune algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1852-1857.

Figures/Tables 5

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	交叉验证LSSVM	贝叶斯验证LSSVM	免疫算法优化LSSVM
MSE	11.400 1	9.411 4	3.454 6
MAE	3.745 6	2.757 4	1.556 4
MAXRE	0.323 5	0.263 4	0.265 6

Short⁃term traffic flow prediction based on LSSVMoptimized by immune algorithm

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