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The use of LS-SVM for short-term passenger flow prediction

    Qian Chen Affiliation
    ; Wenquan Li Affiliation
    ; Jinhuan Zhao Affiliation

Abstract

Transit flow is the basement of transit planning and scheduling. The paper presents a new transit flow prediction model based on Least Squares Support Vector Machine (LS-SVM). With reference to the theory of Support Vector Machine and Genetic Algorithm, a new short-term passenger flow prediction model is built employing LSSVM, and a new evaluation indicator is used for presenting training permanence. An improved genetic algorithm is designed by enhancing crossover and variation in the use of optimizing the penalty parameter γ and kernel parameter s in LS-SVM. By using this method, passenger flow in a certain bus route is predicted in Changchun. The obtained result shows that there is little difference between actual value and prediction, and the majority of the equal coefficients of a training set are larger than 0.90, which shows the validity of the approach.


First Published Online: 12 Apr 2011

Keyword : short-term passenger flow prediction, least squares support vector machine, genetic algorithm

How to Cite
Chen, Q., Li, W., & Zhao, J. (2011). The use of LS-SVM for short-term passenger flow prediction. Transport, 26(1), 5-10. https://doi.org/10.3846/16484142.2011.555472
Published in Issue
Mar 31, 2011
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This work is licensed under a Creative Commons Attribution 4.0 International License.