THE USE OF LS-SVM FOR SHORT-TERM PASSENGER FLOW PREDICTION

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 LS-SVM, 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 gamma and kernel parameter sigma 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.