Bus Arrival Time Prediction based on PCA-GA-SVM

Zixuan Peng, Yonglei Jiang, Xiaoli Yang, Zhigang Zhao, Liu Zhang, Yitian Wang


Considering the correlations of the input indexes and the deficiency of calibrating kernel function parameters when support vector machine (SVM) is applied, a forecasting method based on principal component analysis-genetic algorithm-support vector machine (PCA-GA-SVM) is proposed to improve the precision of bus arrival time prediction. And the No.232 bus in Shenyang City of China is taken as an example. The traditional SVM and Kalman Filtering model and GA-SVM are also employed to make comparative analysis on the prediction rate, respectively. The result indicates that PCA-GA-SVM obtains more accurate prediction results of bus arrival time prediction.


Bus arrival time prediction; Principal Component Analysis (PCA); Support Vector Machine (SVM); Kalman Filter


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DOI: http://dx.doi.org/10.14311/NNW.2018.%25x


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