2018/06/27 by Xuemei Wang, Ning Zhang, Yunlong Zhang +1 · 53 citations
Engineering · Mathematics · Social Sciences · #Artificial neural network #Autoregressive integrated moving average #Autoregressive model #Computer science #Econometrics #Engineering #Human Mobility and Location-Based Analysis #Machine learning #Mathematics #Nonlinear system #Support vector machine #Term (time) #Time series #Traffic Prediction and Management Techniques #Train #Transportation Planning and Optimization
paper · pdf · doi:10.1155/2018/3189238
published in Journal of Advanced Transportation 2018, 1-13 (Hindawi Publishing Corporation)
crossref issued 2018/06/27 · crossref published 2018/06/27 · crossref published-print 2018/06/27 · openalex publication_date 2018/06/27 · crossref created 2018/06/27 · crossref deposited 2020/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29 · crossref indexed 2026/08/06
Forecasting for short-term ridership is the foundation of metro operation and management. A prediction model is necessary to seize the weekly periodicity and nonlinearity characteristics of short-term ridership in real-time. First, this research captures the inherent periodicity of ridership via seasonal autoregressive integrated moving average model (SARIMA) and proposes a support vector machine overall online model (SVMOOL) which insets the weekly periodic characteristics and trains the updated data day by day. Then, this research captures the nonlinear characteristics of the ridership via successive ridership value inputs and proposes a support vector machine partial online model (SVMPOL) which insets the nonlinear characteristics and trains the updated data of the predicted day by time interval (such as 5-min). Afterwards, to avoid the drawbacks and to take advantages of the strengths of the two individual online models, this research takes the average predicted values of two models as the final predicted values, which are called support vector machine combined online model (SVMCOL). Finally, this research uses the 5-min ridership at Zhujianglu and Sanshanjie Stations of Nanjing Metro to compare the SVMCOL model with three well-known prediction models including SARIMA, back-propagation neural network (BPNN), and SVM models. The resultant performance comparisons suggest that SARIMA is superior for the stable weekday ridership to other models. Yet the SVMCOL model is the best performer for the unstable weekend ridership and holiday ridership. It shows that for metro operation manager that gear toward timely response to real-world unstable and abnormal situations, the SVMCOL may be a better tool than the three well-known models.