Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model

Abstract

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.

Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure

“…A similar spatial autocorrelation across different metro stations was found in commercial property value in Wuhan [19] and metro-bikeshare transfer in Nanjing [20]. Besides, the short-term metro ridership was proven to be autocorrelative in temporal scales in many studies [17,21], which means that the hourly ridership in contiguous periods could present analogous correlations with variables of the characteristics.…”

Period Division-Based Markov Models for Short-Term Traffic Flow Prediction

“…Similarly, other researchers also divided traffic flow data into two parts. But then, they adopted two of the ARIMA model, the support vector machine, the generalized autoregressive conditional heteroscedasticity (GARCH) model and the Markov model to predict the two parts of traffic flow data [25][26][27][28][29][30]. To accurately capture the change rules of short-term traffic flow, Zhang et al [31] and Yang et al [32] divided traffic flow data into three parts, including the periodic trend, the deterministic part and the volatility part, and they pointed out that the volatility part is extremely important for short-term traffic flow prediction.…”

RNN-Based Subway Passenger Flow Rolling Prediction

“…And neural network models like BP, stacked auto-encoder and LSTM always have good performances on travel mode analysis and flow prediction or similar problems [14]- [18]. Variants of SVM were used widely as well [19]- [21]. These models are always compared with classic linear models and have better performance in their studies.…”