Abstract
The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.
Highlights
The rapid development of urban rail transit leads to the rapid growth of its passenger volume
In the aspect of short-term passenger flow prediction method, the autoregressive integrated moving average (ARIMA) model has been widely used because it does not need to consider the diversity of variables [3]
Based on the neural network model, a multi-pattern deep fusion (MPDF) method was proposed by Bai et al [8] which classifies the passenger flow distribution of different clusters to adapt to different types of prediction models
Summary
The rapid development of urban rail transit leads to the rapid growth of its passenger volume. The prediction of short-time passenger flow plays an important role in metro operation management. A highway traffic flow prediction model based on the neural network has been proposed by Li and Lu [7]. Based on the neural network model, a multi-pattern deep fusion (MPDF) method was proposed by Bai et al [8] which classifies the passenger flow distribution of different clusters to adapt to different types of prediction models. An online learning weighted support vector regression model was proposed in short-term traffic passenger flow prediction based on the Support Vector Regression (SVR) model [11]. Wu et al [14] proposed a deep neural network capable of making full use of the temporal and spatial characteristics of traffic flow to improve prediction performance. Polson and Sokolov [15] proposed an end-to-end deep learning structure in metro passenger flow prediction
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