Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

Peikun Li, Chaoqun Ma, Jing Ning, Yun Wang and Caihua Zhu
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Peikun Li: School of Highway, Chang’an University, Xi’an 710064, China
Chaoqun Ma: School of Highway, Chang’an University, Xi’an 710064, China
Jing Ning: School of Highway, Chang’an University, Xi’an 710064, China
Yun Wang: School of Highway, Chang’an University, Xi’an 710064, China
Caihua Zhu: School of Highway, Chang’an University, Xi’an 710064, China

Sustainability, 2019, vol. 11, issue 19, 1-19

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 (R 2 ) 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.

Keywords: metro station; passenger flow prediction; time granularity; forecast error; lookback-volatility (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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