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

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.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

“…The experimental data are the inbound and outbound passenger flow data of Yangji Station of Guangzhou Metro from 1 July 2019 to 28 July 2019 from 6:15 to 23:15. The time series was smoothed by aggregating flow data into nonoverlapping 15-min intervals [ 38 ]. This resulted in 96 samples per day.…”

Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP Algorithm

“…Zhang et al [17] proposed to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. Li et al [18] indicated that the time granularity of the prior passenger flow data and the difference in site classification will also lead to differences in prediction accuracy. Ma et al [19] explored the distribution of passenger spatiotemporal characteristics based on big data of traffic smart cards, laying a foundation for the selection of passenger flow prediction objects.…”

Analysis of optimum time granularity selection in traffic prediction based on Pearson correlation coefficient

“…They study the time granularity from one minute to 24 hours, and the result indicates that when time granularity changes within one minute to 15 minutes, the regularity coefficient increases significantly, and when the time granularity is more than 15 minutes, the change of regularity coefficient is flat. Li et al (2019) analyse the similarity coefficients of passenger flow under different time granularity for Xi'an subway stations. They conclude that the similarity coefficient of passenger flow increases significantly within the time granularity of 5 minutes to 15 minutes, and when the time granularity is more than 15 minutes, the similarity coefficient of the passenger flow changes insignificantly.…”