Browsing by Author "Wei, Yu"

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Apply Ensemble Empirical Mode Decomposition to Discover Time Variants of Metro Station Passenger Flow
(2017-01-01)
Chen, Mu-Chen
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Chen, Long-Sheng
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Wei, Yu
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This paper applies both Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) to extract the EMD and EEMD components from a data set of passenger flows of a station in the metro system, and illustrates the time variants of short-term passenger flow for this data sets. The results indicate that the extracted meaningful EEMD components reveal a more unique pattern than the extracted meaningful EMD components. The patterns of these EEMD components of passenger flow in the metro system are more specific and can be explained more easily for management purposes.
Exploring time variants for short-term passenger flow
(2011-07-01)
Chen, Mu-Chen
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Wei, Yu
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Passenger flow is a fundamental element in a transportation system. It is important to explore the time variants of short-term passenger flow for transportation planning and operation. When the data are sufficiently analyzed, transportation planners not only can make better decisions, but also enhance the performance of transportation systems. The data of short-term passenger flow may be difficult to analyze due to its exotic oscillation. Hilbert-Huang transform (HHT) has recently been developed for analyzing non-linear and non-stationary data. In this paper, the proposed time variants exploration method includes two stages: empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). A real passenger flow dataset is collected from Taipei rapid transit corporation (TRTC) to investigate the viability of the proposed time variants exploration approach. The intrinsic mode functions (IMFs) extracted by EMD can represent the local characteristics of passenger flow and imply its meaningful time variants such as peak period pattern, semi-service period pattern, semi-daily pattern and daily pattern. By comparing the results of HHT with that of fast Fourier transform (FFT), it indicates that HHT can obtain the narrower frequency band, accurately capture time-frequency-energy distribution, and help to enhance the performance of transportation systems. The results show that HHT is an effective approach for exploring the time variants of short-term passenger flow in a metro system. (C) 2010 Elsevier Ltd. All rights reserved.
Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks
(2012-04-01)
Wei, Yu
;
Chen, Mu-Chen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Short-term passenger flow forecasting is a vital component of transportation systems. The forecasting results can be applied to support transportation system management such as operation planning, and station passenger crowd regulation planning. In this paper, a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems. There are three stages in the EMD-BPN forecasting approach. The first stage (EMD Stage) decomposes the short-term passenger flow series data into a number of intrinsic mode function (IMF) components. The second stage (Component Identification Stage) identifies the meaningful IMFs as inputs for BPN. The third stage (BPN Stage) applies BPN to perform the passenger flow forecasting. The historical passenger flow data, the extracted EMD components and temporal factors (i.e., the day of the week, the time period of the day, and weekday or weekend) are taken as inputs in the third stage. The experimental results indicate that the proposed hybrid EMD-BPN approach performs well and stably in forecasting the short-term metro passenger flow. (C) 2011 Elsevier Ltd. All rights reserved.