Exploring time variants for short-term passenger flow

Abstract

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.