Understanding multiple days’ metro travel demand at aggregate level

Understanding multiple days’ metro travel demand at aggregate level

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Day-to-day variation of travel demand has been rarely studied, due to the limitation of traditional transport data collection methods and the difficulty in high-dimensional data processing. In this study, singular value decomposition (SVD) is used to study the day-to-day regularity of metro travel demand, based on four one-month datasets from the metro networks of Shanghai and Shenzhen, China. The results show that SVD is a tool to understand the intrinsic structure of daily metro travel demand. It is found that daily metro travel demand can be decomposed into three constituents: periodic part, burst part and other part. The periodic part varies weekly and accounts for a majority of the travel demand of origin–destination matrix. The burst part exhibits short-lived spikes, which are caused by special events or holidays. Also, other part varies randomly and only contributes a fraction of travel demand. Moreover, the periodic part corresponding to the two largest singular values is very stable in 2 months, and accounts for most of the travel demand. Finally, the burst part is used to analyse the impact of a collision accident. This work is helpful for short-term travel demand prediction, metro operation schedule and emergency management.

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