Understanding multiple days’ metro travel demand at aggregate level

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.