Investigating Holiday Subway Travel Flows with Spatial Correlations Using Mobile Payment Data: A Case Study of Hangzhou

The subway is crucial for urban operations, especially during holidays. Unlike traditional studies using smart card data, this research analyzes National Day holiday subway travel patterns with Hangzhou’s 2021 mobile payment data, covering 42 days from 6 September to 17 October for comprehensive comparison. Considering spatial passenger flow correlations, a Composite Weight (CW) matrix integrating network distance and time is defined and integrated into a Spatial Error Model (SEM), Spatial autoregressive model (SAR), and Spatial Durbin Model (SDM) to create CW-SEM, CW-SAR, and CW-SDM. The CW matrix innovatively considers network distance and time, overcoming traditional spatial weight matrix limitations to accurately and dynamically capture passenger flow spatial correlations. The results show the following: (1) Hangzhou saw 37% and 49% increases in average daily passenger flow during the extended holiday versus workdays and weekends, with holiday peak hour flow declining 16% compared to workdays but increasing 18% versus weekends, likely due to shifted travel purposes from commuting to tourism; (2) strong spatial passenger flow correlations existed in both workdays and weekends, attributed to urban functional zoning and transport network connectivity; (3) key factors such as population, social media activity, commercial facilities and transportation hubs show significant positive correlations with holiday passenger flow. Medical facility reveals significant negative correlations with holiday passenger flow. These findings highlight the need to incorporate spatial variations into major holiday subway travel studies for urban planning and traffic management insights.