Understanding the Spatial Heterogeneity Impact of Determinants on Ridership of Urban Rail Transit Across Different Passenger Groups

Abstract

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life-oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station-level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station-level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian- or bicycle-unfriendly areas. Notably, for tourist and life-oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment.