Bus ridership and its determinants in Beijing: A spatial econometric perspective

Abstract

Understanding the temporal and spatial dynamics and determinants of public transport ridership play an important role in urban planning. Previous studies have focused on exploring the determinants at the station level using global models, or a local model, geographically weighted regression (GWR), which cannot reveal spatial autocorrelation at the global level. This study explores the factors affecting bus ridership considering spatial autocorrelation using the spatial Durbin model (SDM). Taking the community in Beijing as the basic study unit, this study aims to explore the temporal and spatial dynamics of bus ridership and identify its key determinants considering neighboring effects. The results show the following: (1) The temporal dynamics are quite distinct on weekdays and weekends as well as at different time slots of the day. (2) The spatial patterns of bus ridership varied across different time slots, and the hot areas are mainly located near the central business district (CBD), transport hubs, and residential areas. (3)