Effects of spatial units and travel modes on urban commuting demand modeling

Understanding the relationships between commuting demand and built environment (BE) features is advantageous in alleviating travel pressure during the commuting period. Numerous works have explored these relationships under either a single spatial unit or travel modes. However, few efforts have attempted to examine how variations in both spatial units and transport modes affect the relationships. To address the above issues, we investigate the impact of the modifiable areal unit problem (MAUP) on the modeling results across three partitioning schemes (i.e., hexagonal-based, square-based, and administrative boundary) and nine spatial units under four commuting modes (i.e., taxi, bike-sharing, ride-hailing, and bus). A geographically weighted regression is applied to estimate commuting demands considering spatial autocorrelation and heterogeneity. The experiment is conducted using smart card data and vehicle GPS trajectories collected in Shenzhen City, China. The contributions of this study include the following three parts. First, it evaluates the pros and cons of different spatial units for commuting demand modeling. Second, it summarizes the effects of the MAUP on relationships among land use, demographic, and traffic network characteristics and commuting demand according to the partitioning scale and schemes. Third, it examines the similarities and differences in the effect of the MAUP on commuting demand modeling among four travel modes. The findings of this study contribute to the division of traffic analysis zones and the allocation of commuting trips.