Dynamic shopping accessibility modeling for smart cities: a crowd-behavior enhanced G2SFCA approach for Wuhan, China

Shopping services are an essential part of urban life. Accessibility to shopping services is an important indicator of social equity, crucial for assessing urban sustainability. However, existing accessibility measurement methods do not consider the impact of consumer behavioral preferences on shopping accessibility, which may lead to overestimation. To address this, we propose a shopping accessibility measurement method that considers crowd behavior preferences, with the aim of estimating shopping accessibility based on the real supply – demand relationship embedded in crowd activity trajectories, thereby improving objectivity. Using Wuhan in Hubei Province, China, as a case study, we extracted dynamic population distribution changes from ride-hailing trajectories to estimate the real supply – demand relationship. Based on this new relationship, we improved the Gaussian Two-Step Floating Catchment Area method (I-G2SFCA) to measure shopping accessibility by considering crowd behavior preferences. By comparing the I-G2SFCA results with the traditional G2SFCA results, we explore how crowd behavior preferences influence shopping accessibility. The experimental results reveal: (1) Crowd behavior preferences significantly affect shopping accessibility measurements, and the new method provides more objective results, effectively improving the overestimation of shopping accessibility in central urban areas; (2) changes in population size are significantly correlated with changes in shopping accessibility, indicating that crowd behavior preferences influence accessibility by altering local consumer numbers. Our research offers new insights into the relationship between crowd activities and urban shopping convenience, and provides a basis for rationalizing commercial resources and fair competition.