Optimizing urban public transport network to align with urban development holds significant theoretical and practical value. To address the challenge of quantitatively analyzing the complexity of such network, this study introduces fractal dimension as an effective analytical tool. A total of 27 sample cities were selected, and data representing urban development indicators (population, population density, and GDP), along with GIS data of bus and rail transit line network, were collected. After preprocessing the GIS data through coordinate reference system (CRS) transformation, rasterization, and binarization, the fractal dimensions of three types of networks were calculated using the box-counting method. A causality test based on time series data from Shanghai (2000-2008) confirmed that the continuous evolution of the metro network significantly contributes to urban prosperity, providing real-world support for this study. Building on this, the study employed correlation analysis, regression analysis, and Data Envelopment Analysis (DEA) to explore the quantitative relationships between fractal dimension and urban indicators, leading to actionable conclusions. Finally, using Hangzhou and Taiyuan as two cases, specific recommendations for optimizing the public transport networks of these two cities were proposed based on the analytical findings.
Wang et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: