Regional comprehensive transportation infrastructures constitute the fundamental basis for the development of inland urban agglomerations. To elucidate the role of comprehensive transportation in shaping the spatial organization and expansion of urban agglomerations, this study takes the Yangtze River Middle Reaches Urban Agglomeration (YRMRUA) as a case example. It examines the spatial relationships between transportation network layout and spatial expansion patterns using fractal dimension based on traffic accessibility, traffic-weighted linear density, and Pearson correlation analysis. The key findings of this study are as follows: (1) The YRMRUA exhibits a partial fractal growth pattern influenced by transportation development, which indicates that the comprehensive transportation has a significant but limited impact on YRMRUA. (2) There is a moderate correlation between traffic-weighted linear density and spatial expansion intensity within YRMRUA. (3) Specific groups such as the Wuhan–Ezhou–Huanggang–Huangshi group, Changsha–Zhuzhou–Xiangtan group, and Nanchang–Yichun group have formed in areas where transportation development and spatial expansion are at the forefront. (4) Different modes of transportation, including waterway transportation, railway transportation, and road transportation, have varying effects on spatial expansion. The integration of these modes forms the fundamental framework of urban agglomerations.

The Yangtze River Middle Reaches urban agglomeration (YRMRUA) is a typical representation of policy-oriented urban agglomeration in China. In this study, we analyze the morphological characteristics of the built-up area of the YRMRUA, which is the research object, and we try to clarify changes in its development and the supporting role of policies. First, we used satellite image data provided by Google Earth Engine for supervised learning to obtain images of the built-up area land needed by the research. Then, we adopted radius dimension, spatial expansion intensity, and standard deviation ellipse successively to explore the spatial morphological characteristics of the YRMRUA. The following conclusions are drawn: (1) The built-up area of the YRMRUA with significant fractal characteristics has formed two parts, with diffusion-type fractals forming within its boundary and cohesion-type fractals forming outside its boundary. In addition, the fractal dimension has exhibited a gradual decline as time has passed. (2) The YRMRUA has gradually stabilized at a low rate of expansion of 0.2% per year. From the perspective of the grid, the spatial expansion intensity presented breakthrough and expansion in the second and third stages, respectively. The hot spots of space expansion with 95% confidence gradually changed from a point-like shape to a ribbon-like shape. (3) The expansion pattern of the YRMRUA showed a tendency of “northwest–southeast”, and gradually moved towards equilibrium. (4) A policy impetus has limitations, and the stages of space shaping has resulted in spatial solidification and differentiation. In response, we put forward suggestions for the objects and ways of policy functions, in order to provide references for the development of policy-oriented urban agglomerations.

The rapid growth of electric vehicles has intensified the spatial mismatch between the layout of charging infrastructure and user demand, resulting in a structural contradiction in which “local oversupply” and “local shortages” coexist. To systematically diagnose and optimize this issue, this study develops an innovative analytical framework for a “residential area–charging infrastructure” collaborative service network and conducts an empirical analysis using Hongshan District in Wuhan as a case study. The framework integrates actual facility utilization data, complex network analysis, and spatial clustering methods. The findings reveal that the collaborative service network in the study area is overall sparse, exhibiting a distinct “core–periphery” structure, with noticeable patterns of resource concentration and isolation. Residential areas can be categorized into three types based on their supply–demand characteristics: efficient-collaborative, transitional-mixed, and low-demand peripheral areas. The predominance of the transitional-mixed type indicates that most areas are currently in an unstable state of supply–demand adjustment. A key systemic mechanism identified in this study is the significant “collaborative reinforcement effect” between facility utilization rates and network centrality. Building on these insights, we propose a hierarchical optimization strategy consisting of “overall network optimization—local cluster coordination—individual facility enhancement.” This ultimately forms a comprehensive decision-support framework for “assessment—diagnosis—optimization,” providing scientific evidence and new solutions for the precise planning and efficient operation of urban charging infrastructure.