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With the continuous advancement of rail transit construction, the number of rail transit stations in the central areas of China's megacities has gradually increased, forming TOD station clusters with overlapping influence ranges, which enhances the competition and collaboration effects among stations. However, current TOD research primarily focuses on spatial development patterns from a single-station perspective and TOD classifications from a network perspective, lacking sufficient understanding of the development phenomena of TOD station clusters at a meso scale. This paper selects Shanghai as a case study. First, it identifies TOD station clusters through station density and influence range analysis, explicitly addressing the development issues of TOD station clusters. Subsequently, it explores the spatial network characteristics within TOD station clusters from a network perspective, conducts regression analysis with network strength characteristics as the dependent variable, and compares typical cases to analyze the impact of built environment variables on network characteristics. Based on this, the paper proposes planning strategies to enhance the overall spatial development of TOD station clusters.
The main research contents and conclusions include the following five parts:
(1) Identification of TOD Station Clusters: A method combining kernel density analysis, station influence range analysis, and pedestrian-scale constraints is proposed to identify TOD station clusters characterized by high station density and overlapping influence ranges. A total of 23 TOD station clusters are identified in Shanghai.
(2) Analysis of Network Characteristics of TOD Station Clusters: Drawing on social network analysis methods and using mobile signaling data, the network strength and structural characteristics of TOD station clusters are examined based on network density, network centrality, and network connectivity. The results show that network connectivity within TOD station clusters is generally higher than that with external stations, indicating that geographic proximity facilitates network effects. Network strength exhibits a layered attenuation pattern. Structurally, TOD station clusters are classified into four types: area-based, polycentric, strong-central, and weak-central.
(3) Impact of Built Environment on Network Strength: Regression models are established with the built environment as independent variables and network strength as the dependent variable. At the cluster level, factors such as functional mix, housing price variation coefficient, public transport network density, and green coverage positively correlate with network strength, while floor area ratio variation coefficient and degree centrality show negative correlations. At the station level, factors such as floor area ratio, intersection density, and betweenness centrality positively correlate with network strength, whereas functional mix and passenger flow show negative correlations.
(4) Impact of Built Environment on Network Structure: Through comparative analysis of typical cases, the study finds that functional complementarity forms the basis of network connections within clusters, spatial distribution of functions determines network structure, development intensity influences node hierarchy, and improved transportation conditions and environmental quality contribute to balanced network structures.
(5) Optimization Strategies for TOD Station Clusters: Based on the analysis, the study identifies shortcomings in land use layout, transportation links, and environmental quality, leading to low network connectivity and imbalanced structures in some TOD station clusters. Strategies are proposed to enhance network strength and balance network structures, drawing on successful cases of TOD cluster development.
This study provides strategies and recommendations to improve network strength and balance network structures, aiming to promote synergistic effects within TOD station clusters and leverage the agglomeration advantages of multiple stations. It offers practical value for the development and renewal of rail transit-intensive areas. However, limitations in data precision, such as mobile signaling data and POI data, restrict the analysis of pedestrian flow paths and functional interactions. Future research could utilize high-precision data to further explore the impact of the built environment on path selection and functional relationships.
| Keywords | TOD; TOD station cluster; spatial network characteristics; transit-oriented development |
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