TOD（Transit-oriented Development）理念的推廣為軌道交通站域街道空間賦予了新的優化需求，站域建成環境的科學評估與定量研究亟待展開，而街道作為軌道交通站域中重要交通走廊，其空間品質是建成環境的一個重要層面。本文選取成都市73個地鐵站域，以街道網絡、POI（Point of Interest）、街景圖片等多源大數據為支撐，運用機器學習、sDNA分析（Spatial Design Network Analysis）等技術，構建了以便捷性、功能性與舒適性為核心的評價體系，進行站域街道空間品質的大規模定量評價，并針對不同等級的站點提出導控策略。結果表明，在城市整體層面，68.03%的站域街道評分低于中等水平，街道功能性與舒適性普遍較好，便捷性較差；在站域層面，街道空間品質呈現出南高北低、西高東低、內高外低的分布特征。研究使得人本尺度的分析精度、站點尺度的分析深度和城市尺度的分析廣度得以兼顧，有助于創建高效的城市管理動態反饋機制。
The promotion of TOD (transit oriented development) concept has given new optimization requirements to the street space of rail transit station area. The scientific evaluation and quantitative research of station area construction environment need to be carried out urgently. As an important transportation corridor in rail transit station area, the space quality of street is an important level of built environment. In this paper, 73 subway stations in Chengdu are selected to support multi-source big data such as street network, POI (point of interest), street view pictures, etc., using machine learning and spatial design network Analysis (sDNA) and other technologies, constructed an evaluation system with convenience, functionality and comfort as the core, carried out large-scale quantitative evaluation of the station area street space quality, and proposed guidance and control strategies for different levels of stations. The results show that 68.03% of the station area streets score is lower than the medium level, the street function and comfort are generally good, and the convenience is poor; at the station level, the street space quality presents the distribution characteristics of high in the South and low in the north, high in the West and low in the East, high in the inside and low in the outside. The research makes the analysis accuracy of human-oriented scale, the analysis depth of site scale and the analysis breadth of urban scale can be taken into account, which is helpful to create an efficient dynamic feedback mechanism of urban management.