TOD（Transit-oriented Development）理念的推广为轨道交通站域街道空间赋予了新的优化需求，站域建成环境的科学评估与定量研究亟待展开，而街道作为轨道交通站域中重要交通走廊，其空间品质是建成环境的一个重要层面。本文选取成都市73个地铁站域，以街道网络、POI（Point of Interest）、街景图片等多源大数据为支撑，运用机器学习、sDNA分析（Spatial Design Network Analysis）等技术，构建了以便捷性、功能性与舒适性为核心的评价体系，进行站域街道空间品质的大规模定量评价，并针对不同等级的站点提出导控策略。结果表明，在城市整体层面，68.03%的站域街道评分低于中等水平，街道功能性与舒适性普遍较好，便捷性较差；在站域层面，街道空间品质呈现出南高北低、西高东低、内高外低的分布特征。研究使得人本尺度的分析精度、站点尺度的分析深度和城市尺度的分析广度得以兼顾，有助于创建高效的城市管理动态反馈机制。
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.