一直以来人们认为视觉环境是影响交通安全的重要因素，然而受限于图像分析 手段，现有的关于环境视觉因素与交通安全关系的研究主要以定性为主，很难进行大规 模的视觉环境定量分析。本研究利用丰富性、易提取性和不断増长的街景图像作为环境 因素的数据来源，通过膨胀残留网络 ( DＲN) 等方法提取街景的图像特征信息、位置 信息和感官信息，并采用皮尔逊相关系数和岭回归筛选量化指标来构建基于深度学习的 视觉环境与交通安全关联的量化分析框架，为研究城市景观提供了新的量化手段。此 外，统计分析方法被用于确定导致道路交通安全状况改变的影响因素。结果表明: 一方 面，探究了关联城市景观的影响因素对不同城区事故率的贡献差异性，例如商业区和老 城区内 “植物”占比的增加会对交通安全有积极影响，但在郊区却恰恰相反等。另一 方面，还发现了可能推动城市规划理论发展的规律。例如越靠近市中心的道路单元其交 通状况越安全等。本文为定量研究城市景观与交通安全之间的联系提供了一种新的思 路，并为高效率和大规模地评估城市交通安全状况提供了可能性。

The visual environment has long been regarded as an important factor affecting traffic safety． However， limited by image analysis methods，existing studies on the relationship between environmental visual factors and traffic safety are mainly qualitative and it is difficult to conduct large-scale quantitative analysis of the visual environment． This study used rich，easy-to-extract and growing streetscape images as the data source of environmental factors，and extracts the image feature information，location information and sensory information of streetscape through methods such as expanded residual network ( DＲN) ． And Pearson correlation coefficients and ridge regression were used to screen quantitative indicators to construct a quantitative analysis framework for the association between visual environment and traffic safety based on deep learning，providing a new quantitative means to study urban landscapes． In addition，statistical analysis methods were used to identify the influencing factors that lead to changes in road traffic safety conditions． On the one hand，the variability in the contribution of influencing factors associated with the urban landscape to accident rates in different urban areas was explored． For example，an increase in the proportion of ″vegetation″ in commercial and older urban areas has a positive impact on traffic safety， but the opposite is true in suburban areas． On the other hand，patterns that may advance urban planning theory are also discovered． For example，the closer the road unit is to the city center，the safer the traffic conditions． This paper offered a new way of thinking about the link between urban landscape and traffic safety in quantitative terms， and offered the possibility for assessing urban traffic safety conditions efficiently and on a large scale．