街景图像与机器学习相结合的道路环境安全感知评价与影响因素分析

Evaluation of Road Environment Safety Perception and Analysis of Influencing Factors Combining Street View Imagery and Machine Learning

准确识别影响环境安全感知的视觉因素,对于改善城市交通环境与提升行人出行安全具有重要支撑作用。然而,既有研究难以对复杂场景下的环境安全感知进行大规模定量化研究。因此,本文利用图像语义分割和目标检测技术从街景图像中提取视觉要素,通过人工评分结合深度学习的方式构建道路安全感知数据集;再基于轻量梯度提升机和SHAP解释框架,识别出影响环境安全感知的视觉因素;最后,选取道路环境特殊的峡谷性城市兰州市安宁区高校聚集地为例进行实证研究。结果表明:①高校及商业街的安全感知评分较高,城市道路的普遍偏低;②天空、人行道、道路和树木的图像占比值是对环境安全感知影响最大的四类要素,其中,天空的图像占比值为线性关系,人行道和树木的图像占比值近似对数函数,道路的图像占比值则类似二次函数;③视觉要素占比和个数存在交互影响作用,合理的要素分布有助于形成良好的空间视线,以及营造合适的行为活动空间,从而提升环境安全感知。

Accurate identification of visual factors affecting environmental safety perception provides important support for improving urban traffic environment and enhancing pedestrian travel safety.However,it is difficult to quantify environmental safety perception in complex scenes on a large scale in existing studies.Therefore,this study uses image semantic segmentation and object detection techniques to extract visual factors from streetscape images and constructs a road safety perception dataset by manual scoring in combination with deep learning methods.The influencing factors of environmental safety perception are also identified based on light gradient boosting machine algorithm and SHAP interpretation framework.In our study,the Anning District college cluster in Lanzhou City,a canyon city with a special road environment,is selected for the empirical study.Results show that:(1)The safety perception scores of colleges and commercial streets are high,while those of urban roads are generally low;(2)The image ratios of sky,sidewalk,road,and tree are the four factors that have the greatest influence on environmental safety perception,among which the image ratio of sky is linear,the image ratios of sidewalk and tree are similar to a logarithmic function,and the image ratio of road is similar to a quadratic function;(3)The proportion and number of visual factors have an interactive effect.A reasonable distribution of visual factors helps to create good spatial sightlines and suitable behavioral spaces,thus enhancing the perception of environmental safety.