Cyclists' willingness to ride is usually influenced by their subjective perception of the street riding environment. Measuring this perception is crucial for enhancing residents' willingness to ride. We propose an SSB framework (Public Security, Traffic Safety, Scenic Beauty) to quantify cyclists' subjective perception using street view imagery (SVI) and volunteer rating data. To address the issue of imbalanced class distribution in the volunteer rating data and enhance the model's ability to distinguish between positive and negative perception scenes, we employed a combination of the Kmeans Synthetic Minority Over-Sampling Technique (Kmeans-SMOTE) and the Random Forest (RF) classifier. The Kmeans SMOTE-RF model improved Area Under the Curve (AUC) by 0.327 for public safety, 0.2 for traffic safety, and 0.209 for scenic beauty compared to the RF model. Additionally, we incorporated Shapley Additive Explanations (SHAP) to examine how the visual features of SVI impact cyclists' subjective perception. Trees had a positive impact on all dimensions. Fence and sidewalk were key features for enhancing traffic safety perception, while roads positively affected public security and scenic beauty. These insights support urban planners in understanding the relationship between SVI features and cyclists' perceptions, aiding the design of cyclist-friendly street environments.

中文翻译：

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使用 K-means SMOTE-RF 模型和街景图像测量骑车者对街道骑行环境的主观感知

骑自行车者的骑行意愿通常受到他们对街道骑行环境的主观感受的影响。衡量这种认知对于提高居民的骑行意愿至关重要。我们提出了一个 SSB 框架（公共安全、交通安全、风景秀丽），使用街景图像 (SVI) 和志愿者评级数据来量化骑自行车者的主观感知。为了解决志愿者评分数据中类别分布不平衡的问题，并增强模型区分积极和消极感知场景的能力，我们结合使用了 Kmeans 合成少数过采样技术（Kmeans-SMOTE）和随机森林（ RF）分类器。与 RF 模型相比，Kmeans SMOTE-RF 模型将公共安全曲线下面积 (AUC) 提高了 0.327，将交通安全提高了 0.2，将风景优美提高了 0.209。此外，我们还结合了 Shapley 附加解释 (SHAP) 来研究 SVI 的视觉特征如何影响骑行者的主观感知。树木对各个方面都有积极的影响。护栏和人行道是增强交通安全认知的关键设施，而道路则对公共安全和风景优美产生积极影响。这些见解有助于城市规划者理解 SVI 特征与骑车者认知之间的关系，有助于设计适合骑车者的街道环境。