People Flow Trend Estimation Approach and Quantitative Explanation Based on the Scene Level Deep Learning of Street View Images
People Flow Trend Estimation Approach and Quantitative Explanation Based on the Scene Level Deep Learning of Street View Images
复制标题
DOI：
10.3390/rs15051362
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Chenbo Zhao;Y. Ogawa;Shenglong Chen;Takuya Oki;Y. Sekimoto
中科院分区：
文献类型：
--
作者：
Chenbo Zhao;Y. Ogawa;Shenglong Chen;Takuya Oki;Y. Sekimoto
人流趋势预测是交通和城市安全规划和管理的关键。然而，由于隐私问题，收集个人的位置数据进行人流统计分析是困难的，因此，迫切需要一种替代方法。此外，人流的趋势反映在街景因素中，但它们之间的关系在现有的文献中仍然不清楚。为了解决这个问题，我们提出了一种端到端的深度学习方法，该方法结合了街景图像和每个街景的人类主观评分。为进行更详细的人流研究，我们采用不同的时间及人流模式进行估计及分析。因此，我们在测试集上实现了78%的准确率。我们还实现了梯度加权类激活映射深度学习可视化和基于L1的统计方法，并提出了一种定量分析方法来理解街景的景观元素和主观感受，并基于梯度影响方法识别人流量估计的有效元素。综上所述，本研究提供了一种新的端到端的人流趋势估计方法，揭示了街景、人的主观感受和人流趋势之间的关系，从而为现有城市发展的评估做出了重要贡献。
People flow trend estimation is crucial to traffic and urban safety planning and management. However, owing to privacy concerns, the collection of individual location data for people flow statistical analysis is difficult; thus, an alternative approach is urgently needed. Furthermore, the trend in people flow is reflected in streetscape factors, yet the relationship between them remains unclear in the existing literature. To address this, we propose an end-to-end deep-learning approach that combines street view images and human subjective score of each street view. For a more detailed people flow study, estimation and analysis were implemented using different time and movement patterns. Consequently, we achieved a 78% accuracy on the test set. We also implemented the gradient-weighted class activation mapping deep learning visualization and L1 based statistical methods and proposed a quantitative analysis approach to understand the land scape elements and subjective feeling of street view and to identify the effective elements for the people flow estimation based on a gradient impact method. In summary, this study provides a novel end-to-end people flow trend estimation approach and sheds light on the relationship between streetscape, human subjective feeling, and people flow trend, thereby making an important contribution to the evaluation of existing urban development.