Accurate and interpretable prediction of crowd flow would benefit business management and public security. The existing studies are challenged to adapt to the indoor environment due to its complex and dynamic spatial interaction patterns. In this study, we propose a crowd flow predicting method for indoor shopping malls, which simultaneously features temporal variables and semantic factors to suit the shopping mall environment. A deep learning model named DeepIndoorCrowd is presented. The model aims at capturing temporal dependencies and the semantic pattern in crowd flow to generate an accurate multi-horizon prediction. With a multi-term temporal dependency capturing structure, the model is effective in learning both daily and weekly patterns of the indoor crowd flow in a shopping mall and is able to provide the temporal interpretation of the prediction result. Moreover, a semantic-temporal fusion module is introduced to utilize the semantic information of stores in prediction, which has proved to be effective in enhancing the model's ability to learn temporal patterns. Experiments were conducted on a real-world dataset to verify the proposed approach. The ablation study demonstrates that the DeepIndoorCrowd can effectively improve the efficiency and accuracy of the prediction up to 18.7%. In addition, some interesting indoor crowd flow patterns were discovered by analyzing the model's interpretation of the prediction result. The proposed prediction method provides an intuitive way of modeling indoor crowd flow, and the experiment's outcome can help indoor managers better understand stores' flow traffic.

中文翻译：

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DeepIndoorCrowd：使用可解释的变压器网络预测室内购物中心的人群流量

准确且可解释的人群流量预测将有利于商业管理和公共安全。由于室内环境复杂且动态的空间交互模式，现有的研究在适应室内环境方面面临着挑战。在本研究中，我们提出了一种室内购物中心的人群流量预测方法，该方法同时具有时间变量和语义因素以适应购物中心环境。提出了一种名为 DeepIndoorCrowd 的深度学习模型。该模型旨在捕获人群流中的时间依赖性和语义模式，以生成准确的多水平预测。通过多项时间依赖性捕获结构，该模型可以有效地学习购物中心室内人群流量的每日和每周模式，并且能够提供预测结果的时间解释。此外，引入了语义-时间融合模块来利用存储的语义信息进行预测，事实证明这可以有效增强模型学习时间模式的能力。在真实世界的数据集上进行了实验以验证所提出的方法。消融研究表明，DeepIndoorCrowd 可以有效地将预测的效率和准确性提高高达 18.7%。此外，通过分析模型对预测结果的解释，发现了一些有趣的室内人群流动模式。所提出的预测方法提供了一种直观的室内人群流量建模方法，