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Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

机译：具有深度学习功能的软传感器用于城市环境中的功能区域检测

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The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.
机译：城市化的快速发展增加了交通压力，并使城市功能区的识别成为热门的研究课题。一些研究使用兴趣点（POI）数据和智能卡数据（SCD）进行地铁站分类。但是，模型和数据集的统一性限制了预测结果。本文不仅使用SCD和POI数据，还添加了“在线到离线（OTO）”电子商务平台数据，该应用程序可为客户提供有关不同业务的信息，例如位置，得分，评论等。本文将这些数据组合并用于分析每个地铁站，考虑到数据的多样性，获得不同站点的客流特征图，800 m内不同类型的POI的数量以及周围的情况OTO商店。本文提出了一个两阶段的框架，以识别地铁站的功能区域。在客流阶段，提取SCD特征并将其转换为特征图，然后使用ResNet模型获取阶段1的输出。在构建环境阶段，提取POI和OTO特征，并进行深度神经网络分析。使用具有堆叠式自动编码器（SAE–DNN）模型的网络来获取阶段2的输出。最后，连接两个阶段的输出，并使用SoftMax函数对功能区域进行最终识别。我们进行了实验测试，实验结果表明该框架具有良好的性能，对地铁车站及其周边环境的规划具有一定的参考价值，为智慧城市的建设做出了贡献。

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