论文标题
利用高效，语义的位置嵌入，以寻求新的自行车共享服务港口
Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
论文作者
论文摘要
对于在拥挤的城市地区旅行的短距离，由于灵活性和便利性，自行车共享服务变得越来越流行。为了扩大服务覆盖范围，关键任务之一是寻求新的服务端口，该端口需要很好地了解现有服务端口的基本功能。在本文中，我们提出了一个新模型，该模型以高效和语义位置的嵌入（ESLE）命名，该模型具有地理位置的地理空间和语义信息。为了生成ESLE，我们首先通过馈送静态MAP-TILE图像，然后从模型中提取嵌入向量的位置来训练具有深卷积神经网络（CNN）的多标签模型。与最近的相关文献相比，ESLE不仅在计算中便宜得多，而且可以通过系统的语义分析更容易解释。最后，我们应用ESLE为在日本运营的NTT DOCOMO的自行车共享服务寻求新的服务端口。最初的结果证明了ESLE的有效性，并提供了一些可能难以通过使用常规方法发现的见解。
For short distance traveling in crowded urban areas, bike share services are becoming popular owing to the flexibility and convenience. To expand the service coverage, one of the key tasks is to seek new service ports, which requires to well understand the underlying features of the existing service ports. In this paper, we propose a new model, named for Efficient and Semantic Location Embedding (ESLE), which carries both geospatial and semantic information of the geo-locations. To generate ESLE, we first train a multi-label model with a deep Convolutional Neural Network (CNN) by feeding the static map-tile images and then extract location embedding vectors from the model. Compared to most recent relevant literature, ESLE is not only much cheaper in computation, but also easier to interpret via a systematic semantic analysis. Finally, we apply ESLE to seek new service ports for NTT DOCOMO's bike share services operated in Japan. The initial results demonstrate the effectiveness of ESLE, and provide a few insights that might be difficult to discover by using the conventional approaches.
