利用高效和语义位置嵌入寻求自行车共享服务的新端口
Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
摘要：
对于在拥挤的城市地区进行短距离旅行，自行车共享服务由于其灵活性和便利性而变得越来越受欢迎。要扩大服务覆盖面，关键任务之一是寻找新的服务端口，这就需要深入了解现有服务端口的底层特性。在本文中，我们提出了一个新的模型，命名为高效和语义位置嵌入（ESLE），它携带的地理空间和语义信息的地理位置。为了生成ESLE，我们首先通过馈送静态地图瓦片图像来用深度卷积神经网络（CNN）训练多标签模型，然后从模型中提取位置嵌入向量。与最近的相关文献相比，ESLE不仅在计算上便宜得多，而且更容易通过系统的语义分析来解释。最后，我们应用ESLE为NTT DOCOMO在日本运营的自行车共享服务寻找新的服务端口。初步结果表明，ESLE的有效性，并提供了一些见解，可能难以发现使用传统的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
