Representation learning for geospatial areas using large-scale mobility data from smart card
Representation learning for geospatial areas using large-scale mobility data from smart card
复制标题
DOI：
10.1145/2968219.2968416
复制
发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Masanao Ochi;Y. Nakashio;Y. Yamashita;I. Sakata;Kimitake Asatani;M. Ruttley;Junichiro Mori
中科院分区：
文献类型：
--
作者：
Masanao Ochi;Y. Nakashio;Y. Yamashita;I. Sakata;Kimitake Asatani;M. Ruttley;Junichiro Mori
随着现代公共交通基础设施的部署，一些研究利用智能卡数据分析了人们的转变模式，并描述了这些区域的特征。在本文中，我们提出了一种新颖的嵌入方法，利用人们智能卡的大规模数据中的转换模式来获取地理空间区域的矢量表示。我们通过考虑人们在现实世界中过渡的地理限制来扩展网络嵌入。我们在日本关西地区的大型铁路网络中使用智能卡数据进行了实验。我们使用所提出的嵌入方法获得了每个火车站的向量表示。结果表明，在以去火车站为目的的多标签分类任务中，所提出的方法比现有的网络嵌入方法表现更好。我们提出的方法可以通过从流动数据中发现地理空间区域的底层表示来有助于预测人流。
With the deployment of modern infrastructures for public transit, several studies have analyzed the transition patterns of people by using smart card data and have characterized the areas. In this paper, we propose a novel embedding method to obtain a vector representation of a geospatial area using transition patterns of people from the large-scale data of their smart cards. We extend a network embedding by taking into account geographical constraints on people transitioning in the real world. We conducted an experiment using smart card data in a large network of railroads in Kansai areas in Japan. We obtained a vector representation of each railroad station using the proposed embedding method. The results show that the proposed method performs better than the existing network embedding methods in the task of multi-label classification for purposes of going to a railroad station. Our proposed method can contribute to predicting people flow by discovering underlying representations of geospatial areas from mobility data.