ABSTRACT

Stops along taxi trajectories, such as picking up and dropping off passengers, are spatially clustered and related to certain attributes of places where stops are made. To detect the hidden knowledge regarding these places, this article examines the semantics of massive taxi stops in a large city. Each taxi trajectory is modeled as a series of sequential semantic stops labeled by street names. All the trajectories can be examined as a document corpus, from which the hidden themes of the stops are identified through Latent Dirichlet Allocation model. Conventional GIS tools are coupled with topic modeling toolkit to visualize and analyze potential information of stop topics for understanding intra-city dynamics. The effectiveness of this approach is illustrated by a case study using a large dataset of taxi trajectories including approximately 4,000 taxis in Wuhan, China.

摘要

沿着出租车轨迹的停靠站（例如上落乘客）在空间上聚类，并且与停靠处的某些属性相关。为了检测有关这些地方的隐藏知识，本文研究了大城市中大型出租车停靠站的语义。每个滑行轨迹都建模为一系列以街道名称标记的顺序语义停靠点。可以将所有轨迹作为文档语料库进行检查，通过潜在狄利克雷分配模型从中识别出站点的隐藏主题。常规GIS工具与主题建模工具包结合使用，可以可视化和分析停车主题的潜在信息，以了解城市内部动态。通过使用大量滑行轨迹数据集（包括大约4个滑行轨迹）的案例研究说明了这种方法的有效性。