• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on visualization and computer graphics >SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories
【24h】

SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories

机译：SemanticTraj：与大规模的出租车轨迹进行交互的新方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as “What were the taxi trips starting from Main Street and ending at Wall Street in the morning?” or “Where are the taxis arriving at the Art Museum at noon typically coming from?”, tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as “Main Street”, “Wall Street”, and “Art Museum”. In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system.
机译：大量的出租车轨迹数据被用于交通和城市规划中的知识发现。现有工具通常要求用户在检索和探索出租车轨迹和乘客旅行时选择并刷涂地图上的地理空间区域。要回答看似简单的问题，例如“从Main Street开始到早上在Wall Street结束的出租车行程是什么？”或“出租车通常在中午从哪里来？”，通常需要乏味且耗时的交互操作，因为轨迹的数字GPS点未直接与诸如“大街”，“华尔街”之类的关键字链接街”和“美术馆”。在本文中，我们介绍了SemanticTraj，这是一种以直观，语义丰富且有效的方式来管理和可视化出租车轨迹数据的新方法。使用SemanticTraj，域和公共用户可以通过基于术语的直接查询轻松地找到上述问题的答案。他们还可以在可视化中交互地探索检索到的数据，可视化由轨迹和行程的语义信息增强。尤其是，滑行轨迹通过文本转换过程转换为滑行文档。此过程将GPS点映射到一系列街道/ POI名称以及上落地点。它还可以将车速转换为用户定义的描述性术语。然后，形成出租车文档语料库并建立索引，以实现对文本搜索引擎的灵活语义查询。语义标签和结果的摘要与SemanticTraj原型中的一组可视化文件集成在一起，可帮助用户快速轻松地研究滑行轨迹。提出了一组使用场景，以显示系统的可用性。我们还收集了领域专家的反馈，并进行了初步的用户研究，以评估视觉系统。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号