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首页> 外文期刊>Journal of Imaging Science and Technology >Discovering and Visualizing Underlying Traffic Regions from Vehicle Trajectories with Multi-Features
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Discovering and Visualizing Underlying Traffic Regions from Vehicle Trajectories with Multi-Features

机译：从具有多种功能的车辆轨迹中发现并可视化潜在的交通区域

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City traffic often exhibits regional characteristics, such as large trucks frequently appearing in the suburbs, and the paths to playgrounds on weekends generally being congested. Discovering and visualizing these hidden traffic regions inside which roads share similar characteristics of traffic conditions simplifies the modeling complexities of whole city traffic conditions and therefore contributes significantly toward city planning. Unfortunately, such traffic regions always have irregular shapes and are time varying, which makes their discovery extremely complicated. In addition, establishing a method to visualize and explore the traffic regions interactively still remains challenging. In this article, the authors propose a latent Dirichlet allocation (LDA)-based approach to the discovery of underlying traffic regions (or region topics) from vehicle trajectories captured by surveillance devices installed along roadsides. They treat vehicle trajectories as documents and the values of different traffic features, such as locations, directions, speeds and vehicle types, as the corresponding words. After applying the LDA model, they obtain a list of region topics with combined feature values, in which the different feature values are clustered with probabilistic assignments. Meanwhile, they build a prototype system to explore the surveillance-device-based vehicle trajectories according to the discovered region topics. The prototype system, which consists of map view, cloud view, treemap view and matrix-table view, visualizes the feature values of hidden traffic regions. The authors finally research a real case based on the traffic data in Wenzhou City, a large city in eastern China with a population of more than nine million. They investigate approximately 157 surveillance devices and 750,000 moving vehicles. The case demonstrates the effectiveness of both their proposed approach and the prototype system. (C) 2016 Society for Imaging Science and Technology.
机译：城市交通通常表现出区域特征，例如郊区经常出现大型卡车，并且周末通向游乐场的道路通常很拥挤。发现并可视化这些隐藏的交通区域，在这些区域内道路共享相似的交通条件特征，简化了整个城市交通条件的建模复杂性，因此对城市规划做出了重要贡献。不幸的是，这样的交通区域总是具有不规则的形状并且是随时间变化的，这使得它们的发现极为复杂。另外，建立一种交互式地可视化和探索交通区域的方法仍然具有挑战性。在本文中，作者提出了一种基于潜在狄利克雷分配（LDA）的方法，用于从路边安装的监视设备捕获的车辆轨迹中发现潜在的交通区域（或区域主题）。他们将车辆轨迹视为文档，并将不同交通特征（例如位置，方向，速度和车辆类型）的值作为相应的单词。应用LDA模型后，他们获得具有组合特征值的区域主题列表，其中不同特征值与概率分配聚在一起。同时，他们建立了一个原型系统，根据发现的区域主题探索基于监视设备的车辆轨迹。原型系统由地图视图，云视图，树图视图和矩阵表视图组成，可将隐藏交通区域的特征值可视化。作者最后根据温州的交通数据研究了一个真实案例，温州是中国东部的一个大城市，人口超过900万。他们调查了大约157个监视设备和75万辆行驶中的车辆。该案例证明了他们提出的方法和原型系统的有效性。 （C）2016年影像科学与技术学会。

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