State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China.
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2020 Feb 17;20(4):1084. doi: 10.3390/s20041084.
Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.
城市交通模式反映了人们的出行方式和货物的运输方式，这对于交通管理和城市规划至关重要。随着传感技术的发展，人们积累了大量用于监测车辆的传感器数据，这也为大数据交通提供了机会，特别是对于实时交互式交通模式分析。我们提出了一个用于识别和可视化多尺度交通模式的三层框架。第一层在精细的时空尺度上计算中间层概要，这些概要被索引并存储在地理数据库中。第二层使用概要高效地提取多尺度交通模式。第三层通过实时交互式可视分析支持最终用户直观地探索。在深圳进行了一个基于一个月内收集的出租车 GPS 轨迹的实验。实时识别和可视化了多种交通模式。结果表明，该框架在交通分析中具有令人满意的性能，这将有助于交通管理和运营。
