Time-space diagrams are essential tools for analyzing traffic patterns and optimizing transportation infrastructure and traffic management strategies. Traditional data collection methods for these diagrams have limitations in terms of temporal and spatial coverage. Recent advancements in camera technology have overcome these limitations and provided extensive urban data. In this study, we propose an innovative approach to constructing time-space diagrams by utilizing street-view video sequences captured by cameras mounted on moving vehicles. Using the state-of-the-art YOLOv5, StrongSORT, and photogrammetry techniques for distance calculation, we can infer vehicle trajectories from the video data and generate time-space diagrams. To evaluate the effectiveness of our proposed method, we utilized datasets from the KITTI computer vision benchmark suite. The evaluation results demonstrate that our approach can generate trajectories from video data, although there are some errors that can be mitigated by improving the performance of the detector, tracker, and distance calculation components. In conclusion, the utilization of street-view video sequences captured by cameras mounted on moving vehicles, combined with state-of-the-art computer vision techniques, has immense potential for constructing comprehensive time-space diagrams. These diagrams offer valuable insights into traffic patterns and contribute to the design of transportation infrastructure and traffic management strategies.

翻译：时空图是分析交通模式、优化交通基础设施及交通管理策略的核心工具。传统数据采集方法在时间与空间覆盖范围上存在局限性。近年来相机技术的发展克服了这些局限，提供了丰富的城市数据。本研究提出一种创新方法，通过利用安装在移动车辆上的摄像机采集的街景视频序列来构建时空图。采用前沿的YOLOv5、StrongSORT算法及摄影测量技术进行距离计算，我们从视频数据中推断车辆轨迹并生成时空图。为评估所提方法的有效性，我们使用了KITTI计算机视觉基准数据集。评估结果表明，该方法能从视频数据中生成轨迹，尽管存在一定误差，但可通过提升检测器、跟踪器及距离计算模块的性能加以缓解。综上所述，利用移动车辆搭载摄像机捕获的街景视频序列，结合前沿计算机视觉技术，在构建综合性时空图方面具有巨大潜力。这些图表可提供对交通模式的深刻洞见，并有助于交通基础设施设计与交通管理策略的制定。