  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.
