Vehicle trajectories, with their detailed geolocations, are a promising data source to compute traffic flow maps which facilitate the understanding of traffic flows at scales ranging from the city/regional level to the road level. The trade-off is that trajectory data are prone to measurement noise. While this is negligible for large-scale flow aggregation, it poses substantial obstacles for small-scale aggregation. To overcome these obstacles, we introduce innovative local alignment algorithms, where we infer road segments to serve as local reference segments, and proceed to align nearby road segments to them. We then deploy these algorithms in an iterative workflow to compute locally aligned flow maps. By applying this workflow to synthetic and empirical trajectories, we verify that our locally aligned flow maps provide high levels of accuracy and spatial resolution of flow aggregation at multiple scales.

翻译：车辆轨迹数据凭借其详细的地理位置信息，成为计算交通流图极具潜力的数据源，有助于从城市/区域尺度到道路尺度理解交通流动规律。然而，轨迹数据易受测量噪声影响，这一局限在大尺度流量聚合中可忽略不计，但在小尺度聚合中会构成显著障碍。为克服此障碍，我们提出了创新的局部对齐算法：通过推断道路路段作为局部参考段，并将邻近路段与其对齐。随后将这些算法部署于迭代工作流中，以计算局部对齐的流量图。通过对合成轨迹与实证轨迹应用该工作流，我们验证了局部对齐流量图能够在多尺度下实现高精度与高空间分辨率的流量聚合。