Using iterated local alignment to aggregate trajectory data into a traffic flow map

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.