Desire line maps are widely deployed for traffic flow analysis by virtue of their ease of interpretation and computation. They can be considered to be simplified traffic flow maps, whereas the computational challenges in aggregating small scale traffic flows prevent the wider dissemination of high resolution flow maps. Vehicle trajectories are a promising data source to solve this challenging problem. The solution begins with the alignment (or map matching) of the trajectories to the road network. However even the state-of-the-art map matching implementations produce sub-optimal results with small misalignments. While these misalignments are negligible for large scale flow aggregation in desire line maps, they pose substantial obstacles for small scale flow aggregation in high resolution maps. To remove these remaining misalignments, 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. With each local alignment iteration, the misalignments of the trajectories with each other and with the road network are reduced, and so converge closer to a minimal flow map. By analysing a set of empirical trajectories collected in Hannover, Germany, we confirm that our minimal flow map has high levels of spatial resolution, accuracy and coverage.

翻译：期望线图因其易于解释和计算的特性，被广泛应用于交通流分析。它们可被视为简化的交通流图，而小尺度交通流聚合的计算挑战阻碍了高分辨率流图的广泛传播。车辆轨迹数据是解决这一难题的潜在数据源。解决方案始于将轨迹与路网进行对齐（或地图匹配）。然而，即使最先进的地图匹配实现也会产生存在微小错位的次优结果。虽然这些错位在大尺度流聚合的期望线图中可忽略不计，但它们对小尺度高分辨率地图中的流聚合构成了重大障碍。为消除这些残留错位，我们提出了创新的局部对齐算法：通过推断路段作为局部参考段，并将邻近路段与之对齐。随着每次局部对齐迭代，轨迹之间及轨迹与路网之间的错位逐渐减少，从而更趋近于最小流图。通过分析在德国汉诺威采集的一组实证轨迹数据，我们证实所构建的最小流图具有较高的空间分辨率、精确度和覆盖度。