The trajectory on the road traffic is commonly collected at a low sampling
rate, and trajectory recovery aims to recover a complete and continuous
trajectory from the sparse and discrete inputs. Recently, sequential language
models have been innovatively adopted for trajectory recovery in a pre-trained
manner: it learns road segment representation vectors, which will be used in
the downstream tasks. However, existing methods are incapable of handling
complex trajectories: when the trajectory crosses remote road segments or makes
several turns, which we call critical nodes, the quality of learned
representations deteriorates, and the recovered trajectories skip the critical
nodes. This work is dedicated to offering a more robust trajectory recovery for
complex trajectories. Firstly, we define the trajectory complexity based on the
detour score and entropy score and construct the complexity-aware semantic
graphs correspondingly. Then, we propose a Multi-view Graph and Complexity
Aware Transformer (MGCAT) model to encode these semantics in trajectory
pre-training from two aspects: 1) adaptively aggregate the multi-view graph
features considering trajectory pattern, and 2) higher attention to critical
nodes in a complex trajectory. Such that, our MGCAT is perceptual when handling
the critical scenario of complex trajectories. Extensive experiments are
conducted on large-scale datasets. The results prove that our method learns
better representations for trajectory recovery, with 5.22% higher F1-score
overall and 8.16% higher F1-score for complex trajectories particularly. The
code is available at this https URL

本研究旨在为复杂轨迹提供更强大的轨迹恢复。通过定义基于绕道分数和熵分数的轨迹复杂度，并相应构建复杂感知语义图，我们提出了一种多视图图和复杂感知变压器（MGCAT）模型，以从两个方面在轨迹预训练中编码这些语义信息：1) 考虑轨迹模式自适应地聚合多视图图特征，并且 2) 在复杂轨迹中对关键节点进行更高的关注。我们的 MGCAT 在处理复杂轨迹的关键场景时具有感知性能，通过在大规模数据集上进行的广泛实验证明，我们的方法提供了更好的轨迹恢复表示，整体 F1 得分提高了 5.22％，对于复杂轨迹特别是 F1 得分提高了 8.16％。