A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
Dedong Li Ziyue Li Zhishuai Li Lei Bai Qingyuan Gong Lijun Sun Wolfgang Ketter Rui Zhao
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 https://github.com/bonaldli/ComplexTraj.