RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

TL;DR

This paper introduces RNTrajRec, a novel transformer-based framework that leverages road network topology to improve the accuracy of recovering low-sample-rate GPS trajectories, benefiting various spatial-temporal applications.

Contribution

It proposes a road network enhanced transformer model with a graph-based embedding and spatial-temporal learning for trajectory recovery, addressing limitations of previous sequence-to-sequence methods.

Findings

Outperforms existing methods on large-scale real-life datasets.

Effectively captures spatial and temporal features for accurate trajectory recovery.

Enhances trajectory data quality for applications like traffic prediction and travel time estimation.

Abstract

GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large amount of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints.We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories. Currently, most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and lack spatial consistency. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model into a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach.