Semi-Traj2Graph: Identifying Fine-grained Driving Style with GPS Trajectory Data via Multi-task Learning

Abstract

In this paper, based on the widely available GPS trajectory big data that records the driving behaviours implicitly, we propose a multi-task learning (MTL) framework called \textsf{semi-Traj2Graph} to recognize the fine-grained driving styles in the temporal dimension accurately. The MTL framework can incorporate the learning capability of graph representation in extracting high-level and interpretable features regarding complex driving behaviours and semi-supervised in exploiting unlabelled data and reducing labelling effort. More specifically, in the graph representation learning, a multi-view graph is first built to capture a more complete view of driving behaviours from the raw GPS trajectory data, then graph convolutional neural networks (Graph-CNNs) are applied. In the semi-supervised learning, a pseudo-label labelling is adopted to make use of the unlabelled data. We evaluate the proposed framework extensively based on two taxi trajectory datasets collected from the city of Beijing and Chongqing, China, respectively. Experimental results show that \textsf{semi-Traj2Graph} outperforms compared to other baselines, achieving an overall accuracy of around 90\%. We also implement the framework on users' smartphones via the collaborative cloud-edge computation manner to demonstrate the system usability in real case