Driving trajectory representation learning is of great significance for
various location-based services, such as driving pattern mining and route
recommendation. However, previous representation generation approaches tend to
rarely address three challenges: 1) how to represent the intricate semantic
intentions of mobility inexpensively; 2) complex and weak spatial-temporal
dependencies due to the sparsity and heterogeneity of the trajectory data; 3)
route selection preferences and their correlation to driving behavior. In this
paper, we propose a novel multimodal fusion model, DouFu, for trajectory
representation joint learning, which applies multimodal learning and attention
fusion module to capture the internal characteristics of trajectories. We first
design movement, route, and global features generated from the trajectory data
and urban functional zones and then analyze them respectively with the
attention encoder or feed forward network. The attention fusion module
incorporates route features with movement features to create a better
spatial-temporal embedding. With the global semantic feature, DouFu produces a
comprehensive embedding for each trajectory. We evaluate representations
generated by our method and other baseline models on classification and
clustering tasks. Empirical results show that DouFu outperforms other models in
most of the learning algorithms like the linear regression and the support
vector machine by more than 10%.

本文提出一种新的轨迹表示联合学习的多模态融合模型 DouFu，它使用多模态学习和注意力融合模块来捕捉轨迹的内部特征，通过设计运动、路线和全局特征并分别使用注意力编码器或前馈网络，结合路线特征和运动特征创建更好的时空嵌入，最终生成每个轨迹的全面嵌入。实验结果表明，DouFu 在大多数学习算法中优于其他模型超过 10%。