Modeling Trajectories with Multi-task Learning

Kaijun Liu, Sijie Ruan, Qianxiong Xu, Cheng Long, Nan Xiao, Nan Hu, Liang Yu, Sinno Jialin Pan

Abstract: With the increasing popularity of GPS modules, there are various urban applications relying on trajectory data modeling. In this work, we study the problem to model the vehicle trajectories by predicting the next road segment given a partial trajectory. Existing methods that model trajectories with Markov chain or recurrent neural network suffer from issues of modeling, context and semantics. In this paper, we propose a new trajectory modeling framework called Multi-task Modeling for Trajectories (MMTraj), which avoids these issues. Specifically, MMTraj uses multi-head self-attention networks for sequential modeling, captures the overall road network as the context information for road segment embedding, and performs an auxiliary task of predicting the trajectory destination to better guide the main trajectory modeling task (controlled by a carefully designed gating mechanism). Extensive experiments conducted on real-world datasets demonstrate the superiority of the proposed method over the baseline methods.