Trajectory-as-a-Sequence: A novel travel mode identification framework

Abstract

Identifying travel modes from GPS tracks, as an essential technique to understand the travel behavior of a population, has received widespread interest over the past decade. While most previous Travel Mode Identification (TMI) methods separately identify the mode of each track segment of a GPS trajectory, in this paper, we propose a sequence-based TMI framework that constructs a feature sequence for each GPS trajectory and sent it to a sequence-to-sequence (seq2seq) model to obtain the corresponding travel mode label sequence, named Trajectory-as-a-Sequence (TaaS). The proposed seq2seq model consists of a Convolutional Encoder (CE) and a Recurrent Conditional Random Field (RCRF), where the CE extracts high-level features from the point-level trajectory features and the RCRF learns the context information of trajectories at both feature and label levels, thus outputting accurate and reasonable travel mode label sequences. To alleviate the lack of data, we adopted a two-stage model training strategy. Additionally, we design two novel bus-related features to assist the seq2seq model distinguishing different high-speed travel modes (i.e., bus, car, and railway) in the sequence. Besides the classical performance metrics such as accuracy, we propose a new metric that evaluates the rationality of the travel mode label sequence at the trajectory level. Comprehensive evaluations corresponding to the real-world TMI applications show that the sequence-based TaaS outperforms the segment-based models in practice. Furthermore, the results of ablation studies demonstrate that the elements integrated into the TaaS framework are helpful to improve the efficiency and accuracy of TMI.