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.

从 GPS 轨迹识别出行模式作为了解人群出行行为的一项基本技术，在过去十年中受到了广泛关注。虽然大多数以前的旅行模式识别 (TMI) 方法分别识别 GPS 轨迹的每个轨道段的模式，但在本文中，我们提出了一个基于序列的 TMI 框架，该框架为每个 GPS 轨迹构建一个特征序列并将其发送到一个序列-to-sequence (seq2seq) 模型获取对应的出行方式标签序列，命名为Trajectory-as-a-Sequence (TaaS)。拟议的 seq2seq 模型由卷积编码器 (CE) 和循环条件随机场 (RCRF) 组成，其中CE从点级轨迹特征中提取高层特征，RCRF在特征级和标签级学习轨迹的上下文信息，从而输出准确合理的出行方式标签序列。为了缓解数据不足，我们采用了两阶段模型训练策略。此外，我们设计了两个新颖的公交车相关特征来帮助 seq2seq 模型区分不同的高速出行模式（即，bus、car和railway ) 顺序。除了准确性等经典性能指标外，我们还提出了一种新指标，用于评估轨迹级别的出行方式标签序列的合理性。对应于真实世界 TMI 应用的综合评估表明，基于序列的 TaaS 在实践中优于基于段的模型。此外，消融研究的结果表明，集成到 TaaS 框架中的元素有助于提高 TMI 的效率和准确性。