Identification of travelers’ transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. In this paper, we aim to identify travelers’ transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments with only one transportation mode. A majority of studies have proposed mode inference models based on hand-crafted features, which might be vulnerable to traffic and environmental conditions. Furthermore, the classification task in almost all models have been performed in a supervised fashion while a large amount of unlabeled GPS trajectories has remained unused. Accordingly, we propose a deep SEmi-Supervised Convolutional Autoencoder (SECA) architecture that can not only automatically extract relevant features from GPS segments but also exploit useful information in unlabeled data. The SECA integrates a convolutional-deconvolutional autoencoder and a convolutional neural network into a unified framework to concurrently perform supervised and unsupervised learning. The two components are simultaneously trained using both labeled and unlabeled GPS segments, which have already been converted into an efficient representation for the convolutional operation. An optimum schedule for varying the balancing parameters between reconstruction and classification errors are also implemented. The performance of the proposed SECA model, trip segmentation, the method for converting a raw trajectory into a new representation, the hyperparameter schedule, and the model configuration are evaluated by comparing to several baselines and alternatives for various amounts of labeled and unlabeled data. Our experimental results demonstrate the superiority of the proposed model over the state-of-the-art semi-supervised and supervised methods with respect to metrics such as accuracy and F-measure.

中文翻译：

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使用 GPS 轨迹数据进行交通模式识别的半监督深度学习方法

识别旅行者的交通方式是解决交通领域中出现的各种问题（如出行需求分析、交通规划和交通管理）的基本步骤。在本文中，我们的目标是纯粹根据他们的 GPS 轨迹来识别旅行者的交通方式。首先，开发了一个分段过程，将用户的行程划分为只有一种交通方式的 GPS 分段。大多数研究提出了基于手工制作的特征的模式推理模型，这些模型可能容易受到交通和环境条件的影响。此外，几乎所有模型中的分类任务都是以监督方式执行的，而大量未标记的 GPS 轨迹仍未使用。因此，我们提出了一种深度半监督卷积自编码器 (SECA) 架构，它不仅可以从 GPS 段中自动提取相关特征，还可以利用未标记数据中的有用信息。SECA 将卷积-反卷积自动编码器和卷积神经网络集成到一个统一的框架中，以同时执行有监督和无监督学习。这两个组件同时使用标记和未标记的 GPS 段进行训练，这些段已被转换为卷积运算的有效表示。还实现了用于改变重建和分类错误之间的平衡参数的最佳计划。所提出的 SECA 模型的性能、行程分割、将原始轨迹转换为新表示的方法、超参数计划和模型配置通过与不同数量的标记和未标记数据的几个基线和替代方案进行比较来评估。我们的实验结果证明了所提出的模型在准确性和 F 度量等指标方面优于最先进的半监督和监督方法。