Transportation agencies are starting to leverage increasingly-available GPS trajectory data to support their analyses and decision making. While this type of mobility data adds significant value to various analyses, one challenge that persists is lack of information about the types of vehicles that performed the recorded trips, which clearly limits the value of trajectory data in transportation system analysis. To overcome this limitation of trajectory data, a deep Convolutional Neural Network for Vehicle Classification (CNN-VC) is proposed to identify the vehicle’s class from its trajectory. This paper proposes a novel representation of GPS trajectories, which is not only compatible with deep learning models, but also captures both vehicle-motion characteristics and roadway features. To this end, an open source navigation system is also exploited to obtain more accurate information on travel time and distance between GPS coordinates. Before delving into training the CNN-VC model, an efficient programmatic strategy is also designed to label large-scale GPS trajectories by means of vehicle information obtained through Virtual Weigh Station records. Our experimental results reveal that the proposed CNN-VC model consistently outperforms both classical machine learning algorithms and other deep learning baseline methods. From a practical perspective, the CNN-VC model allows us to label raw GPS trajectories with vehicle classes, thereby enriching the data and enabling more comprehensive transportation studies such as derivation of vehicle class-specific origin-destination tables that can be used for planning.

运输机构开始利用越来越多的GPS轨迹数据来支持其分析和决策。尽管这种类型的机动性数据为各种分析增加了可观的价值，但仍然存在的挑战是缺乏有关执行记录的行程的车辆类型的信息，这显然限制了运输系统分析中轨迹数据的价值。为了克服这种限制的轨迹数据的，深Ç onvolutional Ñ eural Ñ etwork为V ehicle Ç lassification（CNN-VC）建议从其轨迹中识别出车辆的类别。本文提出了一种新颖的GPS轨迹表示方法，该方法不仅与深度学习模型兼容，而且还捕获了车辆的运动特征和道路特征。为此，还利用开源导航系统来获取有关行进时间和GPS坐标之间距离的更准确信息。在深入研究CNN-VC模型之前，还设计了一种有效的编程策略，以通过虚拟称重站记录获得的车辆信息来标记大规模GPS轨迹。我们的实验结果表明，提出的CNN-VC模型始终优于经典的机器学习算法和其他深度学习基线方法。从实际的角度来看，