A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data

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 CNNVC 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.