Identifying Truck Activity from Global Positioning System Data Based on Satellite Imagery

Road freight transportation plays a crucial role in fostering economic growth and enhancing logistics efficiency. Existing research primarily focuses on analyzing freight truck activity characteristics based on global positioning system (GPS) data of freight truck trajectories. Those studies proposed a variety of identification methods from different perspectives, but they mainly rely on rule-based methods and subjectively defined key threshold parameters, ignoring the complexity of truck freight activities and relying on long sequences of trajectory data. To bridge this gap, we propose the satellite image augmented truck activity recognition method, a structured framework that utilizes satellite imagery to identify truck activities from GPS data. Firstly, we categorize truck activities into three types: loading/unloading, resting, and driving. We extract geographic entities from the area of interest data to create the satellite image dataset of truck activity. Secondly, we evaluate the recognition accuracy of benchmark image classification models on our dataset, and ResNet50 achieves 98.04% accuracy on the test set. Thirdly, we apply the image classification model to identify truck activity from GPS trajectory data and use the recognition results of neighboring points to improve the identification results. The framework delineated in this paper entails the extraction of truck activities from GPS data at the satellite image level. Our approach holds promise in facilitating logistics operation fleets and traffic management authorities to maintain near real-time surveillance of the truck status, thereby fostering enhancements in both the efficiency and safety of freight transport.