TrafficFormer: An Efficient Pre-trained Model for Traffic Data

Traffic data contains deep domain-specific knowledge, making labeling challenging, and the lack of labeled data adversely impacts the accuracy of learning-based traffic analysis. The pre-training technology is widely adopted in the fields of vision and natural language to address the problem of limited labeled data. However, the exploration in the domain of traffic analysis remains insufficient. This paper proposes an efficient pre-training model, TrafficFormer, for traffic data. In the pre-training stage, TrafficFormer introduces a fine-grained multi-classification task to enhance the representation capabilities of traffic data; in the fine-tuning stage, TrafficFormer proposes a traffic data augmentation…