This paper proposes a method based on Gaussian-Mixture-Model Hidden Markov Model (GMM-HMM) to recognize the activity-node type of trip chain for hazardous-materials (hazmat) transportation vehicle. The GPS data of vehicle were pre-processed to identify the activity nodes of the trip chains using a Decision-Tree based move-stop detection algorithm. Then the activity nodes were grouped into the activity hotspots by a dropout-based OPTICS (D-OPTICS) algorithm. A multi-scale feature system was constructed according to the individual features of the activity nodes, the relative features of the corresponding trip chains, and the group features of the related hotspots. These feature vectors were further transformed into low-dimensional vectors using Factor Analysis method. Finally, a GMM-HMM based activity type recognition model for hazmat transportation vehicles was built where Baum-Welch algorithm was used for parameter estimation, and Viterbi algorithm for decoding the hidden state to obtain the recognition results of the activity-node type of trip chains. Not only the accuracy of the proposed method was directly verified based on the small-scale real-activity dataset, but also the effectiveness of the proposed method on the activity type identification of large-scale GPS data was evaluated indirectly using the Point-of-Interest (POI) information. The results demonstrate that the identification rate of the GMM-HMM based activity type recognition method was more than 80% in the task of nine-type activity recognition. The recognition results can help analyzing the activity behavioral patterns, discovering the abnormal activities, and providing effective decision-making support for hazmat transportation supervision.