The widespread adoption of Global Positioning Systems (GPS) in transportation has significantly contributed to the understanding of human behaviour, enabling the extraction of crucial travel information. However, the exploration of transportation modes using GPS data remains an under-researched domain due to its intricate analytical demands. While various methods, ranging from rule-based approaches to advanced machine learning algorithms, have been employed to identify transportation modes from GPS data, most have been tested on limited labelled datasets. This study introduces an innovative clustering method that combines multi-criteria decisionmaking, network analysis, and the meta-heuristic algorithm of particle swarm optimization to effectively cluster transportation modes. Pioneering a hybrid approach, the study utilizes elements from the Analytic Network Process (ANP) super matrix in conjunction with transportation modes as variables, harnessing the particle swarm optimization (PSO) algorithm with a fully unlabelled dataset. The compelling findings underscore the model's effectiveness, achieving an impressive accuracy rate exceeding 88% in transportation mode clustering.