Knowledge Graph-Based Enhanced Transformer for Metro Individual Travel Destination Prediction

Abstract

Accurate and timely destination prediction of subway passengers is of great significance in improving urban residents' travel efficiency, alleviating urban traffic pressure, and recommending the proper location-based service. Although some individual travel destination prediction methods have been proposed, the prediction performance is poor due to the large difference in travel locations of different individuals, the difficulty of evaluating the individual travel intention, the sparsity of individual travel trajectory data, and other problems. To solve these problems, this paper proposes a knowledge graph-based enhanced Transformer method (KG-Trans) for the metro individual travel destination prediction task (MITD-Pre), which contains three main modules: (1) the knowledge graph (KG) module constructs a multilayer individual travel KG from top to bottom, which accurately describes the travel individuals and their travel intentions. By analyzing the association relationship between nodes in the KG, the relationship between travel individuals can be naturally established. The learned similar travel regularity can solve the problem of sparse travel trajectories of some individuals. (2) The enhanced Transformer module extracts the dynamic and hierarchical features from the long-Term sequential travel trajectory data. (3) The classifier module introduces the cross-entropy loss to constrain the uniqueness of the predicted subway travel station. The experimental results show that the proposed method obtains a higher destination prediction accuracy than the previous individual travel destination prediction methods.