Enhancing urban mobility: A multi-modal travel plan recommendation framework integrating the influences of temporal characteristics and candidate sets

Abstract

This paper proposed a travel plan recommendation system that can provide multi-modal, personalized, and door-to-door travel plans to solve travelers’ difficulty in choosing when facing vast and complex travel information. First, we established a dynamic Travel Choice Behavior Graph (TCBG) model, which considers the travel plan candidate set and the temporal characteristics (time-decay and periodicity) of travelers’ behavioral preferences. Next, to effectively learn from TCBG, we constructed a Unified Candidate Set Representation Module (UCSRM) and a new graph neural network called Continuous Dynamic Heterogeneous Graph Attention Networks (CDHAN). UCSRM can employ a multi-head self-attention mechanism for a unified representation of travel plan candidate sets with inconsistent lengths. CDHAN can capture the temporal characteristics of travelers’ preferences by combining the improved Hawkes process. Finally, we validated the effectiveness of the model and framework on multi-modal travel datasets and achieved 0.8172, 0.7994, 0.7859, and 0.9345 on the evaluation metrics of Pre, Rec, F1, and NDCG, respectively. These results show that our model/framework outperforms six existing state-of-the-art models/frameworks in these four evaluation metrics. This study provided a new model and learning framework for travel plan recommendation systems, essential for improving the efficiency of urban transportation and travelers’ travel experience.