Potential destination prediction for low predictability individuals based on knowledge graph

Individuals with low predictability are common due to observation limitation, which shows random movement patterns and makes it difficult to perform mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for destination prediction of low predictability travelers, especially those potential destinations a traveler never visits in history, by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals' unobserved destinations to be chosen in the future can be obtained by calculating triples' distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method significantly outperforms baseline methods, and (ii) the results show strong consistency with human behavioural pattern of choosing potential destinations. Finally, we provide a comprehensive discussion and respond to the innovative points of the methodology.