  Travelers may travel to locations they have never visited, which we call
potential destinations of them. Especially under a very limited observation,
travelers tend to show random movement patterns and usually have a large number
of potential destinations, which make them difficult to handle for mobility
prediction (e.g., destination prediction). In this paper, we develop a new
knowledge graph-based framework (PDPFKG) for potential destination discovery of
low predictability travelers 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 traveler behavior in choosing potential
destinations. Finally, we provide a comprehensive discussion of the innovative
points of the methodology.
