  The operation of urban transportation produces massive traffic data, which
contains abundant information and is of great significance for the study of
intelligent transportation systems. In particular, with the improvement of
perception technology, it has become possible to obtain trip data in
individual-level of vehicles. It has finer granularity and greater research
potential, but at the same time requires higher requirements in terms of data
organization and analysis. More importantly it cannot be made public due to
privacy issues. To handle individual-level urban vehicle trip big data better,
we introduce the knowledge graph for the study. For organization of individual
level trip data, we designed and constructed an individual-level trip knowledge
graph which greatly improves the efficiency of obtaining data. Then we used the
trip knowledge graph as the data engine and designed logical rules to mine the
trip characteristics of vehicles by combining the transportation domain
knowledge. Finally, we further propose an individual-level trip synthesis
method based on knowledge graph generation to address the privacy issue of
individual-level traffic data. The experiment shows that the final generated
trip data are similar to the historical one in mobility patterns and vehicle
associations, and have high spatial continuity.
