Smart City Transportation Data Analytics with Conceptual Models and Knowledge Graphs

Technological advancements have led to easy and rapid generation and collection of huge amounts of varieties of data from of wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by data analytics. Discovered information and knowledge may help to build a smart city and then a smart world. In this paper, we focus on making good fusion of conceptual modelling and knowledge graphs to capture essential data about public transportation (e.g., buses). Specifically, our conceptual model and knowledge graph capture information regarding bus arrival and departure. Some of the captured data can be uncertain (e.g., timestamp, GPS locations of buses) due to the limitations of the measuring devices and methods to collect the data. Nonetheless, the models are helpful in smart city big data analytics of these public transportation data. The discovered knowledge produces insights to users (e.g., city planners, policy makers), which in turn help them to build a smart city.