Detecting Mobility Patterns using Spatial Query Answering over Streams

The development of (semi)-autonomous vehicles and communication between vehicles and infrastructure (V2X) will aid to improve road safety and reduce emissions by identifying dangerous traffic scenes based on movement patterns. A key to this is the Local Dynamic Map (LDM), which acts as an integration platform for static, temporary, and dynamic information about traffic in a geographical context. We have semantically enhanced the LDM to allow an elaborate domain model, captured by a mobility ontology, and queries over data streams that allow for semantic concepts and spatial relationships. Our approach is in the context of ontology-mediated query answering (OQA), which features conjunctive queries over DL-LiteA ontologies that allow for window operators over streams having a pulse and for spatial relations between spatial objects. We have introduced in previous work simple aggregate queries over mobility streams, which often do not suffice to capture more complex movement patterns. Based on three scenarios, we define requirements derived from a domain-specific list of features. Further, we present an extension of the previous aggregate functions with statistical and predictive functionality, which allows us to query more complex mobility patterns such as road networks statistics, vehicles maneuvers, and temporal connected events such as (potential) accidents. We also give a more detailed report on our implementation and analyze it regarding the features and requirements.