Vehicle Data Fusion Based on Spatiotemporal Information and Subgraph Similarity

A computer system for vehicle license plate auditing, the computer system comprising:

• a communication fabric;
• a storage device connected to the communication fabric, wherein the storage device stores program instructions;
• and
• a processor connected to the communication fabric, wherein the processor executes the program instructions to:
• extract distance and time traveling information corresponding to a vehicle from a data network analysis graph model of spatiotemporal traffic flow data based on a license plate number of the vehicle in response to removing irrelevant nodes from the data network analysis graph model;
• determine a distance and time mutually exclusive node relationship within the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time traveling information corresponding to the vehicle extracted from the data network analysis graph model of spatiotemporal traffic flow data;
• determine an anomalous license plate node in the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time mutually exclusive node relationship;
• and
• perform a set of action steps regarding an anomalous license plate number corresponding to the anomalous license plate node.

A computer program product for vehicle license plate auditing, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• extract distance and time traveling information corresponding to a vehicle from a data network analysis graph model of spatiotemporal traffic flow data based on a license plate number of the vehicle in response to removing irrelevant nodes from the data network analysis graph model;
• determine a distance and time mutually exclusive node relationship within the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time traveling information corresponding to the vehicle extracted from the data network analysis graph model of spatiotemporal traffic flow data;
• determine an anomalous license plate node in the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time mutually exclusive node relationship;
• and
• perform a set of action steps regarding an anomalous license plate number corresponding to the anomalous license plate node.