Vehicle mobility patterns based on user location data

A computer-implemented method comprising:

• at a mobility region system:
• receive and determine, by a processor and a memory unit storing computer-executable instructions operating on the mobility region system, location data that includes a plurality of trips by a user between a plurality of origins and a plurality of destinations;
• aggregate, by the processor, the location data;
• detect and filter, by the processor, noise location data from the aggregated location data, wherein the noise location data comprises environmental noise data and inaccuracies from the location data;
• cluster, by the processor, the aggregated location data using one or more cluster levels;
• form, by a machine learning algorithm operating on the processor, one or more location clusters into a plurality of nodes from the aggregated location data and based on the one or more cluster levels;
• generate, by the processor, a mobility graph that includes the plurality of nodes and a plurality of edges, wherein the plurality of nodes is defined from the one or more location clusters, and the plurality of edges is defined by each of the plurality of trips between one of the plurality of origins and one of the plurality of destinations;
• generate, by the processor, a region of mobility based on the mobility graph, wherein the region of mobility is a spatial region based on a specific location included with the plurality of nodes and the plurality of edges that meet a location criteria;
• and
• display, by the processor via a user interface connected to the processor, the region of mobility for the user.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing device, cause the computing device to:

• receive and determine, by a processor operating on the computing device, location data that includes a plurality of trips by a user between a plurality of origins and a plurality of destinations;
• aggregate, by the processor, the location data;
• detect and filter, by the processor, noise location data from the aggregated location data, wherein the noise location data comprises environmental noise data and inaccuracies from the location data;
• cluster, by the processor, the aggregated location data using one or more cluster levels;
• form, by a machine learning algorithm operating on the processor, one or more location clusters into a plurality of nodes from the aggregated location data and based on the one or more cluster levels;
• generate, by the processor, a mobility graph that includes the plurality of nodes and a plurality of edges, wherein the plurality of nodes is defined from the one or more location clusters, and the plurality of edges is defined by each of the plurality of trips between one of the plurality of origins and one of the plurality of destinations;
• generate, by the processor, a region of mobility based on the mobility graph;
• and
• display, by the processor via a user interface connected to the processor, the region of mobility for the user.