Method and algorithm for map-matching trips to POIs

A system for map-matching a probe to a POI during a trip by a vehicle in a region, comprising:

• at least one memory configured to store computer executable instructions;
• and at least one processor configured to execute the computer executable instructions to:
• identify one or more POIs in the region;
• determine, for one or more probes in the region, a latitude location of the one or more probes, a longitude location of the one or more probes and a data collection time stamp for the one or more probes;
• determine, for each of the one or more probes in the region, a proximity between the one or more probes and the one or more POIs within a predetermined radius of the one or more probes;
• determine, for each of the one or more POIs, a POI geographic coverage area;
• calculate an observed POI probability from the proximity and the POI geographic coverage area;
• and use a machine learning model to sequentially process the observed POI probability from a predetermined number of probes of the trip and to predict a POI at which the one or more probes is clustered.

A computer program product comprising a non-transitory computer readable medium having stored thereon computer executable instructions, which when executed by one or more processors, cause the one or more processors to carry out operations for map-matching a probe to a POI during a trip by a vehicle in a region, the operations comprising:

• identifying one or more POIs in the region;
• determining, for one or more probes in the region, a latitude location of the one or more probes, a longitude location of the one or more probes and a data collection time stamp for the one or more probes;
• determining, for each of the one or more probes in the region, a proximity between the one or more probes and the one or more POIs within a predetermined radius of the one or more probes;
• determining, for each of the one or more POIs, a POI geographic coverage area;
• calculating an observed POI probability from the proximity and the POI geographic coverage area;
• and using a machine learning model to sequentially process the observed POI probability from a predetermined number of probes of the trip and to predict a POI at which the one or more probes is clustered.