Patent ID: 11922530
Assignee: VERIZON CONNECT DEVELOPMENT LIMTED
Field: IT methods for management (Electrical engineering)
Classification: CPC G  H | IPC G

Claim 0:
1. A method, comprising:
receiving, by a device, schedule data identifying schedules of appointments for drivers of vehicles and location data identifying geographical locations, of the vehicles, associated with the respective schedules;
receiving, by the device, traffic data identifying traffic conditions associated with the geographical locations of the vehicles;
determining, by the device and with a status estimate model, status data identifying estimated statuses of the appointments based on the schedule data, the location data, and the traffic data;
training, by the device and based on historical data, an isochrone model to generate a plurality of polygons representing sets of isochrones for destinations of the appointments,
wherein the device uses machine learning to train the isochrone model based on the historical data;

generating, by the device and based on inputting the schedule data, the location data, and the traffic data into the isochrone model, the plurality of polygons,
wherein each of the plurality of polygons is associated with a respective portion of a map and encompasses points on the map from which a respective destination, of the destinations, of a respective appointment, of the appointments, can be reached via a particular mode of travel;

identifying, based on generating the plurality of polygons, a smallest polygon, of the plurality of polygons, that includes a current location of a particular vehicle of the vehicles;
identifying, based on generating the plurality of polygons, a particular largest polygon, of a particular subset of polygons of the plurality of polygons, that does not include the current location of the particular vehicle, wherein the particular subset of polygons is associated with a particular appointment, of the appointments, for a particular schedule of the schedules;
calculating, by the device, an estimated time of arrival for the particular vehicle at a subsequent appointment, of the appointments, for the schedule, wherein the estimated time of arrival is based on:
the smallest polygon and the particular largest polygon, and
the status data;

comparing, by the device, an actual time of arrival that the particular vehicle arrived at the subsequent appointment to the estimated time of arrival for the particular vehicle at the subsequent appointment;
determining, by the device and based on comparing the actual time of arrival to the estimated time of arrival, an accuracy associated with calculating the estimated time of arrival; and
modifying, by the device, the isochrone model based on the accuracy,
wherein the device uses the machine learning to modify the isochrone model based on the accuracy.