Abstract: A dispatch database maintains, for a plurality of vehicles available for dispatch, vehicle data and constraints data. A processor is programmed to execute a dispatch server to perform operations including to receive a dispatch request requesting a vehicle to arrive at a request location, utilize a machine-learning model to identify one or more of the plurality of vehicles to respond to the dispatch request, the machine-learning model utilizing the vehicle data and the constraints data as inputs to determine the one or more of the plurality of vehicles, and inform the one or more of the plurality of vehicles of the dispatch request.

Abstract: A cloud service receives locations of priority vehicles (PVs) and non-priority vehicles in a metropolitan area. The cloud service accesses a database storing indicia of restricted road portions in the metropolitan area. The cloud service allocates restricted road portions from the database to the NPVs based on the locations of the PVs, the locations of the NPVs, and locations of the restricted road portions. The allocating includes preventing some of the restricted road portions in the database from being available to be allocated to the NPVs based on determining, according to the locations of the PVs and NPVs and according to pre-stored routes of the PVs, that estimated uses of the road portions by the NPVs would conflict with estimated uses of the road portions by the PVs as they travel along the pre-stored routes. Routes generated to include the respectively allocated road portions are provided to the NPVs.

Abstract: Vehicle fuel tank modeling systems and methods are provided herein. An example method includes introducing known volumes of fuel into the fuel tank, determining correlations of fuel level percentage values relative to the known volumes of fuel placed into the fuel tank, offsetting the correlations to compensate for unusable fuel volume of the fuel tank, selecting a non-linear fuel tank model that fits the offset correlations, and applying the non-linear fuel tank model to a target vehicle having a fuel tank with a fuel tank part number.

Abstract: Vehicle fuel volume estimation systems and methods are provided herein. An example method includes determining a fuel level percentage value for a vehicle, determining a vehicle identification number (VIN) of the vehicle, determining a fuel tank part number using the VIN, converting the fuel level percentage value into a fuel volume value based on the fuel tank part number, and transmitting a message to a recipient, the message that includes the fuel volume value.

Abstract: Vehicle fuel volume consumption estimation and accuracy analysis systems and methods are provided herein. An example method includes determining a fuel level percentage estimate or a fuel volume estimate for a vehicle at a first point in time and a second point in time for a trip, the fuel level percentage estimate or the fuel volume estimate being determined using a fuel tank model of a fuel tank of a vehicle, determining connected vehicle data that includes a determination of accumulated fuel consumed during the trip, and determining an accuracy of fuel tank model using the fuel level percentage estimate or the fuel volume estimate calculated at the first point in time and the second point in time, as well as the accumulated fuel consumed during the trip.

Abstract: Vehicle fuel tank modeling systems and methods are provided herein.

Abstract: Vehicle fuel volume consumption estimation and accuracy analysis systems and methods are provided herein. An example method includes determining a fuel level percentage estimate or a fuel volume estimate for a vehicle at a first point in time and a second point in time for a trip, the fuel level percentage estimate or the fuel volume estimate being determined using a fuel tank model of a fuel tank of a vehicle, determining connected vehicle data that includes a determination of accumulated fuel consumed during the trip, and determining an accuracy of fuel tank model using the fuel level percentage estimate or the fuel volume estimate calculated at the first point in time and the second point in time, as well as the accumulated fuel consumed during the trip.

Abstract: Vehicle fuel volume estimation systems and methods are provided herein. An example method includes determining a fuel level percentage value for a vehicle, determining a vehicle identification number (VIN) of the vehicle, determining a fuel tank part number using the VIN, converting the fuel level percentage value into a fuel volume value based on the fuel tank part number, and transmitting a message to a recipient, the message that includes the fuel volume value.

Abstract: A dispatch database maintains, for a plurality of vehicles available for dispatch, vehicle data and constraints data. A processor is programmed to execute a dispatch server to perform operations including to receive a dispatch request requesting a vehicle to arrive at a request location, utilize a machine-learning model to identify one or more of the plurality of vehicles to respond to the dispatch request, the machine-learning model utilizing the vehicle data and the constraints data as inputs to determine the one or more of the plurality of vehicles, and inform the one or more of the plurality of vehicles of the dispatch request.