Efficient VTOL resource management in an aviation transport network
A transport network management system identifies a service objective for a plurality of VTOL aircraft and retrieves VTOL data including locations of the plurality of VTOL aircraft. An estimate of demand for transport services to be provided at least in part by one of the VTOL aircraft is generated and routing data for the plurality of VTOL aircraft is determined based on the estimated demand and the service objective. Routing instructions based on the routing data are sent to at least a subset of the VTOL aircraft.
1. A method comprising:
identifying a service objective for a plurality of vertical take-off and landing (VTOL) aircraft;
retrieving VTOL data, the VTOL data including locations of the plurality of VTOL aircraft;
generating an estimate of demand for transport services during a given time period;
determining battery levels of each of the plurality of VTOL aircraft;
determining, based on the estimate of demand, the battery levels, and the service objective, a route for each of the plurality of VTOL aircraft, each route specifying a destination, any way points to visit while traveling to the destination, and a time to depart; and
sending routing instructions to at least a subset of the VTOL aircraft, the routing instructions based on the determined route for each of the VTOL aircraft in the subset.
2. The method of claim 1 , wherein the service objective is at least one of:
maximizing a number of people transported by VTOL aircraft;
minimizing a total amount of time spent on the ground by VTOL aircraft; or maximizing an average number of people in each VTOL aircraft when the VTOL aircraft in the air.
3. The method of claim 1 , wherein the routing instructions comprise an instruction to a specific VTOL aircraft to recharge for a specified amount of time.
4. The method of claim 1 , wherein generating the estimate of demand comprises:
retrieving current demand data, the current demand data indicating unfulfilled requests for transport services already made by riders; and
supplementing the current demand data with an estimate of requests for transport services that will be received in the given time period.
5. The method of claim 1 , wherein the estimate of demand comprises a plurality of requests for transport services, each request identifying an origin and a destination.
6. The method of claim 1 , wherein the determining of the route for each of the plurality of VTOL aircraft comprises
providing the VTOL data and estimated demand as input to a network flow model; and
solving the network flow model in view of the identified service objective.
7. The method of claim 1 , wherein the determining of a route for each of the VTOL aircraft comprises:
determining a plurality of different flows of each of the VTOL aircraft through a transportation network;
selecting, for at least s subset of the plurality of VTOL aircraft, a respective one of the different flows; and
determining each of the routes based on each of the selected flows.
8. The method of claim 7 , further comprising:
determining locations of the plurality of vertical take-off and landing (VTOL) aircraft;
determining, based on the estimate of demand, a plurality of transportation requests, each transportation request for travel from a respective origin location to a respective destination location; and
providing the plurality of transportation requests, the locations, and the battery levels to a network flow model, the network flow model configured to concurrently determine each of the routes for each of the plurality of VTOL aircraft by modeling different flows of the plurality of VTOL aircraft through the transportation network.
9. A non-transitory computer-readable storage medium storing executable computer program code that, when executed by one or more processors, causes the one or more processors to perform operations comprising:
identifying a service objective for a plurality of vertical take-off and landing (VTOL) aircraft;
retrieving VTOL data, the VTOL data including locations of the plurality of VTOL aircraft;
generating an estimate of demand for transport services during a given time period;
determining battery levels of each of the plurality of VTOL aircraft;
determining, based on the estimate of demand, the battery levels, and the service objective, a route for each of the plurality of VTOL aircraft, each route specifying a destination, any way points to visit while traveling to the destination, and a time to depart; and
sending routing instructions to at least a subset of the VTOL aircraft, the routing instructions based on the route for each of the plurality of VTOL aircraft in the subset.
10. The non-transitory computer-readable storage medium of claim 9 , wherein the service objective is at least one of: maximizing a number of people transported by VTOL aircraft; minimizing a total amount of time spent on the ground by VTOL aircraft; or maximizing an average number of people in each VTOL aircraft when the VTOL aircraft in in the air.
11. The non-transitory computer-readable storage medium of claim 9 , wherein the routing instructions comprise an instruction to a specific VTOL aircraft to recharge for a specified amount of time.
12. The non-transitory computer-readable storage medium of claim 9 , wherein generating the estimate of demand comprises:
retrieving current demand data, the current demand data indicating unfulfilled requests for transport services already made by riders; and
supplementing the current demand data with an estimate of requests for transport services that will be received in the given time period.
13. The non-transitory computer-readable storage medium of claim 9 , wherein the estimate of demand comprises a plurality of requests for transport services, each request identifying an origin and a destination.
14. The non-transitory computer-readable storage medium of claim 9 , wherein the determining of a route for each of the plurality of VTOL aircraft comprises:
providing the VTOL data and estimated demand as input to a network flow model; and
solving the network flow model in view of the identified service objective.
15. A computer system comprising:
one or more hardware processors; and
a non-transitory computer-readable storage medium comprising executable computer program code, the computer program code when executed causing the one or more processors to perform operations including:
identifying a service objective for a plurality of vertical take-off and landing (VTOL) aircraft;
retrieving VTOL data, the VTOL data including locations of the plurality of VTOL aircraft;
generating an estimate of demand for transport services during a given time period;
determining battery levels of each of the plurality of VTOL aircraft;
determining, based on the estimate of demand, the battery levels, and the service objective, a route for each of the plurality of VTOL aircraft, each route specifying a destination, any way points to visit while traveling to the destination, and a time to depart; and
sending routing instructions to at least a subset of the VTOL aircraft, the routing instructions based on the routes for each of the VTOL aircraft in the subset.
16. The computer system of claim 15 , wherein the service objective is at least one of: maximizing a number of people transported by VTOL aircraft; minimizing a total amount of time spent on the ground by VTOL aircraft; or maximizing an average number of people in each VTOL aircraft when the VTOL aircraft in in the air.
17. The computer system of claim 15 , wherein generating the estimate of demand comprises:
retrieving current demand data, the current demand data indicating unfulfilled requests for transport services already made by riders; and
supplementing the current demand data with an estimate of requests for transport services that will be received in the given time period.
18. The computer system of claim 15 , wherein the estimate of demand comprises a plurality of requests for transport services, each request identifying an origin and a destination.
19. The computer system of claim 15 , wherein determining of each of the routes comprises:
providing the VTOL data and estimated demand as input to a network flow model; and
solving the network flow model in view of the identified service objective.