IP Library Granted Patent US 12,230,146
Granted Patent B2
US 12,230,146 · App. 17/514,823 · Granted Feb 18, 2025

Efficient VTOL resource management in an aviation transport network

Inventors: Nikhil Goel (San Francisco, CA); Jon Petersen (Walnut Creek, CA); John Badalamenti (San Francisco, CA); Mark Moore (San Francisco, CA)
Assignee: JOBY AERO, INC.
G08G5/003B64C29/02G06Q50/40G06Q50/47G08G5/0034G08G5/0039G08G5/0043G06Q30/0202
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,230,146
App. No.
17/514,823
Granted
Feb 18, 2025
Kind
B2
Abstract

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.

Claims (50)

1. A computer-implemented method for intra-city VTOL aircraft routing within a geographic area comprising:

accessing data indicative of user ground-based travel within one or more geographic regions;

accessing data indicative of one or more parameters associated with a transport network configured to provide services using vertical take-off and landing (VTOL) aircraft, the VTOL aircraft comprising a propulsion mechanism for horizontal and vertical thrust;

performing a simulation of the transport network based on the data indicative of user ground-based travel and the one or more parameters associated with the transport network, wherein performing the simulation comprises:

initializing an aerial transportation model based on the data indicative of user ground-based travel and the one or more parameters associated with the transport network, wherein the aerial transportation model comprises a plurality of nodes and a plurality of arcs between the plurality of nodes, at least a portion of the plurality of nodes representing candidate hubs of the transport network for vertical take-off and landing, and at least a portion of the plurality of arcs representing candidate routes for the VTOL aircraft,

computing a flow of riders and the VTOL aircraft through at least the portion of the plurality of nodes representing the candidate hubs and at least the portion of the plurality of arcs representing the candidate routes; and

providing an output based on the simulation, wherein the output indicates:

hub locations for a plurality of hubs where the VTOL aircraft can take off and land via one or more landing or launching pads physically positioned on infrastructure within the geographic area to allow the VTOL aircraft to vertically take off and land for air travel within the geographic area; and

one or more routes between at least a portion of the hub locations.

2. The computer-implemented method of claim 1 , wherein computing the flow of riders and the VTOL aircraft comprises modeling the flow of riders and the VTOL aircraft through at least the portion of the plurality of nodes and at least the portion of the plurality of arcs using the aerial transportation model, and wherein performing the simulation comprises:

selecting at least a portion of the candidate hubs for the output based on the modeling.

3. The computer-implemented method of claim 1 , wherein the output comprises a map interface for display, wherein the map interface comprises a map with the hub locations and the one or more routes overlaid on the map.

4. The computer-implemented method of claim 1 , wherein the one or more parameters associated with the transport network comprise at least one of: (i) one or more parameters associated with the VTOL aircraft; or (ii) one or more parameters associated with one or more of the plurality of hubs.

5. The computer-implemented method of claim 4 , wherein the one or more parameters associated with the plurality of VTOL aircraft comprise at least one of: (i) a rider capacity; (ii) an airspeed; (iii) a charging rate; (iv) a travel distance without recharging, (v) whether a battery can be swapped; (vi) a time needed for take-off or landing; or (vii) a type of VTOL aircraft.

6. The computer-implemented method of claim 4 , wherein the one or more parameters associated with one or more of the plurality of hubs comprise at least one of: (1) a distance between hubs; (ii) time to load or unload riders; or (iii) time restrictions on VTOL aircraft transportation.

7. The computer-implemented method of claim 1 , wherein the one or more parameters associated with the transport network comprise initial candidate locations for the candidate hubs.

8. The computer-implemented method of claim 7 , wherein at least one of the initial candidate locations for the plurality of hubs is identified via user input.

9. The computer-implemented method of claim 7 , wherein at least one of the initial candidate locations for the plurality of hubs is automatically identified.

10. The computer-implemented method of claim 7 , wherein performing the simulation of the transport network comprises:

determining an estimated demand for transport services based on the data indicative of user ground-based travel; and

computing the flow of the riders and the VTOL aircraft through at least the portion of the plurality of hubs of the transport network based on the initial candidate locations and the estimated demand for transport services.

11. The computer-implemented method of claim 10 , wherein the estimated demand comprises: (i) an initial demand that is based on the data indicative of user ground-based travel, the data indicative of user ground-based travel comprising historical data, and (ii) a future demand that is based on transport requests for a configuration of the transport network.

12. The computer-implemented method of claim 1 , wherein the output further comprises a recommendation associated with at least one of: (i) hub infrastructure; (ii) hub design; (iii) hub charging stations; (iv) a distance between hubs; or (v) VTOL aircraft storage at a hub.

13. One or more non-transitory computer-readable storage media comprising executable computer program code, the computer program code when executed causing one or more processors to perform operations for intra-city VTOL aircraft routing within a geographic area comprising:

accessing data indicative of user ground-based travel within one or more geographic regions;

accessing data indicative of one or more parameters associated with a transport network configured to provide services using vertical take-off and landing (VTOL) aircraft, the VTOL aircraft comprising a propulsion mechanism for horizontal and vertical thrust;

performing a simulation of the transport network based on the data indicative of user ground-based travel and the one or more parameters associated with the transport network, wherein performing the simulation comprises:

initializing an aerial transportation model based on the data indicative of user ground-based travel and the one or more parameters associated with the transport network, wherein the aerial transportation model comprises a plurality of nodes and a plurality of arcs between the plurality of nodes, at least a portion of the plurality of nodes representing candidate hubs of the transport network for vertical take-off and landing, and at least a portion of the plurality of arcs representing candidate routes for the VTOL aircraft,

computing a flow of riders and the VTOL aircraft through at least the portion of the plurality of nodes representing the candidate hubs and at least the portion of the plurality of arcs representing the candidate routes; and

providing an output based on the simulation, wherein the output indicates:

hub locations for a plurality of hubs where the VTOL aircraft can take off and land via one or more landing or launching pads physically positioned on infrastructure within the geographic area to allow the VTOL aircraft to vertically take off and land for air travel within the geographic area; and

one or more routes between at least a portion of the hub locations.

14. The one or more non-transitory computer-readable storage media of claim 13 , wherein computing the flow of riders and the VTOL aircraft comprises modeling the flow of riders and the VTOL aircraft through at least the portion of the plurality of nodes and at least the portion of the plurality of arcs using the aerial transportation model, and wherein the operations further comprise:

selecting at least a portion of the candidate hubs for the output based on the modeling.

15. The one or more non-transitory computer-readable storage media of claim 13 , wherein the output comprises a map interface for display, wherein the map interface comprises a map with the hub locations and the one or more routes overlaid on the map.

16. The one or more non-transitory computer-readable storage media of claim 13 , wherein the one or more parameters associated with the transport network comprise at least one of: (i) one or more parameters associated with the VTOL aircraft; or (ii) one or more parameters associated with one or more of the plurality of hubs.

17. The one or more non-transitory computer-readable storage media of claim 13 , wherein the simulation is based on an optimization system and machine-learning techniques.

18. The one or more non-transitory computer-readable storage media of claim 13 , wherein the simulation is based on an objective, the objective comprising at least one of: (i) maximizing a number of the riders transported; (2) maximizing a use of the VTOL aircraft; or (3) minimizing a cost of operations.

19. The one or more non-transitory computer-readable storage media of claim 13 , wherein the simulation models the flow of the riders based on ground-based transport and aviation transport.

20. A computer system intra-city VTOL aircraft routing within a geographic area comprising:

one or more processors; and

one or more non-transitory computer-readable storage media comprising executable computer program code, the computer program code when executed causing the one or more processors to perform operations including:

accessing data indicative of user ground-based travel within one or more geographic regions;

accessing data indicative of one or more parameters associated with a transport network configured to provide services using vertical take-off and landing (VTOL) aircraft, the VTOL aircraft comprising a propulsion mechanism for horizontal and vertical thrust;

performing a simulation of the transport network based on the data indicative of user ground-based travel and the one or more parameters associated with the transport network, wherein performing the simulation comprises:

initializing an aerial transportation model based on the data indicative of user ground-based travel and the one or more parameters associated with the transport network, wherein the aerial transportation model comprises a plurality of nodes and a plurality of arcs between the plurality of nodes, at least a portion of the plurality of nodes representing candidate hubs of the transport network for vertical take-off and landing, and at least a portion of the plurality of arcs representing candidate routes for the VTOL aircraft,

computing a flow of riders and the VTOL aircraft through at least the portion of the plurality of nodes representing the candidate hubs and at least the portion of the plurality of arcs representing the candidate routes; and

providing an output based on the simulation, wherein the output indicates:

hub locations for a plurality of hubs where the VTOL aircraft can take off and land via one or more landing or launching pads physically positioned on infrastructure within the geographic area to allow the VTOL aircraft to vertically take off and land for air travel within the geographic area; and

one or more routes between at least a portion of the hub locations.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: GOEL, NIKHIL; PETERSEN, JON; BADALAMENTI, JOHN; MOORE, MARK
To: UBER TECHNOLOGIES, INC.
Reel/Frame 058934/0777 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: UBER TECHNOLOGIES, INC.
To: UBER ELEVATE, INC.
Reel/Frame 058934/0970 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: JOBY ELEVATE, INC.
To: JOBY AERO, INC.
Reel/Frame 058937/0870 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: UBER ELEVATE, INC.
To: JOBY ELEVATE, INC.
Reel/Frame 058980/0454 →
Continuity (4)
Continuation 17461212 · Aug 30, 2021
Continuation 15961806 · Apr 24, 2018
Provisional Application 62489992 · Apr 25, 2017
Related Publication 20220122467A1 · Apr 21, 2022
References Cited (118)
US 3035789A · Young · 1962 [cited by applicant]
US 4022405A · Peterson · 1977 [cited by applicant]
US 5823468A · Bothe · 1998 [cited by applicant]
US 5839691A · Lariviere · 1998 [cited by applicant]
US 5842667A · Jones · 1998 [cited by applicant]
US 6343127B1 · Billoud · 2002 [cited by applicant]
US 6892980B2 · Kawai · 2005 [cited by applicant]
US 8016226B1 · Wood · 2011 [cited by applicant]
US 8020804B2 · Yoeli · 2011 [cited by applicant]
US 8311686B2 · Herkes et al. · 2012 [cited by applicant]
US 8733690B2 · Bevirt et al. · 2014 [cited by applicant]
US 8737634B2 · Brown et al. · 2014 [cited by applicant]
US 8849479B2 · Walter · 2014 [cited by applicant]
US 9205930B2 · Yanagawa · 2015 [cited by applicant]
US 9301099B2 · Witmer · 2016 [cited by applicant]
US 9387928B1 · Gentry et al. · 2016 [cited by applicant]
US 9415870B1 · Beckman et al. · 2016 [cited by applicant]
US 9422055B1 · Beckman et al. · 2016 [cited by applicant]
US 9435661B2 · Brenner et al. · 2016 [cited by applicant]
US 9442496B1 · Beckman et al. · 2016 [cited by applicant]
US 9550561B1 · Beckman et al. · 2017 [cited by applicant]
US 9550577B1 · Beckman · 2017 [cited by applicant]
US 9663237B2 · Senkel et al. · 2017 [cited by applicant]
US 9694911B2 · Bevirt et al. · 2017 [cited by applicant]
US 9771157B2 · Gagne et al. · 2017 [cited by applicant]
US 9786961B2 · Dyer et al. · 2017 [cited by applicant]
US 9802702B1 · Beckman et al. · 2017 [cited by applicant]
US 9816529B2 · Grissom et al. · 2017 [cited by applicant]
US 9838436B2 · Michaels · 2017 [cited by applicant]
US 10140873B2 · Adler et al. · 2018 [cited by applicant]
US 10152894B2 · Adler et al. · 2018 [cited by applicant]
US 10216190B2 · Bostick et al. · 2019 [cited by applicant]
US 10249200B1 · Grenier et al. · 2019 [cited by applicant]
US 10304344B2 · Moravek et al. · 2019 [cited by applicant]
US 10330482B2 · Chen et al. · 2019 [cited by applicant]
US 10384692B2 · Beckman · 2019 [cited by applicant]
US 10593215B2 · Villa · 2020 [cited by applicant]
US 10593217B2 · Shannon · 2020 [cited by applicant]
US 10627524B2 · Bennett · 2020 [cited by applicant]
US 10663529B1 · Bolotski · 2020 [cited by applicant]
US 10713957B2 · Goel · 2020 [cited by applicant]
US 10752365B2 · Galzin · 2020 [cited by applicant]
US 10759537B2 · Moore et al. · 2020 [cited by applicant]
US 10768201B2 · Luo et al. · 2020 [cited by applicant]
US 10832581B2 · Westervelt et al. · 2020 [cited by applicant]
US 10836470B2 · Liu et al. · 2020 [cited by applicant]
US 10913528B1 · Moore et al. · 2021 [cited by applicant]
US 10948910B2 · Taveira et al. · 2021 [cited by applicant]
US 10960785B2 · Villanueva et al. · 2021 [cited by applicant]
US 11130566B2 · Mikic et al. · 2021 [cited by applicant]
US 11145211B2 · Goel et al. · 2021 [cited by applicant]
US 11238745B2 · Villa et al. · 2022 [cited by applicant]
US 11295622B2 · Goel et al. · 2022 [cited by applicant]
US 20100079342A1 · Smith et al. · 2010 [cited by applicant]
US 20130144831A1 · Atlas · 2013 [cited by applicant]
US 20140179535A1 · Stückl et al. · 2014 [cited by applicant]
US 20150120094A1 · Kimchi et al. · 2015 [cited by applicant]
US 20150279217A1 · Chen et al. · 2015 [cited by applicant]
US 20150336668A1 · Pasko · 2015 [cited by applicant]
US 20160216711A1 · Srivastava · 2016 [cited by applicant]
US 20160244158A1 · Fredericks et al. · 2016 [cited by applicant]
US 20160311529A1 · Brotherton-Ratcliffe · 2016 [cited by applicant]
US 20160371984A1 · Macfarlane · 2016 [cited by examiner]
US 20170021941A1 · Fisher et al. · 2017 [cited by applicant]
US 20170057650A1 · Walter-Robinson · 2017 [cited by applicant]
US 20170090484A1 · Obaidi · 2017 [cited by applicant]
US 20170097240A1 · Murrish · 2017 [cited by applicant]
US 20170129603A1 · Raptopoulos · 2017 [cited by examiner]
US 20170169366A1 · Klein · 2017 [cited by applicant]
US 20170197710A1 · Ma · 2017 [cited by examiner]
US 20170357914A1 · Tulabandhula · 2017 [cited by applicant]
US 20180018887A1 · Sharma et al. · 2018 [cited by applicant]
US 20180053425A1 · Adler et al. · 2018 [cited by applicant]
US 20180081360A1 · Bostick · 2018 [cited by applicant]
US 20180194469A1 · Evans · 2018 [cited by examiner]
US 20180208305A1 · Lloyd · 2018 [cited by applicant]
US 20180216988A1 · Nance · 2018 [cited by applicant]
US 20180305005A1 · Parks · 2018 [cited by applicant]
US 20180354636A1 · Darnell et al. · 2018 [cited by applicant]
US 20190012909A1 · Mintz · 2019 [cited by applicant]
US 20190146508A1 · Dean et al. · 2019 [cited by applicant]
US 20190221127A1 · Shannon · 2019 [cited by applicant]
US 20190316849A1 · Abrego et al. · 2019 [cited by applicant]
US 20190325755A1 · Goel · 2019 [cited by applicant]
US 20190325757A1 · Goel · 2019 [cited by applicant]
US 20200103922A1 · Nonami et al. · 2020 [cited by applicant]
US 20200182637A1 · Kumar et al. · 2020 [cited by applicant]
US 20200388166A1 · Rostamzadeh et al. · 2020 [cited by applicant]
US 20210140777A1 · Balva · 2021 [cited by applicant]
CN 105427003A · 2016 [cited by examiner]
CN 11063221 · 2020 [cited by applicant]
EP 0945841A1 · 1999 [cited by applicant]
EP 2698749A1 · 2014 [cited by applicant]
EP 3499634A1 · 2019 [cited by applicant]
JP 2010095246 · 2010 [cited by applicant]
JP 2013086795A · 2013 [cited by applicant]
JP 2020518070 · 2020 [cited by applicant]
KR 20170080354A · 2015 [cited by examiner]
WO WO2016093905 · 2016 [cited by applicant]
WO WO2018023556A1 · 2018 [cited by applicant]
WO WO2018198038 · 2018 [cited by applicant]
WO WO2019089677A1 · 2019 [cited by applicant]
WO WO2019207377 · 2019 [cited by applicant]
WO WO2020252024A1 · 2020 [cited by applicant]
T. S. L. Prashanth, et al. “Multimodal transport model: Enhancing collaboration among mobility sharing schemes by identifying an optimal transit station,” 2016 Itnl Conf on Internet of Things and Applications (IOTA), Pu… [cited by examiner]
Translation of CN-105427003-A (Year: 2016). [cited by examiner]
International Search Report and Written Opinion for PCT/US2019/052578, mailed Aug. 1, 2019, 8 pages. [cited by applicant]
International Preliminary Report on Patentability for PCT/US2019/052578, mailed Jan. 5, 2020, 7 pages. [cited by applicant]
Extended European Search Report for 18790347.1, mailed on Jan. 8, 2020, 8 pages. [cited by applicant]
International Search Report and Written Opinion for PCT/IB201/052864, mailed Aug. 7, 2018, 11 pages. [cited by applicant]
International Preliminary Report on Patentability for PCT/IB201/052864, mailed Jan. 7, 2019, 8 pages. [cited by applicant]
Melton J. et al., “Combined Electric Aircraft and Airspace Management Design for Metro-Regional Public Transportation”, Feb. 19-27, 2014, NASA Aeronautics Research Mission Directorate (ARMD), 2014 Seedling Technical Sem… [cited by applicant]
Miao, F. et al., “Data-Driven Robust Taxi Dispatch under Demand Uncertainties,” Mar. 20, 2016, 17 pages. [cited by applicant]
Miao, F. et al., “Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: a Receding Horizon Control Approach,” IEEE Transactions on Automation Science and Engineering, vol. 13, No. 2, Apr. 2016, pp. 463-478. [cited by applicant]
Uber Technologies, Inc. “Fast-Forwarding to a Future of On-Demand Urban Air Transportation,” Oct. 27, 2016, 99 pages. [cited by applicant]
“Unmanned aerial vehicle—Wikipedia”, Apr. 19, 2017, XP055833730, Retrieved from the Internet: URL:https://en.wikipedia.org/w/index.php?title=Unmanned-aerial-ehicle&oldid=776106328#Sensors [retrieved on Aug. 20, 2021], 2… [cited by applicant]
Bennaceur et al., “Passenger-centric urban air mobility: Fairness trade-offs and operational efficiency”, Transportation Research: Emerging Technologies, 2021, 29 pages. [cited by applicant]
Jong, “Optimizing cost effectiveness and flexibility of air taxis: a case study for optimization of air taxi operations”, University of Twente, Master's thesis, 2007, 62 pages. [cited by applicant]