Dynamic tracking and coordination of multi-leg shipments using a distributed computing ecosystem

Abstract

An example aspect of the present disclosure relates to dynamic tracking and coordination of multi-leg transportation. An example method includes receiving a request for transportation services. The method includes calculating a multi-modal transportation journey for a user based on the request. The method includes accessing data associated with the multi-modal transportation service and data associated with a state of the user with respect to the multi-modal transportation journey. The method includes calculating a quality measure of the multi-modal transportation service based on the data associated with the multi-modal transportation service and the data associated with the state of the user. Further, the method includes initiating an adjustment action associated with the multi-modal transportation journey for the user based on the quality measure.

Description

[Technical Field]

[0001] (Related Applications) This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 424,292, filed November 10, 2022, which is incorporated herein by reference in its entirety.

[0002] (Field) FIELD OF THE DISCLOSURE The present disclosure relates generally to electric aircraft and operations for transporting users by electric aircraft. [Background technology]

[0003] Transportation service applications allow individual users to request transportation. For example, a service provider can match a driver / vehicle with a request to transport a rider to a requested destination or to deliver luggage, goods, or prepared meals. A computing platform can be used to help facilitate these services. Summary of the Invention [Means for solving the problem]

[0004] Aspects and advantages of implementations of the present disclosure will be set forth in part in the description that follows, and may be learned from the description, or may be learned through practice of the implementations.

[0005] One exemplary aspect of the present disclosure is directed to a computer-implemented method. The computer-implemented method includes accessing data indicating a request for transportation services, the request indicating a user for the transportation services. The method includes calculating a multimodal transportation itinerary for the user based on the request. The multimodal transportation itinerary includes a plurality of transportation legs for providing the multimodal transportation service. The method includes determining a state of the user with respect to the multimodal transportation itinerary. The state indicates a progress of the multimodal transportation service for the user, the state being associated with a transportation leg of the multimodal transportation itinerary. The method includes calculating a quality measure of the multimodal transportation service based on the user state. The method includes determining an adjustment action associated with the multimodal transportation itinerary for the user based on the quality measure. The adjustment action includes adjustments associated with other transportation legs of the multimodal transportation itinerary, the other transportation legs being subsequent to the transportation leg. The method includes transmitting, over a network, instructions to initiate the adjustment action associated with the multimodal transportation itinerary for the user.

[0006] In some example implementations, the other transportation segment includes ground transportation service for the user, and the adjustment action includes at least one of (i) initiating an adjustment to ground transportation for the user, or (ii) initiating an increase in a priority associated with the user for ground transportation.

[0007] In some example implementations, adjusting ground transportation for the user includes at least one of (i) assigning a different ground vehicle to the user, (ii) changing a service level associated with ground transportation for the user, or (iii) changing a service type associated with ground transportation.

[0008] In some example implementations, increasing the priority associated with the user for ground transportation includes adjusting a data structure for allocating ground vehicles to the user to increase the priority with which ground vehicles are assigned to the user.

[0009] In some example implementations, the other transportation segment includes air transportation service for the user, and the adjustment action includes at least one of (i) initiating an adjustment of a seat for the user on an aircraft to be used for the air transportation service, or (ii) initiating an assignment of the user to a different aircraft for the air transportation service.

[0010] In some example implementations, calculating a quality measure of the multimodal transportation service based on a state of the user includes accessing data associated with the multimodal transportation service, where the data associated with the multimodal transportation service includes at least one of timing data, movement data, data associated with another user, or event data received from at least one of a plurality of distributed computing devices associated with the multimodal transportation service, and calculating a quality measure of the multimodal transportation service based on the state of the user and the data associated with the multimodal transportation service.

[0011] In some example implementations, calculating a quality measure of the multimodal transportation service based on data associated with the user's state and the multimodal transportation service includes accessing a time threshold associated with the user's user state, where the time threshold is based on historical transportation data for the user state; calculating a state latency for the user based on data associated with the multimodal transportation service, where the state latency represents a period of time the user remains in the user state; and calculating the quality measure based on a comparison of the time threshold and the state latency.

[0012] In some example implementations, a user is associated with one or more user characteristics, and the time threshold associated with the user state is based on the one or more user characteristics.

[0013] In some example implementations, the time threshold associated with the user state is based on a geographic region associated with the multimodal transportation service.

[0014] In some example implementations, determining an adjustment action associated with the multimodal transportation itinerary for the user based on the quality measurements includes calculating a predicted overall service score for the multimodal transportation service based on the quality measurements, the predicted overall service score indicating a predicted quality of the multimodal transportation service across a plurality of states of the multimodal transportation service; and determining an adjustment action based on the predicted overall service score, the adjustment action being configured to affect the final overall service score.

[0015] In some example implementations, calculating a predicted overall service score for a multimodal transportation service includes determining a weighted quality measure based on a user state and calculating a predicted overall service score based on the weighted quality measure.

[0016] In some example implementations, calculating the predicted overall service score for the multimodal transportation service includes accessing one or more other quality measures of the multimodal transportation service for one or more other states of the user related to the multimodal transportation journey, and calculating the predicted overall service score based on an aggregation of the quality measures with the one or more other quality measures of the multimodal transportation service.

[0017] In some example implementations, the state is one of a plurality of predefined user states, including a transit state, a transition state, a boarding state, a ready state, and an arrival state.

[0018] Another example aspect of the present disclosure is directed to one or more non-transitory computer-readable media storing instructions executable by one or more processors to cause the one or more processors to perform operations. The operations include accessing data indicative of a multimodal transportation journey for a user. The multimodal transportation journey includes a plurality of transportation legs for providing transportation services for the user. The operations include determining a state of the user with respect to the multimodal transportation journey. The state indicates a progress of the multimodal transportation service for the user, the state being associated with a transportation leg of the multimodal transportation journey. The operations include calculating a quality measure of the multimodal transportation service based on the user state. The operations include determining an adjustment action associated with the multimodal transportation journey for the user based on the quality measure. The adjustment action includes adjustments associated with other transportation legs of the multimodal transportation journey, the other transportation legs being subsequent to the transportation leg. The operations include transmitting, over a network, instructions to initiate the adjustment action associated with the multimodal transportation journey for the user.

[0019] In some example implementations, the user is associated with a user device configured to launch a software application associated with the transportation service, and the adjustment action includes a modification to at least one user interface of the software application.

[0020] In some example implementations, at least one user interface of the software application is configured to provide one or more user notifications, and initiating the adjustment action includes accessing alternative transportation data associated with the alternative transportation service, calculating a user notification representing a comparison between the alternative transportation service and the multimodal transportation service, and transmitting data indicating the user notification over a network to a user device during the multimodal transportation service.

[0021] In some example implementations, the adjustment action includes notifying an operator at the air facility to assist the user in transitioning between ground transportation services and air transportation services, and transmitting the instruction to initiate the adjustment action includes transmitting data to a user device associated with the operator indicating the notification to assist the user while at the air facility.

[0022] In some example implementations, the user state is one of a plurality of predefined user states, each respective state being associated with a corresponding weight for determining a weighted quality measure associated with the respective state.

[0023] In some example implementations, the operations further include receiving feedback data associated with the transportation service and modifying a corresponding weight of the at least one respective state based on the feedback data.

[0024] Yet another example aspect of the present disclosure is directed to a computing system including one or more processors and one or more tangible, non-transitory computer-readable media storing instructions executable by the one or more processors to cause the computing system to perform operations. The operations include accessing data indicative of a request for transportation service, the request indicative of a user of the transportation service. The operations include calculating a multimodal transportation itinerary for the user based on the request. The multimodal transportation itinerary includes at least two transportation legs for providing the multimodal transportation service. The operations include accessing data associated with the multimodal transportation service and data associated with a user's status with respect to the multimodal transportation itinerary. The data associated with the multimodal transportation service indicative of a location of the user with respect to the multimodal transportation itinerary. The operations include calculating a quality measure of the multimodal transportation service based on the data associated with the multimodal transportation service and the data associated with the user's status. The operations include initiating an adjustment action associated with the multimodal transportation itinerary for the user based on the quality measure.

[0025] Other example aspects of the present disclosure are directed to other systems, methods, vehicles, apparatus, tangible non-transitory computer-readable media, and devices for improving aircraft operation and associated computational efficiency.

[0026] These and other features, aspects, and advantages of various implementations will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate implementations of the present disclosure and, together with the description, serve to explain the associated principles. [Brief explanation of the drawings]

[0027] A detailed discussion of the implementation, directed to those skilled in the art, is set forth in the specification, which refers to the accompanying figures.

[0028] [Figure 1] FIG. 1 depicts an example multimodal transportation service according to an example implementation of the present disclosure.

[0029] [Figure 2] FIG. 2 depicts a map diagram of an example multimodal transportation itinerary and airline route according to an example implementation of the present disclosure.

[0030] [Figure 3] FIG. 3 depicts a diagrammatic representation of an exemplary aeronautical facility in accordance with an exemplary implementation of the present disclosure.

[0031] [Figure 4] FIG. 4 depicts an example computing ecosystem for providing transportation services according to an example implementation of the present disclosure.

[0032] [Figure 5] FIG. 5 depicts an example computing device register according to an example implementation of the present disclosure.

[0033] [Figure 6] FIG. 6 depicts an example service instance register according to an example implementation of the present disclosure.

[0034] [Figure 7] FIG. 7 depicts an example computing system for providing an example multimodal transportation service according to an example implementation of the present disclosure.

[0035] [Figure 8A] FIG. 8A depicts a state diagram for initiating an adjustment action according to an exemplary embodiment of the present disclosure.

[0036] [Figure 8B] FIG. 8B depicts an exemplary dynamic data model according to an exemplary embodiment of the present disclosure.

[0037] [Figure 9] FIG. 9 depicts a flowchart diagram of an exemplary method for initiating an adjustment action according to an exemplary embodiment of the present disclosure.

[0038] [Figure 10] FIG. 10 depicts a flowchart diagram of an example method for calculating real-time quality measurements according to an example embodiment of the present disclosure.

[0039] [Figure 11] FIG. 11 depicts a flowchart diagram of an example method for calculating a predicted overall service score and associated actions according to an example embodiment of the present disclosure.

[0040] [Figure 12] FIG. 12 depicts a block diagram of an exemplary computing system according to an exemplary implementation of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0041] Generally, the present disclosure is directed to techniques for improving the computational efficiency of transportation services and the associated user experience. A user may request a transportation service to travel to a destination location. In response, a computing system (e.g., a centralized cloud-based platform system) may generate a multimodal transportation journey for the user. The multimodal transportation journey may include multiple transportation legs associated with different transportation modalities. As an example, the journey may include the user traveling by ground vehicle for an initial leg of the trip, by aircraft for intermediate legs, and by another ground vehicle for the final leg.

[0042] The user may transition between multiple states during performance of each of the legs, which may be monitored by the computing system. As further described herein, the states may represent various stages experienced by the user during the transportation service (e.g., first leg traffic state, intermediate leg arrival state, boarding state, ready for takeoff state, intermediate leg traffic state, last leg arrival state, last leg traffic state, etc.).

[0043] As a user progresses along the transportation service (e.g., through various states), the computing system can automatically calculate real-time quality measures for each state of the user. In this exemplary context, the quality measures can be considered “real-time” because they are calculated as the user progresses along the transportation service. The real-time quality measures can include objective scores that represent the quality of at least a portion of the transportation service. For example, a real-time quality measure for a traffic state of a first ground vehicle transportation segment can indicate whether the user is having a favorable experience when the user is being transported in the ground vehicle for the first transportation segment.

[0044] If the real-time quality measurements indicate that the user's experience is currently unfavorable, the computing system can determine an adjustment action. The adjustment action can include modifying subsequent trip segments that would improve the user's experience (and overall service score) associated with the transportation service. This can include, for example, prioritizing the user when matching with a ground vehicle for the final trip segment, reducing user wait time and improving the efficiency of the transportation service for the user. In this way, real-time information associated with the transportation service can be leveraged to improve the delivery of computationally complex multimodal transportation services.

[0045] The disclosed technology can provide several improvements to transportation computing technology. For example, the disclosed technology leverages a specific computing ecosystem and multiple separate user states to improve computing efficiency, accuracy, and personalization for real-time monitoring and adjustment of transportation services. For example, a transportation service can be divided into multiple separate user states that define a user's location or activity at specific points in the transportation service. A distributed computing ecosystem of devices located relative to the multiple separate user states is leveraged to perform real-time quality measurements (e.g., as the user progresses through the transportation service). The real-time quality measurements are correlated with the separate user states to adjust the transportation service in real time as the user is traveling. In this regard, the distributed computing ecosystem of devices, in combination with the multiple separate user states, can be utilized to identify and initiate adjustment actions for a transportation journey (including multiple different transportation modes for the user) that are directly adjusted to address service inefficiencies.

[0046] By correlating real-time quality measurements with different user states, actions to improve transportation services can be tailored to the specific locations or activities associated with negative quality measurements. Thus, the present disclosure presents improved computing systems that can accumulate and distribute newly available information, such as real-time quality measurements tied to different user states, to provide practical applications that improve utilization of computing systems for facilitating transportation services. (Example of a multimodal transportation service)

[0047] 1 depicts an example process flow of a multimodal transportation service according to an example implementation of the present disclosure. The multimodal transportation service may include multiple transportation legs 102, 104, 106 associated with at least two different transportation modalities. For example, the multimodal transportation service may include a first transportation leg 102, one or more second transportation legs 104, and a third transportation leg 106.

[0048] A combination of ground vehicles, aircraft, or other types of vehicles may serve various legs of a multimodal transportation service. Each leg of a multimodal transportation service may be associated with a respective transportation modality. For example, a first transportation leg 102 may be associated with a first transportation modality that uses one or more ground vehicles 108, such as automobiles. A second transportation leg 104 may be associated with a second transportation modality that uses an air-based modality, such as an aircraft 107. A third transportation leg 106 may be associated with a third transportation modality that may be the same as or different from the first or second modality. For example, the third transportation leg 106 may use a ground modality, such as another automobile, bicycle, or walking route.

[0049] Air transportation can include one or more different aircraft, such as airplanes, vertical take-off and landing vehicles ("VTOL"), or other aircraft including conventional take-off and landing vehicles ("CTOL"). For example, VTOL can include one or more different types of rotorcraft (e.g., helicopters, quadcopters, gyrocopters, etc.), tiltrotor aircraft, powered drift vehicles, and / or any other vehicle capable of taking off and / or landing vertically (e.g., without a runway).

[0050] 1, an aircraft used in a multimodal transportation service may include a VTOL configured to operate in multiple flight modes. For example, the aircraft may include a multi-rotor configuration such that the position, orientation, etc. of the aircraft's rotors may be adjusted to enable the aircraft to operate in various flight modes. This may include, for example, a first rotor position that enables the aircraft to take off, land, or hover vertically, and a second rotor position that enables the aircraft to move forward using propulsion (e.g., "cruise").

[0051] The aircraft may include one or more types of power sources, such as batteries, combustible fuel sources, electrochemical sources (such as hydrogen fuel cell systems), or combinations thereof. For example, the aircraft may include an electric VTOL ("eVTOL") capable of operating using one or more electric batteries, a VTOL capable of operating using combustible fuel, or a VTOL that uses a hybrid propulsion system.

[0052] Multimodal transportation services can be provided in an on-demand manner. Services can include ride-sharing, ride-hailing, ride-booking, or delivery services. Multimodal transportation services can be coordinated for users 110 by one or more service providers.

[0053] A service provider may be an entity that provides, coordinates, manages, etc., transportation services. The entity may include a transportation network company, a fleet manager, etc. For example, a user 110 may desire to travel on a journey from an origin location 112 to a destination location 114. The user 110 may interact with a user device 116 through a user interface of a software application to reserve transportation for the journey. The user 110 may interact with the user device 116 through one or more user sessions.

[0054] Based on the user session, at least one service entity can compile one or more options for the user 110 to traverse the journey. The user device 116 of the user 110 can present these options to the user 110 via a user interface of a software application. At least one option for the journey can include a multimodal transportation service. In response to a selection of the multimodal transportation service option by the user 110, a service can be initiated for transportation for the user 110.

[0055] To track and coordinate multimodal transportation services, a user journey can be calculated for the user 110. The user journey (also referred to as a “multimodal journey”) can be defined by a data structure containing various information associated with the user's movement from an origin location to a destination location. As used herein, a user journey may refer to the user journey or the underlying data structure, depending on the context. A user journey may include identifiers for points of interest (e.g., names / coordinates for origin, destination, vertiport, etc.), the time / duration the user will be at each location, transportation modality, specific vehicle assignments, seat assignments, real-time location data, baggage information, or other information. The user journey can be updated in real time as the user 110 progresses along the journey, in response to any changes in the journey, etc. The user journey may be available to the user 110 by the user device 116.

[0056] Building a user journey on demand across modalities can involve centralized or distributed scheduling of resources associated with each modality. For example, an example implementation can involve systems and devices that interact with the user 110, systems and devices associated with a first modality of transportation, and systems and devices associated with a second modality of transportation.

[0057] The user's 110 journey may be based on the user's origin location, destination location, available intermediate locations for transitioning between transportation modalities, vehicle routes, and / or other information. For example, Figure 2 depicts a graphical map view of an example multimodal transportation service within a geographic area 200 according to an example implementation of the present disclosure. The geographic area 200 may be, for example, an urban environment.

[0058] Geographic area 200 may include a network of intermediate locations that can be used to transition users from one transportation modality to another. For example, geographic area 200 may include multiple air facilities 205A-E. Air facilities 205A-E (e.g., vertiports) may enable users to transition from a ground transportation modality to an air transportation modality (or vice versa). Multiple air facilities 205A-E may be located at various locations within geographic area 200. Multiple air facilities 205A-E may be connected by multiple air routes 210A-J. In some implementations, air routes 210A-J may be designed with respect to airspace constraints (e.g., noise constraints, air traffic constraints, etc.). In some implementations, demand modeling may be performed to select high-value infrastructure locations to locate multiple air facilities 205A-E throughout geographic area 200 and generate routes 210A-J between air facilities 205A-E without interfering with airspace constraints. This network of air facilities 205A-E and routes 210A-J can be utilized to create flight plans for aircraft used within the multimodal transportation service 100, indicating how and where a particular aircraft may travel throughout its period of operation.

[0059] Multiple users can be grouped together for multimodal transportation services such that different user journeys may share at least one transportation leg, which may include pooling users to share intermediate transportation legs (e.g., airplane flights) even though the users may have different origin or destination locations.

[0060] As an example, a first user journey for a first user may include three transportation legs 215, 220, and 225 (shown in FIG. 2 by bold lines). The first user journey may include transporting the first user from a first origin location 230, to a first intermediate location (e.g., air facility 205A), to a second intermediate location (e.g., air facility 205B), and finally to a first destination location 235. The first and second intermediate locations may be determined based on their proximity (e.g., being the closest air facility) to the first origin location 230 and the first destination location 235, respectively. The first user journey may include a ground transportation modality (e.g., passenger vehicle, etc.) along the first and final transportation legs 215, 225 and an air transportation modality (e.g., VTOL) along the intermediate transportation leg 220.

[0061] A second user journey for a second user may include three transportation legs 240, 220, and 245 (shown in FIG. 2 by bold lines). The second user journey may include transporting the second user from a second origin location 250, to a first intermediate location (e.g., air facility 205A), to a second intermediate location (e.g., air facility 205B), and finally to a second destination location 255. The second user journey may include a ground transportation modality along the first and last transportation legs 240, 245 and an air transportation modality along the intermediate transportation leg 220.

[0062] A first user and a second user can be pooled together for an intermediate transportation leg 220. For example, a first user journey and a second user journey, respectively, can indicate that the first user and the second user should travel along route 210A and on the same flight of an aircraft transporting the users between air facility 205A and air facility 205B. In this manner, the first and second users can share at least one transportation leg for cost- and power-efficient multimodal transportation. (Example of an aviation facility)

[0063] Intermediate locations within a multimodal transportation service may be configured to help transition users seamlessly from one transportation leg or modality to another. As described herein, these intermediate locations may include aviation facilities to facilitate takeoff (e.g., departure) and landing (e.g., arrival) of aircraft utilized in the multimodal transportation service.

[0064] 3 depicts a diagrammatic view of an exemplary aviation facility 300 in accordance with an exemplary implementation of the present disclosure. The aviation facility 300 may include one or more final approach and landing pads 305, 310 (e.g., FATO pads), one or more vehicle parking locations 315-335, or vertiports with other infrastructure for maintaining and facilitating the functioning of aircraft (e.g., VTOLs). For example, the aviation facility 300 may include infrastructure 340, which may include hardware and software for refueling or recharging aircraft between flights. Various portions of the infrastructure 340 may be accessible at one or more of the vehicle parking locations 315-335.

[0065] Air facility 300 may include a structure or area for transitioning users to and from an air transportation leg of a multimodal transportation service. Air facility 300 may be located within a geographic area in which the multimodal transportation service is provided. For example, air facility 300 may include a building or designated area within the geographic area. In some implementations, air facility 300 may be a portion of a building or structure (e.g., a roof, a dedicated floor, etc.) that may be used for other purposes (e.g., commercial, residential, industrial, parking, etc.).

[0066] Air facility 300 may include one or more sensors 345. The sensors may include visual, auditory, or other types of sensors. For example, the sensors may include cameras, microphones, vibration sensors, motion sensors, RADAR sensors, LIDAR sensors, infrared sensors, temperature sensors, humidity sensors, other weather condition sensors, etc. Sensors 345 may be configured to access sensor data (e.g., noise data, weather data, aircraft-related data, etc.) within and around air facility 300.

[0067] The aviation facility 300 may include one or more output devices. The output devices may include display screens, speakers, lighting elements, or other infrastructure for communicating information to users, facility operators, vehicle operators, or other individuals at the aviation facility 300. For example, a display screen may be utilized to indicate an aircraft assigned to a user and a parking location assigned to the aircraft. The aviation facility 300 may include a route for a user to travel to or from an aircraft. In some implementations, the output devices (e.g., lighting elements) may help indicate the route to the user.

[0068] The aviation facility 300 may include a charging infrastructure configured to charge or otherwise service the energy storage systems of the aircraft. For example, the aviation facility 300 may include chargers configured to physically connect to the aircraft at charging ports. The chargers may provide charge to the aircraft's batteries to increase the charge level of the aircraft. The aviation facility 300 may include various types of chargers to accommodate various types of aircraft, types / configurations of charging stations, types / configurations of batteries, etc.

[0069] The charging infrastructure may also include a system for battery conditioning. This may include, for example, a system for thermal management of the aircraft's batteries. The thermal management system may cool the temperature of the aircraft's batteries. The battery temperature may be cooled to a target temperature for the aircraft to perform a subsequent flight. The target temperature may be based on battery specifications, parameters of the subsequent flight, downtime of the aircraft, etc.

[0070] In some implementations, the charging infrastructure may include one or more other systems for monitoring battery health and status, which may include a system configured to perform diagnostics on the aircraft batteries to detect any anomalies, damage, etc.

[0071] Air facility 300 may include one or more access points 350 for user entry and exit. Access points 350 may include designated areas, elevators, stairwells, etc. Access points 350 may help users transition between transportation segments and their associated different modalities. For example, after being dropped off at air facility 300 by a ground vehicle for a first transportation segment, a user may use access point 350 to enter area 355 for checking in for a flight for the next transportation segment, an area for boarding the aircraft, etc. After unloading from the aircraft at air facility 300, a user may use access point 350 to access area 360 for boarding a ground vehicle for a final transportation segment.

[0072] Air facility 300 may be operated by various entities. For example, a service entity that manages a fleet of aircraft or coordinates transportation services may own, control, operate, etc., air facility 300. In some implementations, air facility 300 may be owned, controlled, operated, etc., by a third-party facility provider. A third-party facility provider may not have its own aircraft fleet but may operate air facility 300 or make air facility 300 available to other entities.

[0073] Air facility 300 can be utilized by a single entity or shared among multiple entities. For example, a service entity that manages / operates a fleet of aircraft can own, lease, control, operate, etc., the entire air facility 300. The service entity and its associated fleet may have exclusive use of air facility 300 such that aircraft outside the service entity's fleet are not permitted in air facility 300 except in an emergency. In another example, a first service entity that manages / operates a first fleet of aircraft can share air facility 300 with a second service entity that manages / operates a second fleet of aircraft.

[0074] In some implementations, certain resources in aviation facility 300 can be allocated to a particular fleet or service entity. For example, a first set of landing pads, parking pads, infrastructure, storage areas, waiting areas, chargers, etc. can be designated for a first service entity and its associated fleet. A second set of landing pads, parking pads, infrastructure, storage areas, waiting areas, etc. can be designated for a second service entity and its associated fleet. In some implementations, resources in aviation facility 300 can be shared, such that shared resources can be dynamically allocated throughout an operational period based on user / aircraft itinerary, charging needs, etc.

[0075] The aviation facility 300 may include an aviation facility computing system (not shown in FIG. 3 ). The aviation facility computing system may be configured to monitor and control various resources of the aviation facility 300. This may include, for example, monitoring and controlling infrastructure such as chargers, sensors, output devices, etc. The aviation facility computing system may include one or more computing devices and may communicate with other computing systems and devices associated with the multimodal transportation service. An exemplary computing ecosystem for multimodal transportation services

[0076] 4 depicts a block diagram illustrating an example networked ecosystem 400 for cross-platform coordination for multimodal transportation services. Multiple networked systems can cooperatively interact within ecosystem 400 to provide multimodal transportation services. As shown, ecosystem 400 may include a distributed computing system with multiple different participating systems / devices communicatively connected via one or more networks 450.

[0077] Ecosystem 400 may include one or more transportation platform systems, such as, for example, an air transportation platform (ATP) system 405 and one or more ground transportation platform (GTP) systems 410. Ecosystem 400 may include third party provider systems 415, airspace systems 420, user devices 425, ground vehicle devices 430, aircraft devices 435, air facility devices 440, or facility operator user devices 445.

[0078] Each of the systems or devices can communicate over one or more wireless or wired networks 450. The networks 450 can include one or more types of networks, including a telecommunications network, the Internet, a private network, or other networks, as further described herein.

[0079] The systems and devices of ecosystem 400 may include multiple software applications operating on the respective systems and devices, which may create an ecosystem of applications for providing and coordinating multimodal transportation services, as further described herein.

[0080] The user devices 425 may include computing devices owned or otherwise accessible to users of the transportation service. For example, the user devices 425 may include handheld computing devices (e.g., phones, tablets, etc.), wearable computing devices (e.g., smart watches, smart glasses, etc.), personal desktop devices, or other devices. The user devices 425 may execute one or more instructions to launch instances of software applications for the respective transportation platforms and present user interfaces associated with the software applications. The user devices 425 may include personal devices (e.g., mobile devices) or shared devices from which a user has initiated a personal session (e.g., by logging in to a public kiosk or display device in a vehicle, etc.).

[0081] The GTP system 410 may be associated with a service entity that provides ground transportation services. The GTP system 410 may include a computing platform (e.g., a cloud services platform, a server system, etc.) that is communicatively connected to one or more of the systems or devices of the networked ecosystem 400 via the network 450.

[0082] The GTP system 410 may include or implement one or more client-enabled software applications accessible to devices in the ecosystem 400. Users may interact with the GTP system 410 (e.g., using user devices 425, ground vehicle devices 430, or aircraft devices 435) to receive various types of transportation services (e.g., delivery, ridesharing, or ridehailing, etc.), including the multimodal transportation services described herein. For example, the GTP system 410 may match one of its associated ground vehicles or operators with a user for ground transportation services.

[0083] The GTP system 410 can be associated with ground infrastructure to facilitate the implementation of ground transportation services. The ground infrastructure can include one or more parking areas, vehicle transfer hubs, charging / refueling locations, storage facilities, etc.

[0084] The GTP system 410 can be associated with a fleet of ground vehicles, and the vehicle operator can include a network of ground vehicle operators. As described herein, the ground vehicles can include automobiles, bicycles, scooters, autonomous vehicles, etc. The network of ground vehicle operators can include drivers or remote operators who facilitate, supervise, or control the movement of available ground vehicles to perform ground transportation services.

[0085] The ground vehicle devices 430 may include computing devices or systems associated with the ground vehicle or operator. For example, the ground vehicle devices 430 may include one or more vehicle computing systems, such as, for example, an onboard computer for operating the vehicle, an autonomous system, an infotainment system, etc. Additionally or alternatively, the ground vehicle devices 430 may include user devices of the operator. For example, the ground vehicle devices may be the driver's mobile phone. In some implementations, the ground vehicle devices 430 may include user devices that remain onboard the ground vehicle, such as, for example, a tablet that is available to the operator or passengers.

[0086] The ATP system 405 may be associated with one or more service entities that provide at least air transportation services to users. The ATP system 405 may include a computing platform (e.g., a cloud services platform, a server system, etc.) that is communicatively connected to one or more of the systems or devices of the networked ecosystem 400 via the network 450.

[0087] The ATP system 405 can include or implement one or more client-enabled software applications accessible to devices in the ecosystem 400. Users (e.g., using user devices 425, ground vehicle devices 430, or aircraft devices 435) can interact with the ATP system 405 to receive various types of information related to transportation services. For example, a user (e.g., a passenger) can interact with the ATP system 405 through an instance of a software application (e.g., a passenger app) running on a user device 425 to request and book multimodal transportation services. A facility operator can interact with the ATP system 405 through an instance of a software application (e.g., an operations app) running on an aviation facility device 440 or a facility operator user device 445 to view / adjust flight information, seat assignments, etc.

[0088] In some implementations, software applications of one system can be launched within or accessed by software applications of another system. For example, the user interface of a software application associated with ATP system 405 can be embedded within and displayed with the user interface of a software application associated with GTP system 410 (or vice versa). This can allow a user to access another application for a particular transportation segment (e.g., air transportation) while utilizing one application.

[0089] The ATP system 405 may be associated with one or more aircraft, aircraft operators, aviation facilities (or portions thereof), facility operators, etc., for facilitating the performance of at least air transportation services. For example, an aircraft may include a fleet of aircraft, and a vehicle operator may include a network of aircraft operators. The network of aircraft operators may include pilots or remote operators who facilitate, supervise, or control the movement of available aircraft to perform air transportation services.

[0090] An air facility used to provide transportation services may include one or more air facility devices 440. The air facility devices 440 may be positioned at various locations within or around the air facility and may collect and receive information associated with the air transportation services. The air facility devices 440 may include one or more charging devices associated with the air facility's charging infrastructure, one or more vehicle positioning devices (e.g., electric tugs, etc.), one or more sensors or monitoring devices (e.g., noise sensors, cameras, etc.), etc.

[0091] A facility operator may be associated with an aviation facility to assist users with security screening, check-in procedures, boarding / deboarding, conducting aircraft checks, etc. Facility operator user devices 445 may include user devices utilized by a facility operator. Facility operator user devices 445 may be used to communicate with transportation platforms or to perform various functions at the aviation facility. For example, facility operator user devices 445 may launch one or more software applications, complete security screening, baggage check-in / out, coordinate recharging / refueling, provide safety briefings, etc.

[0092] The aircraft devices 435 may include one or more aircraft computing systems or aircraft operator user devices. For example, the aircraft devices 435 may include computing systems onboard the aircraft, such as pilot interfaces, avionics systems, infotainment systems, navigation systems, autonomous systems, or any other sensors or devices located on the aircraft and capable of transmitting or receiving information. The aircraft devices 435 may include aircraft operator user devices (e.g., pilot mobile phones). The aircraft devices 435 may include user devices that remain onboard the aircraft, such as tablets or displays that are available to passengers or operators.

[0093] Ecosystem 400 may include one or more airspace systems 420. Airspace system 420 may include one or more airspace data exchanges or may otherwise be associated with a regulatory authority configured to collect real-time, historical, or regulated airspace data. Airspace system 420 may include, for example, (i) an aggregation system that stores airspace data associated with an airspace, (ii) a third-party monitoring system configured to monitor aspects of the airspace (e.g., noise, etc.), or (iii) a regulatory system that may verify, validate, or approve air transportation services prior to takeoff based on one or more policies or criteria set by a regulatory authority (e.g., Federal Aviation Administration, European Aviation Safety Agency, etc.).

[0094] The ecosystem 400 may include one or more third-party provider systems 415. The third-party provider systems 415 may be associated with one or more third parties that provide resources to the ATP system 405 or the GTP system 410. For example, the third-party provider systems 415 may be associated with a third-party aircraft provider that includes one or more “third-party” aircraft. The third-party aircraft may include aircraft provided, leased, rented, or otherwise made available by an entity for use by the ATP system 405 for transportation services, as described further herein.

[0095] Additionally or alternatively, the third-party provider system 415 may be associated with one or more third-party aircraft operator providers. A third-party aircraft operator may include, for example, multiple aircraft pilots who may be available to the ATP system 405 for operation of aircraft for transportation services.

[0096] In some implementations, third-party provider system 415 may be associated with a third-party facility provider that may provide facilities (or facility resources) for use in performing transportation services. For example, the third-party facility provider may own, operate, etc. one or more aeronautical facilities (or portions thereof) that may be rented, leased, or otherwise utilized by the transport platform system to provide air transportation services. ATP system 405 or GTP system 410 may communicate directly or indirectly (e.g., through third-party provider system 415) with third-party aircraft, operators, or infrastructure.

[0097] Systems and devices in ecosystem 400 may be registered for potential use when providing and coordinating multimodal transportation services. Figure 5 illustrates an example device register 455A. The device register 455A may include a table or other data structure that indicates the devices / systems that participate in an on-demand transportation platform ecosystem, such as ecosystem 400. The device register 455A may include fields such as device ID, entity, location, status, availability, etc.

[0098] The device register 455A can be maintained in a local or remote database. Systems and devices can register for participation in the ecosystem 400 by providing information to a registration service. Such information can include a system / device identifier, an associated entity, an IP address, application downloads, sign-up or account creation, or other information for identifying and communicating with the system / device. The device register 455A can be updated to provide a real-time reference regarding the characteristics and status of participating systems / devices. This can include, for example, determining whether a device is online or offline (e.g., powered on and connected or not) or whether a device is available (e.g., not currently being utilized for another task) or unavailable (e.g., being utilized for another task).

[0099] When building a user journey, a service instance register can be created, such as the exemplary service instance register 455B shown in Figure 6. The service instance register 455B can include a data structure with one or more data objects that indicate the devices that should be utilized to facilitate and progress the user along their journey.

[0100] The ATP system 405 (or another system) can establish a service instance register 455B for servicing a particular service request. The service instance register 455B can be associated with a unique (or different) service instance identifier for a particular user journey to provide at least one leg of a transportation service. The service instance register 455B can aggregate a selection of participating devices from the device register 455A. The service instance register 455B can include a minimum set of participating devices to complete at least a leg of the journey. The service instance register 455B can include all participating devices to complete the entire leg of the journey.

[0101] The ATP system 405 (or another system) can update and reconfigure the service instance register 455B as needed to accommodate scheduling changes, delays, device substitutions, etc., or as the journey / journey progresses for a particular user. For example, as a user progresses along the second leg of a particular journey, the ATP system 405 (in communication with the GTP system 410) may identify and select a ground vehicle to service the final leg of the user's journey. In response, the ground vehicle device associated with the selected ground vehicle can be listed in the service instance register 455B. In this way, the service instance register 455B can accurately reflect the systems / devices in ecosystem 400 associated with each particular service instance for a user of a multimodal transportation service. (Example Data Flow for a Computing Ecosystem for Multimodal Transportation Services)

[0102] To help improve the ecosystem 400's ability to efficiently allocate resources across multiple different systems and modalities, an example implementation of the present disclosure provides a vehicle request coordination system that includes a scheduler 700. The scheduler 700 can coordinate requests from multiple client systems for space on one or more vehicles (e.g., aircraft operated by ATP, etc.) to build an on-demand multimodal transportation itinerary 707 (also referred to as a multimodal itinerary 707 or itinerary 707).

[0103] 7 is a block diagram illustrating systems and devices that may respectively correspond to various modalities of an exemplary multimodal transportation service. FIG. 7 illustrates how various computing devices of computing ecosystem 400 may be implemented and interact with each other within the context of the end-to-end multimodal journey described with reference to FIG. 1, and exemplary data flows associated therewith.

[0104] A user 110 can interact with software running on the user device 116 to communicate with one or more service provider systems 705 and request transportation services. The service provider systems 705 can include, for example, an ATP system 405 or a GTP system 410. The service provider systems 705 can also include devices associated with vehicles and facilities used in the multimodal transportation services. The service provider systems 705 can include a scheduler 700 that builds on-demand multimodal itineraries. The service provider can communicate with an airspace system 420 or a third-party provider system 415 to help coordinate transportation services. (Example ATP Client-Enabled System Implementation)

[0105] 7, in some implementations, the ATP system 405 may be a client-facing system that receives user requests and builds an itinerary 707 for the user 110. In such implementations, the scheduler 700 may be a component of (or have access to) the ATP system 405 and help calculate the itinerary 707.

[0106] For example, the user device 116 can communicate with the ATP system 405 and request transportation services for a journey. The user device 116 can execute an application associated with the ATP system 405, which may be an application associated with a service entity that controls the ATP system 405. The ATP system 405 can access availability data for the aircraft and identify or create a flight to transport the user 110 along an air leg of the journey (e.g., the second leg 104). Based on the flight, the ATP system 405 (using the scheduler 700) can request from one or more GTP systems 410 the availability of ground vehicles for transportation along the leg preceding the air leg (e.g., the first leg 102) and the leg following the air leg (e.g., the third leg 106). Additionally or alternatively, user walking instructions may be provided for these legs. The ATP system 405 can use the scheduler 700 to compile a multimodal itinerary 707 for providing transportation services.

[0107] The ATP system 405 can communicate the edited itinerary to the user device 116. The user device 116 can receive the proposed multimodal itinerary 707 from the ATP system 405 and present it for selection by the user 110. For example, the user 110 can view the proposed multimodal itinerary 707 in a user interface of an application associated with the ATP system 405.

[0108] If the user selects a multimodal itinerary 707, the ATP system 405 can communicate with certain devices to coordinate the user's journey. For example, the ATP system 405 can communicate with the GTP system 410 using the scheduler 700. The GTP system 410 can communicate with one or more ground vehicle devices 430 to assign a vehicle for the user 110 for the first transportation leg 102 and a vehicle for the third transportation leg 106. The assignment of the vehicle for the third transportation leg 106 can occur before or during (e.g., during the second leg 104) the user 110 travels along the journey. The ATP system 405 can communicate with air facility devices 440 and provide them with information for the user's journey. This can help facilitate the user's transition between different legs at the respective air facility. The ATP system 405 can communicate with the air vehicle devices 435 to provide seat assignments or other flight information for the aircraft 107 assigned to transport the user 110.

[0109] In some implementations, a service entity associated with the ATP system 405 may have a fleet of ground vehicles for providing ground transportation to users 110. Thus, the GTP system 405 may be an internal client system for communicating with these ground vehicle fleets. (Example GTP client-enabled system implementation)

[0110] 4, in some implementations, the GTP system 420 may be a client-facing system that receives user requests and constructs a multimodal itinerary 707 for the user 110. In such implementations, the scheduler 700 may be a component of (or have access to) the GTP system 410 and may help calculate the itinerary 707.

[0111] For example, a user device 116 can communicate with the GTP system 410 and request transportation services for a journey. The user device 116 can execute an application associated with the GTP system 410. This can be an application associated with a service entity that controls the GTP system 410, such as, for example, a ground vehicle ridesharing / hailing app.

[0112] The GTP system 410 can use the scheduler 700 to request ride slots on one or more ATP system 405 aircraft. The ride slots can include seats for the users 110 and / or cargo space for the users' baggage or delivery items. The ATP system 405 can access availability data for its aircraft and identify or create one or more candidate flights for transporting the users 110 along an air leg of the journey (e.g., the second leg 104).

[0113] In some implementations, the ATP system 405 can provide information associated with the candidate flights to the GTP system 410. This can include takeoff / landing times, boarding times, departure / arrival facilities, aircraft identifiers, or other information. Based on the candidate flights, the GTP system 410 can identify its available ground vehicles for transporting the user 110 along the first transportation leg 102 and the third transportation leg 106. Additionally or alternatively, user walking instructions can be provided for these legs. The GTP system 410 can use the scheduler 700 to compile a multimodal itinerary 707 for providing transportation services.

[0114] The GTP system 410 can communicate the edited itinerary to the user device 116. The user device 116 can receive the proposed multimodal itinerary 707 from the GTP system 410 and present it for selection by the user 110. For example, the user 110 can view the proposed multimodal itinerary 707 in a user interface of an application associated with the GTP system 410.

[0115] If the user selects a multimodal journey, the GTP system 410 can communicate with certain devices to coordinate the user's journey. For example, the GTP system 410 can communicate with the ATP system 405 using the scheduler 700. The ATP system 405 can communicate with the aviation facility devices 440 and provide them with information for the user's journey, as described herein. The ATP system 405 can communicate with the air vehicle devices 435 and provide them with seat assignments or other flight information for the aircraft 107 assigned to transport the user 110.

[0116] The GTP system 410 can communicate with one or more ground vehicle devices 430 to allocate a vehicle for the user 110 for the first transportation leg 102 and a vehicle for the third transportation leg 106. The allocation of the vehicle for the third transportation leg 106 can occur before or while the user 110 travels along the journey (e.g., during the second leg 104).

[0117] In some implementations, the GTP system 410 may be a user-facing system that receives user transportation requests, while the ATP system 405 may be configured to include the scheduler 700 and construct itineraries for the GTP system 410. For example, the GTP system 410 may request a flight or multi-modal itinerary for the user 110 from one or more ATP systems 405. The ATP system 405 may calculate the multi-modal itinerary based on availability data for the aircraft and data indicating ground vehicle availability that may be accessed by the GTP system 410. The ATP system 405 may communicate the itinerary to the GTP system 410 for provision to the user 110. Exemplary Real-Time Computation of Quality Measures for Dynamic User Transport Conditions Using a Distributed Computing Ecosystem

[0118] The techniques of this disclosure can improve the operation of multimodal transportation services by dynamically measuring a user's experience as they progress along the transportation service, which can enable the transportation platform to take corrective action to improve the efficiency of the transportation service and the associated user experience.

[0119] One or more embodiments described herein provide that the methods, techniques, and actions performed by a computing system / device are implemented programmatically or as computer-implemented methods. Programmatically, as used herein, means through the use of code or computer-executable instructions. These instructions can be stored in one or more memory resources of the computing device. Programmatically implemented steps may or may not be automatic.

[0120] One or more embodiments described herein can be implemented using program modules, engines, or components. A program module, engine, or component can include a program, subroutine, portion of a program, or software or hardware component that can perform one or more specified tasks or functions. As used herein, a module or component can exist on a hardware component independent of other modules or components. Alternatively, a module or component can be a shared element or process of other modules, programs, or machines.

[0121] Figure 8A and its accompanying description provide an overview of exemplary programmatic operations implemented by a computing system according to the present disclosure, followed by Figure 8B and its accompanying description illustrating an exemplary data model that may be dynamically generated by the computing system in real time during performance of operations according to the present disclosure.

[0122] 8A depicts a state diagram 800 for initiating an adjustment action according to an exemplary embodiment of the present disclosure. State diagram 800 depicts a computing system 850 (e.g., a transportation platform system) configured to monitor a transportation service and automatically calculate (e.g., in real time) quality measures 855 for a user as the user progresses through the multimodal transportation service. Based on the quality measures 855, computing system 850 can predict an overall service score 860 and initiate actions to improve overall service score 860.

[0123] The computing system 850 may initiate an action by communicating with one or more of the systems and devices of the computing ecosystem 400. The action may modify the first transportation leg 102, the intermediate transportation leg 104, or the final transportation leg 106 of the multimodal transportation service. For example, the action may modify the first transportation modality 145 of the first leg 106, the second transportation modality 150 of the intermediate leg 104, the third transportation modality 155 of the final leg 106, or the first intermediate location 135 or the second intermediate location 140. The transportation modalities may include ground vehicles 108 and / or aircraft 107, as depicted in FIGS. 1 and 7.

[0124] The computing system 850 may be a component of the service provider system 705. For example, the computing system 850 may be implemented as a back-end service for the ATP system 405 coordinating transportation services.

[0125] As described further below, computing system 850 (or its host system) can be configured to access and process various data from various sources. In some implementations, computing system 850 may communicate directly with computing systems / devices to obtain data. As an example, service provider system 705 may be a single orchestrator of transportation services. Computing system 850 may be implemented within service provider system 705 that may communicate directly with users' user devices, ground vehicle devices, air facility devices, aircraft devices, etc. Accordingly, applications running on these devices may be configured to cause the underlying devices to automatically transmit data to computing system 850 via interface layer 851 according to an API that defines a structure for the data to be ingested by computing system 850. In some implementations, computing system 850 may transmit a request for such data to the device, which in response may compute a data payload and communicate the data payload to computing system 850.

[0126] In some implementations, computing system 850 (its host system) may access data from various sources through intermediate computing systems. For example, service provider system 705 may be included among multiple orchestrators that collaborate to provide transportation services. Service provider system 705 may include ATP system 405 or GTP system 410, which communicate with other platforms to coordinate multimodal transportation services. Service level agreements between platforms may define the data, transmission frequency, format, etc., that one platform should provide to another. For example, GTP system 410 may provide computing system 850 with IMU data or location data from a user's user device or ground vehicle 108 while the user is on the first leg 102 and / or last leg 106. ATP system 405 may provide computing system 850 with IMU data or location data from a user's device or aircraft 107 while the user is on the intermediate leg 104.

[0127] In some implementations, the computing system 850 (or its host system) can be configured to query another platform for certain data. For example, the computing system 850 may access an API of the GTP system 405 and formulate a query for certain user feedback data associated with the first leg 102 of the transportation service. In response to the query, the GTP system 405 can return the requested feedback data indicating, for example, the user's rating of the driver for the first leg transportation.

[0128] In this manner, the computing system 850 can access data to track and calculate the user's experience along different states / portions of the requested transportation service. The following provides an example data flow for such calculations.

[0129] More specifically, computing system 850 can receive a request for transportation services. The request can be transmitted from a user device and structured according to an API of computing system 850.

[0130] For example, the request may be a query structured in a manner compatible with the interface layer 851 of the computing system 850. The interface layer 851 may include or implement processes that initiate on the network side of the computing system 850 to establish communication channels with individual devices. For example, the interface layer 851 may establish secure sockets with different types of mobile devices that a service provider / operator may utilize when providing transportation services to users.

[0131] The request can encode a variety of information. For example, the request can indicate one or more users, a start location 112, or a destination location 114. In some implementations, the request can include a user identifier. The user identifier can be utilized by the computing system 850 to access a profile from a database that stores characteristics associated with the user identifier.

[0132] In some implementations, a user can be associated with one or more user characteristics, such as a user score, accessibility, service history, preferences, etc. As an example, a user characteristic can indicate one or more past requested or completed transportation services for the user. In some implementations, a user characteristic can indicate a user's familiarity with transportation services. This can include, for example, the frequency or number of past multimodal transportation services used / requested by the user.

[0133] The computing system 850 can generate a multimodal transportation itinerary for a user based on the request. The multimodal transportation itinerary can include itinerary data for the multimodal transportation service. The itinerary data can indicate multiple transportation legs for providing the multimodal transportation service. As an example, the multimodal transportation itinerary can include a first transportation leg 102 provided by a first ground vehicle, an intermediate transportation leg 104 provided by an aircraft, and a final transportation leg 106 performed by a second ground vehicle.

[0134] The journey data may indicate a modality for each transportation segment, transition points between each segment (e.g., pickup / drop-off locations), and, in some implementations, one or more probabilities for the transportation service. By way of example, the one or more probabilities may identify expected arrival times at transition points, expected vehicles / drivers for transportation segments, or any other predictive information that is subject to change depending on real-world conditions.

[0135] The computing system 850 can monitor the user's progress as the user travels along the multimodal transportation itinerary. As described herein, this can include communication between one or more platforms. In some implementations, the user's progress can be monitored through multiple predefined user states, each of which is associated with a location or activity encountered during the performance of the multimodal transportation service. By way of example, the predefined user states can include an initial booking state 865 and (i) one or more transit states, (ii) one or more transition states, (iii) one or more boarding states, (iv) one or more ready states, or (v) one or more arrival states.

[0136] As an example, the booking state 865 can begin when a software application is launched on a user's device and creates a user-network session with the computing system 850. The booking state 865 can define a period during which the user attempts to reserve a transportation service. The booking state 865 can end, for example, when a vehicle is assigned to transport the user on the first transportation leg 102.

[0137] As another example, the predefined user states may include (i) a first section traffic state 870 in which the user is transported from the origin location 112 to a first intermediate location 135 (e.g., a first airline facility), (ii) a first section transition state 875 in which the user boards an aircraft at the first intermediate location, (iii) an intermediate section traffic state 880 in which the user is transported from the first intermediate location 135 to a second intermediate location 140 (e.g., a second airline facility), (iv) an intermediate section transition state 885 in which the user disembarks from the aircraft at the second intermediate location 140, and (v) a third section traffic state 890 in which the user is transported from the second intermediate location 140 to the destination location 114.

[0138] Each of the states 870, 875, 880, 885, 890 may include one or more sub-states. By way of example, a first leg transition state may include an arrival sub-state, a check-in sub-state, a boarding sub-state, a boarding complete sub-state, a take-off sub-state, etc.

[0139] The trigger conditions for each state (or substate) can be stored in an adaptable data structure (e.g., a table) in accessible memory. These conditions can be referenced by the logic of computing system 850 and can be changed or refined over time. Computing system 850 can be programmed to track and automatically update the user's state based on the conditions defined in the adaptable data structure.

[0140] For example, computing system 850 may transition a user's state based on transportation data 895. Transportation data 895 may include real-time information received from at least one of the distributed computing devices (e.g., systems / devices of computing ecosystem 400) associated with the multimodal transportation service. For example, the distributed computing devices may include one or more user devices, such as, for example, a mobile phone, a desktop computer, one or more smart devices (e.g., a smartwatch, smart glasses, etc.). Additionally, the distributed computing devices may include vehicle devices, such as on-board vehicle computing systems (e.g., autonomous computing systems, on-board aircraft computing systems, etc.), or devices of vehicle operators (e.g., drivers, pilots) that help implement the multimodal transportation service. In some implementations, the distributed computing devices may include one or more facility devices or personnel devices associated with a transition point of the multimodal transportation service.

[0141] Transportation data 895 may include location information for users (or associated vehicles) for multimodal transportation journeys. Location information may include sensor data, such as global positioning system ("GPS") data, inertial measurement unit ("IMU") data, auditory data, image data, etc., recorded by one or more sensors of multiple distributed computing devices. As an example, sensor data may include GPS-based data recorded by a user device or a vehicle device.

[0142] Additionally or alternatively, computing system 850 can transition the user's state based on user input provided to the computing device. The user input can include an indication of the user's location at various locations along the multimodal transportation journey. For example, personnel at a transfer point (e.g., an air facility) for multimodal transportation can provide user input to an associated user device to indicate that a user (e.g., a passenger) has arrived at the transfer point, boarded the aircraft, etc. This can include, for example, adjusting a toggle or other UI element on a user interface to cause the user device to transmit a signal encoding an indication that the user has arrived, boarded, etc. Computing system 850 can access this signal and dynamically update the user's user state during the performance of the multimodal transportation service based on the real-time information.

[0143] In some implementations, transportation data 895 can include at least one of timing data, movement data, or event data. Timing data can indicate periods during which a user is located at various locations along a multimodal transportation journey. Movement data can indicate the user's physical movement and can be used to detect travel conditions. Movement data can include, for example, measurements of a user's movement during one or more time intervals, while user location can refer to the user's location relative to a multimodal transportation journey. Event data can indicate, for example, exceptional travel conditions, such as proximity to a sick passenger.

[0144] The computing system 850 can determine a status of the user with respect to the multimodal transportation journey. The status can indicate the progress of the multimodal transportation service for the user.

[0145] Updating a user's state may include adjusting a data structure to reflect the user's current and / or past states. For example, updating a user's state may include adding one or more data entries to one or more fields in a row of a table. The data entries may indicate the user's location, start time, end time, etc. A row may be associated with a particular state. New entries may be made to the table as the user progresses along the journey 707. Additionally, the computing system 850 may classify an entry / row as associated with a past completed state once the user transitions to the next state or once the transportation service is completed. The data structure may be associated with a particular service instance and stored with or linked to the user's profile.

[0146] The computing system 850 can associate real-time information with a user state that indicates the user's location relative to the multimodal transportation journey. For example, the computing system 850 can identify a current user state and associate data with the current user state. In some implementations, the associated data can include timing data that indicates how long the user remains in each of the predefined user states.

[0147] The timing data can be recorded by a state timer 852 configured to record the amount of time spent in each user state. The state timer 852 can be a virtual timer configured to record the split time between each transition of a plurality of predefined states as the user progresses through the multimodal transportation service. In some implementations, the computing system 850 can operate the timer 852 (e.g., the split timer) based on real-time information (e.g., location information, user input, etc.) received from a plurality of distributed computing devices. Additionally or alternatively, the split time can be recorded or received from one or more of the plurality of distributed computing devices.

[0148] In some implementations, as shown in FIG. 8A, each state can be associated with a specific state timer that is programmed to start and stop based on trigger conditions defined for each state.

[0149] As will be further explained with reference to FIG. 8B, the state times can be utilized to build a unique timeline graph model in real time and to calculate quality measures 855 and overall service scores 860 for each of the states.

[0150] In some implementations, the computing system 850 can access input features 853 associated with a particular state. For example, a software application running on a user device can allow a user to provide feedback about their experience during a particular state. This can include entering text or selecting UI elements that describe the user's experience. The software application can process this information and package it as an interface input signal and as a data set for transmission to the computing system 850. The interface input signal can, for example, encode the user's feedback.

[0151] Computing system 850 can process the interface input signals and store the feedback as input features 853 associated with a particular state. If the interface input signals are provided to an intermediate computing system (e.g., in a GTP system), they can be provided by the intermediate computing system to computing system 850 through interface layer 851 using an API.

[0152] The computing system 850 may calculate quality measures 855 of the multimodal transportation service based on the user states. The quality measures 855 may include a calculated score (e.g., represented as a number, a percentage, a range, a letter, a color, etc.) that represents the quality of at least a portion of the multimodal transportation service. For example, each portion of the multimodal transportation service may be represented by a respective user state, for example, of a plurality of predefined user states. The computing system 850 may calculate a respective quality measure 855 for each respective predefined state of the user.

[0153] Additionally or alternatively, the quality measure 855 may correspond to a sub-score corresponding to the associated transit segment.

[0154] The computing system 850 can calculate an overall service score 860 that quantifies the objective quality of the multimodal transportation service from start to finish. The overall service score 860 can include an aggregation of multiple sub-scores (e.g., quality measures 855) corresponding to different portions of the multimodal transportation service. The aggregation can correspond to a sum, a weighted or unweighted average, a mean, a maximum, a minimum, or any other suitable evaluation measure. As will be further described with reference to FIG. 8B , the computing system 850 can build a dynamic data model in real time, representing / tracking real-time quality measures and changes in the overall service score for a service instance.

[0155] In some implementations, the quality measures 855 for the associated user states can be calculated based on data associated with the multimodal transportation service and one or more thresholds. The thresholds can be stored in a lookup table or other data structure accessible to the computing system 850. Exemplary thresholds can include (i) timing thresholds indicating threshold time periods within which a user should transition in and out of each user state, (ii) movement thresholds indicating tolerances for the amount of movement during each user state, or (iii) event thresholds indicating tolerances for one or more unexpected events during each user state.

[0156] Each threshold can be tailored to a respective user state based on the location or activity involved in each user state. As an example, movement thresholds can be set based on the type of movement that is acceptable for the mode of transportation. This can include, for example, the amount of stop-and-go jerk acceptable for passengers in a passenger vehicle (e.g., as measured by IMU data). As another example, the timing threshold for the boarding state can be higher than the timing threshold for the initial booking state to account for precautionary procedures when boarding an aircraft.

[0157] The thresholds can be determined based on historical data. The historical data can represent multiple historical transportation services or feedback data associated therewith. For example, the historical data can represent historical timing, movements, or events corresponding to individual user states along each of the multiple historical transportation services. The thresholds for each user state can be determined based on the historical timing, movements, or events corresponding to the respective state.

[0158] By way of example, the timing thresholds for each state may include an average or percentage of time period for transitioning users into and out of the first leg traffic state 870 historically.

[0159] In some implementations, thresholds can be tailored to a particular user or geographic region. For example, thresholds for each user state can be determined based on historical timing, movements, or events corresponding to each state of multiple historical transportation services performed for a particular user or within a particular geographic region. By way of example, thresholds associated with user states can be based on (i) one or more user characteristics to account for the user's familiarity with multimodal transportation services (e.g., given that several services have been previously undertaken) or (ii) a geographic region to account for delays (e.g., air traffic delays, etc.), turbulence, etc. that frequently occur in the geographic region. In this way, thresholds can be tailored to better match the expectations of a particular multimodal transportation service.

[0160] The computing system 850 may determine a quality measure 855 for each user state, such as, for example, the first leg traffic state 870. The computing system 850 may do so based on a comparison between a threshold for each state and data associated with the multimodal transportation service. As an example, the computing system 850 may access a time threshold associated with the user's user state (e.g., via a stored lookup table). The computing system 850 may calculate a state latency for the user, representing the period the user remains in each user state. The state latency may be calculated using a timer 852 that measures the duration of a particular state given the conditions for entering and exiting the state. For example, the timer 852 for the first leg traffic state may start when a ground vehicle is assigned to the user and end when the user is disembarked at the first waypoint 135 (e.g., the first air facility). As described herein, the start / end of the timer 852 may be based on the user's monitored location, a signal from the device, etc. Such information can be communicated to computing system 850 by interface layer 851 from individual computing devices or from another platform.

[0161] The computing system 850 can calculate a quality measure 855 based on a comparison of the time threshold and the state latency. The quality measure 855 can be, for example, higher if the state latency does not exceed the time threshold and lower if the state latency exceeds the time threshold. For example, a user may be in a first leg traffic state 870 (e.g., aboard a ground vehicle) en route to the first waypoint 135 for a time that exceeds the time threshold for that traffic state. As a result, the quality measure 855 can be lower to reflect the unfavorable latency. In some implementations, the quality measure 855 can change (e.g., decrease) exponentially as the state latency continues to exceed the time threshold.

[0162] Additionally or alternatively, the quality measure 855 can be based on the input features 853. For example, the quality measure 855 for the first leg traffic state can be higher if the input features indicate a positive user experience during the first transportation leg 102. This can include, for example, a high driver rating. The quality measure 855 for the first leg traffic state can be lower (e.g., a lower driver rating) if the input features indicate a negative user experience during the first transportation leg 102.

[0163] In some implementations, the quality measurement 855 for a particular user can be based on one or more other users associated with the particular user. For example, a first user may be boarding or waiting to board an aircraft for the intermediate transport leg 104. A second user may be assigned to the same aircraft as the first user, thereby pooling the first and second users together for the intermediate transport leg 104.

[0164] The second user may be associated with a different state than the first user. For example, the first user may be associated with a first leg transition state 875 when the first user is waiting to board an aircraft or when the aircraft is waiting to take off. The second user may be associated with a first leg traffic state 870 when the second user is traveling to a first intermediate location 135 (e.g., a first airline facility).

[0165] The computing system 850 may determine a predicted quality measure for the first user based on data associated with the second user. For example, as described herein, the computing system 850 may receive data associated with a multimodal transportation service. The data associated with the multimodal transportation service may include location or status data (e.g., determined using GPS-based data from a mobile device) associated with the second user. The location or status data may indicate the second user's progress along the first transportation leg 102.

[0166] The computing system 850 can determine a predicted quality measure for the first user based on location or status data associated with the second user. For example, the computing system 850 can determine that the second user is or will be delayed in arriving at the intermediate location 135. The computing system 850 can also determine that takeoff for an aircraft (e.g., assigned to the first and second users) will be delayed as a result of the second user's delay. The computing system 850 can determine that the first user will remain in the first leg transition state 875 for an additional period of time. The computing system 850 can aggregate (e.g., sum) the amount of time the first user has already been in this state and the additional period of time (e.g., while waiting for the second user) to predict a total time the first user will be in the first leg transition state 875. The computing system 850 can compare the predicted total time to a threshold for the first leg transition state 875 and predict whether the first user will be in the first leg transition state 875 for an amount of time that exceeds the threshold.

[0167] Based on this comparison, the computing system 875 can determine a predicted quality measurement value for the first user. For example, if the predicted total time is above a time threshold, the computing system 875 can predict that the quality measurement value 855 for the first user (e.g., for at least the first leg transition state 875) will be lower due to the delay of the second user. If the predicted total time is below the time threshold, the computing system 875 can predict that the quality measurement value 855 for the first user (e.g., for at least the first leg transition state 875) will be minimally affected due to the delay of the second user. The computing system 855 can continue to monitor the impact of the delay of the second user on the quality measurement value 855 of the first user to determine whether the quality measurement value 855 will be reduced.

[0168] The computing system 850 can initiate an adjustment action for the transportation journey based on the real-time quality measurements 855 and the user state. The adjustment action can be configured to offset negative quality measurements or emphasize positive quality measurements to help improve the final overall service score 860. To implement the adjustment action, the computing system 850 can adjust previously assigned values ​​or parameters stored in a data structure representing the user's journey 707. Implementing the adjustment action can include transmitting signals to various distributed computing devices to adjust user assignments, update a user interface, provide notifications to an operator, etc.

[0169] The adjustment action can be initiated during a current user state (e.g., corresponding to a currently calculated real-time quality measurement 855), during a user state subsequent to that user state, or after the implementation of the multimodal transportation service.

[0170] The adjustment action may cause an adjustment of a current transportation leg corresponding to the quality measurement 855 or a future transportation leg following the current transportation leg. As an example, the user state may be associated with a first transportation leg 102 of a multimodal transportation journey 707, and the adjustment action may be initiated during a second transportation leg of the multimodal transportation journey 707 following the first transportation leg 102. The adjustment action may include an adjustment of the transportation service associated with the second transportation leg. As further described in the example below, the adjustment action may occur at one or more stages (e.g., 810-830) of the multimodal transportation service.

[0171] In some implementations, the computing system 850 can initiate an adjustment action for the last transportation section 106 to increase the quality measurement value for the third section traffic state 890 to offset or complement the quality measurement value 855 for the first section traffic state 870.

[0172] As an example, a user may be traveling by ground transportation service from an origin location 112 to a first intermediate location 135 according to a first transportation leg 102. This may be, for example, an initial transportation leg of a multimodal transportation service. The computing system 850 may collect (805) transportation data 895 for the first transportation leg. This may include, for example, location data captured or generated by sensors (e.g., GPS, IMU, etc.) of the user's user device. The computing system 850 may calculate a quality measure 855 that indicates the user is having a less favorable experience (e.g., due to wait times, traffic, high jerk, etc.) associated with the first leg traffic conditions 870.

[0173] In response, at 830, the computing system 850 may determine and initiate adjustment actions for a second subsequent transport leg, such as the last transport leg 106. This may include adjustment actions associated with another ground transportation service transporting the user to the destination location 114.

[0174] The adjustment action may include at least one of (i) adjusting ground transportation for the user or (ii) increasing a priority associated with the user for ground transportation. Adjusting ground transportation for the user may include at least one of (i) assigning a different ground vehicle to the user, (ii) changing a service level associated with ground transportation for the user (e.g., from economy service to premium service), or (iii) changing a service type associated with ground transportation (e.g., from combined rider service to single rider service).

[0175] The computing system 850 can increase the priority associated with the user for ground transportation by adjusting a data structure (e.g., a queue, a list, etc.) for allocating ground transportation to the user and increasing the priority at which ground vehicles are assigned to the user. Additionally or alternatively, the data structure can be adjusted to match the user with a particular vehicle class or vehicle service. In some implementations, the computing system 850 can transmit a signal to another computing system (e.g., of a ground transportation provider). The signal can encode a request to increase the user's priority, vehicle type, etc., according to the other computing system's API.

[0176] Additionally or alternatively, at 820, the computing system 850 can determine and initiate an adjustment action for a subsequent transportation leg, such as an intermediate transportation leg 104 including air transportation service. For example, the computing system 850 can initiate the adjustment action for the intermediate transportation leg 104 to increase the quality measurement value for the intermediate transportation leg traffic state 880 to offset or complement the quality measurement value 855 for the first leg traffic state 870. The adjustment action can include at least one of (i) adjusting a seat for the user on an aircraft to be used for the air transportation service or (ii) assigning the user to a different aircraft for the air transportation service. As an example, the adjustment action can include assigning a different aircraft for the air transportation service to shorten an estimated arrival time at the destination location. As another example, a seat can be adjusted to offset a late arrival of the user at the first intermediate location 135 by assigning a seat closer to a vehicle entrance / exit, assigning a premium seat instead of an economy seat, etc.

[0177] Adjustment actions can also be initiated at intermediate locations to help balance the quality measurements 855 and improve the overall score. For example, at 815, the computing system 850 can initiate an adjustment action at the first intermediate location 135 to increase the quality measurement for the first leg transition state 875 to offset or complement the quality measurement 855 for the first leg traffic state 870. Additionally or alternatively, at 825, the computing system 850 can initiate an adjustment action at the second intermediate location 140 to increase the quality measurement for the intermediate leg transition state 885 to offset or complement the quality measurement 855 for the first leg traffic state 870. In some implementations, the adjustment action associated with the first intermediate location 135 or the second intermediate location 140 can include providing information to the user to more efficiently transition to the next transportation mode. This can include notifications or map data to help guide the user, as will be further described herein.

[0178] The computing system 850 can initiate an adjustment action by communicating with at least one of a plurality of distributed devices associated with the multimodal transportation service. For example, the computing system 850 can transmit, over a network, an instruction to initiate an adjustment action associated with the multimodal transportation journey 707 for the user. The instruction can be transmitted to a ground vehicle, an aircraft, or a personnel device associated with the adjustment action.

[0179] For example, the computing system 850 may determine an unfavorable real-time quality measurement 855 (e.g., due to extended latency) for a user state associated with the first transportation leg 102. In response to the unfavorable real-time quality measurement 855, the computing system 850 may determine an adjustment action, identify a subsequent user state corresponding to the adjustment action, and transmit instructions to initiate the adjustment action to at least one of a plurality of distributed devices associated with the subsequent user state.

[0180] In some implementations, initiating an adjustment action can include transmitting a message to another platform. For example, computing system 850 can be included within ATP system 405. Computing system 850 can transmit a message to GTP system 410 to initiate an adjustment action for a transportation segment that includes ground vehicle transportation. For example, computing system 850 can submit a request (e.g., structured according to an API) for a user to be prioritized, for upgrading a vehicle type, etc.

[0181] In another example, computing system 850 can be included within GTP system 410. Computing system 850 can transmit messages to ATP system 405 to initiate adjustment actions for a transportation segment that includes air transportation. For example, computing system 850 can submit a request (e.g., structured according to an API) for a user to be assigned a new seat, an upgrade, etc.

[0182] In some implementations, the adjustment action can include providing a notification to one or more of a plurality of distributed devices associated with the subsequent user state. For example, computing system 850 can identify the subsequent user state corresponding to the adjustment action and provide a notification to the user computing device indicating the adjustment action and the subsequent user state. The notification can indicate an adjustment action for the subsequent transportation leg, such as, for example, changing an aircraft seat, upgrading a subsequent vehicle type, etc.

[0183] As another example, the notification may include information to enable a user to offset a negative / unfavorable real-time quality measurement 855. For example, the computing system 850 may determine a negative / unfavorable real-time quality measurement for a user condition associated with the first intermediate location 135 (e.g., due to difficulty finding a boarding area at an airline facility). In response to the negative / unfavorable real-time quality measurement, the computing system 850 may generate a user notification that includes information regarding the next transition point of the multimodal transportation service (e.g., the second intermediate location 140). The information may include instructions to better orient the user to the second transition point, etc.

[0184] In one example, computing system 850 can access a database that stores maps of aviation facilities. Computing system 850 can generate a custom map for a user that overlays a route from the user's arrival location (e.g., a vehicle drop-off location or an aircraft landing pad) to the user's next relevant location (e.g., a waiting / boarding location for an upcoming vehicle). Computing system 850 can transmit a signal encoding the custom map to a user device. The signal can cause the user device to render the custom map for the user.

[0185] Additionally or alternatively, the adjustment action can include additional assistance at the second transition point, and computing system 850 can provide a notification to a personnel device associated with the second transition point indicating the additional assistance. For example, computing system 850 can transmit a signal (e.g., to a facility operator device) instructing a facility operator to help provide the user with additional assistance.

[0186] The adjustment action may include modifications to at least one user interface of a software application running on the user device. These user interface adjustments and notifications may occur while the user is in a state associated with a low quality measurement. For example, at 810, the computing system 850 may initiate an adjustment action for the first transportation leg 105 while the user is in the first leg traffic state 870 based on the quality measurement 855 for the first leg traffic state 870.

[0187] As an example, a user device of a user may launch a software application associated with a transportation service while the user is in the first leg travel state 870. At least one user interface of the software application may be configured to provide one or more user notifications. The computing system 850 may initiate adjustment actions by causing the user interface to provide one or more user notifications to (i) identify one or more benefits achieved by the multimodal transportation service, (ii) provide instructions to speed up the transportation service (e.g., to direct a lost user), (iii) provide one or more updates (e.g., upgraded service, etc.) to the multimodal transportation service, etc.

[0188] As an example, the computing system 850 may communicate with a user computing device to provide a comparison of timing, reliability, or environmental impact between the multimodal transportation service and alternative transportation services to highlight positive real-time quality measurements 855.

[0189] To that end, computing system 850 may determine alternative transportation data associated with at least one alternative transportation service, such as, for example, a single-leg ground transportation service. The alternative transportation data may indicate an estimated arrival time, one or more delays, carbon emissions, etc. for the alternative transportation service if the user selects the alternative transportation service. This may include, for example, an estimated travel duration or arrival time if the user travels to the destination using only ground vehicle services. Such information may have been previously calculated and stored in response to the transportation service request and the determination of available transportation services, as previously described.

[0190] The computing system 850 can generate a user notification representing a comparison between the alternative transportation service and the multimodal transportation service and provide the user notification to the user during the multimodal transportation service. The notification can be provided during a current or subsequent transportation leg or after completion of the transportation service.

[0191] If the user device is launching an application associated with another service platform, the computing system 850 may generate a query (e.g., based on an API call) to the platform requesting that a particular notification or piece of information be provided to the user by the platform's software application.

[0192] In some implementations, adjustment actions can be based on the predicted overall service score 860. For example, the computing system 850 can calculate the predicted overall service score 860 for the multimodal transportation service based on one or more quality measures 855 and a user state. By way of example, the computing system 850 can extrapolate the predicted overall service score 860 for the multimodal transportation service by using the quality measures 855, one or more previous journey quality measures for the multimodal transportation service, and historical context data indicative of one or more patterns, trends, etc. for subsequent portions of the multimodal transportation service. Adjustment actions can be initiated to affect the final overall service score 860 before the multimodal transportation service is completed. For example, adjustment actions can be implemented to increase journey quality measures for subsequent user states to increase the predicted negative / unfavorable final overall service score 860.

[0193] The final overall service score 860 may be unevenly affected by negative / unfavorable quality measurements for certain user states. To compensate for the relative influence of the quality measurements, the computing system 850 may determine weighted quality measurements 855 based on each user state.

[0194] For example, each of a plurality of predefined user states may be associated with a corresponding weight based on the impact that the quality measure for the respective user state may have on the overall service score 860. The computing system 850 may determine a weighted quality measure based on the user state (e.g., its corresponding weight). The predicted overall service score 860 may be calculated based on the weighted quality measure and one or more previously weighted trip quality measures for the multimodal transportation service.

[0195] The weights corresponding to each of the plurality of user states can be learned or adjusted based on historical transportation data. For example, the computing system 850 can receive feedback data associated with one or more user states of the multimodal transportation service. The feedback data can include user ratings of at least some of the multimodal transportation services, which can help determine influential user states. In some implementations, the feedback data can indicate whether a user continued to book transportation services after the multimodal transportation service. The operations computing system 850 can modify the corresponding weights based on the feedback data, real-time quality measurements, or final overall service score 860 for the corresponding multimodal transportation service.

[0196] The weight assigned to a particular user state can be based on an individual user or an aggregate of users. For example, computing system 850 can store (e.g., in data store 854) not only historical quality measurements 855 associated with user states of past service instances but also their associated adjustment actions. Computing system 850 can analyze historical data associated with a given user to determine the state (or sub-state) with the negative experience that is believed to have the greatest impact on the user's overall experience. Additionally, or alternatively, computing system 850 can analyze historical data for multiple users to determine the state (or sub-state) with the negative experience that is believed to have the greatest impact on the overall experience of a group of users. As an example, one or more users may provide a positive rating of the transportation service for a journey that included a longer than expected boarding time, but a negative rating for a journey that included a longer time to transition to ground transportation for the final transportation leg 106. Thus, computing system 850 can determine that the transition time associated with mid-leg transition state 885 may be more influential for the user and, therefore, apply a higher weight.

[0197] Additionally or alternatively, computing system 850 can analyze previous adjustment actions to determine what adjustment action to undertake for the user's current service instance. Data store 854 can store data indicating previous adjustment actions taken for a given user and the impact of the adjustment actions on the overall service score 860 for that user. As an example, historical data may indicate that a user provided a positive rating for a transportation service when the ground vehicle 108 was upgraded to a luxury car, despite longer than expected wait times in various conditions along the journey. Computing system 850 can determine that this type of adjustment action is preferable for the user.

[0198] In some implementations, the computing system 850 can initiate an adjustment action based on the predicted quality measure. The adjustment action can be a separate action initiated for a user. For example, a first user (e.g., adversely affected by a second user's delay) can be assigned a different seat on the aircraft, provided with a notification, assigned a premium ground ride for the subsequent leg, provided with assistance from a facility operator, provided with a discount, provided with reward points, provided with free products / services at the airline facility, etc. The adjustment action can be initiated to attempt to minimize the impact of the predicted quality measure (e.g., affected by the second user) on the first user's overall score 860.

[0199] In some implementations, computing system 850 can initiate preemptive adjustment actions for a user. For example, based on transportation data 895 collected for multiple users within the past three hours, computing system 850 can determine that an airline facility is experiencing longer than expected wait times for aircraft boarding. Computing system 850 can receive a request for transportation service for a user. Computing system 850 can access data indicative of historical service data and determine that the user's experience is particularly sensitive to long boarding times.

[0200] In response, computing system 850 can determine preemptive adjustment actions for the user based on the historical service data. For example, prior to the user initiating transportation service or computing 850 calculating poor quality measurements 855, computing system 850 can initiate adjustment actions to address potentially extended wait times by providing access to certain areas at the facility (e.g., a VIP lounge), prioritizing user boarding, directing facility personnel to provide additional service, etc.

[0201] In some implementations, a third-party service provider can be selected based on quality metrics 855 associated with that provider. For example, the computing system 850 can calculate and store log data 896 associated with the transportation service. The log data 896 can be stored in accessible memory and used to analyze the third-party service provider.

[0202] Log data 896 may include one or more data fields that capture various information. For example, log data 896 may include fields that indicate a particular journey for multiple transportation services (e.g., a unique ID for the journey), one or more users associated with the service, a vehicle (e.g., a ground vehicle or aircraft that transported the user), a quality measurement for a state or substate, an overall score, a timestamp, a location (e.g., origin, destination, air facility), a route, an event (e.g., traffic, delay), a duration for each state, or other information.

[0203] The computing system 850 can process the log data 896 and calculate quality measurements 855 associated with a particular third-party service provider. The computing system 850 can iteratively analyze the log data 896 to identify certain attributes or combinations of attributes. For example, the computing system 850 can calculate what quality measurements 855 are associated with a traffic state, its associated location, and the third-party service provider. This can include, for example, identifying that a poor quality measurement is associated with a third leg traffic state, in which a passenger vehicle from ground vehicle service provider A is transporting a user from an air facility B to the user's home C in a nearby city. The computing system 850 can perform a similar analysis across the log data 896 for multiple transportation services provided by third-party service provider A.

[0204] Based on the extracted attributes (or attribute combinations), computing system 850 may calculate an overall quality measure for third-party service provider A. This may include, for example, an average of all extracted quality measures associated with transportation services for that third-party service provider A.

[0205] The computing system 850 can utilize the overall quality measure for the service provider to select a service provider for a transportation segment. For example, third-party service providers with higher overall quality measures can be preferred by the transportation platform generating the trip for the requested transportation service. More specifically, the platform can first determine whether such third-party service providers are available for the user's requested transportation when generating the trip (or making the service available) before evaluating other third-party service providers with lower overall quality measures.

[0206] In some implementations, computing system 850 can utilize the overall quality measure for the third-party service provider when performing adjustment actions. For example, computing system 850 can determine that adjusting the third transportation leg 106 is preferable to offset a lower quality measure calculated for the initial state. To help potentially improve overall service score 860, computing system 850 can adjust the third transportation leg such that the user is matched with a ground vehicle from a third-party service provider associated with a higher (or highest) overall quality measure.

[0207] In some implementations, the selection of a third-party service provider can be based on a combination of attributes identified in log data 896. For example, computing system 850 can calculate that a particular third-party service provider generally has a high overall quality measure. However, based on certain log data 896, computing system 850 can determine that aircraft or ground vehicles from a particular service provider have a lower quality measure (e.g., a lower "specific" quality measure) when operating at aviation facility A or within geographic region Y. Thus, computing system 850 can prioritize other service providers when generating portions of a journey associated with this facility or region or when implementing adjustment actions.

[0208] In some implementations, the computing system 850 can perform root cause analysis to determine causes for poor quality measurements and take action to avoid such causes in future services. For example, the computing system 850 can process the log data 896 and identify poor quality measurements associated with states or substates below a particular threshold. The computing system 850 can extract attributes associated with poor quality by analyzing information in other data fields associated with the quality measurements.

[0209] The computing system 850 can process the attributes and identify attribute combinations or patterns that may indicate the cause of a bad user experience. The attribute combinations / patterns can be used to calculate potential causes for poor quality measurements, and the computing system 850 can implement one or more actions to avoid the potential causes.

[0210] In a first example, computing system 850 may determine that there are typically poor quality measurements when a user is transported by car along a highway route to airline facility A during a certain time range. To help avoid this cause, the computing system may limit the ability of cars to take the highway route during that certain time range. Additionally or alternatively, during that time frame, computing system 850 may limit car transport to only within a certain threshold distance from the airline facility. In some implementations, computing system 850 may limit the type of transportation a user utilizes during that time frame to bicycle, scooter, walking, etc. This may also lead to distance restrictions, as service areas may be made to be an appropriate distance for biking, scooter riding, walking, etc. To that end, constraints on matching / routing may be generated in the transportation platform system.

[0211] In another example, computing system 850 may determine that certain aircraft are typically delayed when taking off from a particular airport facility, resulting in poor quality measurements (e.g., because users remain on board for too long). To avoid this potential cause, computing system 850 may avoid making trips that require those aircraft to service the particular airport facility.

[0212] In some implementations, the computing system 850 may attempt to avoid sources of poor quality measurements by limiting matching to only service providers with higher associated quality measurements.

[0213] The root cause analysis can be expanded to include other types of data. For example, the log data 896 can be enriched with information from one or more third-party systems 415. This can include enriching the log data 896 associated with a particular service instance, state, or sub-state with weather or traffic data. This can allow the root cause analysis to capture a broader range of possible causes for a poor user experience.

[0214] In some implementations, information from a third-party system 415 may be added only for certain log data 896. For example, the computing system 850 may access weather or traffic data from a third-party system 415 for a service instance or state / sub-state only if the overall service score 860 for that service instance or the quality measure 855 for a particular state / sub-state is below a certain threshold (e.g., below a score of 70). This can help conserve memory resources of the database that stores the log data 896.

[0215] The root cause analysis can be based on multiple users associated with a common transportation service. For example, four users may be grouped together to board aircraft 107 for the intermediate leg 104 from a first air facility to a second air facility. Each of the users may arrive at the first air facility by a different ground vehicle and depart the second air facility by a different ground vehicle. One of the four users may provide a poor evaluation of the transportation service.

[0216] In performing the root cause analysis, the computing system 850 can compare differences and similarities between different user states to help understand the root causes for that particular user. This can include, for example, determining that users had relatively similar state times in each state, but that users with negative experiences were matched with different ground transportation platforms for the last leg 106. This can help determine how different ground transportation platforms are affecting the user experience for users of the second air facility.

[0217] In some implementations, the root cause analysis can include comparing overall user experiences by market. For example, it can be determined that a first market where users have to walk to a first air facility has a lower overall service score aggregated for that entire population of users than a second, similar market where users are transported by ground vehicle. This can help determine whether the mode of transportation for a first leg in the first market is the root cause of a less favorable user experience.

[0218] FIG. 8B depicts a dynamic data model 950 that may be constructed in real time as a user progresses along a multimodal transportation service. The data model 950 may include a data structure that shows quality measurements for each user state and their relationship to one another. The data structure may be, for example, a timeline graph with multiple state-specific elements 952A-F that are generated in real time as data is collected during the user's service instance. The data structure may be stored as an object in the data store 854 (of FIG. 8A). The object may be associated with an identifier, such as a user identifier or trip identifier, that is associated with a particular service instance.

[0219] Each respective element 952A-D is associated with a particular user state and can store / encode metadata about that state. For example, element 952A is associated with reservation state 865 and includes a start time (t0), an end time (t1), and a quality measurement value for reservation state 865. Element 952B is associated with first section traffic state 870 and includes a start time (t1), an end time (t2), and a quality measurement value for first section traffic state 870. Element 952C is associated with first section transition state 875 and includes a start time (t2), an end time (t3), and a quality measurement value for first section transition state 875. Element 952D is associated with intermediate section traffic state 880 and includes a start time (t3), an end time (t4), and a quality measurement value for intermediate section traffic state 880. Element 952E is associated with the intermediate section transition state 885 and includes the start time (t4), end time (t5), and quality measurement for the intermediate section transition state 885. Element 952F is associated with the third section traffic state 890 and includes the start time (t5), end time (t6), and quality measurement for the third section traffic state 890. end ), and quality measures.

[0220] In some implementations, elements 952A-F can include other metadata associated with each state. For example, elements 952A-F can encode data indicating pickup and drop-off locations, traffic conditions, assigned vehicles (e.g., passenger cars, aircraft), operators, air facilities, occupancy data (e.g., at facilities, on aircraft), unforeseen travel conditions, weather conditions, input characteristics, or any other information associated with a particular state.

[0221] Computing system 850 can generate and refine elements 952A-F as the user is transported to the final destination. For example, computing system 850 can begin generating the data structures at a start time (t0). The start time can coincide with the time a user-network session begins with computing system 850 (e.g., a transportation platform) when a software application is launched on the user device. In some implementations, the start time can coincide with the time a request for transportation services is received by computing system 850.

[0222] The start time (t0) also represents the start of the reservation state 865. For example, a state timer for the reservation state 865 may start at t0. Based on the analysis described with reference to FIG. 8A , the computing system 850 may generate and refine element 952A as time progresses in the reservation state 865. For example, the associated predicted quality measure for the state may be adjusted (e.g., up or down on a timeline graph) as time progresses. When the reservation state 865 ends (e.g., the state timer ends at t1), element 952A may reflect the calculated quality measure for the reservation state 865 and be stored in the data structure.

[0223] The end of one state can coincide with the beginning of the immediately following state. For example, at t1, the reservation state 865 ends and the first leg traffic state 870 begins. The computing system 850 can build a data structure for a particular service instance by iteratively generating each element 952A-F as the user experiences and transitions through each state in real time. The computing system 850 can then determine when the user is ready to travel to their final destination (t end ), the data structure and element generation can be completed.

[0224] In some implementations, elements 952A-F can remain dynamic throughout a user's multimodal transportation service. For example, during a subsequent state, the user (or operator) may provide feedback that a particular state was less favorable than originally calculated. In response, computing system 850 can adjust the elements for that previous state to represent a lower quality measurement than originally calculated.

[0225] The dynamic data model 950 can provide improved resources for calculating the overall service score. For example, using the data model 950, the computing system 850 can more efficiently reference the quality measures of each state when calculating the overall service score 860, which can be an average or weighted average of the individual quality measures. Additionally, the data model 950 (or a replica thereof) can be stored in the log data 896 to aid in analyzing service provider performance, performing root cause analysis, and other computationally intensive evaluations, as previously described herein.

[0226] In some implementations, the overall service score 860 may be initially set to a baseline and adjusted based on each quality measurement 855 as the user's journey progresses. For example, the overall service score 860 may initially be set to 100 points. The number of points may be decreased in response to an unfavorable quality measurement 855. The number of points may be increased in response to a favorable quality measurement 855. In some implementations, the initial score may be a score ceiling. In some implementations, the initial score may be exceeded.

[0227] In some implementations, the overall service score 860 may be relative to a particular user. For example, a particular user may often provide negative feedback, resulting in a lower quality measurement 855 for a condition and a lower overall service score 860. The user may continue to use the transportation service. Thus, negative feedback may indicate an individual user's approach to evaluating the service and, therefore, can be appropriately weighted for root cause analysis.

[0228] 9 depicts a flowchart diagram of an example method 900 for initiating an adjustment action according to an example embodiment of the present disclosure. Method 900 can be performed, for example, by a computing system including one or more computing devices, such as the computing systems described with reference to other figures. Each respective portion of method 900 can be performed by any (or any combination) of one or more computing devices. Furthermore, one or more portions of method 900 can be implemented as an algorithm on a hardware component of a device described herein (e.g., as in FIGS. 4-8, 12, etc.) to initiate an adjustment action as discussed herein, for example.

[0229] Figure 9 depicts elements performed in a particular order for purposes of illustration and discussion. Using the disclosure provided herein, those skilled in the art will understand that elements of any of the methods discussed herein may be adapted, rearranged, extended, omitted, combined, or modified in various ways without departing from the scope of the present disclosure. Figure 9 is described with reference to elements / terminology described with respect to other systems and figures for exemplary illustrative purposes and is not meant to be limiting. One or more portions of method 900 can additionally or alternatively be performed by other systems.

[0230] At 905, a computing system may receive a request for transportation services. The request may indicate a user for transportation services. The request may indicate at least a desired destination. The desired destination may be, for example, a concert venue. In some implementations, the request may be for multimodal transportation services. This may include a user requesting air transportation for at least a portion of a journey to the concert venue.

[0231] At 910, the computing system can calculate a multimodal transportation itinerary for the user based on the request. The multimodal transportation itinerary can include multiple transportation legs for providing multimodal transportation services. For example, a journey for transporting the user to a concert venue can include the user being transported by passenger vehicle to a first vertiport, the user being transported by aircraft from the first vertiport to a second vertiport, and the user being transported by another passenger vehicle from the second vertiport to the concert venue.

[0232] At 915, the computing system can determine a user's state with respect to the multimodal transportation journey. The state can indicate the progress of the multimodal transportation service for the user and / or be associated with a first transportation leg of the multimodal transportation journey. As described herein, the user's state can include predefined states, such as a transit state indicating the user is being transported or a transition state indicating the user is between transportation legs (e.g., transitioning from one modality to another). These states can include sub-states. A sub-state can indicate, for example, that the user is waiting to board an aircraft to transport the user from a first vertiport to a second vertiport.

[0233] The user's state can be determined based on data from the user's mobile device, which can include, for example, GPS-based location data.

[0234] At 920, the computing system can calculate a quality measure of the multimodal transportation service based on the user's state. As described herein, the quality measure can predict / estimate the user's satisfaction with the current state of the transportation service. By way of example, the quality measure can be a points-based score associated with the first traffic state and can continue to decrease the longer the user remains stuck in traffic while being transported to the first vertiport.

[0235] At 925, the computing system can determine an adjustment action associated with the multimodal transportation journey for the user based on the quality measurements. As described herein, the adjustment action can include a separate action adjusting the user's journey or elements thereof. The adjustment action can include an adjustment associated with a second transportation leg of the multimodal transportation journey. In this example, the second transportation leg is after the first transportation leg.

[0236] For example, a quality measure associated with a user's travel condition may be low due to excessive waiting time while the user is stuck in traffic. The quality measure may be low enough to adversely affect the overall service score for the user. In response, the computing system may determine that an adjustment action may be appropriate to attempt to improve the user's overall service score. As described herein, the computing system may analyze the user's previous service instances and determine that an adjustment of the final journey leg may be particularly impactful for this user. Thus, the computing system may determine that an adjustment action to match the user with a vehicle and prioritize upgrading the user to a luxury vehicle may be appropriate.

[0237] At 930, the computing system can transmit, over the network, instructions to initiate adjustment actions associated with the multimodal transportation journey for the user. The instructions can include data, computing instructions, etc. that can be implemented by the computing device. For example, the computing system can call an API and structure a query to be sent to a ground transportation platform to request a luxury vehicle for the user with an increased level of prioritization so that the user has little or no wait time for the vehicle.

[0238] 10 depicts a flowchart diagram of an example method 1000 for calculating a quality measure according to an exemplary embodiment of the present disclosure. Method 1000 can be performed, for example, by a computing system including one or more computing devices, such as the computing systems described with reference to other figures. Each respective portion of method 1000 can be performed by any (or any combination) of one or more computing devices. Furthermore, one or more portions of method 1000 can be implemented as an algorithm on a hardware component of a device described herein (e.g., as in FIGS. 4-8, 12, etc.), for example, to calculate a quality measure.

[0239] Figure 10 depicts elements performed in a particular order for purposes of illustration and discussion. Using the disclosure provided herein, those skilled in the art will understand that elements of any of the methods discussed herein may be adapted, rearranged, extended, omitted, combined, or modified in various ways without departing from the scope of the present disclosure. Figure 10 is described with reference to elements / terminology described with respect to other systems and figures for exemplary illustrative purposes and is not meant to be limiting. One or more portions of method 1000 can additionally or alternatively be performed by other systems.

[0240] Method 1000 may include a sub-operation of operation 920 in FIG. 9, where method 900 includes calculating a quality measure for the multimodal transportation service based on the state of the user.

[0241] At 1005, a computing system may access data associated with the multimodal transportation service. As described herein, the data associated with the multimodal transportation service may include at least one of timing data, movement data, data associated with another user, or other data received from at least one of a plurality of distributed computing devices associated with the multimodal transportation service. The timing data may indicate, for example, when a user was picked up, when a user arrived at a first vertiport, when a user checked in for a flight segment, etc.

[0242] At 1010, the computing system may access a time threshold associated with the user's user state. The time threshold may be based on historical transportation data for the user state, as described herein. The historical transportation data may indicate historical journey times (and their states) for similar transportation services to a particular vertiport. For example, the historical transportation data may indicate that for this particular service instance, the user may expect it to take five minutes to arrive at the first vertiport.

[0243] At 1015, the computing system may calculate a state wait time for the user based on data associated with the multimodal transportation service. The state wait time may represent a period of time that the user remains in the user state. As described herein, the state wait time may be initiated by a state timer. For example, the state wait time for a traffic state may start after the user is picked up. In this example, the state wait time may indicate that the user traveled in the traffic state for 10 minutes to arrive at the first vertiport.

[0244] At 1020, the computing system can calculate a real-time quality measure based on a comparison of the time threshold and the state latency. The quality measure can be calculated in real time, in that it can be calculated while the user is in that state for the transportation service. For example, while the user is being transported to the first vertiport, the quality measure for the user's journey state can be low because the state latency (e.g., 10 minutes) exceeds the time threshold (e.g., 5 minutes). The real-time quality measure can continue to decrease as the state latency increases until the user arrives at the first vertiport.

[0245] 11 depicts a flowchart diagram of an example method 1100 for calculating a predicted overall service score according to an exemplary embodiment of the present disclosure. Method 1100 can be performed, for example, by a computing system including one or more computing devices, such as the computing systems described with reference to other figures. Each respective portion of method 1100 can be performed by any (or any combination) of one or more computing devices. Furthermore, one or more portions of method 1100 can be implemented as an algorithm on a hardware component of a device described herein (e.g., as in FIGS. 4-8, 12, etc.), for example, to calculate a predicted overall service score.

[0246] Figure 11 depicts elements performed in a particular order for purposes of illustration and discussion. Using the disclosure provided herein, those skilled in the art will understand that elements of any of the methods discussed herein may be adapted, rearranged, extended, omitted, combined, or modified in various ways without departing from the scope of the present disclosure. Figure 11 is described with reference to elements / terminology described with respect to other systems and figures for exemplary illustrative purposes and is not meant to be limiting. One or more portions of method 1100 can additionally or alternatively be performed by other systems.

[0247] Method 1100 may include a sub-operation of operation 925 in FIG. 9, in which method 900 includes determining, based on the quality measure, an adjustment action associated with the multimodal transportation journey for the user.

[0248] At 1105, the computing system can determine a weighted quality measure based on a predefined weight for a particular user state, as described herein. This may be a predetermined weight associated with the particular state. As described herein, the weight for a particular user state may be different from that of another user state.

[0249] For example, historical data associated with a user may inform the computing system that the user is particularly sensitive to long transit times while being transported to a first vertiport. Thus, a quality measure for the user's transit state may be given a higher weight than a quality measure for one or more other states.

[0250] At 1110, the computing system may access one or more other quality measures of the multimodal transportation service for one or more other states of the user related to the multimodal transportation journey. For example, the computing system may access a memory that stores a data structure populated with quality measures for each of various states of the user during the multimodal transportation service. This may include quality measures associated with a state in which the user is waiting to board an aircraft, a transit state in which the user is transported by the aircraft, etc. One or more of these other states may be given a lower weight than that assigned to a transit state in which the user is transported by passenger vehicle to the first vertiport.

[0251] At 1115, the method 1100 includes calculating a predicted overall service score based on an aggregation of the quality measures. For example, the computing system may calculate the predicted overall service score based on an aggregation of the quality measures, the weighted quality measures, and / or one or more other quality measures of the multimodal transportation service. The predicted overall service score may indicate a predicted quality of the multimodal transportation service across a plurality of conditions. This may include an aggregation of quality measures for the conditions experienced (or being experienced) by the user.

[0252] In some implementations, future states that have not yet been experienced by a user can be given a default quality measure. The default quality measure can be an initial level (e.g., 100 / 100) that assumes the state will be favorable. In some implementations, the default quality measure for a future state can be predicted based on the experiences of other users. For example, the default quality measure for a future state in which a user waits for the last ride to reach a concert venue can be lowered if a user currently at a second vertiport is experiencing a pickup delay due to traffic.

[0253] At 1120, the computing system can determine an adjustment action based on the predicted overall service score. The adjustment action can be configured to affect the final overall service score as described herein to help improve the overall service score. For example, if the overall service score is adversely affected by a low quality measure for a first leg traffic condition, the computing system can prioritize future ride matchings that assign the user to a better seat on an aircraft, etc.

[0254] 12 depicts example system components of an example system 1200 according to an example implementation of the present disclosure. Example system 1200 may include computing system 1205 and computing system 1250 communicatively coupled via one or more networks 1245. Computing system 1205 / 1205 (and its associated devices) may represent, for example, any of the computing systems / devices described herein.

[0255] Computing system 1205 may include one or more computing devices 1210. Computing device 1210 of computing system 1205 may include one or more processors 1215 and memory 1220. Processor 1215 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a single processor or multiple operatively connected processors. Memory 1220 may include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

[0256] Memory 1220 can store information that can be accessed by processor 1215. For example, memory 1220 (e.g., one or more non-transitory computer-readable storage media, memory devices) can include computer-readable instructions 1225 that can be executed by processor 1215. Instructions 1225 can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, instructions 1225 can be executed in logically or virtually separate threads on processor 1215.

[0257] For example, memory 1220 may store instructions 1225 that, when executed by processor 1215, cause processor 1215 to perform operations such as any of the operations and functions of any of a computing system (e.g., an ATP system, a GTP system, a third-party provider system, an airspace system, etc.) or computing device (e.g., a user device, a ground vehicle device, an aircraft device, an air facility device, a facility operator user device, etc.) as described herein.

[0258] Memory 1220 can store data 1230, which can be obtained, received, accessed, described, manipulated, created, or stored. Data 1230 can include, for example, quality measurements, overall scores, or any other information described herein or other data / information. In some implementations, computing device 1210 can access data from or store data in one or more memory devices remote from computing device 1205, such as one or more memory devices of computing system 1250.

[0259] Computing device 1210 may also include a communications interface 1235 used to communicate with one or more other systems (e.g., computing system 1250). Communications interface 1235 may include any circuits, components, software, etc. for communicating over one or more networks (e.g., 1245). In some implementations, communications interface 1235 may include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software, or hardware for communicating data / information.

[0260] Computing system 1250 may include one or more computing devices 1255. Computing device 1255 may include one or more processors 1260 and memory 1265. The one or more processors 1260 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a single processor or multiple operatively connected processors. Memory 1265 may include one or more non-transitory computer-readable storage media such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

[0261] Memory 1265 can store information that can be accessed by processor 1260. For example, memory 1265 (e.g., one or more non-transitory computer-readable storage media, memory devices) can store data 1275 that can be accessed (e.g., obtained, received, accessed, written, manipulated, created, stored, etc.). Data 1275 can include, for example, any of the data or information described herein. In some implementations, computing system 1250 can access data memory from one or more devices that are remote from computing system 1250.

[0262] Memory 1265 may also store computer-readable instructions 1270 that may be executed by processor 1260. Instructions 1270 may be software written in any suitable programming language or may be implemented in hardware. Additionally, or alternatively, instructions 1270 may be executed in logically or virtually separate threads on processor 1260. For example, memory 1265 may store instructions 1270 that, when executed by processor 1260, cause processor 1260 to perform any of the operations or functions described herein, including, for example, any of the operations and functions of any of a computing system (e.g., an ATP system, a GTP system, a third-party provider system, an airspace system, etc.) or computing device (e.g., a user device, a ground vehicle device, an aircraft device, an air facility device, a facility operator user device, etc.) as described herein.

[0263] Computing device 1255 may also include a communications interface 1280 used to communicate with one or more other systems. Communications interface 1280 may include any circuits, components, software, etc. for communicating over one or more networks (e.g., 1245). In some implementations, communications interface 1280 may include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software, or hardware for communicating data / information.

[0264] Network 1245 can be any type of network or combination of networks that enables communication between devices. In some implementations, network 1245 can include one or more of a local area network, a wide area network, the Internet, a secure network, a cellular network, a mesh network, a peer-to-peer communication link, or some combination thereof, and can include any number of wired or wireless links. Communication over network 1245 can be accomplished by, for example, a network interface using any type of protocol, protection scheme, encoding, format, packaging, etc.

[0265] 12 illustrates an example system 1200 that may be used to implement the present disclosure. Other computing systems may be used as well. Computing tasks discussed herein as being performed on a computing device remote from one system (e.g., a vehicle) may instead be performed on another system (e.g., by a vehicle computing system), or vice versa. Such configurations may be implemented without departing from the scope of the present disclosure.

[0266] The use of a computer-based system allows for a wide variety of possible configurations, combinations, and divisions of tasks and functionality among and between components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implemented tasks or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.

[0267] Aspects of the present disclosure have been described in terms of illustrative implementations thereof. Numerous other implementations, modifications, or variations within the scope and spirit of the appended claims may occur to those skilled in the art from a review of this disclosure. Any and all features in the following claims may be combined or rearranged in any possible manner. Thus, the scope of the present disclosure is by way of example, not limitation, and disclosure of the subject matter does not exclude the inclusion of such modifications, variations, or additions to the subject matter as would be readily apparent to one of ordinary skill in the art. Also, terms are described herein using lists of exemplary elements joined by conjunctions such as "and," "or," "but," etc. It should be understood that such conjunctions are provided for illustrative purposes only. For example, a list joined by a particular conjunction such as "or" may refer to "at least one of" or "any combination of" the exemplary elements listed therein, with "or" being understood as "or" unless otherwise indicated. Also, terms such as "based on" should be understood as "based at least in part on."

[0268] Those skilled in the art will understand, using the disclosure provided herein, that elements of any of the claims, operations, or processes discussed herein may be adapted, rearranged, extended, omitted, combined, or modified in various ways without departing from the scope of the present disclosure. Occasionally, elements may be listed in the specification or claims using letter references for exemplary, illustrative purposes and are not meant to be limiting. When used, letter references do not imply a particular order of operations or a particular importance of the listed elements. For example, letter identifiers such as (a), (b), (c), ..., (i), (ii), (iii), ... may be used to illustrate operations or different elements within a list. Such identifiers are provided for the reader's convenience and do not represent a particular order, importance, or priority of steps, operations, or elements. For example, an operation illustrated by a list identifier such as (a), (i), etc., may be performed before, after, or in parallel with another operation illustrated by a list identifier such as (b), (ii), etc. It is also to be understood that any of the claims provided herein may be combined or dependent on each other.

Claims

1. 1. A computer-implemented method, the computer-implemented method comprising: accessing data indicative of a request for transportation services, said request indicative of a user for said transportation services; calculating a multimodal transportation itinerary for the user based on the request, the multimodal transportation itinerary comprising a plurality of transportation legs for providing the multimodal transportation service; determining a status of the user with respect to the multimodal transportation journey, the status indicating a progress of the multimodal transportation service for the user, the status being associated with a transportation leg of the multimodal transportation journey; calculating a quality measure of the multimodal transportation service based on the state of the user; determining an adjustment action associated with the multimodal transportation journey for the user based on the quality measurements, the adjustment action including an adjustment associated with another transportation leg of the multimodal transportation journey, the other transportation leg being after the transportation leg; and transmitting, over a network, instructions to initiate the adjustment action associated with the multimodal transportation journey for the user; 11. A computer-implemented method comprising:

2. 2. The computer-implemented method of claim 1, wherein the other transportation segment comprises ground transportation service for the user, and the adjustment action includes at least one of (i) initiating an adjustment to the ground transportation for the user, or (ii) initiating an increase in a priority associated with the user for the ground transportation.

3. 3. The computer-implemented method of claim 1, wherein adjusting the ground transportation for the user includes at least one of (i) assigning a different ground vehicle to the user, (ii) changing a service level associated with the ground transportation for the user, or (iii) changing a service type associated with the ground transportation.

4. 4. The computer-implemented method of claim 2, wherein increasing the priority associated with the user for the ground transportation includes adjusting a data structure for allocating ground vehicles to the user to increase the priority for the ground vehicle to be assigned to the user.

5. The computer-implemented method of any of claims 1-4, wherein the other transport segment comprises an air transport service for the user, and the adjustment action includes at least one of (i) initiating an adjustment of a seat for the user on an aircraft to be used for the air transport service, or (ii) initiating an assignment of the user to a different aircraft for the air transport service.

6. Calculating the quality measure of the multimodal transportation service based on the state of the user includes: accessing data associated with the multimodal transportation service, the data associated with the multimodal transportation service comprising at least one of timing data, movement data, data associated with another user, or event data received from at least one of a plurality of distributed computing devices associated with the multimodal transportation service; calculating the quality measure of the multimodal transportation service based on the state of the user and the data associated with the multimodal transportation service; 6. The computer-implemented method of any of claims 1-5, comprising:

7. Calculating the quality measure of the multimodal transportation service based on the state of the user and the data associated with the multimodal transportation service includes: accessing a time threshold associated with the user state of the user, the time threshold based on historical transportation data for the user state; calculating a state latency for the user based on the data associated with the multimodal transportation service, the state latency representing a period of time the user remains in the user state; calculating the quality measure based on a comparison of the time threshold and the state latency; The computer-implemented method of claim 6 , comprising:

8. The computer-implemented method of claim 7 , wherein the user is associated with one or more user characteristics, and the time threshold associated with the user state is based on the one or more user characteristics.

9. The computer-implemented method of claim 7 or 8, wherein the time threshold associated with the user state is based on a geographic region associated with the multimodal transportation service.

10. Determining the adjustment action associated with the multimodal transportation journey for the user based on the quality measurements includes: calculating a predicted overall service score for the multimodal transportation service based on the quality measurements, the predicted overall service score indicating a predicted quality of the multimodal transportation service across a plurality of states of the multimodal transportation service; determining the adjustment action based on the predicted overall service score; and Including, The computer-implemented method of any of claims 1-9, wherein the adjustment actions are configured to affect a final overall service score.

11. Calculating the predicted overall service score for the multimodal transportation service comprises: determining a weighted quality measure based on said user state; calculating the predicted overall service score based on the weighted quality measures; and The computer-implemented method of claim 10, comprising:

12. Calculating the predicted overall service score for the multimodal transportation service comprises: accessing one or more other quality measures of the multimodal transportation service for one or more other conditions of the user related to the multimodal transportation journey; calculating the predicted overall service score based on an aggregation of the quality measure and the one or more other quality measures of the multimodal transportation service; 12. The computer-implemented method of claim 10, comprising:

13. 14. The computer-implemented method of claim 1, wherein the state is one of a plurality of predefined user states, the plurality of predefined user states comprising a transit state, a transition state, a boarding state, a ready state, and an arrival state.

14. One or more non-transitory computer-readable media storing instructions, the instructions being executable by the one or more processors to cause the one or more processors to perform operations, the operations including: accessing data indicative of a multimodal transportation journey for a user, the multimodal transportation journey comprising a plurality of transportation legs for providing transportation services for the user; determining a status of the user with respect to the multimodal transportation journey, the status indicating a progress of the multimodal transportation service for the user, the status being associated with a transportation leg of the multimodal transportation journey; calculating a quality measure of the multimodal transportation service based on the state of the user; determining an adjustment action associated with the multimodal transportation journey for the user based on the quality measurements, the adjustment action including an adjustment associated with another transportation leg of the multimodal transportation journey, the other transportation leg being after the transportation leg; and transmitting, over a network, instructions to initiate the adjustment action associated with the multimodal transportation journey for the user; one or more non-transitory computer readable media,

15. 15. The one or more non-transitory computer-readable media of claim 14, wherein the user is associated with a user device configured to launch a software application associated with the transportation service, and the adjustment action includes a modification to at least one user interface of the software application.

16. The at least one user interface of the software application is configured to provide one or more user notifications, and initiating the adjustment action includes: accessing alternative transportation data associated with the alternative transportation service; calculating a user notification representing a comparison between the alternative transportation service and the multimodal transportation service; transmitting data indicating the user notification to the user device during the multimodal transportation service via the network; and 16. One or more non-transitory computer readable media according to claim 15, comprising:

17. One or more non-transitory computer-readable media as described in any of claims 14-16, wherein the adjustment action includes notifying an operator at an air facility to assist the user in transitioning between ground transportation services and air transportation services, and transmitting the instruction to initiate the adjustment action includes transmitting data to a user device associated with the operator indicating a notification to assist the user while at the air facility.

18. 18. One or more non-transitory computer-readable media according to any of claims 14-17, wherein the user state is one of a plurality of predefined user states, each respective state being associated with a corresponding weight for determining a weighted quality measure associated with the respective state.

19. The operation is receiving feedback data associated with the transportation service; modifying the corresponding weight of at least one respective state based on the feedback data; and 20. The one or more non-transitory computer-readable media of claim 18, further comprising:

20. 1. A computing system, comprising: one or more processors; one or more tangible, non-transitory computer-readable media storing instructions; Equipped with The instructions are executable by the one or more processors to cause the computing system to perform operations, the operations including: accessing data indicative of a request for transportation services, said request indicative of a user of said transportation services; calculating a multimodal transportation itinerary for the user based on the request, the multimodal transportation itinerary comprising at least two transportation legs for providing the multimodal transportation service; accessing data associated with the multimodal transportation service and data associated with the user's status with respect to the multimodal transportation journey, the data associated with the multimodal transportation service indicating the user's location with respect to the multimodal transportation journey; calculating a quality measure of the multimodal transportation service based on the data associated with the multimodal transportation service and the data associated with the state of the user; Initiating an adjustment action associated with the multimodal transportation journey for the user based on the quality measurements; and a computing system including:

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