Patent Application
20200182637
Transportation systems and transport platforms are an important part of the infrastructure used to enable commerce and the movement of people between locations. As such, they are essential services for the growth of an economy and the development of a society. Over the years, several types of transportation systems have been developed, each typically with their own focus, advantages and drawbacks.
A key challenge for transportation is the opening up of a “gap” over regional distances, where much of the world is left without a high-speed mode. Competitive pressures have driven a 70-year decline of air over these distances. And the impact of high-speed rail is limited by economics to a few dense corridors. As a result, the last time the US travel survey was conducted in 2001, 90 percent of all long-distance trips were over regional distances from 50 to 500 miles. Yet just 2 percent of these were by air, with auto at a staggering 97 percent. This has had a tremendous impact on mobility and economic development. Regional door-to-door mobility has stagnated: travel times by highway have not improved for decades; and flight times have stretched given slower cruise speeds and increasingly congested airports. Meanwhile, the steady consolidation of air services to a declining set of major hubs has left many communities disconnected from the global air network, with severe impact on their economy and ability to attract investment.
The Inventors have recognized and appreciated that the rigidity of the current conventional air transport system is poorly suited to regional trips, contributing to the minute and declining role of air over these distances. Shorter regional trips are much more numerous than long-distance travel. They are also much more fluid. Travel over these distances is planned far less in advance and around events that are much more variable. And even modest shifts in the schedule, a few hours here or there, can swing preference away from air. As a result, current revenue and change management practices are hugely damaging. Fare levels discourage trips on short notice, and change penalties discourage trips where dates or schedules are uncertain. And processes to execute change are often restrictive and complex. For example, change is limited to services of the original operator, and the remaining legs need to be separately modified to match.
The Inventors also have recognized and appreciated that another shortcoming relating to the current conventional air transport system today is the inability of operators to tailor schedules and equipment to evolving demand over timeframes shorter than weeks or months. This translates to sub-optimal yields and more critically, to lost revenue as operators rationalize routes to focus on the more profitable. The concentrated air network, comprised of large aircraft serving a declining set of major hubs, restricts this today. Standardization of fleets to a few large platforms means there is limited ability to tailor capacity to demand. Moreover, landing slots at many hubs are fixed far in advance. This rigidity of the air network is compounded by business practices. Yield and revenue management are based on discrete itineraries locked months in advance. And absence of information on passenger or shipper needs, beyond discrete itineraries, means that operators are presented with artificially rigid demand.
The Inventors further recognize and appreciate that the landscape of regional transport is poised for dramatic change as sharing, electrification and autonomy converge to enable fast and flexible regional transport at scale. Intra-urban travel is being reshaped by ridesharing, car sharing, car and van pooling. Electric vehicles and driverless technologies will take this further. Lower fares, improved productivity and reduced congestion will dramatically expand utility and extend range. As recognized by the inventors, the transformation on the ground will take to the skies, extending impact of this “new transport” to regional, and eventually, intracontinental ranges. A new breed of small- to mid-sized hybrid-electric aircraft, some of which have been innovated by the Inventors, will usher in a new golden era of regional air. Frequent departures of smaller aircraft from a large number of smaller airfields will enable air travel much faster than today and at much lower fares. Over time, rapidly improving batteries will further reduce costs and extend range, while increasing acceptance of drones will gradually reduce the need for pilots onboard, accelerating the trend.
As recognized by the inventors, the scale-out of air transport via small to mid-sized aircraft flying regional ranges is a direct result of the unique operating economics of range-optimized hybrid-electric aircraft, enhanced further by future autonomy. Conventional aircraft are scale and range advantaged. Larger aircraft are more efficient, as are flights over longer ranges, than smaller aircraft or shorter flights. These economies of scale and range have powered the long-term transition of aviation to large, long-haul aircraft and high-volume hubs, destroying the utility of regional air. In contrast, hybrid-electric aircraft are free of the scale and range economics that plague regional aviation today. Smaller electric aircraft fly as efficiently as (or in some instances more efficiently than) larger ones. And relatively small electrics flying regional are competitive with the largest conventional jets flying long-haul. Released from the constraints of scale and range, operators of future long-distance trips will mold aircraft, frequency and routes to travel patterns. The air network that emerges will be distributed, with frequent flights to a large number of community and urban airports, a contrast to the concentrated network of today.
As noted earlier, electric aircraft are poised to upturn the rigidity of air transport via vastly expanded services to numerous urban and community airfields. The inventive systems and methods complement this shift by expanding the flexibility of managing journeys (or “trips”) by travelers, or for objects transported on behalf of shippers, to include flexible long-distance legs as well as one or more local legs to address the full extent of door-to-door transport needs. In various aspects, these systems and methods include dynamic capacity management processes for improved tailoring of capacity to demand, from individual itineraries, to schedules, equipment and even routes.
As recognized by the inventors, a focus on the full extent of door-to-door transport needs creates a pathway to a much more flexible transport system. Platforms today are focused on legs connecting one transport terminal to another, most often on a single mode. This narrow scope translates to a system that is artificially rigid given operators and travelers engage over to a discrete itinerary, which once reserved can be expensive to change. In reality, transport needs are much more fluid, in spite of some portions of trips often being restricted to time windows defined by hard constraints (where perceived benefits of the journey decrease steeply, or perceived costs increase steeply). Within a time window, however, benefits and costs vary more gradually, creating room for flex. By addressing the full extent of a traveler's or shipper's needs, the inventive system and methods facilitate this enhanced flexibility to transport, enabling leverage by travelers, shippers and operators in a variety of ways for system-wide value.
These and other aspects of the inventive concepts disclosed herein are designed to enable one or more of the following, which collectively deliver fast and flexible regional transport, coupled with mechanisms to enable operators to maximize yield:
Embodiments of the invention are directed to systems, apparatuses, and methods for providing fast and flexible intermodal transport for passengers and cargo. In some embodiments, the inventive air transport system and associated aircraft include one or more of the following elements, functionality, or features:
In some inventive aspects, a method for generating a multi-modal itinerary for a journey is described herein. The journey can comprise of at least a first segment defined by a first origin and a first destination. The first segment can include a plurality of legs. The plurality of legs can include at least a first long-distance leg that is defined by a first departure terminal and a first arrival terminal respectively that correspond to first start and end points of the first long-distance leg. The plurality of legs can also include at least a first local leg that is defined by one of: the first origin of the first segment and the first departure terminal of the first long-distance leg and the first arrival terminal of the first long-distance leg and the first destination of the first segment. The first long-distance leg can include at least a first long-distance transport mode having one of a Fixed Schedule (FS) model, a Variable Schedule (VS) model, and an On-Demand (OD) schedule model provided by a first operator or a first marketplace participant between the first departure terminal and the first arrival terminal.
In some inventive aspects, the method comprises receiving a first objective for the first segment of the journey. The first objective comprises first origin coordinates for the first origin of the first segment, first destination coordinates for the first destination of the first segment, and a first time window for the first segment. The first time window comprises a departure time specification for the first origin of the first segment and an arrival time specification for the first destination of the first segment.
The method also comprises using the received first objective, querying a mode/origin-destination (M/OD) database. The M/OD database can include a plurality of origin-destination pairs respectively corresponding to different long-distance leg. At least a first origin-destination pair of the plurality of origin-destination pairs can include a departure terminal for a long-distance leg, an arrival terminal for the long-distance leg, a long-distance transport mode and corresponding duration for the long-distance mode between the departure terminal and the arrival terminal for the long-distance leg, at least one of an operator, a platform, and a marketplace that provides the long-distance transport mode, and at least one local leg transport mode option corresponding to the departure terminal and the arrival terminal for the long-distance leg. The long-distance transport mode can include at least one of a long-haul air transport mode, a regional air transport mode, a railway transport mode, and a highway transport mode. The long-distance mode can have one of a VS model and an OD schedule model.
In some inventive aspects, in response to querying the M/OD database, the method also comprises building a list of long-distance transport mode options for the first long-distance leg by selecting from the M/OD database at least a first candidate origin-destination pair as at least a first long-distance transport mode option for the first long-distance leg. The first candidate origin-destination pair can include a first candidate long-distance transport mode having a VS model or an OD schedule mode. The first candidate origin-destination pair can be selected based at least in part on at least one of: at least one of a first distance and a first transit time between the first origin coordinates for the first origin of the first segment and the departure terminal of the first candidate long-distance transport mode and at least one of a second distance and a second transit time between the arrival terminal of the first candidate long-distance transport mode and the first destination coordinates for the first destination of the first segment.
In some inventive aspects, for at least the first long-distance transport mode option of the list of long-distance transport options that are built as described above, the method further comprises determining an estimated duration for the at least one local leg transport mode option corresponding to the departure terminal for the first candidate long-distance transport mode. The method comprises converting the first departure specification of the first time window for the first segment to a converted first departure specification for the first candidate long-distance transport mode based at least in part on the estimated duration for the at least one local leg transport mode option. The method also comprises transmitting via the Internet a first generalized transport request to at least one of a first candidate operator, a first candidate platform, and a first candidate marketplace indicated in the first candidate origin-destination pair corresponding to the first long-distance transport mode option. The first generalized transport request can include the departure terminal and the arrival terminal indicated in the first candidate origin-destination pair corresponding to the first long-distance transport mode option and the converted first departure specification.
In some inventive aspects, the method comprises in response to the first generalized transport request, receiving via the Internet, from the at least one of the first candidate operator, the first candidate platform, and the first candidate marketplace, a first candidate itinerary for the first long-distance leg of the first segment. The first candidate itinerary is a generalized itinerary comprising a first travel departure window, a first travel duration, and a first fare for the first long-distance leg of the first segment.
In some inventive aspects, a computer-facilitated method for monitoring the progress of a traveler or a shipped object during a first segment of a journey and updating an itinerary for the first segment of the journey to meet a first objective for the first segment is described herein. The first segment can be defined by a first origin and a first destination. The first objective can comprise first origin coordinates for the first origin of the first segment, first destination coordinates for the first destination of the first segment, and a first time window for the first segment. The first time window can comprise a departure time specification for the first origin of the first segment and an arrival time specification for the first destination of the first segment. The first segment can include a plurality of legs. The first plurality of legs can include at least a first long-distance leg defined by a first departure terminal and a first arrival terminal respectively corresponding to first start and end points of the first long-distance leg. The first plurality of legs can also include at least a first local leg defined by one of: the first origin of the first segment and the first departure terminal of the first long-distance leg and the first arrival terminal of the first long-distance leg and the first destination of the first segment. The first long-distance leg can include at least a first long-distance transport mode having one of a Fixed Schedule (FS) model, a Variable Schedule (VS) model and an On-Demand (OD) schedule model provided by a first operator between the first departure terminal and the first arrival terminal. The first local leg can be provided by a second operator different from the first operator.
In some inventive aspects, the method comprises building a timeline for the first segment prior to the journey. The timeline can be built based at least in part on respective forecasted durations for the first long-distance leg and the first local leg. The timeline comprises a plurality of expected times and corresponding locations of the traveler or shipped object during the first segment.
In some inventive aspects, during the journey, the method comprises determining a current leg of the first segment from the timeline for the first segment and a current time. If the current leg of the first segment is the first long-distance leg, the method comprises, transmitting via the internet, a first query to the first operator and receiving from the first operator a first long-distance leg arrival status of the first long-distance leg. If the current leg of the first segment is the first local leg, the method comprises, monitoring a current location of the traveler or the shipped object. If the first long-distance leg arrival status significantly deviates from a corresponding expected time according to the timeline, or if the current location of the traveler or the shipped object significantly deviates from a corresponding expected location according to the timeline, the method comprises, generating an updated itinerary for at least one subsequent leg of the first segment.
In some inventive aspects, a computer-facilitated method for generating a daily schedule for a plurality of long-distance transport mode trips by a transportation operator is disclosed herein. The method comprises determining a first provisional schedule on a given day for the plurality of long-distance transport mode trips based on a forecast demand for capacity. The respective long-distance transport mode trips of the plurality of long-distance transport mode trips can have corresponding itineraries. A first number of the corresponding itineraries can be specific itineraries including a departure terminal, an arrival terminal, a departure time, and a duration for a corresponding long-distance transport mode trip. A second number of the corresponding itineraries can be generalized itineraries including at least one of a departure terminal, an arrival terminal, a travel window, and a duration for a corresponding long-distance transport mode trip.
After determining the first provisional schedule, in some inventive aspects, the method comprises, receiving from a plurality of travelers and/or shippers, a plurality of transport requests for at least some of the plurality of long-distance mode trips on the given day.
In some inventive aspects, the method comprises, updating the first provisional schedule based at least in part on the plurality of transport requests received. The first provisional schedule can be updated to determine a second schedule for the plurality of long-distance transport mode trips by increasing the first number of specific itineraries and decreasing the second number of generalized itineraries.
It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.
Other systems, processes, and features will become apparent to those skilled in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, processes, and features be included within this description, be within the scope of the present invention, and be protected by the accompanying claims.
The skilled artisan will understand that the drawings primarily are for illustrative purposes and are not intended to limit the scope of the inventive subject matter described herein. The drawings are not necessarily to scale; in some instances, various aspects of the inventive subject matter disclosed herein may be shown exaggerated or enlarged in the drawings to facilitate an understanding of different features. In the drawings, like reference characters generally refer to like features (e.g., functionally similar and/or structurally similar elements).
The present disclosure describes inventive systems, apparatus, and methods for implementing multi-modal transportation. In various aspects, the systems and methods disclosed herein transform generalized transport objectives into generalized and specific transport requests, and corresponding generalized and specific itineraries from various providers of transport services. In addition, real-time monitoring of a journey leverages flexibility and addresses changeability options. In some inventive aspects, the systems and methods disclosed herein enable operators and providers of transport services to significantly improve matching of capacity to demand for the transport services in a flexible manner, based at least in part on generalized transport requests and generalized itineraries as part of the planning process for one or more segments of a journey.
As mentioned above, a key challenge for transportation is the opening up of a “gap” over regional distances, where much of the world is left without a high-speed mode. The regional distances are usually covered by long-distance commercial flights. In fact, a large fraction of commercial aircraft and business jet flights are less than 1500 miles. With the advent of small to mid-sized regional electric aircrafts, regional transportation is poised for a change. Thus, transportation over “long-distances,” such as distances between approximately 50 to 500 miles, or a couple of hundred miles up to approximately 1000 miles, or from approximately 1000 miles to 3500 miles, or even greater than 3500 miles can be covered by short-haul regional flights, medium-haul regional flights, and long-haul flights, respectively. In addition, other modes of transportation (e.g., train, bus, personal vehicles, personal flights, and/or the like) may also be used to cover long distances.
Consequently, an expanded source of providers and/or operators can provide transport options for long-distance legs of a journey, including providers/operators that may offer variable schedule models or on-demand schedule models. With these expanded sources of providers/operators, a need for a platform that is compatible with a variety of scheduling models and platforms/operators arises. In response to this need, the systems and methods disclosed herein provide end-to-end transport needs over multiple modes of transport from multiple providers/operators. These systems and methods provide compatibility with a wide range of scheduling models and enable an expanded source of operators (e.g., fixed schedule, variable schedule, and on-demand). Thus, the travel segments of multiple legs of a trip can be planned in a flexible manner. In particular, an initial flexible itinerary can be created, reserved, and held before the journey begins. The itinerary can be periodically refreshed and compared with other options to determine a candidate itinerary (e.g., optimal itinerary). The itinerary can be modified and/or changed based on this comparison to accommodate for lower fares and lower travel times for one or more legs or for the overall segment, or to accommodate for faster connections between legs, a combination thereof, and/or the like.
In some inventive aspects, one or more itineraries for respective legs of a journey can be booked prior to the start of the journey. Once the journey begins, the systems and methods disclosed herein can monitor the journey via applications such as Global Positioning System (GPS) to determine the location of the traveler/shipped object at a given point in time. Various providers of the respective legs (e.g., long-distance operators or local platforms) can be queried for status updates such that the itinerary is modified to account for delays. In addition, the systems and methods disclosed herein can facilitate real-time monitoring of various platforms (e.g., weather, traffic, etc.) to determine possible delays or issues that may occur during a leg. Once the itinerary is updated in real time, changes can be made to subsequent legs remaining in the journey such that the original objectives of the travel request can be approximately met (e.g., arrival time/arrival window at segment destination).
Current transport platforms are focused on the long-distance leg and the limited and typically rigid alternatives for this leg, such as air, rail, bus. As a result, they are far less effective for regional journeys where local legs, including transfer through long-distance terminals, account for a much greater fraction of the journey. The limited usefulness of these platforms will erode even further in coming years as transport modes multiply and as flexibility of the modes increases. Emerging trends around sharing, autonomy and electrification on the ground and in the air will give rise to a new breed of fast and flexible transport options that are multi-modal, increasingly electrified and increasingly autonomous.
In contrast, the focus of the systems and methods disclosed herein is end-to-end transport, from an origin to a destination of a segment of a journey (for example, origin 102 to destination 104 in
In one example, the platform determines five possible departure terminals (also referred to as “origin hubs”), for example, 103a-103e (collectively, departure terminals 103) and four possible arrival terminals (also referred to as “destination hubs”), for example, 105a-105d (collectively, arrival terminals 105). In some inventive aspects, the traveler can be connected from the origin 102 to the departure terminal 103 via flexible local transportation (e.g., taxi, personal vehicle, Uber™, local bus, local subway). In a similar manner, the traveler can be connected from the destination 104 to the arrival terminal 105 via flexible local transportation.
In some inventive aspects, the platform can determine a single mode or multiple modes of transportation for every departure terminal 103—arrival terminal 105 pair. For example, the platform determines that regional aircraft can connect the departure terminal 103c—arrival terminal 105b pair as well as the departure terminal 103d—arrival terminal 105d pair. In a similar manner, the platform determines that a train can connect the departure terminal 103b—arrival terminal 105a pair. In some inventive aspects, the platform can determine a single mode of transportation that connects origin 102 to destination 104. In the example illustrated in
In some inventive aspects, the platform can divide the journey segment between a departure terminal 103 to a arrival terminal 105 into multiple legs and determine different modes of transportation for each leg. For example, the platform determines one or more mid-terminal (also referred to as “mid-hub”) 106 that is located in between the origin 102 and the destination 104. The platform then determines a mode of transportation for the departure terminal 103e—mid-terminal 106 pair and for the mid-terminal 106—arrival terminal 105c pair. For example, the platform determines that the departure terminal 103e can be connected to the mid-terminal 106 via a long-distance aircraft and that the mid-terminal 106 can be connected to the destination-terminal 105c via a personal flight. In this manner, inventive aspects disclosed herein provides a seamless platform that stitch multi-modes of transportation for fast and flexible transport of passengers and cargo.
The range of transport modes considered for the local and long-distance legs are shown in Table 1. The range of modes include, but are not limited to, (A) Long-haul air to hub airports, (B) Regional air to hub and non-hub airports, piloted and drones, conventional or vertical take-off and landing (CTOL or VTOL), (C) Rail to terminals, low and high-speed, (D) Highway to terminals or door-to-door, driven or autonomous. These different modes may be served by a range of operators with differing models such as Fixed schedules (FS), e.g., long-haul air, regional air, rail; Variable schedules (VS), e.g., regional air, highway; On-demand (OD), e.g., regional air, highway. Local legs between Origin, Destination and terminals may be covered by personal vehicle, Public transit on a fixed schedule (FS) (e.g., bus, rail, ferry), or a large variety of FS, VS and OD operators offering two-wheelers, cars, vans, buses, trucks, even aircraft.
A specific example is shown in
The system and methods disclosed herein orchestrate door-to-door transport across the local and long-distance legs, across modes and across the range of operator segments, such as fixed (FS) or variable schedules (VS), on-demand (OD) and shared services (mode and operator segment examples listed in Table 1). Several of these operate very differently to transport modes today. Table 4 contrasts the operating processes of conventional air today with regional air of the future, hybrid-electric, piloted or drone, STOL or VTOL.
System to Orchestrate Fast and Flexible Multi-Modal Transport
Trips are orchestrated door-to-door, across modes, segments and legs (as illustrated in
In some inventive aspects, traveler Preferences are built over time, via the personalization module 340 in
The system then calculates a candidate window, such as, an optimal window, for reserving the trip based on fare outlooks for the selected modes, and proceeds based on traveler input on timing. Options are reduced to three tiers of tracked itineraries based on their Utility, or alternate metric preferred by the traveler, such that collectively they offer a high likelihood of generating a candidate trip (e.g., an optimal trip). The first tier is itineraries of superior Utility that are targeted for reservation at optimal fare levels. The second tier is itineraries of just lower Utility targeted as backup, and the third is itineraries with preferred schedules but uncompetitive fare levels, targeted for shift to the first tier if fares drop sufficiently.
A Timeline is built for each tracked itinerary reflecting a best forecast for the future journey across local and long-distance legs and personalized to the traveler. Timelines for the long-distance legs are loaded from the M/OD database with stages tailored to the adjacent local legs. Future times are determined by forecasting the duration of each stage based on operator input, or a Transfer outlook that estimates durations of individual transfer stages for a future arrival or departure. Timelines for the local legs are estimated from local platforms, similarly for the future arrival or departure.
Tracked itineraries are also tagged based on fare and capacity outlooks to provide guidance for reservation such as “Hold”, “Reserve now, fare increase likely” or “Wait, fare reduction likely”. Tracked itineraries are refreshed periodically to reflect changing fares or capacities. And itineraries are added or removed from the tracked list based on traveler input or automated prioritization. Itineraries are held or reserved by the traveler, or if automated reservation is selected, by the system.
Unlike the rigidity of transport today, the system is designed for a much more fluid future. To enable this, circumstances impacting the trip are monitored for change across external platforms. These include transport needs, itinerary options and transport conditions. Timelines for tracked itineraries are updated, and risk to transport objectives is assessed. If significant, transport needs and priorities are redesigned to accommodate and the build process is restarted. This process of building and redesigning itineraries may be repeated several times ahead of the journey, enabled by flexible business practices. The process continues during the journey as well, accommodating changes in travel needs, unexpected disruptions, or the availability of improved itineraries.
Ahead of travel, a workflow for the management of the journey is built, defining traveler and System actions based on Support level requested by the traveler and anchored to stages of the segment. Operator and Transfer management sequences, defined in the Operator and M/OD database respectively, are assigned based on Support levels. Also assigned to the Workflow are Notifications based on Preferences, as well as Transfer checklists. These pre-wire the traveler ahead of key stages of the journey, and provide simple checklists of documents required or actions to be taken. As the time for travel approaches, individual items on the Workflow are triggered, starting with the pre-travel Notifications. The System executes items assigned to it, notifying the traveler, while items assigned to the traveler or the operator may be triggered by the System and may include assistance by the System.
Once the journey is underway, progress of the traveler across modes is tracked via smartphone, vehicle location feeds, or other mechanisms. This is used along with travel conditions to continue refreshing itinerary Timelines, modifying Workflow execution to match. The system also uses the journey feed to build proprietary data around the duration of key transfer stages, such as time from airport parking to terminal, through terminal to gate, from gate to curb, from curb to rental car exit, immigration and customs. These are displayed in a convenient way for the traveler in a Travel dashboard as illustrated in
The architecture and process flows shown on
Method to Describe Door-to-Door Transport Needs Via Traveler or Shipper Objectives and Preferences
Unlike current approaches, focused on discrete legs or modes of a trip, the full extent of a traveler or shipper's door-to-door transport need is captured, including flexibility of the need and its changeability over time. Leverage of this inherent flexibility is critical to building a responsive transport system, unlike the rigidity of today. This equates to significant benefit for travelers or shippers and operators. For travelers, the system is much more responsive, given change requested by a traveler can be accommodated by adjustments of itineraries elsewhere. For operators, the same flex can be used to lift yield and revenue for operators, improving economics, or lift transport utility for customers, improving loyalty.
A trip is defined by the Objectives and Preferences of the traveler or shipper. Trips are described as a collection of segments (e.g., segment-origin 202 to destination 204 in
For example, Objectives of a future trip to Pasadena and a same-day trip to Tahoe are show in Table 6. The Pasadena trip includes time varying Origin from Preferences, Destination via a calendar link, a departure to arrival time window with preferred sub-window.
Preferences applicable to a segment are traveler or shipper Preferences modified by choices specific to the trip, segment or leg. Preferences may be described explicitly by the traveler or deduced from past behavior, or some combination thereof. Preferences may define modes, operators, Loyalty information, trade-offs such as cost-of-time for leisure or business, flexibility, reliability, travel times, connections, terminals. Choices specific to a trip may include factors such as purpose (business or recreation), modes, locations and times.
Objectives and Preferences defining a trip are changeable, in the weeks leading up to a trip, on the days of travel, or even after travel is underway. Changes may be triggered by the traveler or shipper, by events that define Objectives, by changing travel conditions or by changing itineraries.
Method to Use Generalized Transport Requests and Reservations for Flexibility
In order to extend the inherent flexibility of transport needs to the system, the methods disclosed herein go beyond conventional rigid itineraries and reservations that refer to specific departures from specific terminals. This is done via “generalized” versions that leave a few or many aspects of an itinerary open. Examples of use include generalized transport requests as way to query operators for itineraries that meet the traveler's or shipper's needs, generalized itineraries as way for operators to communicate available capacity, and generalized reservations whereby operators commit to offer services within parameters defined by the reservation.
Specific transport requests define specific locations and times for a trip, along with select characteristics of the mode. Examples of specific requests are disclosed in Table 7. Specific itineraries and reservations, in addition, define the mode in full detail, for example, via connections, operators, equipment and comfort. In contrast, generalized transport requests leave location or time, or both, in generalized form, and couple these with select characteristics of the mode. Generalized itineraries and reservation are similar, but with added detail on the mode to enable the traveler or shipper make a transport decision. Generalized Origin, for instance, may be defined to within prescribed distance or transit time from the Origin or a Nearby location, or as a set of Departure terminals that are convenient for the traveler or shipper.
Specific itineraries define end points, schedules and operators for each leg. Generalized itineraries balance the need of the traveler or shipper to have certainty around key aspects of the transport, against need of the operator to optimize schedules for improved yield. An example of specific an generalized travel requests and itineraries for a Palo Alto to Pasadena trip is disclosed in Table 8. A traveler seeking transport services defines these using a generalized form that specifies a travel window (departure, arrival or a combination), Origin and Destination. The latter may refer to specific locations and may vary based on time of travel or be set by real-time location of the traveler. This is converted to a generalized request for operators by identifying viable Departure and Arrival terminals, and calculating time windows for these based on durations of the local legs and transfer stages of the long-distance legs. The operator then returns with a generalized itinerary that specifies a travel window (departure, arrival or a combination), duration, class of service and fare, which the traveler chooses to reserves. Closer to travel, the operator finalizes service schedules and defines remaining detail on the generalized reservation to make it specific.
Itineraries are tagged as reserved, held or Requested (or other similar categories). A reserved itinerary, specific or generalized, guarantees travel as described but comes with defined change processes for the traveler or shipper and operator. A held itinerary, specific or generalized, guarantees travel as described typically with limited time for conversion to reserved, but comes with fewer or no change penalties. A Requested itinerary, specific or generalized, describes travel services sought by a traveler. Alternate itineraries tracked include those tagged as preferred by a traveler, or those Offered by others and awaiting traveler response. Reserved, held and Requested itineraries may have additional descriptors, such as flexibility to change of a reserved itinerary, the likelihood of a held itinerary being reserved or the likelihood of an itinerary coming available.
To further improve responsiveness of the system, itineraries are changeable. Travelers or shippers are able change itineraries where capacity is available subject to fees. Change fees may be negotiated based on estimated or actual revenue impact on the operator. Alternately, novel change insurance or flexible reservations may allow no-fee changes in certain situations. Travelers and shippers may also trade itineraries with each other, subject to restrictions, on a barter, auction or fee basis. Operators may also change itineraries with traveler or shipper consent subject similarly to a change fee. The change fee may not apply and consent may be automatic for some changes, such as those that improve the Utility of transport. The fee may be established by auction, or determined based on the impact to the Utility of the traveler or shipper.
Methods to enable greater changeability of reservations include the following:
Method to Use Generalized Records for Extended Transport Clearing
Generalized descriptors are also used to enable broad matching of transport supply with demand that goes far beyond capacity offered by primary operators. This extends to excess or distressed inventory of primary operators, capacity available at long-tail operators, excess or unused capacity with travelers or shippers, among others.
This is enabled via a specific or generalized transport record that is designed for portability and security. The record is a reduced and sanitized description of transport demand or available capacity that enables first-level matching. Sensitive information is kept secure till later in the matching process. For instance, elements that identify the traveler, shipper or operator may be disguised. Origin and Destination are replaced by Nearby locations or Location windows. Identifiers are replaced with links to enable second-level matching and transaction within secure marketplaces or clearing platforms.
Demand records define generalized Origin and Destination and travel windows, appropriately disguised. In addition, the records may include mode preferences, fare targets, reserved or preferred itineraries. Supply records define specific or generalized Origin and Destination and travel windows. They include detail on mode, fare basis, and capacity.
Method to Identify Itinerary Options that Maximize Door-to-Door Utility
A key function of the BUILD element (e.g., Build 310 in
In some inventive aspects, the method disclosed herein uses a number of metrics to prioritize itineraries. Key among these is a measure of the Utility of an itinerary tailored to the traveler or shipper. Other metrics used in the prioritization process include trip duration, Total fare, Departure and Arrival times, Reliability. The Utility of an itinerary equals a measure of the benefit less a measure of the cost. The benefit and cost measures are tailored to the traveler or shipper via Preferences. The benefit measure equals a composite of factors such as mode preference, transfer quality, comfort, loyalty program benefits, safety and reliability. Costs are the sum of direct costs, incidentals, environmental costs, and cost of time. Direct costs include fares, rental fees, parking, fuel, insurance, depreciation, tolls. Incidentals include food, accommodation incurred along the way. Environmental impact includes the cost of greenhouse gas emissions, community noise, among others. Cost of time equals a unit cost of time for the travel or shipper (may vary by type of trip, business or recreation) multiplied by duration and adjusted for factors such as increased productivity on some modes relative to others.
Segments are constructed to meet traveler or shipper Objectives by building on any pre-booked legs to complete the journey. Viable long-distance modes are identified in the vicinity of start and end points of undefined legs using a Mode/Origin-Destination database. Table 9 discloses an example Mode/Origin-Destination database. The Mode/Origin-Destination database is a library of transport options by Origin-Destination pair. Mode options are built by defining specific modes for each long-distance leg as further illustrated in
Metrics such as trip duration, total fare and Utility are calculated for each segment option to enable prioritization by the system and the traveler or shipper. Modes options are displayed by leg in a table or on a map, enabling the traveler or shipper to prioritize options, add new ones, or define modes for local legs.
For each shortlisted mode option determined in
Queries return specific or generalized itineraries, along with their availability or likelihood of becoming available and fares. Some responses are immediate, others arrive over time. Fixed schedule operators add capacity, variable schedule operators modify schedules, spare capacity on on-demand services is offered to others, and travelers or shipper with changed needs offer their Itineraries to others. In addition, capacity and fares of Itineraries change over time as inventory positions change and fares are adjusted to optimize revenue.
Travel Objectives and Preferences are assessed to see if they lead to a sufficiently large option set. If not, additional itineraries are generated by relaxing Objective, and added to the option set pending approval. Timelines and key metrics are determined for each itinerary option as further illustrated in
Method to Assist with the Optimal Reservation of Multi-Modal Transport
Another function of the BUILD element (e.g., Build 310 in
An important element of this capability is decision support to the traveler or shipper. This is offered at two levels. First is decision support for the candidate reservation (e.g., optimal reservation) of itineraries from an operator or a class of operators serving a mode, for example, conventional air operators offering fixed schedules. Second is decision support for reservations across itineraries from multiple modes, or multiple classes of operators of a mode.
The first is a significant challenge today given aggressive revenue management practices resulting in fares on an itinerary varying on average by a multiple of 3.2 from low to high. Fares typically drop over 30 to 120 day windows ahead of a flight, then increase sharply at the 21, 14 and 7 day marks. And patterns vary widely based on competition, available capacity, day of the week, business versus leisure mix. These aspects of revenue management can extend to regional transport (e.g., regional air). However, the unique environment can drive significant change. Operators may face several other modes with distinct cost structures (e.g., highways, regional air, conventional air, high-speed rail). Competitive dynamics may be reshaped by the distributed nature of regional air versus the concentrated hub network today. Moreover, the much greater need for flexibility on regional trips can limit the ability of operators to vary fares as widely as they do today.
Given the uncertainty around future revenue management practices, the methods and systems disclosed herein are based on a targeted set of capabilities that can be tuned to a wide range of processes. Elements of these may also be sourced from 3rd party platforms where these offer more robust solutions. The first of these capabilities are methods to estimate a candidate reservation window (e.g., an optimal reservation window) for an operator or a class of operators given the likely variation of fares to departure. The candidate reservation window is selected to maximize the likelihood of fares lower than current, less the likelihood of fares higher than current. One approach is to determine this from the probability distribution of normalized fares by days to departure and available capacity for the Mode/Origin-Destination. The latter is determined from historical fare and capacity data, derived from models of revenue management practices, or some combination of the two. Another approach is to determine candidate windows from models for normalized fares by days to departure and capacity for the Mode/Origin-destination, based on revenue management practices and historical fare data. Yet another approach leverages machine learning to factor a broader set of variables, including fare levels on competing modes, to determine the candidate window.
The second of the capabilities are methods to determine fare outlooks for an itinerary. These, coupled with capacity feeds from operators and Platforms, create a robust basis for reservation decisions (referred to as “fare outlook” in the following). On the one hand, assessing optimality of a fare based on likelihood of a lower fare becoming available for the itinerary, on the other, monitoring capacity levels to ensure preferred itineraries remain available. One approach uses machine learning to forecast fares based on historical fare and capacity data, and applies it to recent fare and capacities to arrive at assessments. All of these may be supplemented by heuristics that model revenue management practices, e.g., current airline approach to raise fares at 21, 14 and 7 days. These enable recommendations for the specific itinerary, such as “Reserve” (e.g., Hold or Reserve 710 in
Given changeability of travel needs and itineraries, reservation recommendations are based on Utilities adjusted for the greater option value of itineraries subject to lower rather than higher change penalties. To do this, the systems and methods disclosed herein add to Utility a measure of the benefit of reserving (or holding) an itinerary as the difference of the likelihood a better itinerary will not be offered times the delta in Utility of the itinerary from poorer alternatives, less the likelihood a better itinerary will be offered times the delta in Utility of the better itinerary to that being reserved. The probabilities and Utilities are derived from a combination of fare histories and recent fare trajectories using techniques similar to those described to calculate candidate reservation windows (e.g., optimal reservation windows) and fare outlooks described previously. The cost of this benefit is accounted for reducing the Utility by an amount equal to the total cost incurred when changing the itinerary times the likelihood the better itinerary will be offered. Costs include change penalties or hold fees, but may also include an allocated cost of change insurance if purchased for flexibility on the trip.
Leveraging these capabilities, the platform determines a candidate window for reserving the trip based on the prioritized modes, their relative traffic and utilities. This is determined by maximizing the weighted likelihood of fares lower than the current, less the weighted likelihood of fares higher than current, across the highest Utility modes. Weighting is by a combination of the relative traffic and Utilities of the modes, unless otherwise specified by the traveler or shipper. This is displayed as guide which the traveler or shipper may accept as Reservation timeline, or respond with alternatives such as “Now” for same-day reservation or “By date” for reservation by the provided date. The traveler or shipper may also provide a fare target for outreach to operators, to guide itinerary notifications or itinerary reservation.
To support decisions across modes and operators, itineraries for the shortlisted modes are split into tiers based on Utility or other metric preferred by the traveler. The first tier includes the highest utility itineraries that collectively have a high likelihood of clearing. These are targeted for “Hold” or “Reserve” as they become available at optimal fares. The second tier are back-up itineraries, targeted for upgrade to Tier 1 in event the risk of Tier 1 not clearing becomes high. These may include a highly available mode (e.g., own vehicle) for searches where a traveler of shipper seeks alternatives only if aggressive fare targets are met. The third tier includes itineraries marked by the traveler or shipper as preferred given Utility comparable to Tier 1 if fares were to drop.
These three tiers of tracked itineraries and their comparable alternatives, including itinerary requests, are refreshed periodically. This includes real-time monitoring of fares and capacities, assessment of fare and capacity outlooks based on reservation timeline, refresh of Timelines and a recalculation of Utilities given changing fares. The itineraries are then resorted and tagged for display to guide reservation. The tags are some combination of labels, colors and symbols that are designed to communicate the fare and capacity outlook in a compact way, such as “Reserve now, fare likely to increase” (e.g., 712 in
The reservation process, once initiated, continues through the end of travel for the segment, but with varying objectives.
The platform executes to guidance automatically if “Managed” reservation is selected, and guides the traveler or shipper through execution in event of “Assisted.”
Method to Manage Multi-Modal Journeys in Real-Time
A key function of the MANAGE element (e.g., Manage 320 in
A day or more ahead of travel, determined by Preferences, the Timeline for the reserved itinerary is refreshed as illustrated in
Timelines continue to be refreshed periodically to reflect changes in itineraries and transport conditions. Once the Journey starts real-time feeds on progress, via smartphone or other device, from operator platforms, trigger further updates as illustrated in
As progress along the Timeline triggers items in the Workflow, starting with pre-travel Notifications, those assigned to the Platform are executed, followed by updates to the traveler or operator based on Workflow. On other items, the role of the Platform is limited to providing assistance, monitoring execution and delivering notifications, varying based on Service level requested. Workflow for segment from Palo Alto to Pasadena comprised of three legs is shown in Table 10, with detail on anchoring of the Workflow to the Timeline in Table 11, and a sample Travel dashboard in
Method to Detect Risks or Opportunities to Travel Objectives, Execute Redesign
A key function of the MONITOR element (e.g., Monitor 330 in
Redesign is a key function of the BUILD element (e.g., Build 310 in
Method to Determine Door-to-Door Itineraries Options for a Future Journey
An important element of the system and methods disclosed herein is bridging a traveler's door-to-door transport need to the terminal to terminal transport offered by most operators. This is done by translating traveler objectives at the Origin O and Destination D, to a time window that applies at the departure and arrival terminals of the chosen long-distance leg. For example, consider objectives that require departing O after tO and arriving at D before tD, for a 3-leg segment: local; long-distance; and local. First departure t2 for long-distance leg 2 is calculated by querying local platforms for duration of the local leg 1 for future travel at tO from D to the departures terminal, adjusted for preferences, travel conditions. Similarly, last departure t3 for leg 3 is calculated by querying local platforms for duration of the leg for future travel from the arrival terminal to D, arriving at tD, adjusted for preferences, travel conditions. The times t2 and t3 bracket the long-distance leg 2. These are tightened further to account for the pre- and post-travel stages. Stepping forward from t2, the duration of each future pre-travel stage is calculated using the transit database. This determines the first departure time for the long-distance mode. Similarly, stepping backward from t3, the duration of each future post-travel stage is calculated using the transit database. This determines the last arrival time for the long-distance mode. This pair, first departure and last arrival for the long-distance mode, is then used to identify itinerary options.
Methods for Multi-Modal Transport Monitoring and Forecasting
A function of the MONITOR element (e.g., Monitor 330 in
Another function of the MONITOR element (e.g., Monitor 330 in
The Transfer database is built and maintained in several ways. One by tracking a large number of individual journeys as described earlier, for instance, via smartphones with high-precision location capabilities such as advanced GPS or camera-based positioning. Second, by monitoring stage duration feeds from operators or terminal facilities. In both cases, the raw data is maintained in a cache for processing to the structured statistics that are then stored in the Transfer database.
Another function of the MONITOR element (e.g., Monitor 330 in
Forecast duration for a transfer stage is then determined by a transfer outlook function, drawing on the Transfer database and its supporting cache. For forecasts more than a few days ahead of travel the function calculates future durations based on historical averages stored in the Transfer database. These are modified using the stored variance to reflect the speed and risk tolerance of the traveler from preferences. These are set based on a combination of traveler behavior and stated preference. For forecasts close to the day of travel, the historical averages and variances are adjusted based on recent duration feeds in the cache and travel conditions, to increasing degrees based on proximity to time of travel.
Methods to Optimize Supply to Changing Transport Demand
The capture of door-to-door travel needs and the use of demand records enable operators to optimize their capacity based on evolving demand in ways not possible in the rigid long-distance transport systems of today. Examples for air operators include, optimizing supply and refining itineraries for reserved, held and requesting travelers, leveraging visibility to their door-to-door travel needs. Table 13 discloses range of opportunities to optimize supply by operator type created by the flexible transport system disclosed herein. Operators may trigger updates to traveler itineraries due to schedule changes or delays. Operators may upgrade travelers to improve itineraries or accommodate itinerary requests. Operators may offer to downgrade travelers to less preferred itineraries. Operators may alter schedules and equipment to better match demand leveraging segment flexibility. The capability also enables operators to collaborate to optimize supply to collective demand based on travelers end-to-end needs, or to respond jointly to abnormal events in ways to minimize adverse impact to travelers.
Capacity management in this fluid environment proceeds in stages, from initialization of the network many months ahead of travel, refinement over the months that follow as travelers and shippers express demand, closing over the final days leading to travel, and orchestration on the day of travel. Processes vary across these periods, but split roughly to the following: forecast demand, define footprint and fleet; determine candidate itineraries (e.g., optimal itineraries); assign capacity to transport requests.
Initialization Phase
Prior to opening of reservations, often many months ahead of the day of travel, a first provisional schedule for the day is determined to serve an initial demand forecast. A portion of this schedule may be treated as “fixed” given high likelihood of those itineraries being offered; the remainder is treated as “provisional”. Operators are able to commit to specific itineraries within the fixed schedule, but only on a generalized basis in the provisional schedule. As demand is expressed over time and the forecast refreshed, increasing portions of the schedule shift from provisional to fixed, leading in stages to a fully fixed schedule that is molded to demand as illustrated in
In the initialization phase, demand is forecast using single or multiple product origin-destination multiplicative demand models with factors for market elasticity to door-to-door times and fares. Demand may be forecast for multiple scenarios of varying aggressiveness. Demand is assessed based on traveler origin and destination pair (or nearby landmarks) and time objectives relative to these, versus transport terminals as is conventional practice. For example, demand may be described via origin and destination zip codes and associated departure or arrival times. Corresponding door-to-door times and fares are then calculated by summing across the air and ground legs, the latter weighted by use across the variety of ground modes.
Terminals and equipment to serve the targeted demand are determined. Terminals include hubs and non-hubs, former typically with defined departure and arrival slots, the latter much more flexible. Equipment is conventional and electric aircraft, the latter of much greater variety of sizes, including piloted aircraft as well as drones. Tiers of terminals and equipment may be identified aligned with varying levels of demand, and reflecting operator ability to vary capacity in response to overall demand (e.g., service offerings, maintenance schedules, lessor or 3rd party agreements). Specific itineraries are then calculated via time-space network optimization methods with heuristics. These seek to maximize the utility of the demand served at minimal operating cost across the defined terminals and equipment, factoring in a set of pre-fixed itineraries, and varying constraints at the terminals served. Mathematical approach is typically as a mixed integer problem (MIP), with nonlinear optimization methods such as Sequential Quadratic Programming (SQP). Heuristics are used extensively to make “larger” (i.e. realistic) problems tractable within a useful timeframe.
The degree of certainty of individual itineraries is assessed to differentiate itineraries that have a high likelihood of being offered (e.g., pre-fixed routes) from those that are more changeable. This is determined via a combination of factors, including forecast utilization or profitability of the service, utility of the route relative to nearby alternatives (e.g., varying based on origin-destination locations, time preferences, traffic conditions to-terminal and from-terminal), overall demand and capacity. For example, certainty may be determined by optimizing the network for varying levels of demand and travel conditions to generate alternate schedules from which the certainty of individual itineraries can be derived.
A reservations strategy is defined to guide how the two sides of the demand matching process proceed: the operator-facing assignment of capacity from a provisional schedule to demand; and the traveler or shipper-facing commitments made. This also determines what schedule is published, and the details of specific and generalized itineraries listed. As noted earlier, this will vary by operator based on individual revenue management approaches.
Reservation Phase
As transport requests arrive from the opening of reservations to travel, the demand forecast is refined to leverage this “expressed demand” and the provisional schedule, including the fixed vs. provisional split, is refreshed to match the updated forecast. This process is repeated periodically so that the schedule is molded optimally to demand. And as expressed demand increases relative to the forecast, increasing portions of the schedule shift from provisional to fixed.
Operator response to transport requests is determined by the reservation strategy. Capacity is assigned from the provisional schedule to meet the transport need, typically via a specific itinerary along with back-up alternatives that satisfy objectives. And a commitment is made to the traveler or shipper, in form of a specific or generalized itinerary, door-to-door or terminal-to-terminal. In event of a generalized reservation, a timeline for conversion to specific is also defined.
To accomplish this, the demand forecast is first refined by coupling the demand model with an expressed demand predictor/corrector algorithm. Expressed demand includes specific and generalized reservations, held and preferred itineraries, and may also include other signals of demand, e.g., search volumes. This enables continuing improvement of the demand forecast as expressed demand grows to increasing fractions of the forecast. Terminals and equipment are updated based on the improved demand forecast and other contingencies. This may include additions to the fleet and extensions to additional terminals to accommodate higher demand, terminal or ground travel disruptions.
Following this, the time-space network optimization to determine schedules is refreshed based on the refined demand forecast. Baseline optimization is supplemented with additional constraints to reflect the committed itineraries, reserved or held. Where modifying requirements of select travelers and shippers significantly improves the solution, opportunity to adjust terms is assessed and the optimization is adjusted on that basis. Certainties of individual itineraries are then updated using methods described previously, with the added constraint from the already committed itineraries. Published schedules and reservations strategy are updated to reflect these changed certainties.
Committed itineraries are reassigned to the updated schedule based on the optimized solution, including negotiation with travelers and shippers where the change is contrary to prior agreement. The refreshed fixed vs. provisional split is translated to updated commitments to travelers and shippers, typically increasing the volume of specific reservations. Some or all of these changes are communicated.
Travelers and shippers express demand by requesting reservations and holds, or indicating preferred itineraries, defined in generalized form by origin-destination and preferred times (or their equivalent at the origin-destination terminals), or by indicating a published itinerary. Operators respond to this expressed demand by assigning each provisionally to an itinerary or itineraries (e.g., primary with alternates) that meet transport objectives while maximizing traveler or shipper utility and operator profit margin. And by returning with a commitment to the traveler or shipper, in form of a specific or generalized itinerary, door-to-door or terminal-to-terminal. In event of a generalized reservation, a timeline for conversion to specific is also defined.
If capacity to meet a request is not available, options to reassign existing demand within constraint of individual travel objectives are explored, or the request is accepted provisionally pending update of schedules based on likelihood that the request will clear (determined as described previously or be some other method). This likelihood may be communicated to the traveler or shipper to assist with their decision. Provisional reservations are reviewed following schedule updates, changes or cancellations, and refreshed to reflect availability or likelihood of coming available.
Travelers and shippers also request changes or cancelations to their committed itineraries. Changes are handled much like new demand described previously. Both changes and cancelations involve a release of capacity, and an assessment of costs determined by the terms of the committed itinerary.
Closing Phase
In the final days ahead of travel, the demand forecast and schedule is further optimized based on expressed demand but also by factoring in travel conditions and other contingencies that have a material impact on the operator, traveler or shipper. Arriving transport requests are handled as described previously. And given impending travel, most commitments are converted to specific itineraries, or windows with timeline for conversion to specific.
The demand forecast is refined periodically by enhancing the demand model and expressed demand predictor/corrector algorithm with a model for the effect of travel conditions. These include factors such as traffic, weather, events, holidays that impact traveler and shipper objectives or utilities. For instance, challenging weather or traffic conditions may alter departure or arrival times to avoid the affected period, or alter departure and arrival terminals to those that are positioned to skirt the challenge. Terminals and equipment are adjusted to respond to evolving demand and contingencies.
The time-space network optimization is refreshed based on combination of committed itineraries, the updated demand forecast, travel conditions and contingencies. For instance, the utility of demand includes the condition of traffic on ground legs to and from the terminals, and deterioration of these may shift preference to alternate terminals. Certainties of individual itineraries are then refreshed, along with updates to published schedules and reservations strategy. Given proximity to travel, much of the schedule is typically fixed at this point, with only targeted uncertainty remaining.
Requests for transport, or change to committed itineraries are handled as described previously.
Committed itineraries are reassigned to near final schedules based on the optimized solution, including negotiation with travelers and shippers where the change is contrary to prior agreement. The refreshed fixed vs. provisional split is translated to updated commitments to travelers and shippers. Given proximity to travel, most itineraries are converted to specific, or clear paths to this conversion are determined.
Final transport guidance is communicated to travelers and shippers. This may include specific itineraries, or their generalized equivalents along with timeframe for these being made specific. For instance, this may include detail on a departure window along with timeline by when a specific departure time will be communicated. This guidance is also communicated to transport orchestrators or adjacent transport providers to enable multi-modal coordination.
Orchestration Phase
On the day of travel, capacity is adjusted in response to unexpected traveler, shipper and operational needs. Based on the nature of issue, the adjustment may involve network-wide optimization, or be limited to discrete itineraries. The adjustments are communicated to the range of stakeholders in real-time to enable responsive and seamless multi-modal orchestration.
Traveler and shipper progress is monitored directly or via orchestrators to identify if itinerary is at risk, e.g., delays, no-shows. Operations are monitored for risk to on-time performance. Terminal and equipment alternatives are identified in response where needed to address schedule gaps. If issues have significant impact on more than a few itineraries, a time-space network optimization is triggered to update the schedule, leveraging visibility to transport objectives to determine alternate itineraries that maximize utility.
More localized issues are addressed individually. In event of a delayed departure, for instance, travelers and shippers whose objectives are at risk are rerouted to itineraries that meet their objectives. This may include change for some that are not affected, within constraints of their objectives and utilities, to create capacity for the displaced. Individual journeys are similarly adjusted proactively in event of a disruption or delay so the traveler or shipper is rerouted in ways that minimize impact on objectives.
Final schedules and payloads are also used determine optimal energy plans and onboard storage for each flight, factoring for turnaround required, as well as recharge, swap and refuel capabilities at each of the terminals served.
In accordance with some inventive aspects, the system, apparatus, methods, elements, processes, functions, and/or operations for enabling the inventive platform and transport system disclosed herein may be wholly or partially implemented in the form of a set of instructions executed by one or more programmed computer processors such as a central processing unit (CPU) or microprocessor. Such processors may be incorporated in an apparatus, server, client or other computing or data processing device operated by, or in communication with, other components of the system. As an example,
As used herein, the terms “optimal,” “optimized,” “optimizing,” used in specification and claims are intended to generally cover, for example, best possible solution, most favorable solution, and/or merely an improved solution. For example, in some instances it is possible that “optimizing/optimal” described herein may generate a solution that may not be the best possible solution or most favorable solution, but instead an improved solution (that may fall short of the best possible solution). In such instances, methods described herein may optionally generate the best possible solution, the most favorable solution or an improved solution, depending on one or more aspects such as one or more input data, model parameters, updated parameters, variables associated with the input data, the type of input source devices, other characteristics associated with the input source devices, and/or type of constraints involved in performing “optimization.” In a similar manner, in some instances, it is possible that the best possible solution may not necessarily be an improved solution and vice versa.
Furthermore, although embodiments of the present disclosure may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the present disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s).
While various inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.
The above-described embodiments can be implemented in any of numerous ways. For example, embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.
Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.
Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety.
All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”
The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.
As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03.
This application claims priority under 35 U.C.S. § 371 to, and is a U.S. National Phase entry of, International Application No. PCT/US2017/041266, filed Jul. 7, 2017, which claims a priority benefit to U.S. Application Ser. No. 62/359,211, entitled “System and Method for Implementing Fast and Flexible Multi-modal Transport,” filed on Jul. 7, 2016, the disclosure of each of which is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2017/041266 | 7/7/2017 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62359211 | Jul 2016 | US |
