INTERMEDIATE DESTINATION AND SUPPLEMENTAL TRANSPORTATION FOR OPTIMIZED TRANSPORT

Abstract

An example operation includes one or more of establishing, by a processor, an intended destination of an autonomous transport, and determining, by the processor, an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold, wherein the intended destination and the alternate destination are not the same.

Description

TECHNICAL FIELD

This application generally relates to automated supplemental transportation, and more particularly, to intermediate destination and supplemental transportation for optimized transport.

BACKGROUND

Vehicles or transports, such as cars, motorcycles, trucks, planes, trains, etc., generally provide transportation needs to occupants and/or goods in a variety of ways. Functions related to transports may be identified and utilized by various computing devices, such as a smartphone or a computer.

A person may intend to ride from a starting place to an intended destination using an autonomous transport. However, the person's choice of autonomous transport from the starting place to the intended destination might not be the most optimal form of transport. There may exist other combinations of transport, using intermediate destinations and supplemental transports that may optimize the total travel for the person. However, current systems do not provide a user with information or alternative transports in order to optimize the trip for the user.

Accordingly, what is needed is a solution for providing options of supplemental transportation and/or intermediate destinations when a user desires to use a transport from a starting place to an intended destination using an autonomous transport.

SUMMARY

One example embodiment provides a method that includes one or more of establishing, by a processor, an intended destination of an autonomous transport, and determining, by the processor, an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold, wherein the intended destination and the alternate destination are not the same.

Another example embodiment provides a system that includes a processor and a memory, wherein the processor is configured to perform one or more of establish an intended destination of an autonomous transport, and determine an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold, wherein the intended destination and the alternate destination are not the same.

A further example embodiment provides a non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform one or more of establish an intended destination of an autonomous transport, and determine an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold, wherein the intended destination and the alternate destination are not the same.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A an example system for intermediate destination and supplemental transportation determination for optimized transport, according to example embodiments.

FIG. 1B shows an example display image for intermediate destination and supplemental transportation for optimized transport according to example embodiments.

FIG. 1C shows another example system for intermediate destination and supplemental transportation for optimized transport according to example embodiments.

FIG. 2A illustrates a transport network diagram, according to example embodiments.

FIG. 2B illustrates another transport network diagram, according to example embodiments.

FIG. 2C illustrates yet another transport network diagram, according to example embodiments.

FIG. 2D illustrates a further transport network diagram, according to example embodiments.

FIG. 2E illustrates a yet further transport network diagram, according to example embodiments.

FIG. 3A illustrates a flow diagram, according to example embodiments.

FIG. 3B illustrates another flow diagram, according to example embodiments.

FIG. 3C illustrates yet another flow diagram, according to example embodiments.

FIG. 4 illustrates a machine learning transport network diagram, according to example embodiments.

FIG. 5A illustrates an example vehicle configuration for managing database transactions associated with a vehicle, according to example embodiments.

FIG. 5B illustrates another example vehicle configuration for managing database transactions conducted among various vehicles, according to example embodiments

FIG. 6A illustrates a blockchain architecture configuration, according to example embodiments.

FIG. 6B illustrates another blockchain configuration, according to example embodiments.

FIG. 6C illustrates a blockchain configuration for storing blockchain transaction data, according to example embodiments.

FIG. 6D illustrates example data blocks, according to example embodiments.

FIG. 7 illustrates an example system that supports one or more of the example embodiments.

DETAILED DESCRIPTION

It will be readily understood that the instant components, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of at least one of a method, apparatus, non-transitory computer readable medium and system, as represented in the attached figures, is not intended to limit the scope of the application as claimed but is merely representative of selected embodiments.

The instant features, structures, or characteristics as described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, the usage of the phrases “example embodiments”, “some embodiments”, or other similar language, throughout least this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at one embodiment. Thus, appearances of the phrases “example embodiments”, “in some embodiments”, “in other embodiments”, or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the diagrams, any connection between elements can permit one-way and/or two-way communication even if the depicted connection is a one-way or two-way arrow. In the current application, a transport may include one or more of cars, trucks, motorcycles, scooters, bicycles, boats, recreational vehicles, planes, and any object that may be used to transport people and or goods from one location to another.

In addition, while the term “message” may have been used in the description of embodiments, the application may be applied to many types of network data, such as, a packet, frame, datagram, etc. The term “message” also includes packet, frame, datagram, and any equivalents thereof. Furthermore, while certain types of messages and signaling may be depicted in exemplary embodiments they are not limited to a certain type of message, and the application is not limited to a certain type of signaling.

Example embodiments provide methods, systems, components, non-transitory computer readable media, devices, and/or networks, which provide at least one of: a transport (also referred to as a vehicle herein), a data collection system, a data monitoring system, a verification system, an authorization system and a vehicle data distribution system. The vehicle status condition data, received in the form of communication update messages, such as wireless data network communications and/or wired communication messages, may be received and processed to identify vehicle/transport status conditions and provide feedback as to the condition changes of a transport. In one example, a user profile may be applied to a particular transport/vehicle to authorize a current vehicle event, service stops at service stations, and to authorize subsequent vehicle rental services.

Within the communication infrastructure, a decentralized database is a distributed storage system, which includes multiple nodes that communicate with each other. A blockchain is an example of a decentralized database, which includes an append-only immutable data structure (i.e., a distributed ledger) capable of maintaining records between untrusted parties. The untrusted parties are referred to herein as peers, nodes or peer nodes. Each peer maintains a copy of the database records and no single peer can modify the database records without a consensus being reached among the distributed peers. For example, the peers may execute a consensus protocol to validate blockchain storage entries, group the storage entries into blocks, and build a hash chain via the blocks. This process forms the ledger by ordering the storage entries, as is necessary, for consistency. In a public or permissionless blockchain, anyone can participate without a specific identity. Public blockchains can involve cryptocurrencies and use consensus based on various protocols such as proof of work (PoW). On the other hand, a permissioned blockchain database provides a system, which can secure interactions among a group of entities, which share a common goal, but which do not or cannot fully trust one another, such as businesses that exchange funds, goods, information, and the like. The instant application can function in a permissioned and/or a permissionless blockchain setting.

Smart contracts are trusted distributed applications, which leverage tamper-proof properties of the shared or distributed ledger (i.e., which may be in the form of a blockchain) database and an underlying agreement between member nodes, which is referred to as an endorsement or endorsement policy. In general, blockchain entries are “endorsed” before being committed to the blockchain while entries that are not endorsed are disregarded. A typical endorsement policy allows smart contract executable code to specify endorsers for an entry in the form of a set of peer nodes that are necessary for endorsement. When a client sends the entry to the peers specified in the endorsement policy, the entry is executed to validate the entry. After validation, the entries enter an ordering phase in which a consensus protocol is used to produce an ordered sequence of endorsed entries grouped into blocks.

Nodes are the communication entities of the blockchain system. A “node” may perform a logical function in the sense that multiple nodes of different types can run on the same physical server. Nodes are grouped in trust domains and are associated with logical entities that control them in various ways. Nodes may include different types, such as a client or submitting-client node, which submits an entry-invocation to an endorser (e.g., peer), and broadcasts entry-proposals to an ordering service (e.g., ordering node). Another type of node is a peer node, which can receive client submitted entries, commit the entries and maintain a state and a copy of the ledger of blockchain entries. Peers can also have the role of an endorser, although it is not a requirement. An ordering-service-node or orderer is a node running the communication service for all nodes, and which implements a delivery guarantee, such as a broadcast to each of the peer nodes in the system when committing entries and modifying a world state of the blockchain, which is another name for the initial blockchain entry, which normally includes control and setup information.

A ledger is a sequenced, tamper-resistant record of all state transitions of a blockchain. State transitions may result from smart contract executable code invocations (i.e., entries) submitted by participating parties (e.g., client nodes, ordering nodes, endorser nodes, peer nodes, etc.). An entry may result in a set of asset key-value pairs being committed to the ledger as one or more operands, such as creates, updates, deletes, and the like. The ledger includes a blockchain (also referred to as a chain), which is used to store an immutable, sequenced record in blocks. The ledger also includes a state database, which maintains a current state of the blockchain. There is typically one ledger per channel. Each peer node maintains a copy of the ledger for each channel of which they are a member.

A chain is an entry log, which is structured as hash-linked blocks, and each block contains a sequence of N entries, where N is equal to or greater than one. The block header includes a hash of the block's entries, as well as a hash of the prior block's header. In this way, all entries on the ledger may be sequenced and cryptographically linked together. Accordingly, it is not possible to tamper with the ledger data without breaking the hash links. A hash of a most recently added blockchain block represents every entry on the chain that has come before it, making it possible to ensure that all peer nodes are in a consistent and trusted state. The chain may be stored on a peer node file system (i.e., local, attached storage, cloud, etc.), efficiently supporting the append-only nature of the blockchain workload.

The current state of the immutable ledger represents the latest values for all keys that are included in the chain entry log. Because the current state represents the latest key values known to a channel, it is sometimes referred to as a world state. Smart contract executable code invocations execute entries against the current state data of the ledger. To make these smart contract executable code interactions efficient, the latest values of the keys may be stored in a state database. The state database may be simply an indexed view into the chain's entry log, it can therefore be regenerated from the chain at any time. The state database may automatically be recovered (or generated if needed) upon peer node startup, and before entries are accepted.

A blockchain is different from a traditional database in that the blockchain is not a central storage but rather a decentralized, immutable, and secure storage, where nodes must share in changes to records in the storage. Some properties that are inherent in blockchain and which help implement the blockchain include, but are not limited to, an immutable ledger, smart contracts, security, privacy, decentralization, consensus, endorsement, accessibility, and the like.

Example embodiments provide a way for providing a vehicle service to a particular vehicle and/or requesting user associated with a user profile that is applied to the vehicle. For example, a user may be the owner of a vehicle or the operator of a vehicle owned by another party. The vehicle may require service at certain intervals and the service needs may require authorization prior to permitting the services to be received. Also, service centers may offer services to vehicles in a nearby area based on the vehicle's current route plan and a relative level of service requirements (e.g., immediate, severe, intermediate, minor, etc.). The vehicle needs may be monitored via one or more sensors, which report sensed data to a central controller computer device in the vehicle, which in turn, is forwarded to a management server for review and action.

A sensor may be located on one or more of the interior of the transport, the exterior of the transport, on a fixed object apart from the transport, and on another transport near to the transport. The sensor may also be associated with the transport's speed, the transport's braking, the transport's acceleration, fuel levels, service needs, the gear-shifting of the transport, the transport's steering, and the like. The notion of a sensor may also be a device, such as a mobile device. Also, sensor information may be used to identify whether the vehicle is operating safely and whether the occupant user has engaged in any unexpected vehicle conditions, such as during the vehicle access period. Vehicle information collected before, during and/or after a vehicle's operation may be identified and stored in a transaction on a shared/distributed ledger, which may be generated and committed to the immutable ledger as determined by a permission granting consortium, and thus in a “decentralized” manner, such as via a blockchain membership group.

Each interested party (i.e., company, agency, etc.) may want to limit the exposure of private information, and therefore the blockchain and its immutability can limit the exposure and manage permissions for each particular user vehicle profile. A smart contract may be used to provide compensation, quantify a user profile score/rating/review, apply vehicle event permissions, determine when service is needed, identify a collision and/or degradation event, identify a safety concern event, identify parties to the event and provide distribution to registered entities seeking access to such vehicle event data. Also, the results may be identified, and the necessary information can be shared among the registered companies and/or individuals based on a “consensus” approach associated with the blockchain. Such an approach could not be implemented on a traditional centralized database.

Every autonomous driving system is built on a whole suite of software and an array of sensors. Machine learning, lidar projectors, radar, and ultrasonic sensors all work together to create a living map of the world that a self-driving car can navigate. Most companies in the race to full autonomy are relying on the same basic technological foundations of lidar +radar +cameras +ultrasonic, with a few notable exceptions.

In another embodiment, GPS, maps and other cameras and sensors are used in autonomous vehicles without lidar as lidar is often viewed as being expensive and unnecessary. Researchers have determined that stereo cameras are a low-cost alternative to the more expensive lidar functionality.

The instant application includes, in certain embodiments, authorizing a vehicle for service via an automated and quick authentication scheme. For example, driving up to a charging station or fuel pump may be performed by a vehicle operator and the authorization to receive charge or fuel may be performed without any delays provided the authorization is received by the service station. A vehicle may provide a communication signal that provides an identification of a vehicle that has a currently active profile linked to an account that is authorized to accept a service, which can be later rectified by compensation. Additional measures may be used to provide further authentication, such as another identifier may be sent from the user's device wirelessly to the service center to replace or supplement the first authorization effort between the transport and the service center with an additional authorization effort.

Data shared and received may be stored in a database, which maintains data in one single database (e.g., database server) and generally at one particular location. This location is often a central computer, for example, a desktop central processing unit (CPU), a server CPU, or a mainframe computer. Information stored on a centralized database is typically accessible from multiple different points. A centralized database is easy to manage, maintain, and control, especially for purposes of security because of its single location. Within a centralized database, data redundancy is minimized as a single storing place of all data also implies that a given set of data only has one primary record.

The current solution allows a person to utilize supplemental transport(s) to arrive at an intended destination. A functional relationship value (e.g. value that indicates the various parameters and their importance in determining what type of alternate transport to utilize) is used in determining the supplemental transport.

For example, a person may be traveling to an intended destination via a transport. A processor may determine that there is a faster route by taking the person to an alternate destination. At the alternate destination the person will take a supplemental transport (e.g. train, bus, scooter, etc.) to the intended destination. The alternate destination and alternate transport are determined by a functional relationship value, which indicates the various parameters and their importance in determining what type of supplemental transport to utilize.

A parameter can be one or more of traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters, etc.

FIG. 1A illustrates an example system 100A for intermediate destination and supplemental transportation determination for optimized transport, according to example embodiments. Referring to FIG. 1A, a processor 114 within a management server 102 is able to wirelessly communicate, by way of a wireless network 104, with a processor 116 within a transport 106, with a processor 118 within a transport 108, with a processor 120 within a transport 110 and with a processor 122 within a transport 112. A person 124 desires to be transported from a starting place 126 to an intended destination 128. In this example, the person 116 becomes a passenger in the transport 106 at the starting place 126, wherein the transport 106 may travel along a navigational path 132 to transport the person 124 from the starting place 126 to the intended destination 128. For purposes of explanation only, in this example embodiment, the transport 106 is a car, the transport 108 is a car, the transport 110 is a metro train and the transport 112 is a scooter.

FIG. 1A shows an example system 100A for intermediate destination and supplemental transportation for optimized transport according to example embodiments. Referring to FIG. 1A, a person 124 is intending to travel from a starting place 126 to an intended destination. Initially, the person 124 intends on using a transport 106 to travel from the starting place 126 to the intended destination 128. However, a management server 102, having a processor 114 therein, is in communication with a processor 116 of the transport 106 via a wireless network 104. Further, the processor 114 of the management server 102 is additionally in communication with: a processor 118 of a transport 108 via the wireless network 104, a processor 120 of a transport 110 via the wireless network 104 and a processor 122 of a transport 112 via the wireless network 104. The processor 114 of the management server 102 establishes the intended destination 128 of the transport 106. In some non-limiting example embodiments, the person 124 may choose the intended destination 128, for example via their phone, a computer, or a kiosk. In some other non-limiting example embodiments, a headend of a common carrier (not shown) may choose the intended destination 128 for the person 124.

The processor 114 of the management server 102 then determines an alternate destination based on a parameter by comparing a functional relationship value with a threshold, wherein the functional relationship value is of a provided measurement of the parameter and a weighting value associated with the parameter. Non-limiting examples of such parameters include traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters and combinations thereof.

A functional relationship is a mathematical relationship between parameters. For example, if a single parameter is used, then the functional relationship is just that single parameter. However, if there are two or more parameters, then the functional relationship may be any mathematical function relating the two or more parameters, non-limiting examples of which include a sum, a quotient, a product, etc. For purposes of discussion, let the number, n, of parameters, P_i, have respective measurements, P₁, P₂, . . . P_n−1, P_n. Further, let each parameter have a corresponding weighting value, W_i, associated therewith, for multiplication. Therefore, a functional relationship would be a function of each parameter value multiplied by its respective weighting value, ƒ(P₁*W₁, P₂*W₂, . . . P_n−1*W_n−1, P_n*W_n). For example, a functional relationship might be a sum of the each parameter value multiplied by its respective weighting value, as shown in equation (1) below:

ƒ(P₁*W₁, P₂*W₂, . . . P_n−1*W_n−1,P_n*W_n)=P₁*W₁,+P₂*W₂+ . . . P_n−1*W_n−1,+P_n*W_n.   (1)

In another example, suppose there are only three parameters, for purposes of discussion, such that the functional relationship is shown below in equation (2):

ƒ(P₁*W₁, P₂*W₂, P₃*W₃)=[P₁*W₁*e^(P₂*W₂)]/P₃*W₃.   (2)

The functional relationship value is the calculated value of the functional relationship. For example, for purposes of discussion, let an example system use two parameters: wherein P₁ is total estimated time to travel from the starting place 126 to the intended destination 128 using transport 106, and P₂ is the total cost of traveling from the starting place 126 to the intended destination 128 using transport 106. In this example, let goal be to minimize the total estimated time from the starting place to the intended destination and to minimize the total cost of traveling from the starting place to the intended destination. To achieve this goal, the functional relationship would be inversely related to both parameters. Further, in this example, let minimizing time be more important that minimizing cost. Accordingly, in this example, the weighting value, W₁, for P₁, will be larger than the weighting value, W₂, for P₂. Therefore in this example, the functional relationship is:

ƒ(P₁*W₁, P₂*W₂)=[W₁/P₁]*[W₂/P₂].   (3)

In this example, let W₁ be 0.6 and the total estimated time, P₁, be one hour and 45 minutes, or 1.75 hrs. Further, let W₂ be 0.4 (again in this example, time is more important than cost) and the total estimated cost, P2, be $26.75. In such an example the functional relationship value would be determined by processor 114 by inserting these values into equation (3), as shown in equation (4) below:

ƒ(1.75*0.6, 26.75*0.4)=[0.6/1.75]*[0.4/26.75]=0.00512.   (4)

Accordingly, for two parameters for example, the functional relationship value is based on a functional relationship between a provided measurement of the first parameter and a second provided measurement of the second parameter.

The processor 114 may then set this determined functional relationship value as a threshold, f_th, for determining alternate destinations and modes of transport, as will be described in more detail below.

A parameter may be selected from a group of parameters consisting of traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters, transport cargo availability and combinations thereof. Transport 106 includes many systems and detectors that provide values for values parameters, non-limiting examples of which include wireless network connections, global positioning systems (GPS), thermostats, etc. With respect to time parameters and temperature parameters, for example, if the person will be expected to travel from the alternate destination 130 to the intended destination 128 by foot, then the temperature parameter may be a factor in choosing an alternate transport. Ambient temperature parameters may include temperatures outside a train station and inside a train station for example. With respect to transport temperature parameters, these may include temperatures inside a train or other transport. Further, for example, the outside temperature may be 85° F., whereas if the person 124 were to be traveling by a scooter at 35 mph, the effective temperature may be much lower, e.g., 78° F. With respect to transport type parameters, these may differentiate between a car, scooter, a train, etc. For example, the person 124 may have a large piece of luggage that would need to be stored during transport and may not want a scooter, as there is no room for to store such a piece of luggage.

As for obtaining parameter values, in some embodiments, the processor 114 of the management server 102 obtains the parameter values from the processor 116 of the transport 106 via the wireless network 104. In this sense, the obtained parameter values are provided measurements of the parameters.

The transport 106 may provide an estimated time of arrival at the intended destination 128 to the passenger. In particular, in some embodiments, the instant solution may include determining, by the server 102, the starting place 126 of the transport 106. This may be performed by a Global Positioning System (GPS), or other location systems, wherein the transport 106 includes a GPS that provides the location of the transport 106 to the server 102. The instant solution may additionally include determining, by the server 102, the alternate navigational path 134 to the alternate destination 130. The instant solution may additionally include determining, by the server 102, an estimated time of arrival of the transport 106 to the alternate destination 130. This may be performed by a navigation system, wherein the server 102 is operable to determine an estimated time of arrival based on parameters, non-limiting examples of which include navigational paths of travel, speed limits along the navigational paths of travel, traffic flow along the navigational paths of travel, and combinations thereof.

In some embodiments, the transport 106 may provide, via the wireless network 104, the values of the parameters of the transport 106 to the server 102, when the person 124 enters the transport 106. For example, the processor 116 in the transport 106 may have a memory therein, which stores information related to the values of the parameters of the transport 106. When the person 124 enters the transport 106, the processor 116 may transmit the stored information related to the values of the parameters of the transport 106 to the server 102 via the wireless network 104. If the values of the parameters of the transport 106 change, then the information related to the values of the parameters of the transport will be updated in the memory of the processor 116. Accordingly, when the processor 116 transmits the stored information related to the values of the parameters of the transport 106 at any time, the stored information is the most current state of the configuration of the transport 106.

In some embodiments, the server 102 may periodically ping the transport 106, via the wireless network 104, for updates on the values of the parameters of the transport 106. For example, the processor 116 in the transport 106 may have a memory therein, which stores information related to values of the parameters of the transport 106. When the processor 116 receives the ping from the server 102, the processor 116 may transmit the stored information related to the values of the parameters of the transport 106 to the server 102 via the wireless network 104. If the values of the parameters of the transport 106 change, e.g., the occupant 116 changes the ambient temperature within transport 106, then the information related to the values of the parameters of the transport will be updated in the memory of the processor 116. Accordingly, when the processor 116 transmits the stored information related to the values of the parameters of the transport 106 at any time, the stored information is the most current state of the values of the parameters of the transport 106.

In some embodiments, the transport 106 may periodically provide, via the wireless network 104, updates on values of the parameters of the transport 106 to the server 102, without a need for a ping from the server 102. For example, the processor 116 in the transport 106 may have a memory therein, which stores information related to the values of the parameters of the transport 106. The processor 116 may transmit the stored information related to the values of the parameters of the transport 106 to the server 102 via the wireless network 104 at preset times or at predetermined intervals. If the values of the parameters of the transport 106 change, then the information related to values of the parameters of the transport will be updated in the memory of the processor 116. Accordingly, when the processor 116 transmits the stored information related to the values of the parameters of the transport 106 at any time, the stored information is the most current state of the values of the parameters of the transport 106. In some alternate embodiment, when the transport 106 periodically provides, via the wireless network 104, updates on the values of the parameters of the transport 106 to the server 102, without a need for a ping from the server 102, the transport 106 provides only changes to the values of the parameters as compared to the most recent update that had been sent to the server 102. For example, suppose for purposes of explanation that the transport 106 were to send an initial message of the values of the parameters of the transport 106 to server 102 via the wireless network 104. Then, after the initial message is sent, the occupant changes the internal temperature within transport 106. In this situation, for this type of alternate embodiment, the information related to the change in the values of the parameters of the transport will be updated in the memory of the processor 116. Accordingly, the processor 116 transmits the stored change in information related to the values of the parameters of the transport 106, which reflects the most current state of the values of the parameters of the transport 106, to server 102 via the wireless network 104.

The processor 114 of the management server 102 may determine an intermediate destination and supplemental transportation for the person 124 to optimize transport of the person 124 from starting place 126 to the intended destination 128. Multiple alternate destinations and multiple alternate supplemental forms of transportation might exist for consideration. However, for purposes of explanation only, consider only a single alternate destination, which in this case is the alternate destination 130. Further, the alternate destination 130 might have multiple available supplemental modes of transportation of each type. However, for purposes of explanation only, consider only having a single car at the alternate destination 130 as the transport 108, only a single metro train at alternate destination 130 as the transport 110 and only a single scooter at the alternate destination 130 as the transport 112.

Each of the processor 118 of the transport 108, the processor 120 of the transport 110 and the processor 122 of the transport 112 is able to provide their respective parameters to the processor 114 of the management server 102 via the wireless network 104 in a manner similar to the processor 116 of the transport 106 as discussed above.

The processor 114 determines the functional relationship value for each alternate mode of transportation. For example, the processor 114 determines the functional relationship value, ƒ₁, for the option of the person 124 using the transport 106 to travel from the starting place 126 to the alternate destination 130 and then using the transport 108 from the alternate destination 130 to the intended destination 128 via alternate navigational paths 136, 138, and 140. The processor 114 determinesfi for the option of the person 124 using the transport 108 by using the parameters and weighting values as discussed above for the transport 106 for the duration of the travel from the starting place 126 to the alternate destination 130 and using the parameters and weighting values for the transport 108 for the duration of the travel from the alternate destination 130 to the intended destination 128. In some embodiments, ƒ₁<ƒ_th, then the person 124 would not be notified of the alternate form of transport that includes the transport 108 at the alternate destination 130. Accordingly, processor 114 determines whether transport 108 is a supplemental transport that is operable to navigate from the alternate destination 130 to the intended destination 128.

Further, the processor 114 determines the functional relationship value, ƒ₂, for the option of the person 124 using the transport 110 by using the parameters and weighting values as discussed above for the transport 106 for the duration of the travel from the starting place 126 to the alternate destination 130 and using the parameters and weighting values for the transport 110 for the duration of the travel from the alternate destination 130 to the intended destination 128. In some embodiments, if ƒ₂<ƒ_ih, then the person 124 would not be notified of the alternate form of transport that includes the transport 110 at the alternate destination 130. Accordingly, processor 114 determines whether transport 110 is a supplemental transport that is operable to navigate from the alternate destination 130 to the intended destination 128.

Still further, the processor 114 determines the functional relationship value, ƒ₃, for the option of the person 124 using the transport 112 by using the parameters and weighting values as discussed above for the transport 106 for the duration of the travel from the starting place 126 to the alternate destination 130 and using the parameters and weighting values for the transport 112 for the duration of the travel from the alternate destination 130 to the intended destination 128. In some embodiments, if ƒ₃<ƒ_ih, then the person 124 would not be notified of the alternate form of transport that includes the transport 112 at the alternate destination 130. Accordingly, processor 114 determines whether transport 112 is a supplemental transport that is operable to navigate from the alternate destination 130 to the intended destination 128.

If the determined functional relationship value is less than the threshold, fth, then the processor 114 may determine that the alternate destination does not qualify as an alternate destination option to suggest to the person 124. However, if the determined functional relationship value is equal to or great than the threshold, fth, then the processor 114 may determine that the alternate destination qualifies as an alternate destination option to suggest to the person 124.

After the alternate transport options are determined, the processor 114 of the management server 102 may transmit an instruction, via the wireless network 104, to the processor 116 of the transport 106 to provide the person 124 with an indication of the alternate transport options.

FIG. 1B shows an example display image for intermediate destination and supplemental transportation for optimized transport according to example embodiments. Referring to FIG. 1B, in response to receiving the instruction, the processor 116 may instruct a graphic user interface within the transport 106 to display the alternate transport options.

As shown in FIG. 1B, a display image 100B includes information related to the primary transport 106 and alternate transport options. Accordingly, the transport 106 includes a GUI (not shown) that provides the person 124 with an interactive choice of multiple supplemental transports. In this example embodiment, the display image 100B indicate to the person 124 that the primary transport, which corresponds to the transport 106 from the starting place 126 to the intended destination 128: is estimated to take 43 minutes to arrive at the train station, which corresponds to the intended destination 128, has an inside cabin temperature of 71° F., and will have a total cost of $57.00.

Further, the display image 100B indicate to the person 124 that a first alternate transport, which corresponds to a combination of the transport 106 from the starting place 126 to the alternate destination 130 and then the transport 110 from the alternate destination 130 to the intended destination 128: is estimated to take 32 minutes to arrive at the train station, which corresponds to the intended destination 128, has an inside cabin temperature of 74° F., and will have a total cost of $118.00. In this manner, the person 124 can easily see that the first alternate transport option will be: overall a little quicker—11 minutes quicker, a little warmer for the second leg of the trip, about 3° F. warmer during the ride from the alternate destination 130 to the intended destination 128, and more expensive—$61.00 more expensive.

Further, the display image 100B indicate to the person 124 that a second alternate transport, which corresponds to a combination of the transport 106 from the starting place 126 to the alternate destination 130 and then the transport 108 from the alternate destination 130 to the intended destination 128: is estimated to take 47 minutes to arrive at the train station, which corresponds to the intended destination 128, has an inside cabin temperature of 71° F., and will have a total cost of $50.00. In this manner, the person 124 can easily see that the second alternate transport option will be: overall a little longer—4 minutes quicker, the same temperature for the entire trip, and little less expensive—$7.00 less expensive.

Finally, the display image 100B indicate to the person 124 that a third alternate transport, which corresponds to a combination of the transport 106 from the starting place 126 to the alternate destination 130 and then the transport 112 from the alternate destination 130 to the intended destination 128: is estimated to take 20 minutes to arrive at the train station, which corresponds to the intended destination 128, has an outside temperature of 92° F., and will have a total cost of $45.00. In this manner, the person 124 can easily see that the third alternate transport option will be: overall much quicker—23 minutes quicker, a substantially higher temperature for the second leg of the trip, and less expensive—$13.00 less expensive.

With the visualization of the display image 100B, the person 124 can determine which option is the most preferred option based on the criteria of the user, e.g., whether arriving earlier is more important than paying more money. In these non-limiting example embodiments, the intended destination 128 and the alternate destination 130 are not the same place.

In this example, the option that the person 124 chooses, by way of the GUI displaying the display image 100B, will be the option that is used to transport the person 124 from the starting place 126 to the intended destination 128. If the primary transport of the display image 100B is chosen, then the transport 106 will transport the person 124 from the starting place 126 to the intended destination 128.

Alternatively, if the Alternate 1 transport of the display image 100B is chosen, then the processor 116 of the transport 106 may inform the processor 114 by way of the wireless network 104 that the transport 106 will be transporting the person 124 to the alternate destination 130 wherein the person will then use the transport 110 to transport the person 124 from the alternate destination 130 to the intended destination 128. Further, processor 114 may then inform the processor 120 of the transport 110, via the wireless network 104, that the person 124 will be using the transport 110 to travel from the alternate destination 130 to the intended destination 128. In some embodiments, if the Alternate 1 transport of the display image 100B is chosen, then the processor 116 of the transport 106 may directly inform the processor 120 of the transport 110 by way of the wireless network 104 that the transport 106 will be transporting the person 124 to the alternate destination 130 wherein the person will then use the transport 110 to transport the person 124 from the alternate destination 130 to the intended destination 128.

Further, if the Alternate 2 transport of the display image 100B is chosen, then the processor 116 of the transport 106 may inform the processor 114 by way of the wireless network 104 that the transport 106 will be transporting the person 124 to the alternate destination 130 wherein the person will then use the transport 108 to transport the person 124 from the alternate destination 130 to the intended destination 128. Further, processor 114 may then inform the processor 118 of the transport 108, via the wireless network 104, that the person 124 will be using the transport 108 to travel from the alternate destination 130 to the intended destination 128. In some embodiments, if the Alternate 1 transport of the display image 100B is chosen, then the processor 116 of the transport 106 may directly inform the processor 118 of the transport 108 by way of the wireless network 104 that the transport 106 will be transporting the person 124 to the alternate destination 130 wherein the person will then use the transport 108 to transport the person 124 from the alternate destination 130 to the intended destination 128.

Further, if the Alternate 3 transport of the display image 100B is chosen, then the processor 116 of the transport 106 may inform the processor 114 by way of the wireless network 104 that the transport 106 will be transporting the person 124 to the alternate destination 130 wherein the person will then use the transport 112 to transport the person 124 from the alternate destination 130 to the intended destination 128. Further, processor 114 may then inform the processor 122 of the transport 112, via the wireless network 104, that the person 124 will be using the transport 112 to travel from the alternate destination 130 to the intended destination 128. In some embodiments, if the Alternate 1 transport of the display image 100B is chosen, then the processor 116 of the transport 106 may directly inform the processor 122 of the transport 112 by way of the wireless network 104 that the transport 106 will be transporting the person 124 to the alternate destination 130 wherein the person will then use the transport 112 to transport the person 124 from the alternate destination 130 to the intended destination 128.

In the embodiments discussed above with reference to FIG. 1B, the person 124 is provided with three alternate transports. However, in some embodiments, the processor 114 may instruct the processor 116 of the transport 106 to only provide the person 124 with one optional alternate transport. For example, the processor 114 may determine the functional relationship values of multiple alternate transports at alternate destination 130 and only provide the option associated a transport having the greatest functional relationship value, ƒ_max.

In the embodiments discussed above with reference to FIG. 1B, the person 124 is provided with three alternate transports at a single alternate destination. However, in some embodiments, the processor 114 may instruct the processor 116 of the transport 106 to provide the person 124 with multiple options at multiple alternate destinations. This will be described in greater detail with reference to FIG. 1C.

FIG. 1C shows another example system 100C for intermediate destination and supplemental transportation for optimized transport according to example embodiments. Referring to FIG. 1C, the person 124 is intending to travel from the starting place 126 to the intended destination 128. Initially, the person 124 intends on using the transport 106 to travel from the starting place 126 to the intended destination 128. However, the management server 102, having the processor 114 therein, is in communication with the processor 116 of the transport 106 via the wireless network 104. Further, the processor 114 of the management server 102 is additionally in communication with: the processor 122 of the transport 112 via the wireless network 104 and a processor 146 of a transport 144 via the wireless network 104. The processor 114 of the management server 102 establishes the intended destination 128 of the transport 106 in a manner similar to that discussed above with reference to FIG. 1A.

Each of the processor 116 of the transport 106, the processor 122 of the transport 112 and the processor 146 of the transport 144 is able to provide their respective parameters to the processor 114 of the management server 102 via the wireless network 104 in a manner similar to that as discussed above, for example with reference to FIG. 1A.

The processor 114 determines the functional relationship value for each alternate mode of transportation. For example, the processor 114 determines the functional relationship value, ƒ₄, for the option of the person 124 using the transport 106 to travel from the starting place 126 to the alternate destination 130 and then using the transport 112 from the alternate destination 130 to the intended destination 128 via alternate navigational path 140. The processor 114 determines ƒ₄ for the option of the person 124 using the transport 112 by using the parameters and weighting values as discussed above for the transport 106 for the duration of the travel from the starting place 126 to the alternate destination 130 and using the parameters and weighting values for the transport 112 for the duration of the travel from the alternate destination 130 to the intended destination 128. In some embodiments, if ƒ₄<ƒ_ih, then the person 124 would not be notified of the alternate form of transport that includes the transport 112 at the alternate destination 130. Accordingly, processor 114 determines whether transport 112 is a supplemental transport that is operable to navigate from the alternate destination 130 to the intended destination 128.

Further, the processor 114 determines the functional relationship value, f5, for the option of the person 124 using the transport 144 by using the parameters and weighting values as discussed above for the transport 106 for the duration of the travel from the starting place 126 to the alternate destination 142 and using the parameters and weighting values for the transport 144 for the duration of the travel from the alternate destination 142 to the intended destination 128. In some embodiments, if ƒ₅<ƒ_ih, then the person 124 would not be notified of the alternate form of transport that includes the transport 146 at the alternate destination 142. Accordingly, processor 114 determines whether transport 146 is a supplemental transport that is operable to navigate from the alternate destination 142 to the intended destination 128.

The instant solution may additionally include determining, by the server 102, the alternate navigational paths 134 and 148 to the alternate destination 130. If either of the determined functional relationship values is less than the threshold, fth, then the processor 114 may determine that the respective alternate destinations do not qualify as an alternate destination option to suggest to the person 124. However, if either of the determined functional relationship values is equal to or great than the threshold, fth, then the processor 114 may determine that the respective alternate destinations qualify as an alternate destination option to suggest to the person 124.

After the alternate transport options are determined, the processor 114 of the management server 102 may transmit an instruction, via the wireless network 104, to the processor 116 of the transport 106 to provide the person 124 with an indication of the alternate transport options that include two alternate destinations.

In the embodiments discussed above with reference to FIG. 1B, the person 124 is provided with three alternate transports at a single alternate destination. However, in some embodiments, the processor 114 may instruct the processor 116 of the transport 106 to provide the person 124 with multiple options at multiple alternate destinations so as to combine the multiple options for one alternate destination as discussed above with reference to FIG. 1A with the multiple options for alternate destinations as discussed above with reference to FIG. 1C. Further, in some embodiments, the processor 114 may instruct the processor 116 of the transport 106 to provide the person 124 with a single most optimized option of all options.

FIG. 2A illustrates a transport network diagram 200, according to example embodiments. The network comprises elements including a transport node 202 including a processor 204, as well as a transport node 202′ including a processor 204′. The transport nodes 202, 202′ communicate with one another via the processors 204, 204′, as well as other elements (not shown) including transceivers, transmitters, receivers, storage, sensors and other elements capable of providing communication. The communication between the transport nodes 202, 202′ can occur directly, via a private and/or a public network (not shown) or via other transport nodes and elements comprising one or more of a processor, memory, and software. Although depicted as single transport nodes and processors, a plurality of transport nodes and processors may be present. One or more of the applications, features, steps, solutions, etc., described and/or depicted herein may be utilized and/or provided by the instant elements.

FIG. 2B illustrates another transport network diagram 210, according to example embodiments. The network comprises elements including a transport node 202 including a processor 204, as well as a transport node 202′ including a processor 204′. The transport nodes 202, 202′ communicate with one another via the processors 204, 204′, as well as other elements (not shown) including transceivers, transmitters, receivers, storage, sensors and other elements capable of providing communication. The communication between the transport nodes 202, 202′ can occur directly, via a private and/or a public network (not shown) or via other transport nodes and elements comprising one or more of a processor, memory, and software. The processors 204, 204′ can further communicate with one or more elements 230 including sensor 212, wired device 214, wireless device 216, database 218, mobile phone 220, transport node 222, computer 224, I/O device 226 and voice application 228. The processors 204, 204′ can further communicate with elements comprising one or more of a processor, memory, and software.

Although depicted as single transport nodes, processors and elements, a plurality of transport nodes, processors and elements may be present. Information or communication can occur to and/or from any of the processors 204, 204′ and elements 230. For example, the mobile phone 220 may provide information to the processor 204, which may initiate the transport node 202 to take an action, may further provide the information or additional information to the processor 204′, which may initiate the transport node 202′ to take an action, may further provide the information or additional information to the mobile phone 220, the transport node 222, and/or the computer 224. One or more of the applications, features, steps, solutions, etc., described and/or depicted herein may be utilized and/or provided by the instant elements.

FIG. 2C illustrates yet another transport network diagram 240, according to example embodiments. The network comprises elements including a transport node 202 including a processor 204 and a non-transitory computer readable medium 242C. The processor 204 is communicably coupled to the computer readable medium 242C and elements 230 (which were depicted in FIG. 2B).

The processor 204 performs one or more of establishing an intended destination of an autonomous transport 244C, and determining an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold 246C, providing an instruction, to a user interface of the transport, to provide an interactive choice of the intended destination of the autonomous transport and the alternate destination of the autonomous transport 248C, and determining a supplemental transport operable to navigate from the alternate destination to the intended destination 250C, wherein the intended destination and the alternate destination are not the same.

In some embodiments, the parameter is selected from a group of parameters consisting of traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters and combinations thereof. Further, in some embodiments, the parameter comprises a combination of at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters, wherein each of the at least one of the combination of traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters has a respective weighting value associated therewith, and wherein the determining comprises comparing the functional relationship value, of each of the at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters with the respective weighting value associated therewith, with the threshold. Further, in some embodiments, the functional relationship value is based on a functional relationship between the provided measurement of the parameter and a second provided measurement of a second parameter.

FIG. 2D illustrates a further transport network diagram 260, according to example embodiments. The network comprises elements including a transport node 202 including a processor 204 and a non-transitory computer readable medium 242D. The processor 204 is communicably coupled to the computer readable medium 242D and elements 230 (which were depicted in FIG. 2B).

The processor 204 performs one or more of establishing an intended destination of an autonomous transport 244D, and determining an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold 246D, providing an instruction, to a user interface of the transport, to provide an interactive choice of the intended destination of the autonomous transport and the alternate destination of the autonomous transport 248D, determining a second alternate destination based on a second parameter 250D, providing, by the processor and to a user interface of the autonomous transport, an instruction to provide an interactive choice of the intended destination of the autonomous transport, the alternate destination and the second alternate destination of the autonomous transport 252D, wherein the intended destination and the alternate destination are not the same and wherein the intended destination, the alternate destination and the second alternate destination are not the same.

FIG. 2E illustrates a yet further transport network diagram 270, according to example embodiments. The network comprises elements including a transport node 202 including a processor 204 and a non-transitory computer readable medium 242E. The processor 204 is communicably coupled to the computer readable medium 242E and elements 230 (which were depicted in FIG. 2B).

The processor 204 performs one or more of establishing an intended destination of an autonomous transport 244E, and determining an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold 246E, providing an instruction, to a user interface of the transport, to provide an interactive choice of the intended destination of the autonomous transport and the alternate destination of the autonomous transport 248E, determining a supplemental transport operable to navigate from the alternate destination to the intended destination 250E, determining a second supplemental transport operable to navigate from the alternate destination to the intended destination 252E, and providing, to a user interface of the autonomous transport, an instruction to provide an interactive choice of the first supplemental transport and the second supplemental transport 254E.

The processors and/or computer readable media may fully or partially reside in the interior or exterior of the transport nodes. The steps or features stored in the computer readable media may be fully or partially performed by any of the processors and/or elements in any order. Additionally, one or more steps or features may be added, omitted, combined, performed at a later time, etc.

FIG. 3A illustrates a flow diagram 300, according to example embodiments. Referring to FIG. 3A, an example method may be executed by the processor 114 of the management server 102 (see FIG. 1A). It should be understood that method 300 depicted in FIG. 3A may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 300. The description of the method 300 is also made with reference to the features depicted in FIGS. 1B-1C for purposes of illustration. Particularly, the processor 114 of the management server 102 may execute some or all of the operations included in the method 300.

With reference to FIG. 3A, at block 302, the processor 114 may establish an intended destination of an autonomous transport, for example as shown with the intended destination 128 of FIG. 1A. This determination may be performed by receiving a message from the transport 106 via the wireless network 104, wherein the message indicates that the transport 106 intended to travel to intended destination 128. At block 304, the processor 114 may determine an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold. This may be performed by the processor 114 receiving parameters from the transport 106 via the wireless network 104 and determining the functional relationship value in a manner discussed above. At block 306, the processor 114 may provide an instruction to a user interface of the transport 106 to provide an interactive choice of the intended destination of the autonomous transport and the alternate destination of the autonomous transport. This may be performed by transmitting a message to the transport 106 via the wireless network 104. Further, a user interface of the transport may provide the interactive choice as discussed above with reference to FIG. 1B. At block 308, the processor 114 may determine a supplemental transport operable to navigate from the alternate destination 130 to the intended destination 128, for example the transport 110 in FIG. 1A.

FIG. 3B illustrates a flow diagram 320, according to example embodiments. Referring to FIG. 3B, an example method may be executed by the processor 114 of the management server 102 (see FIG. 1A). It should be understood that method 320 depicted in FIG. 3B may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 320. The description of the method 320 is also made with reference to the features depicted in FIGS. 1B-1C for purposes of illustration. Particularly, the processor 114 of the management server 102 may execute some or all of the operations included in the method 320.

With reference to FIG. 3B, at block 322, the processor 114 may establish an intended destination of an autonomous transport, for example as shown with the intended destination 128 of FIG. 1A. This determination may be performed by receiving a message from the transport 106 via the wireless network 104, wherein the message indicates that the transport 106 intended to travel to intended destination 128. At block 324, the processor 114 may determine an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold. This may be performed by the processor 114 receiving parameters from the transport 106 via the wireless network 104 and determining the functional relationship value in a manner discussed above. At block 326, the processor 114 may provide an instruction to a user interface of the transport 106 to provide an interactive choice of the intended destination of the autonomous transport and the alternate destination of the autonomous transport. This may be performed by transmitting a message to the transport 106 via the wireless network 104. Further, a user interface of the transport may provide the interactive choice as discussed above with reference to FIG. 1B. At block 328, the processor 114 may determine a second alternate destination based on a second parameter, for example the alternate destination 142 in FIG. 1C. At block 330, the processor 114 may provide an instruction to a user interface of the transport 106 to provide an interactive choice of the second alternate destination, such that the interactive choice would then include intended destination of the autonomous transport, the alternate destination of the autonomous transport and the second alternate destination of the autonomous transport. This may be performed by transmitting a message to the transport 106 via the wireless network 104. Further, a user interface of the transport may provide the interactive choice as discussed above with reference to FIG. 1B, so as to include options with the second alternate destination 142.

FIG. 3C illustrates a flow diagram 340, according to example embodiments. Referring to FIG. 3C, an example method may be executed by the processor 114 of the management server 102 (see FIG. 1A). It should be understood that method 340 depicted in FIG. 3C may include additional operations and that some of the operations described therein may be removed and/or modified without departing from the scope of the method 340. The description of the method 340 is also made with reference to the features depicted in FIGS. 1B-1C for purposes of illustration. Particularly, the processor 114 of the management server 102 may execute some or all of the operations included in the method 340.

With reference to FIG. 3C, at block 342, the processor 114 may establish an intended destination of an autonomous transport, for example as shown with the intended destination 128 of FIG. 1A. This determination may be performed by receiving a message from the transport 106 via the wireless network 104, wherein the message indicates that the transport 106 intended to travel to intended destination 128. At block 344, the processor 114 may determine an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold. This may be performed by the processor 114 receiving parameters from the transport 106 via the wireless network 104 and determining the functional relationship value in a manner discussed above. At block 346, the processor 114 may provide an instruction to a user interface of the transport 106 to provide an interactive choice of the intended destination of the autonomous transport and the alternate destination of the autonomous transport. This may be performed by transmitting a message to the transport 106 via the wireless network 104. Further, a user interface of the transport may provide the interactive choice as discussed above with reference to FIG. 1B. At block 348, the processor 114 may determine a supplemental transport operable to navigate from the alternate destination to the intended destination, for example the alternate transport 112 in FIG. 1A. This determination may be performed by comparing functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, of the alternate transport 112 with a threshold as discussed above. At block 350, the processor 114 may determine a second supplemental transport operable to navigate from the alternate destination to the intended destination, for example the alternate transport 108 in FIG. 1A. This determination may be performed by comparing functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, of the alternate transport 108 with a threshold as discussed above. At block 352, the processor 114 may provide an instruction to a user interface of the transport 106 to provide an interactive choice of the second supplemental transport, such that the interactive choice would then include the first supplemental transport and the second supplemental transport. This may be performed by transmitting a message to the transport 106 via the wireless network 104. Further, a user interface of the transport may provide the interactive choice as discussed above with reference to FIG. 1B, but so as to include options for the alternate transport 112 and the alternate transport 108.

FIG. 4 illustrates a machine learning transport network diagram 400, according to example embodiments. The network 400 includes a transport node 402 that interfaces with a machine learning subsystem 406. The transport node includes one or more sensors 404.

The machine learning subsystem 406 contains a learning model 408, which is a mathematical artifact created by a machine learning training system 410 that generates predictions by finding patterns in one or more training data sets. In some embodiments, the machine learning subsystem 406 resides in the transport node 402. In other embodiments, the machine learning subsystem 406 resides outside of the transport node 402.

The transport node 402 sends data from the one or more sensors 404 to the machine learning subsystem 406. The machine learning subsystem 406 provides the one or more sensor 404 data to the learning model 408, which returns one or more predictions. The machine learning subsystem 406 sends one or more instructions to the transport node 402 based on the predictions from the learning model 408.

In a further embodiment, the transport node 402 may send the one or more sensor 404 data to the machine learning training system 410. In yet another embodiment, the machine learning subsystem 406 may sent the sensor 404 data to the machine learning subsystem 410. One or more of the applications, features, steps, solutions, etc., described and/or depicted herein may utilize the machine learning network 400 as described herein.

FIG. 5A illustrates an example vehicle configuration 500 for managing database transactions associated with a vehicle, according to example embodiments. Referring to FIG. 5A, as a particular transport/vehicle 525 is engaged in transactions (e.g., vehicle service, dealer transactions, delivery/pickup, transportation services, etc.), the vehicle may receive assets 510 and/or expel/transfer assets 512 according to a transaction(s). A transport processor 526 resides in the vehicle 525 and communication exists between the transport processor 526, a database 530, a transport processor 526 and the transaction module 520. The transaction module 520 may record information, such as assets, parties, credits, service descriptions, date, time, location, results, notifications, unexpected events, etc. Those transactions in the transaction module 520 may be replicated into a database 530. The database 530 can be one of a SQL database, an RDBMS, a relational database, a non-relational database, a blockchain, a distributed ledger, and may be on board the transport, may be off board the transport, may be accessible directly and/or through a network, or be accessible to the transport.

FIG. 5B illustrates an example vehicle configuration 550 for managing database transactions conducted among various vehicles, according to example embodiments. The vehicle 525 may engage with another vehicle 508 to perform various actions such as to share, transfer, acquire service calls, etc., when the vehicle has reached a status where the services need to be shared with another vehicle. For example, the vehicle 508 may be due for a battery charge and/or may have an issue with a tire and may be in route to pick up a package for delivery. A transport processor 528 resides in the vehicle 508 and communication exists between the transport processor 528, a database 554, a transport processor 528 and the transaction module 552. The vehicle 508 may notify another vehicle 525, which is in its network and which operates on its blockchain member service. A transport processor 526 resides in the vehicle 525 and communication exists between the transport processor 526, a database 530, the transport processor 526 and a transaction module 520. The vehicle 525 may then receive the information via a wireless communication request to perform the package pickup from the vehicle 508 and/or from a server (not shown). The transactions are logged in the transaction modules 552 and 520 of both vehicles. The credits are transferred from vehicle 508 to vehicle 525 and the record of the transferred service is logged in the database 530/554 assuming that the blockchains are different from one another, or, are logged in the same blockchain used by all members. The database 554 can be one of a SQL database, an RDBMS, a relational database, a non-relational database, a blockchain, a distributed ledger, and may be on board the transport, may be off board the transport, may be accessible directly and/or through a network.

FIG. 6A illustrates a blockchain architecture configuration 600, according to example embodiments. Referring to FIG. 6A, the blockchain architecture 600 may include certain blockchain elements, for example, a group of blockchain member nodes 602-606 as part of a blockchain group 610. In one example embodiment, a permissioned blockchain is not accessible to all parties but only to those members with permissioned access to the blockchain data. The blockchain nodes participate in a number of activities, such as blockchain entry addition and validation process (consensus). One or more of the blockchain nodes may endorse entries based on an endorsement policy and may provide an ordering service for all blockchain nodes. A blockchain node may initiate a blockchain action (such as an authentication) and seek to write to a blockchain immutable ledger stored in the blockchain, a copy of which may also be stored on the underpinning physical infrastructure.

The blockchain transactions 620 are stored in memory of computers as the transactions are received and approved by the consensus model dictated by the members' nodes. Approved transactions 626 are stored in current blocks of the blockchain and committed to the blockchain via a committal procedure, which includes performing a hash of the data contents of the transactions in a current block and referencing a previous hash of a previous block. Within the blockchain, one or more smart contracts 630 may exist that define the terms of transaction agreements and actions included in smart contract executable application code 632, such as registered recipients, vehicle features, requirements, permissions, sensor thresholds, etc. The code may be configured to identify whether requesting entities are registered to receive vehicle services, what service features they are entitled/required to receive given their profile statuses and whether to monitor their actions in subsequent events. For example, when a service event occurs and a user is riding in the vehicle, the sensor data monitoring may be triggered, and a certain parameter, such as a vehicle charge level, may be identified as being above/below a particular threshold for a particular period of time, then the result may be a change to a current status, which requires an alert to be sent to the managing party (i.e., vehicle owner, vehicle operator, server, etc.) so the service can be identified and stored for reference. The vehicle sensor data collected may be based on types of sensor data used to collect information about vehicle's status. The sensor data may also be the basis for the vehicle event data 634, such as a location(s) to be traveled, an average speed, a top speed, acceleration rates, whether there were any collisions, was the expected route taken, what is the next destination, whether safety measures are in place, whether the vehicle has enough charge/fuel, etc. All such information may be the basis of smart contract terms 630, which are then stored in a blockchain. For example, sensor thresholds stored in the smart contract can be used as the basis for whether a detected service is necessary and when and where the service should be performed.

FIG. 6B illustrates a shared ledger configuration, according to example embodiments. Referring to FIG. 6B, the blockchain logic example 640 includes a blockchain application interface 642 as an API or plug-in application that links to the computing device and execution platform for a particular transaction. The blockchain configuration 640 may include one or more applications, which are linked to application programming interfaces (APIs) to access and execute stored program/application code (e.g., smart contract executable code, smart contracts, etc.) which can be created according to a customized configuration sought by participants and can maintain their own state, control their own assets, and receive external information. This can be deployed as an entry and installed, via appending to the distributed ledger, on all blockchain nodes.

The smart contract application code 644 provides a basis for the blockchain transactions by establishing application code, which when executed causes the transaction terms and conditions to become active. The smart contract 630, when executed, causes certain approved transactions 626 to be generated, which are then forwarded to the blockchain platform 652. The platform includes a security/authorization 658, computing devices, which execute the transaction management 656 and a storage portion 654 as a memory that stores transactions and smart contracts in the blockchain.

The blockchain platform may include various layers of blockchain data, services (e.g., cryptographic trust services, virtual execution environment, etc.), and underpinning physical computer infrastructure that may be used to receive and store new entries and provide access to auditors, which are seeking to access data entries. The blockchain may expose an interface that provides access to the virtual execution environment necessary to process the program code and engage the physical infrastructure. Cryptographic trust services may be used to verify entries such as asset exchange entries and keep information private.

The blockchain architecture configuration of FIGS. 6A and 6B may process and execute program/application code via one or more interfaces exposed, and services provided, by the blockchain platform. As a non-limiting example, smart contracts may be created to execute reminders, updates, and/or other notifications subject to the changes, updates, etc. The smart contracts can themselves be used to identify rules associated with authorization and access requirements and usage of the ledger. For example, the information may include a new entry, which may be processed by one or more processing entities (e.g., processors, virtual machines, etc.) included in the blockchain layer. The result may include a decision to reject or approve the new entry based on the criteria defined in the smart contract and/or a consensus of the peers. The physical infrastructure may be utilized to retrieve any of the data or information described herein.

Within smart contract executable code, a smart contract may be created via a high-level application and programming language, and then written to a block in the blockchain. The smart contract may include executable code, which is registered, stored, and/or replicated with a blockchain (e.g., distributed network of blockchain peers). An entry is an execution of the smart contract code, which can be performed in response to conditions associated with the smart contract being satisfied. The executing of the smart contract may trigger a trusted modification(s) to a state of a digital blockchain ledger. The modification(s) to the blockchain ledger caused by the smart contract execution may be automatically replicated throughout the distributed network of blockchain peers through one or more consensus protocols.

The smart contract may write data to the blockchain in the format of key-value pairs. Furthermore, the smart contract code can read the values stored in a blockchain and use them in application operations. The smart contract code can write the output of various logic operations into the blockchain. The code may be used to create a temporary data structure in a virtual machine or other computing platform. Data written to the blockchain can be public and/or can be encrypted and maintained as private. The temporary data that is used/generated by the smart contract is held in memory by the supplied execution environment, then deleted once the data needed for the blockchain is identified.

A smart contract executable code may include the code interpretation of a smart contract, with additional features. As described herein, the smart contract executable code may be program code deployed on a computing network, where it is executed and validated by chain validators together during a consensus process. The smart contract executable code receives a hash and retrieves from the blockchain a hash associated with the data template created by use of a previously stored feature extractor. If the hashes of the hash identifier and the hash created from the stored identifier template data match, then the smart contract executable code sends an authorization key to the requested service. The smart contract executable code may write to the blockchain data associated with the cryptographic details.

FIG. 6C illustrates a blockchain configuration for storing blockchain transaction data, according to example embodiments. Referring to FIG. 6C, the example configuration 660 provides for the vehicle 662, the user device 664 and a server 666 sharing information with a distributed ledger (i.e., blockchain) 668. The server may represent a service provider entity inquiring with a vehicle service provider to share user profile rating information in the event that a known and established user profile is attempting to rent a vehicle with an established rated profile. The server 666 may be receiving and processing data related to a vehicle's service requirements. As the service events occur, such as the vehicle sensor data indicates a need for fuel/charge, a maintenance service, etc., a smart contract may be used to invoke rules, thresholds, sensor information gathering, etc., which may be used to invoke the vehicle service event. The blockchain transaction data 670 is saved for each transaction, such as the access event, the subsequent updates to a vehicle's service status, event updates, etc. The transactions may include the parties, the requirements (e.g., 18 years of age, service eligible candidate, valid driver's license, etc.), compensation levels, the distance traveled during the event, the registered recipients permitted to access the event and host a vehicle service, rights/permissions, sensor data retrieved during the vehicle event operation to log details of the next service event and identify a vehicle's condition status, and thresholds used to make determinations about whether the service event was completed and whether the vehicle's condition status has changed.

FIG. 6D illustrates blockchain blocks 680 that can be added to a distributed ledger, according to example embodiments, and contents of block structures 682A to 682n. Referring to FIG. 6D, clients (not shown) may submit entries to blockchain nodes to enact activity on the blockchain. As an example, clients may be applications that act on behalf of a requester, such as a device, person or entity to propose entries for the blockchain. The plurality of blockchain peers (e.g., blockchain nodes) may maintain a state of the blockchain network and a copy of the distributed ledger. Different types of blockchain nodes/peers may be present in the blockchain network including endorsing peers, which simulate and endorse entries proposed by clients and committing peers, which verify endorsements, validate entries, and commit entries to the distributed ledger. In this example, the blockchain nodes may perform the role of endorser node, committer node, or both.

The instant system includes a blockchain, which stores immutable, sequenced records in blocks, and a state database (current world state) maintaining a current state of the blockchain. One distributed ledger may exist per channel and each peer maintains its own copy of the distributed ledger for each channel of which they are a member. The instant blockchain is an entry log, structured as hash-linked blocks where each block contains a sequence of N entries. Blocks may include various components such as those shown in FIG. 6D. The linking of the blocks may be generated by adding a hash of a prior block's header within a block header of a current block. In this way, all entries on the blockchain are sequenced and cryptographically linked together preventing tampering with blockchain data without breaking the hash links. Furthermore, because of the links, the latest block in the blockchain represents every entry that has come before it. The instant blockchain may be stored on a peer file system (local or attached storage), which supports an append-only blockchain workload.

The current state of the blockchain and the distributed ledger may be stored in the state database. Here, the current state data represents the latest values for all keys ever included in the chain entry log of the blockchain. Smart contract executable code invocations execute entries against the current state in the state database. To make these smart contract executable code interactions extremely efficient, the latest values of all keys are stored in the state database. The state database may include an indexed view into the entry log of the blockchain, it can therefore be regenerated from the chain at any time. The state database may automatically get recovered (or generated if needed) upon peer startup, before entries are accepted.

Endorsing nodes receive entries from clients and endorse the entry based on simulated results. Endorsing nodes hold smart contracts, which simulate the entry proposals. When an endorsing node endorses an entry, the endorsing nodes creates an entry endorsement, which is a signed response from the endorsing node to the client application indicating the endorsement of the simulated entry. The method of endorsing an entry depends on an endorsement policy, which may be specified within smart contract executable code. An example of an endorsement policy is “the majority of endorsing peers must endorse the entry.” Different channels may have different endorsement policies. Endorsed entries are forward by the client application to an ordering service.

The ordering service accepts endorsed entries, orders them into a block, and delivers the blocks to the committing peers. For example, the ordering service may initiate a new block when a threshold of entries has been reached, a timer times out, or another condition. In this example, blockchain node is a committing peer that has received a data block 682A for storage on the blockchain. The ordering service may be made up of a cluster of orderers. The ordering service does not process entries, smart contracts, or maintain the shared ledger. Rather, the ordering service may accept the endorsed entries and specifies the order in which those entries are committed to the distributed ledger. The architecture of the blockchain network may be designed such that the specific implementation of ‘ordering’ (e.g., Solo, Kafka, BFT, etc.) becomes a pluggable component.

Entries are written to the distributed ledger in a consistent order. The order of entries is established to ensure that the updates to the state database are valid when they are committed to the network. Unlike a cryptocurrency blockchain system (e.g., Bitcoin, etc.) where ordering occurs through the solving of a cryptographic puzzle, or mining, in this example the parties of the distributed ledger may choose the ordering mechanism that best suits that network.

Referring to FIG. 6D, a block 682A (also referred to as a data block) that is stored on the blockchain and/or the distributed ledger may include multiple data segments such as a block header 684A to 684n, transaction specific data 686A to 686n, and block metadata 688A to 688n. It should be appreciated that the various depicted blocks and their contents, such as block 682A and its contents are merely for purposes of an example and are not meant to limit the scope of the example embodiments. In some cases, both the block header 684A and the block metadata 688A may be smaller than the transaction specific data 686A, which stores entry data, however, this is not a requirement. The block 682A may store transactional information of N entries (e.g., 100, 500, 1000, 2000, 3000, etc.) within the block data 690A to 690n. The block 682A may also include a link to a previous block (e.g., on the blockchain) within the block header 684A. In particular, the block header 684A may include a hash of a previous block's header. The block header 684A may also include a unique block number, a hash of the block data 690A of the current block 682A, and the like. The block number of the block 682A may be unique and assigned in an incremental/sequential order starting from zero. The first block in the blockchain may be referred to as a genesis block, which includes information about the blockchain, its members, the data stored therein, etc.

The block data 690A may store entry information of each entry that is recorded within the block. For example, the entry data may include one or more of a type of the entry, a version, a timestamp, a channel ID of the distributed ledger, an entry ID, an epoch, a payload visibility, a smart contract executable code path (deploy tx), a smart contract executable code name, a smart contract executable code version, input (smart contract executable code and functions), a client (creator) identify such as a public key and certificate, a signature of the client, identities of endorsers, endorser signatures, a proposal hash, smart contract executable code events, response status, namespace, a read set (list of key and version read by the entry, etc.), a write set (list of key and value, etc.), a start key, an end key, a list of keys, a Merkel tree query summary, and the like. The entry data may be stored for each of the N entries.

In some embodiments, the block data 690A may also store transaction specific data 686A, which adds additional information to the hash-linked chain of blocks in the blockchain. Accordingly, the data 686A can be stored in an immutable log of blocks on the distributed ledger. Some of the benefits of storing such data 686A are reflected in the various embodiments disclosed and depicted herein. The block metadata 688A may store multiple fields of metadata (e.g., as a byte array, etc.). Metadata fields may include signature on block creation, a reference to a last configuration block, an entry filter identifying valid and invalid entries within the block, last offset persisted of an ordering service that ordered the block, and the like. The signature, the last configuration block, and the orderer metadata may be added by the ordering service. Meanwhile, a committer of the block (such as a blockchain node) may add validity/invalidity information based on an endorsement policy, verification of read/write sets, and the like. The entry filter may include a byte array of a size equal to the number of entries in the block data 690A and a validation code identifying whether an entry was valid/invalid.

The other blocks 682B to 682n in the blockchain also have headers, files, and values. However, unlike the first block 682A, each of the headers 684A to 684n in the other blocks includes the hash value of an immediately preceding block. The hash value of the immediately preceding block may be just the hash of the header of the previous block or may be the hash value of the entire previous block. By including the hash value of a preceding block in each of the remaining blocks, a trace can be performed from the Nth block back to the genesis block (and the associated original file) on a block-by-block basis, as indicated by arrows 692, to establish an auditable and immutable chain-of-custody.

The above embodiments may be implemented in hardware, in a computer program executed by a processor, in firmware, or in a combination of the above. A computer program may be embodied on a computer readable medium, such as a storage medium. For example, a computer program may reside in random access memory (“RAM”), flash memory, read-only memory (“ROM”), erasable programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”), registers, hard disk, a removable disk, a compact disk read-only memory (“CD-ROM”), or any other form of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (“ASIC”). In the alternative, the processor and the storage medium may reside as discrete components. For example, FIG. 7 illustrates an example computer system architecture 700, which may represent or be integrated in any of the above-described components, etc.

FIG. 7 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the application described herein. Regardless, the computing node 700 is capable of being implemented and/or performing any of the functionality set forth hereinabove.

In computing node 700 there is a computer system/server 702, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 702 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.

Computer system/server 702 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 702 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

As shown in FIG. 7, computer system/server 702 in cloud computing node 700 is shown in the form of a general-purpose computing device. The components of computer system/server 702 may include, but are not limited to, one or more processors or processing units 704, a system memory 706, and a bus that couples various system components including system memory 706 to processor 704.

The bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 702 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 702, and it includes both volatile and non-volatile media, removable and non-removable media. System memory 706, in one embodiment, implements the flow diagrams of the other figures. The system memory 706 can include computer system readable media in the form of volatile memory, such as random-access memory (RAM) 708 and/or cache memory 710. Computer system/server 702 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, memory 706 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to the bus by one or more data media interfaces. As will be further depicted and described below, memory 706 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments of the application.

Program/utility, having a set (at least one) of program modules, may be stored in memory 706 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules generally carry out the functions and/or methodologies of various embodiments of the application as described herein.

As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method, or computer program product. Accordingly, aspects of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present application may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

Computer system/server 702 may also communicate with one or more external devices via an I/O device 712 (such as an I/O adapter), which may include a keyboard, a pointing device, a display, a voice recognition module, etc., one or more devices that enable a user to interact with computer system/server 702, and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 702 to communicate with one or more other computing devices. Such communication can occur via I/0 interfaces of the device 712. Still yet, computer system/server 702 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via a network adapter. As depicted, device 712 communicates with the other components of computer system/server 702 via a bus. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 702. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Although an exemplary embodiment of at least one of a system, method, and non-transitory computer readable medium has been illustrated in the accompanied drawings and described in the foregoing detailed description, it will be understood that the application is not limited to the embodiments disclosed, but is capable of numerous rearrangements, modifications, and substitutions as set forth and defined by the following claims. For example, the capabilities of the system of the various figures can be performed by one or more of the modules or components described herein or in a distributed architecture and may include a transmitter, receiver or pair of both. For example, all or part of the functionality performed by the individual modules, may be performed by one or more of these modules. Further, the functionality described herein may be performed at various times and in relation to various events, internal or external to the modules or components. Also, the information sent between various modules can be sent between the modules via at least one of: a data network, the Internet, a voice network, an Internet Protocol network, a wireless device, a wired device and/or via plurality of protocols. Also, the messages sent or received by any of the modules may be sent or received directly and/or via one or more of the other modules.

One skilled in the art will appreciate that a “system” could be embodied as a personal computer, a server, a console, a personal digital assistant (PDA), a cell phone, a tablet computing device, a smartphone or any other suitable computing device, or combination of devices. Presenting the above-described functions as being performed by a “system” is not intended to limit the scope of the present application in any way but is intended to provide one example of many embodiments. Indeed, methods, systems and apparatuses disclosed herein may be implemented in localized and distributed forms consistent with computing technology.

It should be noted that some of the system features described in this specification have been presented as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom very large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or the like.

A module may also be at least partially implemented in software for execution by various types of processors. An identified unit of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module. Further, modules may be stored on a computer-readable medium, which may be, for instance, a hard disk drive, flash device, random access memory (RAM), tape, or any other such medium used to store data.

Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.

It will be readily understood that the components of the application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments is not intended to limit the scope of the application as claimed but is merely representative of selected embodiments of the application.

One having ordinary skill in the art will readily understand that the above may be practiced with steps in a different order, and/or with hardware elements in configurations that are different than those which are disclosed. Therefore, although the application has been described based upon these preferred embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent.

While preferred embodiments of the present application have been described, it is to be understood that the embodiments described are illustrative only and the scope of the application is to be defined solely by the appended claims when considered with a full range of equivalents and modifications (e.g., protocols, hardware devices, software platforms etc.) thereto.

Claims

1. A method, comprising: establishing, by a processor, a starting place of an autonomous transport and an intended destination of the autonomous transport; anddetermining, by the processor, an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold,wherein the intended destination and the alternate destination are not the same.

2. The method of claim 1, wherein the parameter is selected from a group of parameters consisting of traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters and combinations thereof.

3. The method of claim 2, further comprising: wherein the parameter comprises a combination of at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters,wherein each of the at least one of the combination of traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters has a respective weighting value associated therewith, andwherein the determining comprises comparing the functional relationship value, of each of the at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters with the respective weighting value associated therewith, with the threshold.

4. The method of claim 1, further comprising determining, by the processor, a supplemental transport operable to navigate from the alternate destination to the intended destination.

5. The method of claim 1, further comprising: determining, by the processor, a second alternate destination based on a second parameter; andproviding, by the processor and to a user interface of the autonomous transport, an instruction to provide an interactive choice of the intended destination of the autonomous transport, the alternate destination and the second alternate destination of the autonomous transport,wherein the intended destination, the alternate destination and the second alternate destination are not the same.

6. The method of claim 1, wherein the functional relationship value is based on a functional relationship between the provided measurement of the parameter and a second provided measurement of a second parameter.

7. The method of claim 1, further comprising: determining, by the processor, a supplemental transport operable to navigate from the alternate destination to the intended destination;determining, by the processor, a second supplemental transport operable to navigate from the alternate destination to the intended destination; andproviding, by the processor and to a user interface of the autonomous transport, an instruction to provide an interactive choice of the first supplemental transport and the second supplemental transport.

8. A server, comprising: a processor; anda memory;wherein the processor is configured to:establish a starting place of an autonomous transport and an intended destination of the autonomous transport; anddetermine an alternate destination based on a parameter by comparison of a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold,wherein the intended destination and the alternate destination are not the same.

9. The server of claim 8, wherein the parameter is selected from a group of parameters that consists of traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters and combinations thereof.

10. The server of claim 9, further comprising: wherein the parameter comprises a combination of at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters,wherein each of the at least one of the combination of traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters has a respective weighting value associated therewith, andwherein the processor is further configured to determine the alternate destination by comparison of the functional relationship value, of each of the at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters with the respective weighting value associated therewith, with the threshold.

11. The server of claim 8, wherein the processor is further configured to determine a supplemental transport operable to navigate from the alternate destination to the intended destination.

12. The server of claim 8, further comprising: wherein the processor is further configured to: determine a second alternate destination based on a second parameter, andinstruct the user interface to provide an interactive choice of the intended destination of the autonomous transport, the alternate destination and the second alternate destination of the autonomous transport, andwherein the intended destination, the alternate destination and the second alternate destination are not the same.

13. The server of claim 8, wherein the functional relationship value is based on a functional relationship between the provided measurement of the parameter and a second provided measurement of a second parameter.

14. The server of claim 8, wherein the processor is further configured to: determine a supplemental transport operable to navigate from the alternate destination to the intended destination;determine a second supplemental transport operable to navigate from the alternate destination to the intended destination; andprovide an interactive choice of the first supplemental transport and the second supplemental transport.

15. A non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to: establish a starting place of an autonomous transport and an intended destination of the autonomous transport; anddetermine an alternate destination based on a parameter by comparing a functional relationship value, of a provided measurement of the parameter and a weighting value associated therewith, with a threshold,wherein the intended destination and the alternate destination are not the same.

16. The non-transitory computer readable medium of claim 15, wherein the parameter is selected from a group of parameters consisting of traffic parameters, time parameters, monetary parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters, transport type parameters and combinations thereof

17. The non-transitory computer readable medium of claim 16, further comprising: wherein the parameter comprises a combination of at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters,wherein each of the at least one of the combination of traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters has a respective weighting value associated therewith, andwherein the non-transitory computer readable medium further comprises instructions, that when read by a processor, cause the processor to further determine the alternate destination by comparing the functional relationship value, of each of the at least one of the traffic parameters, time parameters, weather parameters, distance parameters, ambient temperature parameters, transport temperature parameters and transport type parameters with the respective weighting value associated therewith, with the threshold.

18. The non-transitory computer readable medium of claim 15, wherein the non-transitory computer readable medium further comprises instructions, that when read by a processor, cause the processor to further determine a supplemental transport operable to navigate from the alternate destination to the intended destination.

19. The non-transitory computer readable medium of claim 15, further comprising: wherein the non-transitory computer readable medium further comprises instructions, that when read by a processor, cause the processor to further: determine a second alternate destination based on a second parameter, andinstruct the user interface to provide an interactive choice of the intended destination of the autonomous transport, the alternate destination and the second alternate destination of the autonomous transport; andwherein the intended destination, the alternate destination and the second alternate destination are not the same.

20. The non-transitory computer readable medium of claim 15, wherein the functional relationship value is based on a functional relationship between the provided measurement of the parameter and a second provided measurement of a second parameter.