Patent Application
20230342875
Embodiments relate to transportation systems and methods.
Modern day commerce can involve a complex supply chain with logistics of transporting a high number of goods from various locations. Carriers transporting goods often consider relevant data on the goods transported from various locations and often optimize the logistics of transporting goods based on certain preferences. Typically, carriers planning transportation of goods face a high number of options to choose from, which can include all of the goods to be delivered in one or more locations at a given time, and the carriers may rely upon certain assumptions or oversimplifications in considering options and plan an inefficient and unoptimized transportations of the goods. It can be a challenge to determine a highly efficient and optimized transportations of goods that can satisfy the carrier's preferences. For example, it can be difficult to collect information on the vast number of available goods to be transported in an entire country or other large geographic area and conduct an extensive analysis to optimize which goods to deliver and which routes to take, and even more difficult to do so taking into consideration other factors such as a carrier's preferences.
Logistics systems and methods can automatically collect data on a high number of goods to be transported, and can generate data-driven optimized transportations of goods based on the carrier's preferences. According to aspects of embodiments, a system for a transportation tour, the system comprising: a display device; a memory; and a processor coupled to the memory programmed with executable instructions, the instructions including: a data collector for automatically collecting data for the transportation tour, wherein the data collector is configured to receive a transportation load data and a market data; a user interface configured to collect a user input, the user input comprising a user profile, a transportation tour parameter, and a current information of the user; a data filter configured to filter the transportation load data and the market data based on the user input; a network model engine configured to generate a network model based on the filtered transportation load data and the filtered market data, wherein the network model comprises a network of transportation loads; a simulation engine configured to: simulate the transportation tour based on the network model, and automatically select a predetermined number of simulated tours based on a simulated factor; and the user interface further configured to display the selected predetermined number of the simulated tour.
In some embodiments, the user profile comprises at least one of an overhead cost, a commodity type criterion, a broker criterion, a customer criterion, a region criterion, a weather criterion, a terrain criterion, a certification criterion, or a credential criterion.
In some embodiments, the transportation tour parameter comprises a start date of the transportation tour, a start location of the transportation tour, and/or transport information.
In some embodiments, the transportation tour parameter further comprises an end date of the transportation tour, and an end location of the transportation tour.
In some embodiments, the current information of the user comprises at least one of a current location of the user or a current hour of service by the user operating a transport.
In some embodiments, the network model engine is configured to update the network model based on: a new transportation tour data collected by the data collector; a new market data collected by the data collector; or a new user input collected by the user interface.
In some embodiments, the network model comprises a probability model for the simulated tour.
In some embodiments, the simulated factor comprises at least one of a revenue of the simulated tour, an earning of the simulated tour, or an earning over distance traveled in the simulated tour.
In some embodiments, the transportation tour comprises a series of one or more transportation loads.
In some embodiments, the user interface is further configured to: display the selected predetermined number of the simulated tours for an approval by the user; receive an approval input from the user; book a first transportation load in an approved simulated tour; and display a booked status of the first transportation load in approved simulated tour.
According to aspects of embodiments, a method for a transportation tour, the method comprising: automatically collecting a transportation load data and a market data; collecting, via a user interface, a user input, the user input comprising a user profile, a transportation tour parameter, and a current information of the user; filtering the transportation load data and the market data based on the user input; generating a network model based on the filtered transportation load data and the filtered market data, wherein the network model comprises a network of transportation loads; simulating the transportation tour based on the network model; automatically selecting a predetermined number of simulated tour based on a simulated factor; and displaying, via the user interface, the selected predetermined number of the simulated tour.
In some embodiments, the user profile comprises at least one of an overhead cost, a commodity type criterion, a broker criterion, a customer criterion, a region criterion, a weather criterion, a terrain criterion, a certification criterion, or a credential criterion.
In some embodiments, the transportation tour parameter comprises a start date of the transportation tour, a start location of the transportation tour, a transport information.
In some embodiments, the transportation tour parameter further comprises an end date of the transportation tour, and an end location of the transportation tour.
In some embodiments, the current information of the user comprises at least one of a current location of the user or a current hour of service by the user operating a transport.
In some embodiments, the method further comprises updating the network model based on: a new transportation tour data collected by the data collector; a new market data collected by the data collector; or a new user input collected by the user interface.
In some embodiments, the network model comprises a probability model for the simulated tour.
In some embodiments, the simulated factor comprises at least one of a revenue of the simulated tour, an earning of the simulated tour, or an earning over distance traveled in the simulated tour.
In some embodiments, the transportation tour comprises a series of one or more transportation loads.
In some embodiments, the method further comprises: displaying, via the user interface, the predetermined number of the selected simulated tours for an approval by the user; receiving, via the user interface, an approval input from the user; booking, via the user interface, a first transportation load in an approved simulated tour; and displaying, via the user interface, a booked status of the first transportation load in the approved simulated tour.
While embodiments of the disclosure are amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the invention to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.
According to some exemplary embodiments, systems and methods disclosed herein may simulate and optimize transportation of goods or loads and transportation routes based on collected data and user inputs from a carrier or a transport driver. In some embodiments, the data can be automatically collected and filtered based on the user inputs to generate a network model, which can be continually or periodically updated with any new data collected or any new user input. In some embodiments, the network model can be used to run simulations on tours of transporting certain loads in certain routes. In some embodiments, the systems and methods described herein can select one or more simulated tours of transportation loads and routes, for example, based on the user's selection or approval. In some embodiments, the systems and methods described herein can book or confirm the selected or approved tours, or one or more loads and routes associated with the selected or approved tours.
The term “goods” or “loads” described herein, for example, can be anything that may be transported by transports such as trucks, trailers, vans, rails, ships, aircrafts, or any other vehicles. The term “carrier” described herein, for example, can be a person or an entity that transports goods or loads. The term “driver” described herein, for example, can be a person, a system, or an entity that directly or indirectly operates the transports. The term “tour” described herein, for example, can be a single transportation or a series of transportations of loads (e.g., a tour can include transportations of one or more loads in a series such as Load 1, Load 2, Load 3, Load 4, Load 5, Load 6, Load 7, etc. as illustrated in
The systems and methods described herein, for example, can create value for users by automatically collecting data on transportation of goods or loads and transportation routes and filtering the collected data based on the user inputs, which can avoid the need for the user to manually collect or evaluate the data. In another example, the systems and methods described herein can also create value for users by generating a network model based on the filtered data to run simulation of transportation tours. In another example, the systems and methods described herein can also create value for users by automatically selecting simulated tours based on simulated factors such as earnings. In another example, the systems and methods described herein can also create value for users by better utilizing the data on current and historical data on transportation of goods or loads and transportation routes and/or current and historical market data. In another example, the systems and methods described herein can also create value for users by generating a data-driven optimized tour that can increase or maximize users' earnings. In another example, the systems and methods described herein can also create value for users by providing a highly efficient and optimized tour (e.g., keeping the tour busy with less down time or less empty loads) by minimizing inconsistency and unpredictability in the tour, which can be done, for example, by bundling several shipments in the right markets over a tour. In another example, the systems and methods described herein can also create value for users by allowing the users to charge less compensation per load in a tour (e.g., by lowering a premium in compensation based on uncertainty), while increasing the overall earnings in the tour based on a highly efficient and optimized tour provided by the systems and methods. These and other advantages are described further herein.
The systems and methods described here, for example, can also create value for the shippers, brokers, and customers of the goods or loads that are transported by the users. For example, the systems and methods described herein can also create value for the shippers, brokers, and customers by providing enhanced coverage of the transportation of the goods or loads. In another example, the systems and methods described herein can also create value for the shippers, brokers, and customers by reducing the costs of the transportation. The costs can be reduced, for example, based the systems and methods described herein automatically collecting and better utilizing the data on current and historical data on transportation of goods or loads and transportation routes and/or current and historical market data, which can allow the supply to meet the demand (and vice versa) more efficiently. The costs can also be reduced, for example, based the systems and methods described herein minimizing inconsistency and unpredictability in the tour of transportation and allowing the users (e.g., carriers or drivers of the transport) to charge less compensation per load by lowering a premium based on uncertainty. For example, carriers or drivers can charge a premium on uncertainty in their usual compensation rate for transportation of goods or loads, which can increase the shipping costs for the shippers, brokers, and customers. The systems and methods described herein can provide value by reducing such uncertainty premiums. These and other advantages are described further herein.
In some embodiments, the data collection 102 can collect data on current and historical transportation loads 104 and current and historical market data 106. In some embodiments the data collection 102 can collect data on current and historical transportation loads 104 in certain regions (e.g., city, state, province, country, or continent). For example, the data collection 102 can collect data on current and historical loads 104 that are currently available or were previously available for transportation in North America, United States, Canada, certain states, certain provinces, certain regions, certain cities, and/or the like.
In some embodiments, the data collection 102 can collect current and historical market data 106 that may relate to current and historical goods or loads 104 in certain regions (e.g., city, state, country, or continent). For example, the data collection 102 can collect data on current and historical market data 106 of loads that are or were available in North America, United States, Canada, certain states, certain provinces, certain regions, certain cities, and/or the like. In some embodiments, the data collection 102 can collect or compute current and historical aggregated market data on pricing and volume of goods or loads to be transported. For example, aggregated market data on pricing and volume of goods or loads can be collected or computed on a per lane basis from the origin to the destination of the shipment of goods or loads (e.g., origin-destination or origin-destination-trailer tuple). In some embodiments, the data collection 102 can collect or analyze weather data in certain regions (e.g., city, state, country, or continent) that can be relevant to transportation of goods or loads.
In some embodiments, the data collection 102 can collect data from first party sources (e.g., a party implementing the systems and methods described herein, or other parties that may have access to the systems and methods, for example, through software-as-a-service (SaaS) product offerings by the party implementing the systems and methods). In some embodiments, the data collection 102 can collect data from third party sources (e.g., parties that may provide data on current and historical goods or loads for transport in certain regions, which can include freight brokers, load boards, load posting services, shippers, integration with transportation management systems (TMS), or any other third parties). In some embodiments, the data collection 102 can collect data from various sources including first party sources and third party sources, and build aggregated pools of goods or loads for transportation. In some embodiments, the data collection 102 can perform natural language processing (NLP) to interpret the text data when collecting and interpreting data such as the current and historical loads data 104 and the current and historical market data 106.
In some embodiments, the data collection 102 can provide value by automatically collecting the current and historical data on transportation of goods or loads, and the current and historical market data from various sources, which can, for example, better utilize all available data related to the transportations. For example, the value can be created for the users by automatically collecting data from all available data related to the transportations from various sources. In another example, the value can be created for the various sources including shippers, brokers, and customers (e.g., parties that may provide data on current and historical goods or loads for transport and/or data on current and historical market data) by providing enhanced coverage of the transportation of the goods or loads, and also by reducing the costs of the transportation.
In some embodiments, user inputs 108 can collect inputs from a user (e.g., a carrier or a driver) relating to transportation of goods or loads such as a user profile 110, tour parameters 112, and user's current information 114. In some embodiments, the transport profile 110 (e.g., a profile of a carrier or a driver) can include transport operation costs (e.g., overhead cost per mile), certain preferences or criteria on commodity type, broker, customer, region, weather, terrain, certification, or credential. In some embodiments, one or more aspects of the transport profile 110 can be collected from the user once, and may not need to collect again, unless, for example, the user provides new inputs.
In some embodiments, the operation costs can be overhead costs for operating a transport such as loan payments (e.g., if the transport was purchased on a loan), insurance fees, license and registration fees, permit fees, fuel costs, transport maintenance costs, and any other expenses. In some embodiments, the operation costs can be averaged over a distance or a period of time. For example, averaged operation costs can include overhead cost per mile, cost per kilometer, and cost per day.
In some embodiments, the commodity type criterion can relate to preferences or requirements on certain types of good or loads, for example, based on ethical preferences, religious preferences, personal preferences, or equipment requirements. For example, transportation of pork or certain types of meat may not be preferred due to religious reasons. In another example, transportation of loads that must remain frozen or stored in cold temperature during the transportation may not be preferred if the transport lacks proper refrigeration systems that can meet certain temperature requirements.
In some embodiments, the broker criterion can relate to preferences on certain brokers that broker the shipment of loads or goods based on reputation, prior experience, or any other reasons. For example, transportation of loads brokered by certain brokers with good or bad reputations may be preferred or not preferred. In another example, a transportation of loads brokered by certain brokers may be preferred or not preferred based on prior experiences with such brokers.
In some embodiments, the customer criterion can relate to preferences on certain customers based on reputation, prior experience, or any other reasons. For example, certain customers may be preferred or not preferred based on reputations or prior experiences. For example, certain customers with difficult or inflexible appointment times for loading or unloading the loads may not be preferred.
In some embodiments, the regions criterion can relate to preferences on certain regions such as cities, states, countries, or continents based on traffic, permit requirements, laws and regulations, or any other reasons. For example, certain cities such as New York, Los Angeles, or Washington D.C. with reputations for having bad traffic congestion may not be preferred in some instances. In another example, certain states or countries may not be preferred for requiring permit(s) to transport goods or certain undesirable laws and regulations.
In some embodiments, the weather criterion can relate to preferences on certain weather conditions based on safety, equipment requirements such as snow chains for tires on snowy/icy roads, or any other reasons. For example, transportation of goods in bad weather such as operating a transport when snowing can pose a safety risk and may not be preferred. In another example, bad weather may not be preferred due to certain equipment requirements such as snow chains for tires when operating a transport in snowy or icy road conditions.
In some embodiments, the terrain criterion can relate to preferences on certain terrain based on safety, personal preferences, or any other reasons. For example, transportation of goods through mountainous terrains may not be preferred due to difficulty in operating a transport in curvy or steep mountain roads, risks, etc.
In some embodiments, the certification criterion can relate to a requirement for transporting certain goods or loads. For example, a commercial driver's license may be needed to operate commercial vehicles or transports. In another example, a hazardous material (“HAZMAT”) endorsement certification may be needed for a qualification to transport hazardous materials.
In some embodiments, the credential criterion can relate to a requirement for accessing certain regions, areas, buildings, facilities, etc. For example, a transportation worker identification credential (“TWIC”) card may be needed to access to ports, docks, vessels, ships, or similar locations or environments in United States. Such a credential may be needed or preferred in order to load or unload goods at ports, docks, vessels, ships, or similar locations or environments.
In some embodiments, the user inputs 108 can include tour parameter 112 for a tour of transporting loads, which can include a start location of the tour, an end location of the tour, a start date and time of the tour, an end date and time of the tour, transport information (e.g., type, size, length, and maximum payload weight of the transport such as truck, van, trailer, and/or the like), and any other information related to the tour. In some embodiments, a tour can be when a carrier or a driver delivers, for example, a series of one or more transportations of loads (e.g., a tour can include transportations of one or more loads in a series such as Load 1, Load 2, Load 3, Load 4, Load 5, Load 6, Load 7, etc. as illustrated in
In some embodiments, the start date and the end date of the tour can indicate when a carrier or a driver can start and end the tour of transporting loads. In some embodiments, the start and end dates and also times on those dates can be collected. The start date and the end date of the tour can be the same (e.g., the tour can start and end on the same day), or can be one or more days apart (e.g., the tour can span two or more days).
In some embodiments, the starting location and the ending location of the tour can indicate locations where a carrier or a driver can start and end the tour of transporting loads. In some embodiments, the starting and ending locations can be a city, county, town, district, borough, GPS coordinate, geofenced area, or any geographic location. For example, the starting location of the tour can be a city where the first load of the tour can be picked up for transporting, or the first load can be picked up within a predetermined threshold distance from the starting location (e.g., a predetermined threshold distance away from the city). In another example, the end location of the tour can be a city where the last load of the tour can be unloaded, or the last load can be unloaded within a predetermined threshold distance from the ending location (e.g., a predetermined threshold distance away from the city). The start location and the end location, for example, can be the same location or different locations such that the tour can start and end at the same location or at different locations.
In some embodiments, the transport information can indicate a type, size, length, and maximum payload weight of the transport for the tour such as truck, van, trailer, or any vehicle for the tour of transporting goods. For example, the transport information can indicate the amount or the size of loads that can be transported at a given time.
In some embodiments, the user inputs 108 can include a user's current information 114 such as a current location of the user (e.g., GPS data), current location of the user's transport (e.g., GPS data), current hours of service by a carrier or a driver (e.g., the user's current hours of service) operating the transport. In some embodiments, the user's current information 114 can be manually updated or automatically updated, for example, via a user interface.
In some embodiments, the logistics process 100 of
For example, the transport profile 110 of the user inputs 108 can be used to filter the data from the data collection 102. The commodity criterion of the transport profile 110, for example, can be used to filter certain types of commodities (e.g., filter pork freight due to religious reasons). In another example, the broker criterion can be used to filter certain brokers (e.g., filter certain brokers with bad reputations). The customer criterion of the transport profile 110, for example, can be used to filter out the goods or loads for certain customers (e.g., filter the loads associated with customers with bad reputation). The region criterion of the transport profile 110, for example, can be used to filter certain regions such as cities, states, provinces, countries, or continents (e.g., filter transportations of loads where the transportation routes may involve certain cities with heavy traffic congestion, filter certain states that may require special permits, filter certain countries or continents based on where the user prefers to operate the transport, and/or similar filters). The weather criterion of the transport profile 110, for example, can be used to filter out certain weather conditions (e.g., filter out transportations of certain loads where the transportation routes may experience a high chance of snow, or filter other unpreferred weather conditions). The terrain criterion of the transport profile 110, for example, can be used to filter certain terrains (e.g., filter transportations of certain loads where the transportation routes involve mountain roads). The certification criterion of the transport profile 110, for example, can be used to filter certain goods or loads (e.g., filter transportations of hazardous materials based on a certification status). The credential criterion of the transport profile 110, for example, can be used to filter certain regions, areas, buildings, facilities, etc. (e.g., filter transportations of certain loads where the transportation routes require access to areas such as ports based on a credential status).
In another example, the tour parameter 112 of the user inputs 108 can be used to filter the data from the data collection 102. The start date and the end date of the tour parameter 112, for example, can be used to filter transportation of loads that are not within the start and end dates/times. The start location and the end location of the transport parameter 112, for example, can be used to filter transportation of loads based on locations.
In another example, the user's current information 114 of the user inputs 108 can be used to filter the data from the data collection 102. The current location of the current information 114, for example, can be used to filter transportation of loads based on the current location of the transport. The current hours of service information of the current information 114, for example, can be used to filter the loads based on the calculated or estimated duration for transporting the loads, and also based on hours of service rules and regulations. In certain regions (e.g., country, state, province, and/or other geographic area), a carrier or a driver operating the transport may be required to comply with hours of service rules and regulations on the maximum amount of time permitted on duty such as driving time. The current hours of service information of the current information 114, for example, can be used to filter transportation of loads that can cause violation of hours of service rules and regulations.
In some embodiments, the filter data step 116 can involve natural language processing (NLP) to interpret the text data and to determine whether certain data can be qualified or unqualified for filtering. In some embodiments, the data filtering 116 can provide preferable or qualified options for transportation of goods and routes that can satisfy the user profile 110, tour parameter 112, or user's current information 114. In some embodiments, the data filtering step 116 can be performed when new data is collected by the data collection 102, or a user input in the user inputs 108 is changed or newly provided. In some embodiments, the data filtering 116 can be performed continually, periodically, or triggered by certain events such as detections of new data or a new user input.
In some embodiments, the logistics process 100 of
In some embodiments, the network model 118 can be generated to be stochastic. When the desired tour or a later portion of the tour is several days or weeks out into the future, for example, the data that can be collected by the data collection 102 on transportation of loads on those future dates can be limited, and more data can become available later when the tour or a later portion of the tour becomes closer to the present. For example, the network model 118 can be continually or periodically updated with any new data collected from the data collection 102 or with any new user input from the user inputs 108. In some embodiments, the network model 118 can be used as a probability model to compute and predict a tour of transporting loads, routes, and earnings. The network model 118 can be a probability model, for example, based on an initial set of options or a later updated set of options, that can be used to compute and predict the transportation earnings. For example, the network model 118 can be used as a probability model, where the initial set of options from the data filter 116 can be used to compute probability weighted to maximize the transportation earnings for a carrier or a driver in a given tour.
In some embodiments, the logistics process 100 of
In some embodiments, the logistics process 100 of
In some embodiments, the selected simulated tours 122 can be presented to the user for approval. In some embodiments, the user can be requested to approve one simulated tour, or requested to approve more than one simulated tour, for example, to increase the chance of booking the selected simulated tours and to minimize any future back-and-forth. For example, some loads (e.g., with desirable features such as higher earnings) can be taken more quickly by other carriers or other drivers after becoming available to the industry or to the public (e.g., when a transportation request is published, posted online, or otherwise becomes available). Some selected tours or user approved tours, for example, can include such popular loads that can be taken by others before the loads can be booked for the user, and it can be advantageous in some instances to request the user to approve more than one simulated tour as a backup in case some of the selected or approved tours cannot be booked. In some embodiments, the user may not approve the selected tours, and the logistics process 100 can return to the data collection step 102 or the user inputs step 108 to rerun the process based on new data or a new user input.
In some embodiments, the logistics process 100 of
In some embodiments, when the book tour step 124 may not reserve, book, or confirm any load in the selected or approved simulated tours, the logistics process 100 can return to the data collection step 102 or the user inputs step 108. For example, the logistics process 100 can be performed again based on any new data from the data collection 102 or a new user input from the user inputs 108 until the process can successfully reserve, book, or confirm at least a first load in the selected or approved simulated tours.
In some embodiments, the logistics process 100 of
In some embodiments, the logistics process 100 can provide value to the users by generating a data-driven optimized tour that can increase or maximize the users' earnings. In some embodiments, the logistics process 100 can also provide value to the shippers, brokers, and customers (e.g., parties that may provide data on current and historical goods or loads for transport and/or data on current and historical market data) by providing enhanced coverage, and also by reducing costs of the transportation.
In some embodiments, one or more aspects of the exemplary logistics process 100 and the systems and methods described herein can be implemented in user interfaces as illustrated in
In some embodiments, the user profile 110 can be collected via user interface, for example, as illustrated in
In some embodiments, the tour parameter 112 can be collected via user interface as illustrated in
In some embodiments, the user's current information 114 such as the current location of the user or the transport (e.g., GPS data) can be collected and displayed via user interface, for example, as illustrated in
In some embodiments, the number of data on the transportation loads collected by the data collection step 102 can be indicated via user interface, for example, as illustrated in
In some embodiments, when the tour parameter 112's start date of the tour, or a later portion of the tour is several days or weeks out into the future, for example, the data that can be collected by the data collection 102 on transportation of loads on those future dates can be limited, and more data can become available later when the tour or a later portion of the tour becomes closer to the present.
In some embodiments, the selected simulated tours from the logistics process 100's step 122 can be presented to the user for approval via user interface, for example, as illustrated in
In some embodiments, the user may not approve the selected tour, for example, as illustrated in
In some embodiments, when the user approves the selected simulated tours, the approved tour can be booked, for example, as illustrated in
In some embodiments, when the first load of the user approved simulated tour is booked, the booked tour can be displayed via user interface, for example, as illustrated in FIGS. 17A-17D.
In some embodiments, detailed information of booked Tour 1's Load 1 can be displayed via user interface, for example, as illustrated in
In some embodiments, a status of a booked tour can be displayed via user interface, for example, as illustrated in
In some embodiments, the user's past tour(s) can be displayed via user interface, for example, as illustrated in
In some embodiments, one or more aspects of the exemplary logistics process 100 and the systems and methods described herein can be implemented with one or more computing or processing devices such as mobile devices, laptops, desktops, cloud computing resources, servers, terminals, virtualization tools, communication devices. In some embodiments, one or more such computing devices can include one or more transmitters, receivers, and/or transceivers to communicate using one or more techniques such as wired communication and/or wireless communication. In some embodiments, one or more such computing devices can comprise software such as one or more applications or apps. The steps of a process or algorithm described in connection with the embodiments described herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in one or more memory, such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any form of computer-readable storage medium. In some embodiments, a storage medium such as memory may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In some embodiments, the storage medium may be integral to the processor.
All of the processes described herein can be embodied in, and fully automated via, software code modules executed by one or more general purpose or special purpose computers or processors. The code modules may be stored in one or more of any type of computer-readable medium or other computer storage device or collection of storage devices. Some or all of the methods may alternatively be embodied in specialized computer hardware.
All of the methods and tasks described herein may be performed and fully automated by a computer system. The computer system may, in some cases, include single or multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, etc.) that may communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors or circuitry or collection of circuits, e.g., a module) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium. The various functions disclosed herein may be embodied in such program instructions, although some or all of the disclosed functions may alternatively be implemented in application-specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computer system includes multiple computing devices, these devices may, but need not, be co-located. The results of the disclosed methods and tasks may be persistently stored by transforming physical storage devices, such as solid state memory chips and/or magnetic disks, into a different state.
Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present invention. For example, while the embodiments described above refer to particular features, the scope of this invention also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present invention is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.
This application claims priority to U.S. Provisional Application No. 63/333,026, filed on Apr. 20, 2022, titled “Transportation Logistics Systems and Methods,” which is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63333026 | Apr 2022 | US |
