Digital Twin-Based Systems and Methods for Reducing Peak Power and Energy Consumption in Physical Systems

Abstract

The method (1400) includes identifying (1410) a route for a passenger entering a building. The route includes at least one section of roadway for vehicular access to the building or at least one access point within the building for pedestrian passage. The method includes receiving (1420) traffic flow information corresponding to the route. The method includes determining (1430) whether the received traffic flow information corresponds to a condition in a set of pre-trained conditions that obstructs the route. The method includes identifying and displaying (1440) a list of user-selectable condition options associated with the condition that obstructs the route based on a determination that the received traffic flow information corresponds to the condition.

Description

[Technical Field]

[0001] This disclosure relates generally to system management and control, and more particularly to digital twin-based systems and methods for reducing peak power and energy consumption in physical systems. [Background technology]

[0002] Modern societies increasingly rely on complex, interconnected infrastructures to function in ways that enable sustainable growth. When this infrastructure functions well and meets service delivery requirements, societal activity can continue in a manner consistent with policy incentives, goals, and needs. Conversely, when such infrastructure struggles to meet service requirements due to external stressors and collapse or deterioration, the impacts on societal well-being can be enormous. System-level collapses involving this infrastructure can cause a variety of negative outcomes, including economic losses, impaired human health, reduced social trust, reduced social cohesion, or harmful environmental impacts. Of great concern are situations in which infrastructure collapse contributes to cascading system failures and in which infrastructure collapse percolates through society, causing catastrophic and potentially irreversible consequences. For example, in February 2021, Winter Storm Uri brought cold weather (particularly, eight days of subzero temperatures) to a wide geographic area across North America, including parts of Canada, the United States, and northern Mexico. During Winter Storm Uri, cascading failures of interdependent infrastructure systems within the electrical grid in Texas left millions of people without heat and electricity for extended periods of time.

[0003] Historically, solutions to protect infrastructure from system-level collapse have involved various stakeholders utilizing risk assessment techniques to characterize threats, assess vulnerabilities, and identify direct and indirect (or unintended) consequences associated with collapse. Unfortunately, for modern infrastructure, many threats (e.g., human pathogens or regional extreme weather events) are difficult to anticipate. Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides digital twin-based systems and methods for predicting traffic flow information based on selected options for redirection of traffic flow in a physical system, and methods for predicting delayed departure of transportation vehicles based on traffic flow information in a physical system. [Means for solving the problem]

[0005] In a first embodiment, a digital twin-based system and method are provided for predicting traffic flow information based on selected options for redirecting traffic flow in a physical system. The method includes identifying a route for a passenger entering a building. The route includes at least one section of a roadway for vehicular access to the building or at least one access point within the building for pedestrian passage. The method includes receiving traffic flow information corresponding to the route. The method includes determining whether the received traffic flow information corresponds to a condition in a set of pre-trained conditions that would impede the route. The method includes identifying and displaying a list of user-selectable state options associated with the condition that would impede the route based on a determination that the received traffic flow information corresponds to the condition.

[0006] In a second embodiment, an electronic device supporting a digital twin-based system and method for predicting traffic flow information in a physical system based on selected options for redirecting traffic flow in the physical system is provided. The electronic device includes at least one processor configured to identify a route for passengers entering a building. The route includes at least one section of a road for vehicles to access the building or at least one access point inside the building for pedestrians to pass through. The at least one processor is configured to receive traffic flow information corresponding to the route. The at least one processor is configured to determine whether the received traffic flow information corresponds to a condition from a set of pre-trained conditions that would impede the route. The at least one processor is configured to identify and display a list of user-selectable condition options associated with the condition that would impede the route based on a determination that the received traffic flow information corresponds to the condition.

[0007] In a third embodiment, a non-transitory computer-readable medium is provided containing instructions that, when executed, cause at least one processor to support a digital twin-based system and method for predicting traffic flow information in a physical system based on selected options for redirecting traffic flow in the physical system. The computer program includes computer-readable program code that, when executed by a processor of an electronic device, causes the electronic device to identify a route for passengers entering a building. The route includes at least one section of a road for automobiles to access the building or at least one access point inside the building for pedestrians to pass through. The computer-readable program code causes the electronic device to receive traffic flow information corresponding to the route. The computer-readable program code causes the electronic device to determine whether the received traffic flow information corresponds to a condition from a set of pre-trained conditions that would impede the route. The computer-readable program code causes the electronic device to identify and display a list of user-selectable condition options associated with the condition that would impede the route based on a determination that the received traffic flow information corresponds to the condition.

[0008] Other technical features may be readily apparent to those skilled in the art from the following drawings, descriptions, and claims. [Brief explanation of the drawings]

[0009] For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0010] [Figure 1] FIG. 1 illustrates an exemplary electronic device supporting digital twin-based systems and methods for reducing peak power and energy consumption in physical systems according to the present disclosure.

[0011] [Figure 2] 1 illustrates an example of a digital twin-based system according to the present disclosure.

[0012] [Figure 3] 1 illustrates an aerial view image of a geographic area including multiple physical systems in accordance with the present disclosure.

[0013] [Figure 4] 1 illustrates an exemplary geographic area containing multiple physical systems according to the present disclosure.

[0014] [Figure 5] 1 illustrates an exemplary complex ecosystem that is an airport, according to the present disclosure.

[0015] [Figure 6] 1 illustrates an exemplary data stream to an electronic device associated with an airport according to the present disclosure.

[0016] [Figure 7] 1 illustrates an exemplary user interface generated by a digital twin tool and displayed by a user device according to the present disclosure.

[0017] [Figure 8] 1 illustrates an exemplary list of user-selectable state options displayed by a digital twin tool according to the present disclosure.

[0018] [Figure 9] 9 illustrates a user interface showing vehicular and pedestrian traffic flows generated by a digital twin tool in response to selecting a first state option from the list of user-selectable state options of FIG. 8 in accordance with the present disclosure.

[0019] [Figure 10] FIG. 9 illustrates a user interface showing predicted vehicular and pedestrian traffic flow generated by a digital twin tool in response to selecting a third state option from the list of user-selectable state options of FIG. 8 in accordance with the present disclosure.

[0020] [Figure 11A] This disclosure illustrates a generator function within a digital twin for generating a microscopic simulation including predicted traffic flow of vehicles and pedestrians.

[0021] [Figure 11B] Shows the user interface for traveler origins by mode of transport used to travel to the airport.

[0022] [Figure 11C] Shown is the user interface for domestic travel arrival destinations departing from the same departure airport on a single day.

[0023] [Figure 11D] 10 illustrates shuttle bus routes for reassignment of different numbers of shuttles according to an embodiment of the present disclosure.

[0024] [Figure 11E] 1 illustrates multiple shuttle bus routes for route modification according to an embodiment of the present disclosure.

[0025] [Figure 12] This disclosure provides an example of a digital twin-based method for reducing peak power and energy consumption in a physical system.

[0026] [Figure 13A] This disclosure illustrates an example of a digital twin-based method for predicting late departures of transportation vehicles based on traffic flow information in a physical system. [Figure 13B] This disclosure illustrates an example of a digital twin-based method for predicting late departures of transportation vehicles based on traffic flow information in a physical system. [Figure 13C] This disclosure illustrates an example of a digital twin-based method for predicting late departures of transportation vehicles based on traffic flow information in a physical system.

[0027] [Figure 14] We present an exemplary digital twin-based method for predicting traffic flow information based on selected options for traffic flow redirection in a physical system. DETAILED DESCRIPTION OF THE INVENTION

[0028] 1-14 described below, and the various embodiments used to illustrate the principles of the present invention in this patent document, are merely exemplary and should not be construed as limiting the scope of the invention in any way. Those skilled in the art will understand that the principles of the present invention may be implemented in any type of suitably configured device or system.

[0029] FIG. 1 illustrates an exemplary electronic device 100 that supports digital twin-based systems and methods for reducing peak power and energy consumption in physical systems according to the present disclosure. The embodiment of the electronic device 100 illustrated in FIG. 1 is for illustrative purposes only. Other embodiments may be used without departing from the scope of the present disclosure. The electronic device 100 of FIG. 1 may be used in the digital twin-based system 200 of FIG. 2, for example, to interact with and control the operation of one or more physical systems 202, 204, 206.

[0030] 1, electronic device 100 includes at least one processing device 102, at least one storage device 104, at least one communication unit 106, and at least one input / output (I / O) unit 108. Processing device 102 may execute instructions that may be loaded into memory 110. Processing device 102 includes any suitable number and type of processors or other processing devices in any suitable arrangement. Exemplary types of processing device 102 include one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuits. In some embodiments, processing device 102 is a programmable logic controller (PLC) with user-selectable parameters.

[0031] Memory 110 and persistent storage 112 are examples of storage device 104 and represent any structure capable of storing and facilitating retrieval of information (such as data, program code, and / or other suitable information, either temporary or permanent). Memory 110 may represent random access memory or any other suitable volatile or non-volatile storage device. Persistent storage 112 may include one or more components or devices that support longer-term storage of data, such as read-only memory, a hard drive, flash memory, or an optical disk. Storage device 104 stores at least one digital twin tool 114 (referred to simply as a "digital twin" or "DT") of a physical system. More details regarding DT 114 are provided below and throughout this disclosure. In certain embodiments, the at least one DT 114 includes multiple DTs 114, each a digital twin of a different physical system. In certain embodiments, DT 114 is a software platform running on a group of servers.

[0032] The communication unit 106 supports communication with other systems or devices. For example, the communication unit 106 may support communication with an external system that provides information to the electronic device 100 for use in processing nuts, such as automatically tempering nuts. The communication unit 106 can support communication over any suitable physical or wireless communication link, such as a network or a dedicated connection. For example, the communication unit 106 may be controlled by the processing device 102 to receive performance measurements, such as performance measurements from various components associated with the physical system of FIG. 2. The various components associated with the physical system of FIG. 2 may be communicatively coupled to the communication unit 106 via the network 120. The performance measurements received at the communication unit 106 are input to the DT 114.

[0033] The I / O unit 108 allows for the input and output of data. For example, the I / O unit 108 may provide a connection for user input through a keyboard, mouse, keypad, touchscreen, or other suitable input device. The I / O unit 108 may also send output to a display or other suitable output device. The I / O unit 108 may further support communication with various components of the digital twin-based system 200 of FIG. 2. For example, the input and output data of the I / O unit 108 may be received from or output to one or more sensors 130, one or more databases 132, one or more controllers 134 of physical components, or IoT and other connected data sources 136 in the digital twin-based system 200 of FIG. 2. Each of the sensors 130 may have a sensor identifier (ID) stored in the database 132 that links the sensor ID to the location where the sensor is installed and the sensor detection location where the sensor detects the phenomenon. In the database 132, each of the controllers 134 may have an ID that is linked to the ID of the physical component that is controlled by the controller.

[0034] Input and output data of the I / O unit 108 may also be received from or output to one or more optimizers 138, details regarding the optimizers 138 being further described with reference to FIGS. 8-14 . The optimizer 138 may be triggered by the processing device 102 to activate and process data that the I / O unit 108 sends to the optimizer 138 from the DT 114. For example, the I / O unit 108 is operatively connected to an optimizer 138 implemented on another electronic device 101, such as a local server portion of the same server system. In some embodiments, the optimizer 138 can be stored on the storage device 104 and executed (or controlled) by the processing device 102. In some embodiments, the optimizer 138 may be implemented on an external electronic device (e.g., an external system of servers) communicatively coupled to the communication unit 106 via the network 120.

[0035] Although Figure 1 illustrates one example of an electronic device 100, various modifications may be made to Figure 1. For example, computing devices and systems come in a wide variety of configurations, and Figure 1 is not intended to limit the present disclosure to any particular computing or communication device or system.

[0036] FIG. 2 illustrates an example digital twin-based system 200 according to the present disclosure. The embodiment of the digital twin-based system 200 illustrated in FIG. 2 is for illustrative purposes only. Other embodiments may be used without departing from the scope of the present disclosure. The digital twin-based system 200 of FIG. 2 may incorporate or be used in conjunction with the electronic device 100 of FIG. 1, for example, to train the DTs 114a, 114b, and 114c as virtual representations of corresponding physical systems 202, 204, and 206, respectively. The digital twin-based system 200 of FIG. 2 may incorporate or be used in conjunction with the electronic device 100 of FIG. 1, for example, to utilize the trained DTs 114a, 114b, and 114c to implement a digital twin-based method according to an embodiment of the present disclosure. According to the present disclosure, an exemplary digital twin-based method is for reducing peak power and energy consumption in a physical system by utilizing a trained digital twin-based system as a virtual representation of the physical system, as illustrated in FIG. 12. According to the present disclosure, another exemplary digital twin-based method is for predicting delayed departures of transportation vehicles based on traffic flow information within a physical system by utilizing a digital twin-based system trained as a virtual representation of the physical system, as shown in FIG. 13. According to the present disclosure, another exemplary digital twin-based method is for predicting traffic flow information based on selected options for traffic flow redirection in the physical system by utilizing a digital twin-based system trained as a virtual representation of the physical system, as shown in FIGS. 7-10 and 14. For ease of explanation, the present disclosure provides a non-limiting scenario in which a first DT 114a is a digital twin of one or more buildings (e.g., terminal buildings and office buildings) of an airport, a second DT 114b is a digital twin of the land area within the airport boundary, and a third DT 114c is a digital twin of the airport's airfield and airspace.However, it should be noted that the digital twin-based system 200 of FIG. 2 may be used in any other suitable system with any other suitable devices.

[0037] System 200 includes at least one physical system, e.g., first physical system 202, second physical system 204, and third physical system 206. System 200 includes one or more sensors 130a-130c, one or more databases 132a-132c, one or more controllers 134a-134c, physical components, and one or more IoT and other connected data sources 136a-136c associated with corresponding physical systems 202, 204, and 206, respectively. In certain embodiments, databases 132a-132c are separate, while in other embodiments, databases 132a-132c are integrated as combined database 132. System 200 includes one or more electronic devices 100a-100c, each of which includes a corresponding DT 114a-114c that influences operational control of the corresponding physical systems 202, 204, and 206, respectively. Each of the electronic devices 100a-100c is connected to a database 132, for example, via a network 120. In certain embodiments, each electronic device 100a, 100b, 100c includes a corresponding database 132a, 132b, 132c that may be internally coupled to a corresponding DT 114a, 114b, 114c within the electronic device 100a, 100b, 100c, respectively. The system 200 includes network connections 208 from the DTs 114a, 114b, 114c to one or more service provisioning servers 210, which may provide data for updating the database 132 or as input to functions performed by the DTs 114a-114c. The system 200 includes a network connection 212 from the DTs 114a, 114b, 114c to one or more user devices 214 (also referred to as user equipment (UE)), such as a client computer 214a or a smartphone 214b. The client computer 214a can be a desktop or laptop computer. The smartphone 214b can be a mobile device, such as a tablet or a wearable device (e.g., a smartwatch).

[0038] The first DT 114a is a digital twin of the first physical system 202, the second DT 114b is a digital twin of the second physical system 204, and the third DT 114c is a digital twin of the third physical system 206. For example, the first DT 114a receives sensor measurements 216a from sensors 130a associated with the first physical system 202, e.g., over the network 120. The first DT 114a receives state information and other data 218a from a first IoT data source 136a associated with the first physical system 202, e.g., over the network 120. The first DT 114a communicates control signals 220a to a first controller 134a to control the operation of the first physical system 202, e.g., over the network 120. The second DT 114b and third DT 114c receive sensor measurements 216b, 216c, status information, and other data 218b-218c from sensors 130b and 130c and data sources 136b and 136c, respectively, and communicate control signals 220b, 220c with controllers 134b and 134c associated with the second physical system 204 and third physical system 206, respectively, in a manner similar to the first DT 114 associated with the first physical system 202. In certain embodiments, the DTs 114a, 114b, 114c interface with each other such that output data from one DT (114a) is provided as input data to another DT (114b and / or 114c), e.g., via a network connection.

[0039] The network connection 208 may include a connection to an application programming interface (API) web service provided by the U.S. government's National Weather Service (NWS) or the Texas Department of Transportation (TxDOT). Thus, the service provider server 210 may include a server of a third-party service provider, such as the NWS or TxDOT. For example, the system 200 may be referred to as a server system, and the service provider server 210 is an external server system.

[0040] While Figure 2 illustrates an example of a digital twin-based system 200, various modifications may be made to Figure 2. For example, various components of Figure 2 may be combined, further subdivided, duplicated, omitted, or rearranged, and additional components may be added according to specific needs. As a specific example, physical systems 202-206 may represent other physical systems such as a train station, a marine port, or a corporate headquarters campus.

[0041] FIG. 3 illustrates an aerial-view image of a geographic area 300 including multiple physical systems according to the present disclosure. In particular, the geographic area 300 is the land area of ​​and within a boundary 310 of an airport, such as Dallas-Fort Worth International Airport (DFW). For distinction, the land area outside the boundary 310 is slightly blurred and faded or has a lighter shade, while the land area inside the boundary 310 has a darker shade. The geographic area 300 includes multiple airport buildings 320 (e.g., first physical system 202 in FIG. 2 ), land within the airport boundary (e.g., second physical system 204 in FIG. 2 ), and the airport's airfield and airspace (e.g., third physical system 206 in FIG. 2 ). The airport buildings include terminal buildings and office buildings.

[0042] According to this example, the first digital twin 114a may be a digital representation of an airport building. The first digital twin 114a reduces peak power and overall energy consumption and informs improvements in airport operations, such as using flight schedules to predict building occupancy levels, using weather forecasts to predict heating / cooling demand and grid conditions, and providing flexibility to use on-site renewable energy and storage. For example, based on predicted grid conditions of the utility grid infrastructure (e.g., price per kW exceeds a threshold price; predicted demand response signals command a reduction in electrical load), the digital twin 114 can send control signals to controllers of on-site renewable energy generators and on-site energy storage devices to modify (e.g., increase) their output. A behind-the-meter storage (BTMS) system defines optimal system designs and energy flows for thermal and electrochemical behind-the-meter storage with on-site photovoltaic (PV) generation to enable high-power charging of passenger and airport vehicles.

[0043] The second DT 114b is a land-side digital representation of the airport, facilitating passenger and cargo mobility to and from the airport.

[0044] The third DT 114c is a digital representation of the airspace side of the airport. The third DT 114c optimizes the movement of aircraft and ground support equipment to reduce (e.g., minimize) delays and reduce energy consumption.

[0045] FIG. 4 illustrates an exemplary geographic area 400 including multiple physical systems according to the present disclosure. In particular, geographic area 400 is a top view of land at and within a boundary 410 of an exemplary airport. Geographic area 400 includes one terminal building 420 of the airport (e.g., first physical system 202 in FIG. 2 ), land 430 within the boundary of the airport excluding airfield 440 (e.g., second physical system 204 in FIG. 2 ), and the airport's airfield 440 (e.g., third physical system 206 in FIG. 2 ). An airfield is a space specifically allocated for aircraft to take off and land. In this example, airfield 440 includes a paved runway, although other airfields may include runways that are grass, gravel, or strips of dirt.

[0046] 5 illustrates an exemplary complex ecosystem that is an airport, according to the present disclosure. For ease of explanation, the complex ecosystem is referred to as airport 500. Airport 500 includes natural systems 510, buildings 520, human systems 530, a transportation network 540, utilities 550, and communication and technology systems 560.

[0047] Air pollution, noise pollution, changes in water use, and changes in land use are generated by the airport 500 and input into natural systems 510, as indicated by arrow 502. Climate change impacts the airport's natural systems 510, as indicated by double arrow 504. Human systems 530 input demographics 506 to the airport and occasionally output pandemics 508 that impact the airport. Electrification occurring in the transportation network 540 generates demand on utilities 550, as indicated by arrow 514. Energy reliability and water reliability are provided by utilities 550, as indicated by arrow 516. Emergency management 518 relies on communications and technology systems 560. Policies from local, state, federal, and international government agencies are inputs to airport operations, as indicated by arrow 522. For example, communications and technology systems 560, along with infrastructure upgrades to 5G cellular technology, have caused government agencies to enact policies that impact airport operations.

[0048] Although Figure 5 illustrates one example of a complex ecosystem that is an airport 500, various modifications can be made to Figure 5. For example, various components of Figure 5 may be combined, further subdivided, duplicated, omitted, or rearranged, and additional components may be added according to particular needs.

[0049] 6 illustrates an exemplary data stream 600 to an electronic device 100 associated with an airport according to the present disclosure. The data stream 600 may be stored in the database 132 and analyzed by the DT 114. The embodiment of the data stream 600 illustrated in FIG. 6 is for illustrative purposes only. Other embodiments may be used without departing from the scope of the present disclosure.

[0050] The data stream 600 may be received from the service provider server 210 (FIG. 2). The data stream 600 includes flight schedule data 602, cargo route data 604, fleet analysis data 606, demographic data 608, rideshare data 610, traffic data 612, and transit data 614. The DT 114 may predict air traffic based on the flight schedule data 602. The DT 114 may predict a load factor value based on the cargo route data 604, which may include the value (e.g., volume, weight, and / or monetary value) of the cargo being transported. The load factor value may be the utilization rate of the number of seats and / or cargo space on an aircraft. The DT 114 may predict the value of vendor supplies based on the fleet analysis data 606. The DT 114 may determine the number of electric vehicles at an airport and the number of requested, available, or occupied electric vehicle charging stations based on the demographic data 608. For example, the demographic data 608 may indicate that a parking permit has been issued for an electric vehicle associated with the employee and / or employee badge ID. The demographic data 608 may indicate a request to reserve an electric vehicle charging station. The DT 114 may predict the value of CAVs, bus shuttles, taxi vehicles, and light rail vehicles based on at least one of the demographic data 608, rideshare data 610, traffic data 612, and transit data 614.

[0051] 7 illustrates an exemplary user interface 700 generated by the DT 114 and displayed by the user device 214 according to the present disclosure. The embodiment of the user interface 700 illustrated in FIG. 7 is for illustrative purposes only. Other embodiments may be used without departing from the scope of the present disclosure.

[0052] The user interface 700 displays computer-aided design (CAD) renderings of a road 702 through the airport and airport buildings 320, as well as floor plans in, for example, line drawing format. In this example, the second DT 114b includes a digital model of the second physics system 204, including a two-dimensional (2D) digital model of the road 702 in the form of a CAD rendering. The digital model of the second physics system 204 includes the road 702, including multiple sections of roadway for automobiles to access each building 320 at the curbside. The curbside may be the boundary where automobile passengers transition to pedestrians by exiting their automobiles.

[0053] Sensor 130a detects and measures phenomena in the first physical system 202 and transmits sensor data to the first DT 114a. Sensor 130b detects and measures phenomena in the second physical system 204 and transmits sensor data to the second DT 114b. In this example, sensor 130b transmits traffic flow information (including traffic flow values) to the second DT 114b. The second DT 114b links the location of sensor 130b to the traffic flow information received from the sensor, respectively. For example, sensor 130b counts the number of vehicles entering and exiting through the boundary of the second physical system 204, e.g., by passing through a toll booth 708. These numbers may be referred to as the toll booth throughput. Sensor 130b may include a traffic counter that detects the speed at which vehicles are traveling along each section of road 702. The sensors 130b may include traffic cameras located at each lane of a toll booth, at each bridge, at each ramp for curbside access to a building, etc. The traffic cameras may indicate whether a particular lane of a particular section of road contains parked, idling, or moving vehicles, and may indicate the speed of those vehicles. Based on the location of the sensors 130b, the second DT 114b may determine whether a section of road is congested for automobiles, for example, by determining that the received traffic flow value is outside an expected value (or range of expected values). For example, if the traffic counter and traffic camera sensor 130b indicates that the current speed for a section of road is too slow compared to the expected range of speeds for that section of road, the second DT 114b may generate a visual indicator of congestion. The expected speed range may be based on speeds measured by the sensor 130b at the same time on other days, the same day on other years, etc. A speed outside the expected speed range may represent the slowest 5% of speeds previously measured for that section of road.The second DT 114b may update the database 132 to indicate that a section of the road 702 has been determined to be congested for a particular period of time (e.g., start and end timestamps, or duration).

[0054] The user interface 700 displays visual indicators 704, 706 of traffic flow values, including the amount of vehicle traffic in a road area, the amount of pedestrian traffic inside buildings in specific areas (e.g., security checkpoints, gate areas), and the amount of pedestrian traffic outside buildings in curbside areas. The visual indicators 704 indicate road vehicle traffic, and the visual indicators 706 indicate pedestrian traffic. The visual indicators 704, 706 are different colors to indicate different traffic flow values, such as green, light teal, dark blue, yellow, orange, and red, for various ranges of traffic flow values ​​(e.g., ranges based on standard deviations and / or average values).

[0055] For pedestrians, a green visual indicator 706g in a curbside area may indicate a pedestrian traffic volume within a first range of values ​​(e.g., a normal range for pedestrians, or a range relative to a reference, expected, or average value). A light teal visual indicator 706t in a gated area may indicate a pedestrian traffic volume within a second range of values, which may be greater than the first range of pedestrian traffic volume values ​​and less than the third range of pedestrian traffic volume values. Similarly, the third through sixth ranges of pedestrian traffic volume are greater than the second through fifth ranges of pedestrian traffic volume values, respectively. A dark blue visual indicator in a gated area may indicate a pedestrian traffic volume within a third range of values. A yellow visual indicator 706y in a security checkpoint area may indicate a pedestrian traffic volume within a fourth range of values. An amber visual indicator 706o may indicate a pedestrian traffic volume within a fifth range of values. A red visual indicator 706r in a gated area may indicate a pedestrian traffic volume within a sixth range of values.

[0056] Similarly, for vehicles, a green visual indicator 704g along road 702 may indicate an amount of vehicle traffic within a first range of values ​​(e.g., a normal range for vehicles, or a range relative to a reference, expected, or average value), or that the current speed of vehicle traffic is within a normal range of vehicle speeds. An amber visual indicator 704o diagonally across road 702 may indicate the speed of vehicle traffic accelerating or decelerating to enter or exit a toll booth (including multiple lanes with toll booths), or may indicate an amount of vehicle traffic within a fifth range of values.

[0057] The DT 114 can associate (e.g., link) each of the terminal buildings 320a-320e with a route (or multiple routes) for passengers entering the building. Passengers entering the building can include automobile passengers entering the building as workers whose workplaces are within the building, and passengers ticketed for flights on aircraft (i.e., transportation vehicles) scheduled to depart from a particular boarding gate (e.g., boarding location, arrival / departure gate) within the building. Passengers entering the building can take a route that includes one or more sections of roadway 702 for automobiles to access the building. Passengers accessing a boarding location within the building can take a route that includes at least one access point within the building for pedestrians to pass through (e.g., security screening checkpoint). To create a building-to-route association, DT 114 can associate a particular building, such as a building named Terminal A, with each section of the route including a section of road adjacent to Terminal A's curbside, a section of road that is a bridge to the terminal's curbside, a section of road that is a ramp to the bridge, and a section of road connecting the ramp to a toll booth for vehicles entering physical system 202 via road 702. Additionally, DT 114 can associate Terminal A with an alternative second route for passengers entering public areas within the building (e.g., workers, people without boarding passes) and a different alternative third route for passengers entering restricted areas within the building (e.g., ticketed passengers). Restricted areas include boarding gates. Restricted areas are for people who have passed through a security screening access point, such as ticketed passengers or flight crew members. For example, physical system 202 can include an inter-terminal transfer train rail service that operates stations within each restricted area of ​​building 320.For example, a ticketed passenger scheduled to depart from a boarding location within Terminal A may take an alternative third route by passing through a security screening access point into a restricted area of ​​another building 320, such as Terminal B, and then boarding an inter-terminal transfer train rail service to the restricted area of ​​Terminal A.

[0058] While Figure 7 illustrates one example of a user interface 700, various modifications can be made to Figure 7. For example, various components of Figure 7 may be combined, further subdivided, duplicated, omitted, or rearranged, and additional components may be added according to particular needs. As specific examples, visual indicators 704 and 706 may have different colors than those described above, may have hash patterns instead of colors, or may blink at different blink rates.

[0059] FIG. 8 illustrates an example list 800 of user-selectable state options displayed by the DT 114 in accordance with the present disclosure. FIG. 9 illustrates a user interface 900 showing vehicle and pedestrian traffic flows (actual or predicted) generated by the DT 114 in response to selecting a first state option 802 (shown as "No New Policy—Current Status") from the list of user-selectable state options 800 of FIG. 8. FIG. 10 illustrates a user interface 1000 showing predicted vehicle and pedestrian traffic flows generated by the DT 114 in response to selecting a third state option 804 (i.e., a new policy of "Balance Traffic Among Terminals") from the list of user-selectable state options 800 of FIG. 8. FIGS. 8-10 are used to describe at least three different ways in which the DT 114 can be used: (1) to undergo a machine learning (ML) model training process, (2) to perform simulations, and (3) to perform real-time operational control. To facilitate the explanation of Figures 8-10, the DT 114 with the traffic optimizer 138a (Figure 2) will be simply referred to as the DT 114. As the DT 114 undergoes an ML model training process, it learns historical conditions and the historical outcomes that result from those historical conditions. During ML training, the DT 114 receives inputs, which are historical data called training conditions, and thereby learns that the training conditions are the types of data it accepts as input conditions. Also during ML training, the DT 114 receives historical outcomes corresponding to the training conditions and learns that the historical outcomes are the types of data it outputs as predictions. In this case, the historical outcomes can include historical measurements of traffic flow data (e.g., vehicle speeds, the number of vehicles that have traversed a particular section of road from end to end), and the training conditions can include status information (e.g., open / closed lanes, whether the road is in a construction zone, whether all lanes of a multi-lane section of road are open or some lanes are closed, etc.). The set of pre-trained conditions includes each training condition that the DT 114 learns through ML training.After ML training is complete, the DT 114 can be used to run a simulation, where the DT 114 receives input data, which can be historical or real-time data, and outputs predictions that represent future / predicted outcomes. The predictions include predicted traffic flow data, for example, predicted vehicle speeds, predicted road congestion, etc. Also, after ML training is complete, the DT 114 can be used to perform real-time operational control, such as in real-time operational scenarios where both the input and output of the DT 114 are real-time data.

[0060] 8, the DT 114 can output a list 800 to a display device connected to the electronic device 100 or to a display of the user device 214. The embodiment of the list 800 shown in FIG. 8 is for illustrative purposes only. Other embodiments can be used without departing from the scope of the present disclosure.

[0061] In the illustrated example, the user can select at least one state option from the list of eight state options. The first state option 802 is the current state and has no new policy. When the first state option 802 is selected, the input and output of the DT 114 are real-time data (or near-real-time data). That is, the DT 114 displays a digital representation of the current conditions and current measurements of the traffic flow.

[0062] The second state option is shown as "TNC Remote Curb for Drop-Off / Pick-Up." TNC is an abbreviation for transportation network companies such as Uber™ and Lyft™. When the second state option is selected, the DT 114 can control traffic information signs to display a message (e.g., a predetermined message) to the TNC driver instructing them to relocate their drop-off / pick-up location to the specified new destination, i.e., the TNC Remote Curb. When the TNC driver reads the message corresponding to the second state option, the driver redirects away from the original route to the original destination (e.g., the curbside at Terminal A with vehicle lane closure 814) and toward the new route to the specified new destination (i.e., the TNC Remote Curb).

[0063] The eighth state option is shown as "Change Curbside Allocation." When the eighth state option is selected, the DT 114 can balance traffic by controlling traffic information signs to display messages instructing motorists to redirect away from their original destinations associated with the vehicle lane closure 814 (or other locations the policy maker wants vehicles or pedestrians to avoid). For example, if all of the security checkpoint lanes inside the Terminal A building are closed, the DT 114 can balance pedestrian and vehicle traffic by controlling traffic information signs and pedestrian information signs to display messages instructing passengers (vehicle passengers and ticketed passengers) to drop off / pick up at the curbside at Terminal C and go through the security checkpoint lanes at Terminal C. The DT 114 can be configured with other predetermined messages corresponding to different conditions that the DT 114 is trained to recognize.

[0064] The third state option 804 balances traffic between terminal buildings. To make a selection from the list 800, user input is received at the user device 214 or at a peripheral input device (e.g., a keyboard or mouse) connected to the electronic device 100. In this example, the policy maker user selects the third state option 804, which is visually highlighted (e.g., darkened text) compared to unselected items in the list 800. In some embodiments, the third state option 804 causes the DT 114 to simulate the other options in the list 800, individually or in various combinations. Based on these simulations, the DT 114 can determine which of the other options, or which combinations, best meet the specified objectives. For example, the DT 114 may be configured with objectives such as reducing congestion in curbside areas, maximizing roadway speeds, prioritizing decongestion on bus routes over other objectives, equalizing the number of vehicles passing through various sections of roadway, or equalizing the number of ticketed passengers passing through each of the security access points in the second system 204. That is, in some embodiments, the DT 114 may automatically select from the list 800 and perform operational control based on the selection. For example, to balance traffic between terminals, the DT 114 may identify that the corresponding objective is to equalize the number of vehicles passing through various sections of roadway. To achieve this, the DT 114 may control traffic information signs to display messages instructing motorists to avoid areas associated with congestion (e.g., informing the driver that the road is closed at the curbside of Terminal A) and suggest that the driver redirect to one or more new destinations. The messages may specify one or more new destinations based on congestion measurements; for example, a message may inform the driver that the estimated time to arrive at the curbside of Terminal C is two minutes.The two minutes can be estimated based on the estimated travel time from the physical location of the traffic sign to the new destination.

[0065] The fourth state option is shown as "Taxi, Limousine Pick-up and Drop-off (%)." When the fourth state option is selected, the DT 114 can balance traffic by controlling traffic information signs located along designated routes for taxi and limousine drivers and displaying messages informing taxi / limousine drivers of the number of taxi vehicles already in the taxi queue for a particular terminal building or the current occupancy percentage of the lane reserved for the taxi queue. That is, the DT 114 can output messages to help taxi drivers avoid areas associated with congestion.

[0066] The fifth state alternative is shown as "Schedule shuttles to / from RCA (Rental Car Area)." When the fifth state alternative is selected, the DT 114 can balance traffic by adjusting the number of rental car buses in circulation, as shown in FIG. 11D. When the fifth state alternative is selected, the DT 114 can balance traffic by modifying the routes assigned to the rental car buses, as shown in FIG. 11E. For example, the DT 114 can preconfigure multiple rental car bus routes and switch the bus fleet from a currently assigned route to a newly selected route. The DT 114 can generate automated messages to multicast the new route assignments to dashboard-mounted output devices (including transceivers) inside the bus dashboard.

[0067] A sixth state alternative is shown as "Schedule shuttles to / from parking." When the sixth state alternative is selected, the DT 114 may balance traffic by adjusting the number of shuttles (e.g., shuttle buses) in operation and modifying the routes assigned to the shuttles in operation in a manner similar to that performed when the fifth state alternative is selected.

[0068] The seventh state option is shown as "Public Transportation Recommendation." When the seventh state option is selected, the DT 114 can balance traffic by sending a predetermined message, such as an SMS message or a notification message, to the subscriber device via a mobile application installed on the subscriber device. The message corresponding to the seventh state option can instruct the owner of the subscriber device (e.g., a smartphone owned by a traveler or personnel working within the physical systems 202, 204, 206) to change their mode of transportation to public transportation (e.g., light rail). For example, if all lanes at a toll booth are closed or if a roadway entrance / exit to an airport is closed, the DT 114 can push a notification message to display a predetermined message on the subscriber device informing the subscriber of the road closure, the expected delay time associated with the road closure, or the time window when public transportation is recommended.

[0069] The DT 114 can determine whether to display a list of options, such as list 800. That is, the DT 114 displays list 800 when at least one condition from a predefined list of conditions is met. In this example, each condition in the predefined list of conditions defines a condition that blocks a route for a passenger attempting to enter a building 320, such as Terminal A. One condition from the predefined list of conditions is met when a multi-lane section of roadway required for a vehicle to access Terminal A is impassable, e.g., when each of multiple lanes (812 and 814) in that section of roadway has a simultaneous vehicle lane closure. Another condition from the predefined list of conditions is met when a single-lane section of roadway required for a vehicle to access Terminal A is impassable. The predefined list of conditions is not limited to conditions related to sections of roadway within the second physical system 204, but may also include conditions defined by at least one access point within a building or at least one passageway within a building (e.g., security checkpoint, concourse, gate area). For example, the predefined list of conditions may include a condition that is met when each of multiple access points at terminal A is closed or impassable. The predefined list of conditions is not limited to conditions related to impassable or closed sections of the route. The predefined list of conditions may include conditions that are defined by multiple factors, such as the portion of the route that is congested, the congestion time period associated with the congestion state (e.g., the duration during which sensors 130a-130b detect congestion), and the number of people that are expected to pass through the congested portion of the route if there is no congestion relative to the time.

[0070] DT 114 displays list 800 in response to receiving data 806 indicating a vehicle lane closure on a particular section of roadway, data 808 indicating high demand for airport transportation, or both 806 and 808. In particular, list 800 is displayed when a vehicle lane is closed on a section of roadway required for motor vehicles to access a terminal building, and a large volume of passengers are scheduled to enter or exit the terminal building (i.e., access boarding gates within the terminal building) during a time margin 816 that includes the time during the vehicle lane closure (and margin times before and after). Time margin 816 is calculated by DT 114 based on received data 806 and 808. As an example of data 806, traffic data 612 ( FIG. 6 ) may include the open / closed status of a particular section of roadway, and data 806 includes the closure status. As examples of data 808, cargo route data 604 (Figure 6) may include the volume or weight of cargo transported by an aircraft; flight schedule data 602 (Figure 6) may include the number of sold / available or occupied / empty passenger seats, scheduled takeoff / landing times, and terminal building IDs associated with each scheduled departure / arrival gate.

[0071] In certain embodiments, the DT 114 may generate and display a user interface 810 showing multiple sections 812 of roadway required for vehicles to access various terminal buildings. The user interface 810 indicates vehicle lane closures 814, such as by highlighting (e.g., red, flashing, enlarging) the particular section of roadway that has the closure.

[0072] 9, the policy maker-user selects a first state option 802 (i.e., the current state) from the list of user-selectable state options 800 of FIG. 8. In response to selecting the first state option 802, the DT 114 displays a user interface 900 showing the vehicular and pedestrian traffic flow (actual or predicted) during the time margin 816. In this user interface 900, some road sections are highlighted using a red visual indicator 704r to indicate extremely heavy vehicular traffic, such as within the sixth value range.

[0073] In a training embodiment, user interface 900 shows actual traffic flow of vehicles and pedestrians during time margin 816. For example, (historical or current) measurements of traffic flow data (from sensors 130a-130b) can be input to traffic flow optimizer 138 to train optimizer 138 to output user interface 900 as traffic flow resulting from a training condition in which a (historical or current) vehicle lane closure 814 on a particular section of roadway (highlighted in user interface 810 of FIG. 8 ) coincides with a (historical or current) measurement of high demand (similar to 808).

[0074] In an operational embodiment, the user interface 900 shows predicted vehicular and pedestrian traffic flow during the time margin 816. After the DT 114 is trained, it can be used in a stress test scenario or in a real-time operational scenario. When the DT 114 is used in a stress test scenario, the data 806 includes hypothetical parameters that are input to the DT 114 as input conditions. The trained DT 114 recognizes that the input conditions are similar to the training conditions (e.g., exhibit a similar pattern to the training conditions) and generates predicted traffic flow-based similarities between the input conditions and the training conditions. For example, the DT 114 can predict the amount of pedestrian traffic in an above-average range of values ​​(e.g., a sixth range of values) inside a building in an area near a boarding gate where the flight schedule indicates a delayed flight associated with the boarding gate.

[0075] In a real-time operating scenario, the data 806 includes traffic flow information detected in real time by sensors 130b sensing the flow of road vehicle traffic and sensors 130a sensing the flow of pedestrian traffic inside a building. The DT 114 may include a traffic flow optimizer that generates predicted traffic flows based on the similarity between the real-time traffic flow information (as input conditions) and training conditions.

[0076] 10, the policy maker user selects the third state option 804 (i.e., a new policy for "balance traffic among terminals") from the list of user-selectable state options 800 of FIG. 8. In response to selecting the third state option 804, the DT 114 displays a user interface 1000 showing the predicted flow of vehicles and pedestrians during the time margin 816. The user interface 1000 also indicates that some of the road sections shown are highlighted using a green visual indicator 704g to indicate a low volume of vehicle traffic, fewer of the road sections are highlighted with a yellow or orange visual indicator 704y or 704o, and even fewer are highlighted with a red visual indicator 704r.

[0077] By balancing traffic between terminal buildings, the DT 114 indicates that some road sections that currently did not have a visual indicator in the user interface 900 (FIG. 9) now have a visual indicator 704 in the new policy user interface 1000 (FIG. 10). By balancing traffic between terminal buildings, fewer vehicles will be waiting with their engines idling along airport roads, thereby reducing air pollution released into the airport's natural systems 510.

[0078] While Figure 10 shows an example of predicted traffic flow of vehicles and pedestrians, various modifications can be made to Figure 10. As a specific example, if another state option other than the third state option 804 is selected from the list 800 of Figure 8, the DT 114 generates and outputs a user interface showing different values ​​of traffic flow. As another specific example, the DT 114 allows the user to zoom in on the user interface to a granularity where the visual indicator 704 represents the space occupied by a single vehicle within a lane of a road section.

[0079] 11A illustrates a generator function 1102 within DT 114 for generating a microscopic simulation 1104 including predicted traffic flow of vehicles and pedestrians in accordance with the present disclosure. The microscopic simulation 1104 may be displayed as a user interface similar to user interface 1000 of FIG. 10. Other embodiments may be used without departing from the scope of the present disclosure.

[0080] Generator function 1102 generates microscopic simulation 1104 based on input 1106 including years for scaling demand 1108 and corresponding daily travel time series 1110, travel mode distribution 1112, and recorded on-curve dwell times 1114. For example, generator function 1102 converts 1116 years for scaling demand 1108 and corresponding daily travel time series 1110 into people information 1118 entering a physical system (such as first physical system 202, or airport building 320). People information 1118 may include the number of passengers, the number of ground crew not boarding the aircraft, and the number of air crew boarding the aircraft.

[0081] In certain embodiments, the transformation 1116 is also based on input data such as population growth values ​​for the local geographic region that includes the people's trip origin locations. For example, Figure 11B shows an "Origin by Mode Type" user interface generated by the DT 114, which shows eight examples of local geographic regions defined by trip purpose (i.e., business or personal), by mode of transportation to the airport (e.g., drop-off, parking, taxi, or TNC) that may be included in the travel mode distribution 1112, and by the origin from which the person's trip to the airport began (i.e., trip origin location, hotel, residence, work).

[0082] Based on the people information 1118, the generator function 1102 assigns 1120 boarding gates by outputting gate assignment information 1122. The gate assignment information 1122 can be based on configuration or user selections for reducing peak power and energy consumption within a physical system (e.g., within a particular terminal building 420 or set of buildings 320). In particular, to reduce peak power and energy consumption within a physical system, the generator function 1102 can perform a "promote public transportation" or "schedule shuttle" function, as shown in list 800 of FIG. 8. For example, FIGS. 11D-11E show that the DT 114 can optimize and modify existing shuttle bus routes to produce energy and emission reductions of 25-50%.

[0083] Based on the gate assignment information 1122 and the travel mode distribution 1112, the generator function 1102 predicts values ​​1124-1142 of how many people are likely to enter the airport boundary using taxi, transit, limousine, rental car center, private car, parking, or TNC, respectively. Additionally, the generator function 1102 includes a mode-route algorithm 1144 for generating predicted routes 1146 (including predicted parking areas) according to the transportation mode values ​​1124-1142 for people who are expected to enter the terminal building corresponding to the gate assignment information 1122. In certain embodiments, a respective predicted route 1146 is generated for each of the people considered in the people information 1118. The microscopic simulation 1104 shows a graphical representation 1146a of the predicted routes 1146 entering and exiting roads that access terminal building A 1150. The DT 114 sends the microscopic simulation 1104 output from the generator function 1102 to a display device.

[0084] In certain embodiments, the DT 114 estimates the amount of people expected to be present in a particular area (e.g., security area, gate area) in a particular terminal building (e.g., terminal building A 1150) at the same time. The DT 114 estimates the change in indoor air temperature in the particular area based on the level of congestion or the estimated amount of people expected to be present in the particular area at the same time. The DT 114 outputs a control signal to the HVAC controller to modify control parameters to maintain the indoor air temperature in the particular area within a room temperature range. In certain embodiments, the DT 114 can selectively prioritize energy conservation or maintaining a particular room temperature setpoint over the other.

[0085] Although Figure 11A illustrates one example of generator functionality 1102, various modifications can be made to Figure 11A. For example, various components of Figure 11A may be combined, further subdivided, duplicated, omitted, or rearranged, and additional components may be added according to particular needs.

[0086] 12 illustrates an example of a digital twin-based method 1200 for reducing peak power and energy consumption in a physical system according to the present disclosure. For ease of explanation, the method 1200 is described as including the use of the DT 114 of FIG. 1, which may be used within the system 200 of FIG. 2. However, the method 1200 may include the use of any other suitable device in any other suitable system.

[0087] As shown in FIG. 12 , in block 1202, the DT 114 receives freight / cargo movement data. In block 1204, the DT 114 generates a demand forecast based on the received freight / cargo movement data. In block 1206, the DT 114 generates a microscopic simulation based on the demand forecast, which may be overlaid on a street map. In block 1208, the DT 114 generates a metric-based analysis result based on the microscopic simulation. The DT 114 outputs a user interface showing the metric-based analysis result, which is displayed via a display device. In block 1210, a list of policies and future scenarios is output by the DT 114, for example, displayed via a user interface.

[0088] More specifically, in block 1202, the freight / cargo movement data 1212 may include freight route data 604 and fleet analysis data 606. The freight / cargo movement data 1212 may be received from the service provider server 210, for example, from a third-party freight company (e.g., UPS™), a transportation analysis company (e.g., INRIX™), an airline (e.g., American Airlines™), or a navigation company (e.g., TomTom™).

[0089] More specifically, in block 1204, the demand forecast 1214 generated by the DT 114 includes a predicted number of vehicles and the corresponding time when the vehicles are expected to be in the physical system. Each predicted vehicle count is based on a normal range, a periodicity of time (e.g., time of day / week / year), and freight / cargo movement data 1212. The DT 114 obtains a normal range (e.g., minimum, maximum, and average) for periodic measurements of the vehicle count. For example, the normal range may be based on historical measurements captured every 30 minutes per day over a 10-year period. The user interface 1216 includes a shaded region representing the normal range, which is labeled "90% Cl," indicating that 90% of the historical measurements fall within the normal range and the remaining 10% are outliers. The user interface 1216 includes a curve showing the actual value of the vehicle count. The user interface 1216 includes plot points representing each predicted value. Another area of ​​the user interface 1216 shows a bell curve overlaid on a bar graph, which represents the number of vehicles versus the residual.

[0090] More specifically, in block 1206, the microscopic simulation may simulate microscopic simulation data 1220 and overlay a street map 1218 of a local area surrounding the physical system (e.g., a 10-mile radius around an airport). The microscopic simulation data 1220 includes cargo / cargo vehicle routes, e.g., routes for each cargo / cargo vehicle predicted to travel within the physical system (e.g., an airport). The microscopic simulation data 1220 includes fuel efficiency per cargo / cargo vehicle. The fuel efficiency may take into account the volume / weight of the cargo being transported. For each pairing of a cargo / cargo vehicle and its route, the microscopic simulation data 1220 includes corresponding emissions values ​​attributable to the cargo being transported along the route and corresponding fuel consumption values.

[0091] More specifically, in block 1208, based on the microscopic simulation data 1220, the DT 114 calculates 1222 fuel consumption values, or costs (e.g., investments) that can be paid for air pollution equipment, or road maintenance / repair / upgrades. Also, based on the microscopic simulation data 1220, the DT 114 calculates 1224 impacts, such as pollution values ​​(e.g., arrow 502 in FIG. 5 ). Further, in block 1208, metric-based analysis results 1226 are generated based on the microscopic simulation data 1220, the impacts 1224, and a selected policy from a list of policies and future scenarios 1228.

[0092] More specifically, in block 1210, an exemplary list 1228 of user-selectable policies and future scenarios is displayed to the policy-maker user. The list 1228 may be displayed and receive user input in a manner similar to the exemplary list 800 of FIG. 8. When the first policy 1230a (denoted as “No New Policy—Current Condition”) is selected, the DT 114 generates a metric-based analysis result 1226 corresponding to the current conditions. When any of the second through fifth policies 1232-1236 is selected, the DT 114 detects a policy change and generates a metric-based analysis result 1226 corresponding to the selected policy, which may include repeating the functions of blocks 1206-1208 according to the detected policy change. For ease of explanation, it is assumed that the policy-maker user selects the second policy 1232 (denoted as “Relocate Cargo Facility”).

[0093] The DT 114 outputs the metrics-based analysis results 1226 as a user interface, which shows a fuel usage comparison graph, i.e., gallons of fuel (y-axis) versus minutes of a day (x-axis). In the user interface, peak terminal times 1238 (e.g., 6:30 AM to 7:30 AM) are shown in the shaded area, gallons saved 1240 by the selected policy 1232 are shown in a first color (green) as a negative value, and gallons lost 1242 by the selected policy 1232 are shown in a second color (red) as a positive value. For each policy in the list of policies and future scenarios 1228, the DT 114 estimates the corresponding metrics-based analysis results 1226 (e.g., fuel usage and emissions attributable to cargo).

[0094] While Figure 12 illustrates an example of a method 1200 for reducing peak power and energy consumption in a physical system, various modifications may be made to Figure 4. For example, while shown as a series of steps, various steps in Figure 4 may overlap, occur in parallel, occur in a different order, or occur any number of times. As a specific example, the functions in blocks 1208 and 1210 may generally operate in parallel, and thus their associated steps may be performed in parallel.

[0095] 13A-13C (collectively FIG. 13) illustrate an example of a digital twin-based method 1300 for predicting a delayed departure of a transportation vehicle based on traffic flow information in a physical system, according to the present disclosure. In FIG. 13A, the method 1300 includes analyzing information related to the first system 202 to predict a likelihood of a departure gate delay. In FIG. 13B, the method 1300 includes analyzing information related to the second system 204 to predict a likelihood of a departure gate delay. In FIG. 13C, the method 1300 includes determining whether to reassign a gate based on an on-time / late determination for the departure gate. The embodiment of the method 1300 illustrated in FIG. 13 is for illustrative purposes only; other embodiments may be used without departing from the scope of the present disclosure. The method 1300 is implemented by an electronic device including at least one processor, such as the electronic device 100 having the processing device 102 executing the DT 114 of FIG. 1 or the system 200 of FIG. 2. For ease of explanation, the method 1300 is described as being performed by a system 200 having a processing device that executes the DTs 114a-114c of Figure 2. In the description of Figure 13, boarding time refers to the end of a scheduled boarding period.

[0096] Operating a flight with too low a load factor means too many empty seats or too much empty cargo space, which is uneconomical for an airline. Sometimes, airlines may decide to delay a flight to increase the load factor. For example, the decision to delay a flight may mean waiting to board later ticketed passengers who arrive after boarding time. For example, if an aircraft has a total of T seats, including U unsold seats and S sold seats, the expected load factor is defined as S / T. The airline knows that the flight must depart on time even if some of the sold seats remain empty at boarding time (for example, due to passenger illness or passenger lateness). The on-time load factor is defined as K / T. Here, an aircraft will depart on time if K passengers board the aircraft at or before the boarding time, where K is the sum of K and S.L where T L indicates the acceptable loss threshold number of empty seats that meets the on-time load factor condition. System 200 assumes that the aircraft will depart on time if the on-time load factor condition is met. Method 1300 calculates the loss threshold T L system 200 can predict a late departure of an aircraft when the number of delayed passengers exceeds L, generating an increased likelihood that the airline will decide to delay the flight. A delayed departure decision may cause the aircraft to burn jet fuel longer than originally planned while parked and idling at the gate, which means increased pollution within the third physical area 206. Delayed departure of an aircraft based on an excess number of delayed passengers (L). If, prior to boarding time, an airline receives and relies on probability information from system 200 indicating that L passengers are likely to be delayed, the airline can decide to convert the L waiting passengers to on-time passengers to meet the on-time load factor, for example, by providing boarding passes to waiting passengers physically located at the boarding location. That is, the probability information and predictions generated as part of method 1300 can reduce pollution, increase on-time departures that improve the traveler experience, and reduce gate reassignments.

[0097] Referring to FIG. 13A, the system 200 receives at least one flight schedule 1302. Each of multiple airlines utilizing the third physical system 206 may possess one or more service provision servers 210a (referred to as airline 210a for simplicity) that generate and transmit the flight schedule 1302 to the system 200 via the network connection 208. The system 200 is communicatively coupled to the airline 210a via the network connection 208a. For ease of explanation, the flight schedule 1302 represents flight schedules received from multiple airlines. The flight schedule 1302 may include flight-specific information, aircraft-specific information, and an airline ID indicating the source of the flight schedule. The aircraft-specific information may specify the total number of passenger seats and total cargo space. The flight schedule 1302 may include flight dates, flight IDs (flight numbers), a timetable of scheduled departure and arrival times, boarding times, departure airports, arrival airports, etc.

[0098] In block 1304, the system 200 assigns boarding gates to each flight in the flight schedule 1302 and transmits gate assignment information 1306 to the airline 210a. In some embodiments, the system 200 generates the gate assignments 1306 based on the received flight schedule 1302 using the generator function 1102 of FIG.

[0099] Airlines sell inventory, including seats and cargo space, to customers. A person who purchases a ticket from an airline is called a ticketed passenger for a particular flight ID linked to the ticket. The ticket may reserve a seat and reserve a certain amount of cargo space on an aircraft linked to the flight ID. The airline 210a includes a software application that allows the ticketed passenger to check in for a flight by using a user device UE1 approximately 24 hours before the flight's scheduled departure time. Typically, the ticketed passenger is not located in any of the physical systems 202, 242, 206 while checking in, but instead is located at home or at work. In block 1308, the airline 210a provides the ticketed passenger with a boarding pass, such as an electronic boarding pass transmitted to a mobile app on the UE1, in response to the check-in.

[0100] A security screening entity operates at least one security screening access point (AP) within each terminal building 320 and may operate multiple security screening APs per building 320. Generally, security screening entities are not owned or controlled by the owner of system 200. At each security screening AP location, the security screening entity performs primary security screening and then secondary security screening. In block 1310, the security screening entity performs primary security screening by using a scanner device to scan a person's boarding pass or photo ID. A security screening entity may operate multiple scanner devices at the same primary security screening location. In database 132, the location of each security screening AP may be linked to the security screening AP's ID and the scanner device's ID. To distinguish multiple access points within the same building, the location of each security screening AP may designate a building and a specific area within the building (e.g., the north side of Terminal A or the south side of Terminal A).

[0101] After a photo ID is scanned by a scanner device, the security screening AP includes one or more lanes through which ticketed passengers pass to undergo secondary security screening (e.g., carry-on bag screening). The location where the scanner device scans the photo ID is sometimes referred to as the entrance to the secondary security screening lane. The location where secondary security screening is completed is sometimes referred to as the exit of the secondary security screening lane or the exit of the security screening AP. The security screening entity owns one or more service provision servers 210b (referred to as security APs 210b for simplicity) that track information 1312 including the location and ID of each security screening AP and the corresponding number of open and closed secondary security screening lanes at each security screening AP. The system 200 receives information 1312 from the security APs 210b via a network connection 208b.

[0102] In block 1314, system 200 adjusts the travel time from the primary security screening location of a particular security screening AP to a particular boarding location (e.g., a boarding gate) within first physical system 202 based on information 1312. For example, database 132 stores the travel time from a security scanner device located on the south side of Terminal A to each boarding gate within the Terminal A building. Similarly, for each boarding gate within the Terminal A building, database 132 stores another travel time from a security scanner device located on the north side of Terminal A. In some embodiments, the adjustment to the travel time is obtained from a look-up table (LUT). In this example, the LUT is part of DT 114, but in other embodiments, the LUT can be stored in database 132. Database 132 can store the average walking time from the exit of a secondary security screening lane of a particular security screening AP to a particular boarding location, which can be a value that does not change based on the number of lanes open at the security screening AP. The LUT may include a relationship between the number of open secondary security screening lanes at a particular security AP and the secondary screening time it takes for a person to pass through those open lanes (i.e., extending from the scanner device to the exit of the secondary security screening lane). In this LUT, an increase in the number of open lanes directly correlates to a decrease in the secondary screening time it takes for a person to pass through the secondary screening lane. That is, to adjust travel time, system 200 retrieves the average walking time in database 132 and combines it with the secondary screening time retrieved from the LUT. In another embodiment, the travel time from the security AP scanner device to a particular boarding location is represented as a function that varies according to the number of open lanes at the security screening APs located on the north and south sides, respectively.In this function, an increase in the number of open lanes is associated with a decrease in travel time from security checkpoints to each boarding gate, and a decrease in the number of open lanes is associated with an increase in travel time.

[0103] In block 1316, system 200 determines the likelihood that a passenger will be late to their boarding location based on information 1312 including the number of open secondary security lanes. More specifically, system 200 can estimate the number (L) of ticketed passengers who are likely to arrive at their boarding location too late, i.e., after their boarding time. System 200 can send likelihood information 1318 to airline 201a, including L as the estimated number of late passengers. For example, if the security screening APs within Terminal A's building are closed and gate assignments 1306 remain unchanged, it is likely that a large proportion of ticketed passengers will arrive at Terminal A attempting to access a boarding gate within Terminal A before realizing that they will need to detour to a different building (e.g., Terminal C) to find an open security screening AP. Similarly, if the number of open secondary security lanes is too low for a terminal building 320, the number of passengers who arrive too late to their boarding location will increase.

[0104] In block 1320, the security AP 210b receives passenger ID information associated with the photo ID card scanned by the security scanner device. For example, the passenger ID information includes the passenger's name, date of birth, and gender. The security AP 210b includes a secure flight database in which airline-provided passenger ID information is linked to flight details for ticketed travel for that day (e.g., the next 24 hours). The security AP 210b determines whether the passenger ID information received from the security scanner device corresponds to (e.g., matches) passenger ID information stored in the secure flight database, and if so, sends a passenger arrival indication 1322 to the system 200. For simplicity, the passenger arrival indication 1322 is referred to as a match indication 1322. The match indication 1322 may include a flight ID and the number of passengers (e.g., number of boarding passes) corresponding to the passenger ID information. For example, the secure flight database may link an adult's passenger ID information to the flight details of any children accompanying that adult. In this embodiment, system 200 avoids receiving passenger ID information because match indication 1322 does not include passenger ID information. Similarly, airline 210a and security 201b can update the Secure Flight database without transmitting passenger ID information to system 200. In some embodiments, system 200 receives match indication 1322 each time a scan of a photo ID card (or boarding pass) by a security scanner device causes security AP 210b to determine that corresponding passenger ID information is stored in the Secure Flight database.

[0105] In block 1324, system 200 determines or otherwise confirms that the number of passengers corresponding to match indication 1322 have the same physical location as the security scanner device in block 1310. Based on the flight ID corresponding to match indication 1322, system 200 identifies a corresponding boarding location based on gate assignment 1306 and identifies a corresponding boarding time based on flight schedule 1302. Based on the identified boarding location, system 200 identifies a corresponding concourse walk time determined in block 1314 and calculates an estimated time of arrival (ETA) based on the current time plus the corresponding concourse walk time.

[0106] In block 1326, system 200 increments the count of N on-time passengers for the flight ID corresponding to match indicator 1322 based on a determination that the ETA calculated in block 1324 is equal to or before the boarding time corresponding to match indicator 1322. On the other hand, if the ETA is after the boarding time, system 200 updates the estimated number of L late passengers to include the number of passengers corresponding to match indicator 1322. That is, when a person undergoes initial security screening, system 200 sends probability information 1328 to airline 210a informing the airline 210a that the flight ID corresponding to match indicator 1322 is likely to have N on-time passengers and L late passengers.

[0107] In some embodiments, as shown in block 1330, system 200 can determine that the ticketed passenger's physical location is at the location of a self-service kiosk inside the building. Inside the terminal building, airline 210a can include a kiosk that allows the ticketed passenger to check in for a flight by using a photo ID scanner or by entering a ticket ID (e.g., a confirmation number provided by the airline), as shown in block 1332. The kiosk includes a computer screen, a photo ID scanner, a payment card reader, and a printer. In response to completing check-in at the kiosk, airline 210a can send passenger arrival instructions 1334 to system 200 that include the location of the kiosk and a flight ID corresponding to the ticket ID.

[0108] The database 132 stores the number of minutes before the flight boarding time when a kiosk-based passenger arrival indication 1334 is received from the airline 210a or an AP-based passenger arrival indication 1322 is received from security 210b. (ふんすう) , which allows the second DT 114b to generate a passenger arrival forecast 1336. From a sampling of flight IDs over a historical period, a graph can be generated from the database 132 where the x-axis includes every minute within 24 hours prior to boarding time and the y-axis includes the amount of ticketed passengers for the particular flight ID. The y-axis can represent the percentage of seats sold, the percentage of total seats on the aircraft, or the percentage of total matched instructions 1334 received for a particular departing flight. Statistical analysis can be performed on this graph to derive one or more trend functions, which can be the passenger arrival forecast 1336. The trend functions can be used to calculate the percentage of passengers that arrive a given number of minutes after the corresponding boarding time. (ふんすう)The passenger arrival forecast 1336 may provide the proportion of ticketed passengers arriving before / after. The passenger arrival forecast 1336 may be generally applicable to any flight departing from the third physical system 206, or a different passenger arrival forecast may be generated based on sampling similarity and applied to a subset of flights having that similarity. After block 1330, the system 200 may generate likelihood information (similar to likelihood information 1318) by using the second DT 114b to estimate L late ticketed passengers who are likely to arrive too late at their boarding location based on the passenger arrival indication 1334 received from the airline 210a and the applied passenger arrival forecast 1336. Such likelihood information may be generated with or without the matching indication 1322.

[0109] To estimate L, the system 200 can receive an estimated wait time for a queue ending at a primary security screening location of a particular security AP located within the first physical system 202. For example, the estimated wait time may be determined by the DT 114a based on data received from a camera sensor 130a or IoT device 136a monitoring the queue, or from a third-party server 210 that generates estimated wait times based on crowdsourced data. For example, the likelihood of L late ticketed passengers may increase if the estimated wait time for each of multiple security APs in a building is too long or if there are too few open security screening lanes. Based on the time a passenger arrival indication was generated by the airline 210a and the expected passenger arrival schedule, the system 200 can use the second DT 114b to determine the likelihood that the L ticketed passengers will arrive at the boarding location after the boarding time for the flight ID corresponding to the ticket ID.

[0110] Additionally, system 200 may calculate an estimated time of arrival (ETA) based on the current time, the estimated wait time for the queue terminating at the primary security screening location for a particular security AP, and the concourse walk time from the particular security AP to the boarding location corresponding to the ticket ID. However, when both passenger arrival indications (i.e., kiosk-based and AP-based) are received, system 200 may generate more accurate likelihood information 1328. In some embodiments, system 200 increments a count of N on-time passengers for a flight ID corresponding to a kiosk-based passenger arrival indication 1334 based on a determination that the ETA (i.e., calculated based at least in part on the estimated wait time for the queue terminating at the primary security screening location) is equal to or before the boarding time for that flight ID.

[0111] Method 1300 continues in Figure 13B, where system 200 analyzes information related to second system 204 to predict potential departure gate delays. In comparison to Figure 13A, system 200 establishes network connection 208c to one or more service provision servers 210c, thereby receiving road condition information regarding local roads external to second system 204. Road conditions (e.g., construction, road closures, impassable congestion) of local roads affect the rate at which road vehicles can enter and exit roads within second system 204 (e.g., road 702 in Figure 7).

[0112] The system 200 receives road traffic flow information 1340 from a sensor 130b associated with a section of road in the second system 204. In block 1342, the system obtains a prediction that the number of vehicles entering and exiting the second system 204 (e.g., toll booth throughput) will change (e.g., decrease). To obtain this prediction, the second DT 114b system 200 can be trained to recognize certain conditions that have previously caused significant changes in the number of vehicles able to enter and exit the second system 204. For example, the second DT 114b can recognize that such a condition is met when a regional interstate that feeds the second physical system 204 includes a road closure condition associated with a section of road within a specified distance from a boundary (e.g., boundary 310 in FIG. 3 ). To recognize such a condition, the second DT 114b can access a LUT that lists highways or interstates that typically feed a large volume of road vehicles into the second system 204. The second DT 114b may also have access to a LUT that lists expected toll booth throughputs and corresponding traffic speeds or other traffic measurements expected from the sensor 130b.

[0113] In block 1344, the traffic counter sensor 130b measures and transmits (e.g., to the second DT 114b) traffic flow information including measurements of actual toll booth throughput or speed as a function of time to the system 200. In block 1346, the traffic camera sensor 130b detects and transmits (e.g., to the second DT 114b) traffic flow information including counts of vehicles taking routes entering and exiting each road to each terminal building to the system 200.

[0114] In block 1348, system 200 uses second DT 114b to adjust the travel time for a route from an entrance of physical system 204 to the curbside of a building, for example, from the entrance of a toll booth (shown in FIG. 7 as toll booth 708) to the curbside of the Terminal A building. The procedure performed in block 1348 is similar to the procedure performed in block 1314 of FIG. 13A. Database 132 stores driving times associated with predefined routes from a particular entrance of physical system 204 (e.g., a particular toll booth) to the curbside of a particular building, which may be a value that varies based on the number of lanes or sections of roadway that are open between these two locations and the speed of travel through these lanes or sections of roadway that are included in the predefined route.

[0115] In block 1350, the system 200 determines the likelihood that each of the N timely passengers will arrive at the boarding location on time. The system 200 may also determine the likelihood that the L late passengers will arrive at the boarding location after the boarding time. The system 200 sends probability information 1352 to the airline 210a, including the likelihood of the N timely passengers and the likelihood of the L late passengers.

[0116] In block 1354, the system 200 predicts the likelihood that the flight ID will be delayed based on the likelihood information 1352. To make this determination, the system 200 obtains parameters 1358 for on-time load factor (e.g., a threshold minimum passenger load factor) and K (e.g., a threshold minimum number of non-empty seats). In particular, if N on-time passengers (obtained from the likelihood information 1352) are greater than or equal to the threshold K, the on-time load factor is met and the system 200 predicts that the flight ID will depart on time. If L late passengers (obtained from the likelihood information 1352) are greater than or equal to the loss threshold T LIf the on-time load factor exceeds 0, the on-time load factor will not be met and the system 200 predicts that the flight ID will have a delayed departure. To help the airline maintain the flight ID's on-time status, the system 200 sends a recommendation 1356 to the airline 210a based on whether the on-time load factor is met. If the on-time load factor is not met, the system 200 sends a recommendation 1356 including a message notifying the airline 210a that the on-time load factor will not be met at the boarding time and recommending converting waiting travelers to ticketed passengers to fill the seats currently reserved for the L delayed passengers. The recommendation 1356 can be displayed on a computer screen associated with an airline employee authorized to make a decision on whether to delay or depart the flight while the on-time load factor is not met. The airline can read the recommendation 1356 received by the airline 210a and begin allowing waiting travelers to fill the L seats before the boarding time, thereby increasing the actual load factor. If at least L waiting travelers fill the seats reserved for the L delayed passengers, the on-time load factor can be met and the flight ID can have an on-time departure from the boarding gate.

[0117] In some embodiments, if the on-time fill rate is not met, the system 200 may obtain an ETA corresponding to each of the L delayed passengers associated with the arrival instructions 1334, 1322. The system 200 may determine whether the flight will arrive within the specified number of minutes (defined according to the obtained ETA). (ふんすう) If a flight is delayed by a specified number of minutes, a specified portion of the L delayed passengers (ふんすう)A message can be generated advising that the passengers will arrive at their boarding location and be able to board the aircraft within 10 minutes. For example, recommendation 1356 can state, "If this flight is delayed by 10 minutes, five of the seven delayed passengers will be able to board," where the ETA for the five delayed passengers is less than or equal to 10 minutes, but the ETA for the two delayed passengers is the largest ETA of L = 7 delayed passengers. If the on-time load factor is met before the scheduled boarding time, recommendation 1356 includes a message recommending on-time departure.

[0118] 13C, method 1300 continues. At block 1360, the airline notifies Air Traffic Control (ATC) via communication to 210d of a determination of whether the gate departure for a particular flight ID has an on-time status or a delayed status. In some scenarios, the airline is not required to notify the airport of this determination of the delayed status, but is required to notify ATC. For example, airline 210a sends a determination notification 1362 to a server system 210d associated with air traffic control (hereinafter ATC 210d) including the delayed status for the flight ID.

[0119] According to an embodiment of the present disclosure, system 200 establishes network connection 208d with ATC 210d. In block 1364, ATC 210d transmits a forwarded decision notification 1366 corresponding to decision notification 1362 to system 200 via network connection 208d. More specifically, by receiving forwarded decision notification 1366, system 200 can identify that decision notification 1362 included a decision of a delay status determination for a specific flight ID. If ATC does not transmit forwarded decision notification 1366, a wait time will occur until system 200 receives information from third-party service 210c indicating a delay status determination for the specific flight ID. If the specific flight ID corresponds to a shared gate, such a wait time may adversely affect another airline's operations. If system 200 receives forwarded decision notification 1366 at the same time that airline 210 sends original decision notification 1362, then without waiting, system 200 can reallocate gates based on delays at shared gates and transmit the new gate assignments to other airlines affected by the delays.

[0120] In block 1368, the system 200 identifies whether the decision to delay a gate departure for a particular flight ID corresponds to a shared gate. For example, an exclusive boarding gate is subject to a lease agreement in which a single airline obtains exclusive rights to use the gate, while a shared boarding gate is subject to one or more lease agreements in which multiple airlines share rights to use the gate. An airline's decision to delay a flight corresponding to an exclusive gate may trigger the airline to perform gate reallocation. However, an airline's decision to delay a flight corresponding to a shared gate may trigger the system 200 to perform gate reallocation. The database 132 stores a list of all boarding gates within the first system 202 and a list of tenants for each boarding gate.

[0121] In block 1370, the system 200 accesses the current gate assignment schedule to determine whether to perform a gate reassignment. If the delay determination for a particular flight adversely affects a co-tenant of a shared gate, the system 200 performs the gate reassignment and sends an updated gate assignment 1372 to the airline 210a. If the delay determination corresponds to an exclusive gate, the system 200 may send an acknowledgement of receipt of the delay determination 1374 to the airline 210a.

[0122] While Figure 13 illustrates an exemplary digital twin-based method 1300 for predicting delayed departures of transportation vehicles based on traffic flow information in a physical system, various modifications can be made to Figure 13. For example, although shown as a series of steps, the various steps in Figure 13 can overlap, occur in parallel, occur in a different order, or occur any number of times.

[0123] FIG. 14 illustrates an exemplary digital twin-based method for predicting traffic flow information based on selected options for redirecting traffic flow in a physical system, according to an embodiment of the present disclosure. The embodiment of method 1400 illustrated in FIG. 14 is for illustrative purposes only, and other embodiments may be used without departing from the scope of the present disclosure. Method 1400 is performed by an electronic device including at least one processor, such as electronic device 100 having processing device 102 executing DT 114 of FIG. 1 or system 200 of FIG. 2. For ease of explanation, method 1400 is described as being performed by electronic device 100 having processing device 102 executing DTs 114a-114c of FIG. 2.

[0124] At block 1410, the processing device 102 identifies a route for the passenger entering the building. In some embodiments, the entire route is included in the first system 202 and the second system 204. The route includes at least one section of roadway for vehicles to access the building or at least one access point inside the building for pedestrians to pass through. For example, as shown in FIG. 8, the route may include a multi-lane section of roadway (812 and 814) for vehicles to access the Terminal A building. At least one access point of the route may include a security access point inside the Terminal A building, which includes a secondary security screening lane through which ticketed passengers pass from the public area to the restricted area.

[0125] In block 1420, the processing device 102 receives traffic flow information corresponding to the route. In block 1422, the processing device 102 receives traffic flow information corresponding to the route by receiving current traffic flow information from a sensor 130b that detects a phenomenon on the route. For example, as described in FIG. 9, block 1422 represents an operational embodiment of the second DT 114b, and block 1424 represents a training embodiment of the second DT 114b. In block 1424, the processing device 102 receives traffic flow information corresponding to the route by receiving historical traffic flow information from the database 132. The historical traffic flow information may be an input condition input to the traffic flow optimizer 138 to train the optimizer 138.

[0126] In block 1430, the processing device 102 determines whether the received traffic flow information corresponds to a condition that obstructs a route. The second DT 114b includes a set of pre-trained conditions (e.g., a list of known conditions) that obstruct a route, such as obstructing the at least one section of a road for automobiles to access a building. The first DT 114a includes a set of pre-trained conditions that obstruct a route, such as obstructing the at least one access point within a building for pedestrians to pass through. The DTs 114a-114c are trained to analyze the received traffic flow information and recognize a pattern that corresponds to any of the set of pre-trained conditions that are known to obstruct a route. For example, the lane closure shown in 812 of FIG. 8 is a condition that obstructs a route to Terminal A, and the second DT 114b is trained to recognize the lane closure as a condition that obstructs a section of a road from among the set of pre-trained conditions. According to the present disclosure, the list of conditions is known by the DT 114 but is not publicly known due to the training conditions used to train the DT 114. From the set of pre-trained conditions, a closed security access point is a condition that blocks a route for passengers not only attempting to enter the building but also walking through the access point to a boarding location inside the building. A security incident, such as an active shooter situation, is another condition from the set of pre-trained conditions that the DT 114 is trained to recognize as blocking a route.

[0127] In block 1440, the processing device 102 identifies and controls a display to display a list of user-selectable state options associated with the condition that obstructs the route based on a determination that the received traffic flow information corresponds to the condition. In some embodiments, the first state option corresponds to the current state from the list of user-selectable state options.

[0128] At block 1450, the processing device 102 receives a selection of a selected state option from among a list of user-selectable state options. For example, the selection may be obtained from a user input into list 800 of FIG. 8 or list 1228 of FIG. 12.

[0129] At block 1460, the processing device 102 obtains predicted traffic flows corresponding to the route based on the selected state options. In some embodiments, as shown at block 1462, the processing device 102 obtains the predicted traffic flows by transmitting the selected state options to the traffic flow optimizer 138 and receiving the predicted traffic flows from the traffic flow optimizer 138 in response to the transmitted selected state options.

[0130] As shown in block 1464, if the selected state option is the first state option corresponding to the current state, the processing device obtains a predicted traffic flow by using the received traffic flow information as a predicted traffic flow. In other words, when the selected state option is the first state option, the predicted traffic flow is the same as the actual traffic flow of the route and is not different from the current traffic flow information received from the sensor 130b that detects the phenomenon on the route.

[0131] In block 1466, the electronic device 100 enables the policy maker to take action to attempt to mimic the predicted traffic flow. More specifically, the processing system 102 is further configured to control an output device (or multiple output devices) located along the route (e.g., installed on sections of roads within the second system 204 and installed inside buildings) to display a predetermined message corresponding to the selected condition option. While the term "output device" is used in this disclosure for ease of explanation, examples of such output devices include traffic information signs directing vehicular traffic or passenger information signs directing pedestrian traffic. The predetermined message is associated with at least one of a condition that impedes the route or a change in transportation mode. For example, if the selected condition option is the third option ("Balance traffic between terminals") as shown in list 800 of FIG. 8, and if the policy maker user selects (e.g., inputs a command) to mimic the predicted traffic flow, the predetermined message can state "The curbside lane at Terminal A is closed," and this message is associated with the condition. This predetermined message may further state, "Terminal A passenger disembarks at another terminal. Takes inter-terminal rail to Terminal A. Security wait time is 3 minutes at Terminal E and 10 minutes at Terminal C," and this message is associated with a change in transportation mode (e.g., modified to add rail). Automobile passengers accessing Terminal A may instead choose to disembark curbside at a different building (Terminal B, C, D, or E), allowing ticketed passengers to pass through a security access point at the different building and then take the rail to their boarding gate at Terminal A. Automobile passengers accessing Terminal A may still choose to join the congestion caused by vehicle lane closure 814 and disembark using the other lane (812) that remains open at the curbside of Terminal A.

[0132] In block 1470, the processing device 102 controls the display to display a user interface including a route and a visual indicator representing predicted traffic flow based on the selected state option. For example, in response to a first state option (e.g., current state) being selected, the electronic device displays a user interface including a route and a visual indicator representing traffic flow information corresponding to the route.

[0133] At block 1480, the processing device 102 determines N as the number of on-time ticketed passengers based on at least one passenger arrival indication 1322, 1334 and the travel time through the at least one access point of the route (e.g., a secondary security screening lane). For example, as shown in block 1314 of FIG. 13A, the secondary screening time is an example of the travel time through the at least one access point of the route, which may be obtained from the LUT.

[0134] To determine N, the processing device 102 receives at least one passenger arrival indication 1322, 1334. The at least one passenger arrival indication is received from an external device located in the building (e.g., a security scanner device associated with block 1310 of FIG. 13A or a kiosk associated with block 1332). Each passenger arrival indication 1322, 1334 includes passengers entering the building, arriving at the building, and ticketed passengers for a transportation vehicle (e.g., an airplane) scheduled to depart from the building (e.g., Terminal A). Ticketed passengers are distinct from automobile passengers and flight crew members working in transportation vehicles.

[0135] To determine N, the processing device 102 calculates the ETA from the location of the external device (e.g., a kiosk or security scanner device) that transmitted the passenger arrival indicator to the boarding location (e.g., a boarding gate associated with the received passenger arrival indicator). The ETA is the estimated travel time from the location of the external device through the at least one access point of the route to the boarding location within the building for the transportation vehicle.

[0136] Based on the received passenger arrival indicator and the calculated ETA, the processing device 102 increments the number of on-time passengers for the transportation vehicle, N. For example, as shown in block 1326 of FIG. 13 a, the processing device 102 increments N if the calculated ETA is equal to or before the boarding time for that flight ID.

[0137] At block 1490, to predict whether the transport vehicle will have a delayed departure, the processing device 102 determines an on-time fill factor (K / T) corresponding to an on-time departure of the transport vehicle. In some embodiments, the on-time fill factor (K / T) can be a parameter 1358 that includes a predetermined value within a range, such as a range of 65% to 85% (inclusive). In another embodiment, as shown in block 1354 of FIG. 13B, the on-time fill factor for a particular flight ID can be provided to the system 200 by the airline 210a. Once the on-time fill factor is determined (or received), the processing device 102 converts the on-time fill factor into K passenger seats for the transport vehicle. For example, the database 132 stores aircraft-specific information (including T total seats) for each flight ID.

[0138] In block 1490, the processing device 102 predicts that the transportation vehicle will have a delayed departure based on the possibility that the K passenger seats corresponding to the scheduled departure of the transportation vehicle are greater than the number N of scheduled passengers at boarding time. In other words, at the current time, the processing device 102 determines the possibility that N < K is the situation at boarding time for the flight ID corresponding to the received passenger arrival indicator. In certain embodiments, the processing device 102 transmits a recommendation message 1356 (FIG. 13B) to the airline 210a based on whether the scheduled loading rate (K / T) is met. For example, if the scheduled loading rate is not met, the processing device 102 transmits a recommendation message 1356 notifying that waiting passengers should be allowed to fill the seats currently reserved for L late passengers with ETAs beyond the boarding time. The content of the recommendation message 1356 can be based on the ETA associated with each of the L late passengers.

[0139] FIG. 14 shows an exemplary method 1400 for predicting traffic flow information based on selected options for redirecting traffic flow in a physical system, but various changes can be made to FIG. 14. For example, although shown as a series of steps, the various steps of FIG. 13 can be repeated, performed in parallel, performed in a different order, or performed any number of times.

[0140] In some embodiments, various functions described in this patent document are implemented or supported by a computer program formed from computer-readable program code and embodied in a computer-readable medium. The phrase "computer-readable program code" includes any type of computer code, including source code, object code, and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as read-only memory (ROM), random-access memory (RAM), hard disk drive, compact disc (CD), digital video disc (DVD), or any other type of memory. "Non-transitory" computer-readable media excludes wired, wireless, optical, or other communication links that carry transient electrical or other signals. Non-transitory computer-readable media include media on which data can be permanently stored and media on which data can be stored and later overwritten, such as rewritable optical disks or erasable memory devices.

[0141] It may be advantageous to provide definitions of certain words and phrases used throughout this patent document. The terms "application" and "program" refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, associated data, or portions thereof adapted for implementation in suitable computer code (including source code, object code, or executable code). The term "communicate" and its derivatives encompass both direct and indirect communication. The terms "comprise" and "have" and their derivatives mean inclusion without limitation. The term "or" is inclusive and / or. The word "associated" and its derivatives may mean including, contained within, interconnected with, including, contained in, connected to, coupled to, communicable with, cooperate with, interleaved, juxtaposed, adjacent to, coupled to, having, having a property of, having a relationship with, etc. The phrase "at least one of," when used in conjunction with a list of items, means that different combinations of one or more of the listed items may be used, or that only one item in the list may be required. For example, "at least one of A, B, and C" includes any of the following combinations: A, B, C, A and B, A and C, B and C, A, B, and C.

[0142] Nothing in this patent document should be read as suggesting that any particular element, step, or function is a required or essential element required for inclusion in the scope of any claim. Furthermore, no claim is intended to invoke 35 U.S.C. 112(f) with respect to any of the appended claims or claim elements unless the precise words "means for" or "step for" are expressly used in a particular claim along with a participial phrase identifying the function. The use of terms such as, but not limited to, "mechanism," "module," "device," "unit," "component," "element," "member," "apparatus," "machine," "system," "processor," "processing device," or "controller" in the claims is understood and intended to refer to structures known to those skilled in the art, as further modified or enhanced by the features of the claims themselves, and is not intended to invoke 35 U.S.C. 112(f).

[0143] While this disclosure has described certain embodiments and generally associated methods, modifications and permutations of these embodiments and methods will be apparent to those skilled in the art. Thus, the above description of exemplary embodiments does not define or constrain the disclosure. Other modifications, substitutions, and alterations are also possible without departing from the spirit and scope of the disclosure, as defined by the following claims.

Claims

1. A method (1400) comprising: Identifying (1410) a route for passengers entering the building, said route comprising: At least one section of roadway providing motor vehicle access to the building; or at least one access point inside the building for pedestrian passage; Stages and; receiving (1420) traffic flow information corresponding to the route; determining (1430) whether the received traffic flow information corresponds to a condition in a set of pre-trained conditions that obstructs the route; and identifying and displaying (1440) a list of user-selectable condition options associated with the condition that obstructs the route based on a determination that the received traffic flow information corresponds to the condition. method.

2. receiving (1450) a selection of a selected status option from the list of user-selectable status options; obtaining (1460) a predicted traffic flow corresponding to the route based on the selected state option; and displaying (1470) a user interface including the route and a visual indicator representing the predicted traffic flow based on the selected state option. The method of claim 1.

3. Obtaining the predicted traffic flow includes: sending (1462) the selected state options to a traffic flow optimizer; receiving (1462) the predicted traffic movement from the traffic movement optimizer in response to the transmitted selected state option. The method of claim 2.

4. From said list of user-selectable status options, a first status option corresponds to a current state; the selected status option is the first status option; Obtaining the predicted traffic movement includes using the received traffic movement information as the predicted traffic movement (1464); displaying (1470) a user interface including the route and a visual indicator representing the traffic flow information corresponding to the route; The method of claim 2.

5. receiving (1450) a selection of a selected status option from the list of user-selectable status options; and controlling (1466) output devices located along the route to display a predetermined message corresponding to the selected status option; The predetermined message may be: the condition that prevents the route; or Change of transport mode The method of claim 1 , wherein the method is associated with at least one of:

6. receiving (1480) a passenger arrival (1322) indication from an external device located at the building that the passengers entering the building include passengers who have arrived at the building and who are ticketed for a transportation vehicle scheduled to depart from the building; and incrementing (1480, 1326) a count N of on-time passengers for the transportation vehicle based on the received passenger arrival indicator and an estimated travel time from the location of the external device through the at least one access point to a boarding location within the building for the transportation vehicle. The method of claim 1.

7. determining (1490, 1354, 1358) an on-time load factor corresponding to an on-time departure of the transport vehicle; converting the on-time load factor into K passenger seats for the transportation vehicle (1490); predicting (1490) that the transportation vehicle will have a delayed departure based on a likelihood that the K passenger seats corresponding to the on-time departure of the transportation vehicle will be greater than the count N of on-time passengers at a boarding time, and modifying (1370) boarding gate assignments (1306) based on the predicted delayed departure. The method of claim 6.

8. An electronic device (100) having at least one processor (102), the at least one processor (102) being: identifying (1416) a route for passengers entering the building, the route comprising: At least one section of road (702, 812, 814) providing vehicular access to the building; or at least one access point inside the building for pedestrian passage; Stages and; receiving traffic flow information (216, 218, 220, 602, 612, 1340, 1344) corresponding to the route; determining whether the received traffic flow information corresponds to a condition (814) in a set of pre-trained conditions that obstructs the route; identifying and displaying a list (800) of user-selectable condition options associated with the condition that obstructs the route based on a determination that the received traffic flow information corresponds to the condition; 2. An electronic device configured to:

9. The at least one processor: receiving a selection of a selected status option from the list of user-selectable status options; obtaining a predicted traffic flow (1000) corresponding to the route based on the selected state option; displaying a user interface (1000) including the route and a visual indicator representing the predicted traffic flow based on the selected state option; The electronic device of claim 8 further configured to perform

10. To obtain the predicted traffic flow, the at least one processor: sending the selected state options to a traffic flow optimizer (138); receiving the predicted traffic flow from the traffic flow optimizer in response to the transmitted selected state option; The electronic device of claim 9 , further configured to:

11. From said list of user-selectable status options, a first status option (802) corresponds to the current state; the selected status option is the first status option; To obtain the predicted traffic flow, the at least one processor is further configured to use the received traffic flow information as the predicted traffic flow; the at least one processor is further configured to display a user interface (900) including the route and visual indicators (704, 706) representing the traffic flow information corresponding to the route.

10. The electronic device of claim 9.

12. The at least one processor: receiving a selection (804) of a selected state option from the list of user-selectable state options; controlling output devices located along the route to display a predetermined message corresponding to the selected status option; The predetermined message may be: the condition that prevents the route; or Change of transport mode 9. The electronic device of claim 8, wherein the electronic device is associated with at least one of:

13. The at least one processor: receiving a passenger arrival indication (1322) from an external device (210b) located at the building that the passengers entering the building include passengers who have arrived at the building and are ticketed for a transportation vehicle scheduled to depart from the building; incrementing an on-time passenger count (1328) N for the transportation vehicle based on the received passenger arrival indicator and an estimated travel time from the location of the external device through the at least one access point to a boarding location within the building for the transportation vehicle; The electronic device of claim 8 , further configured to perform:

14. The at least one processor: determining an on-time load factor corresponding to an on-time departure of the transportation vehicle; converting the on-time load factor into K passenger seats for the transportation vehicle; predicting that the transportation vehicle will have a delayed departure based on a likelihood that the K passenger seats corresponding to the on-time departure of the transportation vehicle will be greater than the count N of on-time passengers at a boarding time, and modifying (1370) boarding gate assignments (1306) based on the predicted delayed departure; The electronic device of claim 13 , further configured to:

15. A non-transitory computer-readable medium (112) embodying a computer program including computer code that, when executed by a processor (102) of an electronic device (100), causes the electronic device to: identifying a route for passengers entering the building, said route comprising: At least one section of roadway providing motor vehicle access to the building; or at least one access point inside the building for pedestrian passage; Stages and; receiving traffic flow information corresponding to the route; determining whether the received traffic flow information corresponds to a condition in a set of pre-trained conditions that obstructs the route; and based on a determination that the received traffic flow information corresponds to the condition, identifying and displaying a list of user-selectable condition options associated with the condition that obstructs the route. Non-transitory computer-readable medium.

16. The program code, when executed, causes the electronic device to: receiving a selection of a selected status option from the list of user-selectable status options; obtaining a predicted traffic flow corresponding to the route based on the selected state option; displaying a user interface including the route and a visual indicator representing the predicted traffic movement based on the selected state option; 16. The non-transitory computer-readable medium of claim 15,

17. The program code, which when executed causes the electronic device to obtain the predicted traffic flow, when executed causes the electronic device to: sending the selected state options to a traffic flow optimizer; receiving the predicted traffic flow from the traffic flow optimizer in response to the transmitted selected state option; 20. The non-transitory computer-readable medium of claim 16,

18. From said list of user-selectable status options, a first status option corresponds to a current state; the selected status option is the first status option; The program code, which when executed causes the electronic device to obtain the predicted traffic flow, when executed causes the electronic device to use the received traffic flow information as the predicted traffic flow; the program code, when executed, causes the electronic device to display a user interface including the route and a visual indicator representing the traffic flow information corresponding to the route.

17. The non-transitory computer-readable medium of claim 16.

19. The program code, when executed, causes the electronic device to: receiving a selection of a selected status option from the list of user-selectable status options; controlling output devices located along the route to display a predetermined message corresponding to the selected status option; The predetermined message may be: the condition that prevents the route; or Change of transport mode 16. The non-transitory computer-readable medium of claim 15, wherein the non-transitory computer-readable medium is associated with at least one of:

20. The program code, when executed, causes the electronic device to: receiving, from an external device located at the building, a passenger arrival indication that the passengers entering the building include passengers who have arrived at the building and who are ticketed for a transportation vehicle scheduled to depart from the building; incrementing an on-time passenger count N for the transportation vehicle based on the received passenger arrival indicator and an estimated travel time from the location of the external device through the at least one access point to a boarding location within the building for the transportation vehicle; 16. The non-transitory computer-readable medium of claim 15,

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