Prediction device, and prediction method

Abstract

Provided is a prediction device comprising: a reception unit that receives first information obtained by means of a mobile network and second information pertaining to the operation of transport equipment of transit facilities; and a control unit that, on the basis of the first information and the second information, predicts a future number of people for each area related to the transit facilities.

Description

Prediction device and prediction method

[0001] The present invention relates to a technology for predicting the number of people.

[0002] 3GPP (registered trademark) (3rd Generation Partnership Project) has introduced a wireless communication system called 5G or NR (New Radio) (hereinafter, the wireless communication system will be referred to as "5G" or "NR") in order to achieve a larger system capacity, a higher data transmission speed, and a lower latency in wireless sections. 5G introduces various wireless technologies to meet the requirement of achieving a throughput of 10 Gbps or more while reducing the latency in wireless sections to 1 ms or less (for example, Non-Patent Document 1). Furthermore, 6G, a future communication system, is also being studied.

[0003] By utilizing networks such as the above-mentioned 5G, various communications, including communications in public transportation, are being carried out. Public transportation is an important social infrastructure, and disruptions to public transportation schedules have a significant impact on society. When schedules are disrupted, public transportation adjusts train intervals to prevent further delays caused by increased boarding and disembarking times due to congestion at stations or on trains.

[0004] 3GPP TS 38.300 V18.0.0 (2023-12)

[0005] However, conventional technology cannot predict the number of people in each area (e.g., station) of public transportation, making it impossible to resolve disruptions to schedules promptly. Note that this type of problem can also occur in transportation systems other than public transportation.

[0006] The present invention has been made in view of the above points, and aims to provide a technology for predicting the number of people in each area of ​​a transportation facility.

[0007] According to the disclosed technology, a prediction device is provided that includes: a receiving unit that receives first information obtained through a mobile network and second information related to the operation of transportation equipment in a transportation facility; and a control unit that predicts the future number of people in each area related to the transportation facility based on the first information and the second information.

[0008] According to the disclosed technology, it is possible to predict the number of people in each area of ​​a transportation facility.

[0009] FIG. 1 is a diagram for explaining an example of a communication system. FIG. 2 is a diagram for explaining an example of a communication system in a roaming environment. FIG. 3 is a diagram showing an example of the configuration of a system including a prediction device 30. FIG. 4 is a diagram showing a processing sequence in an embodiment of the present invention. FIG. 5 is a diagram showing an example of mapping information in a virtual space. FIG. 6 is a diagram showing a specific example of number of people prediction. FIG. 7 is a diagram showing an example of the functional configuration of a prediction device 30 in an embodiment of the present invention. FIG. 8 is a diagram showing an example of the hardware configuration of a prediction device 30 in an embodiment of the present invention. FIG. 9 is a diagram showing an example of the configuration of a vehicle 2001 in an embodiment of the present invention.

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the embodiment described below is an example, and the embodiment to which the present invention is applied is not limited to the following embodiment.

[0011] In the operation of the wireless communication system according to the embodiment of the present invention, existing technologies are used as appropriate. However, the existing technologies include, but are not limited to, the existing LTE or the existing NR.

[0012] In the following, an example of the configuration of a mobile network used in this embodiment will be first described, and then the configuration and operation relating to the number of people forecasting in public transport will be described.

[0013] Fig. 1 is a diagram illustrating an example of a communication system corresponding to a mobile network. As shown in Fig. 1, this communication system is composed of a UE and multiple network nodes. Hereinafter, it is assumed that one network node corresponds to each function, but multiple functions may be realized by one network node, or multiple network nodes may realize one function. Furthermore, the "connection" described below may be a logical connection or a physical connection.

[0014] A Radio Access Network (RAN) is a network node having a radio access function, which may include a base station, and is connected to a UE, an Access and Mobility Management Function (AMF), and a User plane function (UPF). The AMF is a network node having functions such as terminating the RAN interface, terminating the Non-Access Stratum (NAS), registration management, connection management, reachability management, and mobility management. The UPF is a network node having functions such as a Protocol Data Unit (PDU) session point to the outside that interconnects with a Data Network (DN), packet routing and forwarding, and user plane Quality of Service (QoS) handling. The UPF and the DN constitute a network slice.

[0015] The AMF is connected to the UE, RAN, SMF (Session Management function), NSSF (Network Slice Selection Function), NEF (Network Exposure Function), NRF (Network Repository Function), UDM (Unified Data Management), AUSF (Authentication Server Function), PCF (Policy Control Function), and AF (Application Function). The AMF, SMF, NSSF, NEF, NRF, UDM, AUSF, PCF, and AF are network nodes interconnected via interfaces based on their respective services: Namf, Nsmf, Nnssf, Nnef, Nnrf, Nudm, Nausf, Npcf, and Naf.

[0016] The SMF is a network node that has functions such as session management, UE IP (Internet Protocol) address allocation and management, DHCP (Dynamic Host Configuration Protocol) function, ARP (Address Resolution Protocol) proxy, and roaming function. The NEF is a network node that has the function of notifying other NFs (Network Functions) of capabilities and events. The NSSF is a network node that has functions such as selecting a network slice to which a UE connects, determining the allowed NSSAI (Network Slice Selection Assistance Information), determining the NSSAI to be configured, and determining the AMF set to which the UE connects. The PCF is a network node that has the function of controlling network policies. The AF is a network node that has the function of controlling application servers. The NRF is a network node that has the function of discovering NF instances that provide services. The UDM is a network node that manages subscriber data and authentication data. The UDM is connected to a UDR (User Data Repository) that stores the data.

[0017] 2 is a diagram illustrating an example of a communication system in a roaming environment. As shown in FIG. 2, the network is made up of a UE and a plurality of network nodes.

[0018] The SEPP is a non-transparent proxy that filters control plane messages between PLMNs (Public Land Mobile Networks). The vSEPP shown in Figure 2 is a SEPP in a visited network, and the hSEPP is a SEPP in a home network.

[0019] As shown in Figure 2, a UE is in a roaming environment connected to a RAN and an AMF in a Visited PLMN (VPLMN). The VPLMN and a Home PLMN (HPLMN) are connected via a vSEPP and an hSEPP. The UE can communicate with a UDM in the HPLMN via the AMF in the VPLMN, for example.

[0020] (Regarding Digital Twin) As will be described later, in this embodiment, a mechanism of Digital Twin is used, and therefore an overview of Digital Twin will be described here.

[0021] Digital Twin is a technology that uses data collected from the real world to recreate things, people, and processes that exist in physical space on a computer.

[0022] By using Digital Twin, it is possible to obtain real-world data in real time, perform analysis and simulations such as future predictions in a virtual space, and then realize a system that feeds the results back into reality.

[0023] (Regarding Issues) Issues with the technology according to the present embodiment will be described. As mentioned above, public transportation is an important social infrastructure, and disruptions to the schedules of public transportation have a significant impact on society. When a schedule is disrupted, public transportation adjusts the intervals between trains to prevent further delays caused by increased boarding and alighting times due to congestion at stations or on trains.

[0024] In order to optimally adjust train intervals to prevent congestion and quickly resolve train schedule disruptions, it is necessary to more accurately predict the number of people at stations and on trains, etc. However, conventional technology is unable to predict these numbers.

[0025] Furthermore, while there is conventional technology that visualizes the congestion rate on trains and provides information to train operators, it does not take into account the congestion situation, including around stations.

[0026] (System Configuration Example, Operation Overview) In order to solve the above problems, this embodiment uses a prediction device 30. The prediction device 30 may be called a Digital Twin system. The prediction device 30 may be a network node in a 3GPP network as shown in FIG. 1 . The prediction device 30 may also be a device external to the 3GPP network. The prediction device 30 may be a base station or a terminal.

[0027] Fig. 3 shows an example of the configuration of a system including a prediction device 30. As shown in Fig. 3, a mobile network (NW) 40 and a public transportation system 50 are connected to the prediction device 30. A terminal 20 carried by a user is connected to the mobile NW 40. The "public transportation system 50" is, for example, a web server of a public transportation facility.

[0028] The technology according to the present embodiment can also be applied to non-public transportation facilities. For example, the technology according to the present embodiment can also be applied to transportation facilities within a company. Public transportation facilities and non-public transportation facilities are collectively referred to as transportation facilities. Furthermore, equipment used for transportation in transportation facilities is referred to as transportation equipment. A train is an example of transportation equipment.

[0029] The transportation may be a railroad, a tram (streetcar), a bus, a taxi, an airplane, a ship, or any other means of transportation. In this embodiment, the transportation will be described using a public transportation railroad as an example.

[0030] The mobile network 40 includes a wireless sensing function, which is a technology for detecting objects, estimating states, and the like, based on fluctuations in radio waves between a base station and a terminal.

[0031] The prediction device 30 grasps, for example, the number of people in the station area, the number of people in the area around the station, and the number of people riding on a train based on information acquired from the mobile NW 40, recreates the state in which people are present in each area on the Digital Twin (i.e., on a computer), and predicts the future number of people in each area on the Digital Twin. The prediction device 30 provides the prediction results to the public transportation system 50. Based on these prediction results, the public transportation system 50 can make more optimal decisions, such as adjusting train intervals.

[0032] That is, the prediction device 30 collects information regarding the number of people in each area related to transportation from the mobile network 40, predicts the future number of people in each area, and provides the predicted results of the future number of people in each area to the public transportation system 50 in response to a request from the public transportation system 50.

[0033] In this embodiment, the "area related to transportation" refers to, for example, a station (the entire station premises), an area outside the station around the station (e.g., the area in front of the station), and a train (the entire train), but is not limited to these. For example, each of multiple areas within a station may be an "area related to transportation."

[0034] (Processing Sequence) An example of a processing sequence will be described with reference to FIG.

[0035] In S1 (step 1), the prediction device 30 receives a request to predict passenger count information for each area and information about train operations from the public transportation system 50. The information about train operations is, for example, current train position information and train position information for the time range for which prediction is to be made. For example, if a prediction is to be made for a time point one hour from the present, the "time range for which prediction is to be made" is the time range from the present to a time point one hour from the present.

[0036] The train location information may be the location information itself or information from which location information can be acquired (such as an operation pattern). The operation pattern may be, for example, the arrival and departure times of each train at each station.

[0037] The prediction device 30 may obtain information about train operations from a source other than the public transportation system 50 .

[0038] In S2a, the mobile NW 40 performs wireless sensing for each requested area to identify people (or groups of people) in each area. For example, the location of each person is identified. Note that wireless sensing does not have to be performed for each requested area. It is sufficient to know the number of people in each area based on the information obtained by wireless sensing. Wireless sensing can also be performed on a train. In S2a, the mobile NW 40 obtains information on the results of wireless sensing.

[0039] In S2b, each terminal registers its location information with the mobile NW 40. This allows the mobile NW 40 to grasp the location of each terminal.

[0040] Regarding the above S2a and S2b, either one of them may be performed, or both may be performed.

[0041] In S3, the prediction device 30 receives from the mobile NW 40 the wireless sensing result obtained in S2a or the location information of each terminal obtained in S2b.

[0042] In S3, the prediction device 30 may receive the wireless sensing result obtained in S2a and the location information of each terminal obtained in S2b from the mobile NW 40. In this embodiment, it is assumed that each user of public transportation carries a terminal.

[0043] In the following description, "A / B" means that either one of A and B or both A and B may be used.

[0044] Steps S2a / S2b and S3 are executed, for example, periodically, allowing the prediction device 30 to store time-series data of the information obtained in steps S2a / S2b.

[0045] In S4, the prediction device 30 predicts the number of people in each area. Specifically, the prediction device 30 executes the following process.

[0046] In S4-1, the prediction device 30 obtains (calculates) the number of people in each area from the current "location information / wireless sensing information of the terminal (user of the terminal)" and maps this information onto a map (for example, a map of the area including stations, railway tracks, station squares, etc.).

[0047] The result of this mapping may be displayed (visualized), for example, on the public transportation system 50. The prediction device 30 stores the number of people for each area acquired from the past to the present together with current data as time-series data.

[0048] In S4-2, the prediction device 30 predicts the future number of people for each area from time-series data based on train operation information (e.g., train location information). For example, for a certain station, the prediction device 30 predicts that a train will arrive at the station at the time of prediction, and therefore the number of people at the station will increase / decrease by X people. The prediction device 30 may also create a regression model based on the time-series data for each area and predict the future number of people from the regression model.

[0049] The prediction device 30 can also predict the future number of people in each area using AI (e.g., a neural network model). For example, the model is trained to input "train position information by time" and "time-series data of the number of people in each area" and output the number of people in each area at a desired time, and the trained model is used to predict the number of people in each area at a desired time.

[0050] If the area is a "station," the prediction device 30 outputs a prediction result of the future number of people for each station. If the area is a divided area within a station, the prediction device 30 outputs a prediction result of the future number of people for each area within the station.

[0051] In S5, the prediction device 30 transmits the prediction result of the number of people for each area, which is the prediction result obtained in S4, to the public transportation system 50.

[0052] Alternatively, the mobile network 40 or the prediction device 30 may be equipped with a camera, and the prediction device 30 may obtain the number of people in each area from an image captured by the camera.

[0053] (Specific Example) A specific example of mapping (visualization) and number of people prediction by the prediction device 30 will be described below.

[0054] FIG. 5 is a diagram showing the current number of people in each area around a station reproduced in a virtual space by the prediction device 30.

[0055] The prediction device 30 maps on a map the number of people in each area calculated based on the "location information / wireless sensing information of each terminal (each user)" obtained from the mobile network 40, and the train location information obtained from the public transportation system 50.

[0056] In the example of FIG. 5, as a result of the mapping, images of people are shown in the area of ​​Station A and its surrounding area, the area of ​​Station B and its surrounding area, and the train area, the number of people corresponding to the number of people.

[0057] The prediction device 30 predicts a future increase or decrease in the number of people for each area based on the current number of people reproduced as shown in Fig. 5. Specifically, for example, the prediction device 30 predicts the number of people in the area in front of the station, the number of people in the station (the area inside the station), the number of people on the train, etc.

[0058] 6 is a diagram showing a specific example of headcount prediction performed by the prediction device 30. In the example of Fig. 6, the prediction device 30 predicts in a virtual space a change in the number of people in each area x minutes from now, relative to the current number of people in each area shown in (a). (b) is a diagram showing the case where the change is added to (a).

[0059] As shown in (b), the prediction device 30 predicts that x minutes from now, the three people who were in the area in front of Station B will move to Station B, and so the number of people in the area in front of Station B will be 0. The prediction device 30 also predicts that x minutes from now, the three people who were in Station B will board a train, and the number of people on the train will increase to 5.

[0060] The above-mentioned change in the number of people can be predicted based on time-series data for each area from the past to the present. As mentioned above, the prediction may also be made using AI.

[0061] (Effects of the embodiment) The technology according to the embodiment can predict the number of passengers in each area of ​​a transportation facility, so that it is possible to grasp the precise future congestion situation taking into account the current congestion situation in the surrounding area. This makes it possible to optimally reschedule transportation operations and to quickly resolve disruptions to transportation schedules.

[0062] (Device Configuration) Next, an example of the functional configuration of the prediction device 30 that performs the processes and operations described above will be described.

[0063] Fig. 7 is a diagram showing an example of the functional configuration of the prediction device 30. As shown in Fig. 7, the prediction device 30 has a transmitting unit 310, a receiving unit 320, a setting unit 330, and a control unit 340. The functional configuration shown in Fig. 7 is merely an example. The names of the functional divisions and functional units may be any as long as they can perform the operations related to the embodiment of the present invention.

[0064] The transmitter 310 has a function of generating a signal to be transmitted to another device and transmitting the signal via a wired or wireless connection. The receiver 320 has a function of receiving various signals transmitted from other devices and acquiring, for example, information of a higher layer from the received signals. A communication unit including the transmitter 310 and the receiver 320 may be configured.

[0065] The setting unit 330 stores preset setting information and various setting information to be transmitted to other devices in a storage device, and reads the information from the storage device as needed. The control unit 340 controls the prediction device 30. The function unit related to signal transmission in the control unit 340 may be included in the transmitting unit 310, and the function unit related to signal reception in the control unit 340 may be included in the receiving unit 320.

[0066] (Hardware Configuration) The block diagram ( FIG. 7 ) used to explain the above embodiment shows functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining software with the single device or the multiple devices.

[0067] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0068] For example, the prediction device 30 according to an embodiment of the present disclosure may function as a computer that performs processing of the processing method of the present disclosure. Fig. 8 is a diagram illustrating an example of a hardware configuration of the prediction device 30 according to an embodiment of the present disclosure. The prediction device 30 described above may be physically configured as a computer device including a processor 1001, a storage device 1002, an auxiliary storage device 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0069] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the prediction apparatus 30 may be configured to include one or more of the apparatuses shown in the figure, or may be configured to exclude some of the apparatuses.

[0070] Each function in the prediction device 30 is realized by loading specified software (programs) onto hardware such as the processor 1001, the memory device 1002, etc., so that the processor 1001 performs calculations, controls communication via the communication device 1004, and controls at least one of reading and writing data in the memory device 1002 and the auxiliary memory device 1003.

[0071] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned control unit 340 may be realized by the processor 1001.

[0072] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the auxiliary storage device 1003 and the communication device 1004 into the storage device 1002 and executes various processes in accordance with the programs. A program that causes a computer to execute at least some of the operations described in the above-described embodiments is used as the program. For example, the control unit 340 of the prediction device 30 shown in FIG. 7 may be implemented by a control program stored in the storage device 1002 and running on the processor 1001. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0073] The storage device 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), etc. The storage device 1002 may also be called a register, a cache, a main memory, etc. The storage device 1002 can store executable programs (program codes), software modules, etc. for implementing a communication method according to an embodiment of the present disclosure.

[0074] The secondary storage device 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of the storage device 1002 and the secondary storage device 1003.

[0075] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, a transmission / reception antenna, an amplifier unit, a transmission / reception unit, a transmission path interface, etc. may be realized by the communication device 1004. The transmission / reception unit may be implemented as a transmission unit and a reception unit that are physically or logically separated.

[0076] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0077] Furthermore, each device, such as the processor 1001 and the storage device 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0078] The prediction device 30 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0079] 9 shows an example configuration of a vehicle 2001. As shown in FIG. 9 , the vehicle 2001 includes a drive unit 2002, a steering unit 2003, an accelerator pedal 2004, a brake pedal 2005, a shift lever 2006, front wheels 2007, rear wheels 2008, an axle 2009, an electronic control unit 2010, various sensors 2021 to 2029, an information service unit 2012, and a communication module 2013. Each aspect / embodiment described in the present disclosure may be applied to a communication device mounted on the vehicle 2001, and may be applied to the communication module 2013, for example. For example, the prediction device 30 may be included in the communication module 2013.

[0080] The drive unit 2002 is configured, for example, by an engine, a motor, or a hybrid of an engine and a motor. The steering unit 2003 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels and the rear wheels based on the operation of the steering wheel operated by the user.

[0081] The electronic control unit 2010 is composed of a microprocessor 2031, a memory (ROM, RAM) 2032, and a communication port (IO port) 2033. Signals are input to the electronic control unit 2010 from various sensors 2021 to 2029 provided in the vehicle 2001. The electronic control unit 2010 may also be called an ECU (Electronic Control Unit).

[0082] The signals from the various sensors 2021 to 2029 include a current signal from a current sensor 2021 that senses the current of the motor, a rotation speed signal of the front and rear wheels obtained by a rotation speed sensor 2022, an air pressure signal of the front and rear wheels obtained by an air pressure sensor 2023, a vehicle speed signal obtained by a vehicle speed sensor 2024, an acceleration signal obtained by an acceleration sensor 2025, an accelerator pedal depression amount signal obtained by an accelerator pedal sensor 2029, a brake pedal depression amount signal obtained by a brake pedal sensor 2026, a shift lever operation signal obtained by a shift lever sensor 2027, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 2028.

[0083] The information service unit 2012 is composed of various devices, such as a car navigation system, an audio system, speakers, a television, and a radio, for providing (outputting) various types of information, such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 2012 uses information acquired from external devices via the communication module 2013 or the like to provide various types of multimedia information and multimedia services to the occupants of the vehicle 2001. The information service unit 2012 may include input devices (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.) that accept input from the outside, and may also include output devices (e.g., a display, a speaker, an LED lamp, a touch panel, etc.) that output information to the outside.

[0084] The driving assistance system unit 2030 is composed of various devices that provide functions for preventing accidents and reducing the driving burden on the driver, such as millimeter-wave radar, LiDAR (Light Detection and Ranging), cameras, positioning locators (e.g., GNSS, etc.), map information (e.g., high-definition (HD) maps, autonomous vehicle (AV) maps, etc.), gyro systems (e.g., IMU (Inertial Measurement Unit), INS (Inertial Navigation System), etc.), AI (Artificial Intelligence) chips, and AI processors, as well as one or more ECUs that control these devices. In addition, the driving assistance system unit 2030 transmits and receives various information via the communication module 2013 to realize the driving assistance function or the autonomous driving function.

[0085] The communication module 2013 can communicate with the microprocessor 2031 and components of the vehicle 2001 via the communication port. For example, the communication module 2013 transmits and receives data via the communication port 2033 to and from the drive unit 2002, steering unit 2003, accelerator pedal 2004, brake pedal 2005, shift lever 2006, front wheels 2007, rear wheels 2008, axle 2009, microprocessor 2031 and memory (ROM, RAM) 2032 in the electronic control unit 2010, and sensors 2021 to 29, which are provided in the vehicle 2001.

[0086] The communication module 2013 is a communication device that can be controlled by the microprocessor 2031 of the electronic control unit 2010 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 2013 may be located either inside or outside the electronic control unit 2010. The external device may be, for example, a base station, a mobile station, or the like.

[0087] The communication module 2013 may transmit, via wireless communication, to an external device at least one of signals from the various sensors 2021-2028 input to the electronic control unit 2010, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 2012. The electronic control unit 2010, the various sensors 2021-2028, the information service unit 2012, etc. may be referred to as input units that accept input.

[0088] The communication module 2013 receives various information (traffic information, traffic signal information, vehicle-to-vehicle information, etc.) transmitted from external devices and displays it on an information service unit 2012 provided in the vehicle 2001. The information service unit 2012 may be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH (or data / information decoded from the PDSCH) received by the communication module 2013). The communication module 2013 also stores the various information received from external devices in a memory 2032 that can be used by the microprocessor 2031. Based on the information stored in the memory 2032, the microprocessor 2031 may control the drive unit 2002, steering unit 2003, accelerator pedal 2004, brake pedal 2005, shift lever 2006, front wheels 2007, rear wheels 2008, axles 2009, sensors 2021 to 2029, etc. provided in the vehicle 2001.

[0089] This specification discloses at least the configurations described in the following supplementary notes. <Supplementary Notes> (Supplementary Item 1) A prediction device comprising: a receiving unit that receives first information obtained via a mobile network and second information related to the operation of transportation equipment at a transportation facility; and a control unit that predicts the future number of people in each area related to the transportation facility based on the first information and the second information. (Supplementary Item 2) The prediction device according to Supplementary Item 1, wherein the first information is location information of a terminal or wireless sensing information. (Supplementary Item 3) The prediction device according to Supplementary Item 1, wherein the second information is location information of the transportation equipment. (Supplementary Item 4) The prediction device according to Supplementary Item 1, wherein the control unit generates visualized information in which the number of people in each area related to the transportation facility is mapped on a map. (Supplementary Item 5) A prediction method executed by a prediction device, comprising: receiving first information obtained via a mobile network and second information related to the operation of transportation equipment at the transportation facility; and predicting the future number of people in each area related to the transportation facility based on the first information and the second information.

[0090] Any of Supplementary Items 1 to 5 makes it possible to predict the number of people in each area of ​​a transportation facility. Supplementary Items 2 and 3 make it possible to make predictions using various information collection patterns. Supplementary Item 4 makes it possible to visualize in a virtual space the number of people in each area related to a transportation facility.

[0091] (Supplementary Notes on the Embodiments) Although the embodiments of the present invention have been described above, the disclosed invention is not limited to such embodiments, and those skilled in the art will understand various modifications, alterations, alternatives, and replacements. While specific numerical examples have been used to facilitate understanding of the invention, unless otherwise specified, these numerical values ​​are merely examples, and any appropriate values ​​may be used. The division of items in the above description is not essential to the present invention; matters described in two or more items may be used in combination as needed, and matters described in one item may apply to matters described in another item (as long as there is no contradiction). Boundaries between functional units or processing units in functional block diagrams do not necessarily correspond to boundaries between physical components. The operations of multiple functional units may be performed by a single physical component, or the operations of a single functional unit may be performed by multiple physical components. The order of processing steps described in the embodiments may be reversed as long as there is no contradiction. For convenience of processing description, the prediction device 30 has been described using a functional block diagram, but such a device may be realized by hardware, software, or a combination thereof. Software operated by a processor included in prediction device 30 according to an embodiment of the present invention may be stored in any suitable storage medium, such as random access memory (RAM), flash memory, read-only memory (ROM), EPROM, EEPROM, registers, hard disk drive (HDD), removable disk, CD-ROM, database, server, or the like.

[0092] Furthermore, the notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling), broadcast information (Master Information Block (MIB), System Information Block (SIB)), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0093] Each aspect / embodiment described in the present disclosure may be implemented using any of the following standards: LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (x is, for example, an integer or a decimal number)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802.34 ( The present invention may be applied to at least one of systems using 802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), or other suitable systems, and next-generation systems that are extended, modified, created, or defined based on these systems. The present invention may also be applied to a combination of multiple systems (e.g., a combination of LTE and / or LTE-A with 5G).

[0094] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described herein may be rearranged unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order and are not limited to the particular order presented.

[0095] The information, signals, etc. described in the present disclosure may be output from a higher layer (or a lower layer) to a lower layer (or a higher layer), or may be input / output via multiple network nodes.

[0096] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0097] In the present disclosure, the determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0098] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0099] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0100] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0101] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0102] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0103] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0104] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0105] In the present disclosure, terms such as "base station (BS)," "radio base station," "base station device," "fixed station," "NodeB," "eNodeB (eNB)," "gNodeB (gNB)," "access point," "transmission point," "reception point," "transmission / reception point," "cell," "sector," "cell group," "carrier," and "component carrier" may be used interchangeably. A base station may also be referred to by terms such as a macrocell, a small cell, a femtocell, and a picocell.

[0106] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be partitioned into multiple smaller areas, and each smaller area can also be provided with communication services by a base station subsystem (e.g., a small indoor base station (RRH: Remote Radio Head)). The terms "cell" or "sector" refer to part or all of the coverage area of ​​a base station and / or base station subsystem that provides communication services within that coverage.

[0107] In the present disclosure, the base station transmitting information to a terminal may be interpreted as the base station instructing the terminal to control or operate based on the information.

[0108] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," and the like may be used interchangeably.

[0109] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0110] The prediction device 30 may be referred to as a transmitting device, a receiving device, a communication device, or the like. The prediction device 30 may also be a device mounted on a moving object, the moving object itself, or the like. The moving object refers to a movable object, and may move at any speed. Naturally, this also includes a case where the moving object is stationary. Examples of the moving object include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcars, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones (registered trademark), multicopters, quadcopters, balloons, and objects mounted thereon. The moving object may also be an autonomous moving object based on an operational command. The moving object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned moving object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). Note that the prediction device 30 also includes a device that does not necessarily move during communication operations. For example, the prediction device 30 may be an IoT (Internet of Things) device such as a sensor.

[0111] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0112] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0113] The reference signal may be abbreviated as RS (Reference Signal) or may be called a pilot depending on the applicable standard.

[0114] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0115] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0116] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0117] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0118] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0119] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0120] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0121] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0122] 20 Terminal 30 Prediction device 310 Transmission unit 320 Reception unit 330 Setting unit 340 Control unit 40 Mobile network 50 Public transportation facility 1001 Processor 1002 Storage device 1003 Auxiliary storage device 1004 Communication device 1005 Input device 1006 Output device

Claims

1. A prediction device comprising: a receiving unit that receives first information obtained via a mobile network and second information related to the operation of transportation equipment in a transportation facility; and a control unit that predicts the future number of people in each area related to the transportation facility based on the first information and the second information.

2. The prediction device according to claim 1, wherein the first information is terminal location information or wireless sensing information.

3. The prediction device according to claim 1, wherein the second information is location information of the transportation equipment.

4. The prediction device according to claim 1, wherein the control unit generates visualized information in which the number of people in each area related to the transportation facility is mapped on a map.

5. A prediction method executed by a prediction device, comprising: receiving first information obtained via a mobile network and second information related to the operation of transportation equipment in a transportation facility; and predicting the future number of people in each area related to the transportation facility based on the first information and the second information.

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