Prediction device, system, fee adjustment machine, and prediction method

Abstract

To provide a predictor, a system, a fee adjustment machine, and a prediction method which facilitate avoiding congestion at an exit of a parking lot.SOLUTION: A predictor 20 includes a control unit that identifies the number of first users, who park vehicles in a parking lot in which a grace period from adjustment using a fee adjustment machine 32 to exit is set, and adjust parking fees using the fee adjustment machine 32 before returning to the vehicles, generates predictive data on a degree of congestion of at least one exit of the parking lot according to the identified number of the users, and presents the generated prediction data to second users 12 who have not adjusted the parking fees.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a prediction device, a system, a fee adjustment machine, and a prediction method. [Background technology]

[0002] Patent document 1 discloses a parking management device that predicts the congestion situation at the exit of a parking area based on the scheduled departure time of a vehicle, and outputs a command to send information to occupants of vehicles predicted to be congested, encouraging them to change their scheduled departure time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-006605 A Summary of the Invention [Problem to be solved by the invention]

[0004] In reality, it is difficult to avoid congestion at parking lot exits using conventional technology because there is a risk that many vehicles will not leave the parking lot at the scheduled departure time, or that a scheduled departure time cannot even be determined.

[0005] An object of the present disclosure is to make it easier to avoid congestion at parking lot exits. [Means for solving the problem]

[0006] The prediction device according to the present disclosure comprises: The system includes a control unit that identifies the number of first users who park a vehicle in a parking lot where a grace period is set between payment using a fee payment machine and exiting the parking lot, and who use the fee payment machine to pay the parking fee before returning to the vehicle, generates predictive data regarding the degree of congestion at least one exit of the parking lot based on the identified number of users, and presents the generated predictive data to second users who have not paid their parking fee.

[0007] The prediction method according to the present disclosure includes: Identifying the number of first users who park a vehicle in a parking lot in which a grace period from payment using a fee payment machine until leaving the parking lot is set, and who pay the parking fee using the fee payment machine before returning to the vehicle, by the control unit; generating, by the control unit, prediction data regarding a congestion degree of at least one exit of the parking lot according to the identified number of people; presenting the generated prediction data to a second user who has not yet paid the parking fee by the control unit; Includes. Effect of the Invention

[0008] According to the present disclosure, congestion at parking lot exits can be easily avoided. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a system according to an embodiment of the present disclosure. [Diagram 2] FIG. 1 is a diagram illustrating an example of a parking lot. [Diagram 3] FIG. 1 is a block diagram showing a configuration of a prediction device according to an embodiment of the present disclosure. [Figure 4] 1 is a flowchart illustrating an operation of a prediction device according to an embodiment of the present disclosure. [Diagram 5] 13 is a flowchart illustrating a modified example of the operation of the prediction device according to the embodiment of the present disclosure. [Figure 6] 13 is a flowchart illustrating another modified example of the operation of the prediction device according to the embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings.

[0011] In each drawing, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0012] The present embodiment will be outlined with reference to FIG. 1 and FIG.

[0013] The prediction device 20 identifies the number of first users 11 who park a vehicle in a parking lot 50 in which a grace period Tw from payment using the fee payment machine 32 to exit is set, and who pay the parking fee using the fee payment machine 32 before returning to the vehicle. The prediction device 20 generates prediction data Dp related to the congestion degree of at least one exit of the parking lot 50 according to the identified number of people. The prediction device 20 presents the generated prediction data Dp to a second user 12 who has not paid the parking fee.

[0014] The predicted data Dp is data indicating a predicted congestion degree obtained by predicting a congestion degree of at least one exit of the parking lot 50. Alternatively, the predicted data Dp may be data indicating a predicted time obtained by predicting a time it will take for the second user 12 to exit the parking lot 50. Alternatively, the predicted data Dp may be data indicating a predicted time obtained by predicting a time when the second user 12 will exit the parking lot 50.

[0015] According to this embodiment, it is possible to know whether the second user 12 can smoothly exit the parking lot 50 when paying the parking fee using the fee payment machine 32. Therefore, it becomes easier to avoid congestion at the exit of the parking lot 50.

[0016] In the example shown in FIG. 2, the parking lot 50 has parking spaces 54 for 70 cars. Of these 70 parking spaces 54, some of the parking spaces 54 are located near one of the two store entrances 51 of the parking lot 50. Other parking spaces 54 are relatively close to one of the two store entrances 51. Still other parking spaces 54 are relatively far from one of the two store entrances 51 and far from the other. The remaining parking spaces 54 are far away from both of the two store entrances 51. The number and arrangement of the parking spaces 54, as well as the shape and size of each parking space 54, may be changed as appropriate.

[0017] An entrance gate 52 is installed at the entrance of the parking lot 50. When the vehicle Vn passes through the entrance gate 52, a parking ticket recording the entry time is issued to the vehicle Vn. Alternatively, the entrance gate 52 may read the vehicle registration number from the license plate of the vehicle Vn, and store the entry time in storage in association with the vehicle registration number.

[0018] Each user parks the vehicle Vp in the parking lot 50, and after completing a purpose such as shopping at a store, pays the parking fee using the fee settlement machine 32 before returning to the vehicle Vp. The following method is used for fee settlement. When the user inserts a parking ticket issued at the entrance gate 52 into the fee settlement machine 32, the parking fee according to the elapsed time from the entry time recorded on the parking ticket is automatically calculated and displayed on the display unit of the fee settlement machine 32. Although not required, when the user inserts a service coupon issued at a store into the fee settlement machine 32, the discounted parking fee is automatically calculated and displayed on the display unit of the fee settlement machine 32. If the displayed parking fee is 0 or the user pays the displayed parking fee, the parking ticket is returned with a record that the settlement has been completed. Alternatively, the following method may be used. When the user inputs the vehicle registration number into the fee settlement machine 32, the parking fee according to the elapsed time from the entry time corresponding to the vehicle registration number stored in the storage is automatically calculated and displayed on the display unit of the fee settlement machine 32. Although not essential, when the user inserts a service coupon issued by a store into the fee settlement machine 32, the discounted parking fee is automatically calculated and displayed on the display unit of the fee settlement machine 32. When the displayed parking fee is zero or the user pays the displayed parking fee, a flag indicating that settlement has been completed is stored in storage in association with the vehicle registration number.

[0019] An exit gate 53 is installed at the exit of the parking lot 50. When the vehicle Vp passes through the exit gate 53, a parking ticket with a record indicating that the settlement has been completed is returned. Alternatively, the exit gate 53 may read the vehicle registration number from the license plate of the vehicle Vp and confirm that the settlement has been completed by referring to a flag corresponding to the vehicle registration number stored in storage. However, if the time elapsed since the settlement completion time exceeds the grace period Tw, the exit gate 53 will not be opened until the additional fee is paid. The grace period Tw is preset to an arbitrary period, such as 15 minutes.

[0020] In the example shown in FIG. 2, there are six first users 11 who have already paid the parking fee using the fee payment machine 32 and the elapsed time from the time of payment completion has not exceeded the grace period Tw. Of these six first users 11, one first user 11 is in vehicle V1 and is about to exit the parking lot 50 from the exit. Two first users 11 are in vehicles V2 and V3, respectively, and are waiting in a queue at the exit of the parking lot 50. One first user 11 is in vehicle V4 and is driving in the parking lot 50 toward the exit of the parking lot 50. The two first users 11 are walking in the parking lot 50 from one of the two store entrances 51 toward vehicles V5 and V6.

[0021] In this example, the prediction device 20 identifies the number of first users 11 as "6". The prediction device 20 predicts the degree of congestion at the exit of the parking lot 50 according to the identified number of people "6". Alternatively, the prediction device 20 may predict the time it takes for the second user 12 to exit the parking lot 50 if the second user 12 immediately uses the fee adjustment machine 32 to pay the parking fee according to the identified number of people "6". Alternatively, the prediction device 20 may predict the time it takes for the second user 12 to exit the parking lot 50 if the second user 12 immediately uses the fee adjustment machine 32 to pay the parking fee according to the identified number of people "6". The prediction device 20 generates data indicating the predicted degree of congestion, time, or time as prediction data Dp. The prediction device 20 presents the prediction data Dp to the second user 12 by outputting the prediction data Dp to the first terminal device 30 held by the second user 12. Alternatively, the prediction device 20 may present the predicted data Dp to the second user 12 by outputting the predicted data Dp to a second terminal device 31 installed in a store. Alternatively, the prediction device 20 may present the predicted data Dp to the second user 12 by outputting the predicted data Dp to a fee adjustment machine 32.

[0022] According to this example, if the exit of the parking lot 50 is predicted to be congested, the second user 12 can determine that it is better to avoid immediately using the fee adjustment machine 32 to settle the parking fee. As a result, it becomes easier to alleviate congestion at the exit of the parking lot 50. If the exit of the parking lot 50 is predicted not to be congested, the second user 12 can determine that it is okay to immediately use the fee adjustment machine 32 to settle the parking fee. As a result, it becomes possible to exit the parking lot 50 without avoiding congestion at the exit.

[0023] The configuration of a system 10 according to this embodiment will be described with reference to FIG.

[0024] The system 10 according to this embodiment includes a prediction device 20, a first terminal device 30, a second terminal device 31, and a fee adjustment machine 32. The prediction device 20 is capable of communicating with the first terminal device 30, the second terminal device 31, and the fee adjustment machine 32 via a network 40.

[0025] The prediction device 20 is installed in a facility such as a data center. The prediction device 20 is a computer such as a server belonging to a cloud computing system or other computing system.

[0026] The first terminal device 30 is held by the second user 12 and used by the second user 12. The first terminal device 30 is, for example, a mobile device such as a mobile phone, a smartphone, or a tablet.

[0027] The second terminal device 31 is installed in a store and used by the second user 12. The first terminal device 30 is, for example, a kiosk terminal or a self-checkout.

[0028] The fee settlement machine 32 is installed in a store or parking lot 50 and is used by the second user 12 to settle parking fees.

[0029] Network 40 includes the Internet, at least one WAN, at least one MAN, or any combination thereof. "WAN" is an abbreviation for wide area network. "MAN" is an abbreviation for metropolitan area network. Network 40 may include at least one wireless network, at least one optical network, or any combination thereof. A wireless network may be, for example, an ad-hoc network, a cellular network, a wireless LAN, a satellite communication network, or a terrestrial microwave network. "LAN" is an abbreviation for local area network.

[0030] The configuration of a prediction device 20 according to this embodiment will be described with reference to FIG.

[0031] The prediction device 20 includes a control unit 21, a storage unit 22, and a communication unit .

[0032] The control unit 21 includes at least one processor, at least one programmable circuit, at least one dedicated circuit, or any combination thereof. The processor is a general-purpose processor such as a CPU or a GPU, or a dedicated processor specialized for a specific process. "CPU" is an abbreviation for central processing unit. "GPU" is an abbreviation for graphics processing unit. The programmable circuit is, for example, an FPGA. "FPGA" is an abbreviation for field-programmable gate array. The dedicated circuit is, for example, an ASIC. "ASIC" is an abbreviation for application specific integrated circuit. The control unit 21 executes processes related to the operation of the prediction device 20 while controlling each part of the prediction device 20.

[0033] The storage unit 22 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or any combination thereof. The semiconductor memory is, for example, a RAM or a ROM. "RAM" is an abbreviation for random access memory. "ROM" is an abbreviation for read only memory. The RAM is, for example, an SRAM or a DRAM. "SRAM" is an abbreviation for static random access memory. "DRAM" is an abbreviation for dynamic random access memory. The ROM is, for example, an EEPROM. "EEPROM" is an abbreviation for electrically erasable programmable read only memory. The storage unit 22 functions, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores data used in the operation of the prediction device 20 and data obtained by the operation of the prediction device 20.

[0034] The communication unit 23 includes at least one communication interface. The communication interface is, for example, a LAN interface. The communication unit 23 communicates with the first terminal device 30, the second terminal device 31, and the fee adjustment machine 32. The communication unit 23 receives data used in the operation of the prediction device 20, and transmits data obtained by the operation of the prediction device 20.

[0035] The functions of the prediction device 20 are realized by executing a program according to this embodiment on a processor serving as a control unit 21. That is, the functions of the prediction device 20 are realized by software. The program causes a computer to execute the operations of the prediction device 20, thereby causing the computer to function as the prediction device 20. That is, the computer functions as the prediction device 20 by executing the operations of the prediction device 20 in accordance with the program.

[0036] The program may be stored in a non-transitory computer-readable medium. Examples of the non-transitory computer-readable medium include a flash memory, a magnetic recording device, an optical disk, a magneto-optical recording medium, or a ROM. The program may be distributed, for example, by selling, transferring, or lending a portable medium such as an SD card, DVD, or CD-ROM on which the program is stored. "SD" is an abbreviation for Secure Digital. "DVD" is an abbreviation for digital versatile disc. "CD-ROM" is an abbreviation for compact disc read only memory. The program may be distributed by storing the program in the storage of a server and transferring the program from the server to another computer. The program may be provided as a program product.

[0037] A computer temporarily stores in a main storage device, for example, a program stored in a portable medium or a program transferred from a server. Then, the computer reads the program stored in the main storage device with a processor, and executes processing according to the read program with the processor. The computer may read the program directly from a portable medium and execute processing according to the program. The computer may execute processing according to the received program each time a program is transferred from the server to the computer. Processing may be executed by a so-called ASP-type service that realizes functions only by execution instructions and result acquisition without transferring a program from the server to the computer. "ASP" is an abbreviation for application service provider. A program is information used for processing by a computer, and includes those equivalent to a program. For example, data that is not a direct command to a computer but has the property of defining computer processing falls under "those equivalent to a program."

[0038] A part or all of the functions of the prediction device 20 may be realized by a programmable circuit or a dedicated circuit as the control unit 21. In other words, a part or all of the functions of the prediction device 20 may be realized by hardware.

[0039] The operation of the prediction device 20 according to this embodiment will be described with reference to Fig. 4. This operation corresponds to the prediction method according to this embodiment.

[0040] In step S101, the control unit 21 of the prediction device 20 waits until a request from a second user 12 who has not paid the parking fee is received via the communication unit 23. Specifically, the request from the second user 12 is received in the following procedure.

[0041] The first terminal device 30 accepts input of a request from the second user 12 via an input unit such as a touch screen. The first terminal device 30 transmits the input request to the prediction device 20 via a communication unit compatible with a mobile communication standard such as LTE, 4G standard, or 5G standard, or a wireless LAN communication standard such as IEEE802.11. "LTE" is an abbreviation for Long Term Evolution. "4G" is an abbreviation for 4th generation. "5G" is an abbreviation for 5th generation. "IEEE" is an abbreviation for Institute of Electrical and Electronics Engineers. The control unit 21 of the prediction device 20 receives the request transmitted from the first terminal device 30 via the communication unit 23.

[0042] A request from the second user 12 may be input to the second terminal device 31 instead of the first terminal device 30. In such a modified example, the second terminal device 31 accepts input of a request from the second user 12 via an input unit such as a touch screen. The second terminal device 31 transmits the input request to the prediction device 20 via a communication unit compatible with a wired LAN communication standard such as Ethernet (registered trademark) or a wireless LAN communication standard such as IEEE802.11. The control unit 21 of the prediction device 20 receives the request transmitted from the second terminal device 31 via the communication unit 23.

[0043] A request from the second user 12 may be input to the fee adjustment machine 32 instead of the first terminal device 30 or the second terminal device 31. In such a modified example, the fee adjustment machine 32 accepts input of a request from the second user 12 via an input unit such as a touch screen. The fee adjustment machine 32 transmits the input request to the prediction device 20 via a communication unit compatible with a wired LAN communication standard such as Ethernet (registered trademark) or a wireless LAN communication standard such as IEEE802.11. The control unit 21 of the prediction device 20 receives the request transmitted from the fee adjustment machine 32 via the communication unit 23.

[0044] If a request from second user 12 is received in step S101, the process proceeds to step S102.

[0045] In step S102, the control unit 21 of the prediction device 20 identifies the number of first users 11 who parked their vehicles in the parking lot 50 and paid the parking fee using the fee settlement machine 32 before returning to the vehicles. Specifically, the number of first users 11 is identified by the following procedure.

[0046] Each time the parking fee is paid using the fee payment machine 32, the fee payment machine 32 transmits report data Dr reporting the completion of payment to the prediction device 20 via the communication unit of the fee payment machine 32. The control unit 21 of the prediction device 20 receives the report data Dr transmitted from the fee payment machine 32 via the communication unit 23. Each time the control unit 21 receives the report data Dr, the control unit 21 increments the counter stored in the storage unit 22 by 1. The control unit 21 decrements the counter by 1 when the elapsed time from the time when the report data Dr was received, or when the elapsed time from the payment completion time is reported in the report data Dr, exceeds the grace period Tw. Alternatively, the control unit 21 may decrement the counter by 1 when a vehicle passing through the exit gate 53 is detected. The control unit 21 refers to the counter to identify the number of first users 11. In the example shown in Fig. 2, there are six first users 11 who have already paid the parking fee using the fee payment machine 32 and the elapsed time from the time of payment completion has not exceeded the grace period Tw. If a request from a second user 12 is received at this point, the counter value is "6". Therefore, the control unit 21 refers to the counter and determines the number of first users 11 to be "6".

[0047] In step S103, the control unit 21 of the prediction device 20 generates prediction data Dp regarding the congestion degree of at least one exit of the parking lot 50, according to the number of people identified in step S102. Specifically, the prediction data Dp is generated by one of the following three procedures.

[0048] In the first step, the control unit 21 of the prediction device 20 predicts the congestion degree of at least one exit of the parking lot 50. The control unit 21 generates data indicating the predicted congestion degree as prediction data Dp. In the example shown in FIG. 2, the control unit 21 inputs the number of people "6" identified in step S102 into a model Ma that predicts the congestion degree of the exit of the parking lot 50, and obtains a predicted value of the congestion degree output from the model Ma. The model Ma is generated in advance using machine learning such as deep learning, and is updated as appropriate. The control unit 21 generates data indicating the obtained prediction value as prediction data Dp.

[0049] In the second step, the control unit 21 of the prediction device 20 predicts the time it takes for the second user 12 to exit the parking lot 50. The control unit 21 generates data indicating the predicted time as prediction data Dp. In the example shown in FIG. 2, the control unit 21 inputs the number of people "6" identified in step S102 into a model Mb that predicts the time it takes to queue at the exit of the parking lot 50, and obtains a predicted value of the time output from the model Mb. The model Mb is generated in advance using machine learning such as deep learning, and is updated as appropriate. The control unit 21 calculates a predicted value of the time it takes for the second user 12 to exit the parking lot 50 by summing the obtained predicted value, an average value of the time T1 it takes to settle the parking fee using the fee settlement machine 32, an average value of the time T2 it takes to return to the vehicle from the fee settlement machine 32 and move to the exit of the parking lot 50, and an average value of the time T3 from arriving at the exit of the parking lot 50 to the opening of the exit gate 53. The average values ​​of the times T1, T2, and T3 are set or specified in advance. The control unit 21 generates data indicating the obtained predicted value as predicted data Dp.

[0050] In the third procedure, the control unit 21 of the prediction device 20 predicts the time when the second user 12 will leave the parking lot 50. The control unit 21 generates data indicating the predicted time as predicted data Dp. In the example shown in FIG. 2, the control unit 21 inputs the number of people "6" identified in step S102 into the model Mb as in the second procedure, and obtains a predicted value of the time output from the model Mb. The control unit 21 adds the total value of the obtained predicted value, the average value of the time T1, the average value of the time T2, and the average value of the time T3 to the current time, to obtain a predicted value of the time when the second user 12 will leave the parking lot 50. The control unit 21 generates data indicating the obtained predicted value as predicted data Dp.

[0051] In the example shown in FIG. 2, the parking lot 50 has one entrance and one exit, but as another example, the parking lot 50 may have multiple entrances and multiple exits. The control unit 21 of the prediction device 20 may predict an exit to be used when exiting the parking lot 50 for each first user 11 according to the entrance used when entering the parking lot 50. Which user used which entrance may be recorded on a parking ticket at the time of entry, or may be stored in storage in association with a vehicle registration number at the time of entry. As a method of exit prediction, for example, a method of predicting that an exit closest to the entrance used at the time of entry will also be used at the time of exit, or a method of predicting that an exit facing the same road as the entrance used at the time of entry will also be used at the time of exit, may be used. Machine learning such as deep learning may be used. The control unit 21 may generate data on the congestion degree for each exit as prediction data Dp according to the number of first users 11 at each exit. In the first procedure, the control unit 21 may predict the congestion degree for each exit. The control unit 21 may include in the prediction data Dp not only data indicating the degree of congestion at each exit, but also data guiding a route to an open exit. In the second or third procedure, the control unit 21 may also predict an exit to be used when the second user 12 exits the parking lot 50, according to the entrance used when entering the parking lot 50. In the second procedure, the control unit 21 may predict the time it will take for the second user 12 to exit the parking lot 50 from the predicted exit. In the third procedure, the control unit 21 may predict the time at which the second user 12 will exit the parking lot 50 from the predicted exit.

[0052] In the example shown in FIG. 2, the parking lot 50 has only one parking floor, but as another example, the parking lot 50 may have multiple parking floors. The control unit 21 of the prediction device 20 may generate prediction data Dp according to the identified number of people and the parking floor corresponding to each first user 11. For example, instead of counting each first user 11 as one user, the control unit 21 may apply a weighting coefficient set for each parking floor according to the distance from the exit of the parking lot 50 to the count. As a specific example, the control unit 21 may count a first user 11 who parks at a parking floor close to the exit of the parking lot 50 as one user, and count a first user 11 who parks at a parking floor far from the exit of the parking lot 50 as 1.5 or 2 users. Which user parked at which parking floor can be identified using, for example, a group of sensors installed in the parking lot 50 or a group of sensors mounted on a vehicle in the parking lot 50. The group of sensors includes one or more cameras, one or more LiDARs, or any combination thereof. "LiDAR" is an abbreviation for light detection and ranging.

[0053] In step S104, control unit 21 of prediction device 20 presents predicted data Dp generated in step S103 to second user 12. Specifically, predicted data Dp is presented in the following procedure.

[0054] When a request from the second user 12 is received from the first terminal device 30 in step S101, the control unit 21 of the prediction device 20 presents the prediction data Dp to the second user 12 by outputting the prediction data Dp to the first terminal device 30 via the communication unit 23. More specifically, the control unit 21 transmits the prediction data Dp to the first terminal device 30 via the communication unit 23. The first terminal device 30 receives the prediction data Dp transmitted from the prediction device 20 via the communication unit of the first terminal device 30. The first terminal device 30 displays the congestion degree, time, or time indicated by the received prediction data Dp on the display unit of the first terminal device 30. The congestion degree may be displayed in the form of a message such as, for example, "If you pay the parking fee now, it is predicted that the exit will be congested when you leave" or "If you pay the parking fee now, it is predicted that the exit will not be congested when you leave."

[0055] When a request from the second user 12 is received from the second terminal device 31 in step S101, the control unit 21 of the prediction device 20 causes the second terminal device 31 to output the predicted data Dp via the communication unit 23, thereby presenting the predicted data Dp to the second user 12. More specifically, the control unit 21 transmits the predicted data Dp to the second terminal device 31 via the communication unit 23. The second terminal device 31 receives the predicted data Dp transmitted from the prediction device 20 via the communication unit of the second terminal device 31. The second terminal device 31 displays the congestion degree, time, or time indicated by the received predicted data Dp on the display unit of the second terminal device 31. The congestion degree may be displayed, for example, in the form of a message as described above.

[0056] When a request from the second user 12 is received from the fee adjustment machine 32 in step S101, the control unit 21 of the prediction device 20 causes the fee adjustment machine 32 to output the predicted data Dp via the communication unit 23, thereby presenting the predicted data Dp to the second user 12. More specifically, the control unit 21 transmits the predicted data Dp to the fee adjustment machine 32 via the communication unit 23. The fee adjustment machine 32 receives the predicted data Dp transmitted from the prediction device 20 via the communication unit of the fee adjustment machine 32. The fee adjustment machine 32 displays the congestion degree, time, or time indicated by the received predicted data Dp on the display unit of the fee adjustment machine 32. The congestion degree may be displayed, for example, in the form of a message as described above.

[0057] In this embodiment, because the above-described operation is performed, it is possible to know whether the second user 12 can smoothly exit the parking lot 50 if he or she pays the parking fee using the fee settlement machine 32. This makes it easier to avoid congestion at the exit of the parking lot 50.

[0058] A modified example of the operation shown in Fig. 4 will be described with reference to Fig. 5. In the flow of Fig. 4, control unit 21 of prediction device 20 presents predicted data Dp to second user 12 in response to a request from second user 12. In contrast, in the flow of Fig. 5, control unit 21 presents predicted data Dp to second user 12 in response to a specific action of second user 12.

[0059] In step S201, control unit 21 of prediction device 20 waits until a specific behavior of second user 12 is detected. What behavior corresponds to the specific behavior is defined in advance. Specifically, the specific behavior of second user 12 is detected in the following procedure.

[0060] It is assumed that it is predefined that payment for a product or service provided in a store corresponds to a specific behavior. The second terminal device 31 accepts such a payment operation via an input unit of the second terminal device 31. The second terminal device 31 processes the payment in response to the operation, and transmits a notification that the payment has been made to the prediction device 20 via a communication unit of the second terminal device 31. This notification corresponds to a notification about the specific behavior of the second user 12. The control unit 21 of the prediction device 20 detects the specific behavior of the second user 12 by receiving the notification transmitted from the second terminal device 31 via the communication unit 23.

[0061] It may be predefined that the insertion of a parking ticket into the fee adjustment machine 32 corresponds to a specific behavior. In such a modified example, when the parking ticket is inserted, the fee adjustment machine 32 transmits a notification that the parking ticket has been inserted to the prediction device 20 via a communication unit of the fee adjustment machine 32 before accepting an operation for paying the parking fee. This notification corresponds to a notification about the specific behavior of the second user 12. The control unit 21 of the prediction device 20 detects the specific behavior of the second user 12 by receiving the notification transmitted from the fee adjustment machine 32 via the communication unit 23.

[0062] It may be predefined that input of the vehicle registration number into the fee adjustment machine 32 corresponds to a specific behavior. In such a modified example, the fee adjustment machine 32 accepts input of the vehicle registration number via an input unit of the fee adjustment machine 32. Before accepting an operation for paying the parking fee, the fee adjustment machine 32 transmits a notification that the vehicle registration number has been input to the prediction device 20 via a communication unit of the fee adjustment machine 32. This notification corresponds to a notification about the specific behavior of the second user 12. The control unit 21 of the prediction device 20 detects the specific behavior of the second user 12 by receiving the notification transmitted from the fee adjustment machine 32 via the communication unit 23.

[0063] If a particular action of second user 12 is detected in step S201, the process of step S202 is executed.

[0064] The processes in steps S202 and S203 are the same as those in steps S102 and S103 in FIG. 4, and therefore will not be described.

[0065] In step S204, control unit 21 of prediction device 20 presents predicted data Dp generated in step S203 to second user 12. Specifically, predicted data Dp is presented in the following procedure.

[0066] When a notification about a specific behavior of the second user 12 is received from the second terminal device 31 in step S201, the control unit 21 of the prediction device 20 causes the second terminal device 31 to output the predicted data Dp via the communication unit 23, thereby presenting the predicted data Dp to the second user 12. More specifically, the control unit 21 transmits the predicted data Dp to the second terminal device 31 via the communication unit 23. The second terminal device 31 receives the predicted data Dp transmitted from the prediction device 20 via the communication unit of the second terminal device 31. The second terminal device 31 displays the congestion degree, time, or time indicated in the received predicted data Dp on the display unit of the second terminal device 31.

[0067] When a notification about a specific behavior of the second user 12 is received from the fee adjustment machine 32 in step S201, the control unit 21 of the prediction device 20 causes the fee adjustment machine 32 to output the prediction data Dp via the communication unit 23, thereby presenting the prediction data Dp to the second user 12. More specifically, the control unit 21 transmits the prediction data Dp to the fee adjustment machine 32 via the communication unit 23. The fee adjustment machine 32 receives the prediction data Dp transmitted from the prediction device 20 via the communication unit of the fee adjustment machine 32. The fee adjustment machine 32 displays the congestion degree, time, or time indicated in the received prediction data Dp on the display unit of the fee adjustment machine 32.

[0068] Another modified example of the operation shown in Fig. 4 will be described with reference to Fig. 6. In the flow of Fig. 5, when control unit 21 of prediction device 20 detects a specific behavior of second user 12, it presents predicted data Dp to second user 12. In contrast, in the flow of Fig. 6, when control unit 21 detects second user 12 at a specific location, it presents predicted data Dp to second user 12.

[0069] In step S301, control unit 21 of prediction device 20 waits until second user 12 is detected at a specific location. What type of location corresponds to the specific location is defined in advance. Specifically, second user 12 is detected in the following procedure.

[0070] It is assumed that the cash register in the store corresponds to a specific location. The first terminal device 30 transmits the location information obtained by the GNSS receiver to the prediction device 20 via the communication unit of the first terminal device 30. "GNSS" is an abbreviation for global navigation satellite system. GNSS is, for example, GPS, QZSS, BDS, GLONASS, or Galileo. "GPS" is an abbreviation for Global Positioning System. "QZSS" is an abbreviation for Quasi-Zenith Satellite System. QZSS satellites are called quasi-zenith satellites. "BDS" is an abbreviation for BeiDou Navigation Satellite System. "GLONASS" is an abbreviation for Global Navigation Satellite System. The control unit 21 of the prediction device 20 receives the location information transmitted from the first terminal device 30 via the communication unit 23. If this location information indicates the same location as the cash register in the store, it corresponds to information indicating that the second user 12 is in a specific location. Control unit 21 refers to the received location information and determines whether second user 12 is at the cash register of the store. Determining that second user 12 is at the cash register of the store corresponds to detecting second user 12 at a specific location.

[0071] It may be predefined that the second terminal device 31 corresponds to a specific location. In such a modified example, the second terminal device 31 accepts an operation of payment for a product or service provided at a store via an input unit of the second terminal device 31. The second terminal device 31 processes the payment in response to the operation, and transmits a notification that the payment has been made to the prediction device 20 via a communication unit of the second terminal device 31. This notification corresponds to a notification indicating that the second user 12 is in the specific location. The control unit 21 of the prediction device 20 detects the second user 12 in the specific location by receiving the notification transmitted from the second terminal device 31 via the communication unit 23.

[0072] It may be predefined that the fee adjustment machine 32 corresponds to a specific location. In such a modified example, when the parking ticket is inserted, the fee adjustment machine 32 transmits a notification that the parking ticket has been inserted to the prediction device 20 via the communication unit of the fee adjustment machine 32 before accepting an operation to pay the parking fee. This notification corresponds to a notification indicating that the second user 12 is in a specific location. Alternatively, the fee adjustment machine 32 may accept an input of a vehicle registration number via the input unit of the fee adjustment machine 32. The fee adjustment machine 32 may transmit a notification that the vehicle registration number has been input to the prediction device 20 via the communication unit of the fee adjustment machine 32 before accepting an operation to pay the parking fee. This notification also corresponds to a notification indicating that the second user 12 is in a specific location. The control unit 21 of the prediction device 20 detects the second user 12 in a specific location by receiving the notification transmitted from the fee adjustment machine 32 via the communication unit 23.

[0073] If second user 12 is detected at a particular location in step S301, the process proceeds to step S302.

[0074] The processes in steps S302 and S303 are the same as those in steps S102 and S103 in FIG. 4, and therefore will not be described.

[0075] In step S304, control unit 21 of prediction device 20 presents predicted data Dp generated in step S303 to second user 12. Specifically, predicted data Dp is presented in the following procedure.

[0076] When information indicating that the second user 12 is in a specific location is received from the first terminal device 30 in step S301, the control unit 21 of the prediction device 20 causes the first terminal device 30 to output the predicted data Dp via the communication unit 23, thereby presenting the predicted data Dp to the second user 12. More specifically, the control unit 21 transmits the predicted data Dp to the first terminal device 30 via the communication unit 23. The first terminal device 30 receives the predicted data Dp transmitted from the prediction device 20 via the communication unit of the first terminal device 30. The first terminal device 30 displays the congestion degree, time, or time indicated in the received predicted data Dp on the display unit of the first terminal device 30.

[0077] When a notification indicating that the second user 12 is in a specific location is received from the second terminal device 31 in step S301, the control unit 21 of the prediction device 20 causes the second terminal device 31 to output the predicted data Dp via the communication unit 23, thereby presenting the predicted data Dp to the second user 12. More specifically, the control unit 21 transmits the predicted data Dp to the second terminal device 31 via the communication unit 23. The second terminal device 31 receives the predicted data Dp transmitted from the prediction device 20 via the communication unit of the second terminal device 31. The second terminal device 31 displays the congestion degree, time, or time indicated in the received predicted data Dp on the display unit of the second terminal device 31.

[0078] When a notification indicating that the second user 12 is at a specific location is received from the fee adjustment machine 32 in step S301, the control unit 21 of the prediction device 20 causes the fee adjustment machine 32 to output the prediction data Dp via the communication unit 23, thereby presenting the prediction data Dp to the second user 12. More specifically, the control unit 21 transmits the prediction data Dp to the fee adjustment machine 32 via the communication unit 23. The fee adjustment machine 32 receives the prediction data Dp transmitted from the prediction device 20 via the communication unit of the fee adjustment machine 32. The fee adjustment machine 32 displays the congestion degree, time, or time indicated in the received prediction data Dp on the display unit of the fee adjustment machine 32.

[0079] As yet another modified example of the operation shown in Fig. 4, the process of step S101 may be omitted, and the processes of steps S102 and S103 may be periodically executed. Then, when the congestion degree, time, or time instant predicted in step S103 exceeds a threshold, the process of step S104 may be executed. That is, the control unit 21 of the prediction device 20 may periodically generate predicted data Dp, and when the congestion degree, time, or time instant indicated by the predicted data Dp exceeds a threshold, the predicted data Dp may be presented to the second user 12.

[0080] The present disclosure is not limited to the above-mentioned embodiment. For example, two or more blocks shown in the block diagram may be integrated, or one block may be divided. Instead of executing two or more steps shown in the flowchart in chronological order as described, they may be executed in parallel or in a different order depending on the processing capacity of the device executing each step, or as necessary. Other modifications are possible without departing from the spirit of the present disclosure.

[0081] For example, the prediction device 20 may be provided in a fare settlement machine 32 . [Explanation of symbols]

[0082] 10. System 11 First User 12 Second User 20 Prediction Device 21 Control section 22 Memory section 23 Communications Department 30 First terminal device 31 Second terminal device 32 Fare payment machine 40 Network 50 Parking 51 Store Entrance 52 Entrance Gate 53 Exit Gate 54 Parking Spaces

Claims

1. A prediction device having a control unit that identifies the number of first users who park a vehicle in a parking lot where a grace period is set between payment using a fee payment machine and exiting the parking lot, and who use the fee payment machine to pay the parking fee before returning to the vehicle, generates predictive data regarding the degree of congestion at at least one exit of the parking lot based on the identified number of users, and presents the generated predictive data to second users who have not paid their parking fee.

2. The prediction device according to claim 1 , wherein the control unit predicts a congestion degree of the at least one exit, and generates data indicating the predicted congestion degree as the prediction data.

3. The prediction device according to claim 1 , wherein the control unit predicts a time required for the second user to exit the parking lot, and generates data indicating the predicted time as the prediction data.

4. The prediction device according to claim 1 , wherein the control unit predicts a time when the second user will leave the parking lot, and generates data indicating the predicted time as the prediction data.

5. The prediction device according to claim 1 , wherein the control unit presents the prediction data to the second user in response to a request from the second user.

6. The prediction device according to claim 1 , wherein the control unit is configured to present the prediction data to the second user when a specific action of the second user is detected.

7. The prediction device according to claim 1 , wherein the control unit is configured to present the prediction data to the second user when the second user is detected in a specific location.

8. The parking lot has a plurality of entrances and a plurality of exits, The prediction device described in any one of claims 1 to 7, wherein the control unit predicts, for each first user, the exit to be used when exiting the parking lot based on the entrance used when entering the parking lot, and generates data regarding the degree of congestion at each exit as the predicted data based on the number of first users at each exit.

9. The parking lot has multiple parking levels, The prediction device according to claim 1 , wherein the control unit generates the prediction data in accordance with the identified number of people and a parking floor corresponding to each of the first users.

10. A communication unit that communicates with a terminal device held by the second user, The prediction device according to claim 1 , wherein the control unit presents the prediction data to the second user by causing the terminal device to output the prediction data via the communication unit.

11. The prediction device according to claim 10 ; The terminal device A system comprising:

12. A communication unit that communicates with a terminal device installed in the store is further provided, The prediction device according to claim 1 , wherein the control unit presents the prediction data to the second user by causing the terminal device to output the prediction data via the communication unit.

13. The prediction device according to claim 12 ; The terminal device A system comprising:

14. A communication unit that communicates with the fee adjustment machine is further provided. The prediction device according to claim 1 , wherein the control unit presents the prediction data to the second user by causing the fee adjustment machine to output the prediction data via the communication unit.

15. The prediction device according to claim 14 ; The fee settlement machine; A system comprising:

16. A fee adjustment machine comprising the prediction device according to any one of claims 1 to 9.

17. Identifying the number of first users who park a vehicle in a parking lot in which a grace period from payment using a fee payment machine until leaving the parking lot is set, and who pay the parking fee using the fee payment machine before returning to the vehicle, by the control unit; generating, by the control unit, prediction data regarding a congestion degree of at least one exit of the parking lot according to the identified number of people; presenting the generated prediction data to a second user who has not yet paid a parking fee by the control unit; A prediction method including:

18. The generating step includes predicting a congestion level of the at least one exit; The prediction method according to claim 17 , wherein the prediction data is data indicating a predicted congestion degree.

19. The generating step includes predicting a time it will take for the second user to exit the parking lot; The prediction method according to claim 17 , wherein the prediction data is data indicating a predicted time.

20. The generating step includes predicting a time when the second user will exit the parking lot; The prediction method according to claim 17 , wherein the prediction data is data indicating a predicted time.

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