WO2022064650A1

WO2022064650A1 - Congestion prediction device, congestion prediction method, and recording medium

Info

Publication number: WO2022064650A1
Application number: PCT/JP2020/036366
Authority: WO
Inventors: 昂右吉田; 伸寿高橋
Original assignee: 日本電気株式会社
Priority date: 2020-09-25
Filing date: 2020-09-25
Publication date: 2022-03-31
Also published as: JPWO2022064650A1

Abstract

In the present invention, a congestion state prediction device is provided with a congestion state prediction means for predicting the state of congestion in a license renewal facility, on the basis of the number of persons who have licenses in license renewal periods.

Description

Congestion prediction device, congestion prediction method and recording medium

The present invention relates to a congestion prediction device, a congestion prediction method, and a recording medium.

Techniques for predicting congestion in entertainment facilities have been proposed.
For example, Patent Document 1 describes a congestion status providing system for providing a user with a percentage of the congestion status of an entertainment facility. This congestion status monitoring system records the history of the congestion status ratio according to the number of people waiting in line from the time when the user passes through the management device installed at the entrance of the entertainment facility to the time when the user uses the entertainment facility. to manage. Then, the congestion status monitoring system calculates the ratio of the congestion status according to the current number of people waiting in line from passing through the management device to using the entertainment facility.
In addition, this congestion status provision system calculates the number of units used per unit time in amusement facilities based on information on variable factors such as seasons, days, and time zones, and uses the calculated number of units used per unit time. , Measure the percentage of congestion in recreational facilities.

Further, Patent Document 2 describes a facility congestion status providing system for providing a user with facility congestion information capable of predicting a facility congestion status in the future. This facility congestion status provision system provides users with aggregated results such as the average usage time of facility users and the distribution of the number of elapsed time after admission for current facility users in graphs and the like.
Further, Patent Document 2 describes that the average usage time is calculated by referring to the past data in each season such as each season and the busy season.

Further, Patent Document 3 describes an information providing system for providing the user with the congestion status of the facility according to the day of the week, the time zone, and the like. This information provision system aggregates the congestion status for each day of the week and each weather information using the visit history data, and calculates the recommended visit time for each day of the week.

Japanese Patent Application Laid-Open No. 2006-1418112 Japanese Patent Application Laid-Open No. 2009-2826887 Japanese Patent Application Laid-Open No. 2019-067109

When a driver's license renewal procedure, such as a driver's license renewal, is performed at a specific location, it is possible that people subject to license renewal will gather at that location and become crowded. In that case, it is preferable to be able to predict the congestion situation.

An example of an object of the present invention is to provide a congestion prediction device, a congestion prediction method, and a recording medium that can solve the above problems.

According to the first aspect of the present invention, the congestion status prediction device includes a congestion status prediction means for predicting the congestion status of the license renewal facility based on the number of persons applicable to the license renewal period.

According to the second aspect of the present invention, the congestion status prediction method includes predicting the congestion status of the license renewal facility based on the number of persons applicable to the license renewal period.

According to the third aspect of the present invention, the recording medium is a recording medium for recording a program for causing a computer to predict the congestion status of the license renewal facility based on the number of persons applicable to the license renewal period. ..

According to the congestion prediction device, congestion prediction method and recording medium described above, it is possible to predict the congestion situation when the license renewal procedure is performed at a specific place.

It is a schematic block diagram which shows the structural example of the congestion situation prediction system which concerns on embodiment. It is a schematic block diagram which shows the example of the functional structure of the congestion situation prediction apparatus which concerns on embodiment. It is a schematic block diagram which shows the example of the functional structure of the terminal apparatus which concerns on embodiment. It is a flowchart which shows the example of the processing procedure which performs the congestion prediction of a driver's license center by the congestion situation prediction apparatus which concerns on embodiment. It is a figure which shows the configuration example of the congestion situation prediction apparatus which concerns on embodiment. It is a flowchart which shows the example of the processing procedure in the congestion situation prediction method which concerns on embodiment. It is a schematic block diagram which shows the structure of the computer which concerns on at least one Embodiment.

Hereinafter, embodiments of the present invention will be described, but the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential to the means of solving the invention.
In the following, an example will be described in which an embodiment is provided for providing congestion status information of the license renewal procedure at the driver's license center. The driver's license center is an example of a license renewal facility.

However, the application of the embodiment is not limited to the provision of congestion status information of the license renewal procedure at the driver's license center. For example, the embodiment can be applied to various procedures that can count the upper limit of the number of persons subject to the procedure, such as predicting the number of visitors for receiving a passport.

FIG. 1 is a schematic configuration diagram showing a configuration example of a congestion status prediction system according to an embodiment. With the configuration shown in FIG. 1, the congestion status prediction system 10 includes a congestion status prediction device 100 and a terminal device 200.
The congestion status prediction device 100 and the terminal device 200 communicate with each other via the communication network 900.

The congestion status prediction system 10 is a system that provides forecast information on the congestion status of the license renewal procedure at the driver's license center. The congestion status of the license renewal procedure at the driver's license center is also simply referred to as the congestion status.
The congestion status prediction device 100 predicts the congestion status and provides the congestion status prediction information of the prediction result to the terminal device 200.

In the driver's license renewal procedure, the license renewal period is set, and the number of persons applicable to the license renewal period can be counted. Therefore, the congestion status prediction device 100 predicts the congestion status based on the number of persons corresponding to the license renewal period. In this respect, it is expected that the congestion status prediction device 100 can predict congestion with high accuracy.

The person who falls under the license renewal period here is a person whose congestion forecast target date is included in the license renewal period. The congestion prediction target date is the congestion prediction target date of the congestion status prediction device 100. The number of persons applicable to the license renewal period is also referred to as the number of persons applicable to the license renewal period.
The person who has not completed the renewal here is a person who has not completed the license renewal at the time when the congestion status prediction device 100 performs the congestion prediction among the license renewal target persons. All persons who have a driver's license are eligible for license renewal.

Alternatively, the person who has not completed the renewal has not completed the license renewal at the time when the congestion status prediction device 100 predicts the congestion among the license renewal target persons, and the license renewal reservation is made by the day before the congestion prediction target date. May be a person who is not registered.
The number of people who have not completed renewal is also referred to as the number of people who have not completed renewal.

In addition, in the congestion situation prediction system 10, a part of the procedure for renewing the license can be performed in advance. The prior point here is before going to a facility for license renewal such as a driver's license center and performing the license renewal procedure. The congestion status prediction device 100 acquires information on the pre-procedure from the terminal device 200, and predicts the congestion status based on the implementation status of the pre-procedure. In this respect, it is expected that the congestion status prediction device 100 can predict congestion with higher accuracy.

In addition, the congestion status prediction system 10 can make a reservation for the license renewal procedure. The congestion status prediction device 100 performs reservation registration for the license renewal procedure in response to a reservation application from the terminal device 200. Then, the congestion status prediction device 100 predicts the congestion status based on the reservation status of the license renewal procedure. In this respect, it is expected that the congestion status prediction device 100 can predict congestion with higher accuracy.
The congestion status prediction device 100 may be configured by using a computer such as a Workstation or a personal computer (PC).

The terminal device 200 acquires congestion status information from the congestion status prediction device 100, and notifies the user of the acquired congestion status information. The terminal device 200 may display the congestion status information.
Further, a user who is a license renewal target can use the terminal device 200 to carry out a part of the license renewal procedure as a pre-procedure, for example, taking a license renewal class in advance. The terminal device 200 processes the pre-procedure according to the user operation. Further, the terminal device 200 notifies the congestion status prediction device 100 of the implementation status of the prior procedure.
Further, the terminal device 200 makes a reservation application to the congestion status prediction device 100 according to the user operation of the reservation application for the license renewal procedure.

The terminal device 200 may be configured by using a smartphone owned by a license renewal target person. For example, the function of the terminal device 200 may be executed by the smartphone executing the application program for the license renewal procedure.
However, the configuration of the terminal device 200 is not limited to the configuration using a smartphone. For example, the terminal device 200 may be configured by using a computer other than a smartphone, such as a tablet terminal or a personal computer owned by a license renewal target person.

The communication network 900 mediates communication between the congestion status prediction device 100 and the terminal device 200. The communication network 900 is not limited to a specific type of communication network, and may be various types of communication networks depending on the congestion status prediction device 100 and the terminal device 200. For example, when the terminal device 200 is configured by using a smartphone and connects to the Internet via an access point, the communication network 900 includes a wireless communication network by the access point, the Internet, and a congestion status prediction device 100 connected to the Internet. It may be configured to include a combination with a LAN (Local Area Network) on the side.

FIG. 2 is a schematic block diagram showing an example of the functional configuration of the congestion status prediction device 100. With the configuration shown in FIG. 2, the congestion status prediction device 100 includes a first communication unit 110, a first storage unit 180, and a first control unit 190. The first control unit 190 includes an information processing unit 191 for the number of persons corresponding to the renewal period, a congestion status prediction unit 192, a reservation reception unit 193, a pre-procedure information acquisition unit 194, a window number determination unit 195, and a congestion status notification process. A unit 196 and a feedback processing unit 197 are provided.

The first communication unit 110 communicates with other devices according to the control of the first control unit 190. In particular, the first communication unit 110 communicates with the terminal device 200 via the communication network 900, and transmits the above-mentioned congestion status prediction information to the terminal device 200. In addition, the first communication unit 110 receives information on the pre-procedure from the terminal device 200. Further, the first communication unit 110 receives a reservation application for the license renewal procedure from the terminal device 200.

The first storage unit 180 stores various information. For example, the first storage unit 180 stores actual information (history information) of the congestion status. The first storage unit 180 is configured by using the storage device included in the congestion status prediction device 100.
The first control unit 190 controls each unit of the congestion status prediction device 100 to perform various processes. The function of the congestion status prediction device 100 is executed, for example, by the CPU (Central Processing Unit) included in the congestion status prediction device 100 reading a program from the first storage unit 180 and executing the program.

The number of persons applicable to the renewal period The information processing unit 191 calculates the number of persons who have not completed the renewal among the number of persons applicable to the license renewal period.
In license renewal, the period from one month before to one month after the birthday of the license renewal target person is set as the license renewal period, and in general, the license renewal target person drives within the license renewal period. Go to the license center to renew your license. Once the congestion forecast date is decided, the number of persons applicable to the license renewal period can be counted.

The number of people who have not completed the renewal, which is obtained by subtracting the number of people who have completed the procedure from the number of people who are eligible for the license renewal period, can be treated as the upper limit of the number of visitors who will perform the license renewal procedure at the driver's license center on the day when the congestion is predicted. The factors behind the error in this case are the number of migrants from within the prefecture to outside the prefecture and the number of persons who are eligible for the license renewal period, the number of migrants from outside the prefecture to the prefecture, and the license renewal period. The number of persons and the number of people who perform the renewal procedure before the license renewal period can be mentioned. In all of these cases, the ratio to the number of persons applicable to the license renewal period is small, and it is expected that the impact on congestion forecasts will be small.
A visitor who performs a license renewal procedure at the driver's license center is also referred to as a visitor to the driver's license center or simply a visitor. The number of visitors who perform the license renewal procedure at the driver's license center is also referred to as the number of visitors to the driver's license center, or simply the number of visitors.

The number of persons applicable to the renewal period The information processing unit 191 accesses, for example, the database of the driver's license holder and counts the number of persons applicable to the license renewal period and the number of persons who have completed the renewal procedure among the persons applicable to the license renewal period. .. Then, the information processing unit 191 for the number of persons applicable to the renewal period subtracts the number of persons who have completed the renewal procedure from the number of persons applicable to the license renewal period, and calculates the number of persons who have not completed the renewal among the number of persons applicable to the license renewal period.

The congestion status prediction unit 192 predicts the congestion status based on the number of persons applicable to the license renewal period. Specifically, the congestion status prediction unit 192 predicts the congestion status based on the number of persons who have not completed renewal among the number of persons applicable to the license renewal period. The congestion status prediction unit 192 corresponds to an example of the congestion status prediction means.

As mentioned above, the number of people who have not completed the renewal of the number of people who are eligible for the license renewal period can be treated as the upper limit of the number of visitors who will perform the license renewal procedure at the driver's license center on the day when the congestion is predicted. Since the upper limit of the number of visitors is known, it is expected that the congestion status prediction unit 192 can perform congestion prediction with high accuracy.

Alternatively, if it is statistically known that the ratio of the number of persons who have not completed the renewal procedure to the number of persons who have completed the license renewal period is statistically known, the congestion status prediction unit 192 directly licenses the person instead of the person who has not completed the renewal procedure. Congestion prediction may be performed using the number of people applicable to the renewal period. In this case as well, it is expected that the congestion status prediction unit 192 can perform congestion prediction with high accuracy, as in the case of using the number of people who have not completed the update.

In addition, the congestion status prediction unit 192 predicts the congestion status based on the congestion tendency based on the actual information of license renewal.
For example, statistical data is analyzed in advance for each condition of the congestion forecast target day such as each day of the week, and the degree of congestion of the driver's license center is calculated by the ratio of the number of visitors to the number of people who have not completed the procedure among the number of people who are applicable to the license renewal period. You may calculate it. Then, the congestion status prediction unit 192 predicts the number of visitors by multiplying the number of people who have not completed renewal among the number of people applicable to the license renewal period by the degree of congestion according to the conditions of the congestion prediction target date. May be good. It can be said that the degree of congestion is an index showing the tendency of congestion.

Congestion forecast target days include the day of the week, the month, the week of the month, whether it is during a holiday such as spring break or Golden Week, whether it is after a holiday such as spring break or Golden Week, and the weather. Alternatively, or a combination thereof may be used, but the present invention is not limited thereto.

The congestion status prediction unit 192 may predict the congestion status based on the real-time information of the license renewal procedure acceptance status.
Information on the number of people who have been accepted at the counter may be used as information on the acceptance status of the license renewal procedure. Alternatively, the actual measurement information of the number of visitors to the driver's license center may be used as the information on the acceptance status of the license renewal procedure. A sensor or camera for counting the number of visitors may be installed at the entrance of the driver's license center to count the number of visitors. Alternatively, the congestion status prediction device 100 or the counting device communicates with the terminal device 200 to count the number of users of the terminal device 200 and the number of visitors to the driver's license center, and the total number of visitors to the driver's license center is counted. You may try to estimate the number of people.

The congestion status prediction unit 192 predicts the congestion status by the day before the congestion forecast target date, and updates the congestion status forecast on the day of the congestion forecast target date based on the real-time information of the license renewal procedure acceptance status. You may.

The congestion status prediction unit 192 determines the congestion status as the number of visitors to the driver's license center, or instead, either the waiting time for the license renewal application or the time required to complete the license renewal, or both. You may try to predict.

The waiting time for the license renewal application may be the time from when the license renewal target person visits the driver's license center to when the reception procedure is received at the counter.
For example, after the congestion status prediction device 100 or the device for measuring the waiting time communicates with the terminal device 200 to acquire the position information of the terminal device 200 and the user visits the driver's license center for each terminal device 200. , You may want to acquire the history information of the time until the reception procedure is received at the counter.

Then, statistically analyze the history information of the waiting time of the license renewal application in advance, and apply for the license renewal according to the conditions of the congestion prediction target date similar to the case of the number of visitors prediction and the number of visitors. A calculation formula for calculating the waiting time may be generated. The congestion situation prediction unit 192 may use this calculation formula to calculate the predicted value of the waiting time for the license renewal application based on the conditions of the congestion prediction target date and the prediction of the number of visitors.

The time required to complete the license renewal may be the time from when the license renewal target person visits the driver's license center to when the renewed license is received.
For example, after the congestion status prediction device 100 or the device for measuring the required time communicates with the terminal device 200 to acquire the position information of the terminal device 200 and the user visits the driver's license center for each terminal device 200. , You may want to get the history information of the time until you receive the renewed license.

Then, the history information of the time required to complete the license renewal is statistically analyzed in advance, and the license renewal is completed according to the conditions of the congestion prediction target date similar to the case of the number of visitors prediction and the number of visitors. A calculation formula for calculating the required time may be generated. The congestion situation prediction unit 192 may use this calculation formula to calculate a predicted value of the time required to complete the license renewal based on the conditions of the congestion prediction target date and the prediction of the number of visitors. ..
When the congestion status prediction unit 192 updates the forecast of the number of visitors, the forecast value of the time required to complete the license renewal, the predicted value of the time required to complete the license renewal, or both of them are also updated. You may do so.

The congestion status prediction unit 192 may predict the congestion status based on the reservation status of the license renewal procedure.
For example, the above-mentioned conditions for the congestion status prediction target date may include the rank of the number of people reserved for the license renewal procedure. As a result, the congestion status prediction unit 192 predicts the congestion status based on the reservation status of the license renewal procedure. The number of people reserved for the license renewal procedure is ranked according to the number of people. For example, the number of reserved people may be classified into five stages from rank 1 to rank 5, but the number of reservations is not limited to this.

The congestion status prediction unit 192 may predict the congestion status based on the implementation status of the prior procedure.
For example, the above-mentioned conditions for the congestion status prediction target date may include the rank of the number of people performing the pre-procedure. As a result, the congestion status prediction unit 192 predicts the congestion status based on the implementation status of the prior procedure. The number of people who carry out the pre-procedure is ranked according to the number of people. For example, the number of people who carry out the pre-procedure may be classified into five stages from rank 1 to rank 5, but the number is not limited to this.

Those who have undergone pre-procedures need less time to complete the procedures at the Driver's License Center than those who do not have pre-procedures, and therefore take less time to complete the license renewal. ..
Therefore, the congestion status prediction unit 192 may predict the time required to complete the license renewal for each of the cases where the pre-procedure is performed and the case where the pre-procedure is not performed.

The standard time of the required time, which can be shortened by performing the preliminary procedure, may be predetermined. Then, the congestion status prediction unit 192 may calculate the required time when the pre-procedure is performed by subtracting the predetermined standard time from the predicted value of the required time when the pre-procedure is not performed. good.

The time required to be shortened by performing the pre-procedure may be set for each item that can be pre-procedure, such as attending a course or taking a photo for a driver's license. In this case, the congestion status prediction unit 192 may calculate the time required to complete the license renewal when the pre-procedure for each item is possible.

Alternatively, in common with the items that can be pre-procedured, only one required time that can be shortened by performing the pre-procedure may be defined. In this case, the congestion status prediction unit 192 is requested to calculate two required times, that is, the time required to complete the license renewal when the prior procedure is performed and the time required to complete the license renewal when the prior procedure is not performed. You may do it.
The congestion status prediction unit 192 may predict the congestion status for each time zone, for example, in the morning and afternoon, or every hour.

By displaying the time required for the terminal device 200 to complete the license renewal when the pre-procedure is performed, the user can understand that the time required at the driver's license center will be shortened by performing the pre-procedure. Be expected. This is expected to increase the number of users who perform pre-procedures. It is expected that the congestion at the driver's license center will be alleviated by increasing the number of users who perform pre-procedures and shortening the time required at the driver's license center.

The reservation reception unit 193 accepts reservations for the license renewal procedure. The reservation reception unit 193 corresponds to an example of a reservation reception means.
The Pre-Procedure Information Acquisition Department 194 acquires pre-procedure information indicating the implementation status of the pre-procedure for license renewal. The pre-procedure information acquisition unit 194 corresponds to an example of the pre-procedure information acquisition means.

The terminal device 200 transmits information indicating the implementation of the pre-procedure when the pre-procedure is performed, so that the pre-procedure information acquisition unit 194 can perform the pre-procedure information before the day when the license renewal target person visits the driver's license center. May be obtained. In this case, as described above, the congestion status prediction unit 192 may predict the congestion status based on the implementation status of the prior procedure.

Alternatively, the pre-procedure information acquisition unit 194 may acquire pre-procedure information regarding the license renewal target person who visited the driver's license center. In this case, the congestion status prediction unit 192 may reflect the implementation status of the pre-procedure in the time required to complete the license renewal.

The number of counters determination unit 195 determines the number of operating counters for the license renewal procedure based on the prediction result of the congestion status by the congestion status prediction unit 192. The number of counters determination unit 195 corresponds to an example of a means for determining the number of counters.
The congestion status prediction unit 192 predicts the number of visitors to the driver's license center, and the number of counters determination unit 195 determines the number of operating counters by comparing the predicted value of the number of visitors to the driver's license center with a predetermined threshold value. You may decide.

Then, the congestion status prediction unit 192 divides the predicted value of the waiting time for the license renewal application when the number of operating windows is one by the number of operating windows, and the predicted value of the waiting time for the license renewal application reflecting the number of operating windows. May be calculated.
The congestion status prediction unit 192 may reflect the waiting time shortened according to the number of operating windows in the time required to complete the license renewal.

For example, the congestion status prediction unit 192 is shortened by subtracting the predicted value of the waiting time of the license renewal application reflecting the number of operating windows from the predicted value of the waiting time of the license renewal application when the number of operating windows is one. Calculate the waiting time. Then, the congestion status prediction unit 192 subtracts the shortened waiting time from the time required to complete the license renewal when the number of operating windows is one, and calculates the time required to complete the license renewal reflecting the number of operating windows. calculate.
The congestion status prediction unit 192 may predict the congestion status for each day of a predetermined period, for example, up to one week ahead. The congestion situation prediction unit 192 may update the congestion situation prediction every day.

The congestion status notification processing unit 196 notifies the notification destination such as the terminal device 200 that has inquired about the congestion status of the prediction result of the congestion status by the congestion status prediction unit 192. Specifically, the congestion status notification processing unit 196 controls the first communication unit 110 to transmit the prediction result of the congestion status to the notification destination.

The congestion status notification processing unit 196 may notify related organizations such as a bus company that operates a bus used for going to and from the driver's license center of the prediction result of the congestion status. For example, a bus company can decide the operation of a temporary flight according to the prediction result of the congestion situation.

The feedback processing unit 197 feeds back the actual result of the congestion status to the congestion status prediction by the congestion status prediction unit 192. For example, the feedback processing unit 197 determines in advance as a coefficient of learning degree the error obtained by subtracting the predicted value of the number of visitors by the congestion status prediction unit 192 from the actual number of visitors, divided by the actual number of visitors. The correction value is calculated by multiplying the coefficient α. α is a real constant coefficient of 0 ≦ α ≦ 1. Then, the feedback processing unit 197 corrects the degree of congestion by adding a correction value to the degree of congestion of the driver's license center described above.
As a result, it is expected that the congestion status prediction unit 192 can predict the congestion status with higher accuracy.

FIG. 3 is a schematic block diagram showing an example of the functional configuration of the terminal device 200. With the configuration shown in FIG. 3, the terminal device 200 includes a second communication unit 210, a display unit 220, an operation input unit 230, a second storage unit 280, and a second control unit 290. The second control unit 290 includes a reservation application unit 291, a pre-procedure processing unit 292, a congestion status presentation processing unit 293, and a response processing unit 294.

The second communication unit 210 communicates with other devices according to the control of the second control unit 290. In particular, the second communication unit 210 communicates with the congestion status prediction device 100 via the communication network 900, and receives the above-mentioned congestion status prediction information from the congestion status prediction device 100. Further, the second communication unit 210 transmits the information of the pre-procedure to the congestion status prediction device 100. Further, the second communication unit 210 sends a reservation application for the license renewal procedure to the congestion status prediction device 100.

The display unit 220 includes a display screen such as a liquid crystal panel or an LED (Light Emitting Diode) panel, and displays various images under the control of the second control unit 290. For example, the display unit 220 displays congestion status prediction information.
The operation input unit 230 includes, for example, an input device such as a touch sensor provided on the display screen of the display unit 220 and constitutes a touch panel, and accepts user operations. For example, the operation input unit 230 accepts a user operation for performing a pre-procedure for a license renewal procedure and a user operation for making a reservation application for a license renewal procedure at a driver's license center.

The second storage unit 280 stores various data. The second storage unit 280 is configured by using the storage device included in the terminal device 200.
The second control unit 290 controls each unit of the terminal device 200 to perform various processes. The function of the second control unit 290 is executed, for example, by the CPU included in the terminal device 200 reading a program from the second storage unit 280 and executing the program.

The reservation application unit 291 makes a reservation application for the license renewal procedure at the driver's license center according to the user operation. Specifically, the reservation application unit 291 uses the second communication unit 210 to transmit a reservation application for the license renewal procedure to the congestion status prediction device 100.
The pre-procedure processing unit 292 performs the pre-procedure for the license renewal procedure according to the user operation. For example, when the operation input unit 230 receives a user operation to take a license renewal course in advance, the pre-procedure processing unit 292 communicates with the congestion status prediction device 100 using the second communication unit 210. Get the course content. Then, the pre-procedure processing unit 292 reproduces the training content, such as causing the display unit 220 to reproduce the training content.

When the course content is completed, the pre-procedure processing unit 292 records the completion of the course. The pre-procedure processing unit 292 may confirm whether the license renewal target person is actually taking the course by taking a face image of the student with a camera at the time of taking the course by the course content.

Further, the pre-procedure processing unit 292 uses the second communication unit 210 to transmit the pre-procedure information to the congestion status prediction device 100. When the pre-procedure processing unit 292 completes the pre-procedure, the second communication unit 210 may be used to send a notification of the completion of the pre-procedure to the congestion status prediction device 100.

The congestion status presentation processing unit 293 presents the congestion status prediction information to the user. For example, the congestion status presentation processing unit 293 extracts the congestion status prediction information from the received signal of the second communication unit 210. Then, the congestion status presentation processing unit 293 controls the display unit 220 to display the congestion status prediction information.

The response processing unit 294 responds to the communication from the congestion status prediction device 100 or the device for measuring the waiting time for acquiring the history information of the waiting time of the license renewal application described above. Further, the response processing unit 294 responds to the communication from the congestion status prediction device 100 or the device for measuring the required time for acquiring the history information of the required time until the license renewal is completed described above.

FIG. 4 is a flowchart showing an example of a processing procedure in which the congestion status prediction device 100 predicts congestion at the driver's license center.
In the process of FIG. 4, the information processing unit 191 calculates the number of persons who have not completed the renewal among the number of persons who correspond to the license renewal period (step S101). As described above, the information processing unit 191 for the number of persons applicable to the renewal period subtracts the number of persons who have completed the renewal from the number of persons applicable to the license renewal period to calculate the number of persons who have not completed the renewal.

Next, the congestion status prediction unit 192 acquires information indicating the degree of congestion according to the conditions of the congestion prediction target date (step S102). Then, the congestion status prediction unit 192 predicts the number of visitors by multiplying the number of persons who have not completed renewal among the number of persons applicable to the license renewal period by the degree of congestion according to the conditions of the congestion prediction target date (step S103). ).

Next, the counter number determination unit 195 determines the number of operating counters for the license renewal procedure based on the predicted value of the number of visitors (step S104). For example, the number of counters determination unit 195 determines the number of operating counters by comparing the predicted value of the number of visitors with a predetermined threshold value.
Next, the congestion status prediction unit 192 calculates the predicted value of the waiting time for the license renewal application reflecting the number of operating counters and the time required to complete the license renewal reflecting the number of operating counters (step S105).

Then, the congestion status prediction unit 192 stores the prediction result of the congestion status in the first storage unit 180 (step S106). For example, the congestion status prediction unit 192 sets the number of visitors, the predicted value of the waiting time for the license renewal application reflecting the number of operating windows, and the time required to complete the license renewal reflecting the number of operating windows as the forecast target date. It is linked and stored in the congestion status prediction unit 192.
After step S106, the congestion status prediction device 100 ends the process of FIG.

The congestion status prediction device 100 may transmit the congestion status prediction result to the requesting terminal device 200 in response to the request from the terminal device 200. Alternatively, the congestion status prediction device 100 may periodically transmit the prediction result of the congestion status to each of the terminal devices 200, or may transmit to the terminal device 200 by PUSH notification.

As described above, the congestion status prediction unit 192 predicts the congestion status of the driver's license center based on the number of persons corresponding to the license renewal period.
For example, the number of people who have not completed the renewal of the number of people who are eligible for the license renewal period can be regarded as the upper limit of the number of visitors to the driver's license center. It is considered that there is a correlation with. It is expected that the congestion status prediction unit 192 can predict the congestion status with high accuracy by predicting the congestion status of the driver's license center based on the number of persons applicable to the license renewal period.
As described above, according to the congestion status prediction device 100, the congestion status can be predicted when the license renewal procedure is performed at a specific place such as a driver's license center.

In addition, the congestion status prediction unit 192 predicts the congestion status based on the number of persons who have not completed renewal among the number of persons applicable to the license renewal period.
The number of people who have not completed the renewal of the number of people who are eligible for the license renewal period can be regarded as the upper limit of the number of visitors to the driver's license center. It is expected that the congestion status prediction unit 192 can predict the congestion status with high accuracy by predicting the congestion status based on the number of persons who have not completed the renewal among the number of persons applicable to the license renewal period.

In addition, the congestion status prediction unit 192 predicts the congestion status based on the congestion tendency based on the actual information of license renewal.
As a result, the congestion status prediction unit 192 can reflect the actual congestion status in the congestion status prediction, and in this respect, it is expected that the congestion status can be predicted with high accuracy.

In addition, the congestion status prediction unit 192 predicts the congestion status based on the real-time information of the license renewal procedure acceptance status.
As a result, the congestion status prediction unit 192 can reflect the actual congestion status in the congestion status prediction, and in this respect, it is expected that the congestion status can be predicted with high accuracy.

In addition, the congestion status prediction unit 192 predicts the waiting time for the license renewal application at the driver's license center as the congestion status.
The license renewal target person can grasp the waiting time for the license renewal application by referring to the congestion status prediction result by the congestion status prediction unit 192, which can be useful for determining the schedule.

In addition, the congestion status prediction unit 192 predicts the time required to complete the license renewal at the driver's license center as the congestion status.
The license renewal target person can grasp the time required to complete the license renewal by referring to the congestion status prediction result by the congestion status prediction unit 192, which can be useful for determining the schedule.

In addition, the reservation reception unit 193 accepts reservations for the license renewal procedure. The congestion status prediction unit 192 predicts the congestion status based on the reservation status of the license renewal procedure.
It is considered that the license renewal target person who has reserved the license renewal procedure actually visits the driver's license center on the reservation date and performs the license renewal procedure. In this way, it is considered that there is a correlation between the reservation status of the license renewal procedure and the congestion status of the driver's license center. It is expected that the congestion status prediction unit 192 can predict the congestion status of the driver's license center with high accuracy by predicting the congestion status of the driver's license center based on the reservation status of the license renewal procedure.

In addition, the pre-procedure information acquisition unit 194 acquires pre-procedure information indicating the implementation status of the pre-procedure for renewing the license. The congestion status prediction unit 192 predicts the congestion status based on the implementation status of the preliminary procedure.
It is thought that the license renewal target person who has performed the pre-procedure will perform the license renewal procedure at the driver's license center in the near future, and it is considered that there is a correlation between the implementation status of the pre-procedure and the congestion status of the driver's license center. .. It is expected that the congestion status prediction unit 192 can predict the congestion status with high accuracy by predicting the congestion status of the driver's license center based on the implementation status of the pre-procedure.

In addition, the congestion status prediction unit 192 predicts the time required to complete the license renewal at the license renewal facility, with and without the implementation of the prior procedure.
The driver's license can be renewed by performing the pre-procedure by referring to the forecast of the time required to complete the license renewal at the license renewal facility for each case where the pre-procedure is performed and when the pre-procedure is not performed. It is expected that the time required at the center will be shortened. As a result, it is expected that the number of license renewals subject to pre-procedures will increase. It is expected that the congestion of the driver's license center will be alleviated by increasing the number of people subject to renewal of the license to perform the pre-procedure and shortening the time required at the driver's license center.

In addition, the number of counters determination unit 195 determines the number of operating counters for the license renewal procedure based on the prediction result of the congestion situation.
According to the congestion status prediction device 100, it is expected that the window can be efficiently operated according to the congestion status. Further, according to the congestion status prediction device 100, the window can be operated according to the congestion status, and the congestion can be alleviated.

FIG. 5 is a diagram showing a configuration example of the congestion status prediction device according to the embodiment. With the configuration shown in FIG. 5, the congestion status prediction device 610 includes a congestion status prediction unit 611.
With this configuration, the congestion status prediction unit 611 predicts the congestion status of the license renewal facility based on the number of persons applicable to the license renewal period.
The congestion status prediction unit 611 corresponds to an example of the congestion status prediction means.

For example, the number of people who have not completed the renewal of the number of people who have the license renewal period can be regarded as the upper limit of the number of visitors to the license renewal facility. It is considered that there is a correlation with. It is expected that the congestion status prediction unit 611 can predict the congestion status with high accuracy by predicting the congestion status of the license renewal facility based on the number of persons applicable to the license renewal period.
As described above, according to the congestion status prediction unit 611, the congestion status can be predicted when the license renewal procedure is performed at a specific place.

FIG. 6 is a flowchart showing an example of a processing procedure in the congestion situation prediction method according to the embodiment. The congestion situation prediction method shown in FIG. 6 includes predicting a congestion situation (step S611).
In predicting the congestion status (step S611), the congestion status of the license renewal facility is predicted based on the number of persons applicable to the license renewal period.

For example, the number of people who have not completed the renewal of the number of people who have the license renewal period can be regarded as the upper limit of the number of visitors to the license renewal facility. It is considered that there is a correlation with. In the congestion status prediction method of FIG. 6, it is expected that the congestion status can be predicted with high accuracy by predicting the congestion status of the license renewal facility based on the number of persons applicable to the license renewal period.
As described above, according to the congestion status prediction method of FIG. 6, the congestion status can be predicted when the license renewal procedure is performed at a specific place.

FIG. 7 is a schematic block diagram showing the configuration of a computer according to at least one embodiment.
In the configuration shown in FIG. 7, the computer 700 includes a CPU 710, a main storage device 720, an auxiliary storage device 730, and an interface 740.

Any one or more of the above-mentioned congestion status prediction device 100, terminal device 200, and congestion status prediction device 610 may be mounted on the computer 700. In that case, the operation of each of the above-mentioned processing units is stored in the auxiliary storage device 730 in the form of a program. The CPU 710 reads the program from the auxiliary storage device 730, expands it to the main storage device 720, and executes the above processing according to the program. Further, the CPU 710 secures a storage area corresponding to each of the above-mentioned storage units in the main storage device 720 according to the program. Communication between each device and other devices is executed by the interface 740 having a communication function and performing communication according to the control of the CPU 710.

When the congestion status prediction device 100 is mounted on the computer 700, the operations of the first control unit 190 and each unit thereof are stored in the auxiliary storage device 730 in the form of a program. The CPU 710 reads the program from the auxiliary storage device 730, expands it to the main storage device 720, and executes the above processing according to the program.
Further, the CPU 710 secures a storage area corresponding to the first storage unit 180 in the main storage device 720 according to the program.
Communication by the first communication unit 110 is executed when the interface 740 has a communication device and operates according to the control of the CPU 710.

When the terminal device 200 is mounted on the computer 700, the operations of the second control unit 290 and each unit thereof are stored in the auxiliary storage device 730 in the form of a program. The CPU 710 reads the program from the auxiliary storage device 730, expands it to the main storage device 720, and executes the above processing according to the program.
Further, the CPU 710 secures a storage area corresponding to the second storage unit 280 in the main storage device 720 according to the program.

Communication by the second communication unit 210 is executed when the interface 740 has a communication device and operates according to the control of the CPU 710. The display by the display unit 220 is executed by the interface 740 having a display device and displaying various images according to the control of the CPU 710. The reception of the user operation by the operation input unit 230 is executed by having the interface 740 have an input device and outputting information indicating the received user operation to the CPU 710.

When the congestion status prediction device 610 is mounted on the computer 700, the operation of the congestion status prediction unit 611 is stored in the auxiliary storage device 730 in the form of a program. The CPU 710 reads the program from the auxiliary storage device 730, expands it to the main storage device 720, and executes the above processing according to the program.

Further, the CPU 710 secures a storage area in the main storage device 720 for the congestion status prediction device 610 to perform processing according to the program. The communication performed by the congestion status prediction device 610 is executed by the interface 740 having a communication device and operating according to the control of the CPU 710.

A program for executing all or part of the processing performed by the congestion status prediction device 100, the terminal device 200, and the congestion status prediction device 610 was recorded on a computer-readable recording medium and recorded on the recording medium. The processing of each part may be performed by loading the program into the computer system and executing it. The term "computer system" as used herein includes hardware such as an OS and peripheral devices.
The "computer-readable recording medium" includes a flexible disk, a magneto-optical disk, a portable medium such as a ROM (Read Only Memory) and a CD-ROM (Compact Disc Read Only Memory), and a hard disk built in a computer system. It refers to a storage device such as. Further, the above-mentioned program may be for realizing a part of the above-mentioned functions, and may be further realized for realizing the above-mentioned functions in combination with a program already recorded in the computer system.

As described above, the embodiment of the present invention has been described in detail with reference to the drawings, but the specific configuration is not limited to this embodiment, and the design and the like within a range not deviating from the gist of the present invention are also included.

The embodiment of the present invention may be applied to a congestion status prediction device, a congestion status prediction method, and a recording medium.

10 Congestion

status prediction system

100, 610 Congestion status prediction device 110 1st communication unit 180 1st storage unit 190 1st control unit 191 Update period Number of applicable persons

Information processing unit

192, 611 Congestion status prediction unit 193 Reservation reception unit 194 Preliminary procedure Information acquisition unit 195 Number of contact points determination unit 196 Congestion status notification processing unit 197 Feedback processing unit 200 Terminal device 210 Second communication unit 220 Display unit 230 Operation input unit 280 Second storage unit 290 Second control unit 291 Reservation application unit 292 Preliminary procedure Processing unit 293 Congestion status presentation processing unit 294 Response processing unit 900 Communication network

Claims

License renewal period A congestion status prediction device equipped with a congestion status prediction means that predicts the congestion status of license renewal facilities based on the number of applicable persons.
The congestion status prediction means predicts the congestion status based on the number of persons who have not completed renewal among the number of persons applicable to the license renewal period.
The congestion status prediction device according to claim 1.
The congestion status prediction means predicts the congestion status based on the congestion tendency based on the actual information of license renewal.
The congestion status prediction device according to claim 1 or 2.
The congestion status prediction means predicts the congestion status based on the real-time information of the license renewal procedure acceptance status.
The congestion status prediction device according to any one of claims 1 to 3.
The congestion status predicting means predicts the waiting time for a license renewal application at the license renewal facility as the congestion status.
The congestion status prediction device according to any one of claims 1 to 4.
The congestion status predicting means predicts the time required to complete the license renewal at the license renewal facility as the congestion status.
The congestion status prediction device according to any one of claims 1 to 5.
Further equipped with a reservation reception means to accept reservations for license renewal procedures,
The congestion status prediction means predicts the congestion status based on the reservation status of the license renewal procedure.
The congestion status prediction device according to any one of claims 1 to 6.
Further equipped with a means for acquiring pre-procedure information to acquire pre-procedure information indicating the implementation status of pre-procedures for license renewal.
The congestion status predicting means predicts the congestion status based on the implementation status of the prior procedure.
The congestion status prediction device according to any one of claims 1 to 7.
The congestion status predicting means predicts the time required to complete the license renewal at the license renewal facility for each of the cases where the pre-procedure is performed and the case where the pre-procedure is not performed.
The congestion situation prediction device according to claim 8.
Further provided with a means for determining the number of counters for determining the number of operating counters for the license renewal procedure based on the prediction result of the congestion situation.
The congestion status prediction device according to any one of claims 1 to 9.
License renewal period A congestion status prediction method that includes predicting the congestion status of license renewal facilities based on the number of applicable persons.
On the computer
License renewal period A recording medium that records a program for predicting the congestion status of license renewal facilities based on the number of applicable persons.