Traffic flow measurement device, traffic flow measurement method, and traffic flow measurement program

Abstract

To provide a traffic flow measurement device, a traffic flow measurement method, and a traffic flow measurement program that can obtain information on traffic flow within a building using information on the passage of security gates.SOLUTION: A traffic flow measurement device includes a measurement processing unit. The measurement processing unit acquires gate passage information including gate identification information and the passage time of each of a plurality of security gates in a building through which each of a plurality of persons to which the personal identification information is assigned has passed. The measurement processing unit generates traffic flow information including information indicating the relation between a movement source and a movement destination of the plurality of persons based on the gate passage information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] An embodiment of the present invention relates to a traffic flow measuring device, a traffic flow measuring method, and a traffic flow measuring program. [Background technology]

[0002] Information on traffic flow within an office building or the like, which represents the flow of people within the building, can be used to manage various aspects of the building and provide various services within the building. Security gates are often installed in office buildings, etc. Information on passing through the security gates can be useful information for obtaining information on traffic flow within the building. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-081617 A Summary of the Invention [Problem to be solved by the invention]

[0004] The problem to be solved by the present invention is to provide a traffic flow measuring device, a traffic flow measuring method, and a traffic flow measuring program that are capable of obtaining information on traffic flow within a building by utilizing security gate passage information. [Means for solving the problem]

[0005] In an embodiment, a traffic flow measuring device includes a measurement processing unit. The measurement processing unit acquires gate passage information including gate identification information and passage time of each of a plurality of security gates in a building through which each of a plurality of people to which personal identification information is assigned has passed. The measurement processing unit Based on the gate passage information, the travel time required for multiple people to travel from their origin to their destination is generated as traffic flow information. The measurement processing unit sets the shortest travel time in each section as a reference value. The measurement processing unit calculates the average travel time for each time period in each section. The measurement processing unit identifies a time period in which the difference between the reference value and the average value exceeds a predetermined time as being in a congested state. [Brief description of the drawings]

[0006] [Figure 1]FIG. 1 is a diagram illustrating an overview of an example of a traffic flow measurement system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a configuration of an example of a traffic flow measuring device. [Diagram 3] FIG. 3 is a diagram illustrating an example of the gate passage information. [Figure 4] FIG. 4 is a flowchart showing the operation of the traffic flow measuring device. [Diagram 5] FIG. 5 is a flowchart showing an example of the data conversion process. [Figure 6A] FIG. 6A is a diagram showing an example of an OD table. [Figure 6B] FIG. 6B is a diagram showing an example of a travel time table. [Figure 6C] FIG. 6C is a diagram showing an example of a congestion degree table. [Figure 6D] FIG. 6D is a diagram showing an example of detailed traffic flow information. [Figure 7] FIG. 7 is a flowchart illustrating an example of the data analysis process. [Figure 8] FIG. 8 is a diagram showing a concept of prediction of traffic flow information by the prediction processing unit. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an embodiment will be described with reference to the drawings. FIG. 1 is a diagram showing an overview of an example of a traffic flow measurement system according to an embodiment. The traffic flow measurement system 1 is a system that measures the flow of people inside an office building, for example. Therefore, the traffic flow measurement system 1 is installed in a building B. The traffic flow measurement system 1 has a traffic flow measurement device 10 and security gates 20-1, 20-2, ..., 20-n. The traffic flow measurement device 10 and the security gates 20-1, 20-2, ..., 20-n communicate with each other using a local network environment constructed in the building B. The traffic flow measurement device 10 can also communicate with a cloud server 30 using a public network NW constructed outside the building B.

[0008] In the embodiment, each time a person P passes through any one of the security gates 20-1, 20-2, ..., 20-n, gate passing information is acquired at the security gates 20-1, 20-2, ..., 20-n. The gate passing information includes personal identification information, gate identification information, gate name, passing time, and event. The personal identification information is information for identifying the person P who passed through the security gate, and is, for example, an ID assigned to the person P. If the security gate is of a card authentication type, the personal identification information may be an ID assigned to the card. The gate identification information is information for identifying the security gates 20-1, 20-2, ..., 20-n, and is, for example, a gate number assigned to each of the security gates 20-1, 20-2, ..., 20-n. The gate name is the name of the corresponding security gate. The passing time is the time when the person P passed through the gate. The passing time may be the time when authentication of the person P was performed. An event is information that indicates whether passing through a security gate is for entering a room (entering a building) or for leaving a room (leaving a building). An "event" is information acquired depending on whether authentication was performed on the entrance side of the security gate or on the exit side.

[0009] 1, person P passes through security gate 20-1 when entering building B from the entrance, and then passes through security gate 20-2 when going up to the second floor in elevator EV and entering room R. In this case, gate passage information is acquired by each of security gate 20-1 and security gate 20-2. Security gate 20-1 and security gate 20-2 transmit the gate passage information to traffic flow measuring device 10.

[0010] The traffic flow measuring device 10 specifies the security gate 20-1 through which the person P passed first, i.e., the entrance of building B, as the origin of the person P, and the security gate 20-2 through which the person P passed later, i.e., room R, as the destination of the person P. Then, the traffic flow measuring device 10 calculates the travel time required from the origin to the destination from the difference in the passing time of the security gate. For example, when the passing time of security gate 20-1, which is the origin, is T0, and the passing time of security gate 20-2, which is the destination, is T1, the travel time T is T1-T0. In other words, the person P traveled from the entrance of building B to room R in the travel time T. By collecting such information indicating the relationship between the origin and the destination, and information on the travel time of the section from the origin to the destination, for a large number of people, the traffic flow in building B can be measured.

[0011] The traffic flow measurement system 1 will be further described. FIG. 2 is a diagram showing an example of the configuration of a traffic flow measurement device 10. The traffic flow measurement device 10 is configured by, for example, a computer. As shown in FIG. 2, the traffic flow measurement device 10 has a processor 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, a local communication module 15, and a global communication module 16. Here, the traffic flow measurement device 10 may have elements other than those shown in FIG. 2. For example, the traffic flow measurement device 10 may have elements such as a display and an operation interface.

[0012] The processor 11 is, for example, a central processing unit (CPU) and controls the operation of the traffic flow measuring device 10. The processor 11 does not necessarily have to be a CPU. The processor 11 may be a micro processing unit (MPU), a graphical processing unit (GPU), or the like. The processor 11 may also be an application specific IC (ASIC), a field programmable gate array (FPGA), or the like. The processor 11 does not have to be a single CPU, and may be composed of multiple CPUs. The processor 11 of the embodiment operates as a measurement processing unit 111 and a prediction processing unit 112 by executing a traffic flow measurement program stored in a storage, for example. The measurement processing unit 111 performs various processes related to the measurement of traffic flow in the building. The prediction processing unit 112 predicts future traffic flow in the building based on traffic flow in the building measured in the past by the measurement processing unit 111.

[0013] The ROM 12 is, for example, a flash memory. The ROM 12 stores, for example, a startup program for the traffic flow measuring device 10. The RAM 13 can be used as a working memory for various processes of the traffic flow measuring device 10.

[0014] The storage 14 is a storage such as a hard disk drive (HDD) or a solid state drive (SSD). The storage 14 stores programs and data used in various processes of the traffic flow measurement device 10. For example, the storage 14 stores an operating system (OS) 141, a traffic flow measurement program 142, gate passage information 143, traffic flow information 144, and analysis information 145. The OS 141 is a program for realizing basic functions of the traffic flow measurement device 10. Various programs stored in the storage 14 are executed under the control of the OS 141. The traffic flow measurement program 142 is a program for executing various processes related to the measurement of traffic flow. The gate passage information 143 is gate passage information acquired from the security gates 20-1, 20-2, ..., 20-n. The gate passage information 143 will be described in detail later. The traffic flow information 144 is information related to traffic flow obtained by a data conversion process of the traffic flow measurement program 142. The traffic flow information includes the information indicating the relationship between the movement origin and the movement destination, and the information on the travel time from the movement origin to the movement destination. The analysis information 145 is information related to the analysis result of the traffic flow information obtained by the data analysis process of the traffic flow measurement program 142.

[0015] FIG. 3 is a diagram showing an example of the gate passage information 143. As shown in FIG. 3, the gate passage information 143 includes items such as "date and time", "ID", "gate number", "gate name", and "event". "Date and time" is the date and time when the security gate was passed, and corresponds to the above-mentioned passing time. "ID" is personal identification information for identifying a person who passed through the security gate. Here, the "ID" in the example of FIG. 3 has been anonymized. For the anonymization, for example, a conversion process using a hash function is used, but is not limited to this. In other words, for the anonymization, any method can be used as long as the same conversion result is obtained for the ID of the same person. For example, the "ID" of No. 2, No. 8, and No. 9 in FIG. 3 has the same value. In this case, it means that the same person has passed through the security gate for No. 2, No. 8, and No. 9. "Gate number" is a unique number assigned to each security gate, and corresponds to gate identification information. "Gate name" is the name of each security gate. "Event" is information indicating whether passing through a security gate is for entering or leaving a room.

[0016] 2, the gate passage information 143, the traffic flow information 144, and the analysis information 145 are stored in the storage 14 of the traffic flow measurement device 10. In contrast, the gate passage information 143, the traffic flow information 144, and the analysis information 145 may be stored in a storage separate from the traffic flow measurement device 10. In this case, the traffic flow measurement device 10 acquires the gate passage information 143, the traffic flow information 144, and the analysis information 145 from the separate storage as necessary. For communication in this case, the local communication module 15 is used.

[0017] The local communication module 15 is a communication module used for communication with the security gates 20-1, 20-2, ..., 20-n. The local communication module 15 is, for example, a wired LAN module conforming to the IEEE802.3 standard. The local communication module 15 is not limited as long as it is a communication module compatible with communication in a local environment closed within building B. For example, the local communication module 15 may be a wireless LAN module conforming to the IEEE802.11 standard.

[0018] The global communication module 16 is a communication module used for communication with the cloud server 30. The global communication module 16 is, for example, a wired LAN module, and may be connected to the public network NW via a routing module. The global communication module 16 may be configured to connect to the public network NW via a wireless LAN module.

[0019] The security gates 20-1, 20-2, ..., 20-n are n gates (n is a natural number) equipped with an entrance / exit management function that allows authenticated persons to pass through. The security gates 20-1, 20-2, ..., 20-n are installed in various places in building B and can take various forms depending on the places where they are installed. For example, the security gates may be flapper-type security gates installed at the entrances and exits of building B. The security gates may also be security doors equipped with an authentication-type autolock system. The authentication method of the security gates 20-1, 20-2, ..., 20-n is not limited to a specific method. For example, the authentication method of the security gates 20-1, 20-2, ..., 20-n may be card authentication using an ID card. In addition, the authentication method of the security gates 20-1, 20-2, ..., 20-n may be various types of biometric authentication such as face authentication, fingerprint authentication, and retina authentication. Regardless of the authentication method, the security gates 20-1, 20-2, ..., 20-n acquire personal identification information of the person P passing through during authentication. Then, the security gates 20-1, 20-2, ..., 20-n generate gate passing information by associating the acquired personal identification information with the passing time, gate identification information, and event information, and transmit the generated gate passing information to the traffic flow measuring device 10. Here, authentication of the passing person P does not necessarily have to be performed at the security gate. For example, authentication of the passing person P may be performed by a server linked to the security gate.

[0020] Next, the operation of the traffic flow measuring device 10 will be described. Fig. 4 is a flowchart showing the operation of the traffic flow measuring device 10. The processing of Fig. 4 is performed by the processor 11 of the traffic flow measuring device 10. In parallel with the processing of Fig. 4, the security gates 20-1, 20-2, ..., 20-n transmit gate passage information to the traffic flow measuring device 10. The security gates 20-1, 20-2, ..., 20-n may transmit the gate passage information to the traffic flow measuring device 10 every time a person passes through, or may transmit the gate passage information to the traffic flow measuring device 10 at a certain fixed period such as every hour, every day, or every week.

[0021] In step S1, the processor 11 determines whether or not gate passage information has been received from any of the security gates 20-1, 20-2, ..., 20-n. If it is determined in step S1 that the gate passage information has been received, the process proceeds to step S2. If it is determined in step S1 that the gate passage information has not been received, the process proceeds to step S4. Here, if the security gates are collectively managed by, for example, a security gate management device, the gate passage information may be transmitted by the security gate management device.

[0022] In step S2, the processor 11 performs anonymization processing on the ID included in the received gate passage information. For example, the processor 11 converts the ID into a hash value using a hash function. Here, the ID anonymization processing may be performed in the security gates 20-1, 20-2, ..., 20-n. Also, the ID anonymization processing may be performed by a management device of the security gate. In this case, the processing of step S2 may be omitted.

[0023] In step S3, the processor 11 stores the received gate passage information in the storage 14, for example.

[0024] In step S4, the processor 11 determines whether or not to perform data conversion processing. The data conversion processing is processing for converting gate passage information received from the security gates 20-1, 20-2, ..., 20-n as substantially raw data into traffic flow information with higher anonymity. The data conversion processing may be performed at predetermined intervals, such as once a day or once a week, or may be performed when the accumulated amount of gate passage information reaches a certain amount, or may be performed upon instruction from the manager of building B or the like. When it is determined in step S4 that the data conversion processing is to be performed, the processing proceeds to step S5. When it is determined in step S4 that the data conversion processing is not to be performed, the processing proceeds to step S6.

[0025] In step S5, the processor 11 performs a data conversion process. After that, the process proceeds to step S6. The details of the data conversion process will be described later.

[0026] In step S6, the processor 11 determines whether or not to perform data analysis processing. The data analysis processing is processing for performing various analyses related to the management of building B using the traffic flow information converted in the data conversion processing. The data analysis processing may be performed at predetermined intervals, such as every day or every week, or may be performed when the accumulated amount of data related to traffic flow reaches a certain amount, or may be performed in response to an instruction from the manager of building B, etc. In step S6, when it is determined that the data analysis processing is to be performed, the processing proceeds to step S7. In step S6, when it is determined that the data analysis processing is not to be performed, the processing proceeds to step S8.

[0027] In step S7, the processor 11 performs a data analysis process. Then, the process proceeds to step S8. The details of the data analysis process will be described later.

[0028] In step S8, the processor 11 determines whether or not to transmit data. Data transmission is a process of transmitting various information to the cloud server 30. Data transmission may be performed at predetermined intervals, such as every day or every week, or may be performed when the accumulated amount of data related to traffic flow or the data analysis results reach a certain amount, or may be performed upon receiving an instruction from the manager of building B, etc. In step S8, when it is determined that data transmission is to be performed, the process proceeds to step S9. In step S8, when it is determined that data transmission is not to be performed, the process returns to step S1.

[0029] In step S9, the processor 11 transmits data to the cloud server 30. Then, the process of Fig. 4 ends. Here, the data transmitted to the cloud server 30 is at least one of the traffic flow information 144 and the analysis information 145.

[0030] Fig. 5 is a flowchart showing an example of the data conversion process. In step S101, the processor 11 extracts gate passage information of one ID from the gate passage information 143 stored in, for example, the storage 14. For example, in Fig. 3, the processor 11 extracts gate passage information No. 2, No. 8, and No. 9 of the ID "281CAA8FE9931ED32BD511AC8C9e3439".

[0031] In step S102, processor 11 extracts the final origin (O) and destination (D) of the day from the extracted gate passing information. The final origin and destination are the gate passing information with the latest date and time and the gate passing information immediately before that. For example, in FIG. 3, processor 11 extracts gate passing information No. 9 and No. 8 as the final origin and destination. Here, in the processing of step S102, processor 11 may extract the first origin and destination from the extracted gate passing information.

[0032] In step S103, the processor 11 calculates the travel time required to travel the section from the origin to the destination. When the passing time of the security gate at the origin is T0 and the passing time of the security gate at the destination is T1, the travel time T can be calculated from T1-T0. For example, in the example of FIG. 3, the travel time T is 2 (minutes).

[0033] In step S104, the processor 11 determines whether the difference between the calculated travel time T and the most frequently occurring travel time Tf of the same section as the travel time T is equal to or greater than a predetermined time. The same section here is a section in which the source O and destination D match. Note that the same section may be considered to be the same section even if the source O and destination D are switched. The most frequently occurring travel time Tf may be the most frequently occurring travel time among travel times calculated for the same ID, or may be the most frequently occurring travel time among travel times calculated for a plurality of different IDs. In step S104, when it is determined that the difference between the travel time T and the travel time Tf is equal to or greater than a predetermined time, the process proceeds to step S105. In step S104, when it is determined that the difference between the travel time T and the travel time Tf is not equal to or greater than a predetermined time, the process proceeds to step S106.

[0034] In step S105, the processor 11 stores the calculated travel time T in the storage 14 as the traffic flow information 144 together with the gate number of the security gate of the source O, the gate number of the security gate of the destination D, and the passing time of each security gate. Furthermore, the processor 11 sets an inaccurate flag to the stored travel time T. After that, the process proceeds to step S107.

[0035] The inaccurate flag is a flag indicating that the calculated travel time T may not correctly capture the travel of the person P. Normally, if the travel is within the same section, the travel time is considered to be about the same. In contrast, for example, if there is a detour during the travel and there is no security gate in the detour section, the travel time will be longer by the amount of the detour. In addition, some security gates allow so-called friend entry. Friend entry means passing through a security gate at a timing when it is possible to pass through due to authentication by another person. Furthermore, some security gates restrict passage only when entering, and there are security gates that do not require authentication when leaving. In these cases, gate passage information is not obtained even though the security gate has been passed, so the travel time is longer as a result. However, these travel times do not correctly capture the travel of the person P. Therefore, such travel times T are managed by the inaccurate flag so as not to be used for later analysis.

[0036] In step S106, the processor 11 stores the calculated travel time T together with the gate number of the security gate of the source O, the gate number of the security gate of the destination D, and the passing time of each security gate as the traffic flow information 144 in the storage 14. After that, the process proceeds to step S107. In step S106, the processor 11 does not set the inaccurate flag.

[0037] In step S107, processor 11 determines whether or not the processing for the same ID has been completed. For example, the processing for the same ID is determined to be completed when the calculation of the travel time for the first movement source and the first movement destination is completed. When it is determined in step S107 that the processing for the same ID has not been completed, the process proceeds to step S108. When it is determined in step S107 that the processing for the same ID has been completed, the process proceeds to step S109.

[0038] In step S108, the processor 11 extracts the next source and destination. After that, the process returns to step S103. The next destination is, for example, the security gate of the previous source. The next source is the security gate immediately before the previous source. For example, in FIG. 3, the processor 11 extracts gate passage information No. 8 and No. 2 as the next source and destination.

[0039] In step S109, the processor 11 determines whether or not the process for all IDs has been completed. If it is determined in step S109 that the process for all IDs has not been completed, the process proceeds to step S110. If it is determined in step S109 that the process for all IDs has been completed, the process proceeds to step S111.

[0040] In step S110, the processor 11 extracts the gate passage information of the next ID from the gate passage information 143 stored in, for example, the storage 14. After that, the process returns to step S102.

[0041] In step S111, the processor 11 creates an OD table, and stores the created OD table in the storage 14 as the traffic flow information 144. After that, the processing in FIG. 5 ends.

[0042] FIG. 6A is a diagram showing an example of an OD table. The OD table is a table showing the relationship between the origin O and the destination D. In FIG. 6A, the OD table is created on a floor-by-floor basis. That is, FIG. 6A shows the gate numbers of the security gates of the origin O and the gate numbers of the security gates of the destination D for all IDs during a certain data period, which are tabulated on a floor-by-floor basis. Therefore, the OD table does not have information for each individual. In such an OD table on a floor-by-floor basis, the traffic volume of the departure floor and the destination floor can be known. Here, the numerical value of each cell in the OD table of FIG. 6A is, for example, the total number of people who have passed through the security gate. The numerical value of the cell may be, for example, a value normalized by the total number of people. In addition, the OD table may be created on an area basis defined in building B, or on a security gate basis.

[0043] Furthermore, the OD table may be created for each time period. For example, in the operation of an office building, if an OD table for the commuter time period is created, the amount of traffic during the commuter time period can be known. For example, if there is a lot of movement of person P from the third floor to other floors during the commuter time period, it is possible to control the elevator EV to be called to the third floor in advance during the commuter time period. Furthermore, during the commuter time period, the first destination D is often a room in the department to which person P belongs. For this reason, if an OD table for the commuter time period is created, it is also possible to estimate destinations that are highly related to person P, such as the department to which person P belongs.

[0044] Furthermore, as an extension of the OD table, the processor 11 may create a travel time table. FIG. 6B shows an example of the travel time table. The travel time table is a table of travel times between each of the origins O and destinations D. The numerical values ​​in the cells of the travel time table may be the minimum, average, mode, etc. of the travel time. Moreover, the tables of the minimum, average, and mode of the travel time may be created separately.

[0045] Furthermore, as an extension of the OD table, the processor 11 may create a congestion degree table. FIG. 6C shows an example of the congestion degree table. The congestion degree table is a table that numerically represents the congestion degree in the section between each of the movement origins O and the movement destinations D. The congestion degree can be calculated in a data analysis process that will be described later. In the cell of the congestion degree table in FIG. 6C, "0" represents an empty state, "1" represents a slightly crowded state, and "2" represents a crowded state. In FIG. 6C, the congestion degree is divided into three stages, but it may be divided into two stages or four or more stages.

[0046] In step S9, the processor 11 transmits the traffic flow information to the cloud server 30. For example, the processor 11 may transmit the OD table as the traffic flow information to the cloud server 30, or may transmit the OD table and the travel time table as the traffic flow information to the cloud server 30, or may transmit the OD table and the congestion degree table as the traffic flow information to the cloud server 30, or may transmit the OD table, the travel time table, and the congestion degree table as the traffic flow information to the cloud server 30. Here, as the traffic flow information, the processor 11 may generate detailed traffic flow information as shown in FIG. 6D. The detailed traffic flow information in FIG. 6D includes, for example, items such as "ID", "No.", "Origin passing time", "Origin location information", "Destination passing time", "Destination location information", "travel time", and "accuracy information". "ID" is personal identification information for identifying a person who has made a move between ODs. "ID" may be an ID acquired from a security gate. "No." is a unique number assigned to each piece of traffic flow information. "Origin passing time" is the time when a person with the corresponding ID passed through the security gate of the movement source O. The "Origin location information" is information indicating the floor where the security gate of the movement source O is located. The "Origin location information" may be the gate number of the security gate of the movement source O, or the like. The "Destination passing time" is the time when the person of the corresponding ID passed through the security gate of the movement destination D. The "Destination location information" is information indicating the floor where the security gate of the movement destination D is located. The "Destination location information" may be the gate number of the security gate of the movement destination D, or the like. The "Travel time" is the travel time in the section between the movement source O and the movement destination D. The "Accuracy information" is information on the accuracy of the "Travel time", for example, the above-mentioned inaccuracy flag. If the degree to which the calculated travel time does not correctly capture the movement of the person can be quantified, the "Accuracy information" may be the numerical value. When transmitting the detailed traffic flow information data in step S9, the processor 11 may delete the "ID" from the traffic flow information in FIG. 6D and transmit only the remaining information in the thick frame part of the traffic flow information.This allows the traffic flow information without the personal information to be transmitted to the cloud server 30.

[0047] Here, for example, when considering the movement of a person P in an office building in one day, the person P passes through a security gate installed at the entrance of building B to enter building B, passes through a security gate to enter the room of his / her department, and passes through a security gate installed at the exit of building B to leave building B at the end of the day. Therefore, the gate passage information of the final destination is usually gate passage information of a security gate installed at the exit of building B indicating the exit from building B, and the event is "exit". Similarly, the gate passage information of the initial source is usually gate passage information of a security gate installed at the entrance of building B indicating the entry to building B, and the event is "enter". However, depending on the structure of building B, it may be possible to leave or enter the building without passing through a security gate. In this case, the gate passage information may indicate that the person P is present in the building even though he / she has actually left the building, or the gate passage information may indicate that the person P is present in the building even though there is no record of the person P entering the building. When the gate passage information of the final destination is not "exit" of the security gate installed at the exit of Building B, the gate passage information of the final destination may be switched to "exit" of the security gate installed at the exit of Building B at a certain time. This time may be 4:00 a.m., for example. In practice, this time may be determined according to the operation of Building B. Also, even if the gate passage information of the initial source is not "enter" of the security gate installed at the entrance of Building B, if gate passage information is obtained thereafter, person P may be considered to be in Building B.

[0048] 7 is a flowchart showing an example of the data analysis process. In step S201, the processor 11 selects the shortest time T MIN Extract the shortest time T MINcan be considered as the travel time when one can travel between ODs via the shortest route with almost no waiting time at the elevator EV. This shortest time T MIN is used to analyze the crowded time period. In step S201, the shortest time T MIN may be extracted.

[0049] In step S202, the processor 11 calculates the extracted shortest time T MIN For example, the shortest time T MIN When the deviation is within a predetermined value, the shortest time T MIN It is determined that the shortest time T MIN If it is determined that the shortest time T MIN If it is determined that the value is not an outlier, the process proceeds to step S204.

[0050] In step S203, the processor 11 calculates the next shortest travel time among the travel times between the corresponding ODs as the shortest time T MIN After that, the process returns to step S202.

[0051] In step S204, the processor 11 determines the shortest time T MIN is set as the reference value.

[0052] In step S205, the processor 11 calculates the average value of travel time between each OD for each time period. The time period may be determined appropriately. For example, the time period may be determined as the work start time period (8:00-9:00), the work end time period (17:00-20:00), and others (9:00-12:00, 13:00-17:00, 20:00-28:00). Here, the processor 11 does not use travel times for which an inaccurate flag is set in calculating the average value. Various statistical values ​​such as the median may also be used in step S205.

[0053] In step S206, processor 11 compares the average value for each time period with the corresponding OD reference value, and determines whether or not there is a time period having an average value longer than the reference value by a predetermined time or more. If it is determined in step S206 that there is a time period having an average value longer than the reference value, the process proceeds to step S207. If it is determined in step S206 that there is no time period having an average value longer than the reference value, the process proceeds to step S208.

[0054] In step S207, the processor 11 sets a crowded time zone flag for the corresponding time zone and stores it in the storage 14. The crowded time zone flag is a flag indicating that the elevator EV or the like is crowded during the corresponding time zone. In other words, the average value being longer than the reference value is essentially the same as the reference value, i.e., the shortest time T MIN This means that, although it is possible to move between ODs in about the same time, the average travel time has become longer for some reason. The influence of detours, etc. on the average travel time is excluded by the inaccurate flag mentioned above. Therefore, it can be considered that the increase in travel time is mainly due to the influence of congestion in elevators, etc.

[0055] In step S208, processor 11 extracts, for each ID, the gate number of the security gate that is passed through most frequently during a certain period of time and information on the floor or area in which that security gate is installed. In step S209, processor 11 extracts, for each ID, the gate number of the security gate that is passed through most frequently in the early morning during a certain period of time and information on the floor or area in which that security gate is installed. Here, the certain period of time may be a period of half a year, a quarter, every month, or every last m months. Furthermore, the security gate at the entrance / exit of building B is excluded from the extraction targets in steps S208 and S209.

[0056] In step S210, the processor 11 estimates the department to which the person P having the corresponding personal ID belongs from the gate number of the security gate passed through most frequently and information on the floor or area where the security gate is installed, and the gate number of the security gate passed through most frequently early in the morning and information on the floor or area where the security gate is installed. In the embodiment, since the OD for each ID is also obtained, the security gate that is the most frequent source or destination of the person with the corresponding ID, that is, the location, is specified. This location can be said to be a location that is highly relevant to the person with that ID. For example, in the case of an office building, the location that is highly relevant to the person P is the room of the department to which the person P belongs. Then, it is estimated that the person P passes through the security gate near the room most often. Therefore, the department to which the person P having the corresponding personal ID belongs can be estimated from the gate number of the security gate passed through most frequently and the gate number of the security gate passed through most frequently early in the morning. Here, the gate number of the security gate passed through most frequently and the gate number of the security gate passed through most frequently early in the morning do not necessarily match. When they do not match, which result is to be prioritized may be appropriately determined. Furthermore, if the information on the department to which person P belongs can be obtained by another method, such as obtaining it from another system or manually entering it, the processes in steps S208-S209 may be omitted.

[0057] Here, the information on the department to which person P belongs can also be used to extract the origin and destination of movement described above. As described above, in the case of a security gate that allows entry by friends or a security gate that does not require authentication when exiting, gate passage information is not obtained even if the security gate has been passed through. In such a case, the security gate closest to the room of person P's department may be set as the origin and destination of movement. In this case, the passage time may be calculated backwards from, for example, the average travel time for the same section.

[0058] In step S211, the processor 11 generates analysis information 145 including information on busy time periods and information on departments to which each ID belongs, and stores the generated analysis information 145 in the storage 14. After that, the processing in FIG. 7 ends.

[0059] Here, the analysis process in Fig. 7 is an example. Various analyses can be performed using the traffic flow information 144 generated in the embodiment. For example, the number of people in building B for each time period can be identified using the traffic flow information 144. In addition, the number of people in the building can be identified not for the entire building, but for each floor or area.

[0060] FIG. 8 is a diagram showing a concept of prediction of traffic flow information by the prediction processing unit 112. As shown in FIG. 8, in addition to past traffic flow information, for example, calendar information, weather information, and event information are input to the prediction processing unit 112. The past traffic flow information is traffic flow information 144 generated by the above-mentioned conversion process and stored in the storage 14. The calendar information is calendar information such as date, day of the week, and holidays. The weather information is information on the weather of each day linked with the calendar information. The event information is information on the presence or absence of an event and the contents of the event of each day linked with the calendar information. The prediction processing unit 112 learns the association between the past traffic flow information, calendar information, weather information, and event information. The learning can be performed using machine learning, a deep neural network, or the like. Then, the learned prediction processing unit 112 outputs traffic flow information for a predetermined date and time in the future in response to the input of the calendar information, weather information, and event information.

[0061] As described above, according to the embodiment, traffic flow information such as travel time and OD table for a section is generated based on gate passing information when a person passes through a plurality of security gates installed in a building. In other words, since the traffic flow information is generated based on raw data, the accuracy of the generation of the traffic flow information is maintained.

[0062] Furthermore, the traffic flow information and the analysis information based on it are compiled by collecting gate passage information for each security gate. At this compilation stage, information on an individual basis is no longer collected. This also helps to protect personal information. By outputting the traffic flow information and analysis information to a cloud server, this information can be used flexibly.

[0063] [Variations] Next, a modified example of the embodiment will be described. In the embodiment, the traffic flow information is generated using gate passing information when passing through a security gate. In addition, if the person P has a beacon terminal, the position information of the person P specified by the beacon terminal may be further taken into consideration to generate the traffic flow information. In addition, the position information of the person P specified by the beacon terminal may also be used as teacher information when the prediction processing unit 112 makes a prediction.

[0064] In the embodiment, the traffic flow information is generated using gate passage information when passing through a security gate in building B. In contrast, the technology of the embodiment can also be applied to a security gate installed at the entrance of a store.

[0065] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0066] 1 Traffic flow measurement system, 10 Traffic flow measurement device, 11 Processor, 12 ROM, 13 RAM, 14 Storage, 15 Local communication module, 16 Global communication module, 20-1, 20-2, ..., 20-n Security gates, 30 Cloud server, 111 Measurement processing unit, 112 Prediction processing unit, 141 ... Operating system (OS), 142 Traffic flow measurement program, 143 Gate passage information, 144 Traffic flow information, 145 Analysis information.

Claims

1. acquiring gate passage information including gate identification information and passage times of each of a plurality of security gates in a building through which each of a plurality of persons to which personal identification information is assigned has passed; generating, as traffic flow information, travel times required for the plurality of persons to travel from their origins to their destinations based on the gate passage information; Setting the shortest travel time in each section as a reference value; Calculating an average value of the travel time for each time period in each section; A time period during which the difference between the reference value and the average value exceeds a predetermined time is identified as a congested state. A traffic flow measuring device having a measurement processing unit.

2. The measurement processing unit includes: A travel time whose difference from the most frequently occurring travel time in each section exceeds a predetermined time is excluded from the calculation of the average value. The traffic flow measuring device according to claim 1.

3. The measurement processing unit includes:

3. The traffic flow measuring device according to claim 1 or 2, wherein the security gate that was passed through most frequently for each of the personal identification information is identified based on the gate passage information, and a location within the building that is highly associated with the person is estimated based on the identified security gate.

4. 4. The traffic flow measuring device according to claim 1, further comprising a local communication module configured to communicate the gate passage information with the security gate in a local environment within the building.

5. 5. The traffic flow measuring device according to claim 1, further comprising a global communication module configured to communicate the traffic flow information with equipment outside the building over a public network.

6. A measurement processing unit of the traffic flow measuring device acquires gate passage information including gate identification information and passage time of each of a plurality of security gates in a building through which each of a plurality of persons to which personal identification information is assigned has passed; the measurement processing unit generates, based on the gate passage information, travel times required for the plurality of persons to travel through a section from a movement origin to a movement destination as traffic flow information; the measurement processing unit sets the shortest travel time in each section as a reference value; The measurement processing unit calculates an average value of the travel time for each time period in each section; The measurement processing unit identifies a time period during which a difference between the reference value and the average value exceeds a predetermined time as a congested state; A traffic flow measurement method comprising:

7. acquiring gate passage information including gate identification information and passage time of each of a plurality of security gates in a building through which each of a plurality of persons to whom personal identification information is assigned has passed; generating, as traffic flow information, travel times required for the plurality of persons to travel from their origins to their destinations based on the gate passage information; setting the shortest travel time in each section as a reference value; Calculating an average value of the travel time for each time period in each section; Identifying a time period during which the difference between the reference value and the average value exceeds a predetermined time as a congested state; A traffic flow measurement program for causing a processor to execute the above.

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