Gate control command device, gate control system, gate control device, operation management device, operation management system, gate control method, and gate control program

Abstract

The purpose of the present disclosure is to obtain a gate control command device with which it is possible to prevent two people flows, which include people flows that are formed by passengers alighting from a train, from colliding with each other at narrow places in a train station in a state in which the pressure relationship between the people flows is balanced. The gate control command device according to the present disclosure comprises an alighting personnel calculation unit (37), a gate control command unit, and a transmission unit. The alighting personnel calculation unit (37) calculates an alighting personnel estimation value for the station on the basis of alighting personnel data that indicates alighting personnel for the station. The gate control command unit generates, on the basis of the alighting personnel estimation value, a gate control command for adjusting the people flow that flows into a bottleneck section in which the movement speed of people flows in the station decreases, by using a gate through which people pass. The transmission unit transmits the gate control command to a gate control device that controls the gate.

Description

Gate control command device, gate control system, gate control device, operation management device, operation management system, gate control method, and gate control program

[0001] The present disclosure relates to a gate control command device, a gate control system, a gate control device, an operation management device, an operation management system, a gate control method, and a gate control program for suppressing collisions of the flow of people within a station building.

[0002] Patent Document 1 discloses a transportation system that controls ticket gates according to the degree of congestion within a station building. Specifically, the transportation system described in Patent Document 1 installs human detection sensors in areas outside ticket gates, inside ticket gates, and on station platforms within a station building, calculates the degree of congestion at the station based on the detection results of the presence of people by the human detection sensors, and adjusts the number of ticket gates for entry and the number of ticket gates for exit according to the degree of congestion. In addition, the transportation system described in Patent Document 1 presents information indicating the congestion situation on the signage installed within the station building.

[0003] Japanese Patent Application Laid-Open No. 2016-147620

[0004] A crowd avalanche occurs at a narrow location where crowds of people flowing in different directions with a density above a determined density collide, and at locations where people are likely to fall, such as stairs and slopes. Examples of locations that meet these conditions include stairs and passageways within a station building. When the flow of passengers getting off a train and the flow of people entering through ticket gates converge during congestion within a station building, a crowd avalanche may occur at locations that meet the above conditions.

[0005] However, the technology described in Patent Document 1 is based on the results of detection by a human detection sensor, and is a measure taken after congestion has already occurred. Furthermore, while a human detection sensor can detect the presence of people at the installation location, it cannot determine the direction of people's movement. For this reason, the technology described in Patent Document 1 cannot take into account the collision of people in the direction of flow, which is the principle behind crowd avalanche generation. In addition, the technology described in Patent Document 1 discloses that congestion levels may be determined by photographing various locations in the station premises, including the area outside the ticket gates, the area inside the ticket gates, and the station platforms, and then matching the captured image data with a predetermined human shape pattern, but this method also does not take into account collisions in the direction of flow.

[0006] Thus, in the technology described in Patent Document 1, congestion within the station was judged from surveillance cameras, etc., and the permitted direction of passage for each ticket gate was set individually. For this reason, it was difficult to estimate the number of people disembarking and the expected congestion of pedestrian flow before the train arrived at the station, making it difficult to implement ticket gate restrictions in advance.

[0007] This disclosure has been made in view of the above, and aims to provide a gate control command device that can prevent two flows of people, including the flow of people generated by passengers disembarking from a train, from colliding in a narrow area within a station while their mutual pressure relationships are balanced.

[0008] To solve the above-mentioned problems and achieve the objectives, the gate control command device according to this disclosure comprises a disembarking passenger calculation unit, a gate control command unit, and a transmission unit. The disembarking passenger calculation unit calculates an estimated number of disembarking passengers at a station based on disembarking passenger data indicating the number of passengers disembarking at the station. Based on the estimated number of disembarking passengers, the gate control command unit generates a gate control command to adjust the flow of people into a bottleneck section where the speed of movement of people at the station decreases, using gates through which people pass. The transmission unit transmits the gate control command to a gate control device that controls the gates.

[0009] The gate control command device described herein has the effect of preventing two flows of people, including the flow of people generated by passengers disembarking from trains, from colliding in narrow areas within the station while maintaining a balance of pressure relations between them.

[0010]

[0011] The gate control command device, gate control system, gate control device, operation management device, operation management system, gate control method, and gate control program according to embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0012] Embodiment 1. Embodiment 1 adjusts the flow of people to prevent crowd avalanches from occurring in narrow areas within the station premises, specifically between the disembarking passenger flow (passengers alighting from an arriving train and heading towards the ticket gate) and the entering passenger flow (passengers entering from the ticket gate and heading towards the platform). The disembarking passenger flow and entering passenger flow that cause crowd avalanches are those consisting of groups exceeding a predetermined crowd density. It is also known that crowd avalanches occur when the pressure relationship between the disembarking passenger flow and the entering passenger flow is balanced. Here, we will first explain the adjustment of people flow within the station premises, and then describe the operation management system that realizes the adjustment of people flow.

[0013] Figure 1 shows an example of a station premises. Figure 1 schematically shows the area from platform 110 to ticket gate 120 in station premises 100. Station premises 100 includes platform 110 where train 150 arrives, ticket gate 120 where an automatic ticket gate 121 with a gate 122 is located, and a passageway 130 connecting platform 110 and gate 122 of the automatic ticket gate 121. Here, passageway 130 refers to the space where passengers can walk. Passageway 130 carries two types of people: disembarking passengers from train 150 arriving at platform 110 heading towards ticket gate 120, and entering passengers entering from ticket gate 120 heading towards platform 110. Automatic ticket gate 121 is an example of a ticket gate.

[0014] Generally, the passageway 130 does not have the same width throughout, and there are narrow sections. In the example in Figure 1, the passageway 130 has a narrow section RB, a first section R1 which is wider than section RB and is located on the platform 110 side of section RB, and a second section R2 which is wider than section RB and is located on the ticket gate 120 side of section RB. In one example, section RB is a narrow staircase. The starting point of the passageway 130 on the first section R1 side is the train car of train 150 that is closest to the entrance or exit of the stairs or escalator on platform 110. Note that the starting point on the first section R1 side may be the boarding / alighting door of the train car closest to the entrance or exit of the stairs or escalator, rather than the train car itself. The ending point of the passageway 130 on the second section R2 side is the gate 122 of the automatic ticket gate 121. In this example, we are using the disembarking passengers as the basis for our calculations, so the starting point is defined as a position on platform 110 and the ending point as the ticket gate 120. However, these can be reversed.

[0015] When train 150 arrives at platform 110 and passengers disembark, the disembarking passenger flow passes through the first section R1, section RB, and second section R2 to reach the ticket gate 120. On the other hand, the entering passenger flow from ticket gate 120 passes through the second section R2, section RB, and first section R1 in the opposite direction to the disembarking passenger flow to reach platform 110. At this time, if the disembarking passenger flow and the entering passenger flow collide in the narrow section RB and more people enter from both sides while the disembarking and entering passenger flows are stationary, a crowd avalanche may occur. Therefore, in Embodiment 1, to prevent such a crowd avalanche, that is, to prevent the disembarking passenger flow and the entering passenger flow from colliding in the narrow section RB while the pressure relationship between the two flows is balanced, entry restrictions are implemented at ticket gate 120. Entry restrictions at ticket gate 120 mean adjusting the entering passenger flow.

[0016] Here, the parameters or constants used in Embodiment 1 will be described. Section RB is a narrower section than the first section R1, so generally the speed of disembarking passengers is slower than in the first section R1. The speed of disembarking passengers in the passage 130 is limited by section RB. For this reason, section RB will also be referred to as the bottleneck section RB below. Narrow sections in the station premises 100 can be stairs and escalators that are narrower than other sections, as well as passages under construction, areas where roof leaks occur, and locations for attending to sick people. The reference point can be the width of the entrance or exit of the stairs or escalator in the first section R1. Any section narrower than this width can be considered a narrow section.

[0017] <Section 1 R1> The parameters for Section 1 R1 are defined as follows: L1: The distance from the disembarking point of train 150 to the point where it touches the bottleneck section RB. Hereafter, this will be referred to as distance L1. V1: The average moving speed of passengers disembarking in Section 1 R1. Hereafter, this will be referred to as moving speed V1.

[0018] In adjusting passenger flow, we consider the passengers who will reach the narrowest point in the disembarking passenger flow, i.e., the bottleneck section RB, the fastest. Therefore, the starting point on the platform 110 side at distance L1 will be the train car closest to the ascent or descent of the stairs or escalator closest to the bottleneck section RB. Hereafter, the ascent or descent of the stairs or escalator will also be referred to as the stair entrance 131.

[0019] Figure 2 shows an example of the distance of the first section. As shown in Figure 2, the distance L1 changes depending on the relationship between the stopping position of the train 150 on platform 110 and the position of the staircase entrance 131. As shown in Figure 2(a), when the train 150 stops so that it overlaps with the staircase entrance 131, the starting point of the distance L1 is the vehicle that overlaps with the staircase entrance 131. In this case, there is no travel distance on platform 110 for disembarking passengers. On the other hand, as shown in Figure 2(b), when the train 150 stops away from the staircase entrance 131, the starting point of the distance L1 is the vehicle closest to the staircase entrance 131. In this case, the distance from the staircase entrance 131 to the vehicle closest to the staircase entrance 131 becomes the travel distance on platform 110.

[0020] <Bottleneck Section RB> The parameters in the bottleneck section RB are defined as follows: LB: Distance of the bottleneck section RB. Hereinafter referred to as distance LB. VB: Average travel speed in the bottleneck section RB. Hereinafter referred to as travel speed VB. It is assumed that the travel speed VB in the bottleneck section RB is the same for both disembarking passengers and passengers entering from ticket gate 120. WB: Capacity of the bottleneck section RB. Hereinafter referred to as capacity WB. SB: Inflow velocity (people / sec) of the bottleneck section RB. Hereinafter referred to as inflow velocity SB.

[0021] <Second Section R2> The parameters for the second section R2 are defined as follows: L2: The distance from the point adjacent to the bottleneck section RB to the ticket gate 120. Hereinafter referred to as distance L2. V2: The average speed of passengers entering from the ticket gate 120 in the second section R2. Hereinafter referred to as speed V2. S2: The inflow speed of the second section R2 = ticket gate inflow speed (people / sec). Here, this is the speed when all gates 122 provided at the ticket gate 120 are used for entry. Hereinafter referred to as inflow speed S2.

[0022] In addition, the maximum capacity WB can be set, for example, to a value that is deemed not to cause a crowd avalanche, based on the "Technical Guidelines for Entertainment Venues, etc." issued by the Ministry of Land, Infrastructure, Transport and Tourism, but it may also be a value that is deemed not to cause a crowd avalanche, as determined by other methods.

[0023] <Other> Other parameters are defined as follows: T: Arrival interval or arrival time of train 150. K: Estimated number of passengers disembarking from train 150.

[0024] The movement speeds V1, VB, V2 and inflow speeds SB, S2 listed here are assumed to be the fastest speeds permissible given the width of the passage 130 and the normal walking speed. The parameters or constants shown above are data used to calculate the time required to restrict entry at the ticket gate 120 so that the flow of disembarking passengers, consisting of groups exceeding a predetermined crowd density, and the flow of entering passengers, consisting of groups exceeding a predetermined crowd density, do not collide in a state where the pressure relationship between them is balanced. These parameters are also referred to as station equipment parameters.

[0025] Furthermore, there is a constraint that if the number of disembarking passengers plus the number of entering passengers exceeds the station's capacity of 100, entry restrictions must be in place at all times. However, for the sake of simplicity, the following explanation will assume that this constraint is not met, that is, that the number of disembarking passengers plus the number of entering passengers is less than the station's capacity of 100.

[0026] Figure 3 illustrates an example of a method for adjusting pedestrian flow in a narrow area. In this figure, the horizontal axis represents time, and the vertical axis represents the number of people passing through the bottleneck section RB. When train 150 arrives at platform 110 at time T, the flow of disembarking passengers from train 150 passes through passage 130. When the disembarking flow reaches the bottleneck section RB, which is a narrow area, the movement speed VB is usually lower than the movement speed V1 in the first section R1. As a result, the crowd density increases in the bottleneck section RB. If pedestrian flow adjustment is not performed at the ticket gate 120, the disembarking flow will converge with the flow of people entering from the ticket gate 120 while passing through the bottleneck section RB.

[0027] In Embodiment 1, the collision between the disembarking passenger flow and the entering passenger flow becomes a problem when the collision occurs in a state where the mutual pressure relationship between two opposing passenger flows, each consisting of a crowd density exceeding a predetermined level, is balanced, causing the passenger flow to come to a standstill, and then even more people gather there. This situation is more likely to occur, for example, when the crowd density and size of the two opposing passenger flows are equivalent. Therefore, if the crowd density and size of one passenger flow are significantly smaller than those of the other, the possibility of the passenger flow coming to a standstill due to the collision of the two opposing passenger flows becomes smaller. Accordingly, in Embodiment 1, even if the disembarking passenger flow and the entering passenger flow collide in the bottleneck section RB, the number of visitors is limited so that the entering passenger flow remains below a predetermined crowd density that does not cause a crowd avalanche. As a way to limit the number of entrants, the gates that can be entered at the ticket gate 120 may be controlled so that the flow of entrants is zero, or the gates that can be entered at the ticket gate 120 may be controlled so that the sum of the number of entrants and entrants at the bottleneck section RB while the flow of departing passengers is passing through the bottleneck section RB is less than or equal to the capacity WB.

[0028] An example of a method for calculating the gate adjustment period, which is the period during which entry restrictions are imposed at the ticket gate 120 in Embodiment 1, will be explained with reference to Figure 3. If the scheduled arrival time of train 150 at a certain station is T, the time at which the leading edge of the disembarking passenger flow reaches the bottleneck section RB is T + L1 / V1. By this time, the passenger flow that entered through the ticket gate 120 before the gate adjustment at the ticket gate 120 must have completely passed through the bottleneck section RB. From this, the start time Ts of the ticket gate restriction can be calculated in reverse and shown in the following equation (1).

[0029] Ts=T+L1 / V1-(L2 / V2+LB / VB)...(1)

[0030] As described above, control is implemented to restrict the number of gates that can be used for entry at ticket gate 120 at the start time Ts of the ticket gate restriction. As will be described later, depending on the relationship between the estimated number of disembarking passengers K and the capacity WB, control is implemented at gate 122 of ticket gate 120 to set the number of available entry gates to 0, or to limit the number of available entry gates to a number calculated so that the sum of the number of disembarking passengers and the number of entering passengers in the bottleneck section RB is less than or equal to the capacity WB. By implementing control to restrict the number of available entry gates at the start time Ts of the ticket gate restriction, the flow of entering passengers before the restriction has passed through the bottleneck section RB, and then the leading edge of the disembarking passenger flow reaches the bottleneck section RB.

[0031] Since passenger flow is a group, it takes time K / SB for the entire disembarking passenger flow to enter the bottleneck section RB. It also takes time LB / VB for the entire disembarking passenger flow to exit the bottleneck section RB. Generally, the speed at which a narrow section is entered is less than the speed at which it exits; therefore, the constraint here is the speed at which the passengers enter the narrow section.

[0032] If we work backwards so that the flow of passengers entering the bottleneck section RB enters the same bottleneck section RB at the same time that the flow of passengers disembarking has completely left the bottleneck section RB, the end time of the ticket gate restriction Te can be expressed as shown in equation (2) below.

[0033] Te=T1+L1 / V1+K / SB+LB / VB-L2 / V2...(2)

[0034] At the ticket gate detention end time Te calculated in this way, control is performed to release the restriction on the gates that can be entered at the ticket gate 120 as described above. By releasing the restriction on the gates that can be entered at the ticket gate detention end time Te, the flow of disembarking passengers will pass through the bottleneck section RB, and then the leading edge of the flow of entering passengers after the restriction has been released will reach the bottleneck section RB.

[0035] In the above calculation, if we consider the disembarking passenger flow as a large group, the first passengers to disembark near the staircase entrance 131 will enter the bottleneck section RB at time T + L1 / V1, and the person furthest from the staircase entrance 131 will enter the bottleneck section RB last. In the graph of Figure 3, the person who enters the bottleneck section RB at time T + L1 / V1 + K / SB, when the rise in disembarking passengers is at its peak, will be the passenger furthest from the staircase entrance 131 and the last to get off train 150. However, if the number of people entering from the ticket gate 120 has decreased by time T + L1 / V1, when the first disembarking passengers enter the bottleneck section RB, then the subsequent rise in disembarking passengers in the bottleneck section RB will not affect the occurrence of a crowd avalanche. For this reason, in the above calculation, distance L1 is calculated as the distance the first disembarking passenger travels to reach the staircase entrance 131.

[0036] Furthermore, since the above station equipment parameters change depending on the number of cars, number of doors, and passenger type of the arriving train, it is desirable to define the station equipment parameters according to the type of train cars of the arriving train. The reasons why it is preferable to define the station equipment parameters for each number of cars, number of doors, and passenger type of the arriving train are explained below.

[0037] First, let's explain the differences in station equipment parameters due to the difference in the number of doors on the train cars. There are several types of train cars, with different numbers of doors, such as 1, 2, 3, and 4. When an arriving train has fewer doors, specifically two doors or less, the disembarking speed is slower, so the disembarking speed is determined more by the disembarking speed than by the inflow speed SB, which is determined by the physical constraints of the bottleneck section RB. Conversely, when an arriving train has more doors, for example, more than three doors, the disembarking speed is faster, so the inflow speed SB is suppressed by the physical constraints of section RB. For this reason, it is desirable to make the inflow speed SB for entering the bottleneck section RB variable for each structure of the train cars that make up train 150. In Embodiment 1, it is not necessary to define data for disembarking speed, i.e., the speed at which passengers enter the first section R1, and the disembarking speed is managed by the inflow speed SB into section RB.

[0038] Next, we will explain the differences in station equipment parameters due to the difference in the number of cars in train 150. Train 150 can be of several types, such as 4-car, 6-car, 8-car, and 12-car formations. Therefore, the distance to the staircase entrance 131 may differ depending on the number of cars in the arriving train. However, although platform 110 can physically accommodate disembarking passengers, it is not expected that disembarking passengers will deliberately walk to a location far from the staircase entrance 131, for example, to the end of platform 110 on the opposite side of the staircase entrance 131. Therefore, the distance L1 will be less than or equal to the distance L1 value for the shortest number of cars in the train 150 arriving at platform 110. In other words, it is desirable that the distance L1 when a short train 150 arrives and the distance L1 when a long train 150 arrives be different.

[0039] The differences in the number of doors on the vehicles and the difference in the number of cars in train 150 can be summarized as differences in vehicle type. Here, vehicle type is determined by the combination of the vehicle type, which identifies the type of vehicle, and the number of vehicles used.

[0040] Next, we will explain the differences in station facility parameters due to differences in passenger demographics. Train 150 includes commuter trains used for daily travel and long-distance trains used for relatively longer distances than commuter trains. Long-distance trains are also called express trains. Commuter trains are mainly used for commuting to work and school, while long-distance trains are mainly used for travel, business trips, etc. Thus, the passenger demographic for commuter trains is mainly commuters and students, while the passenger demographic for long-distance trains is mainly tourists and business travelers. From this, it can be considered that the passenger demographics of train 150 are related to the type of train.

[0041] Train types are determined by differences in the stations they stop at or pass through, and include types such as local, rapid, express, and limited express. For example, local, rapid, and express trains can be classified as commuter trains, while limited express trains can be classified as long-distance trains. As mentioned above, local, rapid, and express trains have a high proportion of commuters and students, while limited express trains have a high proportion of tourists and business travelers.

[0042] And since tourists and business travelers often have a lot of luggage and may sometimes have large luggage such as suitcases, their walking speed tends to be slow. On the other hand, commuters and students can move more lightly with less luggage compared to tourists and business travelers.

[0043] Therefore, compared to train types such as local, rapid, and express trains with a higher proportion of commuters and students, the movement speed V1 of the alighting passenger flow tends to be slower for train types such as limited express trains with a higher proportion of tourists and business travelers. From the above, it is desirable to make the movement speed V1 variable depending on the train type.

[0044] In one example, the station facility parameter includes a movement speed weighting ratio Δv1, which is a correction coefficient for adjusting the value of the movement speed V1 of the station facility parameter according to the train type. Then, the movement speed V1 is corrected using the movement speed weighting ratio Δv1 corresponding to the train type. The movement speed weighting ratio Δv1 is a coefficient for adjusting the movement speed V1 of other train types based on train 150 whose train type is local. In this case, the movement speed weighting ratio Δv1 when the train type is local can be set to "1", and the movement speed weighting ratios Δv1 for train types such as rapid, express, and limited express can be set to values less than or equal to "1". When using the movement speed weighting ratio Δv1 corresponding to the train type in this way, the formula (1) for obtaining the ticket gate inhibition start time Ts becomes the following formula (1A), and the formula (2) for obtaining the ticket gate inhibition end time Te becomes the following formula (2A).

[0045] Ts = T + L1 / (Δv1 · V1) - (L2 / V2 + LB / VB) ··· (1A) Te = T1 + L1 / (Δv1 · V1) + K / SB + LB / VB - L2 / V2 ··· (2A)

[0046] As described above, the parameters assumed to have differences depending on the vehicle type and train type are the distance L1, the movement speed V1, and the inflow speed SB. Also, the distances LB, L2, the movement speeds VB, V2, the allowable capacity WB, and the inflow speed S2 are independent parameters that do not depend on the vehicle type and train type.

[0047] Next, the operation management system 10 capable of performing the above-mentioned passenger flow control will be described. FIG. 4 is a diagram showing an example of the configuration of the operation management system according to the first embodiment. The operation management system 10 is a system for managing the operation of trains 150 and the passenger flow at stations in a railway facility having a plurality of stations and a plurality of trains 150. The operation management system 10 includes a vehicle information management device 20, an operation management device 30, and a ticket gate control device 50. The vehicle information management device 20, the operation management device 30, and the ticket gate control device 50 are connected via a communication line such as a network. The communication line may be a WAN (Wide Area Network) such as the Internet or a LAN (Local Area Network). Also, the communication method may be wired or wireless.

[0048] The vehicle information management device 20 manages the power running and braking of vehicles in terms of formation units of vehicles, that is, train units, and various in-vehicle devices. The vehicle information management device 20 is mounted on the train 150.

[0049] The vehicle information management device 20 has a passenger occupancy rate measurement unit 21 and a measurement result transmission unit 22. The passenger occupancy rate measurement unit 21 measures the passenger occupancy rate for each vehicle of the train 150. In one example, the passenger occupancy rate measurement unit 21 measures the weight of passengers on the vehicle by a sensor provided for each vehicle, divides the measured weight by the average weight of the passengers to calculate the number of passengers, and measures the passenger occupancy rate from the number of passengers and the seating capacity of the vehicle. In another example, the passenger occupancy rate measurement unit 21 photographs the interior of the vehicle by a camera provided for each vehicle, analyzes the photographed video to measure the number of passengers in the vehicle, and measures the passenger occupancy rate from the number of passengers and the seating capacity of the vehicle. The passenger occupancy rate measurement unit 21 can measure the passenger occupancy rate at a determined cycle. The measurement result transmission unit 22 transmits the passenger occupancy rate data, which is the passenger occupancy rate for each vehicle measured by the passenger occupancy rate measurement unit 21, as a measurement result to the operation management device 30. In one example, the passenger occupancy rate data is data including train identification information for identifying the train 150, the car number for identifying the vehicle in the train 150, and the passenger occupancy rate in the vehicle with the car number. The car number is a number assigned in order from the vehicle at one end of the train 150.

[0050] The train operation management device 30 performs train operation management processing to manage the operation of multiple trains 150 on the railway, and passenger flow adjustment processing to adjust the flow of people within the station premises 100. In the following, since the train operation management processing is the same as conventionally performed, its explanation in this specification will be omitted, and only the passenger flow adjustment processing will be explained. The train operation management device 30 may be configured by a computer system, or by one or more cloud servers or on-premise servers. A cloud server is a server built in a cloud environment that includes computer resources provided on a cloud service platform.

[0051] The train operation management device 30 includes a passenger occupancy rate acquisition unit 31, a command input unit 32, a station equipment data management unit 33, a timetable management unit 34, an event data management unit 35, a storage unit 36, a disembarking passenger calculation unit 37, a ticket gate control command unit 38, and a command transmission unit 39. In the example shown in Figure 4, the storage unit 36 ​​stores seven databases: a vehicle database 361, a train passenger occupancy rate database 362, a station equipment database 363, a timetable information database 364, a disembarking passenger statistics database 365, an event database 366, and an estimated disembarking passenger database 367. In Figure 4, databases are denoted as DB (Data Base). The same applies in Figure 5 and subsequent figures. In Figure 4, seven databases are stored in one storage unit 36, but the train operation management device 30 may have a storage unit 36 ​​for each database.

[0052] The passenger occupancy rate acquisition unit 31 acquires passenger occupancy rate data, which is the measurement result, from the vehicle information management device 20 and stores the acquired passenger occupancy rate data in the train passenger occupancy rate database 362.

[0053] The command input unit 32 receives commands from the operator of the operation management device 30 or from an external device to the operation management device 30. If the command input unit 32 is related to a change in station equipment information, it passes the command to the station equipment data management unit 33. As will be described later, the station equipment data is data that defines station equipment parameters and a reference car number, which is the car number of the vehicle closest to the entrance 131 of the stairs etc. on the station platform 110. If the command input unit 32 is related to a change in timetable information, it passes the command to the timetable management unit 34. If the command input unit 32 is related to a change or setting of event data, it passes the command to the event data management unit 35.

[0054] In one example, the command input unit 32 may be configured to accept automatic commands from the applicable section when coordinating with disaster information. This makes it possible, in one example, to modify event data, etc., according to the automatic commands. Alternatively, the command input unit 32 may accept the various commands described above that are entered manually.

[0055] The station equipment data management unit 33 manages the station equipment database 363. When the operation management device 30 is started, the station equipment data management unit 33 reads a configuration file that defines station equipment information and saves the station equipment information in the configuration file to the station equipment database 363. The configuration file is, for example, an external file generated by another information processing device, and may be read via a communication line or from a portable storage medium. Furthermore, when the station equipment data management unit 33 receives a command to change station equipment information from the command input unit 32, it updates the station equipment database 363 according to the command. For example, if an incident such as a medical emergency or a roof leak occurs in a passage 130 within a station premises 100, the command input unit 32 accepts a command input that includes the changed movement speeds V1, VB, V2 and the station to which the movement speeds V1, VB, V2 will be changed. This provides an input mechanism that can change station equipment information in real time. Furthermore, if an incident such as a medical emergency, roof leak, or construction occurs in passageway 130, the capacity of the section including the location of the incident may decrease, and in this case, the capacity WB may also be changed. The station equipment data management unit 33 corresponds to the station equipment information management unit.

[0056] The timetable management unit 34 manages the timetable information database 364. Furthermore, when the timetable management unit 34 receives a command from the command input unit 32 that includes time change information indicating a time change for a timetable managed in the timetable information database 364, it saves the contents of the command as timetable change information in the timetable information database 364.

[0057] The event data management unit 35 manages the event database 366. When the operation management device 30 is started, the event data management unit 35 reads a configuration file containing event data in which all weight ratios are set to "1", and saves the event data in the configuration file to the event database 366. The configuration file is, for example, an external file generated by another information processing device, and may be read via a communication line or from a portable storage medium. Furthermore, when the event data management unit 35 receives a command from the command input unit 32 that includes event data change information indicating a change in the weight ratios managed in the event database 366 (described later), it updates the event database 366 according to the event data change information included in the command.

[0058] The vehicle database 361 stores vehicle information. This vehicle information includes the vehicle type, the number of doors for each vehicle type, and the passenger capacity for each vehicle type, which are all relevant to the vehicle type used in train 150. Figure 5 shows an example of vehicle information. The vehicle information shown in Figure 5 includes information on the vehicle type, number of doors, and passenger capacity. Vehicle information is registered for all vehicle types used in train 150. Hereafter, the vehicle type will also be referred to as the vehicle type.

[0059] Returning to Figure 4, the train occupancy rate database 362 stores occupancy rate data. The occupancy rate data includes information on the occupancy rate for each car number of train 150 operating according to the timetable information managed by the timetable information database 364. Figure 6 shows an example of occupancy rate data. The occupancy rate data shown in Figure 6 includes information on the train number and occupancy rate. The train number is train identification information that identifies train 150. In one example, the train number is a number used in the timetable information to identify train 150. The occupancy rate is the occupancy rate for each car of train 150 indicated by the train number. Here, the occupancy rate is stored for each car number assigned to each car within train 150. The occupancy rate indicates the ratio of the actual number of passengers to the capacity of each car, and the unit of the occupancy rate is %.

[0060] Returning to Figure 4, the station equipment database 363 stores station equipment information. Station equipment information is data about station equipment necessary for adjusting passenger flow. More specifically, station equipment information defines station equipment parameters, which are data about station equipment necessary for calculating the gate adjustment period described above, i.e., the start time Ts and end time Te of ticket gate restriction; a movement speed weighting ratio Δv1 that corrects the movement speed V1 of the station equipment parameters; and a reference car number, which is the car number of the vehicle closest to the entrance 131 of the stairs, etc. As described above, station equipment parameters include those that depend on the vehicle type, those that depend on the train type, and those that do not depend on the vehicle type, so the data is managed by dividing it into these groups. The station equipment database 363 corresponds to the station equipment information storage unit.

[0061] Figure 7 shows an example of station facility information. The station facility information shown in Figure 7 includes vehicle type-dependent station facility data, train type-dependent station facility data, and vehicle type-independent station facility data.

[0062] The vehicle type-dependent station equipment data includes information on vehicle type and station equipment data. The vehicle type includes information on vehicle type and number of cars. The vehicle type indicates the vehicle type used in train 150. The number of cars is the number of cars that make up train 150.

[0063] The station equipment data includes station equipment parameters and reference car number information. The reference car number indicates the car number of the vehicle closest to the staircase entrance 131 in train 150. In the example in Figure 7, it is assumed that there are multiple vehicles closest to the staircase entrance 131. If there is only one vehicle closest to the staircase entrance 131, for example, if there is only one staircase or escalator on platform 110, or if train 150 is short, then only reference car number 1 is defined. In this case, reference car number 2 is defined as "N / A", meaning not applicable.

[0064] The station equipment parameters are data necessary for calculating the flow adjustment at the ticket gate 120 for the bottleneck section RB in the passage 130 that adjusts the flow of people from the starting position on platform 110 to the gate 122 of the automatic ticket gate 121 for each train type, the first section R1 on the platform 110 side of the bottleneck section RB, and the second section R2 on the ticket gate 120 side of the bottleneck section RB. Here, the distance L1 and movement speed V1 of the first section R1, which are station equipment parameters that depend on the train type, and the inflow speed SB of the bottleneck section RB are defined. In the example in Figure 7, the train type-dependent station equipment data is defined for each station. As described above, the train type-dependent station equipment data is information that defines the train type-dependent station equipment parameters and the reference car number for each train type and each station.

[0065] The train type-dependent station equipment data includes information on the train type and the travel speed weighting ratio Δv1. The train type is information indicating the type of train 150, and examples of train types include local, rapid, express, and limited express. The travel speed weighting ratio Δv1 is a value used to correct the travel speed V1, one of the station equipment parameters, according to the difference in train type. Here, the travel speed weighting ratio Δv1 is set based on a train type of "local". As mentioned above, passengers disembarking from limited express trains often have large or many pieces of luggage, so their travel speed V1 tends to be slower than that of passengers disembarking from commuter trains. For this reason, in the example in Figure 7, a value smaller than "1" is set for the travel speed weighting ratio Δv1 of limited express trains. In the example in Figure 7, the train type-dependent station equipment data is assumed to be constant regardless of the station. The travel speed weighting ratio Δv1 corresponds to a correction coefficient that adjusts the value of station equipment parameters according to the train type, and the train type-dependent station equipment data including the travel speed weighting ratio Δv1 corresponds to the correction coefficient information.

[0066] The vehicle type-independent station equipment data includes information on station names and station equipment parameters. The station name is the name assigned to the station on the line on which train 150 operates. In the example in Figure 7, it is called the station name, but any station identification information that can identify a station is acceptable. The station equipment parameters are data necessary for calculating the passenger flow adjustment at the ticket gate 120 for the bottleneck section RB, the first section R1, and the second section R2. Here, the inflow speed S2, distance L2, and movement speed V2 of the second section R2, as well as the distance LB, movement speed VB, and capacity WB of the bottleneck section RB, which are station equipment parameters that are not dependent on the vehicle type, are defined. In the example in Figure 7, the vehicle type-independent station equipment data is defined for each station. As described above, the vehicle type-independent station equipment data is information that defines station equipment parameters that are not dependent on the vehicle type for each station.

[0067] Returning to Figure 4, the timetable information database 364 stores timetable information. The timetable information is information about the timetable of train 150 managed by the operation management device 30. The timetable information database 364 is used to identify train 150 from the timetable information and to obtain attributes of this train 150 such as train type, vehicle type, and time. The timetable information includes train identification information that uniquely identifies train 150. The train identification information is, in other words, the train number. For each train number, the train information of train 150, the vehicle type of train 150, the arrival time of train 150 at stations, and time change information in case the time is changed are defined. The time change information is data input by the command input unit 32.

[0068] The disembarking passenger statistics database 365 stores disembarking passenger statistics. Disembarking passenger statistics are statistical data on the number of passengers disembarking at all stations within the train's operating range for each time period. Figure 8 shows an example of disembarking passenger statistics. The disembarking passenger statistics shown in Figure 8 include station names and disembarking passenger data for each time period. The station names are the names assigned to stations on the line on which train 150 operates. In the example in Figure 8, station names are used, but any station identification information that can identify a station is acceptable. The disembarking passenger data is the number of passengers disembarking at each station for each time period within a specified period, and is statistically determined from past actual passenger numbers. In one example, the specified period can be one hour. In the example in Figure 8, disembarking passenger statistics for each station are recorded for every hour, such as 5:00, 6:00, 7:00, and so on. Note that the number of passengers disembarking at a station is thought to vary depending on the day of the week. For example, the number of passengers disembarking varies between weekdays and holidays, including Saturdays, Sundays, and public holidays. Furthermore, even on weekdays, the pattern of passenger numbers may differ between Mondays, Wednesdays, and Fridays. In addition, the number of passengers disembarking at a station is likely to vary from month to month. For example, the pattern of passenger numbers disembarking may differ between months when schools are closed and months when they are not. For this reason, in order to estimate the number of passengers disembarking more accurately, it is desirable that disembarking passenger statistics be defined by day of the week and by month.

[0069] Returning to Figure 4, the event database 366 stores event data. The event data is information that defines weighting ratios for each train 150 and each station to adjust for the increase in the number of passengers disembarking at stations due to the holding or occurrence of an event. More specifically, the event data is information that defines weighting ratios for each train 150 and each station to correct for the increase in the number of passengers disembarking due to an event, etc., when calculating the number of passengers disembarking from trains 150 scheduled to arrive at each station using passenger disembarking statistics. For example, if the number of passengers disembarking at a particular station increases due to the holding of an event, etc., the weighting ratio is set according to the period during which the increase in passengers disembarking at that particular station is expected. The particular station includes not only stations close to the location where the event is held, etc., but also stations where transfers are required to reach that station, etc., and other stations where the number of passengers disembarking will be higher than usual due to the holding of an event, etc. Initially, the weighting ratio for all train sequences in all time periods is set to "1". Furthermore, the weighting ratio for the train sequence in a time period is changed based on a command that includes event data change information from the command input unit 32. The event database 366 corresponds to the event data storage unit.

[0070] Figure 9 shows an example of event data. The event data shown in Figure 9 includes information such as train order and weighting ratios for each time period. The train order is information indicating the order of 150 trains that stop at specific stations in each time period, obtained from the timetable information. The train order may also be obtained by arranging train identification information obtained from the timetable information in the order of stopping at specific stations. The weighting ratio is associated with the train order for each time period within a defined period. In one example, the defined period can be one hour. Figure 9 shows an example of event data when an event starts around 17:00 near station A. In the example in Figure 9, no adjustment is made to the number of disembarking passengers until 15:00, and the weighting ratio is "1". However, the number of disembarking passengers gradually increases from around 16:00, and the weighting ratio is defined so that the number of disembarking passengers gradually returns to normal from the 17:00 time period. In the example in Figure 9, an example is shown in which event data is defined for each station.

[0071] In addition to events such as baseball games, soccer matches, concerts, festivals, and other events, the event data stored in the event database 366 is also changed when a service suspension occurs on a parallel line operated by another company due to a personal injury accident. When a service suspension occurs on a parallel line operated by another company due to a personal injury accident, event data change information, with a weighting ratio of "2" or similar value, is input from the command input unit 32 to all stations in the parallel section with the affected line as soon as the suspension is discovered.

[0072] Returning to Figure 4, the estimated disembarking passenger database 367 stores the estimated disembarking passenger information. The estimated disembarking passenger information includes the estimated disembarking passenger value K, which is the estimated number of disembarking passengers at all stations on train 150 in the timetable, calculated by the disembarking passenger calculation unit 37.

[0073] Figure 10 shows an example of passenger disembarking estimation information. The passenger disembarking estimation information shown in Figure 10 includes the train order and the estimated passenger disembarking value K for each time period. The train order is information indicating the order of trains 150 that stop at the target stations for each time period, obtained from the timetable information. The train order may also be obtained by arranging the train identification information obtained from the timetable information in the order in which they stop at the target stations. The passenger disembarking value K is a value calculated by the passenger disembarking calculation unit 37. The passenger disembarking value K is stored in association with the train order for each time period within a predetermined period. In one example, the predetermined period can be one hour. In the example of Figure 10, the passenger disembarking value K for each station every hour, such as 5 o'clock, 6 o'clock, 7 o'clock, ..., is recorded. Note that the passenger disembarking estimation information only needs to be recorded for trains 150 within a predetermined range on the timetable. In one example, the predetermined range can be a range from the current time to two hours ahead, a range to three hours ahead, etc. Figure 10 shows an example where estimated passenger disembarkation information is defined for each station.

[0074] Returning to Figure 4, the disembarking passenger calculation unit 37 calculates the estimated disembarking passenger value K, which is an estimated value of the number of passengers disembarking at a station, based on the disembarking passenger data, which is statistical data indicating the number of passengers disembarking at a station. The disembarking passenger calculation unit 37 calculates the estimated disembarking passenger value K for trains 150 that are scheduled to arrive at the station during a predetermined period prior to the calculation time.

[0075] The first method for calculating the estimated number of disembarking passengers K will now be explained. The disembarking passenger calculation unit 37 calculates the first estimated number of disembarking passengers Ks by multiplying the disembarking passenger data for the station during the specified time period by the first estimated number of disembarking passengers Ks, which is the ratio of the train capacity of the trains scheduled to stop at the station to the sum of the train capacities of all trains 150 that will operate during the specified time period. The disembarking passenger calculation unit 37 then uses the first estimated number of disembarking passengers Ks as the estimated number of disembarking passengers K. The disembarking passenger calculation unit 37 performs this for each station that each train 150 scheduled to stop at during the specified time period.

[0076] Furthermore, when calculating the first estimated number of disembarking passengers Ks, the disembarking passenger calculation unit 37 may refer to event data in which a weighting ratio is defined for each train 150 and each station to adjust for the increase in the number of disembarking passengers at a station due to the holding or occurrence of an event, obtain the weighting ratio corresponding to the station and the train 150 scheduled to stop at the station, and calculate the first estimated number of disembarking passengers Ks by multiplying the disembarking passenger data by the first estimated number of disembarking passengers ratio and the weighting ratio. The weighting ratio is "1" when there is no event, and is a value greater than "1" set in the event data when an event is held.

[0077] The first method for calculating the estimated number of disembarking passengers K will now be described in more detail. The disembarking passenger calculation unit 37 determines the time period and stations for calculating the estimated number of disembarking passengers K from the current time. The determined stations will also be referred to as the calculation target stations below. In one example, the time period for calculation can be a range of two or three hours from the current calculation time. The time period for calculation can be arbitrarily determined by the operator of the operation management device 30.

[0078] The disembarking passenger calculation unit 37 obtains disembarking passenger data for the determined time period from the disembarking passenger statistics database 365. If N is an integer from 0 to 23, the determined time period is N from N to N+1, and the target station is station X, then the disembarking passenger data at target station X is a set of R(X,N). At this time, the disembarking passenger calculation unit 37 obtains the current day of the week and month, and obtains disembarking passenger data R(X,N) for the determined time period corresponding to the combination of day of the week and month. The disembarking passenger data R(X,N) is statistical data calculated based on disembarking passengers measured in the past.

[0079] The disembarking passenger calculation unit 37 refers to the event database 366 to obtain the weight ratio P(X, N, m) for the mth train 150 in time period N at the target station X. If there are many disembarking passengers at a specific station due to an event or other reason, event data change information, which is set as the increase ratio compared to the normal disembarking passenger forecast, is input from the command input unit 32 for each train 150 in the time period when many disembarking passengers are expected. The event data management unit 35 registers the contents of the event data change information in the event database 366. If the target station X in time period N is not a specific station, the event data is not changed, and all weight ratios registered in the event database 366 are "1".

[0080] The disembarking passenger calculation unit 37 obtains timetable information, time change information, train type, and car type for all 150 trains in a time period determined from the current scheduled arrival time at the target station X from the timetable information database 364. The disembarking passenger calculation unit 37 also obtains the number of doors and capacity corresponding to the car type of all 150 trains obtained from the car database 361. Then, for each of the 150 trains, the disembarking passenger calculation unit 37 calculates the train capacity for each of the 150 trains using the capacity and number of cars corresponding to the car type. The disembarking passenger calculation unit 37 also obtains the weighting ratio at the target station X from the event database 366. Here, if the train capacity of the mth arriving train at the target station X in time period N is C(N,m) and the weighting ratio is P(X,N,m), then the first estimated disembarking passenger Ks(X,N,m) of the mth train 150 in time period N at the target station X is expressed by the following equation (3).

[0081]

[0082] By changing X, the first estimated number of passengers disembarking at other scheduled arrival stations of the m-th train 150, Ks(X, N, m), is calculated. Similarly, by changing m, the first estimated number of passengers disembarking at other scheduled arrival stations of other trains 150 in time period N, Ks(X, N, m), is calculated. In this manner, the passenger disembarking calculation unit 37 calculates the first estimated number of passengers disembarking at each station of each train 150 in time period N, Ks(X, N, m), and stores the first estimated number of passengers disembarking Ks(X, N, m) as the estimated number of passengers disembarking K in the estimated number of passengers disembarking database 367. The passenger disembarking calculation unit 37 then calculates the above estimated number of passengers disembarking K at regular intervals and updates the estimated number of passengers disembarking database 367 at regular intervals.

[0083] The estimated number of disembarking passengers K calculated using this first method is based on statistical data, and is therefore considered to have a certain degree of accuracy when there are no events. Furthermore, if an event is known to be held near a specific station in advance, the event data in the event database 366 can be updated, and the estimated number of disembarking passengers K can be corrected using a weighting ratio for the duration of the event. However, the first method, which calculates the estimated number of disembarking passengers K using statistical data on the number of disembarking passengers at stations for each time period in the past, does not account for the possibility of a sudden increase in disembarking passengers. For example, in the case of an event such as a personal injury accident occurring on train 150 preceding the currently running train 150, causing a delay, the event data may not be registered in the event database 366 in time, making it impossible to calculate an estimated number of disembarking passengers K that is close to the actual number of disembarking passengers. Therefore, a second method for calculating an estimated number of disembarking passengers K that can also account for the possibility of a sudden increase in disembarking passengers will be described next.

[0084] The disembarking passenger calculation unit 37 refers to station equipment information and extracts stations where the combination of train type and reference car number of the train 150 scheduled to arrive at the station is the same. The disembarking passenger calculation unit 37 also calculates a second estimated disembarking passenger ratio, which is the ratio of the station's disembarking passenger data to the sum of the disembarking passenger data for a predetermined time period at the station and the extracted stations. Furthermore, the disembarking passenger calculation unit 37 calculates the estimated number of passengers K' for the target vehicle, which is obtained from the occupancy rate data indicating the occupancy rate of the train 150 scheduled to arrive at the station and the capacity of the vehicles within a predetermined range, including the reference car number of the train 150. Then, the disembarking passenger calculation unit 37 calculates a second estimated number of disembarking passengers Kd by multiplying the second estimated number of disembarking passengers K' for the target vehicle, and uses the second estimated number of disembarking passengers Kd as the estimated number of disembarking passengers K.

[0085] A second method for calculating the estimated number of disembarking passengers K will be described in more detail. The disembarking passenger calculation unit 37 searches the train occupancy rate database 362 for the occupancy rates of F cars before and after the acquired reference car number of the target train 150. The disembarking passenger calculation unit 37 also searches for E cars from the acquired reference car number and the F cars before and after the target train 150 that will be excluded from the calculation of disembarking passengers. Here, F and E are adjustment parameters for prediction and are integers of 0 or more. 2F - E + 1 cars, including the reference car number, become the cars to be calculated for the estimated number of disembarking passengers K.

[0086] The disembarking passenger calculation unit 37 searches the station equipment database 363 for stations with the same reference car number as the reference car at station X for the target train 150. Specifically, the disembarking passenger calculation unit 37 searches the station equipment database 363 for station equipment information using the combination of the train type and reference car number of the target train 150 as the key. As a result, it is assumed that n stations from station Y1 to station Yn have been found, where n is an integer greater than or equal to 0. The disembarking passenger calculation unit 37 obtains disembarking passenger data for time period N from station X and from station Y1 to station Yn from the disembarking passenger statistics database 365, and defines the ratio of the disembarking passenger data at station X to the sum of the disembarking passenger data for station X and from station Y1 to station Yn in the target train as the second assumed disembarking passenger ratio. This second assumed disembarking passenger ratio represents the proportion of passengers who disembark at station X out of the 2F-E+1 cars of the target train including the reference car.

[0087] The disembarking passenger calculation unit 37 then allocates the passengers of the target vehicle according to the second assumed disembarking passenger ratio to obtain the second assumed disembarking passenger Kd at station X. Specifically, the disembarking passenger calculation unit 37 obtains the vehicle type of the target train 150 from the timetable information in the timetable information database 364, obtains the vehicle capacity from the vehicle information in the vehicle database 361, and obtains the occupancy rate data from the train occupancy rate database 362. The disembarking passenger calculation unit 37 calculates the sum of the occupancy rate × capacity of each of the 2F-E+1 target vehicles of train 150 as the assumed passengers on board the target vehicle K'. The disembarking passenger calculation unit 37 then calculates the second assumed disembarking passenger Kd(X, N, m) of the m-th train 150 at station X in time period N using the following equation (4). Hereinafter, i is an integer between 0 and n, and the disembarking passenger data for station Yi in time period N is R(Yi, N).

[0088]

[0089] By changing X, the second estimated number of passengers disembarking at other scheduled arrival stations of the m-th train 150, Kd(X, N, m), is calculated. Similarly, by changing m, the second estimated number of passengers disembarking at other scheduled arrival stations of other trains 150 in time period N, Kd(X, N, m), is calculated. In this manner, the passenger disembarking calculation unit 37 calculates the second estimated number of passengers disembarking at each station for each train 150 in time period N, Kd(X, N, m), and stores the second estimated number of passengers disembarking Kd(X, N, m) as the estimated number of passengers disembarking K in the estimated number of passengers disembarking database 367. The passenger disembarking calculation unit 37 then calculates the above estimated number of passengers disembarking K at regular intervals and updates the estimated number of passengers disembarking database 367 at regular intervals.

[0090] As described above, by using passenger occupancy rate data in addition to statistical data, it becomes possible to calculate the second estimated number of disembarking passengers Kd at each station, when there are more passengers than usual, i.e., more than the number of passengers according to the statistical data, with greater accuracy than the first estimated number of disembarking passengers Ks, which is calculated based on statistical data. Furthermore, since passenger occupancy rate data can only be obtained when the target train 150 is in operation, the second estimated number of disembarking passengers Kd can only be calculated while the target train 150 is running. However, since the passenger occupancy rate data is updated periodically during the operation of train 150 (for example), the accuracy of the second estimated number of disembarking passengers Kd increases as train 150 approaches the target station, and the previously calculated value is corrected. As a result, even if there is a sudden increase in disembarking passengers, the estimated number of disembarking passengers K at each station can be calculated with accuracy.

[0091] Here, we will explain the adjustment parameters for prediction. Figure 11 is a diagram illustrating the adjustment parameters for prediction. Figure 11 shows an example where a 15-car train 150 arrives at station platform 110. Note that cars 4 and 5 are special cars that are different from the other cars. Stairs and an escalator are located at platform 110, with car 6 being the reference car for the stairs and car 9 being the reference car for the escalator.

[0092] The stairs and escalators are located near the center of platform 110. In stations where such stairs and escalators are located near the center of platform 110, in one example, it is thought that passengers in cars 1 and 15 at both ends, which are far from the stairs and escalators, will not disembark, or if they do, the number will be extremely small. This is because it is thought that almost no passengers will walk the long distance from the cars at the ends of train 150 to the stairs or escalators near the center of platform 110. Therefore, in such cases, it is possible to consider only the cars of train 150 that have passengers disembarking. The adjustment parameter F for prediction is used in this case.

[0093] As an example, let's set the adjustment parameter F for prediction to "4". For the stairs, the cars included in the calculation are car 6, cars 2 through 5, and cars 7 through 10. Similarly, for the escalator, the cars included in the calculation are car 9, cars 5 through 8, and cars 10 through 13. Combining these, a total of 12 cars, from car 2 to car 13, are included in the calculation.

[0094] If all 15 cars of a 15-car train (150) are similar, the cars to be included in the calculation can be determined as described above. On the other hand, a 15-car train (150) may include special cars, such as Green Cars, which have a smaller passenger capacity and fewer doors than the other cars. In such cases, if the cars to be included in the calculation are determined using the method described above and the estimated number of disembarking passengers K is calculated, there may be a discrepancy from the actual number of disembarking passengers. Therefore, if special cars are present, the calculation of disembarking passengers in these special cars is excluded. The number of these special cars becomes E.

[0095] In the example shown in Figure 11, cars 4 and 5 are special cars, so the adjustment parameter E for prediction becomes "2". As a result, the vehicles included in the calculation in the example shown in Figure 11 are cars 2, 3, and 10 cars from 6 to 13.

[0096] The adjustment parameters F and E used for prediction can be changed depending on the station. Specifically, the adjustment parameters F and E are used at stations other than the terminal station of train 150, but not at the terminal station. This is because all passengers disembark at the terminal station, making the adjustment parameters F and E unnecessary.

[0097] The first and second methods described above for calculating the estimated number of disembarking passengers K in the disembarking passenger calculation unit 37 are examples, and other methods may be used. Furthermore, the largest value among the estimated number of disembarking passengers calculated using multiple methods may be used as the estimated number of disembarking passengers K. In the example described above, the larger of the first estimated number of disembarking passengers Ks obtained by the first method and the second estimated number of disembarking passengers Kd obtained by the second method may be used as the estimated number of disembarking passengers K and stored in the estimated number of disembarking passengers database 367.

[0098] Incidentally, the second estimated number of disembarking passengers Kd calculated using the second method may be overestimated. For example, if the target station X is a large hub station and train 150 continues beyond it, and almost all passengers disembark at the large hub station, with a small number of passengers boarding at subsequent stations, and then almost all passengers disembarking again at station Y1, then the second estimated number of disembarking passengers Kd calculated using the second method described above, which apportions disembarking passengers according to statistical data, will be underestimated. Also, if passengers disembark from train 150 at target station X but do not go to the ticket gate 120, but instead transfer to another train 150 arriving at the opposite platform 110, the second estimated number of disembarking passengers Kd calculated using the second method may also be overestimated. At these stations, the accuracy of the second estimated number of disembarking passengers Kd is clearly worse than that of the first estimated number of disembarking passengers Ks. Therefore, as a way to deal with the existence of such stations, the calculation of the first assumed number of disembarking passengers Ks may be made mandatory, while the calculation of the second assumed number of disembarking passengers Kd may be made optional, allowing the system to enable or disable the calculation of the second assumed number of disembarking passengers Kd on a station-by-station basis. For example, the calculation of the second assumed number of disembarking passengers Kd may be disabled at large hub stations in the middle of a line, while it may be enabled at medium-sized stations along the route and at the terminal station.

[0099] In the case of older train models 150 that do not have a passenger occupancy measurement unit 21 in the vehicle information management device 20, it is not possible to calculate the second estimated number of disembarking passengers Kd using passenger occupancy data. In this case, the first estimated number of disembarking passengers Ks calculated using disembarking passenger data can be used as the estimated number of disembarking passengers K. Furthermore, if all of the trains 150 managed by the operation management device 30 are older train models 150 that do not have a passenger occupancy measurement unit 21, the operation management device 30 does not need to have a passenger occupancy acquisition unit 31 and a train passenger occupancy database 362.

[0100] Furthermore, in a new type of train 150 equipped with a passenger occupancy rate measurement unit 21 in the vehicle information management device 20, it is possible to calculate an estimated number of disembarking passengers K using at least one of the disembarking passenger data and the passenger occupancy rate data. Alternatively, in a new type of train 150, it is possible to use the larger of the first estimated number of disembarking passengers Ks calculated using the disembarking passenger data and the second estimated number of disembarking passengers Kd calculated using the passenger occupancy rate data as the estimated number of disembarking passengers K.

[0101] Returning to Figure 4, the ticket gate control command unit 38 generates a ticket gate control command to adjust the flow of people into the bottleneck section RB, where the speed of movement of people in the station decreases, using the gates 122 through which people pass, based on the estimated number of disembarking passengers K. Specifically, the ticket gate control command unit 38 generates a ticket gate control command to limit the number of gates that can be entered in order to adjust the flow of people entering the station, which is the flow of people disembarking from the train 150 and the flow of people entering the station. More specifically, the ticket gate control command unit 38 calculates a gate adjustment period before the arrival of the train 150 to minimize the number of gates that can be entered among the multiple gates 122 of the ticket gate 120, so that the flow of disembarking passengers and the flow of people entering the station do not collide in a state where the pressure relationship between them is balanced in the narrow parts of the station premises 100, and so that the flow of disembarking passengers passes through the bottleneck section RB before the flow of people entering the bottleneck section RB, and generates a ticket gate control command to limit the number of gates that can be entered during the gate adjustment period.

[0102] In one example, the ticket gate control command unit 38 generates a ticket gate control command that sets the number of available entry gates to 0 when the estimated number of disembarking passengers K is greater than the capacity WB set for the bottleneck section RB. In other words, the ticket gate control command unit 38 generates a ticket gate control command that controls gate 122 so that the flow of entering passengers passing through the bottleneck section RB becomes 0 while the flow of disembarking passengers is passing through the bottleneck section RB. By performing this control, the flow of disembarking passengers from the train 150 arriving at platform 110 does not cross paths with entering passengers in the bottleneck section RB. As a result, the flow of disembarking passengers and the flow of entering passengers do not collide in a state where the pressure relationship between them is balanced in the narrow part of the station premises 100, so the occurrence of crowd avalanches can be suppressed.

[0103] In another example, the ticket gate control command unit 38 generates a ticket gate control command to adjust the number of available gates when the estimated number of disembarking passengers K is smaller than the capacity WB set for the bottleneck section RB, so that the sum of the number of disembarking passengers and the number of entering passengers in the bottleneck section RB is less than or equal to the capacity WB set for the bottleneck section RB. By performing such control, the flow of entering passengers does not become a flow in which the pressure relationship between them and the flow of disembarking passengers is balanced. In other words, the flow of disembarking passengers and the flow of entering passengers do not collide in a state where the pressure relationship between them is balanced in the narrow part of the station premises 100, so the occurrence of crowd avalanches can be suppressed.

[0104] Furthermore, the ticket gate control command unit 38 uses station equipment parameters and the estimated number of disembarking passengers K calculated by the disembarking passenger calculation unit 37 to calculate the ticket gate restriction start time Ts, which is the time to start controlling gate 122, and the ticket gate restriction end time Te, which is the time to end controlling gate 122. At the ticket gate restriction start time Ts, it generates a ticket gate restriction start command that limits the number of gates that can be entered, and at the ticket gate restriction end time Te, it generates a ticket gate restriction end command that releases the restriction.

[0105] The specific gate processing performed by the ticket gate control command unit 38 will now be explained. The ticket gate control command unit 38 obtains the station arrival time T, vehicle type, and train type of train 150 from the timetable information database 364. The ticket gate control command unit 38 also obtains the distance L1, LB, L2, travel speed V1, VB, V2, and inflow speed SB, as well as the travel speed weighting ratio Δv1, from the station equipment database 363. Furthermore, the ticket gate control command unit 38 obtains the estimated number of passengers K disembarking at the target station for train 150 from the estimated number of disembarking passengers database 367.

[0106] The ticket gate control command unit 38 then uses the acquired data to calculate the start time Ts of ticket gate restriction from equation (1A) and the end time Te of ticket gate restriction from equation (2A). The period from the start time Ts of ticket gate restriction to the end time Te of ticket gate restriction corresponds to the gate adjustment period. When the calculated start time Ts of ticket gate restriction arrives, the ticket gate control command unit 38 generates a ticket gate restriction start command to the ticket gate control device 50 that controls the automatic ticket gates 121 at the corresponding station, which includes an instruction to limit the number of gates that can be entered. Furthermore, when the calculated end time Te of ticket gate restriction arrives, the ticket gate control command unit 38 generates a ticket gate restriction end command to the ticket gate control device 50 that controls the automatic ticket gates 121 at the corresponding station, which includes an instruction to release the limit on the number of gates that can be entered.

[0107] Here, an example of the process by which the ticket gate control command unit 38 determines the number of gates that can be entered during the gate adjustment period will be described. The ticket gate control command unit 38 calculates the number of gates that can be entered for the generated ticket gate deterrence control command using the capacity WB set for the bottleneck section RB, the estimated number of disembarking passengers K, and the inflow speeds SB and S2. The ticket gate control command unit 38 sets the number of gates that can be entered to 0 if the estimated number of disembarking passengers K is greater than the capacity WB. Here, if the next (m+1)th train 150 is approaching the station during the gate adjustment period for the mth train 150, the estimated number of disembarking passengers K of the mth train 150 is used. On the other hand, if the next (m+1)th train 150 is approaching the station during the gate adjustment period for the mth train 150, the estimated number of disembarking passengers K of the mth and (m+1)th trains 150 is used.

[0108] Furthermore, if the estimated number of disembarking passengers K, as explained above, is smaller than the capacity WB, then passengers can be allowed to pass through the bottleneck section RB as the disembarking passenger flow passes through, provided that the number of passengers does not exceed the capacity WB of the bottleneck section RB. In this case, the number of passengers who enter from the ticket gate 120 during the time K / SB until the disembarking passengers with the estimated number of disembarking passengers K have entered the bottleneck section RB, plus the number of disembarking passengers, equals the capacity WB of the bottleneck section RB. This number of passengers is the maximum number of passengers that can be accommodated in the bottleneck section RB while the disembarking passenger flow is passing through it.

[0109] The condition under which the sum of the number of passengers entering and the number of passengers disembarking equals the capacity WB of the bottleneck section RB is given by the following equation (5).

[0110]

[0111] Here, G represents the percentage of gates that are open for entry out of the total number of gates. G × S² represents the entry speed through the ticket gates. From equation (5), the percentage of gates that are open for entry G is expressed as shown in equation (6).

[0112]

[0113] In this way, the percentage of available entry gates G that can accommodate the maximum number of people in the bottleneck section RB (WB) is calculated. The number of available entry gates is then the largest integer that does not exceed the value obtained by multiplying the number of automatic ticket gates 121 installed at the station by the calculated percentage of available entry gates G.

[0114] The ticket gate control command unit 38 generates a ticket gate deactivation start command that includes the ticket gate deactivation start time Ts and the number of entry gates determined as described above. If the estimated number of disembarking passengers K is equal to the capacity WB, the number of entry gates may be set to 0, or it may be set to the largest integer not exceeding the value obtained by multiplying the entry gate ratio G calculated by equation (6) by the number of automatic ticket gates 121.

[0115] The command transmission unit 39 transmits a ticket gate deactivation start command, generated by the ticket gate control command unit 38 at the ticket gate deactivation start time Ts, to the ticket gate control device 50 that controls the automatic ticket gate 121 at the corresponding station. The command transmission unit 39 also transmits a ticket gate deactivation end command, generated by the ticket gate control command unit 38 at the ticket gate deactivation end time Te, to the ticket gate control device 50 that controls the automatic ticket gate 121 at the corresponding station.

[0116] In this example, the ticket gate control command unit 38 generates a ticket gate control command and outputs it to the command transmission unit 39, but the ticket gate control command unit 38 may also transmit the contents of the ticket gate control command. In one example, the ticket gate control command unit 38 can output the contents of the ticket gate control command in a way that can be visually displayed. Specifically, the ticket gate control command unit 38 may display the contents of the ticket gate control command on the screen of a display device (not shown) provided by the operation management device 30. In this case, the ticket gate control command unit 38 can display a display screen on the display device that includes information including the target station, the start time Ts of ticket gate restriction and the number of gates that can be entered, or information including the target station and the end time Te of ticket gate restriction.

[0117] The ticket gate control device 50 is a device that sets entry and exit settings for each gate 122 of the automatic ticket gates 121 installed in the station premises 100. Each gate 122 of the automatic ticket gates 121 is set to one of the following: entry only (allowing entry from outside to inside the ticket gate 120, but not allowing exit from inside to outside the ticket gate 120), exit only (allowing exit, but not allowing entry), entry and exit (allowing both entry and exit), or disabled (not allowing entry or exit), and operates according to the setting. Here, we will explain using the case where multiple gates 122 are provided as an example. The number of gates that allow entry is the number of gates 122 that are set to at least one of the following: entry only or entry and exit.

[0118] The ticket gate control device 50 includes a command receiving unit 51 and a ticket gate control processing unit 52. The command receiving unit 51 receives ticket gate control commands from the operation management device 30, which are ticket gate deactivation start commands and ticket gate deactivation end commands. The ticket gate control processing unit 52 changes the settings of the gate 122 of the automatic ticket gate 121 according to the ticket gate control commands from the operation management device 30.

[0119] When the ticket gate control processing unit 52 receives a command to start ticket gate deactivation from the operation management device 30, it changes the settings of gates 122 to match the number of gates that are allowed to enter as specified in the command to start ticket gate deactivation. At this time, it decides which of the multiple gates 122 will be allowed to enter and changes the selected gate 122 to an allowed gate. The automatic ticket gate 121 that has received the changed settings allows people to pass through in the direction of travel as instructed. Furthermore, when the ticket gate control processing unit 52 receives a command to end ticket gate deactivation from the operation management device 30, it cancels the settings from the command to start ticket gate deactivation.

[0120] If the ticket gate deactivation start command sets the number of available entry gates to 0, the ticket gate control processing unit 52 sets the gates 122 that are set to be entry-only and those that are set to be both entry and exit-only to be exit-only or unusable. As a specific example, consider a case where at some point in the ticket gate 120, three gates 122 are set to be entry-only and three gates 122 are set to be exit-only. If the ticket gate deactivation start command is received from the operation management device 30 setting the number of available entry gates to 0, the ticket gate control processing unit 52 sets all three gates 122 that are set to be entry-only to be exit-only. Subsequently, if the ticket gate deactivation end command is received from the operation management device 30 in this state, the ticket gate control processing unit 52 leaves three of the six gates 122 that are set to be exit-only as exit-only, and sets the remaining three gates 122 to be entry-only.

[0121] If the ticket gate deactivation start command has a non-zero number of entry gates set, the ticket gate control processing unit 52 sets the number of accessible gates so that the number of gates 122 set for entry only and entry / exit is equal to the number of entry gates set. Specifically, the ticket gate control processing unit 52 sets any gates 122 that are set for entry only or entry / exit exceeding the number of entry gates set to exit only or unusable. Note that accessible gates may be set for entry only or entry / exit. As a specific example, consider a case where at some point in the ticket gate 120, three gates 122 are set for entry only and three gates 122 are set for exit only. When the ticket gate deactivation start command is received from the operation management device 30 with the number of entry gates set to "1", the ticket gate control processing unit 52 sets only one of the three gates 122 set for entry only to entry only and the other two gates 122 to exit only. In other words, of the six gates 122, one is for entry only and five are for exit only. Subsequently, if the ticket gate detention is terminated from the operation management device 30 in this state, the ticket gate control processing unit 52 will keep three of the five exit-only gates 122 as exit-only and set the other two gates 122 to entry only.

[0122] The ticket gate control device 50 may be included in an automatic ticket gate 121 having gates 122, or it may be a server device that controls multiple gates 122.

[0123] Next, we will explain the ticket gate control method, which is the operation of such a ticket gate control system. In one example, after the last train of the previous day has finished running and before the first train of the following day has started running, the following processes are executed: reading station equipment information into the station equipment database 363, setting event data into the event database 366, and updating the train occupancy rate database 362. After that, the ticket gate control process is executed. Here, we will first explain the process of setting station equipment information, setting event data, and updating occupancy rate data, and then explain the ticket gate control process.

[0124] <Station Equipment Information Setting Process> When the train operation management device 30 is started, the station equipment information reading process is executed. In the station equipment information reading process, the station equipment data management unit 33 reads a setting file in which the station equipment information is defined. After that, the station equipment data management unit 33 saves the station equipment information in the setting file to the station equipment database 363. With this, the station equipment information reading process is completed.

[0125] Furthermore, if there are any changes to the station equipment information, the station equipment information update process is executed. Figure 12 is a flowchart showing an example of the procedure for updating station equipment information. The command input unit 32 receives a command regarding the input of changes to the station equipment information (step S11) and passes the command to the station equipment data management unit 33. The station equipment data management unit 33 saves the contents related to the changes to the station equipment information included in the command to the station equipment database 363 (step S12). With the above steps, the station equipment information update process is completed.

[0126] For example, if it is necessary to change station equipment parameters such as passenger capacity and travel speed in a section of passage 130 where an incident such as a medical emergency or a roof leak has occurred, the operator of the operation management device 30 inputs a command related to the change of station equipment information via the command input unit 32. This updates the station equipment information in the station equipment database 363.

[0127] <Event Data Setting Process> When the operation management device 30 is started, the event data reading process is executed. In the event data reading process, the event data management unit 35 reads a setting file containing event data in which all weight ratios are set to "1". After that, the event data management unit 35 saves the event data in the setting file to the event database 366. With this, the event data reading process is completed.

[0128] Furthermore, if there are changes to the event data, an event data update process is executed. Figure 13 is a flowchart showing an example of the procedure for updating the event data. In one example, the event data update process is executed when an operator of the operation management device 30 inputs a command containing event data change information via the command input unit 32. First, the command input unit 32 receives a command containing event data change information for a specific station where an increase in the number of passengers disembarking due to the event is expected (step S31), and passes the command to the event data management unit 35. The event data change information includes information on changing the weight ratio for each train 150 according to the duration of the event. In one example, the command input unit 32 accepts the event data change by reading an external file or the like that defines the event data change. In another example, the command input unit 32 accepts the event data change by reading the content of the event data change input by the operator of the operation management device 30. Next, the event data management unit 35 updates the event database 366 according to the event data change information (step S32). With this, the event data update process is completed.

[0129] In one example, if an event is known in advance, the operator inputs a command via the command input unit 32 that includes event data change information, specifying stations where an increase in disembarking passengers is expected due to the event, and setting a weighting ratio for every 150 trains according to the duration of the event. This updates the event data in the event database 366.

[0130] <Occupancy Rate Data Update Process> Figure 14 is a flowchart showing an example of the procedure for updating the occupancy rate data. In the initial state when train 150 is not running, all occupancy rate data are set to "0". In one example, the occupancy rate data update process is performed each time occupancy rate data is acquired from the vehicle information management device 20. In one example, the vehicle information management device 20 measures the occupancy rate at regular intervals or at predetermined times and transmits the occupancy rate data to the operation management device 30. In one example, the occupancy rate measurement unit 21 of the vehicle information management device 20 measures the weight of each vehicle identified by the car number of train 150, identifies the number of passengers in the vehicle from the weight, calculates the occupancy rate from the number of passengers and the vehicle's capacity, and generates occupancy rate data for train 150 by associating the calculated occupancy rate with the car number. The measurement result transmission unit 22 transmits the occupancy rate data as a measurement result to the operation management device 30.

[0131] When the passenger occupancy rate acquisition unit 31 of the operation management device 30 acquires passenger occupancy rate data from the vehicle information management device 20 (step S51), it uses the passenger occupancy rate data to update the passenger occupancy rate of the corresponding train 150 in the passenger occupancy rate database 362 (step S52). With this, the passenger occupancy rate data update process is completed.

[0132] <Ticket Gate Control Processing> After the above-mentioned station equipment information setting processing, event data setting processing, and passenger occupancy rate data update processing are completed, ticket gate control processing is executed periodically. Figure 15 is a flowchart showing an example of the procedure for ticket gate control processing. First, the disembarking passenger calculation unit 37 performs disembarking passenger estimation processing to calculate the estimated disembarking passenger value K, which is an estimated value of the number of passengers disembarking at the station, based on the disembarking passenger data, which is statistical data of the number of passengers disembarking at the station (step S61). Step S61 corresponds to the disembarking passenger calculation step. Next, the ticket gate control command unit 38 performs ticket gate control command generation processing to generate a ticket gate control command that adjusts the flow of people into the bottleneck section RB, where the speed of movement of people flowing through the station decreases, using the gate 122 through which people pass (step S62). Step S62 corresponds to the gate control command step. Then, the command transmission unit 39 performs command transmission processing to transmit the ticket gate control command to the ticket gate control device 50 that controls the gate 122 (step S63). Step S63 corresponds to the transmission step.

[0133] The process of estimating the number of disembarking passengers in step S61 and the process of generating ticket gate control commands in step S62 will be explained in detail below.

[0134] (Processing to estimate the number of passengers disembarking) Figure 16 is a flowchart showing an example of the procedure for the processing to estimate the number of passengers disembarking. First, the passenger disembarking calculation unit 37 determines whether a certain period has elapsed (step S71). In one example, the certain period can be the period since the last time the passenger disembarking estimation process was started. Alternatively, it may be the period since the operation management device 30 was started. If the certain period has not elapsed (if the result is No in step S71), the system enters a waiting state.

[0135] If a certain period has elapsed (if the answer is Yes in step S71), the disembarking passenger calculation unit 37 executes a disembarking passenger estimate calculation process to calculate the estimated number of disembarking passengers K for the mth train 150 that will arrive at station X in time zone N (step S72). The disembarking passenger calculation unit 37 executes an estimated disembarking passenger database update process to update the estimated disembarking passenger database 367 with the calculated estimated number of disembarking passengers K (step S73). The disembarking passenger calculation unit 37 repeatedly executes the disembarking passenger estimate calculation process in step S72 and the estimated disembarking passenger database update process in step S73 for a predetermined number of trains 150 arriving at station X, i.e., for the number of the most recent fixed trains (step S74). Subsequently, the disembarking passenger calculation unit 37 repeatedly performs the process of calculating the estimated number of disembarking passengers in step S72 and the process of updating the estimated disembarking passenger database 367 in step S73 for the number of stations managed by the operation management device 30, for a predetermined number of minutes from the calculation point. Then, the process returns to Figure 15.

[0136] Next, we will explain a specific example of the process for calculating the estimated number of passengers disembarking in step S72. Below, we will explain using the following examples: (A) calculating the estimated number of passengers disembarking K using the passenger disembarking data, (B) calculating the estimated number of passengers disembarking K using the passenger disembarking data and the passenger occupancy rate data, and (C) calculating the estimated number of passengers disembarking K by combining (A) and (B).

[0137] (A) When calculating the estimated number of disembarking passengers K using disembarking passenger data, this corresponds to the first method described above. Figures 17 and 18 are flowcharts showing an example of the procedure for calculating the estimated number of disembarking passengers when using disembarking passenger data. First, the disembarking passenger calculation unit 37 selects the target station X and the mth train 150 that arrives at the target station X during time period N (step S91). In one example, the operation management device 30 selects the second station from the starting station of a certain line as the target station X, and selects the first train 150 that arrives at the target station X during time period N.

[0138] Next, the disembarking passenger calculation unit 37 refers to the disembarking passenger statistics database 365 to obtain the disembarking passenger data R(X, N) for the target station X during time period N (step S92). The disembarking passenger calculation unit 37 also refers to the event database 366 to obtain the weighting ratio P(X, N, m) for the mth train 150 arriving at the target station X during time period N (step S93).

[0139] Furthermore, the disembarking passenger calculation unit 37 refers to the timetable information database 364 to obtain the vehicle type and train type for all 150 trains arriving at the target station X during time period N (step 94). The vehicle type includes the vehicle model and the number of cars. The train type includes the service type such as local, rapid, express, and limited express. The disembarking passenger calculation unit 37 also refers to the vehicle database 361 to obtain the capacity corresponding to the vehicle model for each 150 train arriving at the target station X during time period N (step S95). In the above process, the period to be calculated is time period N, but it may also be from the immediate vicinity to a certain time in the future. The certain time may be less than one hour, longer than one hour, or one hour. The period to be calculated may also be multiple consecutive time periods. Furthermore, in the above process, it is assumed that there are M trains 150 arriving at the target station X during time period N. Here, M is a natural number.

[0140] Subsequently, the disembarking passenger calculation unit 37 calculates the train capacity C(N, m) from the number of cars and the capacity corresponding to the type of train cars of the train 150 that arrives at the target station X as the mth train in time period N (step S96). The disembarking passenger calculation unit 37 also calculates the sum of the train capacities of all trains 150 arriving at the target station X in time period N from the number of cars and the capacity corresponding to the type of train cars of each of the trains 150 arriving at the target station X in time period N (step S97).

[0141] Next, the disembarking passenger calculation unit 37 uses the disembarking passenger data R(X,N), the weighting ratio P(X,N,m), the train capacity C(N,m) of the m-th train 150, and the sum of the train capacities of all 150 trains to calculate the first estimated disembarking passenger Ks(X,N,m) at the target station X for the m-th train 150 in time period N (step S98). Equation (3) is used to calculate the first estimated disembarking passenger Ks(X,N,m). After that, the disembarking passenger calculation unit 37 uses the calculated first estimated disembarking passenger Ks(X,N,m) as the estimated disembarking passenger K (step S99), and the process returns to Figure 16.

[0142] (B) When calculating the estimated number of disembarking passengers K using disembarking passenger data and occupancy rate data, this corresponds to the second method described above. Figures 19 and 20 are flowcharts showing an example of the procedure for calculating the estimated number of disembarking passengers when calculating the estimated number of disembarking passengers using disembarking passenger data and occupancy rate data. First, the disembarking passenger calculation unit 37 performs the same process as steps S91 to S92 in Figure 17 (steps S111 to S112). Also, the disembarking passenger calculation unit 37 performs the same process as step S94 in Figure 17 (step S113).

[0143] Next, the disembarking passenger calculation unit 37 refers to the station equipment database 363 to obtain the reference car number for the m-th train 150 at the target station X, and extracts n stations Y1-Yn among the stations where the m-th train 150 stops that have the same reference car number as the reference car number at the target station X (step S114). At this time, n stations Y1-Yn are extracted from the station equipment database 363 using the combination of the vehicle type of the m-th train 150 and the reference car number at the target station X. m is a natural number less than or equal to M, and n is a non-negative integer. After that, the disembarking passenger calculation unit 37 refers to the train occupancy rate database 362 to obtain the occupancy rate of the reference car of the m-th train 150, as well as the cars in front of and behind the reference car (step S115). In other words, the occupancy rate of 2F+1 cars including the reference car is obtained. Furthermore, the passenger disembarking calculation unit 37 obtains the number E of special cars that are excluded from the calculation of passenger disembarking from the 2F+1 cars, including the reference car acquired for the mth train 150, based on the car type (step S116). In one example, by further registering the train formation information of train 150 in the station equipment database 363 and referring to the car information, it becomes possible to identify the location of the special cars. The number obtained by subtracting the E special cars from the sum of the reference car and the F cars before and after the reference car is also referred to as the calculation target cars below. That is, the calculation target cars are 2F-E+1 cars, including the reference car.

[0144] The disembarking passenger calculation unit 37 refers to the vehicle database 361 to obtain the capacity corresponding to the vehicle type of the 2F-E+1 vehicle that will be used for calculation (step S117). The disembarking passenger calculation unit 37 also calculates the estimated number of passengers K' for the target vehicle from the occupancy rate and capacity of the 2F-E+1 vehicle that will be used for calculation (step S118). Subsequently, the disembarking passenger calculation unit 37 refers to the disembarking passenger statistics database 365 to obtain disembarking passenger data R(Yi, N) for each of the extracted n stations Y1-Yn during time period N (step S119). Here, i is an integer between 0 and n, inclusive.

[0145] Next, the disembarking passenger calculation unit 37 uses the estimated number of passengers boarding the target vehicle K', the disembarking passenger data R(X,N) for the target station X during time period N, and the disembarking passenger data R(Yi,N) for n stations Y1-Yn during time period N to calculate the second estimated number of disembarking passengers Kd(X,N,m) at the target station X for time period N (step S120). Equation (4) is used to calculate the second estimated number of disembarking passengers Kd(X,N,m). After that, the disembarking passenger calculation unit 37 uses the calculated second estimated number of disembarking passengers Kd(X,N,m) as the estimated number of disembarking passengers K (step S121), and the process returns to Figure 16.

[0146] (C) When calculating the estimated number of disembarking passengers K by combining (A) and (B), Figure 21 is a flowchart showing an example of the procedure for calculating the estimated number of disembarking passengers when calculating the estimated number of disembarking passengers from the estimated number of disembarking passengers calculated by multiple methods. First, the disembarking passenger calculation unit 37 executes a first estimated number of disembarking passengers calculation process to calculate the first estimated number of disembarking passengers Ks (X, N, m) (step S131). In one example, the first estimated number of disembarking passengers calculation process executes the processes from steps S91 to S98 in Figures 17 and 18. Next, the disembarking passenger calculation unit 37 executes a second estimated number of disembarking passengers calculation process to calculate the second estimated number of disembarking passengers Kd (X, N, m) (step S132). In one example, the second estimated number of disembarking passengers calculation process executes the processes from steps S114 to S120 in Figures 19 and 20. Subsequently, the disembarking passenger calculation unit 37 uses the larger of the first assumed disembarking passenger Ks(X, N, m) and the second assumed disembarking passenger Kd(X, N, m) as the estimated disembarking passenger K for the target station X (step S133). After that, the process returns to Figure 16.

[0147] This section describes an example of updating the estimated disembarking passenger information in the estimated disembarking passenger database 367 in this case. Figure 22 shows an example of the update status of the estimated disembarking passenger information. Figure 22 shows the estimated disembarking passenger information for train 150, which is scheduled to arrive at station A in the 7 o'clock hour. Below, we will use the calculation of the estimated disembarking passenger value K for train 150, which is train number "1" and is scheduled to arrive at station A in the 7 o'clock hour, as an example.

[0148] First, in the pre-start state, train 150 is not running, so it is not possible to obtain passenger occupancy data from train 150. In other words, it is not possible to calculate the second estimated number of disembarking passengers Kd. For this reason, in the pre-start state, only the first estimated number of disembarking passengers Ks is calculated based on the disembarking passenger data, which is statistical data, and this value of the first estimated number of disembarking passengers Ks is registered as the estimated number of disembarking passengers K in the disembarking passenger estimation information. This disembarking passenger estimation information is shown in Figure 22(a).

[0149] Next, the operation of the first train has begun, but if train 150, which is scheduled to arrive at station A first in the 7 o'clock hour, has not yet started operation, then train 150 is ready, but there are no passengers on board yet. In this case, the vehicle information management device 20 of train 150 transmits the occupancy rate data to the operation management device 30. In this state, the first estimated number of disembarking passengers Ks based on the disembarking passenger data and the second estimated number of disembarking passengers Kd based on the disembarking passenger data and the occupancy rate data are calculated. However, since there are no passengers on board train 150 at this time, the second estimated number of disembarking passengers Kd will be less than the first estimated number of disembarking passengers Ks. As a result, the value of the first estimated number of disembarking passengers Ks is registered as the estimated number of disembarking passengers K in the disembarking passenger estimation information. The disembarking passenger estimation information in this state is shown in Figure 22(b).

[0150] Subsequently, when train 150, the first train scheduled to arrive at station A in the 7 o'clock hour, begins operation, passengers will be on board before arriving at station A. Therefore, the train information management device 20 of train 150 transmits occupancy rate data to the operation management device 30. In this state, similar to Figure 22(b), a first estimated number of disembarking passengers Ks based on the disembarking passenger data and a second estimated number of disembarking passengers Kd based on the disembarking passenger data and occupancy rate data are calculated. If the number of passengers on train 150 obtained from the occupancy rate is less than the statistical data, the second estimated number of disembarking passengers Kd will be less than the first estimated number of disembarking passengers Ks. In this case, the value of the first estimated number of disembarking passengers Ks is registered as the estimated number of disembarking passengers K in the disembarking passenger estimation information. On the other hand, if the number of passengers on train 150 obtained from the occupancy rate is more than the statistical data, the second estimated number of disembarking passengers Kd will be more than the first estimated number of disembarking passengers Ks. In this case, the value of the second assumed number of disembarking passengers Kd is registered as the estimated number of disembarking passengers K in the disembarking passenger estimation information. Note that, in one example, the occupancy rate is updated at a shorter interval than the interval it takes for train 150 to travel between stations. The second assumed number of disembarking passengers Kd, calculated using the occupancy rate data obtained just before arrival at station A, will be close to the actual number of disembarking passengers. For this reason, the value of the second assumed number of disembarking passengers Kd, calculated using the occupancy rate data, will be corrected each time the occupancy rate is updated. In Figure 22(c), train 150 is just about to arrive at station A, and the number of passengers on train 150 obtained from the occupancy rate is assumed to be higher than that obtained from the statistical data. For this reason, the value of the second assumed number of disembarking passengers Kd is adopted as the estimated number of disembarking passengers K. The disembarking passenger estimation information in this state is shown in Figure 22(c).

[0151] Let's assume that the first train of the 7 o'clock hour, train 150, arrives at station A. After arriving at station A, there is no longer any point in calculating the estimated number of passengers K disembarking at station A for train 150, so the calculation and update processes for the estimated number of passengers K are not performed. The passenger disembarking estimate information retains the last adopted passenger disembarking estimate K. This state of passenger disembarking estimate information is shown in Figure 22(d). Note that the final actual values, which are the last values ​​adopted at each station for each train 150 in the passenger disembarking estimate information, may be stored as daily log data in one example. This daily log data can be used as reference data when manually adjusting the values ​​of the prediction adjustment parameters F and E.

[0152] (Gate Control Command Generation Process) Figures 23 and 24 are flowcharts showing an example of the procedure for the gate control command generation process. The gate control command generation process is performed at regular intervals. First, the gate control command unit 38 determines whether a regular interval has elapsed (step S151). In one example, the regular interval can be the period since the last time the gate control command generation process was started. Alternatively, it may be the period since the operation management device 30 was started. If a regular interval has not elapsed (if the answer is No in step S151), the system enters a waiting state. If a regular interval has elapsed (if the answer is Yes in step S151), the gate control command unit 38 selects one train 150 and one station from all the stations where this train 150 is scheduled to stop, from the timetable information database 364 (step S152).

[0153] Next, the ticket gate control command unit 38 refers to the timetable information database 364 and obtains the arrival time, vehicle type, and train type of the selected train 150 at the selected station (step S153). The ticket gate control command unit 38 also refers to the station equipment database 363 and obtains the station equipment parameters corresponding to the vehicle type and the travel speed weighting ratio Δv1 obtained from the station equipment information of the selected station (step S154). Furthermore, the ticket gate control command unit 38 refers to the estimated number of disembarking passengers database 367 and obtains the estimated number of disembarking passengers K of the selected train 150 at the selected station (step S155).

[0154] Next, the ticket gate control command unit 38 calculates the start time Ts and end time Te of ticket gate restriction for the selected station using the acquired arrival time, station equipment parameters, travel speed weighting ratio Δv1, and estimated number of disembarking passengers K (step S156). The start time Ts of ticket gate restriction is calculated using equation (1A), and the end time Te of ticket gate restriction is calculated using equation (2A).

[0155] Subsequently, the ticket gate control command unit 38 determines whether the estimated number of disembarking passengers K is greater than the capacity WB of the bottleneck section RB (step S157). If the estimated number of disembarking passengers K is greater than the capacity WB (if the answer is Yes in step S157), the ticket gate control command unit 38 sets the number of available entry gates to 0 (step S158).

[0156] On the other hand, if the estimated number of disembarking passengers K is less than or equal to the capacity WB (if the answer is No in step S157), the ticket gate control command unit 38 calculates the number of available gates based on the condition that the sum of the number of entering passengers and the number of disembarking passengers is equal to the capacity WB of the bottleneck section RB (step S159). In this case, the ratio of available gates G is calculated using the above-mentioned formula (6), and the number of available gates is calculated as the largest integer that does not exceed the product of the ratio of available gates G and the number of ticket gates installed at the target station.

[0157] Subsequently, or after step S158, the ticket gate control command unit 38 generates a ticket gate deactivation start command and a ticket gate deactivation end command for the selected station (step S160). The ticket gate deactivation start command includes the number of gates that can be entered. Then, when the ticket gate deactivation start time Ts arrives, the command transmission unit 39 transmits a ticket gate deactivation start command including the number of gates that can be entered to the selected station (step S161). Also, when the ticket gate deactivation end time Te arrives, the command transmission unit 39 transmits a ticket gate deactivation end command to the selected station (step S162).

[0158] Subsequently, the ticket gate control command unit 38 repeatedly executes the process from step S152 to step S162 for the number of stations on which the selected train 150 is scheduled to stop (step S163). The ticket gate control command unit 38 also repeatedly executes the process from step S152 to step S162, which is executed for the number of stations on which the selected train 150 is scheduled to stop, for the number of trains 150 that will operate during time period N (step S164). Then, the process returns to Figure 15.

[0159] Next, the hardware configuration for realizing the operation management device 30 and ticket gate control device 50 according to Embodiment 1 will be described. In Embodiment 1, the operation management device 30 functions as an operation management device 30 when a program, which is a computer program describing the processing in the operation management device 30, is executed on the computer system. Figure 25 is a block diagram showing an example of the configuration of a computer system that realizes the operation management device according to Embodiment 1. As shown in Figure 25, this computer system includes a control unit 901, an input unit 902, a storage unit 903, a display unit 904, a communication unit 905, and an output unit 906, which are connected via a system bus 907.

[0160] In Figure 25, the control unit 901 is, in one example, a processor such as a CPU (Central Processing Unit), which executes a program describing the processing in the operation management device 30 of Embodiment 1. The input unit 902 is, in one example, composed of a keyboard, mouse, etc., and is used by the user of the computer system to input various information. The storage unit 903 includes various types of memory such as RAM (Random Access Memory) and ROM (Read Only Memory), and storage devices such as a hard disk, and stores the program to be executed by the control unit 901, necessary data obtained in the process of processing, etc. The storage unit 903 is also used as a temporary storage area for the program. The display unit 904 is composed of a display, liquid crystal display panel, etc., and displays various screens to the user of the computer system. In one example, the input unit 902 and the display unit 904 may be configured as a touch panel in which the input unit 902 and the display unit 904 are integrally formed. The communication unit 905 is a receiver and transmitter that perform communication processing. The output unit 906 is a printer, speaker, etc. Note that Figure 25 is just one example, and the configuration of the computer system is not limited to the example shown in Figure 25.

[0161] Here, we will describe an example of the operation of the computer system until the program becomes executable. In a computer system with the above configuration, for example, a program is installed in the storage unit 903 from a CD-ROM or DVD-ROM set in a CD (Compact Disc)-ROM drive or DVD (Digital Versatile Disc)-ROM drive (not shown). When the program is executed, the program read from the storage unit 903 is stored in the main memory area of ​​the storage unit 903. In this state, the control unit 901 performs processing as the operation management device 30 of Embodiment 1 according to the program stored in the storage unit 903.

[0162] In the above description, a program describing the processing in the operation management device 30 is provided on a CD-ROM or DVD-ROM as the recording medium. However, the system is not limited to this, and depending on the configuration of the computer system, the capacity of the program to be provided, a program provided via a transmission medium such as the Internet via the communication unit 905 may also be used.

[0163] The station equipment data management unit 33, timetable management unit 34, event data management unit 35, disembarking passenger calculation unit 37, and ticket gate control command unit 38 shown in Figure 4 are realized by the execution of a program stored in the storage unit 903 shown in Figure 25 by the control unit 901 shown in Figure 25. The storage unit 903 shown in Figure 25 is also used to realize the station equipment data management unit 33, timetable management unit 34, event data management unit 35, disembarking passenger calculation unit 37, and ticket gate control command unit 38. The command input unit 32 shown in Figure 4 is realized by the input unit 902 shown in Figure 25. The occupancy rate acquisition unit 31 and command transmission unit 39 shown in Figure 4 are realized by the communication unit 905 shown in Figure 25. The storage unit 36 ​​shown in Figure 4 is realized by the storage unit 903 shown in Figure 25.

[0164] The processing function of the ticket gate control device 50 may be implemented by a computer system as shown in Figure 25, or by a processing circuit. The processing circuit may be a circuit in which a processor executes software, or it may be a dedicated circuit.

[0165] Figure 26 shows an example of a hardware configuration for realizing a ticket gate control device used in the operation management system according to Embodiment 1. The ticket gate control device 50 includes a processing circuit 920 having a processor 922 and a memory 923, an input unit 921, and an output unit 924.

[0166] The input unit 921 is an interface circuit that receives data transmitted to the ticket gate control device 50 from outside the ticket gate control device 50 and provides it to the processor 922. The output unit 924 is an interface circuit that outputs data from the processor 922 or memory 923 to the outside of the ticket gate control device 50.

[0167] The processing unit of the ticket gate control device 50 is implemented by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in memory 923. In the processing circuit 920, each function of the processing unit is realized when the processor 922 reads and executes the program stored in memory 923. In other words, the processing circuit 920 is equipped with memory 923 for storing the program that will result in the execution of each function of the processing unit. This program can be said to be a program that causes the computer to execute the procedures and methods to be carried out by the ticket gate control device 50. The ticket gate control processing unit 52, which is a processing unit of the ticket gate control device 50, is realized when the processor 922 reads and executes the program stored in memory 923. Memory 923 is also used as temporary memory when the processor 922 executes various processes. In addition, the command receiving unit 51 of the ticket gate control device 50 is realized by the input unit 921.

[0168] The processor 922 includes, in one example, one or more of a CPU, a DSP (Digital Signal Processor), and a system LSI (Large Scale Integration). The memory 923 includes one or more of RAM, ROM, flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM® (Electrically Erasable Programmable Read Only Memory). The memory 923 also includes a recording medium on which a computer-readable program is recorded. Such a recording medium includes one or more of non-volatile or volatile semiconductor memory, magnetic disks, flexible memory, optical disks, compact disks, and DVDs.

[0169] In the above explanation, since the station equipment parameter V1 can vary depending on the train type, an example was shown in which train type-dependent station equipment data is provided in the station equipment database 363, and the movement speed V1 is corrected by a movement speed weighting ratio Δv1 for each train type. This is just one example, and if the station equipment parameters change due to other factors, station equipment parameters may be prepared for each factor, or correction coefficients may be prepared to correct the station equipment parameters for each factor. For example, on rainy days the movement speed may be slower than on sunny days because the floor gets wet, or on snowy days the movement speed may be slower than on rainy days because the floor becomes slippery. In such cases, it is possible to divide the station equipment parameter movement speed into sunny day speed, rainy day speed, and snowy day speed, and it is also possible to have a separate database of correction coefficients that correct the movement speed on rainy days and snowy days, respectively, based on the movement speed on sunny days. In this case, a database that holds correction coefficients that correct the movement speed on rainy days and snowy days, respectively, based on the movement speed on sunny days, is an example of correction coefficient information.

[0170] The above description explains an operation management device 30 and an operation management system 10 that control the flow of passengers entering the station using the gate 122 of the ticket gate 120 installed in the station premises 100. The contents shown in Embodiment 1 can be applied not only to the gate 122 of the ticket gate 120 installed in the station premises 100, but also to the control of other gates. For example, Embodiment 1 can be applied to a gate control command device that issues commands for the control of a general gate installed in a station. Furthermore, Embodiment 1 can be applied to a gate control system that includes a gate installed in a station and a device that issues commands for the control of this gate.In addition, Embodiment 1 can be applied to a gate control device that controls the gate installed in a station itself.

[0171] In these cases, the ticket gate control command unit 38 and the command transmission unit 39 of the operation management device 30 shown in Embodiment 1 correspond to the gate control command unit and the transmission unit, respectively. Furthermore, the ticket gate control command generated by the ticket gate control command unit 38 corresponds to the gate control command generated by the gate control command unit. The ticket gate detention start command and the ticket gate detention end command generated by the ticket gate control command unit 38 correspond to the gate detention start command and the gate detention end command, respectively.

[0172] Furthermore, when used as a gate control device, the gate control command corresponding to the ticket gate control command includes not only the number of gates that can be entered, but also the setting of which gates 122 should remain as gates that can be entered.

[0173] Furthermore, the ticket gate control method and ticket gate control program described above can also be applied to the overall control of the gates 122 installed at the station. In this case, the ticket gate control method and ticket gate control program can be referred to as the gate control method and gate control program, respectively.

[0174] As described above, the gate control command device according to Embodiment 1 includes: a disembarking passenger calculation unit 37 that calculates an estimated value K of disembarking passengers at a station based on disembarking passenger data indicating the number of disembarking passengers at the station; a gate control command unit that generates a gate control command to adjust the flow of people into the bottleneck section RB, where the speed of movement of people at the station decreases, using a gate 122 through which people pass, based on the estimated value K of disembarking passengers; and a transmission unit that transmits the gate control command to a gate control device that controls the gate 122. This has the effect of suppressing collisions between two flows of people, including the flow of people generated by passengers disembarking from the train 150, in the bottleneck section RB, which is a narrow area within the station premises 100, while maintaining a balance of pressure relations between them. In particular, it can suppress collisions in the narrow area between the flow of disembarking passengers from the train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it becomes possible to suppress the flow of people passing through gate 122 in advance based on the prediction. In this way, by implementing gate control to avoid collisions of people, disasters caused by crowd avalanches can be prevented.

[0175] Furthermore, conventionally, to control the flow of passengers getting on and off during peak hours, personnel were stationed in bottleneck sections such as RB to guide passengers. On the other hand, the gate control command device according to Embodiment 1 has the effect of reducing costs for railway operators because the flow of people in narrow bottleneck sections RB is restricted, eliminating the need to station personnel to guide passengers.

[0176] In Embodiment 1, the disembarking passenger calculation unit 37 calculates the first assumed disembarking passenger Ks by multiplying the disembarking passenger data for stations during a predetermined time period by the first assumed disembarking passenger ratio, which is the ratio of the train capacity of the train 150 scheduled to stop at a station to the sum of the train capacities of all trains 150 operating during a predetermined time period, and sets the first assumed disembarking passenger Ks as the estimated disembarking passenger K. This has the effect of being able to predict the estimated disembarking passenger K at the station where the train 150 is scheduled to arrive, based on statistical data.

[0177] In Embodiment 1, the disembarking passenger calculation unit 37 refers to event data in which weighting ratios are defined for each train 150 and each station to adjust for the increase in the number of passengers disembarking at a station due to the holding or occurrence of an event. The unit obtains the weighting ratios corresponding to the station and the trains 150 scheduled to stop at the station, and calculates the first estimated number of disembarking passengers Ks by multiplying the disembarking passenger data by the first estimated number of disembarking passengers and the weighting ratio. This makes it possible to predict an estimated number of disembarking passengers K that is close to the actual number of disembarking passengers, even when an increase in the number of disembarking passengers is expected compared to statistical data due to the holding or occurrence of events such as sports matches, concerts, or accidents on other companies' lines parallel to the line.

[0178] In Embodiment 1, the gate control command device further includes an event data storage unit for storing event data, a command input unit 32 for receiving commands to change the weighting ratio of event data, and an event data management unit 35 for updating event data according to the commands. This makes it possible to reflect in the event data a weighting ratio that corrects the number of passengers disembarking at a specific station in accordance with the duration of the event, when the holding of an event such as a sports match or a concert is known in advance. Furthermore, in the event of an accident involving a person occurring on another company's line parallel to the line, for example, a setting file that sets the weighting ratio for stations parallel to the other company's line when an accident occurs can be stored in advance, and by inputting this setting file into the command input unit 32, it is possible to respond quickly when an accident involving a person occurs on another company's line parallel to the line, and to restrict the flow of people at each station.

[0179] In Embodiment 1, the gate control command device further includes a station equipment information storage unit that stores station equipment information, including a reference car number, which is the car number of the train closest to at least one location of the stairs and escalators provided on the platform 110 of the train 150, as defined for each train type and each station. The disembarking passenger calculation unit 37 refers to the station equipment information and extracts stations where the combination of train type and reference car number of the train 150 scheduled to arrive at the station is the same. The disembarking passenger calculation unit 37 also calculates the second estimated disembarking passenger Kd by multiplying the second estimated disembarking passenger ratio, which is the ratio of the station's disembarking passenger data to the sum of the disembarking passenger data for a predetermined time period at the station and the extracted stations, by the target vehicle's estimated passenger count K', which is obtained from the passenger count data indicating the passenger count of the train 150 scheduled to arrive at the station and the passenger capacity of the vehicles within a predetermined range including the reference car of the train 150, and uses the second estimated disembarking passenger Kd as the estimated disembarking passenger K. This makes it possible to accurately determine the estimated number of passengers disembarking at the station K, even in cases where there is a sudden increase in passengers on train 150 and the data deviates from the statistical data. In particular, the occupancy rate data is data that changes each time train 150 departs from a station, and by using this changing occupancy rate data, the previously calculated estimated number of disembarking passengers K is corrected. Furthermore, even in situations where it is difficult to respond with guidance personnel due to a sudden increase in passengers getting on and off, restricting the flow of people entering the station has the effect of preventing two different flows of people from colliding in the bottleneck section RB, which is a narrow part of the station premises 100, while maintaining a balance of pressure between them.

[0180] In Embodiment 1, the passenger disembarking calculation unit 37 uses the larger of the first assumed passenger disembarking number Ks and the second assumed passenger disembarking number Kd as the estimated passenger disembarking number K. By using the larger of the assumed passenger disembarking numbers obtained by multiple methods as the estimated passenger disembarking number K, it becomes possible to control the flow of passengers with a margin of safety. Furthermore, it becomes possible to respond to situations where a sudden increase in the number of passengers disembarking is expected compared to the first assumed passenger disembarking number Ks based on statistical data.

[0181] In Embodiment 1, the gate control command unit generates a gate control command that limits the number of gates that can be entered in order to adjust the flow of people entering the station, which is the flow of people disembarking from the train 150 and the flow of people entering the station. This has the effect of preventing disembarking passengers from accumulating on the platform 110, stairs, or escalators, and preventing the flow of disembarking passengers and the flow of people entering the station from colliding in the bottleneck section RB, which is a narrow part of the station premises 100, while maintaining a balance of pressure relations between them.

[0182] In Embodiment 1, the gate control command unit generates a gate control command that sets the number of accessible gates to 0 when the estimated number of disembarking passengers K is greater than the capacity WB set for the bottleneck section RB. As a result, while the disembarking passenger flow passes through the bottleneck section RB, the entering passenger flow does not enter the bottleneck section RB, thus preventing collisions between the disembarking passenger flow and the entering passenger flow in the bottleneck section RB. Therefore, the occurrence of crowd avalanches in the bottleneck section RB can be suppressed.

[0183] In Embodiment 1, when the estimated number of disembarking passengers K is smaller than the capacity WB set for the bottleneck section RB, the gate control command unit generates a gate control command to adjust the number of available gates so that the sum of the number of disembarking passengers and the number of entering passengers in the bottleneck section RB is less than or equal to the capacity WB set for the bottleneck section RB. As a result, even while the disembarking passenger flow is passing through the bottleneck section RB, the entering passenger flow will enter the bottleneck section RB, but the pressure of the entering passenger flow will be sufficiently smaller than the pressure of the disembarking passenger flow. As a result, it is possible to suppress collisions between the disembarking passenger flow and the entering passenger flow in the bottleneck section RB while their mutual pressure relationships are in equilibrium.

[0184] In Embodiment 1, the gate control command device further includes a station equipment information storage unit that stores station equipment information having station equipment parameters including: distance L1 and movement speed V1 of disembarking passengers in a first section R1 which is wider than the bottleneck section RB and includes a passage leading to platform 110; distance LB, movement speed VB of disembarking passengers and inflow speed SB in the bottleneck section RB; and distance L2 and movement speed V2 of entering passengers in a second section R2 which is wider than the bottleneck section RB and includes a passage leading to gate 122. The gate control command unit uses the station equipment parameters and the estimated disembarking passenger count K calculated by the disembarking passenger count calculation unit 37 to calculate a gate suppression start time, which is the time to start controlling gate 122, and a gate suppression end time, which is the time to end controlling gate 122. At the gate suppression start time, it generates a gate suppression start command that limits the number of gates that can be entered, and at the gate suppression end time, it generates a gate suppression end command that releases the limit. As a result, even though the incoming passenger flow enters the bottleneck section RB while the outgoing passenger flow is passing through it, the pressure of the incoming passenger flow is significantly smaller than that of the outgoing passenger flow. Consequently, it is possible to prevent the outgoing passenger flow and the incoming passenger flow from colliding in the bottleneck section RB while maintaining a balance of their respective pressure relationships.

[0185] In Embodiment 1, the gate control command device further includes a command input unit 32 that receives commands to change station equipment parameters, and a station equipment information management unit that updates the station equipment parameters according to the commands. This makes it possible to change the station equipment parameters of the station in question when an incident such as a medical emergency or a roof leak occurs in a passageway 130 within the station premises 100, and to calculate the number of passengers disembarking according to the actual situation within the station premises 100.

[0186] In Embodiment 1, the gate control command unit corrects the station equipment parameters by referring to correction coefficient information, which includes correction coefficients that adjust the values ​​of station equipment parameters according to the train type or weather. This makes it possible to handle cases where the movement speed V1 of disembarking passengers changes depending on the train type, or where the movement speed V1 is slower on rainy or snowy days compared to sunny or cloudy days. In other words, even if the movement speed V1 of disembarking passengers changes depending on the train type or weather, by making the station equipment parameters variable, it becomes possible to generate gate control commands by the gate control command unit, and more specifically, to calculate the gate detention start time and gate detention end time with greater accuracy.

[0187] Furthermore, the gate control system according to Embodiment 1 also relates to a gate control system that controls the operation of a gate 122, which controls whether or not people are allowed to pass in the direction toward platform 110 and whether or not people are allowed to pass away from platform 110 by passengers disembarking from train 150, by opening and closing the gate. The gate control system according to Embodiment 1 includes a passenger disembarking calculation unit 37 that calculates an estimated value K of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station, a gate control command unit that generates a gate control command to adjust the flow of people flowing into the bottleneck section RB, where the movement speed VB of the station's passenger flow decreases, using the gate 122, based on the estimated value K of passenger disembarking, and a gate control processing unit that controls the gate 122 according to the gate control command. This has the effect of preventing two flows of people, including the flow of people generated by passengers disembarking from train 150, from colliding in the bottleneck section RB, which is a narrow part of the station premises 100, while maintaining a balance of their mutual pressure relationships. In particular, it is possible to suppress collisions in the narrow area between the flow of disembarking passengers from train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it is possible to suppress the flow of people passing through gate 122 in advance based on the prediction.

[0188] In Embodiment 1, the gate 122 is the gate 122 of an automatic ticket gate 121 installed at the ticket gate 120 in the station premises 100. This allows for restricting the flow of incoming passengers in order to suppress collisions in the bottleneck section RB, which is a narrow area in the station premises 100, while maintaining a balance between the mutual pressure relationship between the flow of disembarking passengers and the flow of entering passengers. If the flow of disembarking passengers is restricted, disembarking passengers will accumulate on the platform 110, but by restricting the flow of incoming passengers, it is possible to prevent disembarking passengers from accumulating on the platform 110.

[0189] In Embodiment 1, the gate control command unit outputs the contents of the gate control command in a way that can be visually displayed. This allows the operator of the train operation management device 30 to visually confirm the contents of the gate control command issued to each station.

[0190] Furthermore, the gate control device according to Embodiment 1 includes a gate 122 that opens and closes to determine whether or not people are allowed to pass in the direction toward platform 110 and whether or not people are allowed to pass away from platform 110 by passengers disembarking from train 150; a passenger disembarking calculation unit 37 that calculates an estimated value K of passengers disembarking at the station based on passenger disembarking data indicating the number of passengers disembarking at the station; a gate control command unit that generates a gate control command to adjust the flow of people into the bottleneck section RB, where the movement speed VB of the station's passenger flow decreases, using the gate 122, based on the estimated value K of passenger disembarking; and a gate control processing unit that controls the gate 122 according to the gate control command. This has the effect of preventing two flows of people, including the flow of people generated by passengers disembarking from train 150, from colliding in the bottleneck section RB, which is a narrow part of the station premises 100, while maintaining a balance of their mutual pressure relationships. In particular, collisions can be suppressed in the narrow area between the flow of disembarking passengers from train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it is possible to suppress the flow of people passing through gate 122 in advance based on the prediction. In this way, by implementing gate control to avoid collisions of people, disasters caused by crowd avalanches can be prevented.

[0191] Furthermore, the operation management device 30 according to Embodiment 1 includes: a passenger disembarking calculation unit 37 that calculates an estimated value K of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station; a ticket gate control command unit 38 that generates a ticket gate control command to adjust the flow of people entering the bottleneck section RB, where the speed of movement VB of the flow of people at the station decreases, using the gates 122 of the ticket gates through which people pass, based on the estimated value K of passenger disembarking; and a transmission unit that transmits the ticket gate control command to a ticket gate control device 50 that controls the ticket gates. This has the effect of suppressing collisions between two flows of people, including the flow of people generated by passengers disembarking from the train 150, in the bottleneck section RB, which is a narrow part of the station premises 100, while maintaining a balance of pressure relations between them. In particular, it can suppress collisions in the narrow area between the flow of disembarking passengers from the train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it becomes possible to suppress the flow of people passing through gate 122 in advance based on the prediction. In this way, by implementing gate control to avoid collisions of people, disasters caused by crowd avalanches can be prevented.

[0192] Furthermore, the operation management system 10 according to Embodiment 1 includes: a passenger disembarking calculation unit 37 that calculates an estimated value K of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station; a ticket gate control command unit 38 that generates a ticket gate control command to adjust the flow of people entering the bottleneck section RB, where the speed of movement of people at the station decreases, using the gates 122 of the ticket gates through which people pass, based on the estimated value K of passenger disembarking; and a ticket gate control processing unit 52 that controls the ticket gates according to the ticket gate control command. This has the effect of suppressing collisions between two flows of people, including the flow of people generated by passengers disembarking from the train 150, in the bottleneck section RB, which is a narrow part of the station premises 100, while maintaining a balance of pressure relations between them. In particular, it can suppress collisions in the narrow area between the flow of disembarking passengers from the train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it becomes possible to suppress the flow of people passing through gate 122 in advance based on the prediction. In this way, by implementing gate control to avoid collisions of people, disasters caused by crowd avalanches can be prevented.

[0193] Furthermore, the gate control method according to Embodiment 1 includes: a disembarking passenger calculation step of calculating an estimated number of disembarking passengers K based on disembarking passenger data indicating the number of disembarking passengers at the station; a gate control command step of generating a gate control command that adjusts the flow of people into the bottleneck section RB, where the speed of movement of people at the station decreases, using a gate 122 through which people pass, based on the estimated number of disembarking passengers K; and a transmission step of transmitting the gate control command to a gate control device that controls the gate 122. This has the effect of suppressing collisions between two flows of people, including the flow of people generated by passengers disembarking from the train 150, in the bottleneck section RB, which is a narrow area within the station premises 100, while maintaining a balance of pressure relations between them. In particular, it can suppress collisions in the narrow area between the flow of disembarking passengers from the train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it becomes possible to suppress the flow of people passing through gate 122 in advance based on the prediction. In this way, by implementing gate control to avoid collisions of people, disasters caused by crowd avalanches can be prevented.

[0194] Furthermore, the gate control program according to Embodiment 1 causes the computer to execute: a disembarking passenger calculation step, which calculates an estimated disembarking passenger value K based on disembarking passenger data indicating the number of disembarking passengers at the station; a gate control command step, which generates a gate control command based on the estimated disembarking passenger value K to adjust the flow of people entering the bottleneck section RB, where the speed of movement of people at the station decreases, using the gate 122 through which people pass; and a transmission step, which transmits the gate control command to a gate control device that controls the gate 122. This has the effect of suppressing collisions between two flows of people, including the flow of people generated by passengers disembarking from the train 150, in the bottleneck section RB, which is a narrow area within the station premises 100, while maintaining a balance of pressure relations between them. In particular, it can suppress collisions in the narrow area between the flow of disembarking passengers from the train 150 heading from platform 110 to ticket gate 120 and the flow of entering passengers heading from ticket gate 120 to platform 110. Furthermore, by using the estimated number of disembarking passengers K predicted before train 150 arrives at the station, it becomes possible to suppress the flow of people passing through gate 122 in advance based on the prediction. In this way, by implementing gate control to avoid collisions of people, disasters caused by crowd avalanches can be prevented.

[0195] Embodiment 2. Figure 27 is a schematic diagram showing an example of the structure of a station premises. In the station premises 100A shown in Figure 27, there is a first platform 110A used by trains 150 on the first route and a second platform 110B used by trains 150 on the second route. In this station premises 100A, there is a first ticket gate 120A shared by the first and second routes and a second ticket gate 120B used by the second route. The second ticket gate 120B is located inside the first ticket gate 120A.

[0196] In the passage 130A from the first platform 110A to the first ticket gate 120A, there is a first section R1, a second section R2, and a bottleneck section RB, and the operation management system 10 described in Embodiment 1 can be applied in this passage 130A. In this case, before the train 150 arrives at the first platform 110A, control may be performed to set the number of gates 122A available for entry among the automatic ticket gates 121A at the first ticket gate 120A to 0. In other words, during the gate adjustment period until the flow of passengers disembarking from the train 150 arriving at the first platform 110A enters and passes through the bottleneck section RB, it is not possible to enter from the first ticket gate 120A. However, during this gate adjustment period, some passengers attempting to enter from the first ticket gate 120A may be using the train 150 that arrives at the second platform 110B of the second operation route, rather than the first platform 110A of the first operation route. In other words, when controlling the entry gates as shown in Embodiment 1 in a station premises 100A with the configuration shown in Figure 27, there was a problem that passengers could not go to the second platform 110B during the gate adjustment period targeting the passage 130A from the first platform 110A to the first ticket gate 120A. Embodiment 2 solves this problem.

[0197] Figure 28 is a schematic diagram showing an example of a station premises structure to which the gate control system according to Embodiment 2 is applied. The gate control system shown in Figure 28 further includes a pedestrian flow adjustment device 125 having a gate 126, which is a gate structure that adjusts the flow of people in passage 130A, from the first platform 110A to the first ticket gate 120A in the station premises 100A shown in Figure 27, at the boundary between the bottleneck section RB and the second section R2. When the operation management system 10 of Embodiment 1 is applied to passage 130A having two gates 122A and 126 in this way, the operation management device 30 will control the gate 126 of the pedestrian flow adjustment device 125 that is closer to platform 110, rather than the gate 122A of the first ticket gate 120A. In other words, in Figure 28, the gate 126 is located at the end of the bottleneck section RB on the first ticket gate 120A side, so it can be considered that there is no second section R2 and the distance L2 is 0. The control of gate 126 is the same as that described in Embodiment 1, so the explanation of the control of gate 126 will be omitted. The gate 126 of the pedestrian flow adjustment device 125 may be permanently installed or temporary.

[0198] Thus, a passenger flow control device 125 with a gate 126 is installed at the end of the bottleneck section RB on the side of the first ticket gate 120A, and this gate 126 is controlled by the operation management device 30. As a result, the gate 122A of the first ticket gate 120A is not restricted by the number of gates that can be entered by passengers disembarking from the train 150 arriving at the first platform 110A. Therefore, the first ticket gate 120A can be operated as usual, and even when the train 150 arrives at the first platform 110A, passengers can enter through the first ticket gate 120A and proceed to the second platform 110B through the second ticket gate 120B.

[0199] In Embodiment 2, the gate 122 includes the gate 122A of the automatic ticket gate 121A installed at the first ticket gate 120A in the station premises 100A, and the gate 126 of the pedestrian flow adjustment device 125 installed at the end of the bottleneck section RB on the first ticket gate 120A side, and the gate control processing unit controls the gate 126 of the pedestrian flow adjustment device 125. If there is a second ticket gate 120B for another line within the first ticket gate 120A, restricting the entrance gate of the first ticket gate 120A would prevent the use of the second ticket gate 120B. However, by installing a gate 126 at the end of the bottleneck section RB on the first ticket gate 120A side, and making this gate 126 the target of pedestrian flow adjustment control, the first ticket gate 120A is no longer the target of control. As a result, passengers using another line can enter from the first ticket gate 120A at any time and then pass through the second ticket gate 120B.

[0200] Embodiment 3. In Embodiment 1, the passenger disembarkation data in the passenger disembarkation statistics database 365 is statistical data, but it may contain errors when compared to the actual number of passengers disembarking. Embodiment 3 describes an example of correcting the passenger disembarkation data in the passenger disembarkation statistics database 365.

[0201] Figure 29 shows an example of the configuration of the operation management system according to Embodiment 3. Components identical to those in Embodiment 1 are denoted by the same reference numerals, and their descriptions are omitted. In the operation management system 10A according to Embodiment 3, the occupancy rate measurement unit 21 of the vehicle information management device 20 measures the occupancy rate of at least each car of the train 150 immediately before arriving at a station and between arriving at a station and departing. Furthermore, the configuration of the operation management device 30A in the operation management system 10A according to Embodiment 3 differs from that of Embodiment 1. The storage unit 36 ​​of the operation management device 30A further stores a disembarking passenger data database 368, and the operation management device 30A further comprises a disembarking passenger data generation unit 40 and a disembarking passenger statistical data correction unit 41.

[0202] The disembarking passenger data database 368 stores disembarking passenger data. Disembarking passenger data includes disembarking passenger data, which is the actual number of passengers who disembarked at all stations on the train 150 in the timetable, generated by the disembarking passenger data generation unit 40.

[0203] Figure 30 shows an example of disembarking passenger data. The disembarking passenger data shown in Figure 30 includes the train order and the disembarking passenger data value Kr for each time period. The train order is information indicating the order in which trains 150 stop at the target stations for each time period, obtained from the timetable information. The train order may also be obtained by arranging the train identification information obtained from the timetable information in the order in which they stop at the target stations. The disembarking passenger data value Kr is generated by the disembarking passenger data generation unit 40 and is stored in association with the train order for each time period within a defined period. In one example, the defined period can be one hour. In the example in Figure 30, the disembarking passenger data value Kr for each station is recorded for every hour, starting from 5am, 6am, 7am, and so on. Note that disembarking passenger data is recorded for all trains 150 on the timetable. Disembarking passenger data is defined for each station. Furthermore, in the example shown in Figure 30, the disembarking passenger data has the same data structure as the disembarking passenger estimate data.

[0204] Returning to Figure 29, the disembarking passenger data generation unit 40 uses the occupancy rate data acquired by the occupancy rate acquisition unit 31 to generate actual passenger data for each car of each train 150, and saves it as disembarking passenger data in the disembarking passenger data database 368.

[0205] Here, the generation of disembarking passenger data by the disembarking passenger data generation unit 40 will be explained in detail. However, in Embodiment 3, the train occupancy rate database 362 is assumed to hold at least occupancy rate data measured immediately before arriving at the target station and multiple occupancy rate data measured between arriving at the target station and departing. Multiple occupancy rate data measured between arriving at the station and departing is desirable because the more measurement results there are, the more accurate the actual value of disembarking passengers can be. Normally, when train 150 arrives at a station, passengers in the train cars disembark onto platform 110, and then passengers on platform 110 board the train cars. Therefore, if the occupancy rate can be obtained just before the passengers in each car disembark onto platform 110 and the passengers on platform 110 board each car, it is possible to calculate the actual value of disembarking passengers from train 150 by combining it with the occupancy rate data just before arriving at the station. For this reason, it is desirable that multiple occupancy rate data be measured between arriving at the station and departing.

[0206] The disembarking passenger data generation unit 40 obtains multiple passenger occupancy rate data from the train occupancy rate database 362, collected between the arrival and departure of the target train 150. The disembarking passenger data generation unit 40 searches for the minimum passenger occupancy rate from the multiple passenger occupancy rate data for all car numbers of the target train 150. The minimum passenger occupancy rate is obtained because it is considered to be the closest to the state just before passengers disembark from the train 150 while it is stopped at the station and passengers board from the station platform 110.

[0207] The disembarking passenger data generation unit 40 calculates the difference between the minimum occupancy rate data immediately before arrival at the target station and the minimum occupancy rate data between arrival and departure for all cars of the target train 150. The disembarking passenger data generation unit 40 obtains the car type of the target train 150 from the timetable information database 364 and obtains the capacity corresponding to the car type from the car database 361. The disembarking passenger data generation unit 40 also calculates the number of passengers for all cars of the target train 150 by multiplying the capacity by the difference in occupancy rates. The disembarking passenger data generation unit 40 then calculates the disembarking passenger data value Kr, which is the sum of the number of passengers in all cars of the target train 150, and saves the calculated disembarking passenger data value Kr in the disembarking passenger data database 368. The disembarking passenger data value Kr is saved in the disembarking passenger data database 368 when the target train 150 departs from the target station. Furthermore, the actual number of passengers disembarking at each station, Kr, up to the last train of the day according to the timetable, is stored in the disembarking passenger data database 368.

[0208] The actual number of passengers alighting from train 150, Kr, calculated here, is the actual number of passengers who alighted at the target station, and is usually different from the first estimated number of passengers alighting, Ks, calculated by the first method of Embodiment 1, and the second estimated number of passengers alighting, Kd, ​​calculated by the second method. However, the actual number of passengers alighting, Kr, can only be measured after train 150 has departed the target station. In other words, it is not in time to reflect this in the ticket gate detention start time Ts and the ticket gate detention end time Te. For this reason, the actual number of passengers alighting, Kr, calculated by the passenger alighting information generation unit 40 is not used in the ticket gate detention control command, but is used to correct the statistical data of passengers alighting, R(X,N).

[0209] The disembarking passenger statistics data correction unit 41 corrects the disembarking passenger data in the disembarking passenger statistics database 365 using the disembarking passenger data from the disembarking passenger data database 368, and updates the disembarking passenger data in the disembarking passenger statistics database 365 with the corrected disembarking passenger data. Specifically, the disembarking passenger statistics data correction unit 41 uses the disembarking passenger data from the disembarking passenger data database 368 to calculate disembarking passenger data by adding the disembarking passenger data Kr for each time period within a predetermined period at the target station. The predetermined period is the same as the time period defined in the disembarking passenger statistics information of the disembarking passenger statistics database 365. Then, the disembarking passenger statistics data correction unit 41 calculates the average value of the calculated disembarking passenger data and the disembarking passenger data for the corresponding time period at the corresponding station in the disembarking passenger statistics database 365, and saves the calculated average value as new disembarking passenger data in the disembarking passenger statistics database 365. In other words, the value of the disembarking passenger data after correction is (disembarking passenger data before correction + actual disembarking passenger data) ÷ 2. The disembarking passenger statistics data correction unit 41 performs this correction process for disembarking passenger data for all stations and all time periods, thereby correcting the disembarking passenger statistics information.

[0210] Let's explain the correction of disembarking passenger statistics using a specific example. Figure 31 shows an example of the process for updating disembarking passenger statistics. Here, we assume that the number of commuters is generally higher than the statistics due to the start of work and school in April. Disembarking passenger actual information 3681a shows the update status of the disembarking passenger actual value Kr at station A. In the example in Figure 31, the disembarking passenger actual value Kr for train sequence "1" in the 7 o'clock hour, which is hatched, has been updated. Comparing the disembarking passenger actual information 3681b after the operation of train 150 on this day has ended with the disembarking passenger estimated information 3671 calculated by the disembarking passenger calculation unit 37, it can be said that although there are some trains 150 where the number of disembarking passengers has decreased, the total number has increased.

[0211] Once the acquisition of the actual number of disembarking passengers Kr for the day is complete, the actual number of disembarking passengers Kr for each time period from the disembarking passenger data 3681b is added together to calculate the actual disembarking passenger data. In the example in Figure 31, the actual number of disembarking passengers at station A during the 5:00, 6:00, 7:00, and 8:00 hours are 55, 104, 298, and 371 people, respectively.

[0212] Subsequently, the disembarking passenger data is corrected using the calculated actual disembarking passenger data for a certain time period at Station A and the disembarking passenger data for the corresponding time period at Station A from the uncorrected disembarking passenger statistics information 3651a. Here, as shown in the disembarking passenger statistics information 3651b, the average value of the actual disembarking passenger data and the disembarking passenger data is calculated, and this calculated average value is used as the new disembarking passenger data. In this way, the disembarking passenger statistics information is updated daily.

[0213] The discrepancy between the second assumed number of disembarking passengers Kd calculated by the second method described in Embodiment 1 and the actual disembarking passenger data Kr in the disembarking passenger data database 368 is assumed to be due to errors in measuring the vehicle's occupancy rate, errors in recognizing the proportion of disembarking stations, and the fact that a large number of passengers disembark from vehicles with car numbers that were discarded as adjustment parameters F and E for prediction. The second assumed number of disembarking passengers Kd calculated using the second method is intended to predict in advance the need to restrict entry at ticket gates in response to sudden surges in occupancy rates, and here, we will not consider correcting the second assumed number of disembarking passengers Kd using the actual disembarking passenger data Kr. This is because correcting the disembarking passenger data, which is statistical data, using the actual disembarking passenger data results in higher accuracy.

[0214] Next, the method for correcting the disembarking passenger statistics database 365 will be explained. Figures 32 and 33 are flowcharts showing an example of the procedure for correcting disembarking passenger statistics information. Here, it is assumed that the operation management device 30A has a function for detecting the departure of train 150 from a station. First, the disembarking passenger actual information generation unit 40 determines whether a certain period has elapsed (step S191). In one example, the certain period can be the period since the last correction process for disembarking passenger statistics information was started. Alternatively, it may be the period since the operation management device 30A was started. If a certain period has not elapsed (if the result is No in step S191), the unit enters a waiting state.

[0215] If a certain period has elapsed (if the answer is Yes in step S191), the disembarking passenger data generation unit 40 selects the target station X and the mth train 150 that will arrive at the target station X in time zone N (step S192). Here, the disembarking passenger data generation unit 40 is assumed to have obtained the train number of the selected train 150 by referring to the timetable information in the timetable information database 364. Next, the disembarking passenger data generation unit 40 determines whether there was a departure time between the previous certain period and the current certain period (step S193). If there was a departure time (if the answer is Yes in step S193), the disembarking passenger data generation unit 40 refers to the train occupancy rate database 362 and obtains the occupancy rate of train 150 immediately before arriving at the target station X and the minimum value of the occupancy rate from after arriving at the target station X until departure (step S194).

[0216] Next, the disembarking passenger data generation unit 40 calculates the difference in passenger occupancy for all cars of the train 150, which is the difference between the passenger occupancy data immediately before arrival at the target station X and the minimum passenger occupancy data from arrival until departure (step S195). After that, the disembarking passenger data generation unit 40 obtains the car type of the train 150 from the timetable information database 364 and obtains the capacity corresponding to the car type from the car database 361 (step S196).

[0217] Next, the disembarking passenger data generation unit 40 calculates the disembarking passenger data Kr(X, N, m) for train 150, which is selected from the difference in occupancy rates of all cars of train 150 and the capacity, and stores the calculated disembarking passenger data Kr(X, N, m) in the disembarking passenger data database 368 (step S197). Here, Kr(X, N, m) represents the disembarking passenger data at the target station X for the m-th train 150 in time period N.

[0218] Subsequently, or if there is no departure time recorded in step S193 (if the result is No in step S193), the calculation process for the actual number of disembarking passengers Kr(X, N, m) at the target stations, as described in steps S192 to S197 above, is repeated for the number of trains currently running, which are recognized by the operation management system 10A (step S198). In addition, the calculation process for the actual number of disembarking passengers Kr(X, N, m) at the target stations, as described in steps S192 to S197, which is performed for the trains currently running, is repeated for the number of stations (step S199). Note that the correction process for disembarking passenger statistics can be performed even if the order of steps S198 and S199 shown in Figures 32 and 33 is reversed.

[0219] Subsequently, the disembarking passenger statistics data correction unit 41 determines whether the operation of train 150 for the day has ended (step S200). If the operation of train 150 for the day has not ended (if the result is No in step S200), the process returns to step S191.

[0220] If the operation of train 150 for the day has ended (if the answer is Yes in step S200), the disembarking passenger statistics data correction unit 41 calculates disembarking passenger data by adding the disembarking passenger actual values ​​Kr(X, N, m) for the target stations in the disembarking passenger actual database 368 for each time period within a specified period (step S201). Next, the disembarking passenger statistics data correction unit 41 calculates the average value of the calculated disembarking passenger actual data and the disembarking passenger data for the corresponding time period at the corresponding station in the disembarking passenger statistics database 365, and saves the calculated average value as new disembarking passenger data in the disembarking passenger statistics database 365 (step S202). By performing the processes in steps S201 and S202 for all time periods at all stations, the disembarking passenger statistics information in the disembarking passenger statistics database 365 is corrected. This completes the process.

[0221] Note that Figure 29 is just one example, and disembarking passenger statistics may be corrected using other configurations. Here, we will explain an example of correcting disembarking passenger statistics using machine learning. Figure 34 is a diagram showing an example of the configuration of the disembarking passenger calculation unit and the disembarking passenger statistics data correction unit that constitute the operation management device according to Embodiment 3. Here, the disembarking passenger calculation unit 37 and the disembarking passenger statistics data correction unit 41 use an artificial intelligence unit 420 having artificial intelligence (AI) functions to correct disembarking passenger statistics, and use the corrected disembarking passenger statistics to estimate the first estimated disembarking passenger Ks and the second estimated disembarking passenger Kd.

[0222] The disembarking passenger statistics data correction unit 41 comprises a learning device 410 and an artificial intelligence unit 420.

[0223] The learning device 410 includes a learning data acquisition unit 411 and a model generation unit 412.

[0224] The learning data acquisition unit 411 acquires station names, months, days of the week, time zones, and disembarking passenger statistics, as well as station names, months, days of the week, time zones, and disembarking passenger actual information, as learning data. Disembarking passenger statistics are acquired from the disembarking passenger statistics database 365, and disembarking passenger actual information is acquired from the disembarking passenger actual database 368. Disembarking passenger statistics, as shown in Figure 8, is information that summarizes the disembarking passenger data for each time zone at each station, and is managed separately by month and day of the week. The station name, month, day of the week, and time zone in the disembarking passenger statistics are data that serve as search keys for the disembarking passenger statistics. Disembarking passenger actual information, as shown in Figure 30, is information that shows the actual number of disembarking passengers for each train that stopped at each station during each time zone. The station name, month, day of the week, and time zone in the disembarking passenger actual information are data that serve as search keys for the disembarking passenger actual information. Alternatively, the learning data acquisition unit 411 can acquire disembarking passenger statistics information managed by station name, month, day of the week, and time of day, and disembarking passenger actual information specified by station name, month, day of the week, and time of day, as learning data.

[0225] The model generation unit 412 learns corrected disembarking passenger statistics based on training data created from combinations of station name, month, day of the week, time of day, and disembarking passenger statistics information output from the training data acquisition unit 411, as well as station name, month, day of the week, time of day, and disembarking passenger actual information. In other words, it generates a trained model that infers the optimal corrected disembarking passenger statistics from the station name, month, day of the week, time of day, and disembarking passenger statistics information of the operation management device 30A, as well as station name, month, day of the week, time of day, and disembarking passenger actual information. Here, the training data is data that associates station name, month, day of the week, time of day, and disembarking passenger statistics information, as well as station name, month, day of the week, time of day, and disembarking passenger actual information. Furthermore, the corrected disembarking passenger statistics correspond to information obtained by correcting the disembarking passenger statistics using disembarking passenger actual information for trains from the first train to the last train of the day.

[0226] The learning device 410, the artificial intelligence unit 420, and the passenger disembarking data acquisition unit 371 of the passenger disembarking calculation unit 37 (described later) are used to learn the corrected passenger disembarking statistics of the operation management device 30A. In one example, they may be connected to the operation management device 30A via a network and may be separate devices from the operation management device 30A. At least one of the learning device 410, the artificial intelligence unit 420, and the passenger disembarking data acquisition unit 371 may be built into the operation management device 30A. Furthermore, the learning device 410, the artificial intelligence unit 420, and the passenger disembarking data acquisition unit 371 may reside on a cloud server.

[0227] The learning algorithm used by the model generation unit 412 can be any known algorithm, such as supervised learning, unsupervised learning, or reinforcement learning. As an example, the case in which a neural network is applied will be described.

[0228] In one example, the model generation unit 412 learns corrected passenger disembarkation statistics information using so-called supervised learning, following a neural network model. Here, supervised learning is a method in which a learning device 410 is given pairs of data consisting of inputs and resulting labels, learns features in that learning data, and infers results from the inputs.

[0229] A neural network consists of an input layer made up of multiple neurons, a hidden layer made up of multiple neurons, and an output layer made up of multiple neurons. The hidden layer is also called a hidden layer and can consist of one layer or two or more layers.

[0230] Figure 35 schematically shows an example of a neural network used by the model generation unit. In one example, in a three-layer neural network as shown in Figure 35, when multiple inputs are input from input layer X1 to input layer X3, the values ​​are multiplied by weights w11 to w16 and input to hidden layer Y1 to hidden layer Y2. When weights w11 to w16 are not individually distinguished, they are referred to as weight w1. Furthermore, the results from hidden layer Y1 to hidden layer Y2 are multiplied by weights w21 to w26 and output from output layer Z1 to output layer Z3. When weights w21 to w26 are not individually distinguished, they are referred to as weight w2. The output results from output layer Z1 to output layer Z3 vary depending on the values ​​of weights w1 and w2.

[0231] In Embodiment 1, the neural network learns corrected disembarking passenger statistics information by so-called supervised learning, according to training data created based on combinations of station name, month, day of the week, time of day, and disembarking passenger statistics information acquired by the training data acquisition unit 411, as well as station name, month, day of the week, time of day, and disembarking passenger actual information. Here, the model generation unit 412 calculates disembarking passenger actual data by adding the disembarking passenger actual value Kr in the disembarking passenger actual information for each time period within a predetermined period, and generates processed disembarking passenger actual information showing the disembarking passenger actual data for each time period at each station. The model generation unit 412 then uses the disembarking passenger statistics information and the processed disembarking passenger actual information as training data.

[0232] The neural network learns by inputting station names, months, days of the week, time zones, and disembarking passenger statistics into input layers X1-X3 and adjusting weights w1 and w2 so that the results output from output layers Z1-Z3 approach station names, months, days of the week, time zones, and processed disembarking passenger statistics. As described above, disembarking passenger statistics are compiled information summarizing disembarking passenger data for each time zone at each station, and are managed separately by month and day of the week. Furthermore, processed disembarking passenger statistics also include data on stations, months, days of the week, and time zones. Therefore, the corrected disembarking passenger statistics generated by the neural network are compiled information summarizing disembarking passenger data for each time zone at each station, and are managed separately by month and day of the week. For this reason, when using the corrected disembarking passenger statistics generated by the model generation unit 412, it is possible to obtain disembarking passenger data for a desired time zone at a desired station by specifying station name, month, day of the week, and time zone as search keys.

[0233] Returning to Figure 34, the model generation unit 412 generates and outputs a trained model by performing the learning described above. The model generation unit 412 stores the generated trained model in the disembarking passenger statistics database (after training) 421 of the artificial intelligence unit 420. The disembarking passenger statistics database (after training) 421 corresponds to the trained model storage unit. This trained model is corrected disembarking passenger statistics information obtained by correcting the disembarking passenger statistics information, and the disembarking passenger statistics database (after training) 421 can also be called the corrected disembarking passenger statistics database that stores the corrected disembarking passenger statistics information. Furthermore, the corrected disembarking passenger statistics information, which is the trained model generated by the model generation unit 412, may be stored in the disembarking passenger statistics database 365 as new disembarking passenger statistics information.

[0234] The artificial intelligence unit 420 refers to artificial intelligence equipped with intelligent functions such as reasoning and judgment, and its operating environment. The artificial intelligence unit 420 is a model and its operating environment configured to output passenger disembarking data corresponding to data specification conditions, which are search keys including station name, month, day of the week, and time of day. When the data specification conditions are input from the control unit 432 of the passenger disembarking calculation unit 37, the artificial intelligence unit 420 outputs passenger disembarking data based on the data specification conditions and the trained model. The artificial intelligence unit 420 comprises a passenger disembarking statistics database (after training) 421 and a model control unit 422.

[0235] The disembarking passenger statistics database (after training) 421 stores the trained model. The trained model includes model information described later. For example, the trained model may include model parameters, which are information that defines the behavior of the model, such as constraints, weighting variables, and evaluation functions.

[0236] The models may include, for example, Neural Networks (NN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Diffusion models, Transformers, Large Language Models (LLM), Visual Language Models (VLM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformers (GPT), and Contrastive Language Image Pre-training (CLIP). Note that the above models are not mutually exclusive; for example, LLM, VLM, BERT, and GPT are included in the Transformer category. Also, for example, the Transformer is included in the NN category. Furthermore, the learning algorithm and model may be a combination of multiple types. Models also include multimodal models, which are trained using a combination of multiple different types of data.

[0237] When the model control unit 422 acquires data specification conditions including station name, month, day of the week, and time of day, it outputs passenger disembarking data corresponding to the data specification conditions based on the data specification conditions and the trained model. In other words, when the model control unit 422 acquires data specification conditions, it generates and outputs passenger disembarking data corresponding to the data specification conditions using the model shown by the trained model.

[0238] The trained model and other information used by the artificial intelligence unit 420 may be prepared in advance, or they may be acquired via the network as needed.

[0239] The disembarking passenger calculation unit 37 includes a disembarking passenger data acquisition unit 371, a first assumed disembarking passenger estimation unit 372, a second assumed disembarking passenger estimation unit 373, and a disembarking passenger estimate value determination unit 374.

[0240] When data specification conditions including station name, month, day of the week, and time of day are input, the disembarking passenger data acquisition unit 371 acquires disembarking passenger data based on the data specification conditions and the trained model of the artificial intelligence unit 420. In the example in Figure 34, the disembarking passenger data acquisition unit 371 acquires disembarking passenger data by exchanging information with the artificial intelligence unit 420. The disembarking passenger data acquisition unit 371 passes the disembarking passenger data to the first estimated disembarking passenger estimation unit 372 and the second estimated disembarking passenger estimation unit 373. The data specification conditions may be input by the operator from the command input unit 32, or they may be input by the command input unit 32 reading an external file in which the data specification conditions are set.

[0241] The first estimated disembarking passenger estimation unit 372 calculates the first estimated disembarking passenger Ks, which is the number of passengers who will disembark from train 150 scheduled to stop at the station during a predetermined time period, using disembarking passenger data. In one example, the first estimated disembarking passenger estimation unit 372 calculates the first estimated disembarking passenger Ks using equation (3). The first estimated disembarking passenger estimation unit 372 passes the first estimated disembarking passenger Ks to the disembarking passenger estimate value determination unit 374.

[0242] The second assumed disembarking passenger estimation unit 373 calculates the second assumed disembarking passenger Kd, which is the number of passengers who disembark from train 150 that stops at the station during a predetermined time period, using disembarking passenger data and occupancy rate data. In one example, the second assumed disembarking passenger estimation unit 373 calculates the second assumed disembarking passenger Kd using equation (4). The second assumed disembarking passenger estimation unit 373 passes the second assumed disembarking passenger Kd to the disembarking passenger estimate value determination unit 374.

[0243] The passenger disembarking estimate determination unit 374 stores the larger of the first assumed passenger disembarking number Ks and the second assumed passenger disembarking number Kd as the passenger disembarking estimate K in the passenger disembarking estimate database 367.

[0244] The disembarking passenger data acquisition unit 371 comprises a data acquisition unit 431 and a control unit 432. When data specification conditions including station name, month, day of the week, and time of day are input to the disembarking passenger data acquisition unit 371, it uses the artificial intelligence unit 420 to acquire and output disembarking passenger data. The station name, month, day of the week, and time of day are data that specify the disembarking passenger data to be acquired. The disembarking passenger data is disembarking passenger data obtained from past disembarking passenger statistics for each time of day at each station for the specified month and day of the week.

[0245] The data acquisition unit 431 acquires data specification conditions, including station name, month, day of the week, and time zone, from the operation management device 30A. The data acquisition unit 431 outputs the acquired data specification conditions to the control unit 432.

[0246] The control unit 432 is an interface that can exchange information with the artificial intelligence unit 420. The control unit 432 receives data specification conditions, including station name, month, day of the week, and time of day, from the data acquisition unit 431. The control unit 432 inputs the acquired data specification conditions to the artificial intelligence unit 420 and acquires disembarking passenger data corresponding to the data specification conditions from the artificial intelligence unit 420. The control unit 432 outputs the acquired disembarking passenger data to the first estimated disembarking passenger estimation unit 372 and the second estimated disembarking passenger estimation unit 373. If the disembarking passenger statistics data correction unit 41 or the artificial intelligence unit 420 is configured as an external system, the control unit 432 is an interface that can exchange information with the external system.

[0247] Next, using Figure 36, we will explain the process that the learning device 410 of the disembarking passenger statistics data correction unit 41 learns. Figure 36 is a flowchart showing an example of the procedure for the learning process by the learning device.

[0248] The learning data acquisition unit 411 acquires data for learning (step S211). Specifically, the learning data acquisition unit 411 acquires station names, months, days of the week, time slots, and disembarking passenger statistics from the disembarking passenger statistics database 365, and station names, months, days of the week, time slots, and disembarking passenger actuals information from the disembarking passenger actuals database 368. The disembarking passenger statistics are data classified by station name, month, day of the week, and time slot.

[0249] Next, the model generation unit 412 learns estimated values ​​of the disembarking passenger data and generates a trained model (step S212) according to the training data created based on the combination of station name, month, day of the week, time of day, and disembarking passenger statistics information acquired by the training data acquisition unit 411, and processed disembarking passenger actual information obtained by processing station name, month, day of the week, time of day, and disembarking passenger actual information.

[0250] Then, the disembarking passenger statistics database (after training) 421 stores the trained model generated by the model generation unit 412 (step S213). This completes the process.

[0251] Next, using Figure 37, we will explain the process for obtaining disembarking passenger data using the disembarking passenger data acquisition unit 371 of the disembarking passenger calculation unit 37. Figure 37 is a flowchart showing an example of the procedure for acquiring disembarking passenger data by the disembarking passenger data acquisition unit.

[0252] First, the data acquisition unit 431 acquires data specification conditions including station name, month, day of the week, and time of day (step S231). The data acquisition unit 431 outputs the data specification conditions to the control unit 432.

[0253] Next, the control unit 432 inputs the station name, month, day of the week, and time slot to the artificial intelligence unit 420 and obtains the disembarking passenger data (step S232).

[0254] Subsequently, the control unit 432 outputs the passenger disembarking data obtained from the artificial intelligence unit 420 to the first estimated passenger disembarking unit 372 and the second estimated passenger disembarking unit 373 (step S233).

[0255] Subsequently, the first estimated number of disembarking passengers 372 calculates the first estimated number of disembarking passengers Ks using the disembarking passenger data output from the control unit 432, and the second estimated number of disembarking passengers 373 calculates the second estimated number of disembarking passengers Kd using the disembarking passenger data output from the control unit 432 (step 234).

[0256] Specifically, the first estimated disembarking passenger estimation unit 372 uses the disembarking passenger data, the weighting ratio, and the first estimated disembarking passenger ratio, which is the ratio of the train capacity of the 150 trains scheduled to stop at the station to the sum of the train capacities of all 150 trains operating during the specified time period, to calculate the first estimated disembarking passenger Ks using equation (3).

[0257] Furthermore, the second assumed disembarking passenger estimation unit 373 calculates the second assumed disembarking passenger ratio, which is the ratio of the station's disembarking passenger data to the sum of the disembarking passenger data for a predetermined time period at the station and the extracted stations. The second assumed disembarking passenger estimation unit 373 uses the occupancy rate data, the second assumed disembarking passenger ratio, and the capacity of the train 150 scheduled to arrive at the station to calculate the second assumed disembarking passenger Kd using equation (4).

[0258] In this way, since the disembarking passenger data is corrected to approximate the actual disembarking passenger data based on the difference between the disembarking passenger statistics and the actual disembarking passenger data, the likelihood of obtaining disembarking passenger data that is closer to the actual disembarking passenger data increases. As a result, it is possible to increase the likelihood of obtaining the first assumed disembarking passenger Ks and the second assumed disembarking passenger Kd, which are close to the actual disembarking passengers.

[0259] In the above explanation, the learning data acquisition unit 411 acquired station name, month, day of the week, time of day, and disembarking passenger statistics information, as well as station name, month, day of the week, time of day, and disembarking passenger actual information, but it is not limited to this. For example, both the disembarking passenger statistics information and the disembarking passenger actual information may include other information such as weather.

[0260] In Embodiment 3, the gate control command device further includes a disembarking passenger data generation unit 40 that generates disembarking passenger data, including the actual disembarking passenger value Kr of the train 150 at the station, from passenger occupancy rate data measured immediately before the train 150 arrives at the station and multiple passenger occupancy rate data measured between the arrival at the station and departure; and a disembarking passenger statistical data correction unit 41 that calculates the station's disembarking passenger data from the disembarking passenger data and corrects the disembarking passenger data using the disembarking passenger data. As a result, the disembarking passenger data used to calculate the disembarking passenger estimate K is closer to the actual value, and thus the disembarking passenger estimate K calculated by the disembarking passenger calculation unit 37 is more likely to be closer to the actual value.

[0261] Furthermore, in Embodiment 3, the disembarking passenger calculation unit 37 includes a data acquisition unit 431 that acquires data specification conditions including the station, month, day of the week, and time of day for which the number of disembarking passengers is to be estimated, and a control unit 432 that inputs the data specification conditions acquired by the data acquisition unit 431 to the artificial intelligence unit 420, and acquires and outputs disembarking passenger data corresponding to the data specification conditions from the artificial intelligence unit 420. The first estimated disembarking passenger estimation unit 372 calculates the first estimated disembarking passenger Ks using this disembarking passenger data, and the second estimated disembarking passenger estimation unit 373 calculates the second estimated disembarking passenger Kd using this disembarking passenger data. This increases the likelihood that the passenger disembarking data used to calculate the estimated passenger disembarking value K will be closer to the actual value, thereby improving the accuracy of the first assumed passenger disembarking value Ks calculated by the first assumed passenger disembarking value estimation unit 372 and the second assumed passenger disembarking value Kd calculated by the second assumed passenger disembarking value estimation unit 373, i.e., the accuracy of the estimated passenger disembarking value K.

[0262] The configurations shown in the above embodiments are examples only, and it is possible to combine them with other known technologies, combine different embodiments, and omit or modify parts of the configuration without departing from the gist of the invention.

[0263] 10, 10A Operation Management System, 20 Vehicle Information Management Device, 21 Passenger Ratio Measurement Unit, 22 Measurement Result Transmission Unit, 30, 30A Operation Management Device, 31 Passenger Ratio Acquisition Unit, 32 Command Input Unit, 33 Station Equipment Data Management Unit, 34 Timetable Management Unit, 35 Event Data Management Unit, 36, 903 Storage Unit, 37 Disembarking Passenger Calculation Unit, 38 Ticket Gate Control Command Unit, 39 Command Transmission Unit, 40 Disembarking Passenger Actual Information Generation Unit, 41 Disembarking Passenger Statistical Data Correction Unit, 50 Ticket Gate Control Device, 51 Command Receiving Unit, 52 Ticket Gate Control Processing Unit, 100, 100A Station Premises, 110 Platform, 110A First Platform, 110B Second Platform, 120 Ticket Gate, 120A First Ticket Gate, 120B Second Ticket Gate, 121, 121A Automatic ticket gates, 122, 122A, 126 Gates, 125 Passenger flow control devices, 130, 130A Passageways, 131 Stair entrances, etc., 150 Trains, 361 Vehicle database, 362 Train occupancy rate database, 363 Station equipment database, 364 Timetable information database, 365 Disembarking passenger statistics database, 366 Event database, 367 Estimated disembarking passenger database, 368 Disembarking passenger actual database, 371 Disembarking passenger data acquisition unit, 372 First assumed disembarking passenger estimation unit, 373 Second assumed disembarking passenger estimation unit, 374 Disembarking passenger estimate value determination unit, 410 Learning device, 411 Learning data acquisition unit, 412 Model generation unit, 420 Artificial intelligence unit, 421 Disembarking passenger statistics database (after learning), 422 Model control unit, 431 Data acquisition unit, 432, 901 Control unit, 433 Calculation unit, 902, 921 Input unit, 904 Display unit, 905 Communication unit, 906, 924 Output unit, 907 System bus, 920 Processing circuit, 922 Processor, 923 Memory, 3651a, 3651b Disembarking passenger statistics information, 3671 Disembarking passenger estimation information, 3681a, 3681b Disembarking passenger actual information.

Claims

1. A gate control command device comprising: a passenger disembarking calculation unit that calculates an estimated number of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station; a gate control command unit that generates a gate control command to adjust the flow of people entering a bottleneck section where the speed of movement of people at the station decreases using a gate through which people pass; and a transmission unit that transmits the gate control command to a gate control device that controls the gate.

2. The gate control command device according to claim 1, characterized in that the disembarking passenger calculation unit calculates a first estimated disembarking passenger by multiplying the disembarking passenger data for the station during the predetermined time period by a first estimated disembarking passenger ratio, which is the ratio of the train capacity of the train scheduled to stop at the station to the sum of the train capacities of all trains operating during the predetermined time period, and sets the first estimated disembarking passenger as the estimated disembarking passenger value.

3. The gate control command device according to claim 2, characterized in that the disembarking passenger calculation unit refers to event data which defines weighting ratios for each train and each station to adjust for the increase in the number of disembarking passengers at the station due to the holding or occurrence of an event, obtains the weighting ratios corresponding to the station and the trains scheduled to stop at the station, and calculates the first estimated number of disembarking passengers by multiplying the disembarking passenger data by the first estimated number of disembarking passengers and the weighting ratio.

4. The gate control command device according to claim 3, further comprising: an event data storage unit for storing the event data; a command input unit for receiving a command to change the weighting ratio of the event data; and an event data management unit for updating the event data in accordance with the command.

5. The gate control command device according to claim 1, further comprising a station equipment information storage unit that stores station equipment information including a reference car, which is the car number of the vehicle closest to at least one location of stairs and escalators provided on the train platform, as defined for each vehicle type and each station, wherein the disembarking passenger calculation unit refers to the station equipment information to extract stations where the combination of the vehicle type and the reference car of the train scheduled to arrive at the station is the same, calculates a second estimated disembarking passenger by multiplying a second estimated disembarking passenger ratio, which is the ratio of the disembarking passenger data of the station to the sum of the disembarking passenger data for a predetermined time period at the station and the extracted stations, by the estimated number of passengers on the target vehicle, which is obtained from the occupancy rate data indicating the occupancy rate of the train scheduled to arrive at the station and the capacity of the vehicles in a predetermined range including the reference car of the train, and sets the second estimated disembarking passenger as the estimated disembarking passenger value.

6. The gate control command device according to any one of claims 2 to 4, further comprising a station equipment information storage unit that stores station equipment information including a reference car, which is the car number of the vehicle closest to at least one location of stairs and escalators provided on the platform of the train, as defined for each vehicle type and each station, wherein the disembarking passenger calculation unit refers to the station equipment information to extract stations where the combination of the vehicle type and the reference car of the train scheduled to arrive at the station is the same, calculates a second estimated disembarking passenger by multiplying a second estimated disembarking passenger ratio, which is the ratio of the disembarking passenger data of the station to the sum of the disembarking passenger data for a predetermined time period at the station and the extracted stations, by the estimated number of passengers on the target vehicle, which is obtained from the occupancy rate data indicating the occupancy rate of the train scheduled to arrive at the station and the capacity of vehicles in a predetermined range including the reference car of the train, and sets the larger of the first estimated disembarking passenger and the second estimated disembarking passenger as the estimated disembarking passenger value.

7. The gate control command device according to any one of claims 1 to 6, characterized in that the gate control command unit generates a gate control command that limits the number of gates that can be entered in order to adjust the flow of people entering the station, which is one of the flow of people disembarking from trains and the flow of people entering the station.

8. The gate control command device according to claim 7, characterized in that the gate control command unit generates a gate control command to set the number of accessible gates to 0 when the estimated number of disembarking passengers is greater than the capacity set for the bottleneck section.

9. The gate control command device according to claim 7, characterized in that the gate control command unit generates a gate control command to adjust the number of accessible gates when the estimated number of disembarking passengers is smaller than the capacity set for the bottleneck section, so that the flow of passengers entering the bottleneck section is less than or equal to the capacity set for the bottleneck section.

10. The gate control command device according to any one of 7 to 9, further comprising a station equipment information storage unit that stores station equipment parameters including the distance in a first section wider than the bottleneck section and including a passage leading to the platform, and the movement speed of the disembarking passenger flow; the distance in the bottleneck section, the movement speed and inflow speed of the disembarking passenger flow; and the distance in a second section wider than the bottleneck section and including a passage leading to the gate, and the movement speed of the entering passenger flow, wherein the gate control command unit uses the station equipment parameters and the estimated disembarking passenger number calculated by the disembarking passenger number calculation unit to calculate a gate detention start time, which is the time to start controlling the gate, and a gate detention end time, which is the time to end controlling the gate, and generates a gate detention start command to limit the number of gates that can be entered at the gate detention start time, and generates a gate detention end command to release the limit at the gate detention end time.

11. The gate control command device according to claim 10, further comprising: a command input unit that receives a command to change the station equipment parameters; and a station equipment information management unit that updates the station equipment parameters in accordance with the command.

12. The gate control command device according to claim 10 or 11, characterized in that the gate control command unit corrects the station equipment parameters by referring to correction coefficient information which includes a correction coefficient that adjusts the value of the station equipment parameters according to the type of train or weather.

13. A gate control command device according to any one of claims 1 to 12, further comprising: a passenger disembarkation data generation unit that generates disembarkation data including actual passenger disembarkation values ​​for the train at the station from passenger occupancy data measured immediately before the train arrives at the station and a plurality of passenger occupancy data measured between the arrival at the station and departure; and a passenger disembarkation statistical data correction unit that calculates disembarkation data for the station from the disembarkation data and corrects the disembarkation data using the disembarkation data.

14. The gate control command device according to any one of 2, 3, and 6, wherein the disembarking passenger calculation unit comprises: a data acquisition unit that acquires data specification conditions including the station, month, day of the week, and time of day for which the number of disembarking passengers is to be estimated; and a control unit that inputs the data specification conditions acquired by the data acquisition unit to an artificial intelligence unit, and acquires and outputs the disembarking passenger data corresponding to the data specification conditions from the artificial intelligence unit.

15. The gate control command device according to any one of claims 1 to 14, characterized in that the gate control command unit outputs the contents of the gate control command in a way that can be visually displayed.

16. A gate control system that controls the operation of a gate to determine whether or not people are allowed to pass in the direction toward the platform and whether or not people are allowed to pass away from the platform by passengers disembarking from a train, comprising: a disembarking passenger calculation unit that calculates an estimated number of disembarking passengers at a station based on disembarking passenger data indicating the number of disembarking passengers at the station; a gate control command unit that generates a gate control command to adjust the flow of people into a bottleneck section where the speed of movement of people at the station decreases using the gate, based on the estimated number of disembarking passengers; and a gate control processing unit that controls the gate according to the gate control command.

17. The gate control system according to claim 16, characterized in that the gate is a gate of an automatic ticket gate installed at a ticket gate within a station premises.

18. The gate control system according to claim 16, wherein the gate includes a gate for an automatic ticket gate installed at a ticket gate within a station premises and a gate for a pedestrian flow adjustment device installed at the ticket gate side end of the bottleneck section, and the gate control processing unit controls the gate for the pedestrian flow adjustment device.

19. A gate control device comprising: a gate that opens and closes to determine whether or not people are allowed to pass in the direction toward the platform and whether or not people are allowed to pass away from the platform by passengers disembarking from a train; a disembarking passenger calculation unit that calculates an estimated number of disembarking passengers at a station based on disembarking passenger data indicating the number of disembarking passengers at the station; a gate control command unit that generates a gate control command to adjust the flow of people that enters a bottleneck section where the speed of movement of people at the station decreases using the gate, based on the estimated number of disembarking passengers; and a gate control processing unit that controls the gate in accordance with the gate control command.

20. An operation management device comprising: a passenger disembarking calculation unit that calculates an estimated number of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station; a ticket gate control command unit that generates a ticket gate control command to adjust the flow of people entering a bottleneck section where the speed of movement of people at the station decreases using the gates of the ticket gates through which people pass; and a transmission unit that transmits the ticket gate control command to a ticket gate control device that controls the ticket gates.

21. An operation management system comprising: a passenger disembarking calculation unit that calculates an estimated number of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station; a ticket gate control command unit that generates a ticket gate control command to adjust the flow of people entering a bottleneck section where the speed of movement of people at the station decreases using the gates of ticket gates through which people pass; and a ticket gate control processing unit that controls the ticket gates in accordance with the ticket gate control command.

22. A gate control method comprising: a passenger disembarking calculation step of calculating an estimated number of passengers disembarking at a station based on passenger disembarking data indicating the number of passengers disembarking at the station; a gate control command step of generating a gate control command to adjust the flow of people entering a bottleneck section where the speed of movement of people flowing at the station decreases using a gate through which people pass; and a transmission step of transmitting the gate control command to a gate control device that controls the gate.

23. A gate control program characterized by causing a computer to execute: a disembarking passenger calculation step of calculating an estimated number of disembarking passengers at a station based on disembarking passenger data indicating the number of disembarking passengers at the station; a gate control command step of generating a gate control command to adjust the flow of people entering a bottleneck section where the speed of movement of people at the station decreases using a gate through which people pass; and a transmission step of transmitting the gate control command to a gate control device that controls the gate.

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