Vehicle occupancy rate calculation system, ground server, vehicle information control device, and vehicle occupancy rate calculation method

Abstract

The system accurately calculates the occupancy rate of train cars, regardless of environmental conditions such as season or time of day. [Solution] The vehicle occupancy rate calculation system of the present invention comprises a ground server that receives ticket gate information acquired by a ticket gate system, and a vehicle information control device that is mounted on the vehicle and has vehicle load information for each vehicle constituting the train, and is capable of communicating with the ground server, wherein the ground server transmits train passenger count information estimated from the ticket gate information to the vehicle information control device, and the vehicle information control device estimates the weight per passenger of the train based on the received train passenger count information and train load information calculated from the vehicle load information of each vehicle constituting the train, and calculates the vehicle occupancy rate of the vehicles constituting the train based on the weight per passenger and the vehicle load information.

Description

Technical Field

[0001] The present invention relates to a vehicle occupancy rate calculation system, a ground server, a vehicle information control device, and a vehicle occupancy rate calculation method for managing the occupancy rate of vehicles that make up a train.

Background Art

[0002] Conventionally, the occupancy rate of a vehicle, that is, the ratio of the number of passengers to the seating capacity of the vehicle, has been grasped and utilized for train operation management. Railway operators can use the occupancy rate as a reference for train schedule revisions and provide it to passengers as information on congestion. This enables the leveling of train congestion, improving the stability of train operation by shortening boarding and alighting times and enhancing the in-vehicle environment. For example, Patent Document 1 discloses the following as an invention of a passenger guidance device aimed at shortening boarding times during rush hours and ensuring the punctuality of train schedules by eliminating the bias in occupancy rates. "A passenger rate measurement device 2A that measures the occupancy rate of each vehicle 21 of train 20, a transmission device 6 provided on the train that transmits the measured occupancy rate data, a reception device 8 provided at the station that receives the transmitted occupancy rate data, and a display device 10 provided at the station that displays the received occupancy rate data." As a method for calculating the occupancy rate for each vehicle, as shown in Patent Document 1, a method based on load detection that calculates the occupancy rate of a vehicle from the change in the internal pressure of an air spring arranged on the bogie of the vehicle is a commonly used method.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Air springs are components installed between the vehicle and the bogie that reduce vibrations transmitted from the wheels to the vehicle, thereby improving ride comfort. The internal pressure of the air springs is adjusted to maintain the vehicle's height under various load conditions, from empty to full, and the load can be calculated by measuring and converting this pressure. In load detection methods using air springs, the number of passengers is determined by dividing the load detection result by the weight per person, but if the estimation error for the weight per person is large, the accuracy of the number of passengers deteriorates. Estimating the weight per person is difficult because it fluctuates depending on environmental conditions such as season and time of day, and there are biases in passenger attributes depending on the route and station. This invention was made in view of the above-mentioned problems, and aims to accurately calculate the occupancy rate of train cars, regardless of environmental conditions such as season and time of day. [Means for solving the problem]

[0005] To solve the above problems, one representative vehicle occupancy rate calculation system of the present invention comprises a ground server that receives ticket gate information acquired by a ticket gate system, and a vehicle information control device mounted on the vehicle and having vehicle load information for each vehicle constituting the train, and capable of communicating with the ground server, wherein the ground server transmits train passenger count information estimated from the ticket gate information to the vehicle information control device, and the vehicle information control device estimates the weight per passenger of the train based on the received train passenger count information and train load information calculated from the vehicle load information of each vehicle constituting the train, and calculates the vehicle occupancy rate of the vehicles constituting the train based on the weight per passenger and the vehicle load information. [Effects of the Invention]

[0006] According to the present invention, the occupancy rate of the cars that make up a train can be calculated with high accuracy, regardless of environmental conditions such as season and time of day. Other issues, configurations, and effects not mentioned above will be clarified by the description of the embodiments for carrying out the invention below. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 shows an example of the system configuration of a vehicle occupancy rate calculation system according to an embodiment of the present invention. [Figure 2] Figure 2 is a flowchart showing an example of the internal processing of the weight estimation unit per person. [Figure 3] Figure 3 is a flowchart showing an example of the internal processing of the vehicle passenger count estimation unit. [Figure 4] Figure 4 is a flowchart showing an example of the internal processing of the occupancy rate calculation unit. [Modes for carrying out the invention]

[0008] Embodiments of the present invention will be described below with reference to the drawings. However, the present invention is not limited to these embodiments. Furthermore, in the drawings, identical parts are denoted by the same reference numerals. When there are multiple components with the same or similar function, they may be described using the same symbol but with different subscripts. Furthermore, when it is not necessary to distinguish between these multiple components, the subscripts may be omitted in the description. The positions, sizes, shapes, and ranges of the components shown in the drawings may not represent their actual positions, sizes, shapes, and ranges in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, and ranges disclosed in the drawings. [Examples]

[0009] (Summary of this disclosure) The vehicle occupancy rate calculation system according to an embodiment of the present invention first estimates the weight per person in the train by dividing the load measurement result for the entire train by the estimated number of people in the train obtained from the ticket gate system. Then, it estimates the number of people in each car by dividing the load measurement result for each car by the weight per person, and calculates the occupancy rate of each car by dividing the estimated number of people by the capacity of the corresponding car. Compared to a method that uniformly defines the weight per person, this vehicle occupancy rate calculation system makes it possible to calculate the occupancy rate of the cars that make up a train with high accuracy, regardless of environmental conditions such as season and time of day.

[0010] Furthermore, if the estimated number of passengers on the train obtained from the ticket gate system is unavailable, the system estimates the weight per passenger based on a pre-prepared relational formula showing the relationship between passenger composition and weight per passenger, using the passenger composition of the target train as input. This relational formula is generated regressively based on the weight per passenger estimated using the method described in the previous paragraph and the passenger composition on the train at that time, utilizing the accumulation of past cases in which the estimated number of passengers on the train obtained from the ticket gate system was received. This train occupancy rate calculation system makes it possible to calculate the occupancy rate of the train cars, regardless of environmental conditions such as season and time of day, with greater accuracy than methods that uniformly define the weight per passenger, even when the estimated number of passengers on each train cannot be accurately grasped by the ticket gate system during crowded conditions such as rush hour.

[0011] (Configuration of the vehicle occupancy rate calculation system) Figure 1 shows an example of the system configuration of a vehicle occupancy rate calculation system according to this embodiment of the present invention. The vehicle occupancy rate calculation system 1 is a system for calculating the vehicle occupancy rate of the vehicles that make up a train. The vehicle occupancy rate calculation system 1 includes a ground server (ticket gate acquisition information aggregation unit 110) that receives ticket gate information (estimated number of people on the train 151 and passenger composition information 152) acquired by the ticket gate system 100, and a vehicle information control device (occupancy rate calculation device 120) that is mounted on the vehicle and has vehicle load information (measured value of each vehicle load 160) for each vehicle that makes up the train, and can communicate with the ground server. In the vehicle occupancy rate calculation system 1, the ground server transmits the estimated number of passengers on the train (estimated number of passengers on the train 154) estimated from the ticket gate information to the vehicle information control device, and the vehicle information control device estimates the weight per passenger on the train (per passenger weight estimation unit 121) based on the received train occupancy rate information (estimated number of passengers on the train 154) and train load information calculated from the vehicle load information of each vehicle constituting the train, and calculates the vehicle occupancy rate of the vehicles constituting the train (occupancy rate calculation result 167). In another example, in the vehicle occupancy rate calculation system 1, the ground server has a per capita weight database (per capita weight history management unit 114) that stores per capita weight information (estimated per capita weight 164) including the attribute information of the train passengers included in the ticket gate information and the per capita weight, and estimates the per capita weight based on the per capita weight database (per capita weight estimation unit 121), and the vehicle information control device calculates the vehicle occupancy rate of the vehicles constituting the train based on the per capita weight and the vehicle load information received from the ground server. In addition, the attribute information of the passengers includes gender and age, and the ground server estimates the per capita weight based on the stored per capita weight database using a regression calculation method with the attribute information of the passengers as parameters (per capita weight regression estimation unit 113). A detailed explanation follows below.

[0012] The vehicle occupancy rate calculation system 1 includes a ticket gate system 100, a ticket gate acquisition information aggregation unit 110, an occupancy rate calculation device 120, and multiple processing units other than the ticket gate acquisition information aggregation unit 110 that input and output information to the occupancy rate calculation device 120.

[0013] (Ticket gate system 100) One possible configuration is for the ticket gate system 100 to exist at each station along the line, and each station's ticket gate information to be transmitted to the ticket gate acquisition information aggregation unit 110. Alternatively, the ticket gate system 100 could exist as a centralized entity for managing ticket gate information from each station, with one or more systems (including backups) along the line, and all ticket gate information from each station to be transmitted to the ticket gate acquisition information aggregation unit 110. The ticket gate system 100 is usually a ground-side system (a system including multiple ground devices), but it does not need to be a system composed of computers physically located in a specific location; it may also be a system including a logical unit (control device) located in the cloud. Here, the ticket gate information includes information such as the number of entrants, the number of exiters, attribute information of entrants and exiters, and their time-series information, and does not necessarily need to include payment information. In this embodiment, as an example, the ticket gate information is described as estimated number of people on the train 151 and passenger composition information 152 (the estimated number of people on the train 151 and passenger composition information 152 will be explained later). Furthermore, the ticket gate system 100 only needs to be capable of acquiring and transmitting ticket gate information. The methods for acquiring ticket gate information include, for example, acquiring it from a magnetic IC card at a ticket gate, acquiring it using a camera or radar, or acquiring it using payment equipment or cameras installed inside the train rather than at the station.

[0014] (Ticket gate information aggregation unit 110) Since the ticket gate acquisition information aggregation unit 110 is included in the ground-side system, it can also be referred to as a ground server. The ticket gate acquisition information aggregation unit 110 is a ground server that receives the ticket gate information acquired by the ticket gate system 100. The ground server has a per-person weight database (per-person weight history management unit 114) that stores per-person weight information including the attribute information of the passengers on the train and the weight per passenger on the train, estimates the weight per passenger based on the per-person weight database (per-person weight history management unit 114), and transmits the weight per passenger to a vehicle information control device capable of communicating with the ground server. In another example, the ticket gate acquisition information aggregation unit 110 has a per-person weight database (per-person weight history management unit 114) that stores per-person weight information (estimated weight per person 164) including the attribute information of the passengers included in the ticket gate information and the estimated weight per person 164. When an index representing the accuracy of the train boarding人数 information (estimated number of people in the train 154) is below a predetermined threshold, the ground server estimates a new weight per passenger based on the per-person weight database, and the vehicle information control device calculates the vehicle boarding rate of the vehicles constituting the train based on the new weight per passenger received from the ground server and the vehicle load information.

[0015] Specifically, the ticket gate information aggregation unit 110 includes a train passenger estimation unit 111, a train-specific passenger composition management unit 112, a per capita weight regression estimation unit 113, and a per capita weight history management unit 114. Each component will be described later. There only needs to be at least one ticket gate information aggregation unit 110 for the target line, but multiple ticket gate information aggregation units 110 that function as a main unit and backup units may be deployed. In this embodiment, the ticket gate information aggregation unit 110 is described as a device included in the ground-side system, but it does not need to be a computer (processing unit) that is physically located in a specific place; it may be a logical unit that exists on the cloud. The ticket gate information aggregation unit 110 can be configured to perform its functions as each component as appropriate, as will be described later. For example, the ticket gate information aggregation unit 110 may include memory and a processor, and the memory may be configured to include processing instructions that cause the processor to execute as the train passenger estimation unit 111, the train-specific passenger composition management unit 112, the per-person weight regression estimation unit 113, and the per-person weight history management unit 114.

[0016] (Occupancy rate calculation device 120) The boarding rate calculation device 120 can also be referred to as a vehicle information control device that is mounted on the vehicles constituting a train and calculates the vehicle boarding rate. The boarding rate calculation device 120 has the vehicle load information of each vehicle constituting the train, and receives from a ground server (ticket gate acquisition information aggregation unit 110) that can communicate with the boarding rate calculation device 120, the train boarding passenger number information (estimated number of people in the train 154 and weight regression estimation result per person 158) estimated from the ticket gate information acquired by the ticket gate system 100. Based on the train boarding passenger number information and the train passenger load information (sum of the vehicle load measurement values 160 of each vehicle) calculated from the vehicle passenger load information of each vehicle constituting the train, it estimates the weight per person of the passengers on the train (weight per person estimation unit 121), and calculates the vehicle boarding rate of the vehicles constituting the train based on the weight per person and the vehicle passenger load information (vehicle load measurement values 160 of each vehicle) of the vehicle (boarding rate calculation unit 123). Here, the boarding rate calculation device 120 can also have the vehicle load information of other vehicles constituting the train in which the vehicle on which the boarding rate calculation device 120 is mounted is included, in addition to the vehicle load information of the vehicle on which the boarding rate calculation device 120 is mounted. Also, the train passenger load information indicates the load information of the passengers on the entire train, or in other words, indicates the sum of the vehicle passenger information (vehicle load measurement values 160 of each vehicle) of each vehicle. Also, in another example, when an index representing the accuracy of the train boarding passenger number information (estimated number of people in the train 154) is below a predetermined threshold, the boarding rate calculation device 120 receives a new weight per person estimated by a ground server having a weight per person database that stores weight per person information including the attribute information of the passengers included in the ticket gate information and the weight per person, and calculates the vehicle boarding rate of the vehicles constituting the train based on the new weight per person and the vehicle passenger load information (vehicle load measurement values 160 of each vehicle).

[0017] Specifically, the occupancy rate calculation device 120 includes a per capita weight estimation unit 121, a unit 122 for estimating the number of passengers per vehicle, and an occupancy rate calculation unit 123. Each component will be described later. The occupancy rate calculation device 120 may be located on each train, or it may be included as a common logic unit for multiple trains in a ground-side system (which may be a system including a device located on the ground and a logic unit located in the cloud, or a system composed of a logic unit located in the cloud). The following examples will be based on the premise that the device is located on each train. The occupancy rate calculation device 120 is also composed of a computing unit, and the configuration to enable each component to perform its function can be appropriately set as described later. For example, the occupancy rate calculation device 120 may include memory and a processor, and the memory may be configured to include processing instructions that cause the processor to execute as the per capita weight estimation unit 121, the unit 122 for estimating the number of passengers per vehicle, and the occupancy rate calculation unit 123.

[0018] The multiple processing units other than the ticket gate acquisition information aggregation unit 110, which serve as input sources for information to the passenger occupancy rate calculation device 120, include the vehicle load measurement unit 124, the door opening / closing information management unit 125, and the route information management unit 126. The vehicle load measurement unit 124, the door opening / closing information management unit 125, and the route information management unit 126 are, for example, functions of an on-board monitoring device. A description of each component will follow later.

[0019] (Other processing) The processing units other than the ticket gate acquisition information aggregation unit 110, which are the output destinations for information from the passenger occupancy rate calculation device 120, include the passenger occupancy rate storage unit 128. The passenger occupancy rate storage unit 128 outputs passenger occupancy rate information to the in-car display devices 127 located in each car of the train and the station premises display devices 129 located at stations along the line. The passenger occupancy rate storage unit 128 may, for example, be located on the train, or it may be a device included in the ground-side system (which may be a system including a device located on the ground and a logical unit located in the cloud, or a system composed of a logical unit located in the cloud). The passenger occupancy rate storage unit 128 can store the passenger occupancy rate for one train, or it may store the passenger occupancy rate for multiple trains. A description of each component will be given later.

[0020] (Order of explanation in this disclosure) The vehicle occupancy rate calculation system 1 performs each process in response to input to the occupancy rate calculation device 120. Therefore, to facilitate understanding, the processes will be explained in the order in which they occur, in the following order: (A) the input side of the occupancy rate calculation device 120, (B) the output side of the occupancy rate calculation device 120, (C) the inside of the occupancy rate calculation device 120, (D) the inside of the ticket gate acquisition information aggregation unit 110, and (E) the ticket gate system 100.

[0021] (A) Input side of the occupancy rate calculation device 120 (1) Vehicle load measurement section 124 The input information from the vehicle load measurement unit 124 is the measured vehicle load value 160. Each measured vehicle load value 160 indicates the weight increased by passengers, based on the empty state. Each measured vehicle load value 160 indicates the weight of each individual vehicle. If the load measurement is not performed correctly, it is desirable that error information indicating this be added to each measured vehicle load value 160 so that the occupancy rate calculation device 120 can recognize that the vehicle load measurement unit 124 is not functioning properly.

[0022] Here, an example of a vehicle load measurement unit 124 is an on-board monitoring device. In the on-board monitoring device, the internal pressure of the air springs supporting the vehicle body is detected by information received from a brake control device located on the vehicle, and each vehicle load measurement value 160 is calculated by converting the pressure increase from the unloaded state into weight. Furthermore, this disclosure is not limited to the case of an on-board monitoring device, and the brake control device itself may be used as the vehicle load measurement unit 124.

[0023] (2) Door opening / closing information management unit 125 The input information from the door opening / closing information management unit 125 is door opening / closing information 161. Door opening / closing information 161 indicates whether at least some of the doors inside the train or platform doors are open and passengers can board or alight, or whether all doors are closed and passengers cannot board or alight. In the following explanation, the former state indicated by the door opening / closing information 161 will be referred to as the "door open state," and the latter state as the "door closed state."

[0024] Here, the door opening / closing information management unit 125 can perform its function as any device that holds information regarding the opening and closing status of doors inside the train or platform doors. For example, an on-board monitor device can be used as the door opening / closing information management unit 125.

[0025] (3) Route information management section 126 The input information from the route information management unit 126 is the route information 162 of the train itself. The route information 162 is data such as the operating time and distance of intermediate stations along the route the train is running, and includes information such as the current station, destination station, train type, and train number. Here, the route information management unit 126 can perform its role as any device that can detect at least the train number and position of the train itself, so for example, an onboard monitor can be applied as the route information management unit 126.

[0026] (B) Output side of the occupancy rate calculation device 120 (4) Occupancy rate memory unit 128 The passenger occupancy rate storage unit 128 stores the passenger occupancy rate calculation result 167, which is information indicating the passenger occupancy rate calculated by the passenger occupancy rate calculation device 120. The data stored in the passenger occupancy rate storage unit 128 is analyzed by the railway operator's planning department and used to understand congestion levels and as material for considering future timetable revisions, and is also used to provide information to passengers. Furthermore, as will be described later, the passenger occupancy rate storage unit 128 outputs passenger occupancy rate information 168 for each train car and passenger occupancy rate information 169 for each train.

[0027] (5) In-vehicle display device 127 The in-car display device 127 displays the occupancy rate of each car within the train. To this end, the occupancy rate storage unit 128 outputs train car-specific occupancy rate information 168 to the in-car display device 127. The train car-specific occupancy rate information 168 shows the occupancy rate of each car within the train derived from the occupancy rate calculation result 167. For example, by displaying the train car-specific occupancy rate information 168 on the in-car display device 127, it is expected that passengers in crowded cars will move to relatively empty cars, thus leveling out the congestion within the train.

[0028] (6) Station premises display device 129 The station display device 129 is installed at stations along the line and displays train-specific occupancy rate information 169 from the occupancy rate storage unit 128 for each train on the line. The train-specific occupancy rate information 169 includes the occupancy rate calculation results 167 associated with each train, obtained from multiple trains operating on the line. In other words, it includes and may display train car-specific occupancy rate information 168 for each train. By displaying the congestion status of each train on the line, the station display device 129 enables passengers at the station to select and board less crowded trains, which is expected to equalize congestion between trains. Furthermore, by displaying train car-specific occupancy rate information 168 and guiding passengers to cars with relatively low occupancy rates, it is also expected to equalize congestion between cars within a train. Leveling out congestion within and between trains not only improves passenger comfort but also reduces the occurrence of extremely long boarding and alighting times, contributing to improved punctuality of train operations.

[0029] (C) Inside of the passenger occupancy rate calculation device 120 The passenger occupancy rate calculation device 120 includes a per capita weight estimation unit 121, a per-vehicle passenger number estimation unit 122, and a passenger occupancy rate calculation unit 123.

[0030] (7) Per capita weight estimation unit 121 The information input to and output to the per capita weight estimation unit 121 is described below. The inputs to the per capita weight estimation unit 121 are the measured vehicle load values ​​160, the estimated number of people in the train 154, the per capita weight regression estimation result 158, the per capita weight request 163, and route information 162. The measured vehicle load values ​​160 are obtained from the vehicle load measurement unit 124. The estimated number of people in the train 154 are obtained from the train passenger estimate management unit 111 within the ticket gate acquisition information aggregation unit 110. The per capita weight regression estimation result 158 ​​is obtained from the per capita weight regression estimation unit 113 within the ticket gate acquisition information aggregation unit 110. The per capita weight request 163 is obtained from the passenger passenger estimation unit 122 within the occupancy rate calculation device 120. The route information 162 is obtained from the route information management unit 126.

[0031] Furthermore, the output of the per capita weight estimation unit 121 is the train passenger estimate request 153, the per capita weight regression estimation request 157, and the per capita weight estimate 164. The train passenger estimate request 153 is input to the train passenger estimate management unit 111 within the ticket gate acquisition information aggregation unit 110. The per capita weight regression estimation request 157 is input to the per capita weight regression estimation unit 113 within the ticket gate acquisition information aggregation unit 110. The per capita weight estimate 164 is input to the passenger passenger estimate unit 122 for each car within the occupancy rate calculation device 120 and the per capita weight history management unit 114 within the ticket gate acquisition information aggregation unit 110.

[0032] The content of each piece of information and the internal processing of the per capita weight estimation unit 121 will be described later.

[0033] (8) Estimation unit for the number of passengers in each vehicle 122 The information input to and output to the vehicle passenger capacity estimation unit 122 is described below. The inputs to the vehicle passenger capacity estimation unit 122 are the vehicle load measurement value 160, the estimated weight per person 164, and the vehicle passenger capacity request 165. The vehicle load measurement value 160 is obtained from the vehicle load measurement unit 124. The estimated weight per person 164 is obtained from the vehicle passenger weight estimation unit 121 in the passenger capacity calculation device 120. The vehicle passenger capacity request 165 is obtained from the passenger capacity calculation unit 123 in the passenger capacity calculation device 120.

[0034] Furthermore, the output of the vehicle passenger capacity estimation unit 122 is the per capita weight requirement 163 and the estimated number of passengers for each vehicle 166, both of which are information outputs within the occupancy rate calculation device 120. The per capita weight requirement 163 is input to the per capita weight estimation unit 121. The estimated number of passengers for each vehicle 166 is input to the occupancy rate calculation unit 123.

[0035] The details of each piece of information and the internal processing of the vehicle passenger estimation unit 122 will be described later.

[0036] (9) Occupancy rate calculation unit 123 The inputs to the passenger occupancy rate calculation unit 123 are the estimated number of passengers per vehicle 166, door opening / closing information 161, and route information 162. The estimated number of passengers per vehicle 166 is obtained from the passenger occupancy rate estimation unit 122 in the passenger occupancy rate calculation device 120. The door opening / closing information 161 is obtained from the door opening / closing information management unit 125. The route information 162 is obtained from the route information management unit 126. The output of the occupancy rate calculation unit 123 is the passenger count request 165 for each vehicle and the occupancy rate calculation result 167. The passenger count request 165 for each vehicle is input to the passenger count estimation unit 122 in the occupancy rate calculation device 120. The occupancy rate calculation result 167 is input to the occupancy rate storage unit 128. However, in order to display the vehicle occupancy rate in more real time, it may also be directly input to the in-vehicle display device 127 or station premises display device 129, which are output destinations from the occupancy rate storage unit 128. In addition, the vehicle occupancy rate may be input to a vehicle control device (not shown) or other device (not shown) to be used for air conditioning control, etc., in order to improve passenger comfort, or it may be used to guide passengers by notifying users through an app or the like.

[0037] The details of each piece of information and the internal processing of the occupancy rate calculation unit 123 will be described later.

[0038] (7-1) Internal processing of the per capita weight estimation unit 121 First, with reference to Figure 2, the internal processing of the per capita weight estimation unit 121 will be explained.

[0039] Figure 2 is a flowchart showing an example of the internal processing of the per capita weight estimation unit 121.

[0040] In STEP 201, the per capita weight estimation unit 121 determines whether or not it has received a per capita weight request 163 (output from the per vehicle passenger count estimation unit 122). Receipt of the per capita weight request 163 means that the per vehicle passenger count estimation unit 122 has requested the per capita weight estimation unit 121 to provide the per capita weight estimate 164. If the result of this determination is YES, proceed to STEP 202; otherwise, exit this flow.

[0041] In STEP 202, the per capita weight estimation unit 121 transmits a train passenger estimate request 153 (input to the train passenger estimate management unit 111). The transmission of the train passenger estimate request 153 means that the per capita weight estimation unit 121 is requesting the train passenger estimate management unit 111 to provide the train passenger estimate 154. The train passenger estimate request 153 includes information that specifies the target operating date and time, train number, and station intervals, which is generated based on the route information 162. The train passenger estimate 154 is information that indicates the estimated number of passengers on the train, but the details will be described later.

[0042] In STEP 203, the per-person weight estimation unit 121 takes each vehicle load measurement value 160 as input and determines whether the vehicle load measurement unit 124 is functioning correctly. This determination is made by checking whether error information indicating that the load measurement was not performed correctly is attached to each vehicle load measurement value 160. If the result of this determination is YES (normal operation), proceed to STEP 204. If the result of this determination is NO (not functioning correctly), proceed to STEP 207.

[0043] In STEP 204, the per capita weight estimation unit 121 determines whether or not it has received the estimated number of people on the train 154 (output of the train passenger estimate management unit 111). If the train passenger estimate management unit 111 has not been able to properly generate the train passenger estimate 154 for the train and station specified by the train passenger estimate request 153, an invalid value is set for the train passenger estimate 154, and in that case, the determination is NO. If the result of this determination is YES, proceed to STEP 205; otherwise, proceed to STEP 207.

[0044] In STEP 205, the per capita weight estimation unit 121 calculates the per capita weight by dividing the sum of the loads of all vehicles included in each vehicle load measurement value 160 (output of the vehicle load measurement unit 124) by the estimated number of people in the train 154 (output of the train passenger estimation management unit 111).

[0045] In STEP 206, the per capita weight estimation unit 121 outputs the calculated per capita weight information as the per capita estimated weight 164 to the per vehicle passenger count estimation unit 122 and the per capita weight history management unit 114. When outputting to the per capita weight history management unit 114, the per capita weight estimation unit 121 outputs the per capita estimated weight 164 along with the operating date and time, train number, and station intervals that are the subject of the estimation.

[0046] Steps 207 to 210 are executed when the vehicle load cannot be measured correctly (resulting in a "NO" judgment in Step 203) or when information on the estimated number of people inside the train cannot be obtained (resulting in a "NO" judgment in Step 204).

[0047] In STEP 207, the per capita weight estimation unit 121 sends a per capita weight regression estimation request 157 (input to the per capita weight regression estimation unit 113). The transmission of the per capita weight regression estimation request 157 means that the per capita weight estimation unit 121 is requesting the per capita weight regression estimation unit 113 to provide the per capita weight regression estimation result 158. The per capita weight regression estimation request 157 includes information that specifies the target operating date and time, train number, and station intervals, which is generated based on the route information 162. The per capita weight regression estimation result 158 ​​is information that shows the weight per passenger on the train, but the details will be described later.

[0048] In STEP 208, the per capita weight estimation unit 121 takes the per capita weight regression estimation result 158 ​​(output of the per capita weight regression estimation unit 113) as input and determines whether the result is valid data. If the per capita weight regression estimation result 158 ​​cannot be calculated properly, for example, if the relationship between passenger composition and per capita weight is not satisfied in the per capita weight regression estimation unit 113, error information indicating that the data is invalid is added to the per capita weight regression estimation result 158. If the result of this determination is YES, proceed to STEP 209; otherwise, proceed to STEP 210.

[0049] In STEP 209, the per capita weight estimation unit 121 sets the per capita weight to the value of the per capita weight regression estimation result 158. After STEP 209 is performed, the process proceeds to STEP 206.

[0050] In STEP 210, the per capita weight estimation unit 121 sets a predetermined default value for the per capita weight. After STEP 210 is performed, the process proceeds to STEP 206. Here, it is desirable that the predetermined default value be set based on publicly available average weight statistics, assuming a fixed passenger composition (gender ratio and age group).

[0051] (8-1) Internal processing of the vehicle passenger count estimation unit 122 Next, with reference to Figure 3, the internal processing of the vehicle passenger estimation unit 122 will be explained.

[0052] Figure 3 is a flowchart showing an example of the internal processing of the vehicle passenger count estimation unit 122.

[0053] In STEP 301, the vehicle passenger count estimation unit 122 determines whether or not it has received the vehicle passenger count request 165 (output of the occupancy rate calculation unit 123). Receipt of the vehicle passenger count request 165 means that the occupancy rate calculation unit 123 has requested the vehicle passenger count estimation unit 122 to provide the estimated number of passengers 166 for each vehicle. If the result of this determination is YES, proceed to STEP 302; otherwise, exit this flow.

[0054] In STEP 302, the vehicle passenger estimating unit 122 transmits a per capita weight request 163 (input to the per capita weight estimation unit 121). The presence of a per capita weight request 163 means that the vehicle passenger estimating unit 122 is requesting the per capita weight estimation unit 121 to provide an estimated per capita weight 164.

[0055] In STEP 303, the vehicle occupancy estimation unit 122 takes the vehicle load measurement value 160 as input and determines whether the vehicle load measurement unit 124 is functioning correctly. This determination is made by checking whether error information indicating that the load measurement was not performed correctly is attached to each vehicle load measurement value 160. If the result of this determination is YES (normal operation), proceed to STEP 304. If the result of this determination is NO (not functioning correctly), proceed to STEP 305.

[0056] In STEP 304, the vehicle passenger capacity estimation unit 122 calculates the number of passengers for each vehicle by dividing the load measurement value for each vehicle included in the vehicle load measurement value 160 (output of the vehicle load measurement unit 124) by the estimated weight per person 164, and then proceeds to STEP 306.

[0057] In STEP 305, the vehicle passenger estimation unit 122 sets an invalid value for the estimated passenger count 166 for each vehicle. This means that the vehicle load has not been properly acquired and the passenger count for each vehicle cannot be calculated.

[0058] In STEP 306, the vehicle passenger estimation unit 122 sets the calculated number of passengers for each vehicle into the vehicle passenger estimation unit 166 and outputs it to the passenger occupancy rate calculation unit 123.

[0059] (9-1) Internal processing of the occupancy rate calculation unit 123 Next, with reference to Figure 4, the internal processing of the occupancy rate calculation unit 123 will be explained.

[0060] Figure 4 is a flowchart showing an example of the internal processing of the occupancy rate calculation unit 123.

[0061] In STEP 401, the passenger occupancy rate calculation unit 123 uses the door opening / closing information 161 (output of the door opening / closing information management unit 125) to determine whether it is time to calculate the passenger occupancy rate. The passenger occupancy rate calculation timing is defined as the time from when passenger boarding and alighting at one station is completed until when boarding and alighting at the next station begins. Therefore, if the door opening / closing information 161 changes from "door open" to "door closed" at the next station, and the door opening / closing information 161 changes from "door closed" to "door open", the result of the determination in STEP 401 is YES. Here, station information is recognized by referring to the route information 162. If the determination result is YES, the process proceeds to STEP 402; otherwise, the process proceeds to STEP 406. In STEP 406, the passenger occupancy rate calculation unit 123 saves the passenger occupancy rate calculation result 167 as a saved value.

[0062] Regarding the timing of the above-mentioned passenger occupancy rate calculation, it is also possible to set the start time after a predetermined period of time (e.g., 10 seconds) has elapsed since the doors closed. This method would reduce the influence of fluctuations in load measurement results due to passenger movement immediately after the doors close, enabling more stable passenger occupancy rate estimation.

[0063] In STEP 402, the occupancy rate calculation unit 123 transmits a request for the number of passengers in each vehicle 165 (input to the unit estimating the number of passengers in each vehicle 122). The presence of a request for the number of passengers in each vehicle 165 means that the occupancy rate calculation unit 123 is requesting the unit estimating the number of passengers in each vehicle 122 to provide the estimated number of passengers in each vehicle 166.

[0064] In STEP 403, the occupancy rate calculation unit 123 determines whether it has received the estimated number of passengers 166 for each vehicle (output of the vehicle passenger occupancy estimation unit 122). If the vehicle passenger occupancy estimation unit 122 is unable to generate the estimated number of passengers 166 for each vehicle and the result is invalid, the determination result is NO. If the determination result is YES, proceed to STEP 404; otherwise, proceed to STEP 405.

[0065] In STEP 404, the passenger occupancy rate calculation unit 123 calculates the passenger occupancy rate for each vehicle by dividing the estimated number of passengers in each vehicle by the capacity of each vehicle (it is common to multiply by 100 to express it as a percentage).

[0066] In STEP 405, the occupancy rate calculation unit 123 sets the occupancy rate for each vehicle to an invalid value. This means that the occupancy rate cannot be calculated because the number of passengers in each vehicle has not been properly obtained.

[0067] In STEP 406, the occupancy rate calculation unit 123 saves the occupancy rate of each vehicle as a saved value. If a problem occurs in the occupancy rate calculation in STEP 401 to 405 above, the saved value may be output as the occupancy rate. The selection of the saved value can be set according to the target conditions. For example, regarding the target operating date and time, train number, and distance between stations, it is possible to select the occupancy rate calculation result 167 from the saved value at an occupancy rate calculation timing that goes back a predetermined period (1 week, 1 year, etc.) while keeping the train number and distance between stations the same, and use that as the occupancy rate.

[0068] In STEP 407, the passenger occupancy rate calculation unit 123 outputs the passenger occupancy rate values ​​for each vehicle set in STEP 404, STEP 405, and STEP 406 as the passenger occupancy rate calculation result 167 to the passenger occupancy rate storage unit 128. It is also possible to add information to the passenger occupancy rate calculation result 167 indicating whether or not regression estimation was used. This information is useful for judging the reliability of the information and for analysis.

[0069] (D) Inside the ticket gate information aggregation unit 110 Next, referring to Figure 1, the processing units constituting the ticket gate acquisition information aggregation unit 110 will be described. The ticket gate acquisition information aggregation unit 110 estimates the new per capita weight based on the accumulated per capita weight database using a regression calculation method with the passenger attribute information as a parameter (per capita weight regression estimation unit 113). Specifically, the ticket gate acquisition information aggregation unit 110 includes a train passenger estimation unit 111, a train-specific passenger composition management unit 112, a per capita weight regression estimation unit 113, and a per capita weight history management unit 114.

[0070] (10) Estimated number of people on train management department 111 The information input to and output to the train occupancy estimation unit 111 is described below. The inputs to the train occupancy estimation unit 111 are the estimated number of passengers on the train 151 and the train occupancy estimation request 153. The estimated number of passengers on the train 151 is obtained from the ticket gate system 100. The train occupancy estimation request 153 is obtained from the per capita weight estimation unit 121 in the occupancy rate calculation device 120.

[0071] Furthermore, the output of the train occupancy rate management unit 111 is the estimated number of passengers on the train, 154, which is input to the per capita weight estimation unit 121 in the occupancy rate calculation device 120.

[0072] The estimated number of passengers on the train 151 stores information indicating the estimated number of passengers on the train, along with the target operating date and time, train number, and information between stations. This information is stored in the estimated number of passengers on the train management unit 111. Whenever new information on the estimated number of passengers on a train for a new operating date and time, train number, and between stations is created in the ticket gate system 100, this information is sent to the estimated number of passengers on the train management unit 111 as the estimated number of passengers on the train 151.

[0073] The train passenger estimate management unit 111 searches its stored information based on the operating date and time, train number, and station information included in the received train passenger estimate request 153, and outputs the estimated number of passengers on the train that requested the train passenger estimate request 153 (the train targeted by the train passenger estimate request 153) as the train passenger estimate 154. If the search results show that no information for the corresponding train passenger estimate is found (an invalid value is stored), the train passenger estimate 154 is set to either an invalid value or an estimated number from past data matching conditions such as route, season, or time of day, and output as the train passenger estimate 154.

[0074] (11) Train-specific passenger composition management unit 112 The information input to and output to the train-specific passenger composition management unit 112 will now be described. The input to the train-specific passenger composition management unit 112 is passenger composition information 152, which is obtained from the ticket gate system 100. Furthermore, the output of the train-specific passenger composition management unit 112 is the train-specific passenger composition 155, which is input to the per capita weight regression estimation unit 113.

[0075] The passenger composition information 152 includes information on the target operating date and time, train number, and station locations, as well as composition information (passenger attribute information) regarding passengers on the train, and this information is stored in the train-specific passenger composition management unit 112. Typical examples of composition information include the male-female ratio and age composition (gender and age group), but it is not limited to these. Whenever new operating date and time, train number, and station location information is created in the ticket gate system 100, the passenger composition information 152 stores this information and the composition information and sends it to the train-specific passenger composition management unit 112.

[0076] Regarding the passenger composition for each train 155, whenever the passenger composition information 152 stored in the passenger composition management unit 112 is updated, the updated information is transmitted to the passenger composition estimation unit 113 as the passenger composition for each train 155, along with the relevant operating date and time, train number, and station information.

[0077] (12) Per capita weight regression estimation unit 113 The information input to and output to the per capita weight regression estimation unit 113 will now be explained. The inputs to the per capita weight regression estimation unit 113 are the passenger composition for each train 155, the per capita weight regression estimation request 157, and the per capita weight history 156. The passenger composition for each train 155 is obtained from the passenger composition management unit 112 within the ticket gate acquisition information aggregation unit 110. The per capita weight regression estimation request 157 is obtained from the per capita weight estimation unit 121 within the occupancy rate calculation device 120. The per capita weight history 156 is obtained from the per capita weight history management unit 114 within the ticket gate acquisition information aggregation unit 110. The per capita weight history 156 stores the per capita weight, which is information indicating the weight of one passenger linked to the passenger composition for each train 155, the date and time of operation, the train number, and information regarding the distance between stations.

[0078] Furthermore, the output of the per capita weight regression estimation unit 113 is the per capita weight regression estimation result 158, which is input to the per capita weight estimation unit 121 in the occupancy rate calculation device 120.

[0079] The per capita weight regression estimation unit 113 can also be described as a recording device that regressively calculates and records a relationship between the passenger's attribute information and the per capita weight under the passenger's configuration conditions. The per capita weight regression estimation unit 113 calculates the per capita weight from the relationship based on the passenger's attribute information of the train (per capita weight regression estimation result 158). The relationship is used in the regression calculation method to estimate the per capita weight using the passenger's attribute information as input, and is variable according to operating conditions, including the season.

[0080] Specifically, the per capita weight regression estimation unit 113 manages a relationship between passenger composition and per capita weight, and by inputting the target operating date and time, train number, and passenger composition regarding the distance between stations into this relationship, it has the function of calculating and outputting the per capita weight corresponding to the operating date and time, train number, and distance between stations. An example of the relationship is a multiple regression equation (hereinafter also referred to as the "regression equation") in which the male-female ratio for each age group is used as an explanatory variable. y=Σ(a i ·xi )+b Here, the meaning of each symbol is as follows: y: weight per person a i : Coefficient of age i x i : Percentage of men (or women) in age group i i: Represents a tens digit representing an age group (e.g., 10s, 50s), and can take values ​​from 0 to 9. b: constant

[0081] b and a of the above regression equation i This value is identified using historical accumulated data of passenger composition 155 per train and weight history per person 156. Since the coefficient may differ depending on the season, time of day, or distance between stations, it may be possible to improve accuracy by individually identifying each operating condition such as season, time of day, or distance between stations, and preparing multiple regression equations.

[0082] In the per capita weight regression estimation unit 113, the coefficient identification process for the regression equation is performed periodically (e.g., once a week). Furthermore, when a per capita weight regression estimation request 157 is received, the latest regression equation is analyzed using the target operating date and time, train number, and passenger composition (x) between stations. i The system takes the information from the above as input and outputs the obtained value y as the per capita weight regression estimation result 158.

[0083] The above regression equation is merely an example; any form of the equation is acceptable as long as a regressive estimation formula can be identified from past passenger composition and per capita weight history.

[0084] Furthermore, if the per capita weight regression estimation unit 113 fails to generate a relationship between passenger composition and per capita weight, or if the per capita weight regression estimation result 158 ​​is not calculated appropriately, an error message indicating that the data is invalid will be added to the per capita weight regression estimation result 158.

[0085] (13) Per capita weight history management unit 114 The information input to and output to the per capita weight history management unit 114 will now be explained. The input to the per capita weight history management unit 114 is the estimated per capita weight 164, which is obtained from the per capita weight estimation unit 121 in the occupancy rate calculation device 120. Furthermore, the output of the per capita weight history management unit 114 is the per capita weight history 156, which is input to the per capita weight regression estimation unit 113 within the ticket gate acquisition information aggregation unit 110.

[0086] The per capita weight estimated weight 164 received by the per capita weight history management unit 114 is the result estimated by the per capita weight estimation unit 121 in the occupancy rate calculation device 120, and includes the per capita weight linked to the target operating date and time, train number, and station information. The per capita weight history management unit 114 stores this data as history and also plays a role in providing information necessary for generating and updating the relationship formula between passenger composition and per capita weight in the per capita weight regression estimation unit 113.

[0087] (E) Ticket gate system 100 Finally, the ticket gate system 100 will be described with reference to Figure 1. The ticket gate system 100 has a ticket gate or an optical device as a means of acquiring ticket gate information. The ticket gate system 100 also acquires passenger attribute information from IC card registration information at the ticket gate or from camera images.

[0088] Specifically, the ticket gate system 100 outputs the estimated number of people on the train 151 and passenger composition information 152 to the ticket gate acquisition information aggregation unit 110. The estimated number of people on the train 151 is the result of estimating the number of people present on the train, corresponding to the target operating date and time, train number, and stations. The passenger composition information 152 is information on the composition of passengers present on the train, corresponding to the target operating date and time, train number, and stations. This information is generated when the number of people bidding or issuing tickets and the attributes of the passengers are identified during the ticket gate processing.

[0089] Examples of methods for determining the number of people include counting at automatic ticket gates and counting using cameras or laser radar, but the method is not limited to these.

[0090] Furthermore, examples of attribute identification methods include reading IC cards containing attribute information at automatic ticket gates and making determinations using camera images, but the method is not limited to these.

[0091] Furthermore, during peak hours or other congested times, the accuracy of the link between passengers passing through the ticket gates and trains may deteriorate in the ticket gate system 100, and a method of invalidating the estimated number of passengers on the train 151 is conceivable. Even in such cases, the passenger composition trend can be used to estimate the weight per person (processing of the weight per person regression estimation unit 113), so it is desirable to continue outputting the passenger composition information 152, which is the input for this process. In addition, it is desirable that the estimated number of passengers confidence score, which serves as an indicator when determining that the estimated number of passengers on the train 151 (or estimated number of passengers on the train 154) is an invalid value due to deterioration in accuracy, is also associated with the estimated number of passengers on the train 151.

[0092] (Effects / Actions) According to the present invention, the occupancy rate of the cars that make up a train can be calculated with high accuracy, regardless of environmental conditions such as season and time of day.

[0093] Furthermore, when comparing the method disclosed here with conventional methods, examples of conventional methods include methods using visual observation and methods using optical sensors. In the visual observation method, investigators observe the situation inside the train from outside, such as on a station platform, and estimate the occupancy rate of the train by comparing it with predetermined criteria. Examples of these criteria include a 35% occupancy rate if all seats are occupied, and a 230% occupancy rate if the train is so full that one cannot read a newspaper, etc. While the visual observation method is simple, it lacks objectivity, making it difficult to guarantee accuracy, and it has problems in terms of immediacy and comprehensiveness because it relies on human measurement.

[0094] Another method using optical sensors measures the number of passengers by counting the number of heads of passengers inside the vehicle using image recognition, or by detecting the passage of passengers at the vehicle doors using laser radar. However, this method has difficulty in distinguishing overlapping people from the perspective of the sensor, and its accuracy tends to deteriorate in crowded conditions. Furthermore, processing sensing from multiple angles to improve accuracy requires increasing the types and number of sensors, which leads to increased costs.

[0095] The method disclosed herein accurately estimates the weight per occupant based on ticket gate data and reflects this in the calculation of the vehicle's occupancy rate. The ticket gate data contains real-time and comprehensive information about passengers and can be obtained from a standard ticket gate system, thus minimizing cost increases. As a result, the method disclosed herein can resolve the problems of conventional methods for calculating vehicle occupancy rates based on vehicle load, and further improve the accuracy of the vehicle occupancy rate calculation.

[0096] Although embodiments of the present invention have been described above, the present invention is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of the present invention.

[0097] The following describes, but is not limited to, embodiments that may constitute the present invention. (Aspect 1) A vehicle occupancy rate calculation system that records the vehicle occupancy rate of the vehicles that make up a train, A ground server that receives ticket gate information acquired by the ticket gate system, The vehicle is equipped with a vehicle information control device that is mounted on the vehicle and has vehicle load information for each vehicle constituting the train, and is capable of communicating with the ground server, The ground server transmits the train passenger count information estimated from the ticket gate information to the vehicle information control device. The vehicle information control device estimates the per capita weight of passengers on the train based on the received train passenger count information and the train load information calculated from the vehicle load information of each vehicle constituting the train. Based on the above-mentioned per capita weight and the above-mentioned vehicle load information, the vehicle occupancy rate of the vehicles constituting the train is calculated. A vehicle occupancy rate calculation system characterized by the following features. (Aspect 2) A vehicle occupancy rate calculation system according to Embodiment 1, The ground server has a per capita weight database which stores per capita weight information including the passenger attribute information included in the ticket gate information and the per capita weight, If the indicator representing the accuracy of the train passenger count information falls below a predetermined threshold, the ground server estimates a new per capita weight based on the per capita weight database. The vehicle information control device calculates the vehicle occupancy rate of the vehicles constituting the train based on the new per capita weight and vehicle load information received from the ground server. A vehicle occupancy rate calculation system characterized by the following features. (Aspect 3) In the vehicle occupancy rate calculation system described in Embodiment 1 or Embodiment 2, The aforementioned passenger attribute information includes gender and age, The ground server estimates the new per capita weight based on the accumulated per capita weight database using a regressive calculation method that uses the passenger's attribute information as a parameter. A vehicle occupancy rate calculation system characterized by the following features. (Aspect 4) In the vehicle occupancy rate calculation system described in any one of embodiments 1 to 3, The relational expression used in the regression calculation method to estimate the per capita weight using the passenger attribute information as input is variable depending on operating conditions, including the season. A vehicle occupancy rate calculation system characterized by the following features. (Appendix 5) A vehicle occupancy rate calculation system that records the vehicle occupancy rate of the vehicles that make up a train, A ground server that receives ticket gate information acquired by the ticket gate system, The vehicle is equipped with a vehicle information control device that is mounted on the vehicle and has vehicle load information for each vehicle constituting the train, and is capable of communicating with the ground server, The ground server has a per capita weight database which stores per capita weight information, including the attribute information of the train passengers included in the ticket gate information and the per capita weight. Based on the aforementioned per capita weight database, the per capita weight is estimated. The vehicle information control device calculates the vehicle occupancy rate of the vehicles constituting the train based on the per capita weight and vehicle load information received from the ground server. A vehicle occupancy rate calculation system characterized by the following features. (Aspect 6) In the vehicle occupancy rate calculation system described in Embodiment 5, The aforementioned passenger attribute information includes gender and age, The ground server estimates the per capita weight based on the accumulated per capita weight database using a regressive calculation method that uses the passenger's attribute information as a parameter. A vehicle occupancy rate calculation system characterized by the following features. (Aspect 7) A ground server that receives ticket gate information acquired by the ticket gate system, The ground server has a per capita weight database which stores per capita weight information, including the attribute information of train passengers included in the ticket gate information and the per capita weight of each train passenger. Based on the aforementioned per capita weight database, the per capita weight is estimated. The aforementioned weight per person is transmitted to a vehicle information control device that can communicate with the ground server. A ground server characterized by the following features. (Pattern 8) A vehicle information control device installed in a train that makes up a train, which calculates the vehicle occupancy rate, The train has vehicle load information for each vehicle that makes up the aforementioned train, The ground server, which is capable of communicating with the aforementioned vehicle information control device, receives information on the number of passengers on the train estimated from the ticket gate information acquired by the ticket gate system. Based on the train passenger count information and the train passenger load information calculated from the passenger load information of each car constituting the train, the per capita weight of the passengers on the train is estimated. Based on the per capita weight and the vehicle passenger load information of the vehicle, the vehicle occupancy rate of the vehicles constituting the train is calculated. A vehicle information control device characterized by the following features. (Aspect 9) A vehicle information control device according to embodiment 8, If the indicator representing the accuracy of the train passenger count information falls below a predetermined threshold, a new estimated passenger weight is received from a ground server having a per capita weight database that stores per capita weight information including the passenger attribute information included in the ticket gate information and the per capita weight. Based on the new per capita weight and the vehicle passenger load information of the vehicle, the vehicle occupancy rate of the vehicles constituting the train is calculated. A vehicle information control device characterized by the following features. (Aspect 10) A method for calculating the passenger occupancy rate of the cars that make up a train, Based on the estimated number of passengers on the train estimated from the ticket gate information acquired by the ticket gate system, and the passenger load information for the train, the per capita weight of the passengers on the train is calculated. Based on the above per capita weight and the passenger load information for each vehicle constituting the train, the vehicle occupancy rate of the vehicles constituting the train is calculated. A method for calculating vehicle occupancy rates characterized by the following features. (Aspect 11) A method for calculating the passenger occupancy rate of the cars that make up a train, Based on a per capita weight database that stores passenger attribute information included in the ticket gate information acquired by the ticket gate system, and per capita weight information including per capita weight, the per capita weight is estimated. Based on the per capita weight and vehicle load information, the vehicle occupancy rate of the vehicles constituting the train is calculated. A method for calculating vehicle occupancy rates characterized by the following features. (Aspect 12) A vehicle occupancy rate calculation system according to any one of Embodiments 1 to 6, The ticket gate system has a ticket gate or optical device as a means for acquiring the ticket gate information. A vehicle occupancy rate calculation system characterized by the following. (Aspect 13) A vehicle occupancy rate calculation system according to any one of embodiments 1 to 6 and embodiment 12, The aforementioned ticket gate system acquires the passenger's attribute information from the IC card registration information or camera images at the ticket gate. A vehicle occupancy rate calculation system characterized by the following features. [Explanation of Symbols]

[0098] 1. Vehicle Occupancy Rate Calculation System 100 ticket gate system 110 Ticket Gate Information Aggregation Unit 111 Estimated number of people on train management department 112 Passenger composition management department for each train 113 Per capita weight regression estimation unit 114. Per Capita Weight History Management Department 120 Occupancy Rate Calculation Device 121 Per capita weight estimation unit 122 Estimation section for the number of passengers in each vehicle 123 Occupancy Rate Calculation Unit 124 Vehicle load measurement unit 125 Door Opening / Closing Information Management Department 126 Route Information Management Department 127 In-vehicle display device 128 Occupancy Rate Memory Unit 129 Station premises display device 151 Estimated number of people on the train 152 Passenger composition information 153 Request for estimated number of people on train 154 Estimated number of people on the train 155 Passenger composition per train 156 Per capita weight history 157 Request for per capita weight regression estimation 158 Per capita weight regression estimation results 160 Vehicle load measurement values 161 Door Opening / Closing Information 162 Route information 163 Per capita weight requirement 164 Estimated weight per person 165 Passenger count request for each vehicle 166 Estimated number of passengers per vehicle 167. Calculation results of passenger occupancy rate 168 Train Car Occupancy Information 169 Train-specific passenger occupancy information

Claims

1. A vehicle occupancy rate calculation system that records the vehicle occupancy rate of the vehicles that make up a train, A ground server that receives ticket gate information acquired by the ticket gate system, The vehicle is equipped with a vehicle information control device that is mounted on the vehicle and has vehicle load information for each vehicle constituting the train, and is capable of communicating with the ground server, The ground server transmits the train passenger count information estimated from the ticket gate information to the vehicle information control device. The vehicle information control device estimates the per capita weight of passengers on the train based on the received train passenger count information and the train load information calculated from the vehicle load information of each vehicle constituting the train. Based on the above-mentioned per capita weight and the above-mentioned vehicle load information, the vehicle occupancy rate of the vehicles constituting the train is calculated. A vehicle occupancy rate calculation system characterized by the following features.

2. A vehicle occupancy rate calculation system according to claim 1, The ground server has a per capita weight database which stores per capita weight information, including the passenger attribute information included in the ticket gate information and the per capita weight. If the indicator representing the accuracy of the train passenger count information falls below a predetermined threshold, the ground server estimates a new per capita weight based on the per capita weight database. The vehicle information control device calculates the vehicle occupancy rate of the vehicles constituting the train based on the new per capita weight and vehicle load information received from the ground server. A vehicle occupancy rate calculation system characterized by the following features.

3. In the vehicle occupancy rate calculation system according to claim 2, The aforementioned passenger attribute information includes gender and age, The ground server estimates the new per capita weight based on the accumulated per capita weight database using a regressive calculation method that uses the passenger's attribute information as a parameter. A vehicle occupancy rate calculation system characterized by the following features.

4. In the vehicle occupancy rate calculation system according to claim 3, The relational expression used in the regression calculation method to estimate the per capita weight using the passenger attribute information as input is variable depending on operating conditions, including the season. A vehicle occupancy rate calculation system characterized by the following features.

5. A vehicle occupancy rate calculation system that records the vehicle occupancy rate of the vehicles that make up a train, A ground server that receives ticket gate information acquired by the ticket gate system, The vehicle is equipped with a vehicle information control device that is mounted on the vehicle and has vehicle load information for each vehicle constituting the train, and is capable of communicating with the ground server, The ground server has a per capita weight database which stores per capita weight information, including the attribute information of the train passengers included in the ticket gate information and the per capita weight. Based on the aforementioned per capita weight database, the per capita weight is estimated. The vehicle information control device calculates the vehicle occupancy rate of the vehicles constituting the train based on the per capita weight and vehicle load information received from the ground server. A vehicle occupancy rate calculation system characterized by the following features.

6. In the vehicle occupancy rate calculation system according to claim 5, The aforementioned passenger attribute information includes gender and age, The ground server estimates the per capita weight based on the accumulated per capita weight database using a regressive calculation method that uses the passenger's attribute information as a parameter. A vehicle occupancy rate calculation system characterized by the following features.

7. A ground server that receives ticket gate information acquired by the ticket gate system, The ground server has a per capita weight database which stores per capita weight information, including the attribute information of train passengers included in the ticket gate information and the per capita weight of each train passenger. Based on the aforementioned per capita weight database, the per capita weight is estimated. The aforementioned weight per person is transmitted to a vehicle information control device that can communicate with the ground server. A ground server characterized by the following features.

8. A vehicle information control device installed in a train that makes up a train, which calculates the vehicle occupancy rate, The train has vehicle load information for each vehicle that makes up the aforementioned train, The ground server, which is capable of communicating with the aforementioned vehicle information control device, receives information on the number of passengers on the train estimated from the ticket gate information acquired by the ticket gate system. Based on the train passenger count information and the train passenger load information calculated from the passenger load information of each car constituting the train, the per capita weight of the passengers on the train is estimated. Based on the per capita weight and the vehicle passenger load information of the vehicle, the vehicle occupancy rate of the vehicles constituting the train is calculated. A vehicle information control device characterized by the following features.

9. A vehicle information control device according to claim 8, If the indicator representing the accuracy of the train passenger count information falls below a predetermined threshold, a new estimated passenger weight is received from a ground server having a per capita weight database that stores per capita weight information including the passenger attribute information included in the ticket gate information and the per capita weight. Based on the new per capita weight and the vehicle passenger load information of the vehicle, the vehicle occupancy rate of the vehicles constituting the train is calculated. A vehicle information control device characterized by the following features.

10. A method for calculating the passenger occupancy rate of the cars that make up a train, Based on the estimated number of passengers on the train estimated from the ticket gate information acquired by the ticket gate system, and the passenger load information of the train, the per capita weight of the passengers on the train is calculated. Based on the above per capita weight and the passenger load information for each vehicle constituting the train, the vehicle occupancy rate of the vehicles constituting the train is calculated. A method for calculating vehicle occupancy rates characterized by the following features.

11. A method for calculating the passenger occupancy rate of the cars that make up a train, Based on a per capita weight database that stores passenger attribute information included in the ticket gate information acquired by the ticket gate system, and per capita weight information including per capita weight, the per capita weight is estimated. Based on the per capita weight and vehicle load information, the vehicle occupancy rate of the vehicles constituting the train is calculated. A method for calculating vehicle occupancy rates characterized by the following features.

12. A vehicle occupancy rate calculation system according to claim 1, The ticket gate system has a ticket gate or optical device as a means for acquiring the ticket gate information. A vehicle occupancy rate calculation system characterized by the following.

13. A vehicle occupancy rate calculation system according to claim 1, The aforementioned ticket gate system acquires the passenger's attribute information from the IC card registration information or camera images at the ticket gate. A vehicle occupancy rate calculation system characterized by the following features.

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