JP2012174025A - Vehicle congestion rate prediction system and method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a vehicle congestion rate prediction system for predicting a congestion rate of each vehicle of a train for each unit time of each station.SOLUTION: The vehicle congestion rate prediction system includes a congestion rate prediction device which can be connected through a communication line with a plurality of automatic ticket gates installed in respective stations of respective routes and a user terminal. The link of a ticket gate ID and a vehicle number and the number of times of arrival and departure of trains at the respective stations are stored in a database beforehand. When passing through the automatic ticket gate, entry/exit information is acquired and registered, whether respective passengers go up or down is determined on the basis of the entry/exit information in a predetermined period, and the number of entry/exit persons of an up-train and the number of entry/exit persons of a down-train for each ticket gate ID and in each unit time are counted. The number of the entry/exit persons of the up-train and the down-train for an extracted date are respectively averaged to calculate the average number of the entry/exit persons of the up-train and the down-train. On the basis of the information, the respective number of boarding vehicles of the up-train and the down-train in each unit time at the respective stations are calculated, the number of boarding vehicles is divided by a capacity number, and the congestion rate of the respective vehicles are calculated and displayed.

Description

  The present invention relates to a system and method for predicting and providing vehicle congestion status to train users, and more particularly to a vehicle congestion rate prediction system and method for predicting and providing congestion status for each vehicle on a specific route. It is.

  The congestion of trains when commuting to school is a social problem. The congestion situation of each train vehicle in each day of the week varies depending on the passenger's destination station, weather, vehicle troubles, accidents, events at landmarks near the destination, companies and schools near the destination. Currently. In addition, when using a route that is not normally used, even if it is a time zone other than the time of commuting to school, there is a possibility that it may ride on a congested vehicle due to the above-mentioned influence, which may cause inconvenience. If the passenger using the train can know the congestion status of each vehicle in advance, the passenger will get on a relatively free vehicle, and as a result, congestion can be expected.

  On the other hand, as a method for predicting a vehicle congestion rate and notifying a passenger, Patent Document 1 proposes a system in which a human body detection infrared sensor is arranged in the center of a vehicle to predict the congestion rate. Patent Document 2 proposes a system that predicts the congestion rate of a vehicle by installing a pressure sensor or camera installed in the vehicle as a congestion sensor.

  However, even if the congestion rate of each vehicle can be predicted by the above method, it is necessary to newly install a large facility. For this reason, there was a problem that enormous years and equipment costs were required for initial investment. In addition, in the case of the above method, it is possible to provide real-time vehicle congestion at each station, but it is necessary to predict and provide in advance how crowded the train you are riding at the next station. There was also a problem that it was impossible.

JP 2002-193102 A JP 2007-249705 A JP 2007-249277 A

By the way, in the patent document 3, in the vehicle congestion prediction, a server that obtains the getting-off station information from the regular period of the user who has passed the automatic ticket gate at a certain time, and calculates the number of passengers getting off at each getting-off station in real time; It describes a system for predicting the congestion rate from a storage server that stores statistical data of the boarding ratio at which a user having a boarding ticket specifying the getting-off station gets into each vehicle to the getting-off station. However, in the above system, at the time of statistics acquisition, statistics are acquired only from the information of the getting-off station in the ticket and the linkage information of the vehicle to be boarded, and the congestion rate is predicted. It is ambiguous whether it predicts. Therefore, there is a great possibility that the congestion rate prediction in Patent Document 3 will be significantly different.
The purpose of the present invention is to accurately estimate the congestion rate of each vehicle at each station on a specific route by using a statistical method, not only for the prediction at that time, but also for any unit time of each station on each route, The purpose is to provide a system provided to users.

The vehicle congestion rate prediction system according to the present invention has a congestion rate prediction device that can be connected to a plurality of automatic ticket gates installed at each station of each route and a user terminal via a communication line, and each unit time of each station. Is a vehicle congestion rate prediction system for predicting the congestion rate for each train vehicle, wherein the congestion rate prediction device comprises:
Means for previously storing in the database the association between the ticket gate ID of each automatic ticket gate at each station and the vehicle number of each vehicle on the train;
Means for storing in advance in the database the number of departures and arrivals of each of the up and down trains at each station for each unit time;
Means for obtaining entrance / exit information including a station name, a ticket gate ID, a transit time, an entrance / exit flag, and commuter pass information when each passenger passes the automatic ticket gate, and registering it in a database;
Based on all the entrance / exit information registered in a predetermined period, it is judged whether each passenger is an passenger of an up train or a down train. Means for counting the number of visitors and the number of people leaving and the number of people entering and leaving the descending train, respectively;
The date corresponding to the predetermined condition is extracted from the predetermined period, and the number of visitors and exits of the ascending train and the number of visitors and exits of the descending train for the extracted dates are averaged, respectively. Means for calculating the average number of visitors on the upper train and the average number of visitors leaving on a unit time basis, and the average number of visitors on the lower train and average number of people leaving the train, respectively,
For each unit time of each station, the average number of visitors and exits of the ascending train, the average number of visitors and exits of the descending train, the linkage between the ticket gate ID and the vehicle number, and the stations Based on the number of departures and arrivals, means for calculating the number of vehicles boarding for each vehicle of the up train and the down train for each unit time of each station;
Means for calculating a congestion rate for each vehicle by dividing the number of vehicles on board by the capacity of one vehicle;
And a means for displaying a congestion rate for each vehicle in response to an access from the user terminal.

In the above system, the means for calculating the number of vehicle rides adds the increase / decrease number of the unit time of the station to the base ride number of the unit time of the target station. Calculate the number,
The base ride number of the unit time of the station is obtained from the number of vehicle rides of the unit time at the last stop station when there is no carry-over car at the unit time, while when there is a carry-over car at the unit time , By calculating the average by weighting the number of vehicles carried forward and the number of vehicles not carried forward with respect to the number of vehicles boarding in the unit time immediately preceding the stop station and the number of vehicles boarding in the unit time of the immediately preceding stop station, and ,
The increase / decrease number of the unit time of the station is the sum of the average number of visitors of the up train or down train for all the ticket IDs linked to the vehicle number, and the exit of the up train or down train It is preferable to obtain by subtracting the sum of the average number of persons and dividing by the number of arrivals and departures of the up train or down train of the unit time.

  In the above system, the congestion rate prediction device further associates a date, weather, and a holiday flag indicating whether it is a weekday or a holiday in order to extract a date corresponding to a predetermined condition from the predetermined period. It is preferable to include means for registering in the database and means for extracting a date corresponding to a predetermined condition based on one or both of the registered weather and holiday flag.

  In the above system, when the event occurs, the congestion rate prediction device further includes a means for registering the station name, the occurrence time, the end time, and the event description in association with each other in the database, and the unit time when the event occurs. Congestion at the time of the event by dividing the average number of visitors and exits of the up train and the average number of visitors and exit of the down train by each of those when no event has occurred It is preferable to provide a means for calculating the rate ratio.

  In the above system, the ticket gate ID and the vehicle number are linked to the vehicle number of the closest vehicle when the vehicle closest to the automatic ticket gate can be specified, and the vehicle closest to the automatic ticket gate is When it cannot be specified, it is preferable that the passengers passing through the automatic ticket gate are linked so as to be evenly distributed to the vehicles.

  In the above system, the means for calculating the number of vehicle rides calculates the number of vehicle rides for each of the ordinary train and the express train for the relevant unit time of the target station, and calculates each vehicle of the calculated ordinary train and express train. It is preferable to obtain the number of vehicles in the unit time by calculating the average by weighting the number of departures and arrivals of the ordinary train and the number of arrivals and departures of the express train in the unit time.

  In the said system, when displaying the said congestion rate with respect to the said user terminal, it is suitable to make an upper limit into 300%.

The vehicle congestion rate prediction method according to the present invention uses a congestion rate prediction device that can be connected via a communication line to a plurality of automatic ticket gates and user terminals installed at each station of each route, and for each unit time of each station. A vehicle congestion rate prediction method for predicting a congestion rate for each train vehicle, wherein the congestion rate prediction device comprises:
Storing in advance in the database the association between the ticket gate ID of each automatic ticket gate at each station and the vehicle number of each vehicle on the train;
Storing the number of departures and arrivals of each of the up and down trains at each station in advance in a database for each unit time;
Acquiring entrance / exit information including the station name, ticket gate ID, transit time, entrance / exit flag and commuter pass information when each passenger passes the automatic ticket gate, and registering it in the database;
Based on all the entrance / exit information registered in a predetermined period, it is judged whether each passenger is an passenger of an up train or a down train. Counting the number of visitors and the number of people leaving and the number of people entering and leaving the descending train, respectively,
The date corresponding to the predetermined condition is extracted from the predetermined period, and the number of visitors and exits of the ascending train and the number of visitors and exits of the descending train for the extracted dates are averaged, respectively. A step of calculating the average number of visitors on the upper train and the average number of visitors leaving on a unit time basis, and the average number of visitors on the downstream train and average number of people leaving the train, respectively,
For each unit time of each station, the average number of visitors and exits of the ascending train, the average number of visitors and exits of the descending train, the linkage between the ticket gate ID and the vehicle number, and the stations Calculating the number of vehicles boarding for each vehicle of the up train and the down train for each unit time of each station based on the number of departures and arrivals of
Calculating the congestion rate for each vehicle by dividing the number of vehicles on board by the capacity of one vehicle;
And a step of displaying a congestion rate for each vehicle in response to access from the user terminal.

The vehicle congestion prediction system of the present invention has the following effects.
(1) Using statistical data calculated in advance from the information on the number of entrances and exits per unit time obtained from the automatic ticket gates at each station, and depending on events, weather and vehicle accidents at landmarks near each station Using the information on the number of exits, the congestion rate of each vehicle is predicted. For this reason, since it is not necessary to newly install a sensor etc. in a platform of a station or each vehicle, it becomes possible to implement | achieve a vehicle congestion rate prediction system cheaper than the conventional method.

(2) In the congestion rate information calculated in real time according to Patent Document 3, the prediction basis for the prediction of which vehicle the user gets on is ambiguous, but the prediction basis of the present invention includes: Which vehicle the ticket gates of each station are linked to, the number of train arrivals and departures at each station per unit time, the capacity when the congestion rate of vehicles used on each route is 100%, the presence or absence of events near each station on the predicted day, Since the weather of the day and the accident information of the day are acquired as parameters, more accurate congestion rate prediction can be provided.

(3) The vehicle congestion rate prediction result in the present invention can be viewed by the user by transmitting it to a client terminal such as a portable terminal of a user or a notebook computer via a communication line such as the Internet. In addition, since statistical data of all stations that pass on the route to be boarded is calculated in advance, it is possible to predict the congestion status at all stations that pass, and it is possible to select less crowded vehicles and train congestion This has the effect of mitigating In addition, by providing the time zone and vehicle information, landmarks around the station, and event information that are predicted to be crowded at each station, the congestion rate can be provided to the user more easily.

1 is a system configuration diagram showing an embodiment of the present invention. It is a block diagram of the data stored in station information totalization DB. It is a block diagram of the data stored in station information totalization DB. It is a block diagram of the data stored in information management DB. It is a block diagram of the data stored in information management DB. It is a block diagram of the data stored in information management DB. It is a block diagram of the data stored in information management DB. It is a block diagram of the data stored in information management DB. It is a flowchart which shows the data update process of a station boarding number management DB. It is a block diagram of the data stored in the station boarding number management DB. It is a block diagram of the data stored in information management DB. It is a block diagram of the data stored in information management DB. It is a flowchart which shows the data update process of congestion rate management DB. It is a flowchart which shows a vehicle boarding number calculation process. It is a block diagram of the data stored in congestion rate management DB. It is a flowchart which shows the ratio calculation process at the time of an event. It is a block diagram of the data stored in congestion rate management DB. It is a block diagram of the data stored in information management DB. It is a display example of a screen when a user uses the congestion rate prediction system. It is an example of a display of the information obtained by the user using the congestion rate prediction system.

  FIG. 1 is a system configuration diagram showing an embodiment of a vehicle congestion rate prediction system according to the present invention. First, the outline of the configuration of the vehicle congestion rate prediction system will be described with reference to FIG.

  The vehicle congestion rate prediction system according to this embodiment includes a congestion rate prediction device 131, its input terminal 141 and display unit 142, and information transmission that is installed at each station and connected to the congestion rate prediction device 131 via an appropriate communication line. Unit 100 and terminal devices 151 to 154 connected to the congestion rate prediction device 131 via a public line network.

  The congestion rate prediction device 131 includes a calculation sub-device 111, a statistical calculation sub-device 121, a congestion rate management DB (database) 132, and an information management DB 133.

  The calculation sub-device 111 includes a calculation unit 113 and a station information totaling DB 112. The calculation unit 113 is based on the station information aggregation DB 112 updated by the information collected from the information transmission unit 100 of each station and the information management DB 133 in which the station information of each route is input from the input terminal 141 in advance. The number of visitors per unit time of each automatic ticket gate at each station (hereinafter, unit time is assumed to be 10 minutes as an example) is calculated. Based on the calculation result, the station boarding number management DB 122 of the statistical calculation sub-device 121 is updated. Data for updating the station boarding number management DB 122 is stored in the internal storage device 114.

  The statistical calculation sub-device 121 includes a statistical calculation unit 123 and a station boarding number management DB 122. The statistical calculation unit 123 is based on the information on the station boarding number management DB 122 updated by the computation sub-unit 111 and the information management DB 133 in which the ticket gate information of each station is input in advance from the input terminal 141. The prediction data of the congestion rate for each unit time is statistically calculated for each weekday, holiday, and weather. Based on the calculation result, the congestion rate prediction DB 132 is updated. Data for updating the congestion rate prediction DB 132 is stored in the internal storage device 124.

  The congestion rate management DB 132 holds congestion rate prediction data calculated by the statistical calculation sub-device 121. The information management DB 133 manages information necessary for calculation in the calculation sub-device 111 and statistical calculation in the statistical calculation sub-device 121, such as station order of each route and weather information.

  The input terminal 141 is a terminal connected to the congestion rate prediction device 131, and can view information in the information management DB 133 by the display unit 142 and update the information. The weather information is updated daily. Furthermore, it is an administrator terminal that can update the records of each DB as necessary.

  The information transmission unit 100 installed in each station includes an existing automatic ticket gate 101, an information acquisition unit 102 that acquires information related to the entrance and exit of the passenger when the passenger passes the automatic ticket gate 101, and a station clerk. Are provided with an input terminal 103 for inputting event information such as an accident on that day and surrounding events, and a display unit 104 thereof.

  The information acquisition unit 102 installed in each automatic ticket gate 101 at each station acquires information on an entrance station name, a ticket gate ID, a regular departure station name, a regular destination station name, a regular transit station name, a transit time, an entrance flag, and an exit flag. These pieces of information are transmitted to the congestion rate prediction apparatus 131 via a communication line such as the Internet. With these pieces of information, the station information tabulation DB 112 of the computation sub-device 111 is updated. In addition, event information such as accidents on the day and events near the station entered by the station staff using the input terminal 103 and the display unit 104 are also transmitted to the congestion rate prediction device 131 via a communication line such as the Internet, With these pieces of information, the station information tabulation DB 112 of the computation sub-device 111 is updated.

  The information acquisition unit 102 determines the entrance flag and the exit flag when the card reader of the automatic ticket gate 101 installed at each station reacts to the outside card reader when viewed from the station premises. When the flag is set to “1” and the inner card reader reacts when viewed from inside the station, the information acquisition unit 102 sets “1” as the exit flag.

  A user who uses the service of the vehicle congestion rate prediction system connects to the congestion rate prediction device 131 via the public line network such as the Internet from the portable terminals 151 and 152 and the personal computers 153 and 154, and the desired route and station It is also possible to view the vehicle congestion rate in the time zone.

  FIG. 2 is an example of a data configuration diagram of the entrance / exit person table 200 held in the station information totalization DB 112 of the computation sub-unit 111 of the congestion rate prediction apparatus 131 of FIG. The station information tabulation DB 112 holds the event occurrence table 300 of FIG. 3 in addition to the entrance / exit person table 200. The entrance / exit person table 200 manages entrance / exit information including commuter pass information acquired from the commuter pass when the passenger passes in the information acquisition unit 102 of the information transmitting unit 100. The information item includes an entrance station name 201, a ticket gate ID 202, a regular departure station name 203, a regular destination station name 204, a regular transit station name 205, a transit time 206, an entrance flag 207, and an exit flag 208. The entry station name, ticket gate ID, scheduled departure station name, scheduled destination station name, scheduled transit station name, transit time, entrance flag, and exit flag are registered and stored on a daily basis for each registration date.

  Here, for example, one passenger at Mizonokuchi Station has a commuter pass for the departure station name "Mizoguchi Station", the destination station name "Oimachi Station", and the transit station "Futakotamagawa Station" "January 1, 2010 at 7:01" When entering from the automatic ticket gate “1” at 23 seconds, as shown in the first record in FIG. 2, “Mizoguchi” as the entrance station name, “001” as the ticket gate ID, “Mizoguchi” as the scheduled departure station name, and the scheduled destination station name As “Oimachi”, “Futakotamagawa” as the regular transit station name, “2010-01-01 07:01:23” as the transit time, and “1” as the entrance flag. In this case, the exit flag 208 is set to “null”. In addition, the entrance / exit table 200 is held for each route, and the information of all stations on each route is managed by one table.

  FIG. 3 is an example of a data configuration diagram of the event occurrence table 300 held by the station information aggregation DB 112 of the calculation sub-device 111 of the congestion rate prediction device 131 of FIG. The event occurrence table 300 shows the time before and after the end of the event in the event that an operation is held in the vicinity of the station, the time when the operation stop or delay occurs due to personal injury or the like on each route, or when the event is held near the station. Manage the time of congestion. The information item includes a route name 301, a station name 302, an occurrence time 303, an end time 304, and an event description 305.

  Here, for example, at Futakotamagawa Station on the Oimachi Line, a train delay occurs from "January 1, 2010, 7: 0: 12" to "January 1, 2010, 7: 20: 3" due to a "personal accident". As shown in the first record in FIG. 3, the route name is “Oimachi Line”, the station name is “Futako Tamagawa”, the occurrence time is “2010-01-01 07:00:12”, and the end time is “ 2010-01-01 07:20:03 ", and" personal accident "is stored as the event description.

  If the event overlaps, for example, if a “signal trouble” occurs while the “personal accident” event occurs, the following processing is performed. When a subsequent event occurs and ends during the continuation of the previous event, the occurrence time and end time of only the previous event are stored in one record. If the subsequent event continues at the end time of the previous event and does not end, the end time of the previous event is stored in the new record as the occurrence time of the subsequent event. That is, duplicate events are not considered in the vehicle congestion rate prediction system of this specification. The event occurrence table 300 is preferably input by the station staff from the input terminal 103 when the delay is confirmed at each station on each route, and the station information aggregation DB 112 is updated.

  FIG. 4 is an example of a data configuration diagram of the weather holiday table 400 held by the information management DB 133 of the congestion rate prediction apparatus 131 of FIG. In addition to the weather holiday table 400, the information management DB 133 holds a plurality of tables (tables shown in FIGS. 5, 6, 7, 8, 11, 12, and 18 described later). The weather holiday table 400 manages dates and weather for obtaining statistics on the daily weather and the vehicle congestion rate. The information item includes a date 401, a day of the week 402, weather 403, and a holiday flag 404.

  Here, for example, if the weather of “January 1, 2010 (Monday)” is “Sunny”, the date is “2010-01-01”, the day of the week is “Month”, as shown in one record in FIG. “Sunny” is stored as the weather, and “1” is stored as the holiday flag. The weather holiday table 400 is preferably input daily by an administrator of the congestion rate prediction device 131 using the input terminal 141 and the display unit 142 connected to the congestion rate prediction device 131.

  FIG. 5 is an example of a data configuration diagram of the route station order table 500 held by the report management DB 133 of the congestion rate prediction apparatus 131. The route station order table 500 manages the station name from the first departure on each route, the station order, and the required time from the first departure station. The information item includes a route name 501, a station name 502, a station order 503, and a required time 504.

  Here, for example, when the route is “Oimachi Line”, the first record of the route station order table 500 includes “Oimachi Line” as the route name, “Mizoguchi” as the station name, “01” as the station order, and the required time. “0” is stored. The time required is “0” because Mizonokuchi Station is the first station. In the second record, the station name is “Takatsu”, the station order is “01” because the record of the previous station was “01”, and the required time from the first station is stored in minutes. If the required time is 3 minutes, “03” is stored.

  Note that the numerical values stored in the station order column are used for determining whether the train is an ascending train or a descending train. Therefore, the numbers are stored in ascending order from the first station in the order of the station going up.

  FIG. 6 is an example of a data configuration diagram of the time code table 600 held by the information management DB 133 of the congestion rate prediction apparatus 131. The time code table 600 sets the unit time interval for acquiring the congestion rate to 10 minutes, holds the time code at an interval of 10 minutes from the first departure time, and manages information indicating from what hour to what hour to what minute. . The information item includes a time code 601, a start time 602, a start minute 603, an end time 604, and an end minute 605.

  For example, when the time code “1” is “7:00 to 7:10”, the time code is “001”, the start time is “07”, the start time is “00”, and the end time is “07”. "," 10 "is stored as the end amount.

  FIG. 7 is an example of a data configuration diagram of the event ID table 700 held by the information management DB 133 of the congestion rate prediction apparatus 131. The event ID table 700 assigns and manages IDs for personal accidents occurring at each station and for events near the station. The information item includes an event ID 701 and an event description 702.

  Here, for example, when the event ID “1” is a personal accident, “01” is stored as the event ID, and “personal accident” is stored as the event description.

  FIG. 8 is an example of a data configuration diagram of the ticket gate vehicle association table 800 held by the information management DB 133 of the congestion rate prediction apparatus 131. The ticket gate linking table 800 manages information on which vehicle a passenger who has passed through an automatic ticket gate at each station gets on. The information item includes a station name 801, a ticket gate ID 802, and a vehicle number 803.

  Here, for example, if it is assumed that a passenger who passes through an automatic ticket gate having a ticket gate ID “1” at Mizonokuchi Station gets on a vehicle having a vehicle number “1”, the station name is “Mizoguchi”, the ticket gate ID is “001”, the vehicle “01” is stored as the number. The information in the ticket gate association table 800 is a value that associates which vehicle the passenger who has passed through each automatic ticket gate gets on. The basis for associating the automatic ticket gate with the vehicle is based on the premise that the vehicle is closest to the passing automatic ticket gate. In addition, when the location where the automatic ticket gate is present is biased (or when it cannot be determined only in the distance), it is determined by a calculation that evenly distributes the number of passengers boarding each vehicle from the automatic ticket gate. In this way, it is assumed that vehicles to be associated with all automatic ticket gates are determined in advance.

  4, 5, 6, 7, and 8, data is input in advance from the input terminal 141 connected to the congestion rate prediction device 131, and the weather holiday table 400 of FIG. 4 is used. Will be updated daily by the administrator.

  FIG. 9 is a flowchart showing an outline of processing by the calculation unit 113 on the calculation sub-unit 111 of the congestion rate prediction apparatus 131 of FIG. In this process, the information stored in the attendee table 200 of FIG. 2, the event occurrence table 300 of FIG. 3, the route station order table 500 of FIG. 5, the time code table 600 of FIG. 6, and the event ID table of FIG. Based on the information, the information on the number of visitors per unit time in each automatic ticket gate at each station is inserted into the station information management table 1000 of FIG.

  Step 901: The calculation unit 113 starts from the first record of the entry / exit number table 200 in FIG. 2 and enters the station name, ticket gate ID, scheduled departure station name, scheduled destination station name, scheduled transit station name, transit time, entrance flag, and exit flag. Get information about.

  Step 902: Compares whether the names of the entrance station name and the regular departure station name are the same among the information acquired in Step 901.

  Step 903: If the name of the entry station and the name of the regular departure station match in step 902, the name of the entry station is determined by referring to the route station order table 500 in FIG. And by searching for the route name of the periodic destination station name.

  Step 905: When the name of the entrance station and the name of the regular destination station are the same route in Step 903, the entrance station is determined as the departure station name, and the route name that uses (or uses) the route name is determined. Whether the passenger is an up or down train is determined by referring to the route station order table 500 in FIG. 5 based on the station order of the entrance station name and the periodic destination station name. Based on the information obtained from the above processing, set the route name, departure station name, and ticket gate ID, and count as the corresponding item in the number of ascending visitors, ascending / leaving people, descending visitors, or descending visitors Is stored in the internal storage device 114.

  Step 906: In step 903, if the entrance station name and the scheduled destination station name are not the same route, the entrance station name is determined as the departure station name, and the route name to be used (or used) is determined based on the information of the regular transit station name. To do. Information about whether the passenger is an up or down train is determined in the same manner as in step 905. Based on the information obtained from the above processing, set the route name, departure station name, and ticket gate ID, and count as the corresponding item in the number of ascending visitors, ascending / leaving people, descending visitors, or descending visitors Is stored in the internal storage device 114.

  Step 904: On the other hand, if the name of the entrance station does not match the name of the regular departure station in Step 902, the following processing is performed. Since the passenger has used the station outside the regular service area, it cannot be determined from the information in the entrance / exit table 200 in FIG. 2 which route is used (or used). Therefore, for passengers outside the regular service area, the entry station is first determined as the departure station name. Further, it is possible to determine at least whether the entry / exit is made from the entrance flag and the exit flag of the entrance / exit person table 200. For passengers on the up train or down train, if the time code corresponding to the transit time (see time code table 600 in FIG. 6) is 17:00, it is regarded as an up-passenger passenger and the time code after 17:00 If so, it is determined by considering it as a passenger on a descending train. In addition, for the route name to be used (or used), the route station order table 500 of FIG. 5 is searched based on the name of the entrance station, and the route name is determined. If two or more routes are applicable, one route is selected. The route name that hits the eye is determined. Based on the information obtained from the above processing, set the route name, departure station name, and ticket gate ID, and count as the corresponding item in the number of ascending visitors, ascending / leaving people, descending visitors, or descending visitors Is stored in the internal storage device 114.

  Step 907: Passage time information is sequentially extracted from the first record of the entrance / exit table 200 of FIG. 2, the time code of the passage time is determined with reference to the time code table 600 of FIG. To store.

  Step 908: The above processing from Step 901 to Step 907 is sequentially performed from the first record of the entrance / exit person table 200 in FIG. 2, and it is determined whether or not the time code is advanced. The processing from step 901 to step 907 is repeated until the time code is advanced, and the number of inbound and outbound trains per unit time is counted for each station. When the time code is advanced, it moves to the next process.

  Step 909: If the time is advanced in the process in Step 908, the route name, departure station name, ticket gate ID, time code before the advance, the number of visitors to the upstream, the number of visitors to the exit, the number of visitors to the destination, The number of people leaving the station is inserted into the station information management table 1000 in FIG.

  Step 910: It is determined whether or not the processing for the entrance / exit person table 200 in FIG. If the final record has not been reached yet, the process is executed again from step 901, and if the final record has been reached, the process of step 911 is executed.

  Step 911: Information of the name of the route where the event has occurred, the name of the station, the occurrence time, and the end time are stored in the internal storage device 114 from the event occurrence table 300 of FIG.

  Step 912: Refers to the time code table 600 in FIG. 6 and the event ID table 700 in FIG. 7 based on the information set in step 911, and ends from the event ID and occurrence of the event that occurred in each route and each station. Get the time code of the time until.

  Step 913: The acquired event ID and time code are inserted into the station information management table of FIG.

FIG. 10 is an example of a data configuration diagram of the station information management table 1000 held in the station boarding number management DB 122 of the statistical calculation sub-device 121 of FIG. The station information management table 1000 stores data calculated by the calculation unit 113 of the calculation sub-device 111 of FIG. 1 according to the processing flow of FIG. The information in the station information management table 1000 is basic data for obtaining the congestion rate prediction. The information items include a route name 1001, a departure station name 1002, a time code 1003, a ticket gate ID 1004, an ascending attendance number 1005, a descending entrance number 1006, an ascending exit number 1007, a descending exit number 1008, and an event ID 1009.
Here, “up” of the number of ascending visitors and the number of ascending and leaving people means that the passenger is an ascending train, and “down” as the number of descending visitors and the number of descending and exiting people is that it is a passenger of the descending train. means. In addition, the visitors and the exits may be collectively expressed as “entrance / exit”.

  As a data structure, a primary key is composed of four columns of a route name 1001, a departure station name 1002, a time code 1003, and a ticket gate ID 1004, and data is stored in ascending order of the time code and the ticket gate ID. For example, in the ticket gate ID “1” of “Mioiguchi Station” on the “Oimachi Line”, if the number of visitors with the time code “1” is “98” with only the number of visitors going up and down, "Imachi Line", "Mizoguchi" as the departure station name, "001" as the time code, "001" as the ticket gate ID, "98" as the number of ascending visitors, "Number of descending visitors, ascending exits and descending exits" “0” and “null” are stored as the event ID. For example, if the number of automatic ticket gates at Mizonokuchi Station is 5, the above information is stored in ascending order up to the ticket gate ID “005”, and the time code is “002” in the next line of the ticket gate ID “005”. On the other hand, the data structure stores information from ticket IDs “001” to “005”. That is, it is configured to manage the number of people entering and leaving the up and down trains of all ticket gate IDs in ascending order of time code at each station. The station information management table 1000 is stored so as to be composed of sub-tables for each route, and the sub-table for each route is stored and managed so as to be composed of sub-tables for each registered date on a daily basis.

  FIG. 11 is an example of a data configuration diagram of the capacity table 1100 held in the information management DB 133 of the congestion rate prediction apparatus 131 of FIG. The capacity table 1100 of FIG. 11 manages the capacity of one vehicle on the train used on each route. The information item includes a route name 1101 and a capacity 1102. This number of people is the number of people who can get 100% in one vehicle on each route.

  Here, for example, when the capacity of one vehicle used on the “Oimachi Line” is “160”, “Oimachi Line” is stored as the route name, and “160” is stored as the capacity. In addition, it is assumed that the capacity table 1100 of FIG. 11 also inserts data from the input terminal 141 connected to the congestion rate prediction device 131.

  FIG. 12 is an example of a data configuration diagram of the arrival / departure number table 1200 held by the information management DB 133 of the congestion rate prediction apparatus 131 of FIG. The departure / arrival frequency table 1200 in FIG. 12 manages information on how many times the up and down trains arrive and depart per unit time at each station on each route, distinguishing regular trains and express trains. The information items include a route name 1201, a station name 1202, a time code 1203, an up and down ordinary train departure and arrival number 1204, a down and ordinary train departure and arrival number 1205, an up and down express train arrival and departure number 1206, and a down express train arrival and departure number 1207.

  Here, for example, if the number of trains that arrive and depart in the unit time corresponding to the time code “1” of “Mizonoguchi Station” on the “Oimachi Line” is only one regular train, “Oimachi Line” "Mizoguchi" as the station name, "001" as the time code, "01" as the number of arrivals and departures from the ordinary train, "00" as the number of arrivals and departures from the ordinary train, "00" as the number of arrivals and departures from the express train, " 00 ”is stored. It should be noted that the arrival / departure frequency table 1200 of FIG. 12 is also subjected to data insertion from the input terminal 141 connected to the congestion rate prediction device 131.

  FIG. 13 is a flowchart showing an outline of processing by the statistical calculation unit 123 on the statistical calculation sub-device 121 of the congestion rate prediction apparatus 131 of FIG. In this process, all the dates of the target date corresponding to the predetermined condition are extracted from the weather holiday table 400 of FIG. 4, and the information stored in the station information management table 1000 of FIG. 10 corresponding to all the extracted dates is stored. Based on the above, with reference to the ticket gate vehicle linking table 800 in FIG. 8, the capacity table 1100 in FIG. 11 and the number of arrivals and departures table 1200 in FIG. 12, statistical calculation is performed for each station on each route, Get congestion rate information. Then, the acquired congestion rate information is inserted into a congestion rate management table 1500 shown in FIG.

  The predetermined conditions for extracting the date of the statistical calculation target are, for example, “a weekday and a sunny and cloudy day (hereinafter referred to as“ good weather weekday ”)”, “a weekday and a rainy day” Days (hereinafter referred to as “bad weather weekdays”), “holidays and sunny and cloudy days (hereinafter referred to as“ good weather holidays ”)”, “holidays and rainy days (hereinafter referred to as“ bad weather holidays ”) "Holidays"). Note that the congestion rate management table 1500 shown in FIG. 15 to be described later includes a plurality of tables created for each condition on the target day.

  Step 1301: The statistical calculation unit 123 extracts a date corresponding to a good weather weekday from the weather holiday table 400 of FIG. 4 and stores it in the internal storage device 124. The predetermined period to be extracted is, for example, the past year.

  Step 1302: In the station information management table 1000 of FIG. 10, for all the tables corresponding to the date acquired in Step 1301, the number of inbound visitors and the number of outbound visitors for each route, each station, each time code and each ticket gate ID Calculate the average number of visitors, ascending and leaving, and descending and exiting. These are referred to as “average number of visitors going up”, “average number of visitors going down”, “average number of people going up and down”, and “average number of people going down”. These average numbers are stored in the internal storage device 124. However, in this calculation, the record of the time code in which information is inserted into the event ID is excluded.

Step 1303: Based on the average number of people entering and leaving the up and down trains acquired in step 1302, approximately how many passengers get on each of the up and down trains on each route, each station, and each time code. That is, the “number of vehicles on board” is calculated.
Here, “the number of vehicles boarding” means that when a line, station, time code, ascending train or descending train, and a vehicle number are specified, boarding / exiting at the station and leaving the station It means the number of passengers on the vehicle. Further, the number of passengers who have boarded the vehicle before arriving at the station and boarding / exiting is referred to as “base boarding number”. This base boarding number corresponds to the number of boarding of the vehicle at the previous departure / arrival station. Further, the difference between the base boarding number and the vehicle boarding number is referred to as “increase / decrease number”. The increase / decrease number of passengers is the number of passengers that increase / decrease in the vehicle by getting on and off at the station, and becomes a positive value when it increases and a negative value when it decreases. Therefore, in general, the following equation is established for each vehicle of the up train or the down train in each route, each station, and each time code.
(Formula 1) [Vehicle number] = [Base number] + [Increase / decrease number]
Details of this step will be described later with reference to the flowchart of FIG. The route name, station name, time code, up flag, down flag, vehicle number, and vehicle boarding number are acquired by the processing of this step and stored in the internal storage device 124.

Next, in step 1304, the congestion rate in each route, each station, and each time code is calculated based on the number of vehicles on board acquired in step 1303 and the number of people stored in the number of people table 1100 in FIG. The calculation formula is
(Formula 2) [Congestion rate] = [Number of vehicles on board] ÷ [Number of seats] × 100
And The calculation result is rounded off to the first decimal place and stored in the internal storage device 124.

  In step 1305, the information acquired in steps 1303 and 1304, including the number of vehicles on a good weather weekday and the congestion rate, is inserted into the fine weather weekday table of the congestion rate management table 1500 shown in FIG.

  Subsequently, in step 1306, the date corresponding to the good weather holiday is extracted from the weather holiday table 400 of FIG. 4 and stored in the internal storage device 124.

  In step 1307, by carrying out the same processing from step 1302 to step 1304 for all the tables in the station information management table 1000 of FIG. 10 corresponding to the date acquired in step 1306, The number and congestion rate are calculated, route name, station name, up flag, down flag, time code, vehicle number, and congestion rate information are acquired and stored in the internal storage device 124. In step 1308, the information stored in the internal storage device 124 is inserted into a table for good weather holidays in the congestion rate management table 1500 shown in FIG.

  Subsequently, in step 1309, the date corresponding to the bad weather weekday is similarly extracted and stored in the internal storage device 124.

  In step 1310, the processing from step 1302 to step 1304 is similarly performed on all the tables in the station information management table 1000 in FIG. 10 corresponding to the date acquired in step 1309, and the number of vehicles boarded and the congestion rate on bad weather weekdays. The route name, station name, up flag, down flag, time code, vehicle number, and congestion rate information are acquired and stored in the internal storage device 124. In step 1311, the information stored in the internal storage device 124 is inserted into the bad weather weekday table of the congestion rate management table 1500 shown in FIG.

  Finally, in step 1312, the date of bad weather holiday is extracted and stored in the internal storage device 124.

  In step 1313, the processing from step 1302 to step 1304 is similarly performed on all the tables in the station information management table 1000 in FIG. 10 corresponding to the date acquired in step 1312. The rate is calculated, route name, station name, up flag, down flag, time code, vehicle number, and congestion rate information are acquired and stored in the internal storage device 124. In step 1314, the information stored in the internal storage device 124 is inserted into the bad weather holiday table of the congestion rate management table 1500 shown in FIG.

  By performing the processing of FIG. 13 described above, four tables are stored that store the congestion rate of each vehicle on good weather weekdays, bad weather weekdays, good weather holidays and bad weather holidays.

  FIG. 14 is a flowchart showing an overview of the processing for calculating the number of vehicles in step 1303 in the processing by the statistical calculation unit 123 shown in FIG. As a specific example, this processing will be described by showing a method of calculating the number of vehicles traveling on an upstream train (including ordinary trains and express trains) at the first station and the next second station.

Step 1401: The statistical calculation unit 123 first calculates the number of vehicles in each time code for the first station on each route. The average number of inbound visitors acquired in step 1302 of FIG. 13, the linkage information of ticket gate IDs and vehicle numbers in the ticket gate vehicle association table 800 of FIG. 8, and the number of arrivals and departures of the upstream train in the arrival / departure number table 1200 of FIG. Based on this information, the number of vehicles in each time code is calculated. The formula is
(Formula 3) [Number of vehicles boarding at each time code at the first station] = [Sum of average number of visitors going up for all ticket gate IDs linked to the vehicle number] / [Total number of upstream trains at each time code at the first station] Number of departures and arrivals]
And The reason for dividing by the total number of arrivals and departures of the upward train in the above equation is to obtain the number of vehicles boarding in one vehicle of one train. The total number of arrivals and departures for the upstream train is the sum of the number of arrivals and departures for the regular ordinary train and the express train (the same applies hereinafter). At the first station, the number of exits is zero and the number of base rides is zero. Therefore, the sum of the average number of inbound visitors of all ticket gate IDs linked to the vehicle number is the total number of arrivals and departures of the inbound train. The divided number is directly used as the number of vehicles boarding at each time code of the starting station. The number of vehicle rides for each time code of the starting station obtained in this way is stored in the internal storage device 124.

In the following steps 1402 to 1408, a calculation for an up-bound ordinary train is performed.
Step 1402: For the second station on each route, calculate the number of vehicles traveling on the up-bound ordinary train at the time code “1”. The number of arrivals and departures of the regular train with the time code “1” at the second station is all due to the trains that depart with the time code “1” at the first station. Therefore, the base boarding number of the time code “1” at the second station coincides with the vehicle boarding number of the time code “1” at the starting station calculated at step 1401. The number of vehicles with the time code “1” at the second station is added to the base number of times with the time code “1” at the second station by adding the increase / decrease number of the time code “1” at the second station. Calculate The formula is
(Formula 4) [Vehicle number of the up train on the second station time code “1”] = [Base train number of the up train on the second station time code “1” (Time code of the first station “1”) [Number of vehicles in the vehicle]] + [Number of inbound and outbound passengers on the second station time code "1"]
And
The increase / decrease number of passengers in the time code “1” of the second station in Equation 4 is the average number of inbound / outbound visitors acquired in step 1302 of FIG. 13 in the time code “1” of the second station, and the ticket gate vehicle string in FIG. Calculation is performed based on the association information between the ticket gate ID and the vehicle number in the attaching table 800 and the information on the number of arrivals and departures of the upward train in the departure / arrival number table 1200 of FIG. The formula is
(Formula 5) [Increase / decrease in the number of trains traveling up to the second station with the time code “1”] = {[sum of average number of visitors going up to all ticket gate IDs associated with vehicle numbers] − [strings associated with vehicle numbers] The sum of the average number of people leaving the ticket gates with all of the ticket gate IDs attached}} ÷ [total number of arrivals and departures of the upward train with the time code “1” at the second station]
And By substituting the values obtained by Equation 3 and Equation 5 into Equation 4, the number of passengers traveling on the up-bound ordinary train with the time code “1” at the second station is calculated and stored in the internal storage device 124.

  Next, in Steps 1403 to 1407, the number of vehicles traveling on the up-bound ordinary train at the time code “2” is calculated for the second station on each route.

Step 1403: In order to confirm whether there is an up-bound ordinary train that departs the first station at the time code “1” and arrives at the second station at the time code “2” (hereinafter referred to as “carried-over vehicle”). By referring to the departure / arrival number table 1200 of FIG. 12, the number of departures / arrivals of the ordinary train at the time code “1” of the first station and the second station is compared. Due to these differences, the number of cars carried over from the time code “1” of the first station to the time code “2” of the second station (this is referred to as “the number of cars carried over from the time code“ 2 ”of the second station”). ). The formula is
(Expression 6) [Number of Cars Carrying over at Time Code “2” at Second Station] = [Number of Up-and-Down Trains with Time Code “1” at First Station] − [Upstream at Time Code “1” at Second Station Number of regular trains]
And The calculated number of carry-over cars for the up-bound ordinary train is stored in the internal storage device 124.

  Step 1404: A determination process is performed to determine whether or not the number of carry-over vehicles calculated in Step 1403 is zero, that is, whether or not there is a carry-over vehicle for an up-bound ordinary train with the time code “2” at the second station.

Step 1405: If there is a carry-over vehicle in the determination in step 1404, the total number of rides for the number of carry-on vehicles of the time code “2” at the second station (this is referred to as the “total carry-on ride”) is calculated. The total number of cars carried over is the number obtained by multiplying the number of vehicles boarded by the time code “1” of the first station obtained in step 1401 by the number of cars carried by the time code “2” of the second station obtained in step 1403. The formula is
(Formula 7) [Total number of carry-overs of the time code “2” at the second station] = [Number of vehicles boarding the time code “1” at the first station] × [Number of carry-overs of the time code “2” at the second station]
And Store the calculated total number of carry-overs on the up-bound ordinary train in the internal storage device 124.

Step 1406: The number of arrivals and departures of the ordinary train with the time code “2” at the second station arrives without carrying over from the number of cars carried over at the time code “2” at the second station and the time code “2” at the first station. Since it is the sum of the number of ordinary trains going up and down, the time code “2” of the second station obtained in step 1403 is obtained from the number of departures and arrivals of the second train time code “2” in the departure / arrival table 1200 of FIG. By subtracting the number of cars carried over, the number of trains arriving without a carry-over from the first station to the second station at time code “2” (this is called “the number of cars without carry-over of the time code“ 2 ”at the second station”) )). The formula is
(Formula 8) [Number of cars without carryover of the time code “2” at the second station] = [Number of arrivals and departures of the ordinary train with the time code “2” at the second station] − [Time code “2” at the second station] Cars carried over]
And Subsequently, the number of vehicles boarding with the time code “2” of the starting station is acquired, and the total number of passengers for the number of vehicles not carried over (this is referred to as “the number of boarding without repetition”) is calculated. The formula is
(Formula 9) [Total number of cars without carryover of time code “2” at the second station] = [Number of vehicles boarding with time code “2” at the first station] × [Cars without carryover of the time code “2” at the second station] number]
It is. The calculated total number of passengers without carryover is stored in the internal storage device 124.

Step 1407: By averaging the total number of carry-overs of the second station time code “2” obtained in Steps 1405 and 1406 and the total number of rides without a carry-over, the normal increase of the time code “2” of the second station Calculate the base number of trains. In this case, the average is calculated by weighting the number of vehicles carried forward and the number of vehicles not carried over to the number of vehicles boarded at the time code “1” and the number of vehicles boarded at the time code “2” at the first station. It means to calculate. The formula is
(Formula 10) [Number of base trains on the normal train of the second station time code “2”] = {[Total number of carry-overs of the second station time code “2”] + [Time code of the second station ”2 "Total number of rides without carry-over"} ÷ [Number of times the second station time code "2" goes up and down on regular trains]
And The vehicle with the time code “2” at the second station is added to the base number of the time code “2” at the second station thus obtained by adding the increase / decrease number of the time code “2” at the second station. Calculate the number of rides. The formula is
(Formula 11) [Number of vehicles on the upstream ordinary train with the time code “2” at the second station] = [Number of base passengers on the upstream ordinary train with the time code “2” at the second station] + [Time at the second station] Increase / decrease in the number of ups and down trains with code “2”]
And
The increase / decrease number of passengers in the time code “2” of the second station in Equation 11 is the average number of inbound and outbound visitors acquired in step 1302 of FIG. 13 in the time code “2” of the second station, and the ticket gate vehicle string in FIG. Calculation is performed based on the association information between the ticket gate ID and the vehicle number in the attaching table 800 and the information on the number of arrivals and departures of the upward train in the departure / arrival number table 1200 of FIG. The formula is
(Formula 12) [Number of increase / decrease in the number of trains with the time code “2” at the second station] = {[sum of average number of visitors to the ticket IDs linked to the vehicle number] − [vehicle number The sum of the average number of people leaving and leaving the ticket IDs associated with all of the ticket IDs]} ÷ [total number of arrivals and departures of the up-train at the second station time code “2”]
And By substituting the values obtained from Equation 10 and Equation 12 into Equation 11, the number of passengers traveling on the uphill ordinary train with the time code “2” at the second station is calculated and stored in the internal storage device 124.

  Step 1407: If there is no carryover vehicle in the determination in Step 1404, the process directly moves to Step 1407 to obtain the number of vehicles boarding with the time code “2” of the first station, and this is obtained as the time code “2” of the second station. "The base ride number." The time code “2” of the second station is added to the base number of times of the time code “2” of the second station by adding the increase / decrease number of times of the time code “2” of the second station obtained in Expression 12. The number of passengers traveling on the uphill ordinary train is calculated and stored in the internal storage device 124.

  Step 1408: In order to repeat the above steps 1403 to 1407 until the final time code, it is determined whether or not it is the final time code. If it is the final time code, the process proceeds to the next step of calculating the number of vehicles traveling on the express train at the second station. If it is not the final time code, the process is repeated from step 1403 to the final time code (step 1408).

In the following steps 1409 and 1410, a calculation is performed for an ascending express train.
Step 1409: It is determined from the information in the arrival / departure frequency table 1200 in FIG. If the up-and-coming express train arrives and departs at the second station, the process moves to step 1410. If the up-and-coming express train does not arrive and depart, the second station will only depart and arrive at the ordinary train. Since all the vehicle boarding numbers are determined, the processing is repeated from step 1402 in order to calculate the number of boarding vehicles on the upcoming train at the next third station.

  Step 1410: When an ascending express train arrives and departs at the second station, the processing from step 1403 to step 1408 is similarly performed for the ascending express train, and the number of vehicles on the ascending express train at each time code at the second station is calculated. To do. Here, in general, when calculating the number of carry-over vehicles for an express train, since the station in front of the station is not necessarily an express stop station, which station expresses from the information in the arrival / departure frequency table 1200 of FIG. It is specified whether the station is a station before and after the train (referred to as a “previous stop station”). From the obtained direct train station and the number of arrivals and departures of the express train at the station, the number of cars carried over and the number of cars not carried over are calculated in the same manner as in steps 1402 to 1406, and the base of the express train at the station is calculated based on them. The number of rides will be calculated.

In step 1411 below, calculation is performed for both the up-bound ordinary train and the up-bound express train.
Step 1411: The number of vehicles traveling on the upstream ordinary train in each time code of the second station obtained in Step 1407 and the number of vehicles traveling on the upstream express train in each time code of the second station obtained in Step 1410. By averaging, the vehicle boarding number of each time code of the second station is calculated. The averaging here means that the average is calculated by weighting the number of departures and arrivals with respect to the number of vehicles on the ordinary train and the number of vehicles on the express train. The formula is
(Formula 13) [Number of vehicles on the upstream train for each time code at the second station] = {[Number of vehicles on the upstream ordinary train at the second station] × [Number of arrivals and departures of the upstream ordinary train] + [Number of the second station] Number of passengers traveling on ascending express trains] x [Number of departures and arrivals on ascending express trains}} ÷ [Total number of arrivals and departures on ascending trains for each time code at station 2]
And The calculated vehicle boarding number of each time code of the second station is stored in the internal storage device 124. The congestion rate is calculated in step 1304 of FIG. 13 using the value of the number of vehicles boarded here.

  Step 1412: It is determined whether or not it is the last station of each route. If it is the last station, the process moves to Step 1413 to complete the process. If it is not the last station, the process moves to Step 1402, and the number of vehicles on the next station is calculated. We will calculate.

The process shown in FIG. 14 is generally summarized as follows.
The number of vehicles traveling on an ascending or descending train at a predetermined unit time at a predetermined station is calculated by adding the number of increase / decrease of the unit time at the station to the base number of the unit time at the station. This calculation is performed for each of the ordinary train and the express train.
In the above calculation of the number of vehicle rides, the base ride number of the unit time at the station is the number of vehicle rides on the up and down trains of the unit time at the last stop station when there is no carryover in the unit time. . On the other hand, if there is a carry-over car in the unit time, the number of cars carried over and the number of cars not carried over are weighted to the number of vehicles in the unit time immediately before the stop station and the number of vehicles in the unit time at the stop station, respectively. By calculating the average, the base ride number of the unit time is obtained. The base number of passengers at the first station is zero.
In the above calculation of the number of vehicles, the increase / decrease of the unit time at the station is calculated by subtracting the sum of the average number of visitors from the sum of the average number of visitors to all ticket gates linked to the vehicle number. Calculate by dividing by the number of times.
After calculating the number of vehicles on each of the ordinary train and express train, calculate the number of regular trains and express trains for the number of regular trains and express trains per unit time. By calculating the average by weighting each, the number of vehicles on the up or down train of the unit time at the station is obtained.

  FIG. 15 is an example of a data configuration diagram of a congestion rate management table 1500 held in the congestion rate management DB 132 of the congestion rate prediction apparatus 131 in FIG. The congestion rate management table 1500 stores data calculated by the statistical calculation unit 123 of the statistical calculation sub-device 121 of FIG. 1 according to the processing flow of FIG. The congestion rate management table 1500 manages the congestion rate of each vehicle of the up and down trains that arrives and departs between the time codes in each route and each station. The information item includes a route name 1501, a station name 1502, an up flag 1503, a down flag 1504, a time code 1505, a vehicle number 1506, a congestion rate 1507, and a vehicle ride number 1508.

Here, for example, the number of passengers in the vehicle number “1” of the up-and-coming train that arrives and departs at the time code “1” of “Ooimachi Line” and “Mizoguchi Station” is “150 people” and the congestion rate is “90%” In this case, the route name is “Oimachi Line”, the station name is “Mizoguchi”, the up flag is “1”, the down flag is “null”, the time code is “001”, the vehicle number is “01”, and the congestion rate is “90” "150" is stored as the number of vehicles. This table is composed of four sub-tables of “good weather weekday”, “bad weather weekday”, “good weather holiday” and “bad weather holiday”.
A user of the vehicle congestion rate prediction system accesses the vehicle congestion rate prediction system from his mobile terminal or personal computer, and acquires the congestion rate information from the table of FIG.

  FIG. 16 shows an outline of the statistical calculation unit 123 on the statistical calculation sub-apparatus of FIG. 1. From the tables of FIGS. 4 and 10, “good weather weekday”, “bad weather weekday”, “good weather holiday” and The ratio of the congestion rate compared with the time code in which the event has occurred in the time code in which the event has occurred at each station on each route of “bad weather holidays” is calculated and inserted into the table of FIG. It is a flowchart which shows an outline.

  Step 1601: First, the statistical calculation unit 123 stores all tables of dates having event IDs in the station information management table 1000 of FIG.

  Step 1602: For the date table acquired in Step 1601, it is determined from the weather holiday table 400 of FIG. 4 whether the date is a weekday or a holiday, or whether the weather is sunny and cloudy or rainy. , “Good weather weekday”, “bad weather weekday”, “good weather holiday”, and “bad weather holiday” are classified into four types of tables and stored in the internal storage device 124.

  Step 1603: Next, with respect to each of the four types of tables acquired in Step 1602, for each time code having an event ID, “number of visitors going up”, “number of people going up and down” for each time code, Calculate the average number of “downbound visitors” and “downbound visitors” (referred to as “average number of events at the time of event”), and calculate them as “average number of visitors at the time of event” and “average number of visitors at the time of event” ”,“ Average number of people leaving and leaving the event ”, and“ average number of people leaving and leaving the event ”are stored in the internal storage device 124.

  Step 1604: In the case of an event ID such as a sports event, depending on the station, it may not always be applicable. Such an event is referred to as a “single event”. On the other hand, events such as “personal accident” or “signal trouble” are events that can occur in all stations. Such an event is referred to as an “always occurring event”. Therefore, first, it is determined for each time code whether or not the event ID of the constantly occurring event exists in the information stored in step 1603.

  Step 1605: If there is a time code in which there is no event ID of a constantly occurring event in Step 1604, the average number of the time code is the time when the event ID of a constantly occurring event exists with the closest time code before that. It is replaced with the average number of events in the code and stored in the internal storage device 124. If the event ID of the constantly occurring event exists in all the time codes, the process proceeds to the next process.

Step 1606: Based on the average number of events in each time code obtained in Steps 1603 to 1605, a ratio with the average number in which no event ID exists is calculated. Specifically, it is acquired up to step 1605 based on the average number of ascending visitors, the average number of descending visitors, the average number of ascending exits, and the average number of descending exits for the time code that does not have the event ID obtained in step 1302 of FIG. Calculate the ratio by dividing the average number at each event. Here, the ratio of the ascending train is calculated by comparing the average number of inbound visitors, and the ratio of the descending train is calculated by comparing the average number of descending visitors. The calculation formula is, for example,
(Equation 13): [Rate of up train for each time code at each station] = [Average number of visitors at each time code event] ÷ [Average number of visitors at each time code]
It becomes. Round to the third decimal place and store in the internal storage device 124.

  Step 1607: Information acquired up to Step 1606 is stored in the congestion rate ratio table 1700 of FIG.

  Step 1608: Finally, with respect to the congestion rate ratio table 1700 of FIG. 17 in which information is stored, the ratio “1” is set as the ratio with respect to the ratio “null” in the event ID row of the event that cannot be generated. In order to prevent an error from occurring even when a congestion rate prediction is viewed when a single event occurs at a station where a single event cannot occur.

  FIG. 17 is an example of a data configuration diagram of the congestion rate ratio table 1700 held by the congestion rate management DB 132 of the congestion rate prediction apparatus 131 of FIG. The congestion rate ratio table 1700 manages the congestion rate for each vehicle of the up and down trains between the time codes in each route and each station. The route name 1701, the station name 1702, the time code 1703, the up flag 1704, a down flag 1705, an event ID 1706, and a ratio 1707.

  Here, for example, in the up train that arrives and departs at the time code “1” of “Oimachi Line” and “Mizoguchi Station”, the boarding ratio when the “personal accident” with event ID “1” occurs is “1.25” If there is, the route name is “Oimachi Line”, the station name is “Mizoguchi”, the time code is “001”, the up flag is “1”, the down flag is “null”, the event ID is “01”, and the ratio is “ “1.25” is stored. There are four tables of “good weather weekday”, “bad weather weekday”, “good weather holiday” and “bad weather holiday”.

  During the accident, the user of the vehicle congestion rate prediction system accesses the congestion rate prediction system from his / her mobile terminal or personal computer, acquires the congestion rate information from the congestion rate management table 1500 of FIG. In the congestion rate ratio table, the congestion rate information including the ratio when a personal accident occurs is acquired.

  FIG. 18 is an example of a data configuration diagram of the station vicinity information table 1800 held by the information management DB 133 of the congestion rate prediction apparatus 131 of FIG. The station vicinity information table 1800 manages information for providing users with information specific to each station that cannot be explained by the congestion rate prediction system described above, and includes a station name 1801 and vicinity information 1802. The user of the congestion rate prediction system can use this table at the time of access to grasp additional information specific to the use station.

  Here, for example, if Mizonokuchi Station is always crowded with the ticket gates near Exit A, the station name is “Mizonokuchi”, and as additional information, “Only one ticket gate exit is available for transfer to the Nanbu Line. The explanation that “the ticket gate and the vehicle near the exit are crowded” will be stored.

  Furthermore, if the time zone of a single event such as a sports event is known in advance, the congestion rate prediction system user can insert the time zone of the single event into the table as additional information beforehand. When examining the congestion rate of a station where a single event occurs in a time zone, it is urged to use a congestion rate prediction that takes into account the single event.

  In addition, when there is an event that can occur at any time such as a signal trouble or personal injury on each route, the congestion rate prediction system administrator updates this table in real time, and the congestion rate is calculated by taking into account the event that the user can always occur. It is desirable to be able to investigate. The station vicinity information table 1800 in FIG. 18 is also an input terminal connected to the congestion rate prediction device 131 in the same manner as the tables in FIGS. 4, 5, 6, 7, 8, 11, and 12. 141, data insertion is performed.

  FIG. 19 is an example of a screen image displayed when the user accesses the vehicle congestion rate prediction system from the mobile terminals 151 and 152 and the personal computers 153 and 154 in FIG. The user can view the vehicle congestion rate from the boarding station to the getting-off station by inputting necessary items from the display screen as shown in FIG. The screen image includes a route name selection pull-down menu 1901, a boarding station input field 1902, an exit station input field 1903, a departure date selection pull-down menu 1904, a departure time selection pull-down menu 1905, an event selection pull-down menu 1906, a weather selection pull-down menu 1907, and a determination. The button 1908 is configured.

  Here, for example, a user leaves the "Mizoguchi Station" on the "Oimachi Line" at "8:00 on January 1 (weather: rain)" and is about to get off at "Futako Shinchi". Is delayed, the user selects "Tokyu Oimachi Line" from the route name selection pull-down menu, enters "Mizoguchi" as the boarding station, enters "Futako Shinchi" as the getting off station, and selects the date Select “January 1” from the pull-down menu, select “8:00” from the departure time selection pull-down menu, select “personal accident” from the event selection pull-down menu, and select “rain” from the weather selection pull-down menu. By pressing the enter button, the vehicle congestion rate prediction system can be accessed and the results can be viewed.

  FIG. 20 is an example of a screen image that displays a result obtained by the user accessing the vehicle congestion rate prediction system from the mobile terminals 151 and 152 and the personal computers 153 and 154 in FIG. A station display location 2001 that displays the name and arrival time of each station, a station description display location 2002 that displays station information of each station, and a congestion rate display location 2003 that displays the congestion rate of each vehicle in the time zone that each station wants to search. , 2001 to 2003, the information for one station is displayed. For example, when the search in FIG. 19 is taken as an example, the result shown in FIG. 20 is obtained from the congestion rate prediction system.

In FIG. 20, arrival station information and arrival time information are displayed at station display locations 2001, 2004, and 2007, respectively, and the arrival time is calculated and displayed from the route station order table 500 of FIG. 5 in the vehicle congestion rate prediction system. It will be.
In each station explanation display location 2002, 2005, 2008, station information at each station is displayed by accessing the station vicinity information table 1800 of FIG.

  At the congestion rate display location, the congestion rate for each vehicle is calculated and displayed by accessing the congestion rate management table 1500 in FIG. 15 and the congestion rate ratio table 1700 in FIG. When the rate exceeds 300%, 300% is set as the upper limit of the congestion rate, and 300% is displayed in the congestion rate display portion.

  With the system configuration as described above, it is possible to provide vehicle congestion rate prediction information for each station on a specific route to a user of the vehicle congestion rate prediction system. It is desirable that the update rate of the congestion rate management DB 132 in this vehicle congestion rate prediction system is executed daily so that the latest and accurate congestion rate prediction can always be provided to the user.

100: Information transmission unit of each station, 101: Automatic ticket gate, 102: Information acquisition unit, 103: Input terminal, 104: Display unit, 111: Calculation sub-device, 112: Station information management DB, 113: Calculation unit, 121: Statistical calculation sub-device, 122: Station boarding number management DB, 123: Statistical calculation unit, 131: Congestion rate prediction system, 132: Congestion rate management DB, 133: Information management DB, 141: Input terminal, 142: Display unit, 151 : Mobile terminal, 152: form terminal, 153: personal computer, 154: personal computer

Claims (8)

It has a congestion rate prediction device that can be connected to a plurality of automatic ticket gates and user terminals installed at each station on each route via a communication line, and predicts the congestion rate for each train vehicle per unit time of each station A vehicle congestion rate prediction system, wherein the congestion rate prediction device comprises:
Means for previously storing in the database the association between the ticket gate ID of each automatic ticket gate at each station and the vehicle number of each vehicle on the train;
Means for storing in advance in the database the number of departures and arrivals of each of the up and down trains at each station for each unit time;
Means for obtaining entrance / exit information including a station name, a ticket gate ID, a transit time, an entrance / exit flag, and commuter pass information when each passenger passes the automatic ticket gate, and registering it in a database;
Based on all the entrance / exit information registered in a predetermined period, it is judged whether each passenger is an passenger of an up train or a down train. Means for counting the number of visitors and the number of people leaving and the number of people entering and leaving the descending train, respectively;
The date corresponding to the predetermined condition is extracted from the predetermined period, and the number of visitors and exits of the ascending train and the number of visitors and exits of the descending train for the extracted dates are averaged, respectively. Means for calculating the average number of visitors on the upper train and the average number of visitors leaving on a unit time basis, and the average number of visitors on the lower train and average number of people leaving the train, respectively,
For each unit time of each station, the average number of visitors and exits of the ascending train, the average number of visitors and exits of the descending train, the linkage between the ticket gate ID and the vehicle number, and the stations Based on the number of departures and arrivals, means for calculating the number of vehicles boarding for each vehicle of the up train and the down train for each unit time of each station;
Means for calculating a congestion rate for each vehicle by dividing the number of vehicles on board by the capacity of one vehicle;
A vehicle congestion rate prediction system comprising: means for displaying a congestion rate for each vehicle in response to an access from the user terminal. The means for calculating the number of vehicle rides is
Calculate the number of vehicles on the station by adding the increase / decrease number of the unit time of the station to the base number of units of the unit time of the target station,
The base ride number of the unit time of the station is obtained from the number of vehicle rides of the unit time at the last stop station when there is no carry-over car at the unit time, while when there is a carry-over car at the unit time , By calculating the average by weighting the number of vehicles carried forward and the number of vehicles not carried forward with respect to the number of vehicles boarding in the unit time immediately preceding the stop station and the number of vehicles boarding in the unit time of the immediately preceding stop station, and ,
The increase / decrease number of the unit time of the station is the sum of the average number of visitors of the up train or down train for all the ticket IDs linked to the vehicle number, and the exit of the up train or down train 2. The vehicle congestion rate prediction system according to claim 1, wherein the vehicle congestion rate prediction system is obtained by subtracting the sum of the average number of passengers and dividing by the number of departures and arrivals of the up train or down train of the unit time. The congestion rate prediction apparatus further includes:
Means for associating a date, weather, and a holiday flag indicating whether it is a weekday or a holiday in the database in order to extract a date corresponding to a predetermined condition from the predetermined period;
The vehicle congestion prediction system according to claim 1, further comprising means for extracting a date corresponding to a predetermined condition based on one or both of the registered weather and the holiday flag. The congestion rate prediction apparatus further includes:
When an event occurs, means for registering the station name, occurrence time, end time, and event description in association with the database;
The average number of visitors and average number of people leaving the train and the average number of visitors and average number of people leaving the train at each unit time when an event occurs are as follows. The vehicle congestion prediction system according to any one of claims 1 to 3, further comprising means for calculating a congestion rate ratio at the time of occurrence of an event by dividing each of them.   When the ticket ID is associated with the vehicle number, if the vehicle closest to the automatic ticket gate can be identified, the ticket ID is associated with the vehicle number of the closest vehicle, and the vehicle closest to the automatic ticket gate cannot be identified. The vehicle congestion prediction system according to any one of claims 1 to 4, wherein passengers passing through an automatic ticket gate are linked so as to be evenly distributed to each vehicle. The means for calculating the number of vehicle rides is
Calculate the number of vehicles on each of the regular trains and express trains of the relevant unit time at the target station, and the number of regular trains that arrive and depart in that unit time for each calculated number of regular trains and express trains The vehicle congestion prediction system according to any one of claims 1 to 5, wherein the number of passengers in the unit time is obtained by calculating an average by weighting the number of departures and arrivals of express trains.   The vehicle congestion prediction system according to claim 1, wherein an upper limit is set to 300% when the congestion rate is displayed on the user terminal. Predict the congestion rate for each train vehicle at each station unit time by using a congestion rate prediction device that can be connected via a communication line to multiple automatic ticket gates and user terminals installed at each station on each route. A vehicle congestion rate prediction method, wherein the congestion rate prediction device comprises:
Storing in advance in the database the association between the ticket gate ID of each automatic ticket gate at each station and the vehicle number of each vehicle on the train;
Storing the number of departures and arrivals of each of the up and down trains at each station in advance in a database for each unit time;
Acquiring entrance / exit information including the station name, ticket gate ID, transit time, entrance / exit flag and commuter pass information when each passenger passes the automatic ticket gate, and registering it in the database;
Based on all the entrance / exit information registered in a predetermined period, it is judged whether each passenger is an passenger of an up train or a down train. Counting the number of visitors and the number of people leaving and the number of people entering and leaving the descending train, respectively,
The date corresponding to the predetermined condition is extracted from the predetermined period, and the number of visitors and exits of the ascending train and the number of visitors and exits of the descending train for the extracted dates are averaged, respectively. A step of calculating the average number of visitors on the upper train and the average number of visitors leaving on a unit time basis, and the average number of visitors on the downstream train and average number of people leaving the train, respectively,
For each unit time of each station, the average number of visitors and exits of the ascending train, the average number of visitors and exits of the descending train, the linkage between the ticket gate ID and the vehicle number, and the stations Calculating the number of vehicles boarding for each vehicle of the up train and the down train for each unit time of each station based on the number of departures and arrivals of
Calculating the congestion rate for each vehicle by dividing the number of vehicles on board by the capacity of one vehicle;
And a step of displaying a congestion rate for each vehicle in response to an access from the user terminal.

Images