JP7095892B2 - Information processing system, information processing method and information processing program - Google Patents

Description

The present invention relates to an information processing system, an information processing method, and an information processing program for predicting congestion.

A system for predicting the congestion status of trains is known. For example, Patent Document 1 discloses a vehicle congestion status prediction system that predicts a congestion status based on the history of the occupancy rate for each day of the week and each time zone. In this vehicle congestion situation prediction system, a train equipped with a motion sensor arranged for each seat position and each standing position is shown.

Japanese Unexamined Patent Publication No. 2013-73396

However, in the technique of Patent Document 1, the history of the past occupancy rate is required to predict the congestion situation, but it is necessary to provide a motion sensor in the train in order to acquire the history of the past occupancy rate. be. Therefore, there is a problem that the cost is high in terms of hardware.

The present invention has been made in view of the above problems, and an object of the present invention is to provide an information processing system, an information processing method, and an information processing program capable of predicting congestion at low cost.

According to one aspect of the present invention, from a plurality of route information, at least one of a boarding station and a getting-off station, a train getting on at the boarding station, and a train getting off at the getting-off station. An information processing system is provided that includes an extraction means for extracting one train, and a prediction means for predicting congestion related to the train based on the information about the extracted stations and trains.

According to the present invention, since the route information is used, congestion can be predicted at low cost.

The block diagram which shows the schematic structure of the information processing system 2 which concerns on 1st Embodiment of this invention. The figure which shows an example of the structure of the route information database 221. The flowchart which shows an example of the processing operation of the information processing system 2. The figure which shows the 1st example of the point table created in step S3. The figure which shows the 2nd example of the point table created in step S3. The figure which shows the 3rd example of the point table created in step S3. The figure which shows the 4th example of the point table created in step S3. The figure which shows the 5th example of the point table created in step S3. The figure which shows an example of the structure of the route information database 221. The flowchart which shows an example of the processing operation of the information processing system 2. The figure which shows an example of the point table created in step S3'. The figure which shows an example of the method of extracting a vehicle based on the position of a facility. The figure which shows another example of the method of extracting a vehicle based on the position of a facility. The block diagram which shows the schematic structure of the information processing system 100 which concerns on 4th Embodiment of this invention. The sequence diagram which shows an example of the processing operation of the information processing system 100 of FIG. The figure which shows an example of the screen displayed on a display.

Hereinafter, embodiments according to the present invention will be specifically described with reference to the drawings.

(First Embodiment)
FIG. 1 is a block diagram showing a schematic configuration of an information processing system 2 according to a first embodiment of the present invention. The information processing system 2 includes a storage unit 22 and a control unit 23.

The storage unit 22 stores the route information database 221. The route information database 221 contains a plurality of route information. The route information is information indicating how to move from a departure point to a destination (or at least a part thereof), and in the present embodiment, the route information includes getting on and off of at least one train. I'm assuming.

The route information is, for example, route information obtained by a route search performed in response to a user's request. Such route information may be the result of a route search performed by a route search unit (not shown) in the information processing system 2, or may be the result of a route search performed by another device.

Further, the route information may be route information based on user registration, in addition to the result of route search. For example, if the commuting ticket owned by the user is registered, the commuting route (outbound route) with the arrival time of the work location as the typical work time zone (for example, 8:00 to 10:00 on weekdays), or The route information database 221 may include a commuting route (return route) having a typical leaving time zone (for example, 17:00 to 19:00 on weekdays) as the arrival time at home.

FIG. 2 is a diagram showing an example of the structure of the route information database 221. As shown in the figure, the route information database 221 is associated with date information, boarding information, one or more transfer information, and disembarkation information using the route ID as an index. In the following, the route information whose route ID is "X" will be referred to as "route information X".

The date information indicates the date when the route is moved. The boarding information includes the boarding station, the departure time of the train boarding at the boarding station, the route boarding at the boarding station, and the train ID of the boarding train. The transfer information includes the transfer station, the arrival time at the transfer station, the departure time of the train to be boarded at the transfer station, the route to be boarded at the transfer station, and the train ID of the train to be boarded. The disembarkation information includes the disembarkation station and the arrival time at the disembarkation station. Here, the train is uniquely identified by the date, the route, and the train ID. In the following, a train whose train ID is "Y" will be referred to as "train Y".

For example, the route information 002 indicates that the route is as follows. It will move on September 2, 2013. Then, board the S line train Z005, which departs from S2 station at 11:31. Furthermore, we will arrive at S4 station at 11:35 and get off the train. Then, board the T-line train Y007, which departs from the same station at 11:41. Then, we arrive at T1 station at 11:49.

It is possible that there is no transfer as in the route information 001. Further, although not shown, there may be two or more transfers. Further, the getting-off station and the boarding station may be different in the transfer (for example, when moving from the getting-off station to the boarding station on foot), in which case the transfer information may include the getting-off station and the boarding station.

Further, the route information such as the route from the departure place to the boarding station or the route from the getting-off station to the destination may include information other than the information shown in FIG.

Returning to FIG. 1, the control unit 23 has an information acquisition unit 231, an extraction unit 232, and a prediction unit 233. Each of these parts is a function realized by, for example, a processor (not shown) in the information processing system 2 executing a predetermined information processing program.

The information acquisition unit 231 acquires route information from the route information database 221 in the storage unit 22.

The extraction unit 232 extracts at least one of the boarding station and the getting-off station from the route information. The extraction unit 232 may extract only the boarding station or only the getting-off station. From one of the route information shown in FIG. 2, the extraction unit 232 can extract one boarding station based on the boarding information and can extract one getting-off station based on the getting-off information. Further, the extraction unit 232 can extract one boarding station and one getting-off station based on the transfer information.

For example, for the route information 001, the S1 station on the S line can be extracted as the boarding station, and the S2 station on the S line can be extracted as the getting off station. Further, regarding the route information 002, the S2 station on the S line and the S4 station on the T line can be extracted as the boarding station, and the S4 station on the S line and the T1 station on the T line can be extracted as the getting off station.

Further, the extraction unit 232 extracts at least one train to be boarded at the extracted boarding station and a train to be disembarked at the extracted disembarkation station. For example, with respect to the route information 001, the train Z004 is extracted as a train to get on at the boarding station S1 and a train to get off at the getting off station S2.

The prediction unit 233 performs congestion prediction related to this train based on the extracted information about the boarding station and the getting-off station, and the extracted train, and generates congestion prediction information. By "related to a train", it means that the congestion prediction of the extracted train itself may be performed, or the congestion prediction of a plurality of trains including the extracted train may be performed. Specific examples of congestion prediction will be described later.

It should be noted that each unit in the storage unit 22 and the control unit 23 may be provided in a single device to form the information processing system 2, or may be distributed in a plurality of devices to form the information processing system 2.

FIG. 3 is a flowchart showing an example of the processing operation of the information processing system 2. First, the information acquisition unit 231 acquires route information from the storage unit 22 (step S1). Then, the extraction unit 232 extracts the boarding station and / or the getting-off station and the train from the route information (step S2).
Subsequently, the prediction unit 233 counts the number of route information for each station and each train based on the extracted stations and trains, and creates a point table (details will be described later) (step S3). Then, the prediction unit 233 makes a congestion prediction with reference to the point table (step S4).

Hereinafter, as a congestion prediction, an example in which the prediction unit 233 predicts the number of people getting off from each train at the getting-off station will be shown.

FIG. 4 is a diagram showing a first example of the point table created in step S3. This figure is a table of getting-off points, and the number of route information with each station as the getting-off station is counted as the getting-off point. The disembarkation point is a value according to the number of people getting off at each station. In the figure, it is assumed that the S line has 6 stations from S1 to S6 in this order, with S1 station being the first station and S6 station being the last station.

Considering the route information 001 to 004 of the route information database 221 of FIG. 2, the disembarkation point table of FIG. 4A is created. Specifically, for train Z004, there is one route information (route information 001) for getting off at S2 station, and the getting off point is "1". Further, for train Z005, there are two route information (route information 002,003) for getting off at S4 station, and the getting off point is "2". There is one route information (route information 004) for getting off at the S6 station, and the getting off point is "1".

As a result of totaling all route information, it is assumed that the disembarkation point table shown in FIG. 4 (b) is created.
Based on this disembarkation point table, the prediction unit 233 can predict congestion of train Z004, such as "the number of people getting off at S2 station is small" and "the number of people getting off at S3 station is large". Further, when the correlation between the disembarkation point and the actual number of disembarking people is known, the prediction unit 233 may estimate the number of people disembarking at each station to predict congestion. Similarly, the prediction unit 233 can predict the congestion of the train Z005 as well.

In this way, the prediction unit 233 can make a prediction regarding the number of people getting off each train with each station as the getting-off station. Subsequently, as a congestion prediction, an example is shown in which the prediction unit 233 predicts the number of people boarding each train at the boarding station.

FIG. 5 is a diagram showing a second example of the point table created in step S3. The figure is a boarding point table, and the number of route information with each station as the boarding station is counted as the boarding point. The boarding points are values according to the number of people boarding at each station.

Considering the route information 001 to 004 of the route information database 221 of FIG. 2, the boarding point table of FIG. 5A is created. Specifically, for train Z004, there is one route information (route information 001) for boarding at S1 station, and the boarding point is "1". Further, for train Z005, there is one route information (route information 003) for boarding at S1 station. There is one route information (route information 002) for boarding at the same S2 station. There is one route information (route information 004) for boarding at the same S3 station. Therefore, the getting-off points at the stations S1 to S3 are all "1".

As a result of totaling all route information, it is assumed that the boarding point table shown in FIG. 5 (b) is created.
Based on this boarding point table, the prediction unit 233 can predict congestion of train Z004, such as "the number of passengers at S1 station is medium" and "the number of passengers at S5 station is large". can. Further, when the correlation between the boarding point and the actual number of passengers is known, the prediction unit 233 may estimate the number of passengers to board at each station to predict congestion. Similarly, the prediction unit 233 can predict the congestion of the train Z005 as well.

In this way, the prediction unit 233 can make a prediction regarding the number of passengers of each train having each station as a boarding station. Subsequently, as a congestion prediction, an example in which the prediction unit 233 predicts the degree of congestion at the boarding station or the getting-off station will be shown.

FIG. 6 is a diagram showing a third example of the point table created in step S3. The figure is a congestion point table, and the congestion points are values according to the number of people on the train at the boarding station or the getting-off station. The congestion point table of FIG. 6 can be created by accumulating the difference between the boarding point shown in FIG. 5 (b) and the disembarking point shown in FIG. 4 (b) for each station.

For example, for train Z004, according to FIG. 5B, the boarding point at S1 station is "12". And since S1 station is the first station, the getting-off point is "0". Therefore, as shown in FIG. 6, the congestion point at the S1 station is “12”.

Next, according to FIG. 5 (b), the boarding point at S2 station is "10", and according to FIG. 4 (b), the getting-off point at the station is "1". Therefore, the congestion point at the S2 station increases by 9 (= 10-1) points, and the congestion point at the S2 station becomes “21” (= 12 + 9) as shown in FIG.

Next, according to FIG. 5 (b), the boarding point at S3 station is "18", and according to FIG. 4 (b), the getting-off point at the station is "28". Therefore, the congestion point at the S3 station is reduced by "10" (= 28-18) points, and the congestion point at the S3 station is "11" (= 21-10) as shown in FIG.

In the same manner below, the prediction unit 233 calculates the congestion points at each station. Since S6 station is the terminal station, the congestion point is "0".

Based on this point table, the prediction unit 233 described the train Z004 as "low congestion at S1 station", "medium congestion at S2 station", and "high congestion at S5 station". It is possible to predict congestion such as. Further, when the correlation between the congestion point and the number of people actually on the train is known, the prediction unit 233 may estimate the number of people on the train at each station to predict the congestion. Similarly, the prediction unit 233 can predict the congestion of the train Z005 as well.

In this way, the prediction unit 233 can make a prediction regarding the number of people on each train at each station. Subsequently, as a congestion prediction, an example is shown in which the prediction unit 233 makes an example prediction regarding an increase / decrease in the number of people on a train at a boarding station or a getting-off station.

FIG. 7 is a diagram showing a fourth example of the point table created in step S3. This figure is a table of increase / decrease points, and the increase / decrease points are values according to the difference between the number of passengers getting on and off at each station.
The increase / decrease point table of FIG. 7 can be created from the difference between the boarding point shown in FIG. 5 (b) and the disembarking point shown in FIG. 4 (b).

For example, for train Z004, according to FIG. 5B, the boarding point at S1 station is "12". And since S1 station is the first station, the getting-off point is "0". Therefore, as shown in FIG. 7, the increase / decrease point at the S1 station is “12”.

Next, according to FIG. 5 (b), the boarding point at S2 station is "10", and according to FIG. 4 (b), the getting-off point at the station is "1". Therefore, as shown in FIG. 7, the increase / decrease point at the S2 station is “9” (= 10-1).

Next, according to FIG. 5 (b), the boarding point at S3 station is "18", and according to FIG. 4 (b), the getting-off point at the station is "28". Therefore, as shown in FIG. 7, the increase / decrease points at the S3 station are “-10” (= 18-28) points.

In the same manner below, the prediction unit 233 calculates the increase / decrease points at each station. Based on this point table, the prediction unit 233 can make a congestion prediction such as "the number of people on the train Z004 is greatly reduced at the S3 station". Similarly, the prediction unit 233 can predict the congestion of the train Z005 as well.

The above is an example of performing congestion prediction for each train, but the prediction unit 233 may collectively perform congestion prediction for a plurality of trains included in a predetermined time range.

FIG. 8 is a diagram showing a fifth example of the point table created in step S3. The figure is an example of grouping trains by time zone. More specifically, for all trains departing from S1 station between 11:00 and 11:59 (assuming only two trains, train Z004 and train Z005), the congestion points at each station are totaled. It exemplifies what was done. The point table of FIG. 8 can be created from the congestion point table of FIG.

That is, in FIG. 6, the congestion point at the S1 station is "12" for train Z004 and "19" for train Z005. Therefore, the total number of congestion points is "31" (= 12 + 19) as shown in FIG.

Further, in FIG. 6, the congestion point at the S2 station is "21" for the train Z004 and "30" for the train Z005. Therefore, the total number of congestion points is "51" (= 21 + 30) as shown in FIG.

As described above, the total number of congestion points at each station is calculated. Based on this point table, the prediction unit 233 can make a congestion prediction such as "trains on the S line before noon are particularly crowded at S2 and S5 stations".

The prediction unit 233 may calculate the total number of disembarkation points from FIG. 4B and predict the number of people disembarking from the train at each station for each time zone. Further, the prediction unit 233 may calculate the total number of boarding points from FIG. 5B and predict the congestion of the number of people boarding from the train at each station for each time zone. Further, the prediction unit 233 may calculate the total of the increase / decrease points from FIG. 7 and predict the congestion regarding the increase / decrease in the number of people on the train at each station for each time zone.

In addition, the method of organizing trains is not limited to each time zone, but can be organized by any time range such as by day of the week or by season.

As described above, in the first embodiment, since the route information stored in the storage unit 22 is used, congestion prediction can be performed at low cost.

The prediction unit 233 may make a congestion prediction by taking into account not only the route information but also the congestion actual information. The congestion record information is information indicating the past congestion record, and may be stored in the storage unit 22 of FIG. As a specific example, the prediction unit 233 may predict that the congestion is congested when either the congestion prediction based on the route information or the congestion prediction based on the congestion record information is congested. On the other hand, the prediction unit 233 may predict that congestion will not occur if, for example, the result is that neither the congestion prediction based on the route information nor the congestion prediction based on the congestion record information is congested.

Further, in the above-mentioned example, as shown in FIG. 2, the route information includes the train ID, and the train is extracted based on the train ID. However, the route information does not necessarily have to include the train ID. In this case, the timetable information database (not shown) may be stored in the storage unit 22. The timetable information indicates the departure time and / or the arrival time at each station of each train.

Then, the extraction unit 232 extracts the train with reference to the timetable information. By referring to the timetable information, the extraction unit 232 may use, for example, a train departing from S2 station in FIG. 2 at 11:31 (route information 002) and a train departing from S1 station at 11:29 (route information). You may specify that 003) is the same train.

Further, the extraction unit 232 may extract trains by specifying by a line name, a station, a departure time or arrival time at a station, a category, a destination, or the like without using a train ID. The category may be a type such as "limited express", "express", "local train", or may be a nickname of a train.

(Second embodiment)
In the first embodiment described above, the number of passengers and the number of passengers getting off the train are counted for each train. On the other hand, in the second embodiment described below, the number of passengers and the number of passengers getting off the train are counted for each position (for example, a vehicle or a door) in the train in more detail. Then, it predicts which position on the train will be congested.

The configuration itself of the information processing system 2 is the same as that of the first embodiment shown in FIG. 1, and the processing contents of each part will be described below, focusing on the differences from the first embodiment.

In addition to the boarding station and / or the getting-off station, the extraction unit 232 extracts at least one position of the boarding position in the train at the boarding station and the getting-off position in the train at the getting-off station. The boarding or disembarking position is, for example, which vehicle on the train or which door of which vehicle.

The prediction unit 233 makes a congestion prediction related to this position from the extracted stations and positions.
Specific examples of congestion prediction include the number of people getting on / off at the extracted positions at each station, the increase / decrease in the number of people getting on at the extracted positions at each station, and the prediction of the degree of congestion at the extracted positions at each station. Be done.

Hereinafter, an example in which the extraction unit 232 extracts a vehicle in a train as a boarding position and a disembarking position will be shown. In this case, the route information database 221 as shown in FIG. 9 is used. The difference from FIG. 2 is that the boarding information and the transfer information include the "recommended vehicle". The recommended vehicle is information that can be given when searching for a route, is a vehicle that is convenient for traveling on the route, a vehicle that shortens the time required for transfer, and a vehicle that shortens the time required for traveling from the disembarkation station to the destination. And so on.

As shown in the figure, the recommended vehicle may be specified by a relative vehicle in the train such as "first car from the front". As another example, the recommended vehicle may be specified by an absolute vehicle number assigned to each vehicle of the train, such as "car 1".

Further, there may be two or more recommended vehicles such as route information 004. Further, the recommended vehicle may be a rough one such as "a vehicle closer to the front" or "the first to third cars from the front".

FIG. 10 is a flowchart showing an example of the processing operation of the information processing system 2. Step S1 is the same as the first embodiment.

In step S2', the extraction unit 232 extracts the boarding station and / or the getting-off station, the train, and the boarding vehicle and / or the getting-off vehicle from the train at the getting-off station.

The extraction unit 232 can extract a vehicle based on the recommended vehicle in the route information. For example, in the route information 001 of FIG. 9, based on the fact that the recommended vehicle in the boarding information is "the first car from the front", the extraction unit 232 sets the vehicle to board the train Z004 at the boarding station S1 station as "1 from the front". Both eyes can be extracted. Further, in the route information 004, based on the fact that the recommended vehicle in the boarding information is "the 3rd or 5th car from the front", the extraction unit 232 is "3 from the front" as a boarding vehicle for the train Z005 at the station S3 which is the boarding station. Or the 5th car "can be extracted.

Further, since the vehicle normally disembarks from the boarded vehicle, the extraction unit 232 can extract the "first vehicle from the front" as the disembarking vehicle from the train Z004 at the station S2, which is the disembarking station, in the route information 001.

Then, in step S3', the prediction unit 233 counts the number of route information for each station and each vehicle and creates a point table.

FIG. 11 is a diagram showing an example of a point table created in step S3'. The figure shows a boarding point table.

Considering the route information 001 to 004 of the route information database 221 of FIG. 9, the disembarkation point table of FIG. 11A is created. Specifically, in FIG. 9, for train Z004, there is one route information (route information 001) for boarding the first car from the front at S2 station, and the boarding point is "1". Further, for train Z005, there is one route information (route information 003) for boarding the first car from the front at S1 station, and the boarding point is "1". At the same S2 station, there is one route information (route information 002) to board the first car from the front, and the boarding point is "1".

Here, in the route information 004, board the third or fifth car from the front at S4 station. In this case, as shown in FIG. 11, the boarding points of the third car and the fifth car may be evenly set to "0.5". In addition, points may be assigned by weighting according to the possibility of boarding. For example, if there is a high possibility of getting on the 3rd car (for example, if the 3rd car is closer to facilities such as ticket gates and stairs at S3 station), the 3rd car's boarding point is set to "0.8" and the 5th car's boarding point is set. It may be "0.2", or the boarding point of the third car may be "1" and the boarding point of the fifth car may be "0". In any case, it is desirable that the total number of boarding points is "1".

With reference to the point table created as described above, in step S4 of FIG. 10, the prediction unit 233 predicts congestion related to the vehicle. For example, it is assumed that the boarding point table shown in FIG. 11B is created as a result of totaling all route information. In this case, the prediction unit 233 can make a more detailed congestion prediction for the train Z004, such as "At S2 station, the number of passengers on the first and fourth cars is large, and the number of passengers on other vehicles is small."

Although the boarding point table is illustrated in FIG. 11, a disembarkation point table, a congestion point table, an increase / decrease point table, and the like may be created as in the first embodiment. As a specific example of the congestion prediction, the one described in the first embodiment is not "for each train" but "for each vehicle". For example, the prediction unit 233 predicts congestion by the number of people getting on each vehicle at the boarding station, the number of people getting off each vehicle at the getting-off station, the number of people getting on each vehicle at the boarding station or the getting-off station, and the number of people getting on the train. It is possible to predict the degree of congestion of each vehicle at the station or the station where the train gets off.

Further, as in FIG. 8, it may be summarized for each time range. As a result, the prediction unit 233 can make a congestion prediction such as "the third car of the S line train before noon is particularly crowded".

In the above explanation, after specifying the train, it is decided to extract the boarding vehicle or the disembarking vehicle of the train. However, when grouping by time range, vehicles may be extracted without specifying the train.

For example, even if the route information database 221 of FIG. 9 does not include the train ID, the vehicle can be extracted from the recommended vehicle information. As a specific example, when collecting trains departing from S1 station from 11:00 to 11:59, the recommended vehicle at S1 station of route information 001,003 is "the first car from the front", so the first car. The total of points can be "2".
In this case, the train ID information is unnecessary.

Further, the extraction unit 232 may extract the door in the train as the boarding position or the disembarking position. For that purpose, the route information database 221 may include recommended door information. Then, the prediction unit 233 can predict congestion for each door by creating a point table for each door. For example, the prediction unit 233 predicts congestion by the number of people getting on from each door at the boarding station, the number of people getting off from each door at the getting-off station, the number of people getting on the vicinity of each door at the boarding station or the getting-off station, and so on. It is possible to predict the degree of congestion near each door at the boarding station or the getting-off station.

As described above, in the second embodiment, since the position where passengers get on and off in the train is extracted from the route information stored in the storage unit 22 and used for the congestion prediction, it is possible to perform the congestion prediction in more detail at low cost. can.

In the above-mentioned example, the route information database 221 includes recommended vehicle information, and an example of specifying a vehicle based on the recommended vehicle information is shown. However, the route information database 221 does not necessarily have to include the recommended vehicle information.

For example, the station information database may be stored in the storage unit 22. Station information indicates the structure of each station as a station network, and is a specific location (for example, stops) on facilities such as stairs, elevators, escalator, ticket gates, entrances, exits, transfer gates, toilets, shops, etc. It consists of nodes assigned to (positions corresponding to train doors) and links showing the connection relationships between the nodes. Further, the station information may include not only the station premises but also the network information of the surrounding underground malls.

Then, the extraction unit 232 may specify the vehicle based on the position of the facility at the boarding station or the getting-off station.

FIG. 12 is a diagram showing an example of a method of extracting vehicles based on the position of a facility. As shown in the figure, the extraction unit 232 can use the vehicle closest to the facility such as stairs as the boarding vehicle at the boarding station (or the disembarking vehicle at the getting-off station). In the case of FIG. 12, the "second car" closest to the stairs may be used as the boarding vehicle, and the "first car or the second car" may be used as the boarding vehicle in consideration of the fact that the first car is next.

FIG. 13 is a diagram showing another example of a method of extracting vehicles based on the location of a facility. As shown in the figure, the extraction unit 232 may extract the boarding vehicle from the position of the transfer route according to the combination of the route before the transfer and the route after the transfer. In the case of FIG. 13, there is a transfer route between the vicinity of the first car of the train on the line before the transfer and the vicinity of the second car of the train on the line after the transfer. Therefore, the "second car" is extracted as the boarding vehicle of the train on the route after the transfer. Alternatively, the "first car" is extracted as a vehicle for getting off the train on the route before the transfer.

At the time of creating the point table, the prediction unit 233 may give points to each vehicle according to the distance from the facility such as stairs. For example, in the case of FIG. 12, the point of the second car closest to the stairs may be set to "0.8", and the point of the adjacent first car may be set to "0.2".

Further, the vehicle may be extracted or the point table may be created in consideration of both the recommended vehicle included in the route information database 221 and the location of the facility included in the station information database.

The same can be said not only for vehicles but also for doors.

(Third embodiment)
In the third embodiment described below, congestion is predicted for each train category.

The extraction unit 232 extracts at least one of the categories of trains boarding at the boarding station and the categories of trains disembarking at the getting-off station. Then, the prediction unit 233 creates a point table for each category from the extracted stations and categories, and predicts congestion related to the categories.

Category information may be included in the route information database 221. Further, if the category of trains that stop is limited depending on the station, the category may be estimated from the boarding station or the getting-off station.

The train category may be, for example, a type related to a stop station such as "limited express", "express", or "local train", or may be a nickname of a train such as "Kodama" or "Nozomi". The prediction unit 233 can predict congestion for each category, such as "express is easy to get crowded at station A" and "local trains are easy to get crowded at station B". Of course, congestion may be predicted for each vehicle or door, or trains included in a predetermined time may be grouped into categories for congestion prediction.

(Fourth Embodiment)
The fourth embodiment described below provides the user with congestion prediction.

FIG. 14 is a block diagram showing a schematic configuration of the information processing system 100 according to the fourth embodiment of the present invention. The information processing system 100 includes a terminal device 1 and a server 4 that are communicably connected via a network 3.

The terminal device 1 is used by the user, and may be a mobile electronic device such as a mobile phone, a smartphone or a tablet, or a stationary electronic device such as a personal computer or a car navigation device.

The terminal device 1 has a communication unit 11, an operation input unit 12, an output unit 13, and a control unit 14.

The communication unit 11 is an interface for transmitting and receiving information between the control unit 14 and the server 4 via the network 3.

The operation input unit 12 is an interface for a user to input an operation to the terminal device 1, and is, for example, a touch panel or a microphone in a mobile electronic device, or a keyboard, a touch pad, or a dial button in a stationary electronic device.

The output unit 13 is an interface that outputs various information from the terminal device 1 to the user, and is, for example, a liquid crystal display that displays an image. Specifically, the output unit 13 displays information such as a GUI (Graphical User Interface) for receiving an operation from a user and route information described later. Alternatively, the output unit 13 may be a speaker that outputs information by voice.

Further, the output unit 13 does not have to directly present the information to the user. For example, the output unit 13 may be an interface that outputs a video signal and / or an audio signal to a display means and / or an audio reproduction means connected to the outside, or outputs data to a printing device connected to the outside. It may be an interface to be used, or it may be output to a storage device inside or outside the terminal device 1 and stored.

The control unit 14 includes a search condition setting unit 141, an information transmission unit 142, and an information reception unit 143. Each of these parts is a function realized by, for example, a processor (not shown) in the information processing system 100 executing a predetermined information processing program.

The search condition setting unit 141 sets the search condition. The search condition setting unit 141 can set the search condition by receiving the search condition such as information on the departure place, the destination, and the time from the user via the operation input unit 12. Alternatively, the search condition setting unit 141 may set at least a part of the search conditions from other than the user input, for example, the position of the terminal device 1 may be set as the departure point, or the current time may be set as the departure time.

The information transmission unit 142 transmits various information such as search conditions to the server 4 via the network 3.

The information receiving unit 143 receives various information such as route information generated by the server 4 from the server 4 via the network 3. The received information is output from the output unit 13 by the output control unit (not shown) in the control unit 14.

The server 4 has a communication unit 41, a storage unit 42, and a control unit 43.

The communication unit 41 is an interface for transmitting and receiving information between the control unit 43 and the terminal device 1 via the network 3.

The storage unit 42 stores the congestion prediction information database 421 and the route network information database 422.

The congestion prediction information database 421 includes the congestion prediction information generated by the information processing system 2 of FIG. Even if all or part of the information processing system 2 and a part of the server 4 of FIG. 14 are provided in one device and the congestion prediction information generated by the prediction unit 233 is written in the congestion prediction information database 421. good. Further, the information processing system 2 and the server 4 may be used as separate devices, and the congestion prediction information generated by the prediction unit 233 may be written to the congestion prediction information database 421 via the network 3.

The congestion prediction information may be updated every time one congestion prediction information is generated, or may be updated at regular intervals.

The route network information database 422 includes, for example, map information and traffic network information as route network information.

The map information includes map data such as road maps of the whole country and each region, and map object information associated with the map data. Map data is a map showing actual roads. The map object information includes shape information about the shape of the facility displayed on the map, note information about the notes displayed on the map, symbol information about the symbols displayed on the map, and the like.

Transportation network information is information that defines a transportation network such as a railroad or a bus or a road network. The transportation network information includes transportation route information, timetable information, fee information, and the like. The road network information is represented by a combination of node data, which is a node in the road network representation such as an intersection, and link data, which is a road section between the nodes. Nodes are identified, for example, by latitude and longitude and correspond to the location of the detailed map. Map information and road network information are used for route search.

The control unit 43 includes an information receiving unit 431, a route search unit 432, and an information transmitting unit 433. Each of these parts is a function realized by, for example, a processor (not shown) in the information processing system 100 executing a predetermined information processing program.

The information receiving unit 431 receives the search condition from the terminal device 1 via the network 3.
Further, the information receiving unit 431 receives congestion information and route network information from the congestion prediction information database 421 and the route network information database 422 in the storage unit 42, respectively.

The route search unit 432 uses the route network information, performs a route search based on the search conditions, and generates the route information. The route information includes, for example, information such as a means of transportation from a departure place to a destination, a movement distance, a time and a charge required for the movement, a departure time and an arrival time. Further, the route search unit 432 confirms whether or not there is congestion prediction information related to the generated route information. When there is related congestion prediction information, the information receiving unit 431 is controlled to receive this congestion prediction information.

The information transmission unit 433 transmits route information and congestion prediction information from the communication unit 41 to the terminal device 1 via the network 3.

FIG. 15 is a sequence diagram showing an example of the processing operation of the information processing system 100 of FIG.

First, the search condition setting unit 141 sets the search condition according to the operation to the operation input unit 12 by the user operation. This search condition is transmitted to the server 4 by the information transmission unit 142 (step S11).

The information receiving unit 431 of the server 4 receives the search condition (step S21). Then, the route search unit 432 performs a route search based on the search conditions and generates route information (step S22). The route information generated here may be written in the route information database 221 of FIG. After that, the route search unit 432 confirms whether or not the congestion prediction information regarding the generated route information is in the congestion prediction information database 421 (step S23).

Even if there is congestion prediction information, if the number of route information (the number of points in the point table) is small and it is considered that the accuracy of congestion prediction is low, the route search unit 432 determines that there is no congestion prediction information. You may.

When there is congestion prediction information (YES in step S23), the information receiving unit 431 acquires the congestion prediction information from the congestion prediction information database 421 (step S24). Then, the route search unit 432 adds the acquired congestion prediction to the route information (step S25). The route information is transmitted to the terminal device 1 by the information transmission unit 433 (step S26).

On the other hand, when there is no congestion prediction information (NO in step S23), the route information is transmitted as it is to the terminal device 1 by the information transmission unit 433 (step S26).

The information receiving unit 143 of the terminal device 1 receives the route information. Then, the route information is output from the output unit 13 (step S12). The output form is congestion such as which station is likely to be seated, which station is crowded, which vehicle (near the door) is likely to be seated, which vehicle (near the door) is crowded, etc. It is desirable to be able to grasp the situation visually and intuitively. Hereinafter, an output example will be described when the output unit 13 is a display and the operation input unit 12 is a touch panel arranged on the display.

FIG. 16 is a diagram showing an example of a screen displayed on a display. In FIG. 16A, the boarding station is S1 station on the S line (departure time: 19:15) and the disembarkation station is S6 station on the S line (arrival time: 19:48). This is the screen when there is prediction information. As shown in the figure, an icon A1 indicating that there is congestion prediction information is displayed (when there is no congestion prediction information, such an icon is not displayed or is displayed as non-selectable). Then, when the user selects the position where the icon A1 is displayed on the touch panel, the congestion prediction information is displayed on the display. This congestion prediction information is related to the train departing from S1 station at 19:15.

FIG. 16B shows an example in which the congestion prediction is a prediction regarding the number of people getting off. This screen schematically shows the number of people getting off at each station for each vehicle. For example, the mark M1 of the first car of S5 station, which is an intermediate station, indicates that there is a high possibility that a large number of people can get off and sit down. On the other hand, the mark M2 on the fourth car of S1 to S6 stations indicates that the number of people getting off the train is small and the possibility of sitting is low. In addition, Mark M3 at other stations indicates that the number of people getting off is medium.

With reference to such congestion prediction information, the user can understand that if he / she gets on the first car of S1 station, he / she is more likely to sit at S5 station on the way than when he / she gets on another vehicle.

FIG. 16C shows an example in which the congestion prediction is a prediction regarding the number of passengers at each station. This screen schematically shows the number of passengers at each station for each vehicle. For example, the first car shows that it is crowded from S1 to S6 stations. The third car is relatively vacant at S1 and S2 stations, but it shows that it is a little crowded after S3 station. The fourth car is relatively vacant at S1 station, but it is a little crowded at S2 station, and it shows that it is crowded after S3 station.

With reference to such congestion prediction information, if the user gets on the third car of S1 station, it is more likely that he / she can sit down compared to when he / she gets on another vehicle, and even if he / she cannot sit down, it will not be so crowded up to S6 station. I can grasp it.

Note that FIG. 15 shows an example in which congestion prediction information is added and route information is transmitted from the server 4 to the terminal device 1. However, the information transmission unit 433 of the server 4 may separately transmit the route information and the congestion prediction information to the terminal device 1. For example, initially, only information indicating that there is route information and congestion prediction information may be transmitted to the terminal device 1.

Then, in response to the request from the terminal device 1, the congestion prediction information may be transmitted from the server 4 to the terminal device 1.

In addition, a vehicle on which the user of the terminal device 1 is riding is specified by a position acquisition unit (not shown) such as GPS in the terminal device 1, and position information indicating the position of the terminal device 1 is periodically transmitted to the server 4. ..
Then, the server 4 is based on the location information and the congestion prediction information, and the station where the number of people getting off the vehicle in the user is large and the timing when the congestion degree is eased (specifically, when the user arrives at a specific station). Or at a specific time), the congestion prediction information may be transmitted to the terminal device 1.

As described above, in the fourth embodiment, the congestion prediction information is presented to the user who has performed the route search. Therefore, the user can easily grasp the congestion situation.

At least a part of the information treatment system described in the above-described embodiment may be configured by hardware or software. When configured by software, a program that realizes at least a part of the functions of the information processing system may be stored in a recording medium such as a flexible disk or a CD-ROM, read by a computer, and executed. The recording medium is not limited to a removable one such as a magnetic disk or an optical disk, and may be a fixed recording medium such as a hard disk device or a memory.

Further, a program that realizes at least a part of the functions of the information treatment system may be distributed via a communication line (including wireless communication) such as the Internet. Further, the program may be encrypted, modulated, compressed, and distributed via a wired line or a wireless line such as the Internet, or stored in a recording medium.

Further, the information processing system may be operated by one or a plurality of information processing devices.
When a plurality of information processing devices are used, one of the information processing devices may be a computer, and the function may be realized as at least one means of the information processing system by executing a predetermined program by the computer.

Based on the above description, those skilled in the art may be able to conceive additional effects and various modifications of the present invention, but the embodiments of the present invention are not limited to the individual embodiments described above. do not have. Various additions, changes and partial deletions are possible without departing from the conceptual idea and purpose of the present invention derived from the contents specified in the claims and their equivalents.

1 Terminal device 11 Communication unit 12 Operation input unit 13 Output unit 14 Control unit 141 Search condition setting unit 142 Information transmission unit 143 Information reception unit 2 Information processing system 22 Storage unit 221 Route information database 23 Control unit 231 Information acquisition unit 232 Extraction unit 233 Prediction unit 4 Server 41 Communication unit 42 Storage unit 421 Congestion prediction information database 422 Route network information database 43 Control unit 431 Information reception unit 432 Route search unit 433 Information transmission unit

Claims (17)

A means for generating route information by performing a route search based on a search condition set according to a user's operation, and
From the congestion forecast information database that stores congestion forecast information indicating congestion forecast for each train stop station in relation to each train, multiple trains included in a predetermined time range, or each train category. A means for acquiring train congestion prediction information included in the route information, and
A means for displaying an icon on the display for transitioning to the display of the congestion prediction information together with the route information.
When the icon is selected based on the operation of the user, a means for displaying the congestion prediction for each stop station of the train included in the route information on the display in a graphic form.
An information processing system equipped with. The information processing system according to claim 1, wherein the congestion prediction information is information indicating a prediction of the number of people getting off at each stop of a train. The information processing system according to claim 1, wherein the congestion prediction information is information indicating a prediction of the number of passengers at each stop of a train. The information processing system according to any one of claims 1 to 3, wherein the congestion prediction information is information indicating a congestion prediction for each train vehicle. The information processing system according to any one of claims 1 to 4, wherein the congestion prediction information is information predicted based on the congestion record information indicating the past congestion record. Information acquisition means for acquiring past route information, which is the result of multiple past route searches, from the route information database,
From a plurality of past route information acquired by the information acquisition means,
Extract at least one of the boarding station and the getting-off station,
Further, at least one of the trains to be boarded at the extracted boarding station and the trains to be disembarked at the extracted disembarkation station is extracted.
Extraction means and
Based on the information about stations and trains extracted by the extraction means, the number of past route information is aggregated for each station and each train, and based on the aggregated result, congestion prediction related to the train is performed. A prediction means that generates congestion prediction information and writes it to the congestion prediction information database.
The information processing system according to any one of claims 1 to 5, further comprising. The prediction means, as the congestion prediction,
The number of people getting on or off at the extracted stations,
Increase / decrease in the number of passengers at the extracted stations, and
The information processing system according to claim 6, which makes a prediction regarding at least one of the congestion degrees at the extracted stations. Information acquisition means for acquiring past route information, which is the result of multiple past route searches, from the route information database,
From a plurality of past route information acquired by the information acquisition means,
Extract at least one of the boarding station and the getting-off station,
Further, at least one of the boarding position on the train at the extracted boarding station and the disembarking position on the train at the extracted disembarking station is extracted.
Extraction means and
Based on the information about stations and locations extracted by the extraction means, the number of past route information is aggregated for each station and location, and based on the aggregated results, congestion prediction related to the location is performed. A prediction means that generates congestion prediction information and writes it to the congestion prediction information database.
The information processing system according to any one of claims 1 to 5, further comprising. The prediction means, as the congestion prediction,
The number of people getting on or off at the extracted position,
Increase / decrease in the number of passengers at the extracted positions, and
Congestion level at the extracted position,
The information processing system according to claim 8, wherein the information processing system makes a prediction regarding at least one of them. The route information includes recommended position information indicating a recommended boarding position or disembarking position.
The information processing system according to claim 8 or 9, wherein the extraction means extracts the position based on the recommended position information. The information processing system according to claim 8 or 9, wherein the extraction means extracts the position based on the position of a predetermined facility at the extracted station. The information processing system according to any one of claims 8 to 11, wherein the extraction means extracts a vehicle in a train or a door in a vehicle of a train as the position. Information acquisition means for acquiring past route information, which is the result of multiple past route searches, from the route information database,
From a plurality of past route information acquired by the information acquisition means,
Extract at least one of the boarding station and the getting-off station,
Further, at least one category of the extracted train category to be boarded at the boarding station and the extracted train category to be disembarked at the disembarking station is extracted.
Extraction means and
Based on the information about the stations and categories extracted by the extraction means, the number of past route information is aggregated for each station and category, and based on the aggregated result, congestion prediction related to the category is performed. A prediction means that generates congestion prediction information and writes it to the congestion prediction information database.
The information processing system according to any one of claims 1 to 5, further comprising. The information processing system according to any one of claims 6 to 13, wherein the prediction means collectively predicts congestion of a plurality of the trains included in a predetermined time range. Information processing method executed by a computer
A step of performing a route search based on a search condition set according to a user's operation and generating route information, and a step of generating route information.
From the congestion forecast information database that stores congestion forecast information indicating congestion forecast for each train stop station in relation to each train, multiple trains included in a predetermined time range, or each train category. A step of acquiring train congestion prediction information included in the route information, and
A step of displaying an icon on the display for transitioning to the display of the congestion prediction information together with the route information.
When the icon is selected based on the operation of the user, the step of displaying the congestion prediction for each stop station of the train included in the route information on the display in a graphic form.
Information processing method. On the computer
A step of performing a route search based on a search condition set according to a user's operation and generating route information, and a step of generating route information.
From the congestion forecast information database that stores congestion forecast information indicating congestion forecast for each train stop station in relation to each train, multiple trains included in a predetermined time range, or each train category. A step of acquiring train congestion prediction information included in the route information, and
A step of displaying an icon on the display for transitioning to the display of the congestion prediction information together with the route information.
When the icon is selected based on the operation of the user, the step of displaying the congestion prediction for each stop station of the train included in the route information on the display in a graphic form.
Information processing program to execute. Information processing equipment,
A means for generating route information by performing a route search based on a search condition set according to a user's operation.
From the congestion forecast information database that stores congestion forecast information indicating congestion forecast for each train stop station in relation to each train, multiple trains included in a predetermined time range, or each train category. A means for acquiring train congestion prediction information included in the route information, and
A means for displaying an icon on the display for transitioning to the display of the congestion prediction information together with the route information.
When the icon is selected based on the operation of the user, a means for displaying the congestion prediction for each stop station of the train included in the route information on the display in a graphic form.
An information processing program that functions as an information processing program.

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