JP7353799B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, when performing navigation (hereinafter also referred to as "guidance") using an in-vehicle terminal or mobile terminal, etc., it displays the location of a parking lot attached to a facility such as a store, and also displays whether the parking lot is full or not. There is a known technique for displaying information indicating whether or not the sky is empty.

Japanese Patent Application Publication No. 2011-215049

However, with the above-mentioned conventional technology, it is not known when a full parking lot will become vacant, so the user has to decide whether to wait until the parking lot becomes vacant or to search for another parking lot. The problem was that it was difficult.

The present application has been made in view of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can predict the timing when a parking lot that is full will become vacant. do.

The information processing device according to the present application includes an acquisition section and a prediction section. The acquisition unit acquires behavior information indicating a user's behavior or an event that may affect the behavior. The prediction unit predicts the timing at which the vehicle parked by the user will leave the parking lot based on the behavior information acquired by the acquisition unit. Further, the prediction unit predicts a timing at which the vehicle will leave the parking lot based on a direction in which the user moves.

According to one aspect of the embodiment, it is possible to provide an information processing device, an information processing method, and an information processing program that can predict the timing when a parking lot that is full will become vacant.

FIG. 1 is an explanatory diagram showing an example of an information processing method according to an embodiment. FIG. 2 is an explanatory diagram showing an example of the configuration of the information processing device according to the embodiment. FIG. 3 is an explanatory diagram showing an example of a parking lot database according to the embodiment. FIG. 4 is an explanatory diagram showing an example of a vehicle database according to the embodiment. FIG. 5 is an explanatory diagram showing an example of a user database according to the embodiment. FIG. 6 is a flowchart showing processing executed by the control unit of the information processing device according to the embodiment. FIG. 7 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device according to the embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS An information processing apparatus, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail below with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Moreover, each embodiment can be combined as appropriate within the range that does not conflict with the processing contents. Further, in each of the embodiments below, the same parts are given the same reference numerals, and redundant explanations will be omitted.

[1. Information processing method]
First, an information processing method performed by the information processing apparatus 1 according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an example of an information processing method according to an embodiment. The information processing device 1 is an information processing device such as a server that transmits and receives various information to and from the vehicle C via a network N such as the Internet (see FIG. 2).

Vehicle C is a vehicle such as a car or a motorcycle driven by a user. Furthermore, the vehicle C has a terminal device that can communicate with the information processing device 1 and transmits probe data indicating the speed and position of the vehicle C to the information processing device 1.

The terminal device included in the vehicle C is a terminal device used by a user who uses various services. For example, the terminal device may be any information processing device such as a tablet terminal, a PC, a PDA, a smartphone, or an in-vehicle car navigation system.

In addition, such terminal devices are connected to wireless communication networks such as 3G (Generation), 4G, LTE (Long Term Evolution), GSM (registered trademark) (Global System for Mobile Communications), Bluetooth (registered trademark), wireless LAN (Local It is possible to connect to a network N via short-distance wireless communication such as an area network) and communicate with the information processing device 1.

Then, as shown in FIG. 1A, the information processing device 1 communicates with the user who uses the target parking lot P, that is, the user who uses the facility F attached to the parking lot P, via the network N etc. Information (hereinafter also referred to as "behavior information") indicating the behavior of the user or an event that may influence the behavior is acquired (step S1).

For example, as shown in FIG. 1A, a beacon B is installed at a predetermined location (for example, next to a cash register or at an exit) in a target facility F. Then, for example, when the user passes near the beacon B, the information processing device 1 acquires information indicating that the user has passed as behavior information.

Next, the information processing device 1 predicts the timing at which the vehicle C1 parked in the parking lot P among the vehicles C will leave the parking lot P based on the acquired behavior information (step S2). For example, when information processing device 1 acquires information that two users have passed from beacon B installed next to the cash register of facility F, information processing device 1 may receive information such as “Two vehicles C1 parked by the user will be parked 10 minutes later. It can be predicted that the driver will leave the parking lot P.

In addition, when the information processing device 1 acquires information that one user has passed through from the beacon B installed at the exit of the facility F, the information processing device 1 receives the message “One vehicle C1 parked by the user will be parked in the parking lot 5 minutes later. It can be predicted that the player will be ejected from the game. This is because it can be predicted that the user who passes by the cash register or the exit will soon leave the facility F, that is, will soon get into the vehicle C and leave the parking lot P.

Next, based on the predicted timing when the vehicle C1 will leave the parking lot P, the information processing device 1 provides information on the timing when the parking lot P becomes vacant for the vehicle C2 waiting to park in the parking lot P. (Step S3).

For example, the information processing device 1 can provide information to the effect that "there will be a vacant space in the parking lot P in 5 minutes" to the first vehicle C2 among the vehicles C2 waiting to be parked. Furthermore, the information processing device 1 can provide the second and third vehicles C2 in line among the vehicles C2 waiting to be parked with the information that "there will be a vacant space in the parking lot P in 10 minutes." can. Note that the order of the vehicles C2 waiting to be parked can be detected based on the position information of the vehicles C2.

In this way, in the embodiment, the timing at which the vehicle C1 parked by the user will leave the parking lot P is predicted based on the information indicating the behavior of the user who uses the facility F. Thereby, it is possible to predict the timing when the parking lot P that is full will become vacant.

Note that although the example shown here uses information from beacon B as the information indicating the user's behavior (i.e., behavior information), the behavior information indicating the user's behavior is Not limited to information. Further, the behavior information may be information indicating an event that may affect the user's behavior. Examples of other behavior information will be described later.

Furthermore, in the embodiment, the timing at which the vehicle C1 parked by the user will leave the parking lot P is predicted based on information obtained from a location other than the parking lot P, such as the beacon B. As a result, compared to the case where prediction is made based on information obtained from the parking lot P itself, such as predicting the exit timing based on the average time interval of vehicle C leaving the parking lot P, the parking lot P becomes fuller. The timing when the parking lot P becomes vacant can be predicted more accurately.

Subsequently, as shown in FIG. 1B, the vehicle C1a of the parked vehicles C1 starts from the parking space in the parking lot P, and the parking space where it was parked becomes an empty space E. Further, with the departure of the vehicle C1a, a vehicle C2a among the vehicles C2 waiting to be parked in the parking lot P enters the parking lot P.

Here, the information processing device 1 determines whether the vehicle C in the parking lot P is parked or exits (step S4). For example, the information processing device 1 determines whether the vehicle C traveling in the parking lot P parks or leaves the parking lot, based on a usage flag indicating whether the user driving the vehicle C is using the facility F. do.

In this case, since the usage flag of the user driving the vehicle C1a has a value indicating usage (for example, "1"), the information processing device 1 determines that the vehicle C1a is a vehicle that is about to leave the parking lot P. do. In addition, since the usage flag of the user driving the vehicle C2a is a value indicating unused (for example, "0"), the information processing device 1 determines that the vehicle C2a is a vehicle that attempts to park in the parking lot P. do.

Then, the information processing device 1 provides information regarding an empty parking space in the parking lot P (hereinafter referred to as "vacant space (also referred to as "space information") (step S5). For example, the information processing device 1 notifies the vehicle C2a of information regarding the position of the parking space (that is, empty space E) where the vehicle C1a that is about to leave was parked, as empty space information.

In this way, in the embodiment, it is determined whether the vehicle C in the parking lot P is to park or leave the parking lot, and the vehicle C2a that is trying to park in the parking lot P is assigned an empty parking lot in the parking lot P. Notify free space information about spaces. Thereby, the vehicle C2a attempting to park in the parking lot P can smoothly reach the vacant space E.

Note that in the example shown here, it is determined whether vehicle C parks or leaves based on the usage flag that indicates whether the user is using facility F, but the usage flag is used as a means of determination. It is not limited to the example used. Examples of other determining means will be described later.

Moreover, in the embodiment, among the vehicles C in the parking lot P, only the vehicle C2a that is going to park is notified of the information regarding the empty space E. Thereby, it is possible to suppress notification of information regarding the empty space E to the vehicle C1a that is about to leave the parking lot P.

Therefore, according to the embodiment, since it is possible to suppress the notification of unnecessary information to the vehicle C1a, it is possible to suppress making the user driving the vehicle C1a uncomfortable.

Further, in the embodiment, information regarding the position of the vacant space E where the vehicle C1a attempting to leave the parking lot P was parked is notified to the vehicle C2a as vacant space information. Thereby, information regarding the newly created vacant space E can be notified to the vehicle C2a that is attempting to park. Therefore, according to the embodiment, it is possible to prevent the notified free space E from being filled up after being notified.

Moreover, in the embodiment, it is preferable to notify the empty space information only to the vehicle C2a among the vehicles C trying to park in the parking lot P, which have passed by the vehicle C1a trying to leave. In this way, by reporting limited information regarding the position of one vacant space E, it is possible to prevent a plurality of vehicles C from rushing to park in one vacant space E. Therefore, according to the embodiment, confusion in the parking lot P can be suppressed.

[2. Configuration of information processing device]
Next, with reference to FIG. 2, the configuration of the information processing device 1 according to the embodiment will be described. FIG. 2 is an explanatory diagram showing an example of the configuration of the information processing device 1 according to the embodiment. As shown in FIG. 2, the information processing device 1 includes a communication section 11, a storage section 12, and a control section 13. Note that the information processing device 1 includes an input section (for example, a keyboard, a mouse, etc.) that accepts various operations from an administrator using the information processing device 1, and a display section (for example, a liquid crystal display) for displaying various information. etc.).

(About communication department 11)
The communication unit 11 is realized by, for example, a NIC (Network Interface Card). The communication unit 11 is connected to the network N by wire or wirelessly, and transmits and receives information to and from a terminal device mounted on the vehicle C via the network N.

(About the storage unit 12)
The storage unit 12 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 2, the storage unit 12 includes a parking lot database 121, a vehicle database 122, and a user database 123.

(About parking database 121)
The parking lot database 121 is a database in which information about parking lots that predicts when a parking lot becomes available is registered. FIG. 3 is an explanatory diagram showing an example of the parking lot database 121 according to the embodiment. As shown in FIG. 3, in the parking lot database 121, parking lot IDs, location information, the number of available parking spaces, facility information, and the number of passed check flags are registered in association with each other. .

Here, the parking lot ID is an identifier for identifying a parking lot. Note that the parking lot database 121 may store the parking lot name and the like in addition to the parking lot ID. Further, the position information is information indicating the position of the parking lot indicated by the associated parking lot ID.

Further, the number of vacant parking spaces is information indicating the number of vacant parking spaces in the parking lot indicated by the associated parking lot ID. Further, the facility information is information indicating a facility attached to the parking lot indicated by the associated parking lot ID. In addition, the number of times the user passed the check flag indicates the number of times the user passed various check flags (for example, beacon B) installed at various locations in the facilities attached to the parking lot indicated by the associated parking lot ID. It is information.

(About vehicle database 122)
The vehicle database 122 is a database in which information about vehicles to which parking lot vacant space information is notified is registered. FIG. 4 is an explanatory diagram showing an example of the vehicle database 122 according to the embodiment. As shown in FIG. 4, the vehicle database 122 registers a vehicle ID, location information, speed information, departure point, and destination in association with each other.

Here, the vehicle ID is an identifier for identifying a vehicle. Note that the vehicle database 122 may store the vehicle number, vehicle type, etc. in addition to the vehicle ID. Further, the position information is information indicating the position of the vehicle indicated by the associated vehicle ID. Moreover, the speed information is information indicating the speed of the vehicle indicated by the associated vehicle ID.

Further, the departure place is information indicating the departure place of the vehicle indicated by the associated vehicle ID. Further, the destination is information indicating the destination of the vehicle indicated by the associated vehicle ID.

Note that the above-mentioned position information and speed information are information included in probe data transmitted at predetermined time intervals from a terminal device mounted on a vehicle. Further, the above-mentioned departure point and destination are registered in the vehicle database 122 when the departure point and destination are set by the user when the user is guided through a terminal device mounted on the vehicle.

(About user database 123)
The user database 123 is a database in which information of users who drive vehicles and visit facilities is registered. FIG. 5 is an explanatory diagram showing an example of the user database 123 according to the embodiment. As shown in FIG. 5, in the user database 123, a user ID, location information, usage flag, and vehicle ID are registered in association with each other.

Here, the user ID is an identifier for identifying a user. Note that the user database 123 may store the user's name, attribute information indicating the user's attributes (demographic attributes, psychographic attributes), etc. in addition to the user ID.

Further, location information is information indicating the location of the user indicated by the associated user ID. Such location information can be obtained, for example, from a mobile terminal such as a smartphone owned by the user indicated by the associated user ID.

Further, the usage flag is information indicating whether the user indicated by the associated user ID has used various facilities from before a predetermined time to the present time. For example, if the user indicated by the user ID is using a facility, the information processing device 1 registers the facility in association with a usage flag "1", and the information processing device 1 registers the facility in association with a usage flag "1", and indicates that the user indicated by the user ID is not using the facility. If so, the usage flag "0" is associated with the facility and registered. Further, the vehicle ID is an identifier for identifying the vehicle driven by the user indicated by the associated user ID.

(About the control unit 13)
The control unit 13 is a controller, and for example, executes various programs (information processing programs) stored in a storage device inside the information processing device 1 by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), etc. (corresponding to one example) is realized by being executed using RAM as a work area. Further, the control unit 13 is, for example, a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 2, the control unit 13 includes a collection unit 131, an acquisition unit 132, a prediction unit 133, a provision unit 134, a determination unit 135, and a notification unit 136, and includes information processing described below. To realize or carry out the functions and actions of Note that the internal configuration of the control unit 13 is not limited to the configuration shown in FIG. 2, and may be any other configuration as long as it performs information processing to be described later.

(About the collection unit 131)
The collection unit 131 collects probe data from a terminal device mounted on the vehicle C. The collection unit 131 then updates the position information and speed information in the vehicle database 122 based on the probe data collected from the terminal device.

The collection unit 131 also collects history information regarding the use of the facility F by the user. For example, the collection unit 131 collects user purchase history and the like as history information from a POS (point of sales) system of facility F, which is a store. Then, when the collection unit 131 is able to collect such historical information, it updates the usage flag in the user database 123 to "1". On the other hand, if the collection unit 131 is unable to collect such historical information, it updates the usage flag in the user database 123 to "0".

(About the acquisition unit 132)
The acquisition unit 132 acquires, via the network N, information (that is, behavior information) indicating the behavior of the user who uses the facility F to which the parking lot P is attached, or an event that may affect the behavior. For example, the acquisition unit 132 uses, as the behavior information, the number of users who have passed near a check flag (for example, beacon B) installed at a predetermined location of facility F (for example, next to the cash register or at the exit). get. The acquisition unit 132 then registers the number of users who passed near the check flag, which is the acquired behavior information, in the parking lot database 121.

(About the prediction unit 133)
The prediction unit 133 predicts the timing at which the vehicle C1 parked in the parking lot P among the vehicles C will leave the parking lot P based on the behavior information acquired by the acquisition unit 132. For example, the prediction unit 133 predicts the timing at which the vehicle C1 leaves the parking lot P and the timing at which the vehicle C1 leaves the parking lot P, based on the installation location of the check flag whose behavior information has been acquired and the number of users who have passed near the check flag. It is possible to predict the number of vehicles C1 that will leave the venue.

For example, there is a check flag that indicates that the person will leave the parking lot P in 5 minutes (for example, beacon B installed at the exit), and a check flag that indicates that the person will leave the parking lot P in 10 minutes (for example, a beacon installed next to the cash register). A check flag (for example, a beacon B installed on a product shelf near the cash register) that is predicted to leave the parking lot P in 15 minutes is installed in the facility F in stages.

Then, if two users pass near a check flag that is predicted to leave the venue in 5 minutes, and eight users pass near a check flag that is predicted to leave the venue in 10 minutes, the prediction unit 133 It can be predicted that two vehicles C1 will leave the parking lot P in 5 minutes, and that eight vehicles C1 will leave the parking lot P in 10 minutes.

By providing check flags in stages in the facility F in this way, the prediction unit 133 can more accurately predict the timing at which the vehicle C1 parked in the parking lot P will leave the parking lot P.

The prediction unit 133 can predict the timing at which the vehicle C1 parked by the user will leave the parking lot P based on the distance between the parking lot P and the place where the user has performed the action. Furthermore, the prediction unit 133 determines the timing at which the vehicle C1 parked by the user leaves the parking lot P based on the distance between the location where the user performed the action and the location where the user parked the vehicle C1 in the parking lot P. may be predicted. Further, the prediction unit 133 predicts the timing at which the vehicle C1 parked by the user will leave the parking lot P based on the distance from the location where the user parks the vehicle C1 in the parking lot P to the exit of the parking lot P. It's okay.

In this way, by predicting the timing when the vehicle C1 will leave the parking lot P based on the distance between each location in the facility F and the parking lot P, it is possible to more accurately predict the timing when the parking lot P becomes vacant. I can do it.

Furthermore, the prediction unit 133 may predict the timing at which the vehicle C1 parked by the user will leave the parking lot P based on the direction in which the user moves within the facility F or the parking lot P. Thereby, the timing when the parking lot P becomes vacant can be predicted more accurately. In addition. The user's movement direction can be obtained from a mobile terminal such as a smartphone owned by the user.

Furthermore, the prediction unit 133 generates a model by statistically analyzing the user's behavior in the facility F and the parking lot P, and based on the modeled information, the prediction unit 133 determines whether the vehicle C1 parked by the user is in the parking lot. The timing of exit may be predicted from P. For example, if a certain user has left the parking lot P seven minutes after passing by the cash register of facility F in many past patterns, and the user also passes by the cash register, the prediction unit 133 predicts that “the user It can be predicted that one parked vehicle C1 will leave the parking lot P in 7 minutes.

Further, the prediction unit 133 generates a model by statistically analyzing the behavior of a plurality of users who visited the facility F in the past, and based on the modeled statistical information, the vehicle C1 parked by the user is The timing of leaving the parking lot P may be predicted. For example, by statistically analyzing the behavior of multiple users who visited Facility F in the past, it was found that ``From 11:45 a.m. to 12:00 p.m., 2 out of all visitors went to get lunch, according to a normal distribution. It is assumed that statistical information such as ``A percentage of users leave parking lot P'' is modeled. Then, when there are 300 visitors to facility F as of 11:30, the prediction unit 133 predicts that "vehicle C1 parked by the user between 11:45 and 12:00" is based on the modeled statistical information. It can be predicted that 60 vehicles will exit from parking lot P.

Note that the plurality of users to be analyzed when generating the modeled statistical information described above and the users to be predicted for the timing of leaving the parking lot P may not be the same.

The prediction unit 133 also estimates the state of the user based on the behavior information acquired by the acquisition unit 132, and based on the estimated state of the user, the timing at which the vehicle C1 parked by the user leaves the parking lot P. may be predicted. That is, the prediction unit 133 may estimate the user's context information based on the behavior information, and predict the timing at which the vehicle C1 parked by the user will leave the parking lot P based on the context information.

For example, from the beacon B installed in the facility F, the acquisition unit 132 receives the following information: "The user passed in front of an ice cream shop," "The user passed in front of a restaurant," and "The user passed in front of a flower shop." Suppose that the following information is acquired continuously. In this case, the prediction unit 133 estimates the user's moving direction and moving speed based on the positions of the ice cream shop, restaurant, and flower shop in the facility F. The prediction unit 133 can predict the timing at which the vehicle C1 parked by the user leaves the parking lot P based on the estimated moving direction and moving speed of the user.

In this way, by estimating the user's context information based on the acquired behavioral information and predicting the timing when the vehicle C1 will leave the parking lot P based on the context information, the timing when the parking lot P becomes available can be predicted. Can be predicted more accurately.

(About the providing unit 134)
The providing unit 134 provides information on the timing when a space becomes available in the parking lot P to the vehicle C2 waiting to be parked in the parking lot P, based on the timing when the vehicle C1 leaves the parking lot P predicted by the prediction unit 133. I will provide a.

For example, if the prediction unit 133 predicts that "two vehicles C1 will leave the parking lot P in 5 minutes, and eight vehicles C1 will leave the parking lot P in 10 minutes", the providing unit 134 will: The information to the effect that "there will be space in parking lot P in 5 minutes" is provided to the second vehicle C2 from the front, and the information "10 minutes later," is provided to the third to tenth vehicles C2 in the queue. It is possible to provide information to the effect that there is a vacancy in the parking lot P.

In this way, by providing check flags in stages in the facility F, the providing unit 134 can more accurately provide the timing when the vehicle C2 waiting to be parked can be parked in the parking lot P. .

(About the determination unit 135)
The determining unit 135 determines whether the vehicle C in the parking lot P is parked or exits. For example, the determination unit 135 determines whether the vehicle C driven by the target user is parked or exits, based on the value of the usage flag registered in the user database 123.

For example, the determining unit 135 determines that the user's vehicle C whose use flag of the facility F associated with the parking lot P is "1" is the vehicle C1a that is about to leave the parking lot P. Further, the determining unit 135 determines that the user's vehicle C whose use flag for the facility F associated with the parking lot P is "0" is the vehicle C2a that is intended to be parked in the parking lot P.

(About notification section 136)
The notification unit 136 notifies the vehicle C2a of the vehicles C in the parking lot P that intends to park in the parking lot P of information regarding an empty parking space in the parking lot P. For example, the notification unit 136 notifies the vehicle C2a of information regarding the position of the parking space where the vehicle C1a, which is about to leave the parking lot P, was parked, as empty space information.

Note that the notification unit 136 can detect the position of the parking space where the vehicle C1a is parked based on probe data collected from a terminal device mounted on the vehicle C1a.

Further, the notification unit 136 can notify the vacant space information only to the vehicle C2a among the vehicles C trying to park in the parking lot P, which passed by the vehicle C1a trying to leave. Thereby, confusion in the parking lot P can be suppressed.

Note that the notification unit 136 detects that the vehicle C1a attempting to exit and the vehicle C2a attempting to park have passed each other based on probe data collected from terminal devices mounted on the vehicle C1a and the vehicle C2a. I can do it.

In addition, the location of the parking space where vehicle C1a was parked and the fact that vehicle C1a and vehicle C2a passed each other are not limited to detection based on probe data, but can also be detected using a camera separately installed in parking lot P or various The detection may be based on information from a sensor or the like.

In addition, if the vehicle C1a attempting to leave the parking lot P does not pass each other for a predetermined period of time or more, the notification unit 136 may notify the vehicle C2a attempting to park in the parking lot P. It is a good idea to notify all of the free space information.

In this way, if the vehicle C1a does not pass the vehicle C2a attempting to park in the parking lot P for a predetermined period of time or more, it is presumed that there is no vehicle C2a attempting to park near the empty space E. Therefore, by notifying all the vehicles C2a of the vacant space information, the vacant spaces E can be filled efficiently and the number of vehicles C2a running in the parking lot P can be reduced. Therefore, according to the embodiment, confusion in the parking lot P can be suppressed.

In addition, when the vehicle C1a attempting to leave the parking lot P exits without passing the vehicle C2a attempting to park in the parking lot P, the notification unit 136 informs the vehicle C2a which subsequently enters the parking lot P to park there. It is good to notify free space information.

In this way, if the vehicle C1a does not pass the vehicle C2a attempting to park until it leaves the parking lot P, it is presumed that there is no vehicle C2a attempting to park near the empty space E. Therefore, by notifying the empty space information to the vehicle C2a that will enter the parking lot P after that, the empty space E can be efficiently filled and the number of vehicles C2a running in the parking lot P can be reduced. Therefore, according to the embodiment, confusion in the parking lot P can be suppressed.

The notification unit 136 also informs the vehicle C that is about to visit the parking lot P about the availability of the parking lot P and the number of vehicles C traveling in the parking lot P (i.e., the number of vehicles C1a and the number of vehicles C2a). (total number) may be notified. That is, the notification unit 136 may notify the vehicle C, which is about to visit the parking lot P, of the congestion situation of the parking lot P.

[3. Modified example]
The information processing device 1 according to the embodiment described above may be implemented in various different forms other than the embodiment described above. Therefore, other embodiments of the information processing device 1 will be described below.

[3-1. Obtaining behavior information from POS system]
When the user passed near the beacon B installed in the facility F, the information processing device 1 described above acquired information indicating that the user had passed as behavior information. However, embodiments are not limited thereto. For example, the information processing device 1 may acquire various information obtained from the POS system of the facility F as the behavior information.

For example, each time the user pays for shopping at the cash register of facility F, the information processing device 1 obtains information indicating that the user has paid for the shopping from the POS system of facility F as behavior information. Thereby, the information processing device 1 can predict, for example, that "one vehicle C1 of the user who paid for shopping will leave the parking lot P in 5 minutes". This is because it can be predicted that the user who has paid for shopping at the cash register will soon get into the vehicle C and leave the parking lot P.

Furthermore, each time a user orders a substitute egg at facility F, which is a ramen shop, the information processing device 1 obtains information indicating that the user has ordered a substitute egg from the POS system of facility F as behavior information. Thereby, the information processing device 1 can predict, for example, that "one vehicle C1 of the user who ordered the replacement ball will leave the parking lot P in 5 minutes". This is because it can be predicted that the user who ordered the replacement ball will soon get on the vehicle C and leave the parking lot P.

Furthermore, in the POS system of facility F, if it is known in real time which shelf an item has been taken from, and the position of the shelf from which the item has been taken approaches the cash register, the information processing device 1 may, for example, It can be predicted that the vehicle C1 of the user who took the vehicle will leave the parking lot P in 15 minutes.

In this way, the information processing device 1 can acquire various information obtained from the POS system of the facility F as behavior information. Note that the behavior information that can be obtained from the POS system of the facility F is not limited to the above-mentioned example, and other information that can be obtained from the POS system may be acquired as the behavior information.

[3-2. Obtaining behavioral information from various sensors]
Further, the information processing device 1 may acquire information obtained from various sensors as behavior information. For example, a human sensor that can detect when a user enters the last area of facility F, which is a haunted house, is installed.

When the human sensor detects that one user has entered the last area, the information processing device 1 determines, based on the information obtained from the human sensor, that a user has entered the final area. It can be predicted that one vehicle C1 will leave the parking lot P in 10 minutes. This is because it can be predicted that the user who has entered the final area of the haunted house will soon get on the vehicle C and leave the parking lot P.

Furthermore, for example, a biosensor capable of sensing weight is attached to a user using the facility F. When the biosensor detects that one user has carried a heavy object, the information processing device 1 determines, based on the information obtained from the biosensor, that the vehicle C1 of the user carrying the heavy object is It can be predicted that 1 vehicle will leave parking lot P in 10 minutes. This is because it can be predicted that a user carrying a heavy object will soon load the heavy object into the vehicle C and leave the parking lot P.

In this way, the information processing device 1 can acquire information obtained from various sensors as behavior information. Note that the sensors that can obtain behavioral information are not limited to the above-mentioned examples, and information obtained from other sensors may be acquired as behavioral information.

[3-3. Prediction of exit timing based on departure point]
Further, the information processing device 1 may predict the exit timing of the vehicle C parked in the parking lot P based on the departure point of the vehicle C registered in the vehicle database 122. For example, if the departure point of vehicle C that visited Facility F, which is an amusement park in Chiba Prefecture, is Hokkaido and the next day is a weekday, the information processing device 1 may display, for example, "There is one vehicle C1 that visited from Hokkaido in the afternoon. It can be predicted that the driver will leave the parking lot P at 4 o'clock. This is because, if the next day is a weekday, it can be predicted that the vehicle C visiting from a distant place is unlikely to stay until late and is likely to leave by the evening.

On the other hand, if the departure point of vehicle C that visited Facility F, which is an amusement park in Chiba Prefecture, is Saitama Prefecture, and the next day is a weekday, the information processing device 1 may display, for example, "Vehicle C1 that visited from Saitama Prefecture is It can be predicted that the vehicle will exit from parking lot P at 8 p.m. This is because, even if the next day is a weekday, it can be predicted that the vehicle C visiting from the nearby area is unlikely to leave early and is likely to remain until night.

In this way, the information processing device 1 acquires the departure point of the vehicle C and the location of the facility F as behavior information, and determines the exit timing of the vehicle C based on the departure location of the vehicle C and the location of the facility F. Can be predicted.

[3-4. Prediction of exit timing based on user's voluntary actions]
Further, the information processing device 1 may predict the exit timing of the vehicle C parked in the parking lot P based on a spontaneous action taken by the user. For example, a terminal that gives points is provided near the exit of facility F, and a service is implemented in which points are given to users when they check in when leaving the facility. When one user checks in and earns points, the information processing device 1 determines, based on the checked-in information, that "One vehicle C1 of the user who checked in will be parked in a parking lot in 5 minutes." It can be predicted that the player will be ejected from P.

Further, the information processing device 1 may predict the exit timing of the vehicle C parked in the parking lot P based on a user's spontaneous action on the web. For example, when a user who has visited a facility F, which is a large shopping mall, has researched information on an event to be held at the facility F in advance on the web, the information processing device 1 may, for example, report that "the user's vehicle C1 is It can be predicted that the vehicle will leave the parking lot P 10 minutes after the event end time. This is because a user who has researched event information in advance can predict that he or she will leave the parking lot P in the vehicle C as soon as the event ends.

In this way, the information processing device 1 acquires the spontaneous actions taken by the user as action information, and determines the exit timing of the vehicle C parked in the parking lot P based on the spontaneous actions taken by the user. Can be predicted.

[3-5. Prediction of exit timing based on information other than information attributable to the user]
Further, the information processing device 1 may predict the exit timing based on information other than information attributable to the user himself/herself. For example, the information processing device 1 may predict the timing at which the vehicle C1 parked by the user will leave the parking lot P based on information indicating an event that may affect the user's behavior.

For example, the information processing device 1 may predict the exit timing of the vehicle C parked in the parking lot P based on the weather around the facility F. For example, when thunder starts to rumble around facility F, the information processing device 1 can predict that "10 vehicles C1 will leave the parking lot P for the next 15 minutes" based on the weather information. I can do it. This is because when thunder starts, the psychology of the users visiting facility F changes to "I want to go home early," and it can be predicted that many vehicles C will leave the facility.

Furthermore, even when it starts to rain lightly around facility F, the psychology of the users visiting facility F changes to "I want to go home early." It can be predicted that the player will be ejected.

On the other hand, if it starts to rain heavily around the facility F, the information processing device 1 can predict that "the vehicle C1 will not leave the parking lot P for the next 10 minutes" based on the weather information. I can do it. This is because when it starts to rain, the psychology of the users visiting Facility F changes to "Let's stay in Facility F until the pouring rain subsides." This is because it can be predicted that the player will not be ejected.

In this way, the information processing device 1 can acquire information other than the information attributable to the user himself/herself as behavior information, and predict the exit timing of the vehicle C based on the information other than the information attributable to the user himself/herself. can. Note that the information other than the information attributable to the user himself/herself may be, for example, an action caused by another person toward the user himself/herself (for example, sending an e-mail from another person to the user himself/herself).

[3-6. Vehicle identification based on origin and destination]
The information processing device 1 also determined whether the vehicle C in the parking lot P should park or leave, based on the usage flag indicating whether the user driving the vehicle C is using the facility F or not. However, embodiments are not limited thereto. For example, the information processing device 1 may determine whether the vehicle C in the parking lot P is parked or exits based on the departure point and destination of the vehicle C registered in the vehicle database 122.

For example, if the destination of a vehicle C in the parking lot P is set to a location other than the facility F, such as a home or another facility, the information processing device 1 can determine that the vehicle C is about to leave the parking lot. . On the other hand, if the destination of the vehicle C in the parking lot P is set to the facility F, the information processing device 1 can determine that the vehicle C will be parked from now on.

In this way, the information processing device 1 can determine whether the vehicle C in the parking lot P is parked or exits based on the departure point and destination of the vehicle C registered in the vehicle database 122.

[3-7. Prediction of free space by other means]
Further, the information processing device 1 notified the vehicle C2a of information regarding the position of the parking space where the vehicle C1a, which was determined to be the vehicle attempting to exit the parking lot P, was parked as vacant space information. However, embodiments are not limited thereto. For example, the information processing device 1 may notify the parking space where the vehicle C of the user predicted to leave the facility F by the prediction unit 133 as a vacant space before it becomes the vacant space E.

Thereby, it is possible to further prevent the vacant space E notified to the vehicle C2 from being filled up after being notified.

[4. Processing procedure]
Next, with reference to FIG. 6, the processing executed by the control unit 13 of the information processing device 1 will be described. FIG. 6 is a flowchart showing processing executed by the control unit 13 of the information processing device 1 according to the embodiment.

As shown in FIG. 6, the control unit 13 first determines whether probe data has been received from the terminal device of the vehicle C (step S101). When the probe data is received from the terminal device of the vehicle C (Step S101, Yes), the control unit 13 updates the position information and speed information of the vehicle C in the vehicle database 122 based on the probe data (Step S101, Yes). S102). On the other hand, if probe data is not received from the terminal device of vehicle C (step S101, No), step S102 is skipped.

Next, the control unit 13 determines whether the user is using the facility F (step S103). If the user is using the facility F (step S103, Yes), the control unit 13 updates the use flag of the facility F in the user database 123 to "1" (step S104). On the other hand, if the user is not using the facility F (step S103, No), the control unit 13 updates the usage flag of the facility F in the user database 123 to "0" (step S105).

Next, the control unit 13 receives, from a beacon B etc. installed in the facility F, behavior information indicating the behavior of the user who uses the facility F attached to the parking lot P or an event that may affect the behavior. Acquire (step S106). Then, the control unit 13 predicts the timing at which the vehicle C1 parked in the parking lot P among the vehicles C will leave the parking lot P based on the acquired behavior information (step S107).

Next, the control unit 13 determines whether there is a vehicle C2 waiting to be parked in the parking lot P (step S108). If there is a vehicle C2 waiting to be parked in the parking lot P (step S108, Yes), information on the timing when the parking lot P becomes available is provided to the vehicle C2 (step S109). On the other hand, if there is no vehicle C2 waiting to be parked in the parking lot P (step S108, No), step S109 is skipped.

Next, the control unit 13 determines whether the vehicle C in the parking lot P is parked or exits (step S110). For example, the control unit 13 determines whether the vehicle C traveling in the parking lot P parks or leaves based on the use flag of the facility F updated in steps S103 to S105.

Finally, the control unit 13 notifies the vehicle C2a of the vehicles C in the parking lot P that intends to park in the parking lot P of the vacant space information regarding the vacant parking spaces in the parking lot P. (Step S111), the process ends.

[5. Hardware configuration]
Note that the information processing device 1 in the embodiment is realized, for example, by a computer 50 having a configuration as shown in FIG. FIG. 7 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device 1. The computer 50 includes a CPU (Central Processing Unit) 51, a RAM (Random Access Memory) 52, a ROM (Read Only Memory) 53, an HDD (Hard Disk Drive) 54, a communication interface (I/F) 55, and an input/output interface (I/F). /F) 56, and a media interface (I/F) 57.

The CPU 51 operates based on a program stored in the ROM 53 or the HDD 54 and controls each part. The ROM 53 stores a boot program executed by the CPU 51 when the computer 50 is started, programs depending on the hardware of the computer 50, and the like.

The HDD 54 stores programs executed by the CPU 51 and data used by the programs. The communication interface 55 corresponds to the communication unit 11, receives data from other devices via the network N, sends it to the CPU 51, and sends data generated by the CPU 51 to the other devices via the network N.

The CPU 51 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse via an input/output interface 56. The CPU 51 obtains data from an input device via the input/output interface 56. Further, the CPU 51 outputs the generated data to an output device via the input/output interface 56.

The media interface 57 reads programs or data stored in the recording medium 58 and provides them to the CPU 51 via the RAM 52. The CPU 51 loads the program from the recording medium 58 onto the RAM 52 via the media interface 57, and executes the loaded program. The recording medium 58 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. etc.

For example, when the computer 50 functions as the information processing device 1 according to the embodiment, the CPU 51 of the computer 50 realizes the functions of the control unit 13 by executing a program loaded onto the RAM 52. Furthermore, the data in the storage unit 12 is stored in the HDD 54 . The CPU 51 of the computer 50 reads these programs from the recording medium 58 and executes them, but as another example, these programs may be acquired from another device via the network N.

[6. others〕
Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually can be performed manually. All or part of the process can also be performed automatically using known methods. In addition, information including the processing procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed arbitrarily, unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Furthermore, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured. Further, the embodiments described above can be combined as appropriate within the range that does not conflict with the processing contents.

[7. effect〕
As described above, the information processing device 1 according to the embodiment includes the acquisition section 132 and the prediction section 133. The acquisition unit 132 acquires behavior information indicating a user's behavior or an event that may affect the behavior. The prediction unit 133 predicts the timing at which the vehicle C1 parked by the user will leave the parking lot P based on the behavior information acquired by the acquisition unit 132.

Thereby, the information processing device 1 can predict the timing when the parking lot P that is full will become vacant.

In the information processing device 1 according to the embodiment, the acquisition unit 132 acquires behavior information from a beacon B installed at a facility F visited by the user in the vehicle C1.

Thereby, the information processing device 1 can acquire information about the user who will soon leave the facility F in the vehicle C1 as behavior information.

Furthermore, in the information processing device 1 according to the embodiment, the acquisition unit 132 acquires the purchase history at the facility F that the user visited in the vehicle C1 as behavior information.

Thereby, the information processing device 1 can acquire information about the user who will soon leave the facility F in the vehicle C1 as behavior information.

Furthermore, in the information processing device 1 according to the embodiment, the acquisition unit 132 acquires behavior information from a sensor capable of detecting the state of the user.

Thereby, the information processing device 1 can acquire information about the user who will soon leave the facility F in the vehicle C1 as behavior information.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 predicts the timing at which the vehicle C will leave the parking lot P based on the distance between the parking lot P and the place where the user performed the action.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 determines whether the vehicle C1 is parked based on the distance between the location where the user performed the action and the location where the user parked the vehicle C1 in the parking lot P. Predict the timing of leaving the parking lot P.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 predicts whether the vehicle C1 will leave the parking lot P based on the distance from the place where the user parks the vehicle C1 in the parking lot P to the exit of the parking lot P. Predict when to leave.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

In the information processing device 1 according to the embodiment, the prediction unit 133 predicts the timing at which the vehicle C1 will leave the parking lot P based on the direction in which the user moves.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

In the information processing device 1 according to the embodiment, the prediction unit 133 generates a model by statistically analyzing the user's behavior, and based on the modeled information, the prediction unit 133 determines whether the vehicle C1 leaves the parking lot P. Predict the timing.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 generates a model by statistically analyzing past actions of a plurality of users, and based on the modeled statistical information, the prediction unit 133 Predict the timing of leaving the parking lot P.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

In the information processing device 1 according to the embodiment, the prediction unit 133 estimates the state of the user based on the behavior information acquired by the acquisition unit 132, and based on the estimated user status, the prediction unit 133 estimates whether the vehicle C1 is in a parking lot or not. Predict the timing of exit from P.

Thereby, the information processing device 1 can more accurately predict the timing when the parking lot P becomes vacant.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 predicts the timing at which the vehicle C1 will leave the parking lot P based on the departure point of the vehicle C1 parked by the user.

Thereby, the information processing device 1 can predict the timing when the parking lot P that is full will become vacant.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 predicts the timing at which the vehicle C1 will leave the parking lot P based on a spontaneous action taken by the user.

Thereby, the information processing device 1 can predict the timing when the parking lot P that is full will become vacant.

Furthermore, in the information processing device 1 according to the embodiment, the prediction unit 133 predicts the timing at which the vehicle C1 will leave the parking lot P based on the weather around the facility F that the user visited with the vehicle C1.

Thereby, the information processing device 1 can predict the timing when the parking lot P that is full will become vacant.

The information processing device 1 according to the embodiment also provides information on the timing when a space becomes available in the parking lot P based on the timing at which the vehicle C1 will leave the parking lot P predicted by the prediction unit 133. It further includes a providing unit 134 that provides the information to the vehicle C2.

Thereby, the user of the vehicle C2 waiting to be parked can accurately determine whether to wait until the parking lot P becomes vacant or to search for another parking lot.

Although the embodiments of the present application have been described above in detail based on the drawings, this is merely an example, and various modifications and improvements can be made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure section of the invention. It is possible to implement the invention in other forms.

Further, the above-mentioned "section, module, unit" can be read as "means", "circuit", etc. For example, the prediction section can be read as a prediction means or a prediction circuit.

1 Information processing device 11 Communication unit 12 Storage unit 13 Control unit 121 Parking lot database 122 Vehicle database 123 User database 131 Collection unit 132 Acquisition unit 133 Prediction unit 134 Providing unit 135 Discrimination unit 136 Notification unit

Claims (3)

an acquisition unit that acquires behavior information indicating a user's behavior or an event that may affect the behavior;
a prediction unit that predicts a timing at which a vehicle parked by the user will leave the parking lot based on the behavior information acquired by the acquisition unit;
Equipped with
The prediction unit is
Predicting the timing at which the vehicle will leave the parking lot based on the end time of the event when the user has previously researched on the web information about an event to be held at a facility visited by the vehicle. An information processing device characterized by: An information processing method performed by a computer, the method comprising:
an acquisition step of acquiring behavior information indicating a user's behavior or an event that may influence the behavior;
a prediction step of predicting the timing at which the vehicle parked by the user will leave the parking lot based on the behavior information acquired in the acquisition step;
including;
The prediction step includes:
Predicting the timing at which the vehicle will leave the parking lot based on the end time of the event when the user has previously researched on the web information about an event to be held at a facility visited by the vehicle. An information processing method characterized by: an acquisition procedure for acquiring behavior information indicating a user's behavior or an event that may affect the behavior;
a prediction procedure for predicting a timing at which a vehicle parked by the user will leave the parking lot based on the behavior information acquired in the acquisition procedure;
make the computer run
The prediction procedure is
Predicting the timing at which the vehicle will leave the parking lot based on the end time of the event when the user has previously researched on the web information about an event to be held at a facility visited by the vehicle. An information processing program featuring:

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