JP2019096084A - Information processing device - Google Patents

Abstract

To provide an information processing device, information processing method, and recording medium, which allow for accurately predicting a car park vacancy rate or information corresponding to the vacancy rate.SOLUTION: A server device 10 acquires the number of vehicles parked in a parking lot. The server 10 stores the maximum capacity of the parking lot and a past history of the number of parked vehicles that has been acquired. The server 10 then predicts a vacancy rate or information corresponding to the vacancy rate of the parking lot at a prediction target point in time on the basis of the maximum capacity and past history of the number of parked vehicles that have been stored and the current number of parked vehicles.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus, an information processing method, and a recording medium.

  As a device for predicting the vacant rate of a parking lot, for example, a vehicle guide device described in Patent Document 1 has been proposed. This vehicle guidance apparatus accumulates and stores the vacant information of the past parking space of the parking lot for each date and time and for each time zone, and calculates the availability rate for each date and time and for each time zone based on the statistical result of the accumulated vacant information. It has been described that.

  However, in the above-described conventional vehicle guidance apparatus, since the empty vehicle probability is obtained based on the vacant information of the past parking lot, it can not cope with occurrence of a parking demand different from usual, and it is not accurate. The problem of saying is an example.

JP, 2016-45806, A

  The present invention takes as an example the problem to address such problems. That is, an object of the present invention is to provide, for example, an information processing apparatus, an information processing method, and a recording medium capable of accurately predicting information according to a vacant rate or a vacant rate.

  The information processing apparatus according to claim 1, which has been made to solve the above-described problems, includes an acquisition unit that acquires the number of parked vehicles in a parking lot, a maximum number of accommodated parking spaces, and the number of parked vehicles acquired by the acquisition unit. Storage means for storing the past history, the past history of the maximum accommodation number and the parking number stored in the storage means, and the current number of parkings acquired by the acquiring means And prediction means for predicting an empty car situation of the parking lot at a predicted time.

  The information processing method according to claim 7 includes a step of acquiring the number of parked vehicles in a parking lot, a step of storing the maximum number of accommodated parking spaces, and a past history of the acquired number of parked vehicles, and the storage Predicting an empty car condition of the parking lot at a predicted time based on the stored maximum number of vehicles and the past history of the number of parked vehicles, and the acquired number of currently parked vehicles. It is characterized by

  The information processing program according to claim 8 is an information processing program executed by a computer that controls the information processing apparatus, and is an acquisition unit that acquires the number of parked vehicles in a parking lot, the maximum number of accommodated parking spaces, and the acquisition. Storage means for storing the past history of the number of parked vehicles acquired by the vehicle, the past history of the maximum number of accommodated cars and the number of parked vehicles stored in the storage means, and the present current acquired by the acquisition means The computer is caused to function as a prediction unit that predicts an empty car situation of the parking lot at a predicted time based on the number of parked vehicles.

It is a block diagram which shows the information provision system of the parking lot incorporating the information processing apparatus of this invention. It is a block diagram which shows the structure of the server apparatus shown in FIG. It is a block diagram which shows the structure of the vehicle equipment shown in FIG. It is a block diagram which shows the structure of the terminal shown in FIG. It is a figure which shows an example of the screen displayed on the display part 26 of the vehicle equipment shown in FIG. It is an explanatory view for explaining a parking candidate area and a parking demand forecasting area. It is a flowchart which shows the process sequence of the vehicle equipment shown in FIG. 1, and a server apparatus. It is a flowchart which shows the process sequence of FIG.7 S23. It is a graph which shows the log | history of the number of parking of the day acquired by FIG.8 S231. It is explanatory drawing for demonstrating step S233 of FIG. It is explanatory drawing for demonstrating step S235 of FIG. It is a flowchart which shows the process sequence of step S26 of FIG.

  Hereinafter, an information processing apparatus according to an embodiment of the present invention will be described. An information processing apparatus according to an embodiment of the present invention includes an acquisition unit that acquires the number of parked vehicles in a parking lot, a maximum number of accommodated parking spaces, and a past history of the number of parked vehicles acquired by the acquisition unit. The storage at the predicted time is based on storage means to be stored, the past history of the maximum number of stored cars and the number of parked cars stored in the memory, and the present number of parked cars acquired by the acquiring means. And a prediction means for predicting an empty car situation of a parking lot. As a result, since the information according to the predicted vacant rate or the vacant rate of the parking lot at the predicted time is predicted based on the current number of parked vehicles, even if parking demand different from usual occurs, it is forecasted accurately. It is possible to predict information according to the vacant rate or the predicted vacant rate.

  The vacant status may further include output means for indicating information according to a predicted vacant rate or the predicted vacant rate, and outputting information regarding information according to the predicted vacant rate or the predicted vacant rate. As a result, it is possible to notify the user of information according to the predicted vacant rate or the predicted vacant rate.

  The predicted time may be an estimated arrival time of a vehicle at the parking lot. As a result, the predicted vacancy rate at the estimated arrival time can be predicted.

  Further, the acquisition unit further acquires vehicle information on a parking space in which the vehicle can park, and the history of the maximum accommodation number and the number of parked vehicles stored in the storage unit is acquired by the acquisition unit. The vacant vehicle status may be predicted based on the current number of parked vehicles and the vehicle information.

  Further, the prediction means may predict based on a past history of the number of parked vehicles in a time zone including the same time as the predicted time before the previous day. As a result, the predicted vacancy rate can be predicted more accurately.

  Further, the time zone includes the same time as the current time to a predetermined time before the current time, and classification means for classifying the past history of the number of parked vehicles in the time zone for a plurality of days according to its fluctuation pattern; The history of the number of parked vehicles up to the predetermined time and determination means for determining a day when the variation pattern can be regarded as the same classification may be provided, and the prediction means may predict the predicted vacant rate by the following equation.

ρ(t_ed)：予測時刻t_edにおける予測空車率
P：現在時刻の駐車台数
Pi(t_bg)：前記判定手段によって判定された各日における現在時刻t_bgと同一時刻の駐車台数
Pi(t_ed)：前記判定手段によって判定された各日における予測時刻t_edと同一時刻の駐車台数
ｎ：前記判定手段により判定された日の総和
St：最大収容台数

ρ (t_ed): Predicted vacant vehicle rate at predicted time t_ed
P: Number of parked cars at current time
Pi (t_bg): Number of parked vehicles at the same time as the current time t_bg on each day determined by the determination means
Pi (t_ed): number of parked vehicles at the same time as predicted time t_ed on each day determined by the determination means n: sum of days determined by the determination means
St: Maximum capacity

  As a result, the predicted vacancy rate can be predicted more accurately.

  Further, the information processing method according to an embodiment of the present invention stores the step of acquiring the number of parked vehicles in a parking lot, the maximum number of accommodated parking spaces, and the past history of the acquired number of parked vehicles. Predicting an empty car condition of the parking lot at a predicted time based on the process, the past history of the stored maximum number of vehicles and the number of parked vehicles, and the acquired current number of parked vehicles , And characterized in that. As a result, since the information according to the predicted vacant rate or the vacant rate of the parking lot at the predicted time is predicted based on the current number of parked vehicles, even if parking demand different from usual occurs, it is forecasted accurately. The rate of empty cars can be predicted.

  In addition, an information processing program that causes a computer to execute the above-described information processing method may be used. Since the program is executed by the computer as described above, dedicated hardware and the like are not required, and the program can be installed and functioned in a general-purpose information processing apparatus.

  Further, the above-described information processing program may be stored in a computer readable recording medium. By doing this, it is possible to distribute the program alone as well as incorporating the program into the device, and to easily upgrade the version.

  Hereinafter, an information providing system incorporating a server apparatus as an information processing apparatus according to the present embodiment will be described with reference to FIGS. 1 to 4.

  The information providing system 1 includes a server device 10 as an information processing device, an on-board unit 20 which is a terminal capable of communicating with the server device 10, and a terminal 30 which is a terminal capable of communicating with the server device 10. . The on-vehicle device 20 is configured of, for example, a navigation device mounted on a vehicle or the like. In the present embodiment, the on-vehicle device 20 mounted on a vehicle is described as an example of a terminal capable of communicating with the server device 10, but it may be a mobile terminal such as a smartphone.

  The in-vehicle device 20 has a navigation function to provide route guidance to the destination when the destination is set by the operation of the user. In addition, when setting of a destination is performed by the user, the on-vehicle device 20 can request the server device 10 to transmit, for example, a predicted vacant rate of a parking lot around the destination. The predicted vacant rate is a predicted value of the vacant rate (number of vacant parking spaces / number of accommodated parking spaces) of the parking lot around the destination at the predicted arrival time (predicted time) to the destination. When the in-vehicle device 20 receives the predicted vacant rate from the server device 10 in response to the transmission request, it displays it on the map around the destination, as shown in FIG. Display the predicted vacancy rates at the positions of integers of.

  The terminal 30 is configured of a computer or the like installed in each parking lot. The terminal 30 transmits the number of parked parking spaces to the server device 10.

  The server device 10 is connected to a communication network such as the Internet or a public network, and is provided to be communicable with the on-vehicle device 20 and the terminal device 30.

  As illustrated in FIG. 2, the server device 10 includes a control unit 11, a communication unit 12, and a storage unit 13.

  The control unit 11 is configured to include, for example, a central processing unit (CPU) including a memory such as a random access memory (RAM) or a read only memory (ROM). The control unit 11 controls the entire server device 10. When receiving the transmission request for the predicted vacant rate from the in-vehicle device 20, the control unit 11 calculates the predicted vacant rate of the parking lot around the destination and transmits the calculated estimated vacant rate to the in-vehicle device 20. In addition, the control unit 11 calculates the parking demand of the parking lot around the destination, and corrects the above-described predicted vacant rate based on the calculated parking demand.

  In the present embodiment, the parking demand is the number of vehicles that are not currently parked in the parking lot around the destination, but reach around the destination and parked in the parking lot before the expected arrival time. . The parking demand is, as shown in FIG. 6, traveled with a point existing in the parking candidate area (first predetermined range) A1 among the vehicles existing in the parking demand prediction area (second predetermined range) A2 as a destination. It is determined based on the total number of vehicles in use. The parking candidate area A1 is an area around the destination, and the parking demand prediction area A2 is an area that can arrive at the parking candidate area A1 before the estimated arrival time.

  The communication unit 12 communicates with the in-vehicle device 20 and the terminal device 30 via a communication network such as the Internet or a public network.

  The storage unit 13 stores various types of information necessary for processing in the server device 10. The storage unit 13 has a parking status database (hereinafter, DB) 13A, a destination surrounding status DB 13B, a predicted vacant rate DB 13C, and a parking lot DB 13D as a first storage unit.

  The parking status DB 13A is a memory in which a history of the number of parked vehicles periodically transmitted from each terminal 30 is stored. The destination surrounding situation DB 13B is a memory in which the history and the destination of the position information transmitted from the on-vehicle device 20 present in the parking demand prediction area A2 are stored. The predicted vacant rate DB 13C is a memory in which the predicted vacant rate of each parking lot calculated by the control unit 11 is stored. Parking lot DB13D is memory in which information (a position, the maximum number of parkings, the existence of a private parking space, etc.) about each parking lot is memorized.

  In the present embodiment, only one server device 10 is provided, but the present invention is not limited to this. A plurality of server devices 10 may be provided. For example, the operation performed by the control unit and the operation performed by the communication unit may be divided into separate server devices 10 (an arithmetic server device and a distribution server device). In such a case, the server devices 10 may be installed at mutually separated positions and configured to communicate with each other through a communication network.

  As shown in FIG. 3, the in-vehicle device 20 includes a control unit 21, a communication unit 22, a GPS reception unit 23, a storage device 24, an operation unit 25, and a display unit 26.

  The control unit 21 is configured by a CPU including a memory such as a RAM and a ROM, for example, and controls the entire on-vehicle apparatus 20.

  The communication unit 22 communicates with the server device 10. In the present embodiment, an example in which the communication unit 22 is integrally provided to the in-vehicle device 20 is shown, but the communication unit is separately configured such as connecting a card type communication device or a mobile phone with a cable or the like. It may be detachable from the in-vehicle device 20.

  The GPS receiving unit 23 periodically receives radio waves oscillated from a plurality of GPS (Global Positioning System) satellites as is well known, obtains current position information and time, and outputs the information to the control unit 21. In the present embodiment, an example is shown in which the GPS receiving unit 23 is integrally provided in the in-vehicle device 20. However, the GPS receiving unit 23 may be configured separately and be detachable from the in-vehicle device 20. .

  The storage device 24 is configured of, for example, a hard disk or a non-volatile memory, and is read and written under the control of the control unit 21.

  The operation unit 25 is provided for the user to perform various operations of the in-vehicle apparatus 20. As shown in FIG. 5, the display unit 26 displays the predicted vacant rate of each parking lot transmitted from the server device 10.

  As shown in FIG. 4, the terminal 30 includes a control unit 31, a communication unit 32, and an input unit 33.

  The control unit 31 is configured of a CPU including a memory such as a RAM or a ROM, for example, and controls the entire terminal 30.

  The communication unit 32 communicates with the server device 10.

  By the way, a plurality of parking spaces are provided in the parking lot. Each parking space is provided with a sensor that detects the presence or absence of a vehicle and detects a vacant state. Detection results of sensors provided in a plurality of parking spaces are input to the input unit 33.

  Next, an example of the operation of the information providing system 1 configured as described above will be described with reference to FIG. The flowchart of FIG. 7 is executed by the server device 10. Further, the control unit of the server device 10, with the flowchart as a computer program, becomes an information providing program that causes the computer to execute the information providing method.

First, the control unit 31 of each terminal 30 (hereinafter simply referred to as "terminal 30") periodically determines the number of parked vehicles from the sensor detection result input from the input unit 33, and determines the number of parked vehicles and the maximum number The number of accommodated devices is transmitted to the server device 10. The control unit 11 of the server device 10 (hereinafter simply referred to as "server device 10") functions as an acquisition means, a storage means, and a second storage means, and the number of parked vehicles and the maximum number of vehicles received from the terminal 30 are stored in the parking situation DB 13A. Remember to For example, as shown in Table 1 below, the parking status DB 13A stores the current number of parked vehicles and the past history of the number of parked vehicles by parking lot. The latest number of parked vehicles transmitted from the terminal 30 is the current number of parked vehicles.

  When the destination is set by the operation of the user (hereinafter, referred to simply as "in-vehicle device 20"), the control unit 21 of the in-vehicle device 20 transmits the predicted vacant rate to the server device 10 Send a request (step S11). The transmission request includes the destination set in the on-vehicle apparatus 20 and the position information output from the GPS receiving unit 23.

  The server device 10 works as a request reception means, and when receiving a transmission request for the predicted vacant rate (Y in step S20), acquires a parking lot around the destination from the parking lot DB 13D (step S21). In the example illustrated in FIG. 5, the server device 10 acquires the parking lots PK1 to Pkn. Next, the server device 10 acquires, from the parking status DB 13A, the past number of parked vehicles and the past history of the number of parked vehicles acquired in step S21 (step S22). Thereafter, the server device 10 calculates the estimated arrival time to the destination (step S23).

  Next, the server device 10 functions as a second prediction means and a prediction means, and based on the past history of the current number of parked vehicles and the number of parked vehicles obtained in step S22, the predicted vacant rate of each parking lot PK1 to Pkn at the arrival predicted time It calculates (step S24). Thereafter, the server device 10 calculates the parking demand for the parking lot around the destination by communication with the in-vehicle device 20 present in the parking demand prediction area A2 (step S25).

  After that, the server device 10 corrects the predicted vacant rate calculated in step S24 based on the parking demand calculated in step S25 (step S26), and functions as an output unit to transmit the corrected predicted vacant rate to the on-vehicle device 20 Then (step S27), the process returns to step S20. When the on-vehicle device 20 receives the predicted free rate from the server device 10, the on-vehicle device 20 displays it on the display unit 26 as shown in FIG. 5 (step S13), and ends the process.

  Next, the details of the calculation of the predicted vacancy rate in step S23 described above will be described with reference to the flowchart of FIG. First, the server device 10 records a history of the number of parked vehicles in a time zone from the current time to a predetermined time T1 before each parking lot PK1 to Pkn (hereinafter referred to as "history of the number of parked vehicles on the day") from the parking situation DB 13A. It acquires (step S231). For example, assuming that the current time is 11:30 and the predetermined time T1 is 3 hours, in step S231, as shown in FIG. 9, a history of the number of parked vehicles from 8:30 to 11:30 on the day is extracted.

  Next, the server device 10 records the past history of the number of parked vehicles in the time zone from the same time to the predetermined time T1 from the same time as a plurality of days before the previous day for each of the parking lots PK1 to Pkn (hereinafter, "previous day The history of the number of parked vehicles is acquired from the parking situation DB 13A (step S232). In step S232, a history of the number of parked vehicles from 8:30 to 11:30 for a plurality of days, such as the previous day and the day before, is acquired.

  Next, the server device 10 works as a classification unit, and classifies the history of the number of parked vehicles from 8:30 to 11:30 for a plurality of days according to the fluctuation pattern (step S233). The classification in step S233 is performed by, for example, known similarity calculation. Similarity calculation calculates the history of the number of parkings for the 2nd day into the same classification, for example, if the difference of the plot point of the history of the number of parkings for 2 days is added and the total value is less than a predetermined value. As a result of classification in this manner, for example, as shown in FIG. 9, the history of the number of parked vehicles on days d11 to d1 n (n is an arbitrary integer) that is a weekday is pattern Pa, and days d21 to d2 m (m is arbitrary) that is holidays The history of the number of parked cars is often classified into two as in the pattern Pb. In the example shown in FIG. 10, although the history of the number of parked vehicles is classified into two, the classified number is classified into three or more even when classified into one according to factors such as the environment of the parking lot. In some cases.

  Next, the server apparatus 10 functions as a determination means, and is the history of the number of parked vehicles from 8:30 to 11:30 of the day obtained in step S232 classified into which of the patterns Pa and Pb classified in step S233? Is determined (step S234). The day classified into the pattern determined in step S234 can be determined as the day when the history of the number of parked vehicles on the day and the variation pattern can be regarded as the same classification.

  Thereafter, the server device 10 acquires, from the parking status DB 13A, the number of parked vehicles at the same time as the current time of each day and the estimated arrival time that can be regarded as the same classification in step S234 (step S235). Specifically, if it is determined in step S234 that the server apparatus 10 is determined to be classified into the pattern Pa, as shown in FIG. 11, the current time 11:30 of the day d11 to d1 n classified into the pattern Pa and The number of parked vehicles P1 (t_bg) to Pn (t_bg) and P1 (t_ed) to Pn (t_ed) at the same time as the estimated arrival time 12:00 are extracted.

ρ(t_ed)：到着予想時刻t_edにおける予測空車率
P：現在時刻t_bgの駐車台数
Pi(t_bg)：各日ｄ１１〜ｄ１ｎにおける現在時刻t_bgと同一時刻の駐車台数
Pi(t_ed)：各日ｄ１１〜ｄ１ｎにおける到着予想時刻t_edと同一時刻の駐車台数
ｎ：各日ｄ１１〜ｄ１ｎの総和
St：最大収容台数 Next, the server device 10 calculates the forecasted vacant rate of each parking lot PK1 to Pkn using the following equation (1), stores it in the forecasted vacant rate DB 13C (step S236), and ends the process.

ρ (t_ed): Expected empty car rate at estimated arrival time t_ed
P: Number of parked cars at current time t_bg
Pi (t_bg): Number of parked cars at the same time as current time t_bg on each day d11 to d1 n
Pi (t_ed): number of parked vehicles at the same time as estimated arrival time t_ed on each day d11 to d1 n n: sum of each day d11 to d1 n
St: Maximum capacity

  Next, the details of the calculation of the parking demand in step S26 described above will be described with reference to the flowchart of FIG. First, the server device 10 calculates a parking demand prediction area A2 that can arrive in the parking candidate area A1 around the destination in the predetermined time T2 (step S261). In the present embodiment, the predetermined time T2 is set to a time obtained by subtracting the current time from the estimated arrival time (the required time to the destination).

  In step S261, for example, it is conceivable that the server device 10 sets the inside of the range of the radius according to the predetermined time T2 centering on the destination as the parking demand prediction area A2. In addition, it is also conceivable that the server device 10 calculates many routes with the destination as the departure point, and sets the area which can arrive within the predetermined time T2 as the parking demand prediction area A2.

  Next, the server device 10 communicates with all the on-vehicle devices 20 present in the parking demand prediction area A2 to obtain the number M of the on-vehicle devices 20 present in the parking demand prediction area A2 (step S262). In step S262, the server device 10 transmits, for example, the history (travel history) of position information and the request signal of the destination from the base station installed in the parking demand prediction area A2, and parks the in-vehicle device 20 that has sent the reply. The on-vehicle device 20 in the demand forecast area A2 is assumed. The server device 10 stores the history of the position information and the destination received from the in-vehicle apparatus 20 in the destination surrounding situation DB 13B.

  Next, the server device 10 samples a fixed number of in-vehicle devices 20 at random from the destination surrounding situation DB 13B (step S263). After that, the server device 10 calculates the ratio of the vehicle having the parking candidate area A1 as the destination and the vehicle not setting the parking candidate area A1 as the destination for the fixed number of in-vehicle devices 20 sampled ((1) Step S264).

  In step S264, the server device 10 determines that the in-vehicle device 20 in which the point in the parking candidate area A1 is set as the destination is the vehicle whose destination is the parking candidate area A1. Further, the server device 10 obtains the traveling direction from the history of position information, and determines that the in-vehicle device 20 heading to the parking candidate area A1 is a vehicle whose destination is the parking candidate area A1. Then, the server device 10 calculates the ratio n1 / (n1 + n2) from the number n1 of vehicles having the parking candidate area A1 as the destination and the number n2 of the vehicles not having the destination.

  After that, the server device 10 functions as a number calculating means, and multiplies the number M (calculated) obtained (calculated) at step S262 by the ratio n1 / (n1 + n2) obtained at step S264. This M · n1 / (n1 + n2) is the number of vehicles traveling with a point existing in the parking candidate area A1 as a destination among the on-vehicle devices 20 (vehicles) present in the parking demand prediction area A2 . The server device 20 functions as a first prediction unit, obtains M · n1 / (n1 + n2) as the total parking demand Pr of the parking lots PK1 to Pkn in the parking candidate area A1 (step S265), and ends the processing.

Cor(i)：駐車場PKiの個別駐車需要
Pst(i)：駐車場PKiの最大収容数
Pr：総駐車需要
Ｎ：駐車候補エリアＡ１内の駐車場PK1〜Pknの総和 Next, the details of the correction of the predicted vacant rate in step S27 described above will be described. First, using the parking demand obtained in step S26, the server device 10 obtains an individual parking demand for each of the parking lots PK1 to Pkn using the following equation (2).

Cor (i): Individual parking demand of parking lot PKi
Pst (i): Maximum capacity of parking lot PKi
Pr: Total parking demand N: Sum of parking lots PK1 to Pkn in the parking candidate area A1

ρ(i)：駐車場PKiの予測空車率 Thereafter, the server device 10 corrects the estimated vacant rate of each of the parking lots PK1 to Pkn using the following formulas (3) and (4).

ρ (i): Estimated vacant car rate of parking lot PKi

  According to the embodiment described above, since the server apparatus 10 predicts the predicted vacant rate of the parking lot at the predicted time (the estimated arrival time) based on the current number of parked vehicles, a parking demand different from usual occurs. Even in this case, it is possible to predict the forecasted vacant rate accurately.

  Further, according to the embodiment described above, the server device 10 transmits the calculated predicted vacant rate to the on-vehicle device 20, and displays the predicted vacant rate received by the on-vehicle device 20 on the display unit 26. As a result, it is possible to notify the user of the estimated vacant rate of each of the parking lots PK1 to Pkn.

  Further, according to the above-described embodiment, the predicted time is set to the estimated arrival time of the vehicle at the parking lot. As a result, the predicted vacancy rate at the estimated arrival time can be predicted.

  Further, according to the embodiment described above, the server device 10 predicts based on the past history of the number of parked vehicles in a time zone including the same time as the estimated arrival time before the previous day. As a result, the predicted vacancy rate can be predicted more accurately.

  Further, according to the embodiment described above, the server device 10 classifies the history of the number of parked vehicles before the previous day, and determines the day when the history of the number of parked vehicles on the day and the fluctuation pattern can be regarded as the same classification. The predicted vacant rate is predicted based on the history. As a result, the predicted vacancy rate can be predicted more accurately. In addition, as the history of the number of parked vehicles, it is possible to predict the forecasted vacant vehicle rate with high accuracy without discriminating between holidays and weekdays.

  Further, according to the embodiment described above, the server device 10 predicts the parking demand from the number of vehicles traveling with the point existing in the parking candidate area A1 as the destination, so that the parking demand can be accurately performed. It can be predicted.

  Further, according to the embodiment described above, the server apparatus 10 parks each of the parking lots Pk1 to Pkn based on the total parking demand Pr and the capacity of all the parking lots existing in the parking candidate area A1. We are forecasting demand. As a result, the parking demand can be predicted more accurately.

  Further, according to the embodiment described above, the server device 10 extracts a part of the vehicles present in the parking demand prediction area A2, and the vehicle and the parking candidate area A1 having the parking candidate area A1 as the destination. The ratio with vehicles not having the inside as a destination is determined, and the total parking demand Pr is determined from the determined ratio. As a result, by extracting some of the vehicles, the amount of calculation processing can be reduced compared to the case where it is determined whether all the vehicles in the parking demand forecasting area A2 are the destination vehicles, and the cost can be reduced. Can be

  Further, according to the embodiment described above, whether or not the server device 10 is a vehicle traveling with the point existing in the parking candidate area A1 as the destination based on the destination information acquired from the in-vehicle device 20 Is determined. Thus, it is possible to accurately obtain the number of vehicles traveling with the point existing in the parking candidate area A1 as the destination.

  Further, according to the above-described embodiment, the server device detects a point existing in the parking candidate area A1 based on the traveling direction (traveling information) obtained from the history (traveling history) of the position information acquired from the in-vehicle device 20. It is determined whether the vehicle is traveling as a destination. Thus, it is possible to accurately obtain the number of vehicles traveling with the point existing in the parking candidate area A1 as the destination.

  Further, according to the above-described embodiment, the server device 10 corrects the predicted vacant rate based on the parking demand. As a result, it is possible to predict the forecasted vacant rate accurately.

  Further, according to the embodiment described above, the server device 10 periodically receives the maximum number of vehicles accommodated as well as the number of parked vehicles from the terminal device 30. Thereby, for example, it is possible to cope with the fluctuation of the maximum number of accommodation, such as providing a temporary parking lot.

  Note that according to the embodiment described above, the predicted time may not be the predicted arrival time, and may be set to a time later (future) than the current time. For example, a schedule input by the user, a target time, or a recommended arrival time to a destination calculated by the server device 10 may be used as the predicted time.

Further, according to the above-described embodiment, the server device 10 predicts the predicted vacant rate at the estimated arrival time, but the present invention is not limited to this. For example, the server device 10 may predict information according to the predicted vacant rate, such as the predicted number of parked vehicles at the predicted arrival time, and the predicted number of vacant cars. The estimated number of parked vehicles and the estimated number of vacant vehicles can be obtained from the following equations (5) and (6). In this case, it is not necessary to obtain the predicted vacant rate.

  Moreover, as a prediction method of a predicted vacant rate, it is not limited to the Example mentioned above. The server device 10 may be able to predict the predicted vacant rate based on the maximum capacity, the past history of the number of parked vehicles, and the current number of parked vehicles. For example, the server device 10 obtains an average value of the amount of increase or decrease of the number of parked vehicles at the same estimated time of arrival and the estimated time of arrival for the same time as the current time for a plurality of days such as the previous day The average value of the calculated amounts of change may be added to obtain the predicted number of parked vehicles, and the predicted vacant rate may be obtained from the calculated predicted number of parked vehicles. Alternatively, the server device 10 corrects the present number of parked vehicles based on the average value of the number of parked vehicles at the same time and the estimated number of arrivals for multiple days such as the previous day and the day before, and obtains the estimated vacant rate. May be

  Further, in the above-described embodiment, the server device 10 corrects the predicted vacant rate with the parking demand, but it is not essential to correct the parking demand with the parking demand.

  Further, according to the above-described embodiment, the server device 10 extracts a part of the vehicles present in the parking demand prediction area A2 to obtain the total parking demand Pr, but the present invention is not limited to this. . The total parking demand Pr may be determined by determining whether all vehicles existing in the parking demand prediction area A2 are traveling with a point existing in the parking candidate area A1 as a destination.

  Further, according to the above-described embodiment, the server device 10 corrects the predicted vacant rate based on the parking demand, but the present invention is not limited to this. The server device 10 may transmit the parking demand to the on-vehicle device 20 and provide it to the user.

  Moreover, although the parking demand was used for correction | amendment of the vacant rate in the Example mentioned above, utilization of the parking demand is not restricted to this. For example, it may be used as an index value indicating whether the parking demand for the destination area is high or low. The in-vehicle device 20 displays the index value to notify the user. Furthermore, when the predicted parking demand is higher than a predetermined value, the parking lot search range may be expanded or the use of public transportation may be promoted.

[Modification 1]
Next, in the embodiment described above, a modified example will be described in which the server device 10 considers whether the requested parking lot can be parked using a high accuracy map or the like. The server device 10 acquires the vehicle information of the request vehicle when there is a request for the predicted vacant rate. The vehicle information is, for example, information as to whether or not the vehicle is a mini-vehicle, the width of the vehicle, the length of the vehicle, and the size of the vehicle such as the height of the vehicle. If the vehicle is not a mini-vehicle, there is no parking space exclusively for mini-vehicles in the parking space existing in the parking lot using the high-precision map etc. in the parking lot around the destination in the calculation of the vacant rate mentioned above. It is determined whether the parking space is large enough for the vehicle to park for each parking space, and the calculation is performed ignoring the parking space determined to be inappropriate. That is, the calculation is performed without adding to the number of parked vehicles stopped in the current dedicated parking space or the maximum number of vehicles accommodated.

  Moreover, although the parking space for mini vehicles was mentioned as an example in the above-mentioned modification 1, for example, even if it is a wheelchair exclusive parking lot, if it is a vehicle on which the wheelchair is riding is acquired as vehicle information of a request vehicle, The same process as described above is possible.

  According to the first modification described above, the server device 10 can calculate the vacant rate of the parking lot that matches the vehicle.

[Modification 2]
In the embodiment described above, the parking situation is generated by the information received from the terminal 30 installed in each parking lot, but not limited to this, from the object recognition means such as a rider or a camera provided in the vehicle existing in the parking lot The parking status may be recognized based on the received information.

[Modification 3]
Next, detection of a parking lot that does not exist in the parking lot DB 13D will be described. For example, when a temporary parking lot or a new parking lot is present in the parking lot search range, it is not included in the parking lot DB 13D, so it is not added to the candidate display or the calculation of the vacant rate. Therefore, the server device 10 extracts a vehicle parked in the parking candidate area after that, regarding the vehicle that was present in the parking demand prediction area A2 at the time of calculating the vacant rate. The server device 10 functions as an updating unit, determines whether or not a parked parking lot exists in the parking lot DB 13D, and stores the parking lot DB 13D as a new parking lot if it does not exist. When storing, for example, the maximum number of parked vehicles, the current number of parked vehicles, the temporary parking lot, etc. by position information, an object recognition means such as a rider or camera of the vehicle, or a display prompting the driver to provide information. It stores a parking lot that is opened temporarily or for a fixed period, or an on-site map created from object recognition data in the parking lot. By this, the server device 10 functions as an output means to notify a vehicle guiding a parking lot that there is a new parking lot, or update the vacant rate in a form taking into account the new parking lot. Can do. Even if there is a parking lot which does not exist in parking lot DB13D according to the above-mentioned modification 3, it is possible to calculate a more accurate empty vehicle rate. Furthermore, collecting new parking lot information can be used for future parking lot guidance.

  The present invention is not limited to the above embodiment. That is, various modifications can be made without departing from the scope of the present invention.

  11 control unit (acquisition means, storage means, prediction means, output means, classification means, determination means)

Claims (9)

Acquisition means for acquiring the number of parked vehicles in the parking lot;
Storage means for storing the maximum capacity of the parking lot and the history of the number of parked vehicles acquired by the acquisition means;
Based on the past record of the maximum capacity and the number of parked vehicles stored in the storage means, and the current number of parked vehicles acquired by the acquisition means, the vacant car situation of the parking lot at predicted time is predicted An information processing apparatus comprising: prediction means for The vacant car status indicates information according to a predicted vacant car rate or the predicted vacant car rate,
The information processing apparatus according to claim 1, further comprising an output unit that outputs information related to information according to the predicted vacant rate or the predicted vacant rate.   The information processing apparatus according to claim 1, wherein the predicted time is an estimated arrival time of a vehicle at the parking lot. The acquisition means further acquires vehicle information on a parking space where the vehicle can park.
The present invention is characterized in that the vacant car situation is predicted based on the past record of the maximum accommodation number and the number of parked vehicles stored in the storage means, the current number of parked vehicles acquired by the acquisition means, and the vehicle information. The information processing apparatus according to claim 3.   5. The apparatus according to any one of claims 1 to 4, wherein the prediction means predicts based on a past history of the number of parked vehicles in a time zone including the same time as the predicted time before the previous day. Information processing device. The time zone includes a predetermined time from the same time as the current time, and
Classification means for classifying the past history of the number of parked vehicles in the time zone for a plurality of days according to its fluctuation pattern;
And a determination unit that determines a day on which the history of the number of parked vehicles up to the predetermined time before the current time and the variation pattern can be regarded as the same classification,
The information processing apparatus according to claim 5, wherein the prediction means predicts the predicted vacant rate by the following equation.

ρ (t_ed): Predicted vacant vehicle rate at predicted time t_ed
P: Number of parked cars at current time
Pi (t_bg): Number of parked vehicles at the same time as the current time t_bg on each day determined by the determination means
Pi (t_ed): number of parked vehicles at the same time as predicted time t_ed on each day determined by the determination means n: sum of days determined by the determination means
St: Maximum capacity Obtaining the number of parked vehicles in the parking lot;
Storing the maximum capacity of the parking lot and the past history of the acquired number of parkings;
Predicting an empty car condition of the parking lot at a predicted time based on the stored history of the maximum capacity and the number of parked vehicles, and the acquired current number of parked vehicles;
An information processing method comprising: An information processing program executed by a computer that controls an information processing apparatus,
Acquisition means for acquiring the number of parked vehicles in the parking lot;
Storage means for storing the maximum capacity of the parking lot and the history of the number of parked vehicles acquired by the acquisition means;
Based on the past record of the maximum capacity and the number of parked vehicles stored in the storage means, and the current number of parked vehicles acquired by the acquisition means, the vacant car situation of the parking lot at predicted time is predicted An information processing program characterized by causing the computer to function as prediction means.   A recording medium on which the information processing program according to claim 8 is stored.

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