Information processing device, information processing method, and program

Abstract

Based on travel-related information, the system predicts the travel time from the starting point to the destination. [Solution] The system includes: a travel information acquisition unit that acquires travel information including a departure point, arrival point, departure time, and arrival time; a registration unit that associates the travel information with travel-related information and registers it as a record in a database; an average travel time calculation unit that calculates the travel time from the difference between the departure time and arrival time, and calculates the average travel time from the average of the travel times, based on one or more records in the database that have the same departure point and the same arrival point; a classification unit that classifies the multiple records into multiple groups based on the travel-related information; a correction coefficient calculation unit that calculates a correction coefficient based on the records belonging to each of the multiple groups; and a predicted travel time calculation unit that calculates a predicted travel time based on the average travel time and the correction coefficient.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] Conventionally, a device for predicting the travel time from a departure point to an arrival point has been known. For example, in Patent Document 1, there are provided route selection means for selecting a route to travel, destination specifying means for specifying a destination or a distance post of the destination, current location specifying means for specifying the current location or a distance post of the current location, travel speed setting means for setting the travel speed of a vehicle, calculation processing means for calculating the required time from the current location to the destination based on the route selected by the route selection means, the destination or the destination distance post and the current location or the current location distance post specified by the destination specifying means and the current location specifying means, and the travel speed set by the travel speed setting means, and display means for displaying the route selected by the route selection means, the destination or the destination distance post and the current location or the current location distance post specified by the destination specifying means and the current location specifying means, the travel speed set by the travel speed setting means, and the required time calculated by the calculation means. A required time calculation device for a vehicle navigation system characterized by the above is disclosed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An information processing apparatus or the like for predicting the travel time from a departure point to an arrival point based on information related to travel is desired.

Means for Solving the Problems

[0005] The information processing device disclosed herein includes: a travel information acquisition unit that acquires travel information including a departure point, arrival point, departure time, and arrival time; a registration unit that associates the travel information with travel-related information and registers it as a record in a database; an average travel time calculation unit that calculates travel time from the difference between departure time and arrival time and calculates the average travel time from the average of the travel times, based on one or more records in the database that have the same departure point and the same arrival point; a classification unit that classifies the multiple records into multiple groups based on the travel-related information; a correction coefficient calculation unit that calculates a correction coefficient based on the records belonging to each of the multiple groups; and a predicted travel time calculation unit that calculates a predicted travel time based on the average travel time and the correction coefficient. [Effects of the Invention]

[0006] According to this disclosure, travel time from a departure point to an arrival point can be predicted based on travel-related information. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 is a diagram showing the hardware configuration of an information processing device according to an embodiment of the present disclosure. [Figure 2] Figure 2 is a functional block diagram showing the functions of the control unit. [Figure 3] Figure 3 shows an example of an information registration table. [Figure 4] Figure 4 shows an example of an information extraction table. [Figure 5] Figure 5 shows a concrete example of classifying multiple records contained in an information extraction table into multiple groups. [Figure 6] Figure 6 illustrates how the correction coefficient calculation unit calculates the correction coefficient. [Figure 7] Figure 7 shows an example of the correction coefficient for each group. [Figure 8] Figure 8 shows an example of a system including an information processing device according to an embodiment of the present disclosure. [Figure 9] Figure 9 is a flowchart illustrating the overall processing procedure of the information processing device according to the embodiment of this disclosure. [Figure 10] Figure 10 is a flowchart illustrating the details of the processing in step S104 performed by the information processing device according to the embodiment of this disclosure. [Figure 11] Figure 11 is a flowchart illustrating the details of the processing in step S205 performed by the information processing device according to the embodiment of this disclosure. [Modes for carrying out the invention]

[0008] [Description of Embodiments in this Disclosure] First, the embodiments of this disclosure will be listed and explained. Furthermore, at least some of the embodiments described below may be combined in any way.

[0009] (1) The information processing device of the present disclosure includes: a travel information acquisition unit that acquires travel information including a departure point, an arrival point, a departure time, and an arrival time; a registration unit that associates the travel information with travel-related information and registers it in a database as a record; an average travel time calculation unit that calculates the travel time from the difference between the departure time and the arrival time, and calculates the average travel time from the average value of the travel times, based on one or more records in the database that have the same departure point and the same arrival point; a classification unit that classifies the multiple records into multiple groups based on the travel-related information; a correction coefficient calculation unit that calculates a correction coefficient based on the records belonging to each of the multiple groups; and a predicted travel time calculation unit that calculates a predicted travel time based on the average travel time and the correction coefficient.

[0010] This allows for the prediction of travel time from the starting point to the destination based on travel-related information.

[0011] (2) The information processing apparatus according to (1), wherein the movement information acquired by the movement information acquisition unit includes a stop time, and the movement time may be calculated by subtracting the stop time from the difference between the departure time and the arrival time.

[0012] Thereby, even when stopping at a point other than the departure point and the arrival point, an appropriate predicted movement time can be calculated.

[0013] (3) The information processing apparatus according to (1) or (2), wherein the correction coefficient calculation unit calculates a ratio of the movement time of the records belonging to each group with respect to the average movement time, and the ratio of the records belonging to each group may be used as a correction coefficient.

[0014] Thereby, it is possible to appropriately reflect information related to movement and predict the movement time from the departure point to the arrival point.

[0015] (4) The information processing apparatus according to any one of (1) to (3), wherein the movement-related information is calendar information, and the classification unit may classify the plurality of records into a plurality of groups based on the calendar information.

[0016] Thereby, based on the calendar information, the movement time from the departure point to the arrival point can be predicted.

[0017] (5) The information processing apparatus according to any one of (1) to (4), wherein the movement-related information is weather information, and the classification unit may classify the plurality of records into a plurality of groups based on the weather information.

[0018] Thereby, based on the weather information, the movement time from the departure point to the arrival point can be predicted.

[0019] (6) The information processing apparatus according to any one of (1) to (5), wherein the movement-related information is the departure time, and the classification unit may classify the plurality of records into a plurality of groups based on the departure time.

[0020] This allows us to predict the travel time from the departure point to the destination point based on the departure time.

[0021] (7) An information processing device according to any of (1) to (6), further comprising: an input unit for acquiring information entered by a user of the information processing device; and a predicted travel time acquisition unit for acquiring a predicted travel time from an external map information service if the same departure point acquired by the input unit is not registered in the database records, or if the same arrival point acquired by the input unit is not registered in the database records.

[0022] This allows for accurate prediction of travel time.

[0023] (8) The information processing method of the present disclosure includes the steps of: acquiring travel information including a departure point, an arrival point, a departure time, and an arrival time; registering the travel information as a record in a database in association with travel-related information; calculating travel time from the difference between departure time and arrival time, and calculating the average travel time from the average of the travel times, based on one or more records in the database that have the same departure point and the same arrival point; classifying the multiple records into multiple groups based on the travel-related information; calculating a correction coefficient based on the records belonging to each of the multiple groups; and calculating a predicted travel time based on the average travel time and the correction coefficient.

[0024] This provides an information processing method that enables a computer to function as an information processing device that predicts the travel time from a starting point to a destination point based on information related to travel.

[0025] (9) The program of the Disclosure executes the following processes: acquire travel information including a departure point, arrival point, departure time, and arrival time; register the travel information as a record in a database in association with travel-related information; calculate travel time from the difference between departure time and arrival time based on one or more records in the database that have the same departure point and the same arrival point; calculate average travel time from the average of the travel times; classify the multiple records into multiple groups based on the travel-related information; calculate a correction coefficient based on the records belonging to each of the multiple groups; and calculate a predicted travel time based on the average travel time and the correction coefficient.

[0026] This makes it possible to provide a program that allows a computer to function as an information processing device that predicts the travel time from a starting point to a destination point based on travel-related information.

[0027] [Details of the embodiments of this disclosure] This disclosure will be described in detail with reference to the drawings illustrating its embodiments. However, this disclosure is not limited to these examples and is intended to include all modifications within the meaning and scope of the claims, as indicated by the claims.

[0028] <Hardware configuration of the information processing device> Figure 1 shows an example of the hardware configuration of the information processing device 10 according to this embodiment. The information processing device 10 comprises a control unit 102, a storage unit 103, a communication unit 104, an input unit 105, and an output unit 106. These units are connected by a data bus, an address bus, a PCI (Peripheral Component Interconnect) bus, etc.

[0029] The information processing device 10 is, for example, an in-vehicle device. However, the information processing device 10 is not limited to an in-vehicle device. The information processing device 10 may be a mobile phone (including a smartphone), a tablet terminal, a server device, a laptop computer, or a wearable device.

[0030] The control unit 102 is, for example, a CPU (Central Processing Unit). Alternatively, the control unit 102 may be a GPU (Graphical Processing Unit) or an MPU (Micro Processing Unit). Furthermore, the control unit 102 may include an ASIC (Application Specific Integrated Circuit) as part, and may also include programmable logic devices such as gate arrays or FPGAs (Field Programmable Gate Arrays) as part. The control unit 102 is configured to execute computer programs.

[0031] The storage unit 103 includes volatile memory and non-volatile memory. The volatile memory is, for example, SRAM (Static Random Access Memory). The volatile memory may also be DRAM (Dynamic Random Access Memory). The non-volatile memory is, for example, flash memory. The non-volatile memory may also be HDD (Hard Disk Drive), SSD (Solid State Drive), or ROM (Read Only Memory).

[0032] The storage unit 103 stores a control program 310 and a database 320, which are computer programs. The control program 310 can be stored on a recording medium such as flash memory, ROM, or CD-ROM. The control unit 102 implements some of the functions of the information processing device 10 using the control program 310. The database 320 is, for example, a relational database. In this case, various types of information, which will be described later, are registered as records in tables included in the database 320.

[0033] The communication unit 104 includes a communication port for wired communication and / or an antenna for wireless communication. The communication port for wired communication is, for example, a USB port for connecting a USB (Universal Serial Bus) cable. Alternatively, the communication port for wired communication may be a LAN (Local Area Network) port for connecting an Ethernet cable or the like, and / or a communication port for serial communication. The antenna for wireless communication is, for example, a GPS (Global Positioning System) antenna. Alternatively, the antenna for wireless communication may be a GNSS (Global Navigation Satellite System) antenna. Furthermore, the antenna for wireless communication may be a Wi-Fi, 4G, 5G, or Bluetooth antenna. The communication unit 104 may have multiple antennas for performing different types of wireless communication. The communication unit 104 transmits and receives information to and from the outside world via the communication port for wired communication and / or the antenna for wireless communication.

[0034] The input unit 105 is, for example, a keyboard, touch panel, mouse, and / or microphone. The input unit 105 can acquire information entered by a user using the information processing device 10.

[0035] The output unit 106 is, for example, a display and / or a speaker. The user can obtain information processed by the control unit 102 through the output unit 106.

[0036] <Functions of the control unit> Figure 2 is a functional block diagram showing an example of the functions of the control unit 102 according to this embodiment. The control unit 102 functions as follows: movement information acquisition unit 201, registration unit 202, average movement time calculation unit 211, classification unit 221, correction coefficient calculation unit 222, predicted movement time calculation unit 231, and predicted movement time acquisition unit 241.

[0037] The movement information acquisition unit 201 has the function of acquiring movement information including the departure point, departure time, arrival point, and arrival time. That is, in accordance with the user's movement, the movement information acquisition unit 201 acquires the departure time and departure point from the communication unit 104, and similarly acquires the arrival time and arrival point. Furthermore, if the user makes a stop at a point other than the departure point or arrival point during the journey from the departure point to the arrival point, the movement information acquisition unit 201 acquires the time from the start to the end of the stop as the stop time. Note that if the user does not make a stop at a point other than the departure point or arrival point during the journey from the departure point to the arrival point, the stop time will be 0. For example, if the information processing device 10 is an in-vehicle device such as a car navigation system, the movement information acquisition unit 201 acquires the departure point and departure time of the vehicle the user is riding in using GPS or GNSS. Similarly, the movement information acquisition unit 201 acquires the arrival point and arrival time of the vehicle the user is riding in using GPS or GNSS. If a user stops at a location other than the departure or arrival point for reasons such as taking a break, the movement information acquisition unit 201 acquires the location and the time from the start to the end of the stop (stop time) using GPS or GNSS. The movement information acquisition unit 201 may also acquire the aforementioned movement information if the user inputs some or all of the aforementioned movement information into the input unit 105.

[0038] The registration unit 202 has the function of registering travel information as a record in the database 320, associating it with travel-related information. Travel-related information is information obtained via the communication unit 104 and / or the input unit 105, and is used to calculate travel time, as described later. Travel-related information includes calendar information, weather information, or departure time. Here, travel-related information is not limited to calendar information, weather information, or departure time. For example, it may be event information around the area that includes the travel route. Calendar information is, for example, the year, month, day, and day of the week in which the travel took place. Note that calendar information may also be the week number of the week in which the travel took place, the ten-day period of the month (early, mid, and late), or the date information of the event held. Furthermore, calendar information may be the month and day based on the lunar calendar. Weather information is weather information for the departure point, arrival point, or area that includes the travel route. Weather information is qualitative information such as sunny, cloudy, rainy, or snowy. Note that weather information may also be quantitative information such as temperature, precipitation, snow depth, or wind speed. Furthermore, weather information may combine the qualitative and quantitative information mentioned above.

[0039] Figure 3 shows an example of information registered in the database 320 by the registration unit 202. The database 320 has, for example, an information registration table 321 for registering information. The information registration table 321 contains multiple records, and each record is registered with an ID that identifies the record and various kinds of information associated with it. When the registration unit 202 registers movement information and movement-related information in the database 320, all the information may be registered in a single information registration table 321, or it may be divided and registered in multiple information registration tables 321.

[0040] Referring again to Figure 2, the average travel time calculation unit 211 calculates the average travel time based on one or more records that have the same departure point and the same arrival point. Now, referring to Figure 4, the method by which the average travel time calculation unit 211 calculates the average travel time based on one or more records will be explained. Figure 4 is an information extraction table 322 obtained by extracting records that have the same departure point and the same arrival point from the records contained in the information registration table 321 shown in Figure 3. In the information extraction table 322, the departure time, arrival time, and stopover time are registered for each record. Therefore, the average travel time calculation unit 211 calculates the difference between the departure time and the arrival time, and then subtracts the stopover time from that difference to calculate the travel time from the departure point to the arrival point for each record. Then, the average travel time calculation unit 211 calculates the average travel time by calculating the average of the travel times calculated for each record. For example, in the information extraction table 322 shown in Figure 4, the average travel time is 77.5 minutes. For the sake of explanation, the average travel time calculation unit 211 calculates the average travel time using the information extraction table 322, but it is not limited to this. The average travel time calculation unit 211 may also calculate the average travel time from the information registration table 321 without using the information extraction table 322.

[0041] The classification unit 221 has the function of classifying multiple records into multiple groups based on movement-related information. For example, the classification unit 221 classifies multiple records into multiple groups according to the syntax of SQL (Structured Query Language). Before classifying multiple records into multiple groups, the classification unit 221 may count the number of records and stop the classification if the number of records is less than a predetermined number. In this case, the classification unit 221 may output information to the output unit 106 notifying that the calculation of the predicted movement time has been stopped.

[0042] Here, referring to Figure 5, we will explain how the classification unit 221 classifies multiple records into multiple groups. The column for registering the month, which is calendar information, contains the numbers 6 and 7. Therefore, as shown in Figure 5, the classification unit 221 classifies the multiple records into records belonging to the June group, where the number 6 is registered, and records belonging to the July group, where the number 7 is registered. Note that the movement-related information used by the classification unit 221 for classification is not limited to the month of the calendar information. The classification unit 221 may also classify multiple records into multiple groups using the year or day, which are also calendar information. Furthermore, the classification unit 221 may also classify multiple records into multiple groups based on weather information such as sunny or rainy. For example, in the information extraction table 322, the column for registering the weather contains the weather information of sunny and rainy. Therefore, the classification unit 221 classifies the records into records belonging to the sunny group, where sunny is registered, and records belonging to the rainy group, where rainy is registered. In addition, the classification unit 221 may also classify multiple records into multiple groups based on departure times such as 6:50 or 7:00. For example, in the information extraction table 322, the column for registering departure times contains records with departure times of 6:50 and 7:00. Therefore, the classification unit 221 classifies the records into two groups: the 6 o'clock group, where 6:50 is registered, and the 7 o'clock group, where 7:00 is registered. Note that the classification unit 221 classifies records with departure times between 6:00 and 6:59 as belonging to the 6 o'clock group, and records with departure times between 7:00 and 7:59 as belonging to the 7 o'clock group, but is not limited to this. For example, the departure time could be divided into 10-minute units, and records with departure times between 6:50 and 6:59 could be classified as belonging to the 6:50 group. Furthermore, the classification unit 221 could also divide the departure time into 5-minute, 30-minute, or 60-minute units.

[0043] The classification unit 221 may calculate a week number from the date and classify multiple records into multiple groups for each week number. For the sake of explanation, the classification unit 221 classified multiple records contained in the information extraction table 322 into multiple groups, but is not limited to this. The classification unit 221 may also classify multiple records contained in the information registration table 321 into multiple groups without using the information extraction table 322.

[0044] Referring again to Figure 2, the correction coefficient calculation unit 222 has the function of calculating a correction coefficient based on the records belonging to each group classified by the classification unit 221.

[0045] Here, referring to Figure 6, the method by which the correction coefficient calculation unit 222 calculates the correction coefficient will be explained. The correction coefficient calculation unit 222 calculates the travel time for each record from the departure time, arrival time, and stopover time registered for each record. If the average travel time calculation unit 211 has already calculated the travel time for each record, that travel time may be used. Next, based on the travel time, the correction coefficient calculation unit 222 calculates the average travel time up to the most recent record for each record. For example, in the case of a record with ID 5 in Figure 6, the average travel time is calculated based on the travel time of the record with ID 1 and the travel time of the record with ID 3. That is, the average travel time for the record with ID 5 is the average of these two travel times. In this way, the average value is calculated from the travel time up to the record immediately preceding the target record. Note that, for example, if the target record with ID 1 is the first record, the average travel time up to the most recent record is not calculated.

[0046] Next, the correction coefficient calculation unit 222 calculates the average time ratio based on the travel time up to the most recent point and the average travel time. Specifically, the correction coefficient calculation unit 222 calculates the ratio of travel time to the average travel time up to the most recent point for each record. For example, in the first record of the target records, such as when the ID is 1, the average travel time up to the most recent point is not calculated, and therefore the average time ratio is also not calculated.

[0047] Next, the correction coefficient calculation unit 222 calculates the average value of the average time ratio for each group based on the average time ratio. The correction coefficient calculation unit 222 divides the records into groups and calculates the average value of the average time ratio for each group. For example, if the records are divided into a June group and a July group as shown in Figure 5, the correction coefficient calculation unit 222 calculates two values, as shown in Figure 6: the average value of the average time ratio for records with IDs 1 to 9 and the average value of the average time ratio for records with IDs 11 to 19. Note that the first record of the target records, such as those with ID 1, does not have its average time ratio calculated and is therefore not used in the calculation of the average value. The average value of the average time ratio calculated above becomes the correction coefficient.

[0048] Figure 7 shows the correction coefficients calculated based on the information extraction table 322. The information extraction table 322 is classified into multiple groups, and a correction coefficient is calculated based on the records included in each group. The calculated correction coefficients are used, along with the average travel time, to calculate the predicted travel time. For the sake of explanation, the correction coefficient calculation unit 222 calculated the average travel time using the information extraction table 322, but is not limited to this. The correction coefficient calculation unit 222 may also calculate the average travel time from the information registration table 321 without using the information extraction table 322.

[0049] The predicted travel time calculation unit 231 has the function of calculating the predicted travel time based on the average travel time calculated by the average travel time calculation unit 211 and the correction coefficient calculated by the correction coefficient calculation unit 222. Specifically, the predicted travel time calculation unit 231 obtains the calendar information, weather information, and departure time to be predicted via the communication unit 104 or the input unit 105. Then, the predicted travel time calculation unit 231 obtains the correction coefficient for the group containing the calendar information to be predicted from among multiple groups. Similarly, for weather information and departure time, the predicted travel time calculation unit 231 obtains the correction coefficient for the group containing the weather information to be predicted and the correction coefficient for the group containing the departure time to be predicted. Then, the predicted travel time calculation unit 231 calculates the predicted travel time from the average travel time and the obtained correction coefficient. As a specific example, consider the case where the predicted travel time calculation unit 231 predicts the travel time from point A to point B, departing at 7:00 on July 8, 2025. Also, assume that the forecast weather at that time is rain. In this case, the predicted travel time calculation unit 231 obtains the correction coefficients for the July group, the rainy group, and the 7 o'clock group. Then, it multiplies the average travel time by the previously obtained correction coefficients for the July group, the rainy group, and the 7 o'clock group to calculate a predicted travel time of 78 minutes. After calculating the predicted travel time, the predicted travel time calculation unit 231 outputs the predicted travel time to the output unit 106.

[0050] In the example above, the predicted travel time calculation unit 231 calculated the predicted travel time using a correction coefficient calculated from information stored in the storage unit 103 of the information processing device 10, but it is not limited to this. As shown in Figure 8, the information processing device 10 is connected to multiple information processing devices 10 via a network N in a communicable manner. Therefore, the predicted travel time calculation unit 231 can obtain the average travel time or correction coefficient from other information processing devices 10. Accordingly, the predicted travel time calculation unit 231 may calculate the travel prediction time based on the average travel time or correction coefficient obtained from other information processing devices 10. In addition, when the information processing device 10 obtains the correction coefficient, it may be configured to obtain only correction coefficients that satisfy predetermined conditions. For example, the time zone ID of the area including the departure point and arrival point may be stored in the storage unit 103, and only correction coefficients calculated based on the information of the area with the same time zone ID as that time zone ID may be obtained from other information processing devices 10. By doing so, it becomes possible to calculate a more accurate predicted travel time.

[0051] The predicted travel time acquisition unit 241 has the function of acquiring predicted travel time from an external map information service. External map information services include, for example, Google Maps provided by Google Inc., Apple Maps provided by Apple Inc., or NAVITIME provided by NAVITIME Japan Co., Ltd. Since these external map information services can acquire predicted travel time even for locations not registered in the information extraction table 322, the predicted travel time is output to the output unit 106.

[0052] <Processing flow of information processing equipment> Figure 9 is a flowchart showing the processing flow of the information processing device 10. The information processing device 10 determines whether multiple records are registered in the database 320 (step S101). If the information processing device 10 determines that multiple records are not registered in the database 320 (NO in step S101), it obtains the predicted travel time from an external map information service (step S105). This applies, for example, when using the information processing device 10 immediately after it has been shipped. After that, the information processing device 10 outputs the obtained predicted travel time (S106).

[0053] Furthermore, the information processing device 10 acquires movement information in accordance with the user's movement (step S107). In addition, the information processing device 10 associates the movement information with movement-related information such as weather information and registers it as a record in the database 320 (step S108).

[0054] If the information processing device 10 determines that multiple records are registered in the database 320 (YES in step S101), the information processing device 10 obtains the departure point and destination point in order to predict the travel time (step S102). The information processing device 10 determines whether the same departure point as the obtained departure point is registered in the database 320 (step S103). If the information processing device 10 determines that the same departure point as the obtained departure point is registered in the database 320, and that the same destination point as the obtained destination point is registered in the database 320 (YES in step S103), the information processing device 10 calculates the predicted travel time (step S104).

[0055] If the information processing device 10 determines that a departure point identical to the acquired departure point is not registered in the database 320 (NO in step S103), it obtains a predicted travel time from an external map information service (step S105). Similarly, if the information processing device 10 determines that a destination point identical to the acquired destination point is not registered in the database 320 (NO in step S103), it obtains a predicted travel time from an external map information service (step S105).

[0056] Next, the information processing device 10 outputs the predicted travel time calculated in step S104 or the predicted travel time obtained in step S105 (step S106).

[0057] Figure 10 is a flowchart illustrating the details of the processing performed by the information processing device 10 in step S104. The information processing device 10 calculates the difference between the departure time and the arrival time, and then subtracts the stopover time from that difference to calculate the travel time from the departure point to the arrival point for each record (step S201). Next, the information processing device 10 calculates the average travel time from the travel time calculated earlier (step S202). Specifically, the information processing device 10 targets records from among multiple records that have the same departure point as the departure point obtained in step S102, and also have the same arrival point as the arrival point obtained in step S102, and calculates the average travel time from the average travel time of the targeted records.

[0058] Next, the information processing device 10 decides whether or not to use a correction coefficient for a user other than the user of the information processing device 10 when calculating the predicted travel time (step S203).

[0059] If the correction coefficient of another user is to be used (YES in step S203), the information processing device 10 obtains the correction coefficient of another user from another information processing device 10 via the network N (step S204).

[0060] If the correction coefficient of another user is not used (NO in step S203), the information processing device 10 calculates the correction coefficient (step S205).

[0061] Figure 11 is a flowchart illustrating the details of the processing performed by the information processing device 10 in step S205.

[0062] The information processing device 10 classifies multiple records into multiple groups based on calendar information (step S301). Specifically, the information processing device 10 targets records in which the same departure point as the departure point obtained in step S102 is registered, and in which the same arrival point as the arrival point obtained in step S102 is registered, and classifies the targeted records into multiple groups based on calendar information such as year, month, and day.

[0063] Next, the information processing device 10 calculates a correction coefficient based on each record belonging to a group classified based on the calendar information (step S302). Specifically, the information processing device 10 calculates the average time ratio described above for each record belonging to a group classified based on the calendar information. After that, the information processing device 10 calculates the average value of the average time ratio for each group and uses these as the correction coefficient for each group.

[0064] Next, the information processing device 10 classifies the records into multiple groups based on weather information (step S303). Specifically, the information processing device 10 targets records in which the same departure point as the departure point obtained in step S102 is registered, and in which the same arrival point as the arrival point obtained in step S102 is registered, and classifies the targeted records into multiple groups based on weather information such as sunny or rainy.

[0065] Next, the information processing device 10 calculates a correction coefficient based on each record belonging to one of several groups classified based on weather information (step S304). Specifically, the information processing device 10 calculates the average time ratio described above for each record belonging to one of the groups classified based on weather information. After that, the information processing device 10 calculates the average value of the average time ratio for each group and uses these as the correction coefficient for each group.

[0066] Next, the information processing device 10 classifies the records into multiple groups based on the departure time (step S305). Specifically, the information processing device 10 targets records that have the same departure point registered as the departure point obtained in step S102, and also have the same arrival point registered as the arrival point obtained in step S102, and classifies the targeted records into multiple groups based on the departure time, such as 6:50 or 7:00.

[0067] Next, the information processing device 10 calculates a correction coefficient based on each record belonging to a group classified based on departure time (step S306). Specifically, the information processing device 10 calculates the average time ratio described above for each record belonging to a group classified based on departure time. After that, the information processing device 10 calculates the average value of the average time ratio for each group and uses these as the correction coefficient for each group.

[0068] Referring again to Figure 10, the information processing device 10 calculates the predicted travel time based on the average travel time and correction coefficients (step S206). Specifically, the information processing device 10 acquires the calendar information, weather information, and departure time to be predicted. Then, the information processing device 10 acquires the correction coefficients for the group containing the calendar information to be predicted from among multiple groups. Similarly, for the weather information and departure time, it acquires the correction coefficients for the group containing the weather information to be predicted and the correction coefficients for the group containing the departure time to be predicted. Then, the information processing device 10 calculates the predicted travel time by sequentially multiplying the average travel time and the acquired correction coefficients.

[0069] <Other Embodiments> This disclosure is not limited to the embodiments described above and in the drawings. For example, any combination of the features of the embodiments described above or later is possible as long as it does not contradict each other. Furthermore, any feature of the embodiments described above or later may be omitted unless explicitly stated as essential. In addition, the embodiments described above may be modified as follows:

[0070] In the above embodiment, the predicted travel time was calculated by multiplying the average travel time by a correction coefficient related to calendar information, a correction coefficient related to weather information, and a correction coefficient related to departure time, but the embodiment is not limited to this. The predicted travel time may also be calculated by multiplying the average travel time by at least one of the correction coefficients related to calendar information, weather information, and departure time.

[0071] In the above embodiment, a car was described as the means of transportation between the starting point and the destination, but this is not limited to that. For example, the means of transportation may be a train, walking, bus, bicycle, boat, or airplane. Furthermore, the means of transportation between the starting point and the destination is not limited to a single means of transportation. For example, multiple means of transportation such as walking, bus, and train may be used.

[0072] A column for registering the means of transportation may be created in the information registration table 321, and the means of transportation used may be registered as a record in the information registration table 321 in association with the transportation information. Alternatively, the classification unit 221 may classify multiple records into multiple groups based on the means of transportation. Furthermore, the correction coefficient calculation unit 222 may calculate a correction coefficient for calculating the predicted travel time for each group of multiple means of transportation.

[0073] Predicted travel time may be calculated based on the travel information and travel-related information of multiple users. For example, if multiple vehicles travel from the same starting point to the same destination, the travel information of those vehicles may be associated with travel-related information and registered as records in the information registration table 321. Based on the records of the multiple registered vehicles, the average travel time, correction coefficient, and predicted travel time may be calculated.

[0074] The classification unit 221 may classify multiple records into multiple groups based on combinations of movement-related information. For example, the classification unit 221 may classify multiple records into groups such as the "rainy June" group or the "7 o'clock July" group. The correction coefficient calculation unit 222 may also calculate a correction coefficient based on the records belonging to the groups classified as described above.

[0075] The information processing device 10 may calculate the scheduled departure time based on the predicted travel time calculated by the predicted travel time calculation unit 231 and the scheduled arrival time entered by the user, and output the scheduled departure time to the output unit 106. Alternatively, the information processing device 10 may calculate the scheduled arrival time based on the predicted travel time calculated by the predicted travel time calculation unit 231 and the scheduled departure time entered by the user, and output the scheduled arrival time to the output unit 106. [Explanation of Symbols]

[0076] 10 Information Processing Devices 102 Control Unit 103 Storage section 104 Communications Department 105 Input section 106 Output section 201 Movement information acquisition unit 202 Registration Department 211 Average travel time calculation section 221 Classification Department 222 Correction coefficient calculation unit 231 Predicted travel time calculation unit 241 Predicted travel time acquisition unit 310 Control Program 320 databases 321 Information Registration Table 322 Information Extraction Table

Claims

1. A movement information acquisition unit acquires movement information including the departure point, arrival point, departure time, and arrival time. A registration unit registers the aforementioned travel information as a record in a database, associating it with travel-related information including calendar information, weather information, and departure time. An average travel time calculation unit calculates travel time from the difference between departure time and arrival time, and calculates the average travel time from the average of the travel times, based on one or more records in the database that have the same departure point and the same arrival point. A classification unit that classifies the plurality of records into a plurality of groups, including the group of calendar information, the group of weather information, and the group of departure time, based on the aforementioned travel-related information. A correction coefficient calculation unit calculates a correction coefficient for calendar information, a correction coefficient for weather information, and a correction coefficient for departure time based on records belonging to each of the aforementioned plurality of groups. An information processing device comprising: a predicted travel time calculation unit that calculates a predicted travel time based on a combination of the average travel time, a correction coefficient for calendar information, a correction coefficient for weather information, and a correction coefficient for departure time.

2. A movement information acquisition unit acquires movement information including the departure point, arrival point, departure time, and arrival time. A registration unit registers the aforementioned travel information as a record in a database, associating it with travel-related information including two types of information from calendar information, weather information, and departure time. Among the multiple records included in the aforementioned database, those with the same starting point and arrival point An average travel time calculation unit calculates travel time from the difference between departure time and arrival time based on one or more records of the same location, and calculates the average travel time from the average of the said travel times. A classification unit that classifies the plurality of records into a plurality of groups, including two of the groups of calendar information, weather information, and departure time, based on the aforementioned travel-related information, A correction coefficient calculation unit calculates two types of correction coefficients from among the correction coefficient for calendar information, the correction coefficient for weather information, and the correction coefficient for departure time, based on the records belonging to each of the plurality of groups. An information processing device comprising: a predicted travel time calculation unit that calculates a predicted travel time based on the average travel time and the combination of the two types of correction coefficients.

3. The movement information acquired by the movement information acquisition unit includes stopover time, and the movement time is calculated by subtracting the stopover time from the difference between the departure time and the arrival time, as described in claim 1 or claim 2. Processing device.

4. The information processing apparatus according to claim 1 or 2, wherein the correction coefficient calculation unit calculates the ratio of the travel time of records belonging to each group to the average travel time, and uses the ratio of records belonging to each group as the correction coefficient.

5. The information processing device includes an input unit that acquires information entered by a user of the information processing device, The information processing apparatus according to claim 1 or 2, further comprising: a predicted travel time acquisition unit that acquires a predicted travel time from an external map information service if the same departure point as the departure point acquired by the input unit is not registered in the database records, or if the same arrival point as the arrival point acquired by the input unit is not registered in the database records.

6. An information processing method performed by an information processing device, Steps include obtaining travel information including departure point, arrival point, departure time, and arrival time, The steps include registering the aforementioned travel information as a record in a database, associated with travel-related information including calendar information, weather information, and departure time, The steps include: calculating travel time from the difference between departure time and arrival time based on one or more records in the database that have the same departure point and the same arrival point, and calculating the average travel time from the average of the travel times; A step of classifying the plurality of records into a plurality of groups, including the group of calendar information, the group of weather information, and the group of departure time, based on the aforementioned travel-related information. A step of calculating a correction coefficient for calendar information, a correction coefficient for weather information, and a correction coefficient for departure time based on records belonging to each of the plurality of groups, A step of calculating the predicted travel time based on a combination of the average travel time, the correction coefficient for calendar information, the correction coefficient for weather information, and the correction coefficient for departure time. An information processing method having the following characteristics.

7. An information processing method performed by an information processing device, Steps include obtaining travel information including departure point, arrival point, departure time, and arrival time, The steps include registering the aforementioned travel information as a record in a database, associating it with travel-related information including two types of information from calendar information, weather information, and departure time, Based on one or more records in the aforementioned database that share the same departure point and the same arrival point, the difference between the departure time and the arrival time is calculated. The steps include: calculating travel time and calculating the average travel time from the average of the travel times, A step of classifying the plurality of records into a plurality of groups, including two of the groups of calendar information, weather information, and departure time, based on the aforementioned travel-related information, A step of calculating two types of correction coefficients from among the correction coefficient for calendar information, the correction coefficient for weather information, and the correction coefficient for departure time, based on the records belonging to each of the aforementioned plurality of groups. A step of calculating the predicted travel time based on the average travel time and the combination of the two types of correction coefficients. An information processing method having the following characteristics.

8. On the computer, Obtain travel information including departure point, arrival point, departure time, and arrival time. The aforementioned travel information is registered in the database as a record, associated with travel-related information including calendar information, weather information, and departure time. From among the multiple records included in the aforementioned database, based on one or more records where the departure point and arrival point are the same, the travel time is calculated from the difference between the departure time and the arrival time, and the average travel time is calculated from the average of the said travel times. Based on the aforementioned travel-related information, the plurality of records are classified into a plurality of groups, including the calendar information group, the weather information group, and the departure time group. Based on the records belonging to each of the aforementioned multiple groups, a correction coefficient for the calendar information, a correction coefficient for the weather information, and a correction coefficient for the departure time are calculated. A program that performs a process to calculate a predicted travel time based on a combination of the average travel time, a correction coefficient for calendar information, a correction coefficient for weather information, and a correction coefficient for departure time.

9. On the computer, Obtain travel information including departure point, arrival point, departure time, and arrival time. The aforementioned travel information is registered in the database as a record, associated with travel-related information including two types of information from calendar information, weather information, and departure time. From among the multiple records included in the aforementioned database, based on one or more records where the departure point and arrival point are the same, the travel time is calculated from the difference between the departure time and the arrival time, and the average travel time is calculated from the average of the said travel times. Based on the aforementioned travel-related information, the plurality of records are classified into a plurality of groups, including two of the following groups: the calendar information group, the weather information group, and the departure time group. Based on the records belonging to each of the aforementioned multiple groups, two types of correction coefficients are calculated from among the correction coefficient for calendar information, the correction coefficient for weather information, and the correction coefficient for departure time. A program that performs a process to calculate the predicted travel time based on the combination of the average travel time and the two types of correction coefficients.

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