Information processing apparatus and information processing method

Abstract

To easily quantify the convenience of traffic.SOLUTION: The processing device 21 includes a first acquisition unit 213, a second acquisition unit 214, and a determination unit 215. The first acquiring unit 213 acquires the predicted value Pij {circumflex over ()}. The predicted value Pij {circumflex over ()} indicates a prediction of the number of people who have moved from the departure place I to the destination j. The second acquisition unit 214 acquires the performance value Pij. The performance value Pij indicates a performance of the number of people who have moved from the departure place I to the destination j. The determining unit 215 determines the traffic utility level Tij based on the predicted value Pij {circumflex over ()} and the actual value Pij. The traffic convenience level Tij indicates a degree of convenience of traffic.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to an information processing device and an information processing method. [Background technology]

[0002] Previously, attempts have been made to quantify the convenience of intercity transportation based on data such as the number of public transportation services in operation and the number of transfers. For example, Non-Patent Document 1 discloses an evaluation method for estimating the convenience of a regional public transportation network by multiplying three factors: accessibility, the importance of the travel section, and the importance of the travel time period. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Hiroki Okuda and three others, "Method for evaluating the effect of transportation policies on the convenience of regional public transportation networks," Railway Technical Research Institute Report, Railway Technical Research Institute, February 2020, Vol. 34, No. 2, pp. 17-24 [Non-patent document 2] MC Gonzalez, CA Hidalgo, A.-L. Barabasi, "Understanding individual human mobility patterns", June 7, 2008, Nature 453, 479-482 Summary of the Invention [Problem to be solved by the invention]

[0004] In the method of Non-Patent Document 1, the importance of a time period is determined based on the number of public transportation services that can be used without transfers per unit time period. However, the method of Non-Patent Document 1 has the problem that quantification is not easy because the quantification results vary depending on the type of data used.

[0005] The present invention has been made to solve the above-mentioned problems, and has an object to easily quantify an index showing the degree of convenience of transportation. [Means for solving the problem]

[0006] An information processing device according to a preferred embodiment of the present invention includes a first acquisition unit that acquires a predicted value indicating a predicted number of people traveling from a first city to a second city, a second acquisition unit that acquires an actual value indicating the actual number of people traveling from the first city to the second city, and a determination unit that determines an index indicating the degree of convenience of transportation when traveling from the first city to the second city based on the predicted value and the actual value.

[0007] An information processing method according to a preferred embodiment of the present invention obtains a predicted value indicating the predicted number of people moving from a first city to a second city, obtains an actual value indicating the actual number of people moving from the first city to the second city, and determines an index indicating the degree of convenience of transportation when moving from the first city to the second city based on the predicted value and the actual value. [Effects of the Invention]

[0008] According to the information processing device and information processing method of the present invention, an index showing the degree of convenience of transportation can be easily quantified. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram showing the overall configuration of an information processing system including an information processing device according to a first embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of the configuration of the statistics server of FIG. 1. [Figure 3] FIG. 1 is a schematic diagram showing the movement of a person from a starting point to a destination. [Figure 4] FIG. 1 is a schematic diagram showing the movement of people between cities. [Figure 5] FIG. 2 is a diagram showing an example of a movement information database according to the first embodiment. [Figure 6]2 is a block diagram showing an example of the configuration of the information processing server shown in FIG. 1; [Figure 7] FIG. 10 is a diagram illustrating an example of a relationship between a moving distance and an actual value of a moving amount. [Figure 8] FIG. 10 is a diagram showing an example of determined transportation convenience levels. [Figure 9] 10 is a flowchart illustrating an example of the operation of the processing device. [Figure 10] FIG. 10 is a diagram illustrating an example of ranking of transportation convenience levels. DETAILED DESCRIPTION OF THE INVENTION

[0010] 1. First embodiment The configuration of an information processing device according to a first embodiment of the present invention will be described below with reference to FIGS.

[0011] 1.1. Configuration of the First Embodiment 1.1.1. Overall structure 1 is a diagram showing the overall configuration of an information processing system 1 including an information processing device according to the first embodiment. The information processing system 1 includes a statistical server 10, an information processing server 20, and a communication network NET.

[0012] The statistics server 10 is a server that creates demographic statistics and stores and updates the created demographic data. For example, the statistics server 10 creates demographic statistics using operational data of a mobile phone network.

[0013] The information processing server 20 is a server that mainly processes information relating to population movement based on the demographic statistics created by the statistics server 10.

[0014] The communication network NET is a telecommunications line such as a mobile communication network managed by a telecommunications carrier that provides communication services. The communication network NET includes one or both of a wired communication network and a wireless communication network. For example, the communication network NET may be connected via the Internet to another network (not shown) managed by another telecommunications carrier.

[0015] In the information processing system 1, a statistical server 10 and an information processing server 20 are connected to each other via a communication network NET so that they can communicate with each other.

[0016] 1.1.2.Statistics Server Configuration Fig. 2 is a block diagram showing an example of the configuration of the statistical server 10 in Fig. 1. As shown in Fig. 2, the statistical server 10 includes a processing device 11, a storage device 12, and a communication device 13. The elements included in the statistical server 10 are connected to each other by one or more buses for communicating information.

[0017] The processing device 11 is a processor that controls the entire statistical server 10, and is configured using, for example, one or more chips. The processing device 11 is configured using, for example, a central processing unit (CPU) including an interface with peripheral devices, an arithmetic unit, a register, etc. Note that some or all of the functions of the processing device 11 may be realized by hardware such as a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The processing device 11 executes various processes in parallel or sequentially.

[0018] The storage device 12 is a recording medium that can be read and written by the processing device 11. The storage device 12 includes, for example, a nonvolatile memory and a volatile memory. The nonvolatile memory is, for example, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and an electrically erasable programmable read-only memory (EEPROM). The volatile memory is, for example, a random access memory (RAM).

[0019] The storage device 12 stores a movement information database DB1 and a plurality of programs including a control program PR1 to be executed by the processing device 11. The storage device 12 also functions as a work area for the processing device 11.

[0020] FIG. 3 is a schematic diagram showing the movement of a person from a departure point i to a destination j. In FIG. 3, the departure point i and the destination j are indicated by circles. The movement of a person from the departure point i to the destination j is indicated by an arrow. The length of the arrow indicating the movement of a person is the travel distance D from the departure point i to the destination j. ij Therefore, in this example, the moving distance D ij is the straight-line distance between origin i and destination j. Origin i is an example of a first city. Destination j is an example of a second city.

[0021] Figure 4 is a schematic diagram showing the movement of people between cities. In Figure 4, each circle represents one city. City A, City B, and City C correspond to the departure point i. City X and City Y correspond to the destination j. The travel distance from City A to City X is D AX The travel distance from city A to city Y is D AY The travel distance from City B to City X is D BX The travel distance from City B to City Y is D BY The travel distance from City C to City X is D CX The travel distance from city C to city Y is D CY It should be noted that "city" may be interpreted as an administrative division such as "city," "town," "county," or "village," or as a non-administrative division such as "region" or "provincial area." In other words, the present disclosure is applicable to the movement of people between two arbitrarily defined areas.

[0022] Based on population statistics, the number of people who moved from city A to city X per unit period is the actual value of the migration amount, P AX The unit period is, for example, one week or one month, but any period width can be set. The amount of movement grasped from demographic statistics is stored in the movement information database DB1 as an actual value P ijThe amount of migration is an example of the number of people who moved from the first city to the second city.

[0023] 5 is a diagram showing an example of the movement information database DB1 according to the first embodiment. As shown in FIG. 5, the movement information database DB1 has, as its items, a starting point i, a destination j, a population P i , moving distance D ij , and the actual value of the movement amount P ij "Departure point i" indicates the name of the city from which the user U departs. "Destination point j" indicates the name of the city where the person arrives. "Departure point population P i " indicates the population of the city from which the person departed. "Distance traveled D ij " indicates the travel distance from the departure point i to the destination j. "The actual travel distance P ij ” indicates the number of people who travel from origin i to destination j per unit period.

[0024] The communication device 13 is hardware serving as a transmitting / receiving device for communicating with other devices. The communication device 13 is also called, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 13 may include a connector for wired connection and an interface circuit corresponding to the connector. The communication device 13 may also include a wireless communication interface. Examples of the connector and interface circuit for wired connection include products that comply with wired LAN, IEEE1394, and USB. Examples of the wireless communication interface include products that comply with wireless LAN, Bluetooth (registered trademark), etc.

[0025] The processing device 11 reads and executes the control program PR1 from the storage device 12. The processing device 11 acquires operation data of the mobile phone network from a core system of the mobile phone network (not shown) via the communication device 13, and executes demographic processing based on the acquired operation data.

[0026] Demographic processing is processing that performs statistics on population distribution, population trends by time period, population movement between cities, etc. based on operation data of the mobile phone network. The processing device 11 creates a movement information database DB1 as a result of the statistical processing.

[0027] Demographic processing may include a de-identification process that summarizes mobile phone network operation data so as to remove information that can identify individuals, a tabulation process that estimates the population including people other than mobile phone users by taking into account the mobile phone penetration rate, and a confidentiality process that adds noise based on differential privacy to the tabulation results. The de-identification process, tabulation process, and confidentiality process are merely examples and do not particularly limit the present invention.

[0028] 1.1.3. Information Processing Server Configuration Fig. 6 is a block diagram showing an example of the configuration of the information processing server 20 in Fig. 1. As shown in Fig. 6, the information processing server 20 includes a processing device 21, a storage device 22, and a communication device 23. The elements included in the information processing server 20 are connected to each other by a single bus or multiple buses for communicating information. The information processing server 20 is an example of an information processing device.

[0029] The processing device 21 is a processor that controls the entire information processing server 20, and is configured using, for example, one or more chips. The processing device 21 is configured using, for example, a central processing unit (CPU) including an interface with peripheral devices, an arithmetic unit, and a register. Note that some or all of the functions of the processing device 21 may be realized by hardware such as a DSP, an ASIC, a PLD, or an FPGA. The processing device 21 executes various processes in parallel or sequentially.

[0030] The storage device 22 is a recording medium that can be read and written by the processing device 21. The storage device 22 includes, for example, a nonvolatile memory and a volatile memory. The nonvolatile memory is, for example, a ROM, an EPROM, and an EEPROM. The volatile memory is, for example, a RAM.

[0031] The storage device 22 stores a plurality of programs, including a control program PR2, to be executed by the processing device 21, and a prediction model PM1. The storage device 22 also functions as a work area for the processing device 21.

[0032] The communication device 23 is hardware serving as a transmitting / receiving device for communicating with other devices. The communication device 23 is also called, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 23 may include a connector for wired connection and an interface circuit corresponding to the connector. The communication device 23 may also include a wireless communication interface. Examples of the connector and interface circuit for wired connection include products that comply with wired LAN, IEEE1394, and USB. Examples of the wireless communication interface include products that comply with wireless LAN, Bluetooth (registered trademark), etc.

[0033] The processing device 21 functions as a model learning unit 211, a prediction unit 212, a first acquisition unit 213, a second acquisition unit 214, a determination unit 215, and an evaluation unit 216, for example, by reading and executing a control program PR2 from the storage device 22.

[0034] Figure 7 shows the distance traveled D ij and the actual value of the movement amount P ij 7 is a diagram showing an example of the relationship between the horizontal axis and the moving distance D ij The vertical axis shows the logarithm of the movement amount P ij where the actual value of the movement amount P ij is indicated by a black circle. Generally, the actual value of the movement amount P ij is the travel distance D ij The longer the distance, the smaller the value tends to be.

[0035] In FIG. 7, the solid line indicated by F1 represents the predicted value P ij In the present invention, the predicted value of the movement amount P ij ^(Hat) and the actual value of the movement amount Pij The difference between these is the transportation convenience T ij The transportation convenience level T ij indicates the degree of convenience of transportation when traveling from departure point i to destination j. Convenience of transportation is also called accessibility. Convenience of transportation can also be expressed as the ease of using public transportation or individual transportation. Public transportation refers to transportation that can be freely used by an unspecified number of people by paying a set fare. For example, public transportation includes railways, trams, monorails, buses, taxis, airplanes, ships, etc. Individual transportation refers to transportation that operates based on individual demand. For example, individual transportation includes private cars, motorcycles, minibuses owned by companies, etc. Transportation convenience T ij is an example of an index.

[0036] For example, the actual value P of the movement amount indicated by point Q1 ij is the predicted value of the movement amount P ij ^(hat) is lower. Therefore, the actual value of the amount of movement from city i to city j, P ij is the predicted value P ij Smaller than ^(Hat), with good accessibility T ij It is understood that this is not good.

[0037] The model learning unit 211 learns the prediction model PM1 using a linear regression model. More specifically, the prediction model PM1 learns the travel distance D ij and the population P of departure point i i and the travel volume per unit period. The travel volume is the number of people traveling between cities. In this embodiment, the probability that a person travels from a departure point i to a destination j is expressed as the travel distance D between the departure point i and the destination j. ij Based on this assumption, the predicted value P of the travel distance from the origin i to the destination j is ij ^(hat) is the distance traveled D ij Therefore, the predicted value of the movement amount P ij ^(hat) is expressed by the following equation (1).

[0038]

number

[0039] In other words, the prediction model PM1 predicts the travel distance D ij In FIG. 7, the predicted value of the movement amount P ij ^(hat) is expressed as a linear function F1 with a slope of w1 and an intercept of w0. Therefore, the predicted value of the movement amount P ij ^(hat) is the population P of starting point i i and function f(D ij ) as a product of the function f(D ij ) and the population of departure point i, P i Includes the product with .

[0040]

number

[0041] Actual movement amount P ij is the population P of departure point i i and the travel distance D ij , and transportation convenience T ij Therefore, it can be formulated as the following equation (3).

[0042]

number

[0043] Therefore, the predicted value of the movement amount P ij ^(Hat) and actual value of movement amount P ij By calculating the gap between the ij is quantified.

[0044]

number

[0045] The prediction model PM1 is based on the origin i and the population P i and the travel distance D from the starting point i to the destination j ij This model predicts the number of people traveling from a departure point i to a destination j by inputting travel information related to i and j. For example, the prediction model PM1 uses a machine learning model such as a linear regression model, a neural network model, a logistic regression model, or a support vector machine. Below, as an example, a learning method for the prediction model PM1 using a linear regression model will be described.

[0046] As shown in the above equation (1), the predicted value of the movement amount P ij ^ (hat) has one explanatory variable. Therefore, the predicted value of the movement amount as the objective variable P ij ^(hat) can be predicted by simple regression analysis. Actual value of movement amount P ij When the distance between each data point included in the above equation and the regression line is defined as the error, the sum of squares of the errors corresponding to each point is defined as the error function.

[0047] In simple regression analysis, the coefficients w0 and w1 that minimize the error function are calculated using the least squares method. In this way, the predicted value P ij A prediction model PM1 that outputs ^ (hat) is trained.

[0048] The prediction unit 212 adds the population P of the departure point i to the prediction model PM1. i and the travel distance D from the starting point i to the destination j ij By inputting the above, the predicted value of the movement amount P ij Determine the predicted value P ij ^(hat) denotes the predicted number of people traveling from origin i to destination j.

[0049] The first acquisition unit 213 obtains the predicted value P ij Get ^(hat).

[0050] The second acquisition unit 214 acquires the actual value P ij Obtain the actual value Pij indicates the actual number of people who traveled from departure point i to destination j.

[0051] The determination unit 215 determines the predicted value P ij ^(Hat) and performance value P ij Based on this, the transportation convenience T when traveling from departure point i to destination j is calculated. ij The determination unit 215 determines the predicted value P ij ^(Hat) and performance value P ij Based on the difference between ij Determine.

[0052] Figure 8 shows the determined transportation convenience T ij For example, if the departure point i is City A and the destination j is City X, the population of City A is P i is 10,000 people, travel distance D ij is 25km, and the actual travel distance is P ij is 500 people, and the predicted movement amount is P ij ^(Hat) has 450 people. Therefore, the transportation convenience level is T ij is +50 people.

[0053] Also, if the departure point i is City B and the destination j is City X, the population of City B is P i is 20,000 people, travel distance D ij is 10 km, and the actual travel distance is P ij is 400 people, and the predicted movement amount is P ij ^(Hat) has 500 people. Therefore, the transportation convenience level is T ij Similarly, if the starting point i is City C and the destination j is City X, the population of City C is P i is 5,000 people, travel distance D ij is 59 km, and the actual travel distance is P ij is 140 people, and the predicted movement amount is P ij ^(Hat) has 150 people. Therefore, the transportation convenience level T ij is -10 people.

[0054] Also, if the departure point i is City A and the destination j is City Y, the population of City A is P iis 10,000 people, travel distance D ij , is 55 km, and the actual travel distance P ij is 103 people, and the predicted value of the movement amount is P ij ^(Hat) has 120 people. Therefore, the transportation convenience in this case is T ij is -17 people.

[0055] The evaluation unit 216 calculates the transportation convenience T ij For example, if the departure point i is City A and the destination j is City X, the transportation convenience T ij is 50 people, and the transportation convenience T ij is -100 people. Therefore, the evaluation unit 216 evaluates that the transportation convenience when traveling from City A to City X is higher than the transportation convenience when traveling from City B to City X.

[0056] 1.2. Operation of the information processing server according to the first embodiment 1.2.1. Operation of the processing device 21 (traffic convenience T ij (Decision and Evaluation of 9 is a flowchart showing an example of the operation of the processing device 21. Hereinafter, by referring to FIG. 9, the traffic convenience level T ij The decision-making process will be explained.

[0057] In step S11, the processing device 21 functions as the prediction unit 212 to calculate a predicted value P of the travel distance from the departure point i to the destination j. ij That is, the processing device 21 determines the population P of the departure point i to be evaluated. i and travel distance D ij and the population P i and travel distance D ij By inputting the above, the predicted value of the movement amount P ij Determine ^(hat).

[0058] In step S12, the processing device 21 functions as the first acquisition unit 213 to obtain the predicted value P determined in step S11.ij Get ^(hat).

[0059] In step S13, the processing device 21 functions as the second acquisition unit 214 to acquire the actual travel distance P from the departure point i to the destination j to be evaluated. ij Get.

[0060] In step S14, the processing device 21 functions as the determination unit 215 to determine the predicted value P ij ^(Hat) and performance value P ij Based on this, the transportation convenience level T ij Determine.

[0061] In step S15, the processing device 21 functions as the evaluation unit 216 to calculate the transportation convenience T ij Evaluate.

[0062] In step S16, the processing device 21 functions as the evaluation unit 216 to output the evaluation result to a display device (not shown) or the like, and then ends this routine.

[0063] 1.3. Advantages of the First Embodiment According to the above description, the processing device 21 of the information processing server 20 according to the first embodiment includes the first acquisition unit 213, the second acquisition unit 214, and the determination unit 215. The first acquisition unit 213 acquires the predicted value P ij Obtain the predicted value P ij ^ (hat) indicates the predicted number of people moving from the departure point i to the destination j. The second acquisition unit 214 calculates the actual value P ij Obtain the actual value P ij indicates the actual number of people who traveled from the departure point i to the destination j. The determination unit 215 calculates the predicted value P ij ^(Hat) and performance value P ij Based on this, the transportation convenience T when traveling from departure point i to destination j is calculated. ij Determine the degree of accessibility T ij indicates the degree of accessibility to transportation.

[0064] According to this embodiment, the predicted value P of the number of people moving from the departure point i to the destination j is ij ^(hat) and the actual number of people traveling from departure point i to destination j, P ij Based on this, the transportation convenience level T ij This makes it easy to quantify the index that shows the degree of accessibility to transportation.

[0065] The information processing server 20 according to this embodiment further includes a prediction unit 212. The prediction unit 212 predicts the travel distance D ij and the number of people traveling between cities per unit time. ij By inputting ij Determine ^(hat).

[0066] Distance traveled by a person D ij is an objective indicator of inter-city human movement, so the distance traveled by people D ij and the predicted value P predicted by the forecast model PM1, which shows the relationship between the number of people moving between cities per unit period. ij ^ (hat) is stable. Therefore, according to this embodiment, it is possible to stably quantify the convenience of transportation.

[0067] Furthermore, the determination unit 215 determines the predicted value P ij ^(Hat) and performance value P ij Based on the difference between ij Determine.

[0068] According to this embodiment, the predicted value P ij ^(Hat) and performance value P ij Based on the difference between ij , so that accessibility can be more easily quantified.

[0069] In addition, the prediction model PM1 uses the travel distance D ij A power law function f(D ij ) is included.

[0070] It is generally known that the trajectory of a person's movement follows a power law. For example, Non-Patent Document 2 discloses that, as a result of tracking the trajectories of 100,000 mobile phone users for six months, in a certain data set, the trajectory of a person's movement follows a power law. Therefore, according to this aspect, the movement distance D ij A power law function f(D ij A prediction model with higher prediction accuracy is generated compared to a prediction model that does not include the

[0071] In addition, the prediction model PM1 uses the function f(D ij ) and the population of departure point i, P i Includes the product with .

[0072] The amount of movement is the population P i , the prediction model PM1 is proportional to the function f(D ij ) and the population of departure point i, P i Therefore, according to this aspect, it is reasonable to include the product of the function f(D ij ) and the population of departure point i, P i A prediction model with higher prediction accuracy is generated compared to a prediction model that does not include the product of

[0073] Also, the travel distance D ij is the straight-line distance between origin i and destination j.

[0074] According to this embodiment, the moving distance D ij can be easily quantified.

[0075] The information processing server 20 further includes an evaluation unit 216. The evaluation unit 216 evaluates the determined transportation convenience T ij The convenience of transportation is evaluated by comparing the size of the

[0076] According to this embodiment, the evaluator determines the transportation convenience T ij Depending on the results of the comparison, appropriate actions can be taken to improve the accessibility of transportation.

[0077] Furthermore, the information processing method according to the first embodiment calculates a predicted value P ij ^(hat) is obtained, and the actual number of people who traveled from departure point i to destination j is calculated as P ij Obtain the predicted value P ij ^(Hat) and performance value P ij Based on this, the transportation convenience T indicates the degree of convenience when traveling from departure point i to destination j. ij Determine.

[0078] According to this embodiment, the predicted value P of the number of people moving from the departure point i to the destination j is ij ^(hat) and the actual number of people traveling from departure point i to destination j, P ij Based on this, the transportation convenience level T ij This makes it easy to quantify the index that shows the degree of accessibility to transportation.

[0079] 2. Variations The present disclosure is not limited to the above-described exemplary embodiments. Specific modified embodiments are exemplified below. Two or more embodiments selected from the following examples may be combined. Furthermore, the above-described exemplary embodiments and the following modified embodiments may be combined in any combination as long as they are not mutually inconsistent.

[0080] 2.1. Variation 1 In the above embodiment, it has been shown that the prediction model PM1 is trained using machine learning models such as a linear regression model, a neural network model, a logistic regression model, and a support vector machine. However, the prediction model PM1 may also be generated using various analysis tools without using machine learning.

[0081] 2.2. Variation 2 The prediction model PM1 in the above embodiment may include a plurality of prediction models corresponding one-to-one to a plurality of departure points i. In this case, the prediction model PM1 is a first prediction model, and the prediction unit 212 includes a plurality of prediction models PM1 including the first prediction model. i(i=A, B, C, . . . ). The prediction unit 212 includes a plurality of prediction models PM1 i The prediction unit 212 selects one prediction model corresponding to the departure point i as the first city from the above. The prediction unit 212 calculates a predicted value P of the travel distance from the first city to the destination j using the selected prediction model. ij Determine ^(hat).

[0082] According to this aspect, each prediction model PM1 i In this case, the population of departure point i is P i can be treated as a constant rather than a variable, so the prediction model PM1 i In other words, in the above embodiment, the population P i However, according to the second modification, each prediction model PM1 i The dataset for training is the population P i This modification is not limited to the case of multiple departure points i. In other words, not only in the case of multiple departure points i, but also in the case of a single departure point i, the population P i can be treated as a constant rather than a variable.

[0083] 2.3. Variation 3 In the first embodiment, the accessibility of the transportation is expressed as the transportation convenience T ij The degree of transportation convenience T ij When comparing the magnitude of the transportation convenience level T ij 1 is a diagram illustrating an example of the ranking of the transportation convenience level T determined by the determination unit 215. ij is classified into five ranks A to E according to the example shown in Figure 10. The transportation convenience T ij To normalize the travel convenience T ij The population P of departure point i i The value divided by is used for evaluation. The transportation convenience T when traveling from departure point i to destination j ij The population P of departure point ii The value divided by [value] is the normalized traffic convenience degree T ij and is referred to as T

[0084] Normalized traffic convenience degree T ij When the normalized traffic convenience degree T ij is greater than or equal to the first threshold Th1, the traffic convenience degree T ij from the departure place i to the destination j is classified into rank A. When the normalized traffic convenience degree T ij is greater than or equal to the second threshold Th2 (< Th1) and less than the first threshold Th1, the traffic convenience degree T ij from the departure place i to the destination j is classified into rank B. When the normalized traffic convenience degree T ij is greater than or equal to the third threshold Th3 (< Th2) and less than the second threshold Th2, the traffic convenience degree T ij from the departure place i to the destination j is classified into rank C. When the normalized traffic convenience degree T ij is greater than or equal to the fourth threshold Th4 (< Th3) and less than the third threshold Th3, the traffic convenience degree T ij from the departure place i to the destination j is classified into rank D. When the normalized traffic convenience degree T ij is less than the fourth threshold Th4, the traffic convenience degree T ij from the departure place i to the destination j is classified into rank E.

[0085] When the determination of ranks D and E is made for the normalized traffic convenience degree T

[0086] On the other hand, for the normalized traffic convenience degree T ijIf n is judged as rank A or B, the evaluator may take action such as applying the initiatives to improve the convenience of transportation within the relevant travel distance in that city to areas judged as ranks D and E. Furthermore, by comparing the initiatives implemented in areas judged as rank A with those implemented in areas judged as rank B, it may be possible to obtain knowledge that will contribute to improving the convenience of transportation.

[0087] 2.4. Variation 4 In the third modification, the transportation convenience level T ij Although the method of classifying into five ranks A to E has been explained as an evaluation of the degree of transportation convenience, it is not necessary to classify into five ranks. It may be classified into more than five ranks or into fewer than five ranks. In the third modification, ij Normalization and ranking are performed for the transportation convenience T ij does not necessarily need to be normalized. ij may be ranked without being normalized.

[0088] 2.5. Variation 5 In the above embodiment and modified examples, the transportation convenience T ij As an evaluation of the transportation convenience, ij Although the method of comparing the magnitude of transportation convenience and classifying it into several ranks has been explained, it is not necessary to classify it into ranks. ij is the transportation convenience level T ij The evaluation may be carried out from a perspective other than a comparison of the magnitude of the items and a classification by rank.

[0089] 2.6. Variation 6 In the above embodiment, the moving distance D ij was the straight-line distance between the starting point i and the destination j, while the travel distance D ijmay be the distance along the travel route between the departure point i and the destination j. According to this aspect, the prediction model PM1 can be adapted to the actual state of people's movement between cities, and therefore, improvement in the accuracy of the prediction model PM1 can be expected.

[0090] 2.7. Variation 7 In the above embodiment, information about inter-city movement of people is acquired from a movement information database DB1 based on statistical information statistically processed by the statistical server 10. However, information about inter-city movement of people may also be acquired from official statistics, statistics from various industry organizations, or private research companies. Furthermore, information about inter-city movement of people may also be acquired from location information of users carrying terminal devices such as mobile phones and smartphones. The user's location information may be acquired by a positioning device included in the terminal device, or may be location information related to base stations or Wi-Fi location information. The positioning device may be, for example, a Global Navigation Satellite System (GNSS) receiver. The GNSS receiver receives radio signals transmitted from one or more GNSS satellites. GNSS is a positioning system using positioning satellites from countries around the world, including Global Positioning System (GPS) satellites. The location information related to base stations is location information of terminal devices located within cells corresponding to each base station. The Wi-Fi location information is location information of terminal devices located within an accessible range of each Wi-Fi access point.

[0091] 3.Other (1) In the above-described embodiment, the storage device 12 and the storage device 22 are exemplified by ROM and RAM, but they may also be flexible disks, magneto-optical disks (e.g., compact disks, digital versatile disks, Blu-ray (registered trademark) disks), smart cards, flash memory devices (e.g., cards, sticks, key drives), CD-ROMs (Compact Disc-ROMs), registers, removable disks, hard disks, floppy (registered trademark) disks, magnetic strips, databases, servers, or other suitable storage media. The programs may also be transmitted from a network via telecommunications lines. The programs may also be transmitted from a communications network NET via telecommunications lines.

[0092] (2) In the above-described embodiments, the described information, signals, etc. may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0093] (3) In the above-described embodiment, input and output information may be stored in a specific location (for example, a memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be transmitted to another device.

[0094] (4) In the above-described embodiment, the determination may be made by a value (0 or 1) represented using one bit, by a Boolean value (true or false), or by a comparison of numerical values ​​(e.g., comparison with a predetermined value).

[0095] (5) The order of the process procedures, sequences, flowcharts, etc. illustrated in the above-described embodiments may be rearranged unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0096] (6) Each function illustrated in Figures 1 to 10 is realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (for example, by wire, wirelessly, etc.) and these multiple devices. A functional block may also be realized by combining software with the single device or the multiple devices.

[0097] (7) The programs exemplified in the above-described embodiments should be broadly construed to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., regardless of whether they are called software, firmware, middleware, microcode, hardware description language, or by other names.

[0098] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0099] (8) In each of the foregoing embodiments, the terms "system" and "network" are used interchangeably.

[0100] (9) The information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information.

[0101] (10) In the above-described embodiments, the terminal device may be a mobile station (MS). Those skilled in the art may also refer to a mobile station as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate terminology. In this disclosure, terms such as "mobile station," "user terminal," "user equipment (UE)," and "terminal" may be used interchangeably.

[0102] (11) In the above-described embodiments, the terms "connected," "coupled," or any variations thereof refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be a physical coupling or connection, a logical coupling or connection, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0103] (12) In the above embodiments, the phrase "based on" does not mean "based only on," unless otherwise specified. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0104] (13) As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0105] (14) In the above embodiments, when "include," "including," and variations thereof are used, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, the term "or" as used in this disclosure is not intended to be an exclusive or.

[0106] (15) In this disclosure, where articles are added by translation, such as a, an, and the in English, this disclosure may include that the nouns following these articles are plural.

[0107] (16) In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combined" may also be interpreted in the same way as "different."

[0108] (17) Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0109] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure. [Explanation of symbols]

[0110] 1...information processing system, 20...information processing server, 21...processing device, 212...prediction unit, 213...first acquisition unit, 214...second acquisition unit, 215...determination unit, 216...evaluation unit, D ij …Traveling distance, f(D ij )...function, i...origin, j...destination, P i …Population, P ij ...Actual value, P ij ^(Hat)...Predicted value, PM1, PM1 i …predictive model, T ij ...Transportation convenience.

Claims

1. a first acquisition unit that acquires a predicted value indicating a predicted number of people moving from the first city to the second city; a second acquisition unit that acquires a performance value indicating a performance of the number of people who have traveled from the first city to the second city; a determination unit that determines an index indicating a degree of convenience of transportation when traveling from the first city to the second city based on the predicted value and the actual value; An information processing device comprising:

2. a prediction unit that determines the predicted value by inputting a travel distance from the first city to the second city into a prediction model that represents a relationship between a travel distance traveled by people between cities and the number of people traveling between cities per unit period; The information processing device according to claim 1 .

3. the determination unit determines the index based on a difference between the predicted value and the actual value. The information processing device according to claim 1 .

4. the prediction model includes a power law function of the traveled distance; The information processing device according to claim 2 .

5. The prediction model includes a product of the function and the population of the origin. The information processing device according to claim 4 .

6. The travel distance is a straight-line distance between the first city and the second city. The information processing device according to claim 2 .

7. the travel distance is a distance along a travel path between the first city and the second city; The information processing device according to claim 2 .

8. An evaluation unit is further provided to evaluate the convenience of transportation by comparing the magnitudes of the indices. The information processing device according to claim 1 .

9. the prediction model is a first prediction model; The prediction unit a plurality of prediction models including the first prediction model; the plurality of forecast models correspond one-to-one to a plurality of departure locations; selecting one prediction model from the plurality of prediction models, the departure point of which corresponds to the first city; determining the predicted value using the selected one of the prediction models; The information processing device according to claim 2 .

10. obtaining a forecast value indicating a forecast of the number of people moving from the first city to the second city; acquire a performance value indicating the performance of the number of people who have traveled from the first city to the second city; determining an index indicating the degree of convenience of transportation when traveling from the first city to the second city based on the predicted value and the actual value; Information processing methods.

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