Information processing device and information processing method

Abstract

An information processing device according to one embodiment of the present invention includes: a reception unit that receives designation of one travel means for a route from a departure point to a destination; an identification unit that identifies one or more travel means available for the route; and an output unit that outputs an environmental load index which is for a case in which the designated travel means is used to travel from the departure point to the destination, and which has been corrected according to the degree of difficulty in selecting the designated travel means among the one or more travel means.

Description

Information processing device and information processing method

[0001] The present invention relates to a technique for an information processing device and an information processing method.

[0002] There are known techniques for evaluating user behavior from an eco-friendly perspective. For example, Patent Literature 1 discloses an invention that calculates a predicted value of the amount of carbon dioxide reduction associated with travel based on the travel route and means of travel from a departure point to a destination.

[0003] Japanese Patent Application Laid-Open No. 2022-026418

[0004] The invention described in Patent Document 1 merely calculates the amount of CO2 emissions based on the selected means of transportation.

[0005] In response to this, the present invention provides a technique for evaluating eco-friendly behavior by taking into consideration the difficulty of selecting a means of transportation.

[0006] An information processing device according to one aspect of the present disclosure includes a receiving unit that receives the designation of one means of transportation along a route from a departure point to a destination, an identifying unit that identifies one or more means of transportation that can be used along the route, and an output unit that outputs an environmental load index when traveling from the departure point to the destination using the designated means of transportation, the environmental load index being adjusted according to the difficulty of selecting the designated means of transportation from among the one or more means of transportation.

[0007] An information processing method according to another aspect of the present disclosure includes the steps of accepting the designation of one means of transportation along a route from a departure point to a destination, identifying one or more means of transportation available along the route, and outputting an environmental load index for traveling from the departure point to the destination using the designated means of transportation, the environmental load index being adjusted according to the difficulty of selecting the designated means of transportation from among the one or more means of transportation.

[0008] According to the present invention, eco-friendly behavior can be evaluated taking into consideration the difficulty of selecting a means of transportation.

[0009] 1 is a diagram illustrating the system configuration of an eco-score management system 99 according to related technology. A diagram illustrating a visualization model of transportation means options and difficulty levels. A diagram illustrating the system configuration of an information processing system 1 according to an embodiment. A diagram illustrating the functional configuration of the information processing system 1. A diagram illustrating the hardware configuration of an information processing device 10. A sequence chart illustrating a method of calculating an eco-score in the information processing system 1. A diagram illustrating a movement database 1001. A diagram illustrating a target mesh range 2001. A diagram illustrating an aggregation result 3001. A diagram illustrating classification of score correction in the information processing system 1. A diagram illustrating a coefficient database 5001.

[0010] 1. Overview FIG. 1A is a diagram illustrating the system configuration of an eco-score management system 99 according to related technology. In this example, the eco-score management system 99 (or simply referred to as the "related system") is a system for measuring and evaluating user behavior based on an ecological (i.e., environmentally friendly) perspective. Eco is a concept used to improve the impact of human consumption or production activities on the global environment (or the natural environment). Similar concepts to eco include the Sustainable Development Goals (SDGs), sustainability, and ethical. In recent years, efforts to evaluate the activities of human society as a whole (or each individual) from an ecological perspective have been increasing. The eco-score management system 99 evaluates the activities of users participating in the related system based on this ecological perspective. The main evaluation method is the calculation of an environmental load index (or "eco-score") that quantifies the environmental load (e.g., CO2 emissions) associated with the user's movements. The configuration of the eco-score management system 99 and the method for calculating the environmental load index are described below.

[0011] The eco-score management system 99 includes a management device 91, a user terminal 92, and a computer network (not shown) for connecting various devices. For example, a user U9 can register an account on the management device 91, which functions as a server for a related system, from their own user terminal 92. By linking the devices, the user can receive various services from the related system. Specifically, the eco-score management system 99 records data related to the user's movements, such as the mode of transportation and the distance traveled (see the related database 9001 in the figure). Figure 1A schematically illustrates the movement record of user U9, who traveled on bicycle BC9 (an example of a mode of transportation) at a specific time and location. The base station 81 is a wireless base station that communicates with the user terminal 92 via a mobile network or the like. The base station 81 can collect various terminal data, such as location information, from the wirelessly connected user terminal 92. The management device 91 then calculates the CO2 emissions or reductions associated with user U9's movements based on the collected data from the user terminal 92.

[0012] For example, when user U9 travels 10 km on bicycle BC9, the CO2 emissions are approximately 0 kg (strictly speaking, this includes CO2 from human breathing, but for the sake of simplicity, this is not considered here). On the other hand, when a hypothetical car C9 (a typical gasoline-powered vehicle) travels the same 10 km, the CO2 emissions are 1.64 kg. Therefore, when user U9 travels the above distance on bicycle BC, it can be considered that approximately 1.64 kg (1.64 kg - 0 kg) of CO2 has been reduced (no CO2 emissions were avoided) compared to traveling in a gasoline-powered vehicle. This is called the CO2 reduction amount. The CO2 reduction amount is incorporated into an index calculation formula for evaluating the environmental impact of a user's travel. An index based on the CO2 reduction amount is called the first eco score in the related system. For example, the first eco score is calculated using the following formula (1):

[0013] Here, E1 represents the first eco-score. rrepresents the amount of CO2 reduction. The amount of CO2 reduction is the weight (or mass) of CO2 that is considered to have been reduced by the target user's travel compared to the travel of the gasoline vehicle. s represents the score coefficient. The score coefficient is a coefficient that is predetermined in the related system. Typically, the score coefficient is used to adjust the first eco score to a value that is easy for the user to recognize (familiar). In this example, the higher the first eco score, the more eco-friendly the user's movement is considered to be. As efforts to contribute to carbon neutrality, a social issue, continue to increase, improved methods for evaluating user behavior or calculating environmental impact indices are required to ensure transparency and objectivity in the amount of CO2 reduction. Specifically, they are as follows.

[0014] FIG. 1B illustrates a visualization model of transportation options and their difficulty. For example, when calculating the first eco-score in the above-described eco-score management system 99 according to Equation (1), only the amount of CO2 reduction accounts for the majority of the impact on the score value. Therefore, the first eco-score may be a simple comparison of transportation options (i.e., the score is determined by the transportation option selected). However, in reality, the transportation options available to a user vary depending on the region to which the user is traveling. For example, in sparsely populated rural areas, even if a user wants to use an eco-friendly transportation option, public transportation such as trains or buses may not even be available. In such situations, users are forced to travel by car. In other words, selecting an eco-friendly transportation option is difficult. On the other hand, in urban areas, public transportation is well developed, providing many transportation options, and users can travel by train or bus if they are willing. In this case, the difficulty of switching to an eco-friendly transportation option is low. Currently, it is not possible to calculate an eco-score that takes into account the difficulty of selecting a transportation option that differs between urban and rural areas. Therefore, in this embodiment, a new method for calculating an eco-score (referred to as a second eco-score) that takes into consideration the viewpoint based on the above-mentioned problem will be disclosed.

[0015] 2. Configuration FIG. 2 is a diagram illustrating the system configuration of an information processing system 1 according to one embodiment. In this example, the information processing system 1 (or simply the system) is a system for outputting an environmental impact index adjusted according to the difficulty of selecting a user's transportation mode based on the background described above. The transportation mode referred to here refers to the mode of transportation used by a user to travel from a departure point to a destination within a specific physical section when that user travels (or has traveled) that section. For example, transportation modes include car, bus, train, bicycle, or walking. The information processing system 1 includes an information processing device 10, a user terminal 20, and an analysis system 30. In this example, the components of the system are connected via a network 9 as shown in FIG. 2. In this example, the network 9 is a computer network such as the Internet or a mobile network.

[0016] The information processing device 10 is an information processing device or server device in the information processing system 1. In this example, the information processing device 10 outputs an environmental impact index when a user travels from a departure point to a destination using a specified transportation mode. More specifically, the information processing device 10 outputs an environmental impact index corrected according to the difficulty level of the user selecting the specified transportation mode. The second eco-score described above is an example of this environmental impact index. Transportation options refer to candidate transportation modes that the user can actually use. For example, in urban areas, road networks, bus routes, and train routes are well developed and have frequent schedules, resulting in a wide range of options, including cars, buses, and trains. On the other hand, in rural areas, bus routes or train routes may not exist at all, or even if there are bus routes or train routes, the schedule may be extremely infrequent (e.g., one or two bus routes per day), or the only transportation mode actually available may be a private car. The difficulty level of selection refers to the difficulty of actually using a transportation mode. In other words, in the above example, in urban areas, bus and train routes are well developed and frequently scheduled, making it practically easy to use buses or trains as a means of transportation. On the other hand, in rural areas, even if bus routes exist, they are infrequent, making it practically difficult to use buses as a means of transportation. The correction in this embodiment refers to calculating the eco-score by taking into account the difficulty of selecting a mode of transportation that may arise depending on such regional differences. This makes it possible, for example, to prevent the fairness of the eco-score from being lost. The information processing device 10 corrects the above-mentioned first eco-score based on a predetermined correction method and calculates a second eco-score that takes into account the user's travel patterns in each region. Specific correction methods will be described later.

[0017] The user terminal 20 is a terminal operated by a user. The user terminal 20 includes, for example, a smartphone, a tablet, or a personal computer. The user terminal 20 acquires information regarding the user's movement and transmits the data to the information processing device 10 via a network. This allows the information processing device 10 to acquire information necessary for calculating the first eco-score or the second eco-score. The information processing device 10 calculates the first eco-score or the second eco-score, evaluates the user's movement based on an eco-perspective, and shares information through various services. Specifically, the user can refer to information such as the eco-score by accessing a website or application via the user terminal 20. Alternatively, the user can receive various services related to eco-activities via the user terminal 20.

[0018] The analysis system 30 (an example of an analysis unit or analysis means) is a system, device, or functional element for analyzing the behavior of a specific user based on various information (mainly time-varying changes in location information) acquired from the user terminal 20. In this example, the analysis system 30 acquires the user's location information directly from the user terminal 20 (or via the information processing device 10). The analysis system 30 analyzes the user's behavior from time-varying changes in the location information, and outputs information about the analyzed behavior, such as the movement route, movement speed, and movement distance, to the information processing device 10. This allows the information processing device 10 to acquire the movement pattern of the target user and obtain information about the movements of multiple users.

[0019] 3 is a diagram illustrating an example of the functional configuration of the information processing system 1. In this embodiment, the information processing device 10 has functional blocks (components) including an identification unit 11, a reception unit 12, an output unit 13, a storage unit 18, and a control unit 19. In this example, the storage unit 18 stores various types of data including, for example, a database. The control unit 19 performs various controls.

[0020] The identification unit 11 identifies one or more means of transportation that can be used on a route from the departure point to the destination. The available means of transportation refer to means of transportation that can actually be used by a user at the departure point. In one embodiment, the identification unit 11 identifies one or more available means of transportation based on means of transportation used by other users who actually traveled from the departure point to the destination during a time period corresponding to the time period during which the target user actually traveled from the departure point to the destination.

[0021] The reception unit 12 receives the designation of one of one or more modes of transportation. For example, the user can designate the mode of transportation that they actually used for travel. Alternatively, the system itself may designate the mode of transportation.

[0022] The output unit 13 outputs an environmental load index when traveling from a departure point to a destination using a designated means of transportation, the environmental load index being corrected according to the difficulty of selecting the designated means of transportation from one or more means of transportation (hereinafter referred to as "selection difficulty"). Note that a coefficient is predetermined by the system for each means of transportation.

[0023] In one embodiment, the selection difficulty is a parameter corresponding to the quantity of other means of transportation other than the specified means of transportation among the available means of transportation. Other means of transportation other than the specified means of transportation are means of transportation other than the means of transportation selected by the user. In one embodiment, the quantity of other means of transportation (means of transportation other than the means of transportation selected by the user) includes the number of options for other means of transportation. The quantity of other means of transportation may include the environmental load of the other means of transportation. The environmental load of the other means of transportation is a coefficient determined for each means of transportation. Alternatively, the environmental load of the other means of transportation may be the sum of the coefficients of the other means of transportation. In another example, the quantity of other means of transportation includes the number of users who have used other means of transportation. The quantity of other means of transportation may be the sum of the number of users who have used other means of transportation.

[0024] In one embodiment, the correction includes subtracting a correction value corresponding to the difficulty level from the environmental impact index of the travel by the specified transportation means (e.g., the first eco-score described above). The correction value (hereinafter referred to as the "score correction value") is one of the variables used in the calculation process, which is defined to correct the first eco-score from the perspective of the option or difficulty level of the transportation means selection.

[0025] Alternatively, the correction may include multiplying the environmental impact index of travel by the specified means of transportation by a correction factor corresponding to the degree of difficulty. The correction factor (hereinafter referred to as the "score correction factor") is another example of a variable used in the calculation process to correct the first eco-score, similar to the score correction value.

[0026] FIG. 4 is a diagram illustrating an example of the hardware configuration of the information processing device 10. The information processing device 10 is physically configured as a computer including a processor 101, a memory 102, a storage 103, a communication device 104, and a bus connecting these devices. Each of these devices operates using power supplied from a battery (not shown). In the following description, the term "device" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the information processing device 10 may be configured to include one or more of the devices shown in FIG. 4, or may be configured without including some of the devices. Furthermore, the information processing device 10 may be configured by communicating with multiple devices each having a different housing.

[0027] Each function of the information processing device 10 is realized by loading specified software (programs) onto hardware such as the processor 101, memory 102, etc., so that the processor 101 performs calculations, controls communication via the communication device 104, and controls at least one of reading and writing data in the memory 102 and storage 103.

[0028] The processor 101 controls the entire computer by running, for example, an operating system. The processor 101 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Furthermore, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 101.

[0029] The processor 101 reads programs (program codes), software modules, data, etc. from at least one of the storage 103 and the communication device 104 into the memory 102 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the information processing device 10 may be implemented by a control program stored in the memory 102 and running on the processor 101. Various processes may be executed by one processor 101, or may be executed simultaneously or sequentially by two or more processors 101. The processor 101 may be implemented by one or more chips. The programs may be transmitted to the information processing device 10 via a telecommunications line.

[0030] The memory 102 is a computer-readable recording medium and may be configured by at least one of, for example, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 102 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 102 can store executable programs (program codes), software modules, etc. for implementing the method according to this embodiment.

[0031] Storage 103 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 103 may also be called an auxiliary storage device.

[0032] The communication device 104 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0033] Each device, such as the processor 101 and the memory 102, is connected by a bus for communicating information. The bus may be configured using a single bus, or different buses may be used between each device.

[0034] The information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 101 may be implemented using at least one of these pieces of hardware.

[0035] In this example, the programs stored in the storage 103 include a program (hereinafter referred to as a "server program") for causing a computer to function as a server in the information processing system 1. When the processor 101 is executing the server program, the processor 101, memory 102, storage 103, and communication device 104 are examples of functional blocks for operating the information processing device 10. The processor 101 is an example of an identification unit 11 and a control unit 19. At least one of the memory 102 and the storage 103 is an example of a storage unit 18. The communication device 104 is an example of an output unit 13.

[0036] Although detailed description will be omitted, the user terminal 20 is a computer having a processor, memory, storage, a communication device, an input device, and an output device, specifically, for example, a smartphone, a tablet terminal, or a personal computer. In this example, the programs stored in the storage of the user terminal 20 include a program (hereinafter referred to as a "client program") for causing the computer to function as a client in the information processing system 1. The configuration of the information processing system 1 has been described above. Next, the operation of the information processing system 1 will be described.

[0037] 3. Operation Figure 5 is a sequence chart illustrating a method for calculating an eco-score in the information processing system 1. In step S101, the analysis system 30 acquires various data from the user terminal 20 via the network at predetermined times (e.g., periodically). This data includes information about the movement of the user who owns the user terminal 20 (hereinafter referred to as "movement information"). The analysis system 30 acquires various data from multiple user terminals 20 belonging to the system and analyzes it.

[0038] In step S102, the information processing device 10 accepts a designation of a means of transportation from the user terminal 20 of a target user (hereinafter referred to as the "target user"). More specifically, the information processing device 10 accepts a designation of information regarding travel from a departure point to a destination (hereinafter referred to as "travel information") from the user terminal 20. The travel information includes, for example, a departure point, a destination, a means of transportation, a departure time or arrival time, and other information. The travel information may be information regarding travel that the target user has actually made up to this point, or information regarding travel that the target user plans to make in the future. The user terminal 20 is running a client program, and the client program prompts the user to input this information. Alternatively, if the travel information is information regarding travel that has actually been made, the client program may automatically (i.e., without user input) acquire this information from the location information history of the user terminal 20.

[0039] In step S103, the information processing device 10 identifies the transportation means of the target user. In this example, the information processing device 10 identifies the transportation means from the transportation information acquired from the user terminal 20.

[0040] In step S104, the information processing device 10 transmits a request for analyzing the movement information to the analysis system 30. This request includes the movement information received from the user terminal 20.

[0041] In step S105, the analysis system 30 analyzes the data. First, the analysis system 30 records the movement information of the target user in a database. Here, the database that manages the movement information of the target user will be described.

[0042] FIG. 6 is a diagram illustrating a movement database 1001. In this example, the movement database 1001 includes multiple records related to a target user, user U1. Each record corresponds to a piece of movement information when the target user travels from a departure point to a destination. Note that each record may be uniquely managed for each movement using identification information such as a movement ID. Each record includes a departure (or arrival) timestamp, a departure point, a destination, a means of transportation, and a movement route. The departure (or arrival) timestamp is information indicating the date and time when the target user departs from the departure point or arrives at the destination. The departure point and destination are information indicating the departure point and destination. For example, in an example in which the target user travels from home to their workplace (workplace) during their commute, the departure point S1 represents their home, and the destination G1 represents their workplace, respectively. The means of transportation indicates the means of transportation. The route information includes information related to the movement route, for example, in the above example of "commuting," the route and distance traveled by car from home to the workplace. At least some of this data may be recorded using a separately defined identifier. In the above example of traveling by train, the account identifier (identifier for identifying the user), travel route history ID (identifier for each trip), transportation mode type, starting point type, start date and time, starting point station ID, starting point line ID, end point type, end date and time, end point station ID, end point line ID, or travel distance may be recorded using an identifier.

[0043] The movement database 1001 manages movement information of multiple users, including the target user U1. The multiple users refer to users of the user terminals 20 belonging to the information processing system 1 or users linked to account information.

[0044] Next, the analysis system 30 identifies a target mesh range based on the user's movement information. In this example, the analysis system 30 manages geographical information in units of meshes. A mesh is a unit obtained by dividing a map in the data into two directions (for example, directions parallel to latitude lines and directions parallel to longitude lines) at predetermined reference lengths. Meshes are defined in the analysis system 30. A mesh range is a set of one or more consecutive (adjacent) meshes. The target mesh range here is the mesh range that is the target of processing. In this example, the departure point and destination are identified by mesh IDs. The route is identified by the permutation of the mesh IDs of consecutive meshes.

[0045] The algorithm for identifying the target mesh range is defined in the analysis system 30. For example, the analysis system 30 identifies, as the target mesh range, a set of meshes that includes the departure point and destination and forms a rectangle with the smallest area.

[0046] Fig. 7 is a diagram illustrating an example of a target mesh range 2001. In Fig. 7, it is assumed that a user U1 travels by car C1 from a departure point (starting point) S1 in mesh M1 to a destination (ending point) G1 in mesh M9.

[0047] Furthermore, the analysis system 30 identifies a target time period. The target time period is a time period determined based on the target user's departure time and arrival time, i.e., a time period corresponding to the time period during which the user travels from the departure point to the destination. In one example, the target time period is a time period that includes the target user's departure time and arrival time, and more specifically, a time period that includes a margin before the departure time and a margin after the arrival time. The margin is, for example, approximately 30 minutes to 2 hours. The size of the margin is defined in the analysis system 30. For example, if the target user's departure time is 9:35, the arrival time is 11:12, and the margin is 30 minutes, the target time period is the continuous time period from 9:05 to 11:42.

[0048] Second, after identifying the meshes of user U1's starting and ending points, the analysis system 30 analyzes whether there is a transportation route (e.g., a train or bus route, or a shared spot for mobility sharing such as a shared bicycle) that can physically travel between the two meshes. This analysis uses actual map information, route information, and the like. The analysis system 30 may also identify the transportation method or route depending on whether the travel route is a single route (i.e., whether the travel uses different routes). If there is a transportation method that can travel between meshes M1 and M9, the analysis system 30 identifies the meshes that include the route of that transportation method.

[0049] Third, the analysis system 30 extracts other users (e.g., users U2 and U3) who have the same starting and ending meshes and analyzes their travel patterns. This is intended to take into account time constraints and other constraints on the means of transportation (e.g., temporary unavailability due to an accident). Time constraints, for example, are schedules for public transportation, or business hours for mobility sharing. The analysis system 30 analyzes the behavior of other users who traveled within the target mesh range during the target time period and identifies the means of transportation and travel routes of the other users. Furthermore, the analysis system 30 also identifies meshes that include the travel routes of the other users. The content of the analyzed information includes, for example, an interpretation such as, "User U3 traveled from XX Station ST1 (mesh M1) to △△ Station ST2 (mesh M9) on the subway line □□ (train T3)."

[0050] Next, the analysis system 30 calculates the selection difficulty of the transportation means of the target user. In this example, there are two methods for estimating the selection difficulty: (A) estimation based on the number of transportation means options, and (B) estimation based on the number of users for each transportation means.

[0051] FIG. 8 is a diagram illustrating an example of a tabulation result 3001 of the travel information of other users in the target mesh range. In this example, the tabulation result 3001 includes multiple records. Each record corresponds to the travel information of each user. Here, the tabulation result is broadly divided into two categories: (A) the number of transportation options and (B) the number of users per transportation option. (A) The number of transportation options corresponds to the number of types of transportation identified in the "Transportation" field of the tabulation result 3001. Referring to the example of the target mesh range 2001, if the transportation options are identified as car, bus, and train, the number of transportation options is "3." (B) The number of users per transportation option corresponds to the number of users who actually used each transportation option out of the total number of users tabulated in the tabulation result 3001 (this tabulation may also include the target user U1).

[0052] These two aggregated results are both examples of parameters related to the difficulty of selecting a means of transportation. In particular, with regard to (B) the number of users for each means of transportation, means of transportation with a large number of users (for example, cars in FIG. 8) can be considered to be means of transportation that are relatively easy to select from the perspective of versatility, prevalence, number of users, etc. Conversely, means of transportation with a small number of users (not widely used) are considered to be difficult to select.

[0053] Here, when the level of difficulty depends on the number of users, it is inferred that choosing a less common mode of transportation is more eco-friendly than choosing a mode used by many users. This is based on the psychological aspect of the established theory that people unconsciously behave in an uneconomical manner, and the objective fact that eco-friendly modes of transportation have not yet become widespread in human society over time. For example, when comparing cars, trains, and buses, people still prefer private modes of transportation such as private cars and taxis, which are convenient and safe, to trains and buses, which are prone to congestion and riding stress. In other words, when users have free rein to choose their mode of transportation, cars and other modes that are far from eco-friendly are more likely to be chosen (i.e., not eco-friendly). Alternatively, modes of transportation such as eco-bikes (bike-share) that are not yet fully equipped naturally have fewer users than standard cars, which are widely used (i.e., less common modes of transportation are relatively eco-friendly). Based on these established theories and facts, it cannot necessarily be said that the level of difficulty of choosing a mode of transportation (the number of users) determines whether it is eco-friendly for all modes of transportation, but it is a significant relationship that cannot be ignored. This is an important point to consider when evaluating users' eco-friendly behavior.

[0054] Based on the above-mentioned aggregation results, the analysis system 30 can identify information regarding the target user's transportation options and the difficulty of changing the options, which is then used in the calculation process to calculate an improved eco-score.

[0055] In this example, the analysis process in step S105 includes the calculation of the first eco-score shown in equation (1).

[0056] Returning to Fig. 5, in step S106, the analysis system 30 outputs the analysis result to the information processing device 10. The analysis result includes the first eco-score and the selection difficulty level for the target movement information.

[0057] In step S107, the information processing device 10 calculates an eco-score (second eco-score) based on various calculation formulas. The second eco-score is calculated, for example, by correcting the existing first eco-score based on the difficulty of selecting the means of transportation used by the target user for travel. Details of the correction method will be explained in the following sections 3-1 and 3-2.

[0058] In step S108, the information processing device 10 outputs the calculated second eco-score to the user terminal 20. The method for outputting the eco-score to the user terminal 20 may be any method, and may be, for example, a process of outputting the eco-score triggered by an output request from the target user via the user terminal 20 (or access to an app or the web).

[0059] 3-1. Method for calculating eco-score based on score correction value FIG. 9 is a diagram illustrating an example of classification of score correction in the information processing system 1. A matrix 4001 represents a combination (correction pattern or variation) of two corrections based on a score correction value or a score correction rate and a parameter used to calculate the difficulty of selection. There are two correction methods and two parameters, and four possible correction processes can be achieved by combining these. Here, we will first explain the method for calculating eco-score based on a score correction value.

[0060] For example, the second eco-score based on the score correction value is calculated by the following equation (2).

[0061] In this example, E2 represents the "second eco-score." E1 (first eco-score) is an existing eco-score determined according to the amount of CO2 reduction. V represents the "score correction value." The "score correction value" is a variable used to subtract the first eco-score based on the difficulty of changing the transportation options or selections available to the target user for the trip being calculated. k sThe (score coefficient) is a coefficient for adjusting the score correction value, and in principle, the same numerical value as the score coefficient applied when calculating the first eco score is used. The basic concept of the second eco score obtained from equation (2) is as follows: First, (A) if the means of transportation selected by the target user is more eco-friendly than other means of transportation, the value to be subtracted will be smaller (i.e., the score will be maintained relatively high). Second, (B) if the means of transportation selected by the target user is more difficult to select and change than other means of transportation, the correction value to be subtracted will be smaller. Here, the difficulty of selecting a means of transportation represents a correction process based on the logic that, as already mentioned above, the number of users (i.e., the level of difficulty) affects whether a means of transportation is eco-friendly or not.

[0062] Here, the specific calculation formulas for the score correction value are shown below: (A) the score correction value based on the number of transportation options (hereinafter referred to as "correction value A") and (B) the score correction value based on the number of users for each transportation mode (hereinafter referred to as "correction value B"). For example, correction value A is calculated using the following formula (3).

[0063] In this example, V a represents the "correction value A." T represents the "number of transportation options." The "number of transportation options" is, for example, the number of transportation options included in the tabulation result 3001. In other words, this indicates the number of types of transportation (car, train, bus, etc.) that exist within the same mesh as the mesh M1 where the target user is located in the target mesh range 2001. t u represents the coefficient of the target user's transportation means. sum represents the "sum of coefficients for each transportation option." u and t sum Each of these is based on a coefficient for each mode of transportation. Here, the coefficients for each mode of transportation will be explained.

[0064] FIG. 10 is a diagram illustrating an example of the coefficient database 5001. In this example, the coefficient database 5001 is a record indicating coefficients. The record includes a score coefficient (common) and coefficients for each transportation mode. The score coefficient is a coefficient for adjusting a score correction value determined by the system. The coefficient for each transportation mode corresponds to the type of transportation and indicates the level of environmental impact of the transportation mode. For example, if the types are classified as car, bus, and train in descending order of environmental impact, the corresponding values ​​are set as 4, 3, and 2. The higher the value, the higher the environmental impact and the less eco-friendly the transportation mode. The score correction value (or score correction rate) is calculated based on these coefficients. As an example, when formula (3) is applied based on the data conditions recorded in the aggregation result 3001, the "number of transportation mode options" is "3," the "target user's transportation mode coefficient" is "4 (car)," and the "sum of coefficients for each transportation mode option" is "4 (car) + 3 (bus) + 2 (train) = 9." As a result, the "correction value A" is "approximately 1.33." Although the subsequent calculation process is omitted, the "second eco score" is calculated by subtracting this "correction value A."

[0065] Next, regarding the difficulty of selecting a means of transportation in the score correction value, a correction value B is calculated, for example, by the following formula (4).

[0066] In this example, V b represents the "correction value B". i represents the number of users who use the same transportation means as the target user. dsum represents the total number of users using transportation modes different from those of the target user. i and U dsum is a variable based on, for example, the number of users for each means of transportation (B) included in the counting result of the counting result 3001. Similarly, when formula (4) is applied based on the data conditions recorded in the counting result 3001, the "number of users using the same means of transportation as the target user" is "50 (car)" and the "total number of users using a means of transportation different from that of the target user" is "20 (bus) + 30 (train) = 50." As a result, the "correction value B" is calculated as "1."

[0067] As described above, by using calculation processing including a score correction value, an eco score can be calculated that takes into account the transportation options and the difficulty of changing the options, thereby enabling a more improved eco evaluation.

[0068] Note that the specific example of the calculation process based on the above formula is only for the case where a hypothetical movement case is applied, and in the process according to the actual application destination, the second eco-score may become negative (for example, when the score correction value from which the subtraction is made becomes significantly high). This represents a so-called "failure" in the calculation, and a specific method for dealing with this failure will be explained later in "4. Modifications."

[0069] 3-2. Calculation method of eco-score based on score correction rate Next, we will explain the correction method based on the score correction rate. Unlike the score correction value that subtracts the existing score, the score correction rate has the magnification itself by which the existing score is multiplied as a variable. For example, the second eco-score based on the score correction rate is calculated using the following equation (5):

[0070] In this example, E2 and E1 represent the second eco score and the first eco score. R represents the "score correction rate." The "score correction rate" is a variable used to multiply the first eco score based on the transportation options available to the target user for the trip being calculated or the difficulty of changing the selection. Referring back to FIG. 9 , the correction method based on the score correction rate, like the score correction value, is divided into patterns based on two perspectives: (A) the number of transportation options or (B) the number of users per transportation mode. Note that the basic concept of the value indicated by equation (5) is the same as equation (2): the more eco-friendly the target user's transportation mode, the larger the multiplication value (the higher the final score is maintained), and the more difficult it is to select a transportation mode, the more eco-friendly it becomes (the higher the multiplier and the higher the score is estimated). That is, the difference from equation (2) is whether the score correction value is subtracted or multiplied by the score correction rate (multiplier).

[0071] Here, (A) the score correction factor based on the number of transportation means options (hereinafter referred to as "correction factor A") is calculated, for example, by the following formula (6).

[0072] In this example, R a represents the "correction factor A". T and t u Similarly to equation (3), t represents the "number of transportation options" and the "coefficient of the transportation mode of the target user," respectively. ex represents the "sum of coefficients of transportation modes other than the one selected by the target user." u and t ex are each given according to a coefficient predefined in the coefficient database 5001 described above. As an example, when formula (6) is applied based on the data conditions recorded in the aggregation result 3001, the "coefficient of the target user's transportation means" is "4 (car)," the "total coefficients of transportation means other than those selected by the target user" is "3 (bus) + 2 (train) = 5," and the "number of transportation means options" is "3." As a result, the "correction rate A" is "approximately 0.7." Note that "1" in formula (6) represents a reference value. In other words, in the "correction rate A," if the target user's transportation means is more eco-friendly than other transportation means, the multiplier remains "1," and the negative change in the final second eco score may be small.

[0073] Next, regarding the difficulty of selecting a means of transportation in terms of the score correction factor, a correction factor B is calculated, for example, by the following formula (7).

[0074] In this example, R b represents the "correction factor B". dsum and U iSimilarly to formula (4), each of these variables is based on the number of users for each transportation mode (B) included in the tabulation results of the tabulation results 3001. When formula (6) is applied based on the data conditions recorded in the tabulation results 3001, the "total number of users of transportation modes different from the target user" is "20 (bus) + 30 (train) = 50," and the "number of users of the same transportation mode as the target user" is "50 (car)." As a result, the "correction rate B" is calculated as "1." Note that the "correction rate B" is multiplied lower (below the reference value "1") because people who select transportation modes with a large number of users are considered to be relying on transportation modes that are easy to use and far from being eco-friendly. As a result, the final second eco score is also estimated to be low.

[0075] As described above, by using calculation processing including a score correction factor, an eco score can be calculated that takes into account the transportation options and the difficulty of changing the options, thereby enabling a more improved eco evaluation.

[0076] By using the variations in the correction patterns described above, the final eco-score (second eco-score) can take on different values ​​depending on the method, making it easier to calculate an eco-score that is suited to the conditions of each region, from urban to rural. Note that the administrator who operates the information processing system 1 decides which of these four combinations to use to calculate the second eco-score.

[0077] 4. Modifications The present invention is not limited to the above-described embodiment, and various modifications are possible. Some modifications will be described below. Two or more of the following features may be combined and applied.

[0078] (1) Information Processing System 1 The hardware configuration and network configuration of the information processing system 1 are not limited to those exemplified in the embodiment. The information processing system 1 may have any hardware configuration and network configuration as long as the required functions can be realized. For example, multiple physical devices may cooperate to function as the information processing system 1. For example, at least a part of the analysis system 30 may be implemented in the information processing device 10, and the information processing device 10 may be configured to analyze user movement information acquired from the user terminal 20.

[0079] (2) Information Processing Device 10 Some of the functions of the information processing device 10 may be implemented on another server. This server may be, for example, a physical server or a virtual server (including a so-called cloud). Furthermore, the correspondence between functional elements and hardware is not limited to that illustrated in the embodiment. For example, at least some of the functions described in the embodiment as being implemented on the information processing device 10 may be implemented on another device or system, or conversely, at least some of the functions described as being implemented on another device or system may be implemented on the information processing device 10. In this example, the information processing device 10 may have at least some of the functions of the analysis system 30. In this case, the information processing device 10 may generate mesh data representing the movement information of a target user or multiple users for each specific area on a map. Note that the information processing device 10 may obtain user movement information or its analysis results directly from the user terminal 20 without going through the analysis system 30, for example, by using an application programming interface (API), website tracking, cookies, or the like.

[0080] (3) User Terminal 20 The user terminal 20 is not limited to the one exemplified in the embodiment. The user terminal 20 may perform the above-described processing using any display screen, input device, or various UIs. The user terminal 20 may be equipped with a function to display the eco-score results output from the information processing device 10 to the target user via an application (or a web browser) within the terminal. The user terminal 20 may be equipped with various sensors and may use these sensors to acquire (output to the information processing device 10) movement information of the target user. The sensors include, for example, an acceleration or angular velocity sensor, a Global Navigation Satellite System (GNSS), a Light Detection and Ranging (LiDAR), or an Inertial Measurement Unit (IMU).

[0081] (4) Analysis System 30 The analysis system 30 is not limited to the example shown in the embodiment. The analysis system 30 may have any functional configuration as long as it can realize the required functions or operations. For example, the analysis system 30 may cooperate with a base station 81 (an example of a wireless base station) and perform various analyses of user movement information based on base station information such as the base station's cell ID, radio wave intensity, and communication standard. In this case, the boundaries of each mesh according to geographical divisions in the mesh data created by the analysis system 30 may be changed based on the location of the base station, etc. Note that the analysis system 30 may acquire user information directly from the user terminal 20 (without going through the information processing device 10).

[0082] (5) Method of Calculating Eco-Score The sequence chart shown in FIG. 5 merely shows one example of the operation, and the operation of the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In step S105, the analysis system 30 may simultaneously identify the movement information of the target user and identify information based on the aggregation results of other users. In step S107, the information processing device 10 may calculate both the first eco-score and the second eco-score, or may calculate only the second eco-score (or only either one).

[0083] (6) Method for calculating eco-score based on score correction value The method for calculating eco-score based on score correction value is not limited to the example described in the embodiment. In the above-described embodiment, if the calculation process based on various calculation formulas causes the calculation result to fail at the actual eco-score application destination (movement performed by the target user) (i.e., if the second eco-score becomes a negative value), the information processing device 10 needs to introduce a method for avoiding the failure due to the correction.

[0084] For example, in equation (2), if the score correction value is significantly greater than the first eco-score, the subtraction process may result in a negative second eco-score. This is a potential failure condition in a system configuration where the first eco-score increases simply with travel distance (increasing the contribution to CO2 reduction), regardless of the mode of transportation. In other words, if the target user's travel is limited to very short trips and the first eco-score to be corrected is small to begin with, a problem occurs in which the user makes a negative contribution to the environment, regardless of how eco-friendly the mode of transportation used. In response to this, the information processing device 10 may, for example, set a threshold for travel distance and predetermine the values ​​of various coefficients (score coefficients or coefficients for each mode of transportation) depending on whether the threshold is reached. Alternatively, the coefficient may be adjusted in proportion to the magnitude of the first eco-score (such as travel distance). Alternatively, a method may be used in which the coefficient is adjusted in smaller decimal increments rather than in integer increments. If a negative value still occurs, the information processing device 10 may perform a process to correct the eco-score for that trip to "0."

[0085] (7) Method for calculating eco-score based on score correction factor The method for calculating eco-score based on score correction factor is not limited to the example described in the embodiment. In the above-described embodiment, a method for preventing the eco-score value from being corrupted may be similarly introduced into the system for the score correction factor.

[0086] For example, in equation (6), it is conceivable that the "coefficient of the target user's means of transportation" indicates a numerical value that is significantly higher than the other coefficients, resulting in a negative correction factor A. In this case, too, it is necessary to adjust the coefficient on the system side to avoid a breakdown. In an extreme example, if the coefficient set for a truck carrying a large amount of cargo is "100" (while the coefficients for other means of transportation are single-digit integers), this coefficient value may be reduced, or the coefficient value for the other means of transportation may be increased, or even the second eco score calculated for the target trip may be set to "0."

[0087] (8) Database (Data) The database (or the data itself) of the information processing system 1 shown in Figures 6, 7, 8, and 10 is not limited to the example shown in the embodiment. In this example, any type of data may be registered in the database. For example, the data recorded in the movement database 1001 may be data related to movement based on a movement route (commuting route) previously specified by the target user. In this case, the user terminal 20 may cause the information processing device 10 to record the movement section received from the target user. The target mesh range 2001 may include transportation stop spots, topographical data, or coordinate information. In the aggregation result 3001, other users to be extracted may be users located in other meshes adjacent to the mesh at the start or end point of the target user (mesh M1 or mesh M2 in Figure 8). The score coefficients determined in the coefficient database 5001 for the first eco-score and the second eco-score may be the same or different. Furthermore, the coefficients for each transportation mode may be determined not only by the type of transportation mode, but also by the vehicle type, etc. The data that the information processing device 10 outputs to the user terminal 20 or the analysis system 30 may be any data registered in the database.

[0088] (9) Travel Information Travel information (information about user travel) is not limited to the examples given in the embodiments. Travel information may be, for example, the target user's travel history, travel route, transfer of transportation means, location information, or stay time.

[0089] (10) Eco Score (Environmental Load Index) The eco score is not limited to the examples given in the embodiments. The eco score may include variables other than the means of transportation, the distance traveled, and the amount of CO2 reduction, such as the amount of power consumption.

[0090] (11) Correction The correction is not limited to the example described in the embodiment. The correction may be performed by any method based on (A) the number of transportation options or (B) the number of users for each transportation option. For example, the correction may be performed using a formula that combines a score correction value and a score correction rate.

[0091] (12) Others The various programs executed by the processor 101 may be provided by downloading via a network such as the Internet, or may be provided in a state recorded on a computer-readable non-transitory recording medium such as a DVD-ROM. Each processor may be, for example, a CPU, an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit).

[0092] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or 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 directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may also be realized by combining software with the single device or multiple devices.

[0093] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0094] For example, the information processing device 10 according to an embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0095] Each aspect or embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), or other suitable systems, and next-generation systems enhanced based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G) may also be applied.

[0096] The order of the procedures, sequences, sequence charts, etc. of each aspect or embodiment described in this disclosure may be changed unless it is consistent. 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.

[0097] Input and output information may be stored in a specific location (for example, 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 sent to another device.

[0098] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0099] 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.

[0100] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, should be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Additionally, software, instructions, information, etc. may 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 such wired and / or wireless technologies are included within the definition of a transmission medium.

[0101] The information, signals, etc. described in this disclosure 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 voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0102] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0103] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0104] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0105] The "unit" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," or the like.

[0106] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0107] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0108] In the present 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 "coupled" may also be interpreted in the same way as "different."

[0109] 1...information processing system, 10...information processing device, 20...user terminal, 30...analysis system, 9...network, 11...identification unit, 12...reception unit, 13...output unit, 18...storage unit, 19...control unit, 101...processor, 102...memory, 103...storage, 104...communication device, 1001...movement database, 2001...target mesh range (mesh data), 3001...aggregation results, 4001...matrix, 5001...coefficient database, 81...base station, 9001...associated database, 91...management device, 92...user terminal, 99...eco score management system, U...user, S...departure point, G...destination, M...mesh, R...route, B...bus, BC...bicycle, BS...bus stop, C...car, ST...station, T...train,

Claims

1. An information processing device having: a receiving unit that receives the designation of one means of transportation on a route from a departure point to a destination; a specifying unit that specifies one or more means of transportation that can be used on said route; and an output unit that outputs an environmental load index when traveling from said departure point to said destination using said designated means of transportation, said environmental load index being corrected according to the difficulty of selecting said designated means of transportation from among said one or more means of transportation.

2. The information processing device according to claim 1, wherein the difficulty level is a parameter corresponding to the number of means of transportation other than the specified means of transportation among the available means of transportation.

3. The information processing device according to claim 2, wherein the quantity relating to the other means of transportation includes the number of the other means of transportation.

4. The information processing device according to claim 3, wherein the quantity relating to the other means of transportation includes the environmental load of the other means of transportation.

5. The information processing device according to claim 2, wherein the quantity relating to the other means of transportation includes the number of users who have used the other means of transportation.

6. The information processing device according to claim 1, wherein the correction includes a process of subtracting a correction value according to the difficulty level from the environmental load index of travel by the specified means of travel.

7. The information processing device according to claim 1, wherein the correction includes a process of multiplying the environmental load index of the travel by the specified means of travel by a correction factor according to the difficulty level.

8. The information processing device according to claim 1, wherein the identification unit identifies the one or more available means of transportation according to the means of transportation used by other users who actually traveled from the departure point to the destination during a time period corresponding to the time period during which the target user actually traveled from the departure point to the destination.

9. An information processing method comprising the steps of: accepting the designation of one means of transportation along a route from a departure point to a destination; identifying one or more means of transportation available along said route; and outputting an environmental load index when traveling from said departure point to said destination using said designated means of transportation, said environmental load index being adjusted according to the difficulty of selecting said designated means of transportation from among said one or more means of transportation.

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