Congestion Degree Exploration System

Abstract

The present invention searches for an appropriate crowdedness degree when a person, who has been presented with information corresponding to the crowdedness degree, changes a behavior.　A crowdedness degree search system 10 searches for, regarding the crowdedness degree of a target that is a position or a transportation means, the crowdedness degree of the target in a case where a person is to change a behavior after reception of information corresponding to the crowdedness degree. The system comprises: an estimation unit 11 for estimating the crowdedness degree of the target in a condition that information corresponding to the crowdedness degree set in advance for the target is to be presented to a person making a behavior; and a search unit 12 for setting the crowdedness degree for use in estimation by the estimation unit 11, for causing the estimation unit to repeatedly estimate the crowdedness degree, and for searching for the crowdedness degree of the target such that the difference between the crowdedness degree for use in estimation by the estimation unit 11 and the crowdedness degree estimated by the estimation unit 11 becomes smaller.

Description

Technical Field

[0001] The present invention relates to a congestion search system that searches for the congestion level of a calculation target when a person changes their behavior upon receiving information indicating the congestion level for the calculation target of the congestion level.

Background Art

[0002] Patent Document 1 describes predicting congestion at a predetermined location based on a person's planned behavior.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, as shown in Patent Document 1, predicting congestion at a predetermined location has been proposed. However, it is conceivable that the prediction may deviate as a result of announcing the predicted congestion value. For example, it is conceivable that a person who knows that the destination location is congested or is expected to be congested may change the destination because they dislike congestion. When the number of people who know the predicted congestion value is extremely small compared to the population, the impact of people changing their behavior is small. However, when the number of people changing their behavior is so large that it cannot be ignored, the congestion predicted by the conventional method may become inaccurate.

[0005] One embodiment of the present invention has been made in view of the above, and an object is to provide a congestion calculation system capable of searching for an appropriate congestion level when a person who has been shown information according to the congestion level changes their behavior.

Means for Solving the Problems

[0006] In order to achieve the above object, a congestion level exploration system according to an embodiment of the present invention is a congestion level exploration system that explores the congestion level of a target of congestion, which is a location or a transportation facility, when a person changes their behavior after receiving information indicating the congestion level according to the congestion level. The congestion level exploration system includes an estimation unit that estimates the congestion level of the target under the condition that information according to a preset congestion level for the target is shown to the acting person, and a search unit that sets the congestion level used for the estimation by the estimation unit after changing the congestion level each time the estimation by the estimation unit is performed, repeatedly causes the estimation unit to estimate the congestion level, and explores the congestion level of the target so that the difference between the congestion level used for the estimation by the estimation unit and the congestion level estimated by the estimation unit becomes small. The exploration unit sets the congestion level to be used in the next estimation by the estimation unit by means of an optimization method using an evaluation function based on the congestion level used in the estimation by the estimation unit and the congestion level estimated by the estimation unit.

[0007] In the congestion level exploration system according to an embodiment of the present invention, the congestion level is estimated under the condition that information according to the congestion level is shown to the acting person, and the congestion level is explored so that the difference between the congestion level used for the estimation and the estimated congestion level becomes small. Therefore, according to the congestion level exploration system according to an embodiment of the present invention, it is possible to explore an appropriate congestion level when a person who has been shown information according to the congestion level changes their behavior.

Effects of the Invention

[0008] According to an embodiment of the present invention, it is possible to explore an appropriate congestion level when a person who has been shown information according to the congestion level changes their behavior.

Brief Description of the Drawings

[0009]

Figure 1

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Embodiments for Carrying Out the Invention

[0010] Hereinafter, embodiments of a congestion degree search system according to the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same reference numerals are assigned to the same elements, and redundant descriptions are omitted.

[0011] Fig. 1 shows a congestion degree search system 10 according to the present embodiment. The congestion degree search system 10 is a system (device) that searches for the congestion degree of a target of congestion degree, which is a position or a transportation facility, when a person changes their behavior upon receiving information indicating the congestion degree.

[0012] Conventionally, the congestion degree has been measured or predicted for geographical positions such as facilities or transportation facilities. A person who acts may change their behavior by referring to information corresponding to the measured or predicted congestion degree. For example, a person who knows that the destination is congested or is expected to be congested may change the destination because they dislike congestion. In the present embodiment, when a person who has been shown information corresponding to the congestion degree changes their behavior, it is called behavior modification. Behavior modification is, for example, the change of the destination as described above. Also, behavior modification may be other than the change of the destination.

[0013] When the number of people to whom information corresponding to the congestion degree is shown is extremely small compared to the population, the influence of people's behavior modification is small. However, when the number of people who modify their behavior is so large that it cannot be ignored, the measured or predicted congestion degree may vary due to people's behavior modification and become inaccurate.

[0014] The congestion degree exploration system 10 explores a highly accurate predicted value of the congestion degree under the condition that information corresponding to the congestion degree is presented to the acting person. The explored congestion degree is used for any purpose such as presentation to the acting person.

[0015] The congestion degree exploration system 10 is constituted by a computer such as a PC (Personal Computer) or a server device. The congestion degree exploration system 10 may be constituted by a plurality of computers. The congestion degree exploration system 10 may be able to transmit and receive information to and from another device via a network for acquiring information necessary for realizing functions.

[0016] Subsequently, the functions of the congestion degree exploration system 10 according to the present embodiment will be described. As shown in FIG. 1, the congestion degree exploration system 10 includes an estimation unit 11 and an exploration unit 12.

[0017] The estimation unit 11 is a functional unit that estimates the congestion degree of the target under the condition that information corresponding to the preset congestion degree of the target is presented to the acting person. The estimation unit 11 may simulate the behavior of the person and estimate the congestion degree of the target under the condition that information corresponding to the preset congestion degree of the target is presented to the acting person. The estimation unit 11 may simulate the behavior of each individual person under the condition that information corresponding to the preset congestion degree of the target is presented to the acting person.

[0018] The target of the estimated congestion degree is a preset position or transportation facility. The position is a geographical position such as a facility. The transportation facility is a public transportation facility such as a bus or a train. The congestion degree of the transportation facility is, for example, the congestion degree of the passengers on the transportation facility. The targets of the estimated congestion degree may be plural. The information corresponding to the congestion degree presented to the acting person is, for example, information indicating the congestion degree of the target itself (for example, the number of people at the position of the target or the number of people on the transportation facility of the target). Alternatively, the information may be information related to the congestion degree, for example, the required time, waiting time, presence or absence of delay, or delay time of the transportation facility caused by congestion.

[0019] For example, the estimation unit 11 estimates the congestion level as follows. The estimation unit 11 simulates human behavior to estimate the congestion level. The estimation unit 11 performs multi-agent simulation in the human behavior area. The multi-agent simulation reproduces the behavior of each individual person at each time, mimicking the real world. The multi-agent simulation is schematically shown in FIG. 2. In the multi-agent simulation, in the human behavior area such as an urban area, the movement status of people (agents) is calculated. In FIG. 2, each point indicates an individual person (the position of a person). In the multi-agent simulation, the situation where people use means of transportation such as buses or trains is also calculated.

[0020] The estimation unit 11 performs a simulation considering the behavior when information corresponding to the target congestion level is shown to the person to be simulated, that is, the behavior variation. For example, the simulation may include a decision-making model according to the behavior variation. The decision-making model is, for example, to change the behavior according to the probability p (congestion level) (change rate, behavior variation rate) corresponding to the shown congestion level and change the destination. That is, p% of the people who see the information indicating the congestion level change their behavior.

[0021] Alternatively, the decision-making model calculates a value f by the following formula for each position that can be a destination, and heads for the position where the value f is the largest. f = w 混雑度 × congestion level + w 魅力度 × attractiveness + w インセンティブ × incentive Among the above formulas, the congestion level is a value indicating the congestion level of the position. The attractiveness is a value indicating the attractiveness of the position. The incentive is a value indicating the incentive to go to the position. w 混雑度 、w 魅力度 and w インセンティブis the weight of each value and is a preset value. Among the above formulas, for the congestion level, a value corresponding to the congestion level shown to the person to be simulated is used. The attractiveness and incentive are values preset for each location or in the simulation.

[0022] The above decision-making model is preset by existing methods or the like. For example, it is set through the know-how of the decision-making model, or through questionnaires or empirical experiments. Note that the consideration of behavioral variations when information corresponding to the congestion level is shown in the simulation may be performed by any method other than the above decision-making model.

[0023] By using multi-agent simulation, it is possible to estimate the congestion level (dynamic congestion) of a situation that conforms to reality. Note that the simulation by the estimation unit 11 does not necessarily have to be a multi-agent simulation, and any simulation that can simulate human behavior under the condition that information corresponding to a preset congestion level for the target is shown is acceptable.

[0024] Note that the target (location or transportation facility) of the estimated congestion level is the same as the target for which information corresponding to the congestion level is shown. Also, there may be a plurality of such targets.

[0025] The estimation unit 11 acquires information necessary for the simulation. The estimation unit 11 acquires information about the person to be simulated as information necessary for the simulation. Specifically, the estimation unit 11 acquires OD (Origin-Destination) data indicating who moves from where to where and when. An example of OD data is shown in Fig. 3(a). The OD data is, for example, data in which the departure place, the destination, the number of people, and the time (departure time) are associated with each other. In the example shown in Fig. 3(a), the departure place and the destination of the OD data are identifiers (area IDs) indicating small areas (for example, mesh-like areas) that divide the movement area of the person, which are the departure place and the destination. The number of people in the OD data indicates the number of people moving. The time in the OD data indicates the departure time of the movement. The data in the first row of Fig. 3(a) indicates that 10 people depart from the small area of "4010" and move to the small area of "8050" starting from 9:00 on March 17, 2022. Note that the OD data may also include information other than the above (for example, the arrival time at the destination).

[0026] The OD data may be generated based on the actual positions and movements of people. For example, time-series position information indicating where and how many people are at any given time is acquired from various sensors such as mobile terminals carried by people and sensors that measure traffic volume. An example of a position information database storing this position information is shown in Fig. 3(b). The position information is, for example, information in which the area ID, the number of people, and the time are associated with each other. The area ID of the position information is an identifier indicating a small area (for example, a mesh-like area) that divides the movement area of the person. The number of people and the time in the position information indicate the number of people and the time of the people in the small area indicated by the corresponding area ID. The data in the first row of Fig. 3(b) indicates that 10 people are in the small area of "8050" at 9:00 on March 17, 2022. The OD data may be generated from the above position information by a conventional data assimilation technique.

[0027] The estimation unit 11 may read and obtain OD data from a database in which the OD data is stored in advance, or may read the data from a database in which data capable of generating OD data is stored in advance and generate and obtain OD data. The estimation unit 11 may obtain OD data by any other arbitrary method. In addition to, or instead of, the above information, the estimation unit 11 may obtain information necessary for simulation other than the above information.

[0028] As will be described later, the estimation unit 11 performs a simulation using the congestion level (pre-prediction value) set by the search unit 12 as the congestion level related to the information shown to the person who is the simulation target. The simulation may be performed, for example, by software that performs an existing multi-agent simulation.

[0029] The estimation unit 11 obtains an estimated value (post-prediction value) of the congestion level for the target (location or transportation facility) from the result of the simulation. For example, the estimation unit 11 performs a simulation for a certain period (e.g., 30 minutes) in the simulation and uses the number of people present at the target at the end of the simulation as the estimated value (post-prediction value) of the congestion level. Note that the congestion level may not be the number of people present at the target, and any value indicating the degree of congestion at the target may be used. The estimation unit 11 outputs information indicating the estimation result of the congestion level of the target to the search unit 12. Note that the estimation of the congestion level is repeatedly performed as will be described later.

[0030] Note that the estimation unit 11 may estimate the congestion level of the target other than by simulation. For example, when a model for estimating the congestion level can be simplified and an estimated value (post-prediction value) of the congestion level can be calculated (estimated) based on the congestion level (pre-prediction value) set by the search unit 12 using a preset mathematical solution, that may be used.

[0031] The exploration unit 12 is a functional unit that sets the congestion level used in the estimation by the estimation unit 11, repeatedly causes the estimation unit 11 to estimate the congestion level, and explores the congestion level of the target so that the difference between the congestion level used in the estimation by the estimation unit 11 and the congestion level estimated by the estimation unit 11 becomes small. The exploration unit 12 may set the congestion level used in the next estimation by the estimation unit 11 by an optimization method using an evaluation function based on the congestion level used in the estimation by the estimation unit 11 and the congestion level estimated by the estimation unit 11.

[0032] For example, the exploration unit 12 explores the congestion level as follows. FIG. 4 shows an overview of the exploration of the congestion level by the exploration unit 12. The exploration unit 12 sets a prior prediction value y p i which is the congestion level for the target used in the estimation by the estimation unit 11. i is an index indicating the position or transportation means that is the target of the congestion level. As described above, the targets for which the prior prediction value y p i is set may be plural. The exploration unit 12 notifies the set prior prediction value y p i to the estimation unit 11. The estimation unit 11 performs a simulation using the notified prior prediction value y p i and estimates a posterior prediction value ŷ p i (note that "ˆ" is attached directly above "y", and the same applies hereinafter) which is the estimated value of the congestion level for the target. The estimation unit 11 outputs the posterior prediction value ŷ p i which is the estimation result to the exploration unit 12.

[0033] The exploration unit 12 compares the set prior prediction value y p i with the posterior prediction value ŷ p i input from the estimation unit 11 and determines whether to end the exploration. If it is determined to end the exploration, the exploration unit 12 compares the prior prediction value y p i with the posterior prediction value ŷ p iThe final predicted value y, which is the degree of congestion of the search results based on at least any of them p fin is output. For example, the search unit 12 sets the final predicted value y p fin = the prior predicted value y p i When the search unit 12 determines that the search should be terminated, when it determines not to terminate the search, the search unit 12 sets a new prior predicted value y p i and repeats the above process.

[0034] Hereinafter, the above search for the degree of congestion will be specifically described. First, the search unit 12 sets the first prior predicted value y p i For example, the search unit 12 does not set the prior predicted value y p i That is, under the condition that the information corresponding to the degree of congestion is not shown to the person who acts, the estimation unit 11 estimates the degree of congestion of the target, and the degree of congestion obtained as a result of the estimation is set as the first prior predicted value y p i Or, the search unit 12 estimates the degree of congestion of the target by a method other than the simulation of the estimation unit 11, and the degree of congestion obtained as a result of the estimation is set as the first prior predicted value y p i The method other than the above simulation may be any conventional method. For example, a method using a learning model such as an RNN (recurrent neural network) generated by machine learning may be used.

[0035] When the search unit 12 inputs the posterior predicted value ŷ p i from the estimation unit 11, it compares the prior predicted value y p i with the posterior predicted value ŷ p i Specifically, the search unit 12 calculates the difference |y p i − ŷ p i between the prior predicted value y p i and the posterior predicted value ŷ p iCalculate |. The search unit 12 determines whether the calculated difference is less than a preset threshold value k, p i -y^ p i i.e., whether |<k. The threshold value k is a value that can be regarded as making the prior prediction value y p i and the posterior prediction value y^ p i the same value. When the difference is less than the threshold value k, the search unit 12 sets the final prediction value y p fin = prior prediction value y p i and outputs the final prediction value y p fin as information indicating the search result.

[0036] When the difference is not less than the threshold value k, the search unit 12 sets the next prior prediction value y p i The search unit 12 sets the next prior prediction value y p i so that the difference between the prior prediction value y p i and the posterior prediction value y^ p i becomes smaller. However, in the estimation by the estimation unit 11 using the next next prior prediction value y p i it is not necessarily required that the difference between the prior prediction value y p i and the posterior prediction value y^ p i becomes smaller. For example, the search unit 12 uses an optimization method using an evaluation function (objective function) f based on the prior prediction value y p i and the posterior prediction value y^ p i to set the next prior prediction value y p i Specifically, the next prior prediction value y p i is set (searched) by the gradient method. As the evaluation function f in this case, the following function is used. f=(1 / 2)(y p i-y^ p i ) 2

[0037] In FIG. 5, the following prior prediction value y by the gradient method p i is set (prior prediction value y p i ). The graph in FIG. 5 shows the relationship between the prior prediction value y p i_n (horizontal axis) and the value f of the evaluation function n (vertical axis). n indicates the number of times the prior prediction value y p i is set. y p i_0 is the initially set prior prediction value y p i , and y p i_1 is the prior prediction value y set after the simulation performed under the condition that the information according to the first congestion level is shown by the estimation unit 11 p i . f0 is the value of the evaluation function calculated using the prior prediction value y p i_0 , and f1 is the value of the evaluation function calculated using the prior prediction value y p i_1 .

[0038] As shown in FIG. 5, in the gradient method, the current value f of the evaluation function is compared with the previous value f of the evaluation function, and the search for the next prior prediction value y p i is advanced in the direction in which the value f of the evaluation function becomes smaller. Since there is no value f of the evaluation function before f0, when determining y p i_1 , search is performed in a preset direction or a random direction. As shown in FIG. 5, by repeating the setting of the next prior prediction value y p i in this way, the value of the calculated evaluation function f also becomes smaller. As a result, the difference |y| between the prior prediction value y p i and the posterior prediction value y^ p i and pi -y^ p i also becomes smaller.

[0039] Note that the setting of the next predicted value y p i may be performed by an optimization method other than the gradient method. Also, by repeating the estimation by the estimation unit 11, the difference between the predicted value y p i and the posterior predicted value y^ p i becomes smaller, and if it is possible to search for the final predicted value y p fin the setting of the next predicted value y p i may be performed by a method other than the optimization method.

[0040] The search unit 12 sets the next predicted value y p i and the posterior predicted value y^ p i and repeats the process of estimating the posterior predicted value y^ p i -y^ p i | until it becomes less than the threshold value k. Note that when there are a plurality of objects for which the predicted value y p i is set and the posterior predicted value y^ p i is estimated, it is not necessary to search for the final predicted value y p i for all of them, and it is also possible to search for the final predicted value y p i for only some of them. p i p fin p fin p fin p

[0041] Once the search unit 12 obtains the final predicted value y p fin p fin ​Output the information indicated as the information indicating the search result. For example, the search unit 12 may display the information on a display device included in the congestion search system 10. Alternatively, the search unit 12 may transmit the information to another device, for example, the terminal of a person who acts in the action area including the congestion target (that is, a person who may go to the target). Further, the search unit 12 may output the information by a method other than the above.

[0042] Here, with reference to FIG. 6, a specific example of the search for the final predicted value y p fin is shown. In the example of FIG. 6, for two targets at point A and point B, the prior predicted value y p i is set and the posterior predicted value ŷ p i is estimated, and the final predicted value y p fin for point A is searched.

[0043] In the first round of search, the prior predicted value y p i is set to 300 people for point A and 50 people for point B. The prior predicted value y p i in the first round of search is generated by the estimation by the estimation unit 11 under the condition that the information corresponding to the congestion level is not shown to the acting people as described above, or by a method using a learning model generated by machine learning. As a result of the simulation by the estimation unit 11, the posterior predicted value ŷ p i becomes 87 people for point A and 263 people for point B. The difference |y p i −ŷ p i | between the prior predicted value y p i and the posterior predicted value ŷ p i is equal to or greater than the threshold value k (that is, y p i ≠ŷ p i ), and a second search is performed.

[0044] In the search starting from the second round, the prior predicted value y p i is set by the gradient method or the like as described above. In the search of the second round, the prior predicted value y p i is set to 250 people for point A and 100 people for point B. As a result of the simulation by the estimation unit 11, the posterior predicted value ŷ p i becomes 174 people for point A and 176 people for point B. For point A, the difference between the prior predicted value y p i and the posterior predicted value ŷ p i |y p i −ŷ p i | is equal to or greater than the threshold value k (that is, y p i ≠ŷ p i ), and a new search is performed.

[0045] The above search is repeated. In the search of the nth round, the prior predicted value y p i is set to 221 people for point A and 129 people for point B. As a result of the simulation by the estimation unit 11, the posterior predicted value ŷ p i becomes 221 people for point A and 129 people for point B. For point A, the difference between the prior predicted value y p i and the posterior predicted value ŷ p i |y p i −ŷ p i | is less than the threshold value k (here, y p i =ŷ p i ), and the final predicted value y p fin for point A is set to 221 people, and the search ends. The above is the function of the congestion degree search system 10 according to the present embodiment.

[0046] Next, the process executed by the congestion level search system 10 according to this embodiment (the operation method performed by the congestion level search system 10) will be described using the flowchart of FIG. 7. In this process, first, the estimation unit 11 acquires information necessary for the simulation (S01). Then, the search unit 12 acquires a prior predicted value y p i is set (S02). Then, the estimation unit 11 uses the acquired information and the set prior predicted value y p i The estimation unit 11 calculates a posterior predicted value y^, which is the degree of congestion of the object based on the results of the simulation, under the condition that the person is shown information according to the degree of congestion of the object that is preset, using the above. p i is obtained (S04).

[0047] Next, the search unit 12 calculates the predicted value y p i and the posterior predicted value y^ p i Difference from |y p i -y^ p i It is determined whether |y is less than a threshold k (S05). p i -y^ p i | <kでないと判断された場合(S05のNO)、探索部12によって、再度、事前予測値y p i is set (S02). p i The setting of y p i and the posterior predicted value y^ p i This is done so that the difference between the predicted value y p i The gradient method or the like may be used to set the predicted value y p iis used, and the processes of S03 to S05 described above are repeated.

[0048] In S05, |y p i -y^ p i If it is determined that |<k (YES in S05), as a result of the congestion degree search by the search unit 12, the final predicted value y p fin is set to the prior predicted value y p i at that time (S06). Subsequently, information indicating the final predicted value y p fin which is the result of the congestion degree search by the search unit 12 is output (S07). The above is the process executed by the congestion degree search system 10 according to the present embodiment.

[0049] In this embodiment, under the condition that information corresponding to the prior predicted value y p i is shown to the acting person, the posterior predicted value y^ p i is estimated, and the congestion degree is searched so that the difference between the prior predicted value y p i and the posterior predicted value y^ p i becomes small. Therefore, according to the present embodiment, it is possible to search for the final predicted value y p fin which is an appropriate congestion degree when a person showing information according to the congestion degree changes their behavior. As a result, it is possible to grasp the accurate congestion degree in the case of the above conditions, and for example, it is possible to take an appropriate action according to the congestion degree.

[0050] Also, like in this embodiment, an optimization method using an evaluation function based on the prior predicted value y p i and the posterior predicted value y^ p i , for example, by the gradient method, the prior predicted value y p i used for the next estimation by the estimation unit 11 may be set. According to this configuration, the final predicted value y pfin However, the prior predicted value y p i The setting of does not have to be done as above, and the final predicted value y p fin Any method other than the above may be used as long as it is capable of performing the search.

[0051] In addition, as in this embodiment, the person who will behave is given a predicted value y p i Under the condition that information according to p i Furthermore, the person who will take action can be given a prior predicted value y p i The behavior of each individual person may be simulated under the condition that information corresponding to the condition is displayed. For example, a multi-agent simulation may be performed as described above. With this configuration, an appropriate simulation can be performed, and as a result, an appropriate final predicted value y p fin However, the simulation does not necessarily have to simulate the behavior of an individual person, and the posterior predicted value y^ can be calculated as a result of the simulation. p i It is sufficient to obtain the posterior predicted value y^ p i may be estimated by methods other than simulating human behavior.

[0052] Note that the block diagrams used in the description of the above embodiments show blocks of functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Also, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one physically or logically combined device, or two or more physically or logically separated devices may be directly or indirectly (e.g., using wired, wireless, etc.) connected and realized using these multiple devices. The functional block may be realized by combining software with the above one device or the above multiple devices.

[0053] Functions include, but are not limited to, judgment, decision, determination, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, regarded as, notification (broadcasting), notification (notifying), communication (communicating), forwarding, configuration (configuring), reconfiguration (reconfiguring), allocation (allocating, mapping), assignment (assigning), etc. For example, a functional block (component) that functions to transmit is called a transmitting unit or a transmitter. In any case, as described above, the realization method is not particularly limited.

[0054] For example, the congestion search system 10 in an embodiment of the present disclosure may function as a computer that performs the information processing of the present disclosure. FIG. 8 is a diagram showing an example of the hardware configuration of the congestion search system 10 according to an embodiment of the present disclosure. The above-described congestion search system 10 may physically be configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0055] In the following description, the term "device" can be read as a circuit, device, unit, etc. The hardware configuration of the congestion level search system 10 may be configured to include one or more of each device shown in the figure, or may be configured without including some devices.

[0056] Each function in the congestion level search system 10 is realized by causing a processor 1001 to perform operations and control communication by a communication device 1004, or by controlling at least one of reading and writing data in a memory 1002 and a storage 1003, by loading a predetermined software (program) onto hardware such as the processor 1001 and the memory 1002.

[0057] The processor 1001 controls the entire computer by operating an operating system, for example. The processor 1001 may be constituted by a central processing unit (CPU: Central Processing Unit) including an interface with peripheral devices, a control device, an arithmetic device, registers, etc. For example, each function in the congestion level search system 10 described above may be realized by the processor 1001.

[0058] Also, the processor 1001 reads a program (program code), software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these. As the program, a program for causing a computer to execute at least a part of the operations described in the above embodiments is used. For example, each function in the congestion level search system 10 may be realized by a control program stored in the memory 1002 and operating in the processor 1001. Although it has been described that the above various processes are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. Note that the program may be transmitted from a network via a telecommunication line.

[0059] The memory 1002 is a computer-readable recording medium and may be constituted by at least one of, for example, a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), etc. The memory 1002 may be referred to as a register, a cache, a main memory (main storage device), etc. The memory 1002 can store a program (program code), a software module, etc. that are executable for carrying out information processing according to an embodiment of the present disclosure.

[0060] The storage 1003 is a computer-readable recording medium and may be constituted by 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. The storage 1003 may be referred to as an auxiliary storage device. The storage medium included in the congestion search system 10 may be, for example, a database, a server, or other appropriate medium including at least one of the memory 1002 and the storage 1003.

[0061] The communication device 1004 is hardware (a transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc.

[0062] The input device 1005 is an input device (e.g., keyboard, mouse, microphone, switch, button, sensor, etc.) that receives external input. The output device 1006 is an output device (e.g., display, speaker, LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may have an integrated configuration (e.g., touch panel).

[0063] Also, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus or may be configured using different buses for each device.

[0064] Also, the congestion search system 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 1001 may be implemented using at least one of these hardware components.

[0065] The processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be reordered as long as there is no contradiction. For example, regarding the methods described in this disclosure, the elements of various steps are presented using an exemplary order and are not limited to the specific order presented.

[0066] The input / output information, etc. may be stored in a specific location (e.g., memory) or may be managed using a management table. The input / output information, etc. may be overwritten, updated, or appended. The output information, etc. may be deleted. The input information, etc. may be transmitted to other devices.

[0067] The determination may be made based on a value represented by 1 bit (either 0 or 1), a Boolean value (Boolean: true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0068] Each aspect / embodiment described in the present disclosure may be used alone, in combination, or switched and used during execution. Also, the notification of predetermined information (e.g., the notification of "being X") is not limited to being explicitly performed, and may be performed implicitly (e.g., by not performing the notification of the predetermined information).

[0069] As described in detail above regarding the present disclosure, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented as modified and changed aspects without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is for illustrative purposes and does not have any limiting meaning for the present disclosure.

[0070] Software should be broadly interpreted 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 it is called a software, firmware, middleware, microcode, hardware description language, or other names.

[0071] Also, software, instructions, information, etc. may be transmitted and received via a transmission medium. For example, when software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cables, optical fiber cables, twisted pairs, digital subscriber lines (DSL), etc.) and wireless technologies (such as infrared rays, microwaves, etc.), at least one of these wired technologies and wireless technologies is included within the definition of the transmission medium.

[0072] The terms "system" and "network" used in the present disclosure are used interchangeably.

[0073] Also, the information, parameters, etc. described in the present disclosure may be represented using absolute values, relative values from a predetermined value, or corresponding other information.

[0074] As used herein, the terms "determining" and "deciding" may encompass a variety of operations. "Determining" and "deciding" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up (e.g., searching a table, database, or other data structure), ascertaining, and considering something as having been "determined" or "decided". Further, "determining" and "deciding" may include considering something as having been "determined" or "decided" after receiving (e.g., receiving information), transmitting (e.g., transmitting information), inputting, outputting, accessing (e.g., accessing data in memory), and the like. Additionally, "determining" and "deciding" may include considering something as having been "determined" or "decided" after resolving, selecting, choosing, establishing, comparing, and the like. That is, "determining" and "deciding" may include considering something as having been "determined" or "decided" after performing some operation. Also, "determining (deciding)" may be read as "assuming", "expecting", "considering", or the like.

[0075] The terms "connected" and "coupled" and any variations thereof mean any direct or indirect connection or coupling between two or more elements and can 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 physical, logical, or a combination thereof. For example, "connected" may be read as "accessed". As used in this disclosure, two elements can 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, by way of some non-limiting and non-exhaustive examples, electromagnetic energy having wavelengths in the radio frequency region, microwave region, and optical (both visible and invisible) region.

[0076] As used in this disclosure, the recitation "based on" does not mean "based only on" unless otherwise specified. In other words, the recitation "based on" means both "based only on" and "based at least in part on".

[0077] Any reference to an element using designations such as "first", "second", etc. used in this disclosure does not generally limit the quantity or order of those elements. These designations can be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not mean that only two elements can be employed or that the first element must precede the second element in any way.

[0078] In this disclosure, when the terms "include", "including", and variations thereof are used, these terms are intended to be inclusive in the same manner as the term "comprising". Further, the term "or" used in this disclosure is not intended to be exclusive.

[0079] In the present disclosure, for example, when articles are added by translation, such as a, an, and the in English, the present disclosure may include that the nouns following these articles are in the plural form.

[0080] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other". Note that the term may also mean "A and B are different from C respectively". Terms such as "separate", "coupled", etc. may also be interpreted in the same way as "different".

[0081] The congestion search system of the present disclosure has the following configuration. [1] A congestion search system that searches for the congestion of a target of congestion, which is a location or a transportation facility, when a person changes their behavior after receiving information indicating the congestion according to the congestion, An estimation unit that estimates the congestion of the target under the condition that information according to the preset congestion of the target is shown to the acting person; A search unit that sets the congestion used for the estimation by the estimation unit, repeatedly causes the estimation unit to estimate the congestion, and searches for the congestion of the target so that the difference between the congestion used for the estimation by the estimation unit and the congestion estimated by the estimation unit becomes small; A congestion search system comprising: [2] The search unit according to [1], which sets the congestion used for the next estimation by the estimation unit by an optimization method using an evaluation function based on the congestion used for the estimation by the estimation unit and the congestion estimated by the estimation unit. [3] The estimation unit according to [1] or [2], which simulates the behavior of a person under the condition that information according to the preset congestion of the target is shown to the acting person, and estimates the congestion of the target. [4] The estimation unit according to [3], which simulates the behavior of each individual person under the condition that information according to the preset congestion of the target is shown to the acting person.

Description of Reference Numerals

[0082] 10… Congestion degree exploration system, 11… Estimation unit, 12… Exploration unit, 1001… Processor, 1002… Memory, 1003… Storage, 1004… Communication device, 1005… Input device, 1006… Output device, 1007… Bus.

Claims

1. A congestion search system that searches for the congestion level of a target that is a location or a transportation facility when a person changes their behavior upon receiving information indicating the congestion level according to the congestion level, comprising: An estimation unit that estimates the congestion level of the target under the condition that information corresponding to a preset congestion level for the target is shown to the person who acts; The congestion level used for the estimation by the estimation unit is set after being changed each time the estimation by the estimation unit is performed, and the estimation unit is repeatedly made to estimate the congestion level, so that the difference between the congestion level used for the estimation by the estimation unit and the congestion level estimated by the estimation unit becomes small. A search unit that searches for the congestion level of the target; Comprising; The search unit is a congestion search system that sets the congestion level to be used for the next estimation by the estimation unit by an optimization method using an evaluation function based on the congestion level used for the estimation by the estimation unit and the congestion level estimated by the estimation unit.

2. The estimation unit according to claim 1, wherein the estimation unit simulates the behavior of a person under the condition that information corresponding to a preset congestion level for the target is shown to the person who acts, and estimates the congestion level of the target.

3. The estimation unit according to claim 2, wherein the estimation unit simulates the behavior of each individual person under the condition that information corresponding to a preset congestion level for the target is shown to the person who acts.

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