Traffic congestion determination system, traffic congestion determination method, and traffic congestion determination program

Abstract

To highly accurately determine congestion conditions of each mobile body on more roads.SOLUTION: A congestion determination system includes: a determination device for determining congestion conditions of each mobile body that is constructed by machine learning based on learning information including movement information for learning indicating movement information of each of the plurality of mobile bodies and learning congestion conditions information indicating congestion conditions of the corresponding mobile body; an input movement information acquisition unit for acquiring input movement information indicating movement information of each of at least one mobile body; and a mobile body determination unit that inputs the input movement information to the determination device and determines congestion conditions of each of the at least one mobile body.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a congestion determination system, a congestion determination method, and a congestion determination program for determining a congestion state. [Background technology]

[0002] For some time now, when users travel on roads in mobile vehicles such as automobiles and motorcycles, they have used driving assistance systems installed in in-vehicle navigation devices and various information terminals such as smartphones to select an optimal route to their destination (e.g., a route with less congestion). When a user selects a route with less congestion, the driving assistance system acquires traffic condition information such as congestion status from VICS (registered trademark: Vehicle Information and Communication System), understands the congestion status of the road based on the acquired traffic condition information, searches for a route with less congestion, and provides the searched route to the user. Here, VICS acquires traffic information and the like for mobile vehicles from congestion sensors installed on expressways and major public roads, analyzes traffic conditions such as congestion status based on the acquired traffic information, and provides the analyzed traffic condition information to the driving assistance system.

[0003] In recent years, the spread of road traffic information systems that utilize probe information has been progressing, which not only collect traffic information from congestion sensors installed on expressways and major public roads, but also collect information such as location information and vehicle speed information from moving objects on the road (probe information) and analyze traffic conditions based on large amounts of probe information.Under road traffic information systems that utilize probe information, it is possible to grasp traffic conditions on roads where VICS is not installed, and it is expected that this will enable the analysis of congestion conditions on a wider range of roads.

[0004] Meanwhile, as traffic congestion analysis methods become more sophisticated, it is becoming possible to analyze congestion not only for each road but also for each lane. By understanding the congestion situation for each lane, driving assistance systems can search for routes that avoid unnecessary congestion and unnecessary detours, and provide users with more optimal routes.

[0005] An example of how understanding the congestion status of each lane is effective in optimal route search is an analysis of a road with two lanes on each side that has a fork in the road (road A1 and road A2) in the direction of travel, where only the lane on road A1 is congested just before the fork. If the user heads toward the other road, road A2, which is not congested, a driving assistance system that understands the congestion status of each lane can quickly guide the user to the lane on the fork A2 side. As a result, the user can pass through the fork using the optimal lane route.

[0006] In analyzing the congestion situation for each lane as described above, attention has been focused on analyzing the congestion situation for each moving body. Analysis of the congestion situation for each moving body has a high affinity with road traffic information systems that utilize probe information. In fact, there is a technology (for example, Patent Document 1) that uses a road traffic information system that utilizes probe information to analyze the congestion situation for each moving body based on the speed information of the moving bodies traveling on the road, and grasp the congestion situation for each lane.

[0007] In addition, methods for analyzing congestion conditions for each lane without analyzing congestion conditions for each moving object include an analysis method based on information obtained from VICS (e.g., Patent Document 2) and an analysis method based on in-vehicle camera footage (e.g., Patent Document 3). [Prior art documents] [Patent documents]

[0008] [Patent Document 1] JP 2019-109708 A [Patent Document 2] Re-table 2018-151005 publication [Patent Document 3] JP 2020-4235 A Summary of the Invention [Problem to be solved by the invention]

[0009] However, Patent Document 1 uses vehicle speed information of moving objects in the analysis and determines that moving objects traveling at a certain speed or less are in a traffic jam. Therefore, there is a risk that a slow moving object that is not necessarily causing a traffic jam, such as a taxi waiting for a customer, may be mistakenly determined to be in a traffic jam. Therefore, Patent Document 1 cannot accurately provide the user with the traffic jam situation of slow moving objects that are not causing a traffic jam.

[0010] Furthermore, Patent Document 2 analyzes the congestion status for each lane using traffic condition information collected by VICS. Therefore, Patent Document 2 cannot analyze the congestion status for each lane on roads where VICS is not installed. Therefore, Patent Document 2 cannot provide users with the congestion status for each lane on the many roads where VICS is not installed, given that roads where VICS is installed are currently limited to expressways and major trunk roads.

[0011] Furthermore, Patent Document 3 uses in-vehicle camera footage captured by an in-vehicle camera to analyze the movement status of surrounding moving objects captured in the footage and analyze the congestion status for each lane. Therefore, Patent Document 3 requires that a certain number of moving objects equipped with in-vehicle cameras be present on the road to be analyzed. Therefore, Patent Document 3 cannot provide users with accurate congestion status for roads where the penetration rate of in-vehicle cameras is low.

[0012] Therefore, an object of the present invention is to provide a congestion determination system, a congestion determination method, and a congestion determination program that can determine the congestion status of each mobile object on more roads with high accuracy. [Means for solving the problem]

[0013] A traffic congestion determination system according to one embodiment of the present invention comprises a determiner that determines the traffic congestion status of each of a plurality of moving bodies, constructed by machine learning based on learning information including learning movement information indicating the movement information of each of a plurality of moving bodies and learning traffic congestion status information indicating the traffic congestion status of the corresponding moving body; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving body; and a moving body determination unit that inputs the input movement information to the determiner and determines the traffic congestion status of each of the at least one moving body.

[0014] In addition, a congestion determination method according to one embodiment of the present invention involves a computer having a determiner for determining the congestion status of each of a plurality of moving bodies, constructed by machine learning based on learning information including learning movement information indicating the movement information of each of a plurality of moving bodies and learning congestion status information indicating the congestion status of the corresponding moving bodies, acquiring input movement information indicating the movement information of each of at least one moving body, inputting the input movement information to the determiner, and determining the congestion status of each of the at least one moving body.

[0015] In addition, a traffic congestion determination program according to one embodiment of the present invention causes a computer to implement a determiner that determines the traffic congestion status of each moving body, constructed by machine learning based on learning information including learning movement information indicating the movement information of each of a plurality of moving bodies and learning traffic congestion status information indicating the traffic congestion status of the corresponding moving body; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving body; and a moving body determination unit that inputs the input movement information to the determiner and determines the traffic congestion status of each of the at least one moving body.

[0016] In this invention, a "unit" does not simply mean a physical means, but also includes cases where the functions of the "unit" are realized by software. Furthermore, the functions of one "unit" or device may be realized by two or more physical means or devices, and the functions of two or more "units" or devices may be realized by one physical means or device. [Effects of the Invention]

[0017] According to the present invention, it is possible to provide a congestion determination system, a congestion determination method, and a congestion determination program that can determine the congestion state for each moving object on more roads with high accuracy. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram showing the configuration of a congestion determination system 100 according to an embodiment of the present invention. [Figure 2] 10 is a diagram showing an example of information stored in a learning information storage unit 131. FIG. [Figure 3] 10 is a diagram showing an example of information stored in an input movement information storage unit 141. FIG. [Figure 4] 10 is a diagram showing an example of determination by a moving body determination unit 142 using a determiner 133. FIG. [Figure 5] 10 is a diagram showing an example of information stored in a moving body determination information storage unit 144. FIG. [Figure 6] 10 is a diagram showing an example of information stored in a tally information storage unit 151. FIG. [Figure 7] 10 is a diagram showing an example of information stored in a link determination information storage unit 154. FIG. [Figure 8] 1 is a flowchart showing an example of machine learning processing in the traffic congestion determination system 100. [Figure 9] 1 is a flowchart showing an example of a process for determining congestion in the congestion determination system 100. DETAILED DESCRIPTION OF THE INVENTION

[0019] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described with reference to the accompanying drawings, in which: Figure 1 is a diagram showing the configuration of a congestion determination system 100 according to one embodiment of the present invention;

[0020] The traffic congestion determination system 100 is a system that is communicably connected to an information processing system 110 via a network such as the Internet. The information processing system 110 is further communicably connected to a terminal device 120 via a network such as the Internet. The traffic congestion determination system 100 may be communicably connected to the terminal device 120 via a network such as the Internet. Although FIG. 1 shows three terminal devices 120a, 120b, and 120c as examples of the terminal device 120, the number of terminal devices 120 is not limited to this.

[0021] The traffic congestion determination system 100 acquires learning information including learning movement information indicating the movement information of each of a plurality of moving bodies and learning congestion situation information indicating the congestion situation of the corresponding moving body from the information processing system 110, and generates a determiner that determines the congestion situation of each moving body by machine learning based on the learning information. Then, the traffic congestion determination system 100 inputs input movement information indicating the movement information of at least one moving body acquired from the information processing system 110 or the terminal device 120 to the determiner, and determines the congestion situation of the moving body.

[0022] Furthermore, the congestion determination system 100 determines the congestion state of each of at least one link based on the congestion determination result of at least one mobile object. Then, the congestion determination system 100 provides the congestion determination result of each of at least one mobile object and the congestion determination result of each of at least one link to the information processing system 110. Here, the mobile object is an object such as an automobile or a motorcycle traveling on a road used for the movement of people or goods. Details of the congestion determination system 100 will be described later.

[0023] The information processing system 110 is a system that collects and analyzes traffic condition information, such as movement information (time information, speed information, acceleration information, location information, mileage information, etc.) of each mobile object, congestion status information indicating the congestion status of roads, and information on road accidents and lane restrictions. The information processing system 110 cooperates with a system that manages traffic condition information and mobile objects, etc. to collect traffic condition information. The information processing system 110 may be, for example, a system that analyzes traffic condition information based on the movement information and congestion status information of the mobile objects and provides the analyzed traffic information to a terminal device, a base station, etc., or a driving assistance system that supports the driving of the mobile objects based on the analyzed traffic information. The information processing system 110 may be managed by a public institution or independently by a mobile object manufacturer.

[0024] The terminal device 120 is an information communication terminal mounted on a mobile object or an information communication terminal carried by a user who moves using the mobile object. The terminal device 120 collects movement information (time information, speed information, acceleration information, position information, mileage information, etc.) of each mobile object from each part of the mobile object, and provides the collected movement information to the information processing system 110. The terminal device 120 may also calculate movement information of each mobile object based on GPS coordinate information measured by a GPS system using a GPS device mounted on the mobile object or the terminal device 120, and provide the calculated movement information to the information processing system 110. The terminal device 120 may also provide the movement information to the congestion determination system 100.

[0025] Next, the traffic congestion determination system 100 will be described in detail. As shown in Fig. 1, the traffic congestion determination system 100 includes a learning information acquisition unit 130, a learning information storage unit 131, a machine learning execution unit 132, a determiner 133, an input movement information acquisition unit 140, an input movement information storage unit 141, a moving object determination unit 142, a moving object determination information generation unit 143, a moving object determination information storage unit 144, a compilation information generation unit 150, a compilation information storage unit 151, a link determination unit 152, a link determination information generation unit 153, a link determination information storage unit 154, and a determination information provision unit 160. The computer constituting the traffic congestion determination system 100 includes a processor and a storage area. Each unit shown in Fig. 1 can be realized, for example, by using the storage area or by the processor executing a program stored in the storage area.

[0026] The learning information acquisition unit 130 accesses the information processing system 110, acquires from the information processing system 110 learning information that serves as the basis for teacher data when performing machine learning, and stores the acquired learning information in the learning information storage unit 131. The learning information includes, for example, learning movement information indicating the movement information of each of a plurality of moving objects, and learning traffic congestion status information indicating the traffic congestion status of the corresponding moving objects, which have been accumulated in the information processing system 110 over a certain period of time in the past.

[0027] The learning movement information is movement information for each of multiple moving bodies, and includes, for example, time information indicating the time of data acquisition, speed information indicating the speed of the moving body, and GPS coordinate information indicating GPS coordinates measured by a GPS system using a GPS device installed in the moving body or terminal device 120.

[0028] The learning traffic congestion information indicates the traffic congestion status of the corresponding mobile object, and is indicated by an index such as "traffic jam," "slightly congested," or "empty." The traffic congestion information may be a qualitative index such as "A," "B," or "C," or a quantitative index such as a percentage. The learning traffic congestion information may be collected by having the mobile object for information collection travel along a road and having the user of the mobile object (driver or passenger) make a judgment, or may be calculated based on a traffic congestion judgment result determined by a system different from the traffic congestion judgment system 100 (for example, a system that analyzes traffic congestion status based on road images, etc.).

[0029] The timing at which the learning information acquisition unit 130 acquires the learning information from the information processing system 110 can be set arbitrarily. For example, the learning information acquisition unit 130 may acquire the learning information from the information processing system 110 before machine learning by the machine learning execution unit 132 (described later), or may acquire the learning information from the information processing system 110 periodically, such as once a day. Alternatively, the learning information acquisition unit 130 may acquire the learning information from the information processing system 110 in response to an instruction from the information processing system 110.

[0030] 2 is a diagram showing an example of information stored in the learning information storage unit 131. The information stored in the learning information storage unit 131 includes, for example, a mobile object ID, learning movement information, and learning traffic congestion information. The mobile object ID is mobile object identification information that identifies a mobile object that provides information to the information processing system 110.

[0031] The learning information stored in the learning information storage unit 131 does not have to be the learning information itself acquired from the information processing system 110. In addition, the data included in the learning information may be partially missing or may have different items.

[0032] The machine learning execution unit 132 generates a determiner 133 that determines the congestion status of each mobile object by executing machine learning based on the learning information stored in the learning information storage unit 131. The machine learning by the machine learning execution unit 132 is executed, for example, in response to instructions from a system administrator. Note that the machine learning may also be executed automatically at predetermined times such as during system maintenance. The machine learning algorithm is not particularly limited, and examples that can be used include decision tree learning, deep learning, random forest, light GBM (light gradient boosting machine), and SVM (support vector machine).

[0033] In machine learning by the machine learning execution unit 132, information calculated based on speed information included in the input movement information stored in the input movement information storage unit 141 can be used as a feature quantity. The information calculated based on the speed information includes, for example, the maximum speed, minimum speed, average speed, acceleration, and the variance, standard deviation, skewness, and kurtosis of the frequency distribution within a certain period of time.

[0034] In machine learning by the machine learning execution unit 132, a plurality of pieces of information calculated based on speed information are used as feature quantities, making it possible to perform highly accurate congestion determination.

[0035] For example, by generating the determiner 133 based on machine learning that combines maximum speed information and acceleration information, a more accurate congestion determination system 100 can be constructed compared to a case where a mobile object whose maximum speed is below a certain value is simply determined to be in a "traffic jam." This is because information characterizing the traffic jam situation of a mobile object is contained not only in the maximum speed information of the mobile object but also in the acceleration information. A specific example will be described below.

[0036] Both a mobile body that is simply driving slowly regardless of traffic congestion and a mobile body that is driving slowly while repeatedly starting and stopping due to traffic congestion have low speeds. On the other hand, when looking at acceleration, the acceleration of a mobile body that is simply driving slowly regardless of traffic congestion is small and constant, while the acceleration of a mobile body that is driving slowly while repeatedly starting and stopping due to traffic congestion fluctuates. Therefore, if a mobile body whose maximum speed is below a certain value is simply determined to be in a "traffic jam," it is not possible to distinguish between a mobile body that is simply driving slowly regardless of traffic congestion and a mobile body that is driving slowly while repeatedly starting and stopping due to traffic congestion. However, if machine learning that combines maximum speed information and acceleration information is used, it is possible to distinguish between a mobile body that is simply driving slowly regardless of traffic congestion and a mobile body that is driving slowly while repeatedly starting and stopping due to traffic congestion.

[0037] In this way, in machine learning that combines maximum speed information and acceleration information, by generating a classifier 133 that makes it easier to determine that a moving object with a low maximum speed and high acceleration is in a "traffic jam," it is possible to perform a more accurate traffic jam determination than simply determining that a moving object with a maximum speed below a certain value is in a "traffic jam."

[0038] Furthermore, in machine learning by the machine learning execution unit 132, in addition to the information calculated based on the speed information included in the input movement information stored in the input movement information storage unit 141, GPS coordinate information indicating GPS coordinates measured by a GPS system using a GPS device installed in the mobile body or terminal device 120 can also be used as a feature.

[0039] When the information calculated based on the speed information and the GPS coordinate information are used as feature quantities, for example, a mobile object determined to be located in a link where congestion occurs frequently based on the GPS coordinate information can be more easily determined to be in a "congestion" state, while a mobile object determined to be located in a link where congestion is rare can be more easily determined to be in a "non-congestion" state even if its speed is low. In addition, congestion can also be determined based on movement information (movement distance, average speed, etc.) calculated from the GPS coordinate information.

[0040] Furthermore, in machine learning by the machine learning execution unit 132, GPS travel distance information indicating the travel distance on GPS coordinates based on the input travel information and GPS coordinate information, and reference travel distance information indicating the travel distance of the moving object calculated based on learning travel information excluding the GPS coordinate information, can be used as feature quantities. In machine learning by the machine learning execution unit 132, for example, the discrepancy between the GPS travel distance information and the reference travel distance information can be used as feature quantities.

[0041] Here, GPS travel distance information is information indicating the travel distance of a moving object calculated based on GPS coordinate information. The GPS travel distance information includes processing errors by the GPS system. One factor that can cause processing errors by the GPS system is error due to the frequency with which GPS coordinate information is acquired. Specifically, since GPS coordinate information is merely information about a single point, the travel distance indicated by the GPS travel distance information indicates the length of the trajectory connecting each point. And since this length is the length of the trajectory (broken line) connecting each point, this length is not the length of the actual trajectory (smooth curve) of the moving object, but merely an approximation of the length of the actual trajectory of the moving object. In this case, the greater the speed or acceleration of the moving object, the greater the difference between the trajectory connecting each point included in the GPS coordinate information and the actual trajectory of the moving object.

[0042] On the other hand, the reference travel distance information is, for example, information indicating a travel distance calculated from time information and speed information included in the input travel information or information indicating a travel distance included in the input travel information. The travel distance information is, for example, information calculated based on the number of rotations of the wheels of the moving object.

[0043] Both the GPS travel distance information and the reference travel distance information indicate the travel distance of a moving object over a certain period of time. However, due to processing errors by the GPS system, the GPS travel distance information and the reference travel distance information may not match. As described above, for example, when a moving object is moving at high speed or high acceleration, a discrepancy occurs between the trajectory connecting the points included in the GPS coordinate information and the actual trajectory of the moving object, resulting in a discrepancy between the GPS travel distance information and the reference travel distance. Therefore, the greater the discrepancy between the GPS travel distance information and the reference travel distance information, the more likely the moving object is to be evaluated as moving at high speed or high acceleration. Furthermore, a moving object with a large discrepancy between the GPS travel distance information and the reference travel distance information can be more easily determined to be in a "non-traffic jam." In this way, in machine learning by the machine learning execution unit 132, the GPS travel distance information and the reference travel distance information can be used as feature quantities for traffic jam determination.

[0044] In machine learning by the machine learning execution unit 132, only information based on the movement information of the moving body that is the target of traffic congestion judgment may be used as a feature, or information based on the movement information of other moving bodies, such as moving bodies present in the vicinity, may also be used as a feature.

[0045] The input movement information acquisition unit 140 accesses the information processing system 110, acquires movement information of at least one moving object from the information processing system 110, and stores the information in the input movement information storage unit 141. Note that the input movement information acquisition unit may access the terminal device 120 and acquire the movement information of the moving object directly from the terminal device 120.

[0046] The timing at which the input movement information acquisition unit 140 acquires the input movement information from the information processing system 110 or the terminal device 120 can be set arbitrarily. For example, the input movement information acquisition unit 140 may acquire the input movement information from the terminal device 120 immediately when the information processing system 110 or the terminal device 120 collects the movement information, or the input movement information acquisition unit 140 may acquire the input movement information from the information processing system 110 or the terminal device 120 at a fixed timing, such as every second. Furthermore, the input movement information acquisition unit 140 may acquire the input movement information from the information processing system 110 or the terminal device 120 in response to an instruction from the information processing system 110 or the terminal device 120.

[0047] Furthermore, the input movement information may be movement information immediately before the determination by the moving body determination unit 142, may be past movement information, or may be virtual movement information calculated by simulation. Furthermore, the input movement information may include not only the movement information of the moving body that is the subject of determination by the moving body determination unit 142, but also the movement information of other moving bodies, such as moving bodies existing around the moving body.

[0048] 3 is a diagram showing an example of information stored in the input movement information storage unit 141. The information stored in the input movement information storage unit 141 includes, for example, a moving object ID, time information, speed information, and GPS coordinate information.

[0049] The moving body determination unit 142 inputs the input movement information stored in the input movement information storage unit 141 to the determiner 133 , determines the traffic congestion status of each moving body, and stores the determined information in the moving body determination information storage unit 144 .

[0050] FIG. 4 is a diagram illustrating an example of a determination made by the moving object determination unit 142 using the determiner 133. The determiner 133 classifies the moving object, for example, for each branch, based on the input movement information stored in the input movement information storage unit 141, and ultimately generates a congestion determination result for the moving object. The congestion determination result includes, for example, "congested," "slightly congested," "empty," etc. Note that the congestion status information may be a qualitative indicator such as "A," "B," or "C," or may be a quantitative indicator such as a percentage. Furthermore, the indicators of the congestion determination result for the moving object and the learning congestion status information may be the same or different. Note that while FIG. 4 illustrates a determination method utilizing a decision tree learning method, the machine learning algorithm is not limited to this.

[0051] Based on the congestion determination result by the mobile body determination unit 142 using the determiner 133, the mobile body determination information generation unit 143 generates mobile body determination information indicating information that associates the congestion determination result by the mobile body determination unit 142 using the determiner 133 with mobile body identification information that identifies the mobile body, and stores the information in the mobile body determination information storage unit 144.

[0052] FIG. 5 is a diagram illustrating an example of information stored in the moving object determination information storage unit 144. The information stored in the moving object determination information storage unit 144 includes, for example, a moving object ID, a link ID, time information, and congestion determination result information related to the congestion determination result for each moving object by the determiner 133. The link ID is link identification information that identifies the link on which the moving object is located. The link ID may be generated, for example, on the congestion determination system 100 or the information processing system 110, or may be included in the input movement information stored in the input movement information storage unit 141. The link ID may be information determined based on GPS coordinate information, or may be information based on information acquired by the moving object or the terminal device 120 from equipment installed on the road.

[0053] The tally information generating unit 150 tally the congestion determination results for each moving object by the determiner 133 for each link based on the moving object determination information stored in the moving object determination information storage unit 144, and stores the tally information in the tally information storage unit 151. For example, the tally information generating unit 150 may tally the number of moving objects corresponding to each congestion determination result, or may tally the proportion of the number of moving objects corresponding to each congestion determination result to the total number of moving objects. Furthermore, the tally information generating unit 150 may classify the congestion determination results into multiple categories and then tally the results.

[0054] Fig. 6 is a diagram showing an example of information stored in the tally information storage unit 151. The information stored in the tally information storage unit 151 includes, for example, a link ID, time information, and judgment result tally information. In Fig. 6, the judgment result tally information indicates the number of moving objects that exist within the same link and that have been given each congestion judgment result, but the tallying method is not limited to this.

[0055] The link determination unit 152 determines the congestion status of each link based on the aggregated information stored in the aggregated information storage unit 151, and stores the determined status in the link determination information storage unit 154. The link determination unit 152 determines the congestion status of each link based on, for example, the determination result aggregated information for each link stored in the aggregated information storage unit 151.

[0056] The conditions for determining the congestion status of each link can be, for example, (a) "complete congestion" when the number of "congested" or "slightly congested" mobile units is 1 or more and the number of "free" mobile units is 0; (b) "partial congestion" when the number of "congested" or "slightly congested" mobile units is 1 or more and the number of "free" mobile units is also 1 or more; and (c) "free" when the number of "congested" or "slightly congested" mobile units is 0 and the number of "free" mobile units is 1 or more. Here, "complete congestion" refers to a state in which all lanes are congested, "partial congestion" refers to a state in which only a portion of the link is congested, such as a state in which congested and non-congested lanes are mixed or a state in which only a specific section within the link is congested, and "free" refers to a state in which no lanes or sections are congested. However, the indicators for the congestion determination results for each link are not limited to these. Furthermore, the indicators for the congestion determination results for each link may be qualitative indicators such as "A," "B," or "C," or quantitative indicators such as percentages and the number of mobile units.

[0057] The conditions for determining the congestion status of each link may be a condition based on the number of moving objects other than one or more than one, or a combination of multiple conditions. The conditions for determining the congestion status of each link may also be a condition based on the number of moving objects on links other than the link in question, such as links adjacent to the link in question. The conditions for determining the congestion status of each link may also be a condition based on the ratio of the number of moving objects in each congestion determination result to the total number of moving objects.

[0058] Based on the congestion determination result by the link determination unit 152, the link determination information generation unit 153 generates link determination information indicating information that associates the congestion determination result by the link determination unit 152 with link identification information that identifies the link, and stores the information in the link determination information storage unit 154.

[0059] 7 is a diagram showing an example of information stored in the link determination information storage unit 154. The information stored in the link determination information storage unit 154 includes, for example, a link ID, time information, and congestion determination result information related to the congestion determination result by the link determination unit 152.

[0060] The judgment information providing unit 160 provides at least one of the moving body judgment information stored in the moving body judgment information storage unit 144 and the link judgment information stored in the link judgment information storage unit 154 to the information processing system 110 as reference information when analyzing traffic conditions.

[0061] 8 is a flowchart showing an example of machine learning processing in the traffic congestion determination system 100. First, the learning information acquisition unit 130 acquires learning information from the information processing system 110 and stores it in the learning information storage unit 131 (S801). Then, the machine learning execution unit 132 executes machine learning based on the learning information stored in the learning information storage unit 131, and generates a classifier 133 (S802).

[0062] 9 is a flowchart showing an example of a congestion determination process in the congestion determination system 100. First, the input movement information acquisition unit 140 acquires movement information of moving objects from the information processing system 110 or the terminal device 120, and stores it in the input movement information storage unit 141 (S901). Next, the moving object determination unit 142 inputs the input movement information stored in the input movement information storage unit 141 to the determiner 133, determines the congestion status of each moving object, and stores it in the moving object determination information storage unit 144 (S902).

[0063] The aggregated information generating unit 150 aggregates the moving object determination information stored in the moving object determination information storage unit 144 for each link and stores the aggregated information in the aggregated information storage unit 151 (S903). The link determination unit 152 determines the congestion status of each link based on the aggregated information stored in the aggregated information storage unit 151 (S904). Then, at least one of the moving object determination information and the link determination information is provided to the information processing system 110 as reference information for analyzing the traffic status (S905).

[0064] The above describes one embodiment of the present invention. The traffic congestion determination system 100 can determine the traffic congestion status of at least one mobile object based on a determiner that determines the traffic congestion status of each mobile object, which is constructed by machine learning based on learning information, and input movement information that indicates the movement information of at least one mobile object. This makes it possible to determine the traffic congestion status of each mobile object on more roads with high accuracy.

[0065] Furthermore, the congestion determination system 100 can determine the congestion status of each link and the congestion status of each part of each link based on the congestion status of each mobile object. This makes it possible to grasp the congestion status of a link, a situation in which congested and non-congested lanes are mixed within a link, and a situation in which only a specific section within a link is congested.

[0066] Furthermore, the congestion determination system 100 can provide the congestion determination result for each mobile body and each link to the information processing system 110. This allows the information processing system 110 to utilize the determination result by the congestion determination system 100 for analyzing traffic conditions or supporting the travel of mobile bodies.

[0067] Furthermore, the results of the congestion status determination for each mobile object by the congestion determination system 100 are not limited to being used to determine the congestion status for each link. For example, they can also be used to identify mobile objects to which a service can be provided when providing a unique service to mobile objects in a traffic jam, or to quantify the likelihood of congestion on a road by analyzing the relationship between the movement of congested vehicles and the shape of the road.

[0068] It should be noted that the present embodiment is provided to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention. [Explanation of symbols]

[0069] 100 congestion judgment system, 110 information processing system, 120 terminal device, 130 learning information acquisition unit, 131 learning information storage unit, 132 machine learning execution unit, 133 judger, 140 input movement information acquisition unit, 141 input movement information storage unit, 142 moving body judgment unit, 143 moving body judgment information generation unit, 144 moving body judgment information storage unit, 150 aggregate information generation unit, 151 aggregate information storage unit, 152 link judgment unit, 153 link judgment information generation unit, 154 link judgment information storage unit, 160 judgment information provision unit

Claims

1. a determiner for determining the congestion status of each of the mobile bodies, the determiner being constructed by machine learning based on learning information including learning movement information indicating movement information including speed information indicating the speed of each of the multiple mobile bodies and acceleration information indicating the acceleration, and learning congestion status information indicating the congestion status of the corresponding mobile body; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving object; a moving body determination unit that inputs the input movement information to the determiner and determines a traffic congestion state of each of the at least one moving body; a summary information generating unit that generates summary information by correlating the congestion determination result of the at least one mobile object determined by the mobile object determining unit with link identification information that identifies a link on which the at least one mobile object is located; a link determination unit that determines a congestion state of each of at least one of the links based on the aggregated information, the link determination unit determines a congestion state of each of the at least one links based on a congestion determination result of each mobile body corresponding to the link identification information in the aggregation information; The congestion state includes a partial congestion state in which congested lanes and non-congested lanes are mixed. Traffic congestion detection system.

2. a determiner for determining the congestion status of each of the mobile bodies, the determiner being constructed by machine learning based on learning movement information indicating movement information including speed information indicating the speed of each of the multiple mobile bodies and GPS coordinate information indicating the GPS coordinates measured by a GPS system, learning congestion status information indicating the congestion status of the corresponding mobile body, and a correspondence relationship associating congestion occurrence frequency with the GPS coordinates; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving object; a moving body determination unit that inputs the input movement information to the determiner and determines a traffic congestion state of each of the at least one moving body; a summary information generating unit that generates summary information by correlating the congestion determination result of the at least one mobile object determined by the mobile object determining unit with link identification information that identifies a link on which the at least one mobile object is located; a link determination unit that determines a congestion state of each of at least one of the links based on the aggregated information, the link determination unit determines a congestion state of each of the at least one links based on a congestion determination result of each mobile body corresponding to the link identification information in the aggregation information; The congestion state includes a partial congestion state in which congested lanes and non-congested lanes are mixed. Traffic congestion detection system.

3. A determiner for determining the congestion status of each of a plurality of moving bodies, constructed by machine learning based on learning movement information indicating movement information including speed information indicating the speed of each of a plurality of moving bodies and GPS coordinate information indicating GPS coordinates measured by a GPS system, and learning congestion status information indicating the congestion status of the corresponding moving bodies, as well as a correspondence relationship that associates the frequency of congestion occurrence with GPS coordinates; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving object; a moving body determination unit that inputs the input movement information to the determiner and determines a congestion state of each of the at least one moving body, the learning movement information includes at least the speed information and the GPS coordinate information of each of the plurality of moving bodies; the determiner is constructed by machine learning using, as features, at least the speed information, the GPS coordinate information, and information calculated based on a correspondence relationship between a congestion occurrence frequency and a GPS coordinate; the input movement information further includes GPS coordinate information indicating a GPS coordinate measured by a GPS system; the determiner is constructed by machine learning using, as at least a feature, information calculated based on a discrepancy between GPS movement distance information indicating a movement distance of the moving body on GPS coordinates, which is calculated based on the GPS coordinate information of each of the plurality of moving bodies, and reference movement distance information indicating a movement distance of the moving body, which is calculated based on the learning movement information excluding the GPS coordinate information of each of the plurality of moving bodies; Traffic congestion detection system.

4. The congestion determination system according to claim 1 , wherein the link determination unit determines congestion conditions of a plurality of specific portions included in each of at least one of the links based on the aggregate information.

5. the learning movement information includes at least the speed information and the acceleration information of each of the plurality of moving bodies; the determiner is constructed by machine learning using information calculated based on the speed information and the acceleration information as a feature, the input movement information includes at least the velocity information and the acceleration information of each of the at least one moving object; The congestion determination system according to claim 1 .

6. The congestion determination system of claim 1 or 2, further comprising a determination information providing unit that provides the congestion status of at least one mobile body determined by the mobile body determination unit to an information processing system as reference information when analyzing traffic conditions.

7. The congestion determination system of claim 1 or 2 further comprises a determination information providing unit that provides at least one of the congestion status of the at least one mobile body determined by the mobile body determination unit and the congestion status of the at least one link determined by the link determination unit to an information processing system as reference information when analyzing traffic conditions.

8. a computer including a determiner for determining a congestion status of each of a plurality of moving bodies, the determiner being constructed by machine learning based on learning information including learning movement information indicating movement information including speed information indicating the speed of each of a plurality of moving bodies and acceleration information indicating the acceleration, and learning congestion status information indicating the congestion status of the corresponding moving body; acquiring input movement information indicating the movement information of each of at least one moving object; inputting the input movement information to the determiner to determine a congestion state of each of the at least one moving body; aggregating the congestion determination results of the at least one mobile object in association with link identification information identifying the link on which the at least one mobile object is located, thereby generating aggregate information; determining a congestion state of each of the at least one links based on the aggregated information; determining a congestion state of each of at least one of the links based on a congestion determination result of each mobile body corresponding to the link identification information in the aggregate information; The congestion state includes a partial congestion state in which congested lanes and non-congested lanes are mixed. Method for determining congestion.

9. On the computer, a determiner for determining the congestion status of each of the mobile bodies, the determiner being constructed by machine learning based on learning information including learning movement information indicating movement information including speed information indicating the speed of each of the multiple mobile bodies and acceleration information indicating the acceleration, and learning congestion status information indicating the congestion status of the corresponding mobile body; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving object; a moving body determination unit that inputs the input movement information to the determiner and determines a traffic congestion state of each of the at least one moving body; a summary information generating unit that generates summary information by correlating the congestion determination result of the at least one mobile object determined by the mobile object determining unit with link identification information that identifies a link on which the at least one mobile object is located; a link determination unit that determines a congestion state of each of at least one of the links based on the aggregated information, the link determination unit determines a congestion state of each of the at least one links based on a congestion determination result of each mobile body corresponding to the link identification information in the aggregation information; The congestion state includes a partial congestion state in which congested lanes and non-congested lanes are mixed. Congestion detection program.

10. a computer including a determiner for determining the congestion status of each of a plurality of mobile bodies, the determiner being constructed by machine learning based on learning movement information indicating movement information including speed information indicating the speed of each of a plurality of mobile bodies and GPS coordinate information indicating GPS coordinates measured by a GPS system, learning congestion status information indicating the congestion status of the corresponding mobile bodies, and a correspondence relationship associating congestion occurrence frequency with the GPS coordinates; acquiring input movement information indicating the movement information of each of at least one moving object; inputting the input movement information to the determiner to determine a congestion state of each of the at least one moving body; aggregating the congestion determination results of the at least one mobile object in association with link identification information identifying the link on which the at least one mobile object is located, thereby generating aggregate information; determining a congestion state of each of the at least one links based on the aggregated information; determining a congestion state of each of at least one of the links based on a congestion determination result of each mobile body corresponding to the link identification information in the aggregate information; The congestion state includes a partial congestion state in which congested lanes and non-congested lanes are mixed. Method for determining congestion.

11. On the computer, a determiner for determining the congestion status of each of the mobile bodies constructed by machine learning based on: learning movement information indicating movement information including speed information indicating the speed of each of the multiple mobile bodies and GPS coordinate information indicating GPS coordinates measured by a GPS system; learning information including congestion status information indicating the congestion status of the corresponding mobile body; and a correspondence relationship correlating congestion occurrence frequency with the GPS coordinates; an input movement information acquisition unit that acquires input movement information indicating the movement information of each of at least one moving object; a moving body determination unit that inputs the input movement information to the determiner and determines a traffic congestion state of each of the at least one moving body; a summary information generating unit that generates summary information by correlating the congestion determination result of the at least one mobile object determined by the mobile object determining unit with link identification information that identifies a link on which the at least one mobile object is located; a link determination unit that determines a congestion state of each of at least one of the links based on the aggregated information, the link determination unit determines a congestion state of each of the at least one links based on a congestion determination result of each mobile body corresponding to the link identification information in the aggregation information; The congestion state includes a partial congestion state in which congested lanes and non-congested lanes are mixed. Congestion detection program.

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