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WO2018138803A1 - Dispositif de prédiction d'encombrement et procédé de prédiction d'encombrement - Google Patents

Dispositif de prédiction d'encombrement et procédé de prédiction d'encombrement Download PDF

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Publication number
WO2018138803A1
WO2018138803A1 PCT/JP2017/002539 JP2017002539W WO2018138803A1 WO 2018138803 A1 WO2018138803 A1 WO 2018138803A1 JP 2017002539 W JP2017002539 W JP 2017002539W WO 2018138803 A1 WO2018138803 A1 WO 2018138803A1
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WIPO (PCT)
Prior art keywords
group
characteristic information
person
congestion
congestion prediction
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PCT/JP2017/002539
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English (en)
Japanese (ja)
Inventor
幸成 松田
惇矢 宮城
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Mitsubishi Electric Corp
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Mitsubishi Electric Corp
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Priority to JP2018542308A priority Critical patent/JP6415795B1/ja
Priority to PCT/JP2017/002539 priority patent/WO2018138803A1/fr
Priority to TW106111342A priority patent/TWI632532B/zh
Publication of WO2018138803A1 publication Critical patent/WO2018138803A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Definitions

  • the present invention relates to an apparatus for predicting congestion caused by people's behavior when a disaster occurs or an event that is an event is held.
  • Patent Document 1 discloses an evacuation behavior for a certain refugee that calculates the position information of the refugee for each time step based on spatial data and human data, and simulates how people move with time. A prediction system is described. At that time, the evacuation behavior prediction system also integrates evacuees into groups according to conditions and simulates how people move in groups.
  • the present invention has been made to solve the above-described problems, and is capable of performing congestion prediction calculation after defining the calculation target so that the calculation is close to real people's behavior.
  • the purpose is to obtain a prediction device.
  • the congestion prediction device uses a first characteristic information generation unit that generates person movement characteristic information indicating a movement state of each person, and the person movement characteristic information to determine the proximity of the position and the similarity of the action. And a second characteristic for generating group movement characteristic information indicating the movement of the group for each group formed by the grouping of the correlation analysis unit, and a correlation analysis unit for grouping persons for each group sharing the action.
  • people are divided into groups, and the calculation of congestion prediction is performed for the group. Therefore, the calculation target is defined so that the calculation is close to the behavior of real people, and the congestion prediction is performed. Can perform calculations.
  • FIG. 13A and FIG. 13B are diagrams for explaining the occupied area of the group defined by the information correcting unit.
  • FIG. 1 is a configuration diagram showing a congestion prediction system including a congestion prediction device 1 according to Embodiment 1 of the present invention.
  • the congestion prediction device 1 when an event that attracts a large number of people is held, the congestion prediction device 1 is in a travel route from a place where people visit such as a public transportation such as a railway station or a bus stop or a parking lot to an event venue. It predicts the congestion situation. That is, the movement route becomes a congestion prediction target area.
  • the position where the sensor 2 is installed on the movement path from the place where the occurrence of the event to the place where people arrive such as an event venue is called a measurement point.
  • the sensor 2 includes a camera, for example, and performs image processing on a video imaged by the camera, so that the number of people passing the measurement point in the forward direction or the backward direction, the moving speed of each person, the moving direction of each person, The position and the attributes of each person are detected and output to the congestion prediction device 1 as time series data.
  • FIG. 2 is a diagram illustrating an installation example of the sensor 2.
  • the sensor 2 is installed in the vicinity of a place where people occur, for example.
  • the display device 3 displays the congestion prediction result output from the congestion prediction device 1.
  • the display device 3 is a liquid crystal display, for example.
  • the congestion prediction device 1 uses the measurement data output from the sensor 2 to predict the congestion status of the movement route, and outputs the congestion prediction result to the display device 3 for display on the display device 3.
  • the congestion prediction device 1 includes an information extraction unit 10, a correlation analysis unit 11, an information correction unit 12, a congestion prediction unit 13, a congestion degree analysis unit 14, and a storage unit 15.
  • the information extraction unit 10 uses the measurement data output from the sensor 2 to generate person movement characteristic information indicating the movement of each person.
  • the person movement characteristic information is output to the correlation analysis unit 11.
  • the correlation analysis unit 11 generates group information obtained by grouping persons using the person movement characteristic information.
  • the group information is output to the information correction unit 12.
  • the information correction unit 12 generates group movement characteristic information indicating a movement state for each group, and corrects the generated group movement characteristic information according to the congestion degree calculated by the congestion degree analysis unit 14.
  • the group movement characteristic information is output to the congestion prediction unit 13.
  • the congestion prediction unit 13 calculates congestion prediction using the group movement characteristic information.
  • the congestion prediction result is output to the display device 3 and the congestion degree analysis unit 14.
  • the congestion level analysis unit 14 analyzes the congestion prediction result, calculates the congestion level of the congestion prediction target area, and outputs the calculated congestion level to the information correction unit 12.
  • the storage unit 15 is used as an information storage area in the course of processing performed by each unit included in the congestion prediction device 1.
  • the congestion prediction device 1 includes a processor 101, a memory 102, a data storage storage 103, an input interface 104, an output interface 105, and the like.
  • the input interface 104 is an interface for taking measurement data from the sensor 2.
  • the output interface 105 is an interface for outputting the calculated congestion prediction result to the display device 3.
  • the data storage 103 functions as the storage unit 15.
  • the congestion prediction device 1 includes a processing circuit for executing the steps shown in the flowcharts of FIGS. 4, 5, 6, 9, and 10 described later.
  • the processing circuit is a processor 101 that executes a program stored in the memory 102.
  • the processor 101 is also called a processing device, an arithmetic device, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a microprocessor, a microcomputer, or a DSP (Digital Signal Processor).
  • Each function of the information extraction unit 10, the correlation analysis unit 11, the information correction unit 12, the congestion prediction unit 13, and the congestion degree analysis unit 14 constituting the congestion prediction device 1 is based on software, firmware, or a combination of software and firmware. Realized. Software or firmware is described as a program and stored in the memory 102. The processor 101 reads out and executes the program stored in the memory 102, thereby realizing the function of each unit. That is, when the congestion prediction device 1 is executed by the processor 101, a program in which each step shown in the flowcharts of FIGS. 4, 5, 6, 9, and 10 described later is executed as a result. Is provided. In addition, it can be said that these programs cause the computer to execute the procedure or method of each unit constituting the congestion prediction device 1.
  • the memory 102 and the data storage 103 are data holding means required in accordance with execution of software processed by the processor 101.
  • the data holding means is, for example, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Programmable EPROM), Flash Memory, SSD (Solid State Semiconductor) and the like.
  • a magnetic disk such as an HDD (Hard DiskHDrive) may be used.
  • the data holding means includes CD (Compact Disc), DVD (Digital Versatile Disc), and BD (Blu-ray Disc / Blu-ray are registered trademarks). It may be an optical disk or a magneto-optical disk such as an MO disk (Magneto Optical Disc).
  • the sensor 2 outputs measurement data to the congestion prediction device 1 at an appropriate timing.
  • FIG. 4 is a flowchart showing processing performed by the information extraction unit 10.
  • the information extraction unit 10 takes in the measurement data output from the sensor 2 (step ST100).
  • the information extraction unit 10 extracts person information for one person from the measurement data (step ST110).
  • the person information includes information such as a moving speed, a moving direction, a position, and an attribute regarding one person.
  • the measurement data output from the sensor 2 may be in various formats depending on the specifications and settings of the sensor 2. For example, it is conceivable that information not related to the person information is included in the measurement data. For this reason, the information extraction unit 10 performs the process of extracting from the measurement data only in each piece of information constituting the person information such as the movement speed, the movement direction, the position, and the attribute in step ST110.
  • the information extraction unit 10 assigns a person number to the person information for one person extracted in step ST110, and generates person movement characteristic information including the person information and the person number (step ST120). .
  • the person movement characteristic information indicates how the person corresponding to the person number in the person movement characteristic information moves.
  • the information extraction unit 10 functions as a first characteristic information generation unit.
  • the person movement characteristic information is stored in the storage unit 15. Different person numbers are assigned to the person information of different persons.
  • the information extraction unit 10 determines whether or not person movement characteristic information has been generated for all persons whose movement speeds and the like are indicated in the measurement data captured in step ST100 (step ST130). When there is a person who has not generated the person movement characteristic information (step ST130; No), the processing of the information extraction unit 10 returns to step ST110. At this time, the information extraction unit 10 performs a process of extracting person information for one person who has not generated person movement characteristic information as the process of step ST110.
  • step ST130 when the information extraction unit 10 has generated the person movement characteristic information for all the persons (step ST130; Yes), the information extraction unit 10 reads all the person movement characteristic information stored in the storage unit 15 and correlates the correlation analysis unit. 11 and the process using the measurement data captured in step ST100 is terminated. In addition, when the next measurement data is output from the sensor 2, the information extraction part 10 starts a process again from step ST100.
  • FIG. 5 is a flowchart showing processing performed by the correlation analysis unit 11.
  • the correlation analysis unit 11 takes out the person movement characteristic information for one person from all the person movement characteristic information notified from the information extraction unit 10 as an evaluation target (step ST200). Subsequently, the correlation analysis unit 11 determines whether there is another person who behaves similar to the person in the vicinity of the person corresponding to the person movement characteristic information to be evaluated (step ST210).
  • the correlation analysis unit 11 uses, for example, the movement speed and movement direction of the person indicated in the person movement characteristic information, and the movement speed between the person corresponding to the person movement characteristic information to be evaluated and a person other than the person. If the difference in the moving direction is within the similarity threshold, it is determined that the persons are performing similar actions.
  • the proximity threshold is set to a value that is equal to or less than the distance between persons who can be taken by people in the group who act by forming one group.
  • the similarity threshold is set to a value equal to or less than the difference in moving speed and moving direction that people in the group who act as one group can take.
  • the correlation analysis unit 11 moves the person to be evaluated.
  • a new group number is assigned to the characteristic information (step ST220).
  • the correlation analysis unit 11 generates group information including the person movement characteristic information to be evaluated and the assigned group number, and stores the group information in the storage unit 15 (step ST225).
  • the correlation analysis unit 11 It is determined whether a group number has already been assigned to the person movement characteristic information (step ST230). This can be determined by the correlation analysis unit 11 examining whether the person number assigned to the other person is included in any group information stored in the storage unit 15.
  • the correlation analysis unit 11 determines that the group number has not yet been assigned (step ST230; No)
  • the correlation analysis unit 11 performs other actions that are similar to those of the person movement characteristic information to be evaluated and the step ST210.
  • the same new group number is assigned to the person movement characteristic information of the person determined as a person (step ST240).
  • the correlation analysis unit 11 includes the group movement information including the person movement characteristic information to be evaluated, the person movement characteristic information of the person determined as another person who performs a similar action in step ST210, and the assigned group number. Is stored in the storage unit 15 (step ST245).
  • the correlation analysis unit 11 determines that the group number has already been assigned (step ST230; Yes)
  • the correlation analysis unit 11 uses the assigned group number as the evaluation target person movement characteristic information. (Step ST250). Subsequently, the correlation analysis unit 11 adds the person movement characteristic information to be evaluated to the group information of the assigned group number, updates the group information, and stores it in the storage unit 15 (step ST255). In this way, the correlation analysis unit 11 groups persons for each group that acts together based on the proximity of the positions of the persons and the similarity of the actions of the persons.
  • the correlation analysis unit 11 determines whether all the person movement characteristic information notified from the information extraction unit 10 has been evaluated (step ST260). When the correlation analysis unit 11 performs the process of extracting the person movement characteristic information as the evaluation target in step ST200 for all the notified person movement characteristic information, all the person movement characteristic information is evaluated. Become.
  • step ST260 If the correlation analysis unit 11 has not evaluated all the person movement characteristic information (step ST260; No), the process of the correlation analysis unit 11 returns to step ST200. At this time, the correlation analysis unit 11 performs a process of extracting person movement characteristic information for one person from the unevaluated person movement characteristic information as an evaluation target as the process of step ST200.
  • step ST260 when the correlation analysis unit 11 evaluates all the person movement characteristic information (step ST260; Yes), the correlation analysis unit 11 reads all the group information stored in the storage unit 15 and notifies the information correction unit 12 of the group information. The process using the person movement characteristic information notified in step ST200 is terminated. In addition, when the next person movement characteristic information is notified from the information extraction part 10, the correlation analysis part 11 starts a process again from step ST200.
  • FIG. 6 is a flowchart showing processing performed by the information correction unit 12.
  • amendment part 12 takes out the group information for 1 group as evaluation object among all the group information notified from the correlation analysis part 11 (step ST300).
  • the information correcting unit 12 generates group movement characteristic information indicating the movement of the group using the person movement characteristic information included in the group information that is the evaluation target (step ST310).
  • the group movement characteristic information includes information such as the occupied area of the group and the moving speed of the group.
  • the group movement characteristic information may include both person movement characteristic information corresponding to persons belonging to the group.
  • the information correction unit 12 determines whether the congestion level is notified from the congestion level analysis unit 14 (step ST320). When the congestion degree is not notified (step ST320; No), the information correction unit 12 assumes that no congestion has occurred at a location where there is a group corresponding to the group information to be evaluated, and the group movement characteristic information Is corrected (step ST330). In this case, the same correction as that performed when the degree of congestion described with reference to FIG. 8B described later is as small as the first congestion threshold is performed.
  • the information correction unit 12 corrects the group movement characteristic information according to the congestion degree of the location where the group corresponding to the group information to be evaluated exists. (Step ST340).
  • FIG. 7 is a diagram illustrating how people move. The process of generating and correcting group movement characteristic information by the information correction unit 12 will be described with reference to this figure.
  • FIG. 7 when people move, unrelated others move at a certain distance so as not to intimidate each other and to give a sense of incongruity to each other.
  • persons having a relationship such as family or friends are acting in a group, the persons move within a certain distance so as not to be separated from each other. Therefore, in the calculation of congestion prediction, instead of distinguishing one person from each other as a processing unit, a group consisting of people who act together is used as a processing unit, thereby reducing the amount of calculation and reducing the amount of calculation. Can be reduced.
  • the information correction unit 12 defines the occupation area of the group and defines the moving speed of the group. For example, the information correction unit 12 sets the occupied area of the person using the person movement characteristic information included in the group information. For example, the information correction unit 12 sets an area centered on the position indicated by the person movement characteristic information as the occupied area of the person corresponding to the person movement characteristic information.
  • the size and shape of the region are set in advance in consideration of a standard human physique.
  • the information correction unit 12 sets the size of the occupied area of the person corresponding to the person movement characteristic information to half or two thirds of the preset value. It may be set. Then, the information correction unit 12 defines an area including the occupied area of all the persons belonging to one group as the occupied area of the group, and includes it in the group movement characteristic information. Further, for example, the information correction unit 12 defines the movement speed of the person with the slowest movement speed in the group as the movement speed of the group and includes it in the group movement characteristic information.
  • the movement speed of the person in the group may be specified by the information correction unit 12 using the person movement characteristic information included in the group information of the group.
  • amendment part 12 may define the average value of the moving speed of the person in a group as a moving speed of the said group. Then, the information correction unit 12 corrects the occupation area of the group and the group movement speed defined as the group movement characteristic information in this way according to the congestion level calculated by the congestion level analysis unit 14. For example, the information correction unit 12 corrects so that the occupied area of the group is reduced as the degree of congestion is higher. Further, the information correction unit 12 corrects the moving speed of the group to be slower as the degree of congestion is higher.
  • FIG. 8A to FIG. 8D are image diagrams for explaining processing performed by the information correction unit 12 when the congestion prediction unit 13 performs calculation using the cellular automaton method.
  • the congestion prediction unit 13 performs a congestion prediction simulation by the cellular automaton method
  • the congestion prediction unit 13 performs a calculation based on a region arranged in a lattice shape called a cell.
  • correction of group movement characteristic information in accordance with the degree of congestion is performed by controlling the calculation target in units of cells arranged in two orthogonal directions, ie, the horizontal direction and the vertical direction on the paper surface, as shown in FIGS. It will be.
  • FIG. 8A is an example where each person is acting alone.
  • the information correcting unit 12 assigns one cell to each person, that is, each group as an occupied area.
  • the information correction unit 12 acts so that each person acts without cooperating with others and only one person belongs to one group.
  • the information correction unit 12 When a plurality of people belong to one group, the information correction unit 12 first assigns an arbitrary shape combining cells as many as the number of people belonging to the group as the occupied area of the group. Then, as will be described below with reference to FIGS. 8B to 8D, the information correction unit 12 corrects the shape according to the degree of congestion.
  • FIG. 8B is an example of a case where four people are acting together as a group when the degree of congestion is as small as the first congestion threshold or less. In this case, the information correction unit 12 assigns four cells connected to one row in the horizontal direction as the occupied area to the group. In this way, the information correction unit 12 treats four people as acting in a single horizontal row in the same movement direction.
  • the occupied area of the group has a shape in which cells are arranged along a horizontal direction close to a direction orthogonal to the moving direction of the group among two directions of the horizontal direction and the vertical direction on the paper.
  • FIG. 8C is an example of a case where four people are acting together as a group when the degree of congestion is medium than the first congestion threshold but smaller than the second congestion threshold.
  • the information correction unit 12 assigns, to the group, cells connected in 2 vertical columns ⁇ 2 horizontal columns as the occupied area. In this way, the information correction unit 12 treats four people as acting together in the same movement direction.
  • FIG. 8D is an example of a case where four people are acting together as a group when the degree of congestion is as large as the second congestion threshold or more.
  • the information correction unit 12 assigns four cells connected to one column in the vertical direction of the drawing as the occupied area. In this way, the information correction unit 12 treats four people as acting in one vertical row in the same movement direction.
  • the occupied area of the group has a shape in which cells are arranged along the vertical direction close to the moving direction of the group among the two directions of the horizontal direction and the vertical direction on the paper.
  • FIGS. 8A to 8D the case of four people has been described as an example, but the same applies to other people.
  • the information correction unit 12 corrects the size, shape, and the like of the occupied area of the group according to the degree of congestion.
  • the information correction unit 12 determines whether all group information notified from the correlation analysis unit 11 has been evaluated (step ST350). When the information correcting unit 12 performs the process of extracting the group information as an evaluation target in step ST300 for all the notified group information, all the group information is evaluated.
  • step ST350 If the information correction unit 12 has not evaluated all group information (step ST350; No), the processing of the information correction unit 12 returns to step ST300. At this time, the information correction
  • step ST350 when the information correction unit 12 evaluates all the group information (step ST350; Yes), the information correction unit 12 notifies the congestion prediction unit 13 of the generated and corrected group movement characteristic information, and is notified in step ST300. The processing using the group information is terminated. In addition, when the next group information is notified from the correlation analysis part 11, the information correction
  • FIG. 9 is a flowchart illustrating processing performed by the congestion prediction unit 13.
  • the congestion prediction unit 13 uses the group movement characteristic information notified from the information correction unit 12 to perform a prediction simulation of the movement status of each group. Then, the congestion prediction unit 13 sets one of the groups notified of the group movement characteristic information from the information correction unit 12 as an evaluation target, and searches for a group that has entered the congestion prediction target region in the simulation result (step ST400). ).
  • the congestion prediction unit 13 determines whether there is a group that corresponds to the group set as the evaluation target as a result of the search and that has entered the congestion prediction target region in the simulation result (step ST410). .
  • the congestion prediction unit 13 adds the group movement characteristic information of the group as input data to the congestion prediction process (step ST420).
  • the process of the congestion prediction unit 13 proceeds to step ST430 described later.
  • the congestion prediction unit 13 determines whether all group movement characteristic information notified from the information correction unit 12 has been evaluated (step ST430). When the congestion prediction unit 13 performs the process of setting the evaluation target in step ST400 for all the notified group movement characteristic information, all the group movement characteristic information is evaluated. When the congestion prediction unit 13 has not evaluated all the group movement characteristic information (step ST430; No), the processing of the congestion prediction unit 13 returns to step ST400. At this time, the congestion prediction unit 13 sets the group movement characteristic information for one group of the unevaluated group movement characteristic information as an evaluation target as the process of step ST400.
  • the congestion prediction unit 13 executes a congestion prediction process (step ST440).
  • the congestion prediction unit 13 uses a group force characteristic information that has been input to the congestion prediction process by the processes of steps ST400 to ST430, and applies a multi-agent using a social force model or a cellular automaton method.
  • the prediction process is executed by a known calculation method such as simulation.
  • the congestion prediction unit 13 calculates the movement of people in the congestion prediction target area, that is, the movement of each group in the first embodiment. In this way, the congestion prediction unit 13 calculates congestion prediction for the group. The result of this calculation is the congestion prediction result.
  • the congestion prediction unit 13 sets one of the groups in which the group movement characteristic information is used for input to the congestion prediction process as an evaluation target, and as a result of the congestion prediction process in step ST440, the congestion prediction target area The group that came out of is searched (step ST450).
  • the congestion prediction unit 13 determines whether there is a group that corresponds to the group set as the evaluation target in step ST450 and that is out of the congestion prediction target region (step ST460).
  • the congestion prediction unit 13 excludes the group movement characteristic information of the group from the input data to the congestion prediction process (step ST470).
  • the process of the congestion prediction unit 13 proceeds to step ST480 described later.
  • the congestion prediction unit 13 determines whether or not all group movement characteristic information used for the input data in the congestion prediction process of Step ST440 has been evaluated (Step ST480).
  • the congestion prediction unit 13 performs the process set as the evaluation target in step ST450 for all the group movement characteristic information used for the input data in the congestion prediction process, all the group movement characteristic information is evaluated. Will be.
  • the congestion prediction unit 13 has not evaluated all the group movement characteristic information (step ST480; No)
  • the process of the congestion prediction unit 13 returns to step ST450. At this time, the congestion prediction unit 13 sets unevaluated group movement characteristic information as an evaluation target as the process of step ST450.
  • step ST480 when the congestion prediction unit 13 evaluates all the group movement characteristic information (step ST480; Yes), the congestion prediction unit 13 notifies the congestion prediction result to the congestion degree analysis unit 14 and the display device 3, and in step ST400. The process using the notified group movement characteristic information is terminated. As a result, an image showing the congestion prediction result is displayed on the display device 3.
  • the congestion prediction unit 13 starts the process again from step ST400.
  • FIG. 10 is a flowchart showing processing performed by the congestion degree analysis unit 14.
  • the congestion degree analysis unit 14 divides the congestion prediction result notified from the congestion prediction unit 13 in units of evaluation regions of the congestion prediction target region (step ST500).
  • FIG. 11 is an image diagram showing processing of step ST500. As shown by a dotted line in FIG. 11, the congestion degree analysis unit 14 divides the congestion prediction target area into a plurality of evaluation areas. For example, the congestion degree analysis unit 14 mainly divides the congestion prediction target area at a portion where the movement of a person such as an intersection or a corner changes.
  • the congestion degree analysis unit 14 counts the number of groups existing in each evaluation area, and calculates the congestion degree for each evaluation area (step ST510). For example, the congestion degree analysis unit 14 treats the number of groups per unit area as the congestion degree of the evaluation region. Alternatively, the congestion degree analysis unit 14 may treat the ratio of the total area of the group occupation area to the area of the evaluation area as the congestion degree.
  • the congestion level analysis unit 14 notifies the information correction unit 12 of the calculated congestion level, and ends the process using the congestion prediction result notified in step ST500. When the next congestion prediction result is notified from the congestion prediction unit 13, the congestion degree analysis unit 14 starts the process again from step ST500.
  • the congestion prediction device 1 is applied at the time of an event where a large number of people gather.
  • the congestion prediction device 1 can be applied to other scenes.
  • the congestion prediction device 1 can be applied to predict congestion at the time of a disaster.
  • the information correction unit 12 generates group movement characteristic information in step ST310 and further corrects the group movement characteristic information in the subsequent processing.
  • the information correction unit 12 in order to calculate the congestion prediction for the group, the information correction unit 12 only needs to function as a second characteristic information generation unit that generates at least group movement characteristic information. A function for correcting information may not be provided. In this case, the congestion level analysis unit 14 can be omitted.
  • the congestion prediction device 1 divides people into groups, and uses the group movement characteristic information indicating the movement of the group as input data to calculate congestion prediction for the group. Therefore, it is possible to calculate congestion prediction after defining the calculation target so that the calculation is close to the behavior of real people.
  • the information extraction unit 10 generates person movement characteristic information including movement speed obtained by image processing of the video, and the information correction unit 12 performs movement indicated by the person movement characteristic information corresponding to persons belonging to the same group.
  • the average speed value and the occupied area including the occupied area of the person belonging to the group are included in the group movement characteristic information of the group.
  • an apparatus that performs image processing can be used as the sensor 2.
  • a congestion degree analysis unit 14 that calculates the degree of congestion using the calculation result of the congestion prediction unit 13 and an information correction unit 12 that corrects the generated group movement characteristic information using the calculated degree of congestion are provided.
  • the congestion prediction unit 13 uses the group movement characteristic information corrected by the information correction unit 12. As a result, it becomes easy to predict congestion in accordance with congestion.
  • the information correction unit 12 performs correction to reduce the occupied area of the group as the degree of congestion increases. This facilitates congestion prediction in consideration of the size of the occupied area that the group can take according to congestion density.
  • the congestion prediction unit 13 performs a calculation using the cellular automaton method, and the information correction unit 12 assigns a shape combining cells as many as the number of persons belonging to the group as an occupation region of the group. Thereby, group movement characteristic information in a format suitable for performing congestion prediction using the cellular automaton method is created.
  • the information correction unit 12 has a shape in which cells are arranged along a direction close to a direction orthogonal to the group moving direction, among two orthogonal directions in which the cells are arranged , And the congestion degree is equal to or greater than the second congestion threshold value greater than the first congestion threshold value, the cell is moved along the direction close to the group moving direction among the two orthogonal directions in which the cells are arranged. It was decided to correct the arranged shape as an occupied area of the group. This facilitates congestion prediction in consideration of the shape of the occupied area that the group can take according to congestion density.
  • Embodiment 2 a case will be described in which a situation in which a person moves with a large baggage or the like is considered.
  • the configuration of the congestion prediction system including the congestion prediction device 1 and the congestion prediction device 1 according to the second embodiment is the same as that in FIG.
  • the components having the same or corresponding functions as those in the first embodiment are denoted by the same reference numerals, and the description thereof is omitted or simplified.
  • the congestion prediction device 1 according to the second embodiment will be described with a focus on differences from the first embodiment with reference to FIGS. 12 and 13.
  • FIG. 12 is a diagram illustrating a situation in which a person holding a luggage or the like moves.
  • the area occupied by a walking person in the space differs depending on whether the person has a large baggage such as a carry bag or not.
  • the moving speed of the person who walks is different between when carrying a large baggage such as a carry bag and when not carrying it.
  • a baggage having a size corresponding to a person such as a carry bag is hereinafter referred to as a large baggage. Therefore, the information extraction unit 10 generates person movement characteristic information by including information indicating that the person has a large baggage in the person movement characteristic information of the person having a large baggage. Whether or not a large baggage is held may be determined by image processing by the sensor 2 so as to be acquired as measurement data.
  • the information correction unit 12 defines the occupation area and the moving speed of the group in consideration of having a large baggage. Specifically, the information correction unit 12 defines the occupied area of the group to which the person belongs after making the occupied area double in the movement direction for the person having a large baggage. In addition, the information correction unit 12 defines the moving speed of the group to which the person belongs after adjusting the moving speed of the person who has a large baggage.
  • the same phenomenon as in the case of holding a large luggage as described above can occur for a person pushing a wheelchair, a wheelbarrow, a stroller or a carriage. Accordingly, wheelchairs, wheelbarrows, strollers, and carts may be handled in the same manner as the large luggage described above. That is, the information extraction unit 10 generates person movement characteristic information including information indicating that the wheelchair, the handcart, the stroller, or the carriage is pushed in the person movement characteristic information of the person pushing the wheelchair, the handcart, the stroller, or the carriage. To do. Further, the information correction unit 12 defines the occupation area and the moving speed of the group in consideration of pushing the wheelchair, the handcart, the stroller, or the carriage.
  • the congestion prediction unit 13 performs a congestion prediction simulation by the cellular automaton method
  • the information correction unit 12 allocates one cell even for a large package.
  • a large baggage possessed by a person is treated as acting adjacent to the person.
  • FIG. 13A and FIG. 13B are diagrams for explaining the occupied area of the group defined by the information correction unit 12 when the congestion prediction unit 13 performs a calculation using the cellular automaton method.
  • 13A and 13B show a case where one person forms one group.
  • FIG. 13A shows a case where the person does not have a large luggage.
  • the information correction unit 12 assigns one cell as the occupied area of the group to which the person belongs.
  • FIG. 13B shows a case where a person has a large luggage.
  • the information correction unit 12 assigns two cells including the luggage as the occupied area of the group to which the person belongs.
  • the congestion prediction device 1 according to the second embodiment performs the same processing as the congestion prediction device 1 according to the first embodiment, except for the effects of large luggage, wheelchairs, wheelbarrows, strollers, or carts.
  • the congestion prediction device 1 according to the second embodiment takes into account that a person has a large baggage and that the person's occupancy region is in consideration of pushing a wheelchair, a wheelbarrow, a stroller, or a carriage. As a result, the occupation area of the group to which the person belongs is defined. Therefore, in addition to the effects shown in the first embodiment, the congestion prediction device 1 according to the second embodiment is crowded by a person holding a large baggage, or a person pushing a wheelchair, a wheelbarrow, a stroller, or a carriage. Congestion prediction that reflects the impact on the
  • the information extraction unit 10 generates the person movement characteristic information in which the occupied area doubles in the movement direction for the person who has the luggage corresponding to the person. In this way, it is possible to consider a person who moves with a luggage of a size corresponding to the person.
  • the information extraction unit 10 generates person movement characteristic information in which the occupied area doubles in the movement direction for a person pushing a wheelchair, a wheelbarrow, a stroller, or a carriage. By doing in this way, the person who pushes and moves a wheelchair, a wheelbarrow, a stroller, or a cart can be considered.
  • the congestion prediction device can calculate the congestion prediction after defining the calculation target so that the calculation is close to the behavior of the actual people. It is suitable for predicting the congestion situation on the travel route to the event venue when holding a gathering event.
  • 1 Congestion prediction device 2 sensors, 3 display devices, 10 information extraction unit, 11 correlation analysis unit, 12 information correction unit, 13 congestion prediction unit, 14 congestion degree analysis unit, 15 storage unit, 101 processor, 102 memory, 103 data Storage storage, 104 input interface, 105 output interface.

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Abstract

Dans la présente invention, une unité d'extraction d'informations (10) utilise des données de mesure délivrées par un capteur (2) pour générer des informations de caractéristiques de déplacement de personne indiquant comment chaque personne se déplace. Une unité d'analyse de corrélation (11) génère des informations de groupe dans lesquelles des personnes sont réparties dans des groupes à l'aide des informations de caractéristiques de déplacement de personne. Une unité de correction d'informations (12) génère des informations de caractéristiques de déplacement de groupe indiquant comment chaque groupe se déplace. Une unité de prédiction d'encombrement (13) réalise une opération de prédiction d'encombrement à l'aide des informations de caractéristiques de déplacement de groupe.
PCT/JP2017/002539 2017-01-25 2017-01-25 Dispositif de prédiction d'encombrement et procédé de prédiction d'encombrement Ceased WO2018138803A1 (fr)

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