JP2018088108A - Information processing apparatus, information processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing apparatus capable of detecting abnormality that occurs at a spot where a device for detecting a mobile is not installed, even in a case where a mobile moving on a traffic road does not include a function for collecting/transmitting information.SOLUTION: An information processing apparatus 10 comprises: entering measurement means 11 for measuring an entering number of mobiles entering a target section including an end through which a mobile enters/leaves the target section, at an interval of an entering time; leaving prediction means 12 for predicting a leaving number of mobiles leaving the target section through the end at an interval of a leaving time on the basis of the entering number at the interval of the entering time, and calculating a prediction section for the leaving number at the interval of the leaving time; leaving measurement means 13 for measuring the leaving number of mobiles leaving the target section through the end at the interval of the leaving time; and abnormality discrimination means 14 for discriminating the presence/absence of abnormality in the target section in accordance with whether the leaving number at the interval of the leaving time is included in the prediction section.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus, an information processing method, and a program for detecting occurrence of an abnormality in a traffic road.

  Concerning automobile traffic control, congestion information collected using vehicle sensors installed on the road is collected at the traffic control center, and a large number of traffic lights are collectively controlled based on the collected information to reduce road congestion in a wide area. A wide-area control system has been put to practical use.

  As for pedestrian traffic control, a wide-area control system such as that applied to automobiles has not been put into practical use, but congestion information can be obtained by image analysis using images taken by cameras installed on street corners. It is possible to collect.

  Combining information collected on the traffic situation of automobiles and pedestrians collected as described above with information processing technology will cause abnormalities such as delays even at points that are not directly monitored by sensors, cameras, etc. Can be predicted and detected.

  Patent Document 1 discloses a road traffic information system that collects information from vehicle sensors, inputs the information into a traffic flow simulation, and predicts the position of each vehicle after a predetermined time.

  Patent Document 2 discloses a traffic flow control system that collects route information held by devices mounted on each vehicle, calculates a predicted traffic volume and a delay time, and detects an abnormal delay point.

  Patent Document 3 discloses a traffic flow measuring device that estimates the occurrence of traffic conditions and abnormal events in a blind spot region between sensors.

Japanese Patent No. 4661355 International Publication No. 2010/026630 Japanese Patent Laid-Open No. 08-279093

  According to the system of Patent Document 1, it is possible to predict the occurrence of a delay in a traffic route by simulation. However, the system of Patent Document 1 does not include means for comparing the prediction based on the simulation with the actual traffic conditions. For this reason, the system of Patent Document 1 has a problem that a delay due to an event not included in the prediction such as an unexpected accident cannot be detected.

  The system of Patent Document 2 collects information such as a delay in a traffic road from a vehicle moving on the traffic road. Therefore, there is a problem that if the moving body located on the traffic route does not have the function of collecting and transmitting information such as delay like a general pedestrian, the situation of the delay on the traffic route cannot be grasped. It was.

  According to the apparatus of Patent Document 3, the traffic state in the area that cannot be measured in the target area is estimated by comparing the measured value measured by the plurality of vehicle measuring means and the predicted value predicted by the predicting means. Can do. However, the apparatus of Patent Document 3 has a problem that it is necessary to specify the vehicle by a sensor because it is necessary to measure the time for the vehicle to pass through two points.

  An object of the present invention is to solve the above-described problems, and a device for detecting a moving body is installed even when the moving body moving in the target section does not have a function of collecting and transmitting information. An object of the present invention is to provide an information processing apparatus that can detect an abnormality that has occurred at a point that is not present.

  An information processing apparatus according to an aspect of the present invention is based on an entrance measurement unit that measures the number of entrances for each entry time of a moving body that enters a target section including an end portion where the moving body enters and exits, and an entry number for each entry time. Predicting the number of exits for each exit time of the mobile body that exits from the end of the target section and calculating a prediction section for the number of exits for each exit time; and for the mobile body exiting from the end of the target section There are provided exit measuring means for measuring the number of exits for each exit time, and an abnormality determining means for determining whether there is an abnormality in the target section based on whether or not the number of exits for each exit time is included in the prediction section.

  In the information processing method according to an aspect of the present invention, the number of entries for each entry time of a moving object entering the target section including the end portion where the moving object enters and exits is measured, and the target section is based on the number of entries for each entry time. Predict the number of exits for each exit time of the mobile body that exits from the end of the area, calculate the predicted number of exits for each exit time, and calculate the number of exits for each exit time of the mobile body that exits the end of the target section Measurement is performed, and whether or not there is an abnormality in the target section is determined based on whether or not the number of exits at each exit time is included in the prediction section.

  The program according to one aspect of the present invention is a program that measures the number of entries for each entry time of a moving object that enters an object section that includes an end where the moving object enters and exits, and the number of entries for each entry time based on the number of entries for each entry time. A process for calculating the number of exits for each exit time by predicting the number of exits for each exit time of the mobile body leaving the end, and the number of exits for each exit time of the mobile body exiting from the end of the target section And a process of determining whether there is an abnormality in the target section based on whether or not the number of exits for each exit time is included in the prediction section.

  According to the present invention, even when a moving body moving on a traffic road does not have a function of collecting and transmitting information, an abnormality occurring at a point where a device for detecting the moving body is not installed. Can be detected.

It is a block diagram which shows the structure of the information processing apparatus which concerns on the 1st Embodiment of this invention. It is a conceptual diagram which shows the structural example of the information processing system which concerns on the 1st Embodiment of this invention. It is a graph which shows an example of the result as which the approach measurement means of the information processing apparatus which concerns on the 1st Embodiment of this invention measured the number of approach for every approach time of a mobile body. It is a graph which shows an example of the result of having calculated the prediction area of the number of evacuation for every evacuation time by the evacuation prediction means of the information processor concerning a 1st embodiment of the present invention. It is a conceptual diagram which shows an example of the simulation performed in the information processing apparatus which concerns on the 1st Embodiment of this invention. It is a flowchart for demonstrating the operation | movement regarding the simulation performed in the information processing apparatus which concerns on the 1st Embodiment of this invention. It is a graph which shows an example of the abnormality detection by the abnormality determination means of the information processing apparatus which concerns on the 1st Embodiment of this invention. It is a flowchart for demonstrating operation | movement of the information processing apparatus which concerns on the 1st Embodiment of this invention. It is a block diagram which shows the structure of the information processing apparatus which concerns on the 2nd Embodiment of this invention. It is a flowchart for demonstrating operation | movement of the information processing apparatus which concerns on the 2nd Embodiment of this invention. It is a conceptual diagram which shows an example of the hardware constitutions for implement | achieving the information processing apparatus which concerns on each embodiment of this invention.

  EMBODIMENT OF THE INVENTION Below, the form for implementing this invention is demonstrated using drawing. However, the preferred embodiments described below are technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following. In addition, in all the drawings used for description of the following embodiments, the same reference numerals are given to the same parts unless there is a particular reason. In the following embodiments, repeated description of similar configurations and operations may be omitted. Moreover, the direction of the arrow in the drawings shows an example, and does not limit the direction of movement of a signal or an object between blocks.

(First embodiment)
First, an information processing apparatus according to a first embodiment of the present invention will be described with reference to the drawings.

(Constitution)
FIG. 1 is a block diagram illustrating a configuration of the information processing apparatus 10 according to the present embodiment. As shown in FIG. 1, the information processing apparatus 10 includes an entry measurement unit 11, an exit prediction unit 12, an exit measurement unit 13, and an abnormality determination unit 14.

  In the following, the target section corresponds to one section such as a roadway or a sidewalk on an actual road. Moreover, an edge part is a location verified by detection means, such as an infrared sensor and a camera, in traffic roads, such as a roadway and a sidewalk. The moving body is a moving body such as an automobile or a pedestrian.

  The entry measuring means 11 measures the number of entries for each entry time of the moving body entering the target section from the end of the target section including the end where the moving body enters and exits.

  The exit prediction unit 12 predicts the number of exits for each exit time of the mobile body that exits from the end of the target section based on the number of entries for each entry time, and calculates a prediction section for the number of exits for each exit time.

  The exit measurement means 13 measures the number of exits for each exit time of the moving body that exits from the end of the target section.

  The abnormality determination means 14 determines whether or not there is an abnormality in the target section depending on whether or not the number of exits for each exit time is included in the prediction section. The abnormality determination unit 14 determines whether the number of exits for each exit time measured by the exit measurement unit 13 is included in the prediction section calculated by the exit prediction unit 12 and outputs a determination result.

  FIG. 2 is a conceptual diagram illustrating a configuration example of the information processing system 1 including the information processing apparatus 10. FIG. 2 illustrates a state in which a plurality of moving bodies 40 move on the traffic path 20 on one lane on one side. The mobile body 40 to be verified by the information processing system 1 is, for example, a vehicle such as a vehicle or a bicycle, or a person such as a pedestrian or a runner. Further, the mobile object 40 to be verified by the information processing system 1 may be a subject other than a vehicle or a person.

  In FIG. 2, a first end 21 and a second end 22 are set on the traffic path 20. The information processing system 1 verifies the situation of the moving body 40 in the target section between the first end 21 and the second end.

  Information on the moving body 40 that has passed through the first end 21 is acquired by the first detection means 31. Information on the moving body 40 that has passed through the second end 22 is acquired by the second detection means 32.

  The information processing system 1 includes an information processing apparatus 10, first detection means 31, and second detection means 32. The first detection unit 31 and the second detection unit 32 are connected to the information processing apparatus 10 via the network 100 and transmit the acquired information to the information processing apparatus 10.

  The first detection unit 31 is connected to the entry measurement unit 11 and the exit measurement unit 13 of the information processing apparatus 10 via the network 100. The first detection means 31 is realized by a camera or a sensor for detecting the moving body 40 entering and exiting the target section at the first end 21 on the traffic path 20.

  Similarly, the second detection unit 32 is connected to the entry measurement unit 11 and the exit measurement unit 13 of the information processing apparatus 10 via the network 100. The 2nd detection means 32 is implement | achieved by the camera and sensor for detecting the mobile body 40 which enter / exit in a target area in the 2nd edge part 22 on the traffic path 20. FIG.

  The first detection means 31 and the second detection means 32 output information of the moving body 40 that has entered the target section to the entry measurement means 11. Further, the first detection means 31 and the second detection means 32 output information of the moving body 40 that has left the target section to the exit measurement means 13. Note that the first detection unit 31 and the second detection unit 32 may be configured to output the information of the moving body 40 entering and exiting the target section to the entrance measurement unit 11 and the exit measurement unit 13 without distinguishing them. Good.

  In FIG. 2, in order to assume that the target section is set on a general road, two end portions are shown, but the end portions are not limited to two. For example, when the information processing system 1 is installed at a rotary in front of a station or an entrance / exit of a parking lot, one end may be set. In addition, when the information processing system 1 is installed at the intersection of a crossing or a crossroad, three or four ends may be set. Moreover, when installing the information processing system 1 in the intersection etc. which branched into five or more roads, the road which has five or more entrance / exit parts, five or more edge parts may be set. Further, when the information processing system 1 is installed indoors in a large facility for a person, end portions may be set at a plurality of entrances.

  Here, the components of the information processing apparatus 10 will be described in detail.

  The approach measurement means 11 acquires the information detected by the detection means for monitoring the end of the target section on the traffic road and monitors the traffic road. The approach measurement means 11 measures the number of mobile bodies that enter and the time at predetermined time intervals when a mobile body enters the target section. The predetermined interval can be set to an arbitrary interval. For example, the predetermined interval can be set to an interval of 1 second.

  When the target mobile body is an automobile, the approach measuring means 11 can detect the automobile using a detecting means such as a vehicle sensor including an infrared sensor. Moreover, the approach measurement means 11 may detect a vehicle using the detection means which has the structure which combined the camera and the software which recognizes the passing time of a motor vehicle based on the said camera image. Moreover, the approach measurement means 11 may detect a motor vehicle using detection means, such as a loop coil type, a magnetic detection type, a photoelectric type, an ultrasonic type, and an illumination intensity detection type. However, there is no particular limitation on the type and form of detection means for detecting the automobile by the approach measurement means 11.

  When the target moving body is a pedestrian, the approach measurement unit 11 can detect the pedestrian using a detection unit such as a human sensor including an infrared sensor. Moreover, the approach measurement means 11 may detect a pedestrian using the detection means which has a structure combining a camera and the software which recognizes a pedestrian's approach time and the number of persons based on the said camera image. However, there is no particular limitation on the type and form of detection means for the entry measurement means 11 to detect a pedestrian.

  When the type of the moving body to enter is not known in advance, the approach measuring unit 11 determines the type of the moving body by recognizing the shape of the moving body using a three-dimensional distance image sensor system or an image recognition system using an infrared laser. However, a detection means corresponding to the type of the moving body may be used.

  FIG. 3 is a graph showing an example of the result of measuring the number of entries of the moving object at each entry time by the entry measuring means 11. In the example of FIG. 3, the bar graph shows the result of measuring the number of moving objects entering from 10:00:00 to 10:00:10 at 1 second intervals.

  The exit predicting unit 12 executes a simulation for reproducing the movement of the moving body on the traffic route by using the number of entering the moving body for each approach time measured by the entering measuring unit 11 as an input value. For example, the exit prediction unit 12 executes a traffic route simulation using a method of expressing a traffic route with cells.

  The exit prediction means 12 predicts the number of mobile bodies that exit from the end of the traffic road and the time at predetermined time intervals. The exit prediction means 12 executes the prediction of the number of mobile bodies exiting from the end of the traffic road and the time multiple times, and calculates the prediction section of the predicted value.

  Simulation results that reproduce the movement of a moving object on a traffic route often vary depending on the actual value of the random variable because the model includes a random variable. Therefore, the exit prediction unit 12 executes the simulation many times using different values as seeds for the random numbers in order to aggregate a large number of simulation results. The exit prediction unit 12 executes the simulation about 1000 times, for example. The number of simulations executed by the exit prediction unit 12 can be arbitrarily set.

  For example, the exit prediction unit 12 aggregates the simulation results and sets the confidence level to 99%. And the exit prediction means 12 calculates the prediction section of the number of exits for each exit time for the moving object that exits from the end of the target section. For example, the exit prediction unit 12 applies smoothing by a moving average filter or a Gaussian filter in the calculation of the prediction interval.

  FIG. 4 is a graph showing an example of the result of calculating the number of exit prediction intervals for each exit time by the exit prediction means 12. In the example of FIG. 4, the prediction interval of the number of exits between 10:00:00 and 10:00:30 is calculated at 1 second intervals, and the upper and lower limits of the prediction interval are indicated by broken lines.

  Next, a simulation method by the exit prediction unit 12 will be described with an example. FIG. 5 is a diagram illustrating an example in which a traffic route is simulated by a method of expressing a traffic route with cells.

  In the example of FIG. 5, the traffic path 20 has a first end 21 and a second end 22. The shape of the traffic path 20 is determined according to the situation of an actual road or the like to be simulated, for example. The traffic path 20 is composed of a plurality of cells 23. In the example of FIG. 5, the traffic path 20 is divided into 64 cells 23.

  In the simulation of FIG. 5, it is assumed that the moving body 24 passes through one of the cells 23 included in the traffic path 20 when moving on the traffic path 20.

  Each cell 23 is assumed to correspond to an area where at least one moving body can be located on an actual road associated with the traffic path 20. The length of each side of the cell 23 is set to about 50 centimeters, for example. The shape of the cell 23 may be a square or a rectangle. Further, the shape of the cell 23 may be a polygon in which a part of the square is missing. However, the size and shape of the cell 23 are arbitrarily set.

  The model of FIG. 5 represents a state in which an arbitrary type of moving body moving on the traffic path 20 moves. For example, the moving body 24 corresponds to a vehicle, a pedestrian, or the like that moves on the traffic path 20.

  The moving body 24 is arranged in one of the cells adjacent to the first end 21 or the second end 22 in accordance with the number of moving bodies entered by the entry measuring means 11 and the measurement result of the entering time. Then, it is assumed that the moving body 24 entering from one end moves toward the other end. Each moving body 54 is set with a traveling direction and a moving distance per step. For example, the moving distance per step is set according to the moving speed of a vehicle or a pedestrian associated with each moving body 24 in a normal case.

  Here, how the moving body 24 moves in the simulation by the exit prediction means 12 will be described with reference to the flowchart of FIG.

  In FIG. 6, first, the exit prediction means 12 randomly selects one of the moving bodies 24 existing on the traffic path 20 (step S1).

  Next, the exit prediction means 12 includes the selected moving body 24 and the moving body 24 belonging to the cell 23 within a predetermined range determined based on the traveling direction of the selected moving body 24 and the form of the cell 23. The number is derived (step S2).

  Next, the exit prediction unit 12 updates the position of the selected moving body 24 according to the number of moving bodies 24 belonging to each cell 23 in a predetermined range (step S3). The position of the selected moving body 24 after being updated corresponds to the position of the selected moving body 24 at a time point after a predetermined period from the corresponding time point before the update.

  During the above-mentioned predetermined period, the behavior in a relatively short time of a pedestrian or the like associated with the selected moving body is appropriately represented according to the moving distance per step of the selected moving body 24 or the like. As appropriate. For example, when the selected moving body is associated with a pedestrian or the like, the predetermined period is set to 1 second based on the time required for the pedestrian to move from one step to several steps on an actual road or the like. It is done.

  When the simulation is continued (Yes in step S4), the process returns to step S1. On the other hand, when the simulation is not continued (No in step S4), the process according to the flowchart of FIG. As described above, the state of movement of the moving body 24 can be simulated by repeatedly updating the position of the moving body 24.

  FIG. 7 is a graph showing an example of abnormality detection by the abnormality determination unit 14. The abnormality determination unit 14 determines that the number of exits measured by the exit measurement unit 13 is normal when it is included in the prediction section, and determines that the above is the case when it is not included.

  In the example of FIG. 7, the number of exits measured by the exit measurement means 13 is included in the prediction interval calculated by the exit prediction means 12 between 10:00:00 and 10:00:20. In such a case, the abnormality determination means 14 determines that it is normal.

  On the other hand, in the example of FIG. 7, the number of exits is not included in the prediction interval after 10:00:20. In such a case, the abnormality determination unit 14 determines that there is an abnormality. The reason why the number of exits is not included in the predicted section is that some abnormality occurs at a point that cannot be directly monitored by a sensor or a camera between the ends of the traffic path 20, and a delay occurs in the passage of the moving body 24. Presumed to be because.

(Operation)
Next, the operation of the information processing apparatus 10 according to the present embodiment will be described with reference to the drawings. FIG. 8 is a flowchart for explaining the operation of the information processing apparatus 10 of this embodiment. In addition, the process of step S11-step S16 of the flowchart of FIG. 8 is implemented at a predetermined time interval.

  First, in FIG. 8, the entry measuring means 11 measures the number of entries for each entry time of a moving body that enters the target section from the end of the target section at a predetermined time interval (step S <b> 11).

  Next, the exit prediction means 12 calculates a predicted value of the number of exits for each exit time of the mobile body that exits from the end of the target section based on the number of entrances for each entry time (step S12).

  Next, the exit prediction unit 12 determines whether or not the number of executions of prediction in step S12 has reached a predetermined number (step S13). If the predetermined number of times has been reached (Yes in step S13), the process proceeds to step S14. If the predetermined number of times has not been reached (No in step S13), the process returns to step S12.

  Next, the exit prediction means 12 aggregates the prediction results in step S12 and calculates a prediction interval of the number of exits for each exit time (step S14).

  Next, the exit measurement means 13 measures the actual number of exits for each exit time of the moving body that exits from the end of the target section (step S15).

  Next, the abnormality determination unit 14 determines whether the number of exits for each exit time measured by the exit measurement unit 13 is included in the prediction section calculated by the exit prediction unit 12 (step S16).

  When the measured number of exits for each exit time is not included in the prediction section (No in step S16), the abnormality determination unit 14 detects an abnormality (step S17). On the other hand, when the number of exits for each measured exit time is included in the prediction section (Yes in step S16), the process proceeds to step S18.

  If the current time has not reached the predetermined process end time (No in step S18), the processes after step S11 are repeatedly executed. If the current time has reached the predetermined processing end time (Yes in step S18), the processing according to the flowchart of FIG. 8 is ended.

  The above is description about operation | movement of the information processing apparatus 10 of this embodiment.

  As described above, according to the information processing apparatus of the present embodiment, no sensor or camera is installed even when the subject on the traffic road does not have a function of collecting and transmitting information such as delay. Abnormalities such as delays occurring at the point can be detected.

(Second Embodiment)
Next, an information processing apparatus according to a second embodiment of the present invention will be described with reference to the drawings. FIG. 9 is a block diagram illustrating a configuration of the information processing apparatus 10-2 according to the present embodiment. As illustrated in FIG. 9, the information processing apparatus 10-2 includes an abnormality notification unit 15 in addition to the entry measurement unit 11, the exit prediction unit 12, the exit measurement unit 13, and the abnormality determination unit 14. Note that the configuration other than the abnormality notification unit 15 is the same as that of the information processing apparatus 10 of the first embodiment, and thus detailed description thereof is omitted.

  The abnormality notification unit 15 issues a warning when the abnormality determination unit 14 determines that there is an abnormality. For example, the abnormality notification means 15 can be configured to issue a warning by displaying that there is an abnormality on a display device such as a display (not shown). Further, for example, the abnormality notification means 15 can be configured to issue a warning by emitting a sound for notifying that there is an abnormality from a speaker (not shown). The abnormality notification method by the abnormality notification means 15 can be arbitrarily set.

  Next, the operation of the information processing apparatus 10-2 according to the present embodiment will be described with reference to the drawings. FIG. 10 is a flowchart for explaining the operation of the information processing apparatus 10-2. In addition, the process of step S21-step S26 of the flowchart of FIG. 10 is implemented at a predetermined time interval.

  First, in FIG. 10, the entry measuring means 11 measures the number of entries for each entry time of a moving body that enters the target section from the end of the target section at a predetermined time interval (step S <b> 21).

  Next, the exit prediction means 12 calculates a predicted value of the number of exits for each exit time of the mobile body that exits from the end of the target section based on the number of entrances for each entry time (step S22).

  Next, the exit prediction means 12 determines whether or not the number of executions of prediction in step S22 has reached a predetermined number (step S23). If the predetermined number of times has been reached (Yes in step S23), the process proceeds to step S24. If the predetermined number of times has not been reached (No in step S23), the process returns to step S22.

  Next, the exit prediction means 12 calculates the prediction interval of the number of exits for each exit time by counting the prediction results in step S12 (step S24).

  Next, the exit measurement means 13 measures the actual number of exits for each exit time of the moving body that exits from the end of the target section (step S25).

  Next, the abnormality determination unit 14 determines whether the number of exits for each exit time measured by the exit measurement unit 13 is included in the prediction section calculated by the exit prediction unit 12 (step S26).

  When the number of exits measured for each exit time is not included in the prediction section (No in step S26), the abnormality notification unit 15 issues a warning (step S27). On the other hand, when the number of exits at each measured exit time is included in the prediction section (Yes in step S26), the process proceeds to step S28.

  If the current time has not reached the predetermined process end time (No in step S28), the processes after step S21 are repeatedly executed. If the current time has reached the predetermined processing end time (Yes in step S28), the processing according to the flowchart of FIG. 10 is ended.

  The above is description about operation | movement of the information processing apparatus 10-2 of this embodiment.

  As described above, according to the information processing apparatus of the present embodiment, as in the first embodiment, even if the subject on the traffic road does not have a function of collecting and transmitting information such as delay, It is possible to detect an abnormality such as a delay occurring at a point where no sensor or camera is installed. Further, according to the information processing apparatus of this embodiment, when an abnormality occurs in the target section, the abnormality can be notified to the outside.

  According to the information processing apparatus of each embodiment of the present invention, even if it is difficult to monitor all points with sensors, cameras, etc., the range of traffic paths to be monitored is wide, You can keep track of the location and number of people. Furthermore, according to the information processing apparatus of each embodiment of the present invention, it is possible to detect the occurrence of an abnormality such as a delay due to a trouble or the like by detecting a change in the flow of the crowd.

  The information processing apparatus according to each embodiment of the present invention, for example, at the time of a large-scale event or the like, grasps the situation of the crowd in the venue or its surrounding urban area, detects the occurrence of a trouble, and guides the crowd. It can be used for the purpose of taking measures.

(hardware)
In each of the embodiments of the present invention described above, each component of each device represents a functional unit block. Each component of each device can be realized by any combination of an information processing device 90 and software as shown in FIG. 11, for example. The information processing apparatus 90 includes the following configurations 1 to 8 as an example.
(1) CPU91 (Central Processing Unit)
(2) ROM92 (Read Only Memory)
(3) RAM93 (Random Access Memory)
(4) Storage device 94 for storing a program loaded into the RAM 93
(5) Drive device 95 for reading / writing the storage medium 200
(6) Communication interface 96 connected to the network 100
(7) Input / output interface 97 for inputting / outputting data
(8) Bus 98 for connecting each component
In addition, there are various modifications to the method for realizing each device. For example, each device can be realized as a dedicated device. Each device can be realized by a combination of a plurality of devices.

  A program that causes a computer to execute processing by the information processing apparatus according to the present embodiment is also included in the scope of the present invention. Furthermore, the storage medium 200 storing the program according to the embodiment of the present invention is also included in the scope of the present invention.

  Although the present invention has been described with reference to the embodiments, the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

DESCRIPTION OF SYMBOLS 1 Information processing system 10 Information processing apparatus 11 Entrance measurement means 12 Exit prediction means 13 Exit measurement means 14 Abnormality determination means 20 Traffic road 21 First end 22 Second end 23 Cell 24 Mobile body 90 Information processing apparatus 91 CPU
92 ROM
93 RAM
94 storage device 95 drive device 96 communication interface 97 input / output interface 98 bus 100 network 200 storage medium

Claims (10)

An entry measuring means for measuring the number of entries for each entry time of the moving object entering the target section including the end portion where the moving object enters and exits;
Exit prediction means for predicting the number of exits for each exit time of the mobile body exiting from the end of the target section based on the number of entries for each entry time and calculating a prediction section for the number of exits for each exit time; ,
An exit measuring means for measuring the number of exits for each exit time of the moving body exiting from the end of the target section;
An information processing apparatus comprising: an abnormality determination unit that determines whether or not there is an abnormality in the target section based on whether or not the number of exits for each exit time is included in the prediction section. The exit prediction means includes
Predicting the number of exits for each exit time of the moving body that exits from the end of the target section based on the number of entrances for each entry time and performing the prediction interval for the number of exits for each exit time The information processing apparatus according to claim 1 to calculate. The abnormality determination means includes
When the number of exits for each exit time is between the upper limit and the lower limit of the prediction interval, the target section is determined to be normal, and the number of exits for each exit time is between the upper limit and the lower limit of the prediction interval. The information processing apparatus according to claim 1, wherein the target section is determined to be abnormal when deviating from the range. The exit prediction means includes
Dividing the target section into a plurality of cells, placing the moving body in any of the divided cells, and using the simulation of moving the moving body between the cells, the number of exits for each exit time The information processing apparatus according to any one of claims 1 to 3, wherein a prediction interval is calculated.   The information processing apparatus according to claim 1, wherein the presence or absence of an abnormality in the target section in which two ends are set is determined.   The information processing apparatus according to any one of claims 1 to 4, wherein whether or not there is an abnormality in the target section in which three or more ends are set is determined.   The information processing apparatus according to any one of claims 1 to 6, further comprising an abnormality notification unit that issues a warning when the abnormality determination unit determines that the target section is abnormal. An information processing apparatus according to any one of claims 1 to 7,
Connected to at least one of the approach measurement means and the exit measurement means, detects the moving body that enters and exits the end of the target section, and determines the number of the detected moving bodies as the entry measurement means and the exit measurement means. An information processing system comprising detection means for outputting to a computer. Measure the number of entries for each entry time of the moving body entering the target section including the end where the moving body enters and exits,
Predicting the number of exits for each exit time of the moving object that exits from the end of the target section based on the number of entries for each entry time, and calculating a prediction section for the number of exits for each exit time;
Measure the number of exits for each exit time of the mobile body exiting from the end of the target section,
An information processing method for determining whether or not there is an abnormality in the target section based on whether or not the number of exits for each exit time is included in the prediction section. A process of measuring the number of entries for each entry time of the moving object entering the target section including an end portion where the moving object enters and exits;
A process of predicting the number of exits for each exit time of the moving object that exits from the end of the target section based on the number of entrances for each entry time and calculating a prediction section for the number of exits for each exit time;
A process of measuring the number of exits for each exit time of the mobile body exiting from the end of the target section;
The program which makes a computer perform the process which determines the presence or absence of the abnormality of the said object area by whether the exit number for every said exit time is contained in the said prediction area.

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