WO2018180030A1 - Congestion countermeasure assist system - Google Patents

Congestion countermeasure assist system Download PDF

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Publication number
WO2018180030A1
WO2018180030A1 PCT/JP2018/006151 JP2018006151W WO2018180030A1 WO 2018180030 A1 WO2018180030 A1 WO 2018180030A1 JP 2018006151 W JP2018006151 W JP 2018006151W WO 2018180030 A1 WO2018180030 A1 WO 2018180030A1
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countermeasure
congestion
time
prediction
support system
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PCT/JP2018/006151
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French (fr)
Japanese (ja)
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鋭 寧
加藤　学
正康 藤原
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株式会社日立製作所
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Definitions

  • the present invention relates to a congestion countermeasure support system that provides congestion prediction information and congestion countermeasure information.
  • Patent Document 1 using time-series data of entry / exit of guarded areas such as event venues and passages, a function for predicting subsequent entry / exit and human flow, and when the predicted entry / exit exceeds a predetermined danger alarm set value.
  • the alarm function that notifies the alarm and the traffic flow when traffic restriction is performed by entering the traffic restriction point and time zone to make the entry and exit of the time zone that exceeds the danger alarm setting value less than the danger alarm setting value
  • An event security monitoring device having a function of predicting the above is disclosed.
  • the event security monitoring device described in Patent Document 1 has a function of predicting the congestion situation when the traffic restriction is performed, the time when the traffic restriction should be started and the time when the traffic restriction may be canceled Need to judge.
  • An object of the present invention is to provide a countermeasure release time in a congestion countermeasure support system.
  • a congestion countermeasure support system includes a countermeasure candidate database that stores information on candidate countermeasures that can be implemented for congestion in a predetermined space, and a congestion index value at a future time for the predetermined space.
  • a congestion prediction unit for calculating a transition, a countermeasure required congestion detection unit for detecting a countermeasure required congestion for which the congestion index value exceeds a threshold, and a transition of the congestion index value at a future time under the condition to which the countermeasure candidate is applied
  • a countermeasure effect predicting section for calculating the countermeasure cancellation time and a countermeasure cancellation time calculating section for calculating the countermeasure cancellation possible time.
  • the countermeasure release time can be provided in the congestion countermeasure support system.
  • FIG. 1 is a diagram illustrating a configuration example of a congestion countermeasure support system according to the present embodiment.
  • the congestion countermeasure support system is a computer system that monitors the congestion situation of passengers in a space such as a railway station, and supports congestion countermeasures by a supervisor if necessary.
  • the congestion countermeasure support system is composed of one or a plurality of mutually communicable computers.
  • the congestion countermeasure support system includes a predicted number of people information database 111, a spatial information database 112, a countermeasure candidate database 113, a prediction intermediate state database 114, a congestion prediction section 120, a countermeasure countermeasure congestion detection section 130, a countermeasure effect prediction section 140, and a countermeasure start limit time. It has a calculation unit 150, a measure releasable time calculation unit 160, a measure plan management unit 170, and an input / output unit 180.
  • the congestion countermeasure support system includes the predicted number information 210 shown in FIG. 2, the spatial information 220 shown in FIG. 3, the partial space information 230 shown in FIG. 4, the countermeasure candidate information 240 shown in FIG. 6, the congestion prediction result 250 shown in FIG.
  • the countermeasure required congestion information 260 shown in FIG. 8, the countermeasure plan 270 shown in FIG. 9, and the predicted midway state information 280 shown in FIG. 10 are used as data.
  • the predicted number of people information database 111 is a database that records the predicted number of people information 210, and has functions of recording, searching, and reading.
  • the predicted number of people information 210 stored in the predicted number of people information database is, for example, at a known route level using data such as the number of people who pass through the video analysis of cameras installed in the space and the traffic logs of ticket gates. Using the passenger simulation.
  • the spatial information database 112 is a database that records the spatial information 220 and the partial spatial information 230, and has functions of recording, searching, and reading.
  • the spatial information 220 and the partial space 230 are created in advance for each target station for which congestion prediction is performed.
  • the spatial information 220 is structured map data including attributes of equipment at each position in a target space (for example, a railway station).
  • the partial space information 230 is information in which a range and attributes are given to a partial space defined in a predetermined range included in the target space.
  • the countermeasure candidate database 113 is a database that records the countermeasure candidate information 240, and has recording, searching, and reading functions.
  • the countermeasure candidate information 240 is data in which candidate countermeasures applicable to the congestion are recorded in association with each partial space.
  • the countermeasure candidate information 240 is created in advance for each target space.
  • the prediction halfway state database 114 is a database that records the prediction halfway state information 280, and has recording, searching, and reading functions.
  • Prediction intermediate state information 280 is data in which the congestion prediction unit 120 or the countermeasure effect prediction unit 140 stores the intermediate state during the prediction.
  • the congestion prediction unit 120 or the countermeasure effect prediction unit 140 can resume prediction from the time when the prediction intermediate state information 280 is recorded, with the prediction intermediate state 280 as an input.
  • the congestion prediction unit 120 receives the predicted number of people information 210, the spatial information 220, and the partial space information 230 as input, and in the space when the passenger is moved according to the predicted number of people information 210 in the space represented by the spatial information. Is sequentially predicted using a known pedestrian simulation device, and a congestion prediction result 250 that is a time-series transition of the congestion index value of each partial space is sequentially output.
  • the congestion index value for example, the number of people staying in the space or a specific range in the space, the crowd density, and the flow line density are used.
  • the congestion prediction unit 120 can output an intermediate state in which sequential prediction is performed as the predicted intermediate state information 280, and performs prediction using the predicted intermediate state information 280 as input instead of the predicted number information 210, the spatial information 220, and the partial space information 230. be able to.
  • a known cellular automaton pedestrian simulation device is used as an example of the congestion prediction unit 280.
  • the countermeasure required congestion detection unit 130 receives the congestion prediction result 250 output from the congestion prediction unit 120 as an input, and measures threshold values for determining whether or not each of the partial spaces is in a congestion situation that requires countermeasures for the congestion prediction result. It is determined whether or not a congestion index value exceeding 1 is included. When a congestion index value exceeding the countermeasure threshold is included, countermeasure required congestion information 260 including information on the partial space where the congestion occurs and time is output.
  • the countermeasure threshold for example, a value that indicates that the risk of a crowd accident is higher than a predetermined value or a value that is set in advance by a monitor is used in the congestion index to be used.
  • the countermeasure effect prediction unit 140 receives the countermeasure candidate information 240 in addition to the predicted number of people information 210, the spatial information 220, and the partial space information 230, and uses the same method as the congestion prediction unit 120 under the condition that the countermeasure candidate is applied.
  • the congestion prediction result 250 under the condition where the countermeasure candidate is applied is output. Further, in the same way as the congestion prediction unit 120, it is possible to output an intermediate state in which sequential prediction is performed as prediction intermediate state information 280, and predict using prediction intermediate state information 280 as an input instead of the predicted number information 210, space information 220, and partial space information 230. It can be performed.
  • the countermeasure start limit time calculation unit 150 receives the countermeasure required congestion information 260, the countermeasure candidate information 240 associated with the partial space in which the countermeasure required congestion information 260 is detected, and the countermeasure effect prediction unit 140 outputs the countermeasure candidate as input.
  • the congestion prediction result 250 is input, and the countermeasure start limit time, which is the time when the countermeasure candidate must be executed at the latest because the congestion index value does not exceed the detection threshold, is output.
  • the measure cancelable time calculation unit 160 includes the partial space information 230, the countermeasure necessary congestion information 260, the countermeasure candidate information 240 associated with the partial space in which the countermeasure necessary congestion information 260 is detected, the congestion predicting unit 120, and the countermeasure effect.
  • the countermeasure proposal management unit 170 Based on the congestion prediction result 250 output by at least one of the prediction units 140, the countermeasure proposal management unit 170 outputs a countermeasure releasable time that is a time at which a countermeasure that has already been applied or applied can be cancelled.
  • the countermeasure required congestion information 260 output by the unit 130, the countermeasure start limit time output by the countermeasure start limit time calculating unit 150, and the countermeasure cancelable time output by the countermeasure cancelable time calculating unit 160 are input.
  • the countermeasure proposal information 270 including the congestion alleviation effect of the countermeasure countermeasures related to 260, the countermeasure start limit time, the countermeasure cancelable time, etc. The Also, the countermeasure plan information 270 being implemented is held.
  • the input / output unit 180 has a function of outputting the congestion prediction result 250 and the countermeasure plan information 270 and outputting them to the display device.
  • the display device for example, a computer display device held by a supervisor, a digital signage device that provides information to the user, a computer display device held by the user, or the like is used. In addition, it accepts as input whether or not to implement a countermeasure plan from an operator such as a supervisor or a device such as a ticket gate.
  • each part of the congestion countermeasure support system of the present embodiment described above are realized as one or more computers having an arithmetic device, a control device, a recording device, and an input / output device, and software operating on each computer.
  • FIG. 2 is a diagram illustrating an example of the predicted number of people information 210 recorded in the predicted number of people information database 111.
  • the predicted number of people information 210 is time-series data in which the number of people moving between the departure place and the destination is recorded by time.
  • the station entrance and platform (train) are the departure and destination.
  • train When the departure place is a home (train), data is created with the arrival time of the train as the start time and the train departure time as the end time.
  • the number of people moving from the doorway 1 to the starting point and from the time 7:00 to the time 7:10 with the home 1 as the destination is ten.
  • the number of people who move from home 7 as the destination and from home 7 as the destination between time 7:01 and time 7:02 is 30. This means that 30 passengers who arrive at the home 1 at the time 7:01 and get off from the train 1 leaving at the time 7:02 travel with the home 2 as the destination.
  • FIG. 3 is a diagram showing an example of the spatial information 220 recorded in the spatial information database.
  • Spatial information is data for recording station structure and facility information, and is created in advance based on the station structure.
  • spatial information obtained by dividing a space used in a known cellular automaton pedestrian simulation apparatus into a lattice shape is used.
  • the spatial information 220 is configured as a combination of unit lattices that are units of elements constituting the space.
  • the unit grid is, for example, a passage grid, a wall grid or an impassable grid, a stair grid, a ticket gate grid, an entrance grid, a boarding grid.
  • Each unit grid has, as attributes, for example, whether or not it can pass, speed that can pass, direction that can pass, distance cost required for passing, whether or not it is possible to enter and exit the pedestrian space.
  • FIG. 3 shows an example of a station structure having one ticket gate and two platforms as spatial information using a unit grid.
  • FIG. 4 is a diagram showing an example of the partial space information 230 recorded in the spatial information database 112.
  • the partial space information 230 is data that stores the attributes of the partial space defined in the range included in the space to be subjected to the congestion support.
  • the partial space is defined as a range obtained by dividing the space at a point where the traffic volume can be restricted and the direction of the traffic can be restricted, or where a branch of the route occurs.
  • the subspace information 230 includes, for example, a subspace name, a range, a subspace priority, a connection point, and a connection subspace name.
  • the partial space name is a name for uniquely identifying each partial space.
  • the range represents the definition range of the subspace and is held as a set of coordinates in the space or unit cell.
  • FIG. 5 is an example in which a partial space range included in the partial space information 230 of FIG. 4 is shown in a plan view. Ranges A001 to A004 shown in FIG. 5 correspond to the partial space ranges of FIG. 4 and are separated by points A011 to A014 (stairs) and A021 (ticket gates).
  • the range of the subspace is basically based on the unit for detecting the countermeasure congestion, and may be set in finer units. For example, the partial space with the partial space name “Home 1” may be defined by being divided into a plurality of partial spaces.
  • the partial space priority is an evaluation value that is set in advance to determine the priority for taking countermeasures when countermeasure required congestion information 260 is detected in a plurality of partial spaces. For example, congestion on the platform is difficult to evacuate and the risk of dangerous accidents such as falls and shogi falls is higher than the concourse, so the subspace on the home has a higher priority than the concourse subspace. Set.
  • connection point records the point where the partial space is connected to another partial space.
  • the home 1 (range A001) is connected to another partial space using the staircase 1 (point A011) and the staircase 2 (point A012) as connection points.
  • the connection point is held as a set of coordinates in the space or unit cell like the range.
  • connection subspace name records the subspace name of the other subspace to which the subspace is connected via the connection point.
  • home 1 (range A001) is connected to the inside of the concourse (range A003) via stairs 1 (point A011) and stairs 2 (point A012).
  • FIG. 6 is a diagram showing an example of the countermeasure candidate information 240 recorded in the countermeasure candidate information database 113.
  • the countermeasure candidate information 240 is data that is inputted and held in advance candidate countermeasures that can be implemented for each partial space, and is composed of, for example, a target partial space name, a target point, and a countermeasure state.
  • the target subspace name is data for specifying the subspace to be subjected to the countermeasure.
  • the target point is data for specifying a target whose state is changed as a countermeasure. It is often the same as the connection point of the partial space, but is not limited to this.
  • the same point may be further subdivided and defined. For example, instead of a point representing a set of ticket gates, it may be defined by subdividing into one ticket gate.
  • Measure state indicates the state that the target point can take.
  • the states that can be taken are the state that can be taken by a machine such as a ticket gate, and the state that occurs when an observer conducts traffic control, such as regulation of the direction of stairs.
  • FIG. 7 is a diagram illustrating an example of the congestion prediction result 250 output from the congestion prediction unit 120 or the countermeasure effect prediction unit 140.
  • the congestion prediction result 250 is composed of time series prediction values of the congestion index value of each partial space.
  • the congestion index value of the subspace for example, the number of people staying in the subspace, the crowd density, or the flow line density is used.
  • the example of FIG. 7 represents the number of visitors.
  • FIG. 8 is a diagram illustrating an example of countermeasure required congestion information 260 that is output when the countermeasure required congestion detection unit 130 detects countermeasure required congestion.
  • the detection subspace name is the name of the subspace detected when the countermeasure required congestion occurs.
  • the predicted congestion time is a time zone in which countermeasures are required.
  • the maximum congestion is the maximum value of the congestion index value in the predicted countermeasure required congestion.
  • FIG. 9 is a diagram showing an example of the measure plan information 270 output / held by the measure plan management unit 170.
  • the congestion countermeasure plan information 270 includes target points and countermeasure states included in the countermeasure candidate information 240 corresponding to the detection subspace of the countermeasure countermeasure congestion information 260, the countermeasure start limit time output by the corresponding countermeasure start limit time calculation unit 150, and
  • the countermeasure cancelable time calculation unit 160 outputs the countermeasure cancelable time and the congestion mitigation effect of the congestion prediction using the countermeasure candidate information 240 from the countermeasure start limit time to the countermeasure cancelable time.
  • FIG. 9 is an example of countermeasure plan information when countermeasure required congestion is detected in the partial space home 1.
  • FIG. 10 is a diagram illustrating an example of the prediction intermediate state information 280 output by the congestion prediction unit 120 or the congestion effect prediction unit 140 when the prediction is executed.
  • Prediction in-progress information 280 is data that stores all information necessary for the congestion prediction unit 120 or the congestion effect prediction unit 140 to start prediction again from an arbitrary prediction time.
  • the predicted intermediate state information 280 the predicted recording time when the predicted intermediate state information is stored, the departure point, the destination, the current position coordinates, the traveling direction, and the next action time for each pedestrian agent in the prediction space
  • maintain the state of space are comprised.
  • the congestion countermeasure support system inputs the predicted number of people information 210 that is stored sequentially, predicts the subsequent congestion status in the target space, and detects the congestion that requires countermeasures, and the countermeasure contents and countermeasure time that can be applied. It is a system that supports congestion countermeasures by presenting countermeasure plans including
  • step 1001 is expressed as S1001.
  • the congestion countermeasure support system reads the predicted number information 210 after the current time from the predicted number information database 111 and the spatial information 220 and the partial space information 230 from the spatial information database 112 and inputs them to the congestion prediction unit 120.
  • the congestion prediction unit 120 performs congestion prediction based on the predicted number of people information 210, the spatial information 220, and the partial space information 230 input to the congestion countermeasure support system, and outputs a congestion prediction result 250. Further, the predicted intermediate state information 280 is stored in the predicted intermediate state database 113 at a given time period.
  • the countermeasure-necessary congestion detection unit 130 receives the congestion prediction result 250 output from the congestion prediction unit 120 and determines whether or not congestion requiring countermeasures has occurred. If congestion requiring countermeasures is detected, countermeasure necessary congestion information 260 is output, and the process proceeds to S1005. If congestion requiring countermeasures is not detected, the process proceeds to S1006.
  • the countermeasure plan management unit 170 updates the currently implemented countermeasure plan based on the input information received from the input / output unit 180.
  • the countermeasure plan management unit 170 determines whether there is a countermeasure plan that is currently being implemented. If there is a measure currently being implemented, the process proceeds to S1008. If there is no currently implemented measure, the process proceeds to S1009.
  • the measure cancelable time calculation unit 160 receives the congestion prediction result output from the congestion prediction unit 120 as an input, recalculates the measure cancelable time, and updates the currently applied measure plan.
  • the congestion countermeasure support system outputs a subsequent congestion prediction result, and when a countermeasure necessary congestion is detected, a countermeasure plan and a countermeasure releasable time for the currently implemented countermeasure plan are output.
  • Countermeasure candidate information 240 for the partial space in which the countermeasure required congestion information 260 output by the countermeasure required congestion detection unit 130 is detected is extracted from the countermeasure candidate information database 113.
  • the countermeasure start limit time calculation unit 150 applies the countermeasure candidate information n with the spatial information 220 and the partial space information 230 reflecting the state change caused by the countermeasure specified by the countermeasure candidate information n and the number of people prediction information 210 as inputs. In this case, the countermeasure start time limit is calculated.
  • the measure end possible time calculation unit 160 sets the measure start limit corresponding to the spatial information 220 and the partial space information 230 reflecting the change in state due to the measure specified by the measure candidate information n, the number of people prediction information 210, and the measure candidate information n. Taking the time as an input, calculate the possible countermeasure end time when countermeasure candidate information n is applied from the corresponding countermeasure start limit time.
  • the countermeasure effect prediction unit 120 inputs the spatial information 220 and the partial space information 230 of the condition in which the condition corresponding to the countermeasure candidate information n is applied from the countermeasure start limit time to the countermeasure cancellation time, and the number prediction information 210 as an input.
  • the congestion prediction result when the plan is implemented is calculated, and the difference between the maximum value of the congestion index value and the maximum congestion of the countermeasure required congestion information 260 is calculated as the congestion mitigation effect.
  • the countermeasure plan management unit 170 generates and holds countermeasure plan information using the countermeasure candidate information n, the countermeasure start limit time corresponding to the countermeasure candidate information n, the countermeasure releasable time, and the congestion mitigation effect.
  • S2007 The countermeasure plan information 270 for the countermeasure required congestion information 260 is output.
  • the countermeasure plan information 270 can be created.
  • the congestion countermeasure support system it is basic to create and update countermeasure plans for all the necessary countermeasure congestion information 260 and countermeasure candidate information 240 for each prediction time period, but not necessarily all countermeasure plans are created. And there is no need to update.
  • the period for re-evaluating each countermeasure plan and the time period for prediction may be set separately.
  • the cycle for re-evaluating each countermeasure plan is determined so that, for example, the shorter the time until the countermeasure start limit time, the shorter the cycle. As a result, it is possible to update the information more frequently as the countermeasure plan has a shorter countermeasure start time limit.
  • S3001 Input the first congestion prediction result, which is the congestion prediction result 250 of the partial space in which the countermeasure required congestion information 270 output by the congestion prediction unit 120 is output, and the countermeasure candidate information n for which the countermeasure start limit time is calculated. To do.
  • S3002 Predicting the effect of countermeasures based on the second congestion prediction result to which a countermeasure plan for starting the change of conditions specified by the countermeasure candidate information n from an arbitrary time within the range from the current time until the detection of countermeasure congestion information is detected. Calculated by the unit 140 and used as the second congestion prediction result.
  • An approximate value of the countermeasure start limit time is calculated from the first congestion prediction result and the second congestion prediction result.
  • the approximate value of the countermeasure start limit time is calculated by calculating a time t at which the maximum congestion index value of the first congestion prediction result is taken and a time s at which the minimum value is taken before that.
  • a change in the congestion index value of the second congestion prediction result from time s to time t is linearly approximated, a straight line passing through the point that becomes the detection threshold at time t with the same slope as the approximated straight line, and the first congestion
  • the intersection before the time t with the graph of the congestion index value of the prediction result is calculated, and the time of the intersection is set as the countermeasure start limit time.
  • S3004 From the set of prediction intermediate state information in which the first congestion prediction result is stored at the time of prediction from the prediction intermediate state database 114, the prediction intermediate state information at the time immediately before the approximate value of the countermeasure start limit time is obtained as the first prediction intermediate state. Extract as information. For example, as shown in FIG. 15, the first prediction intermediate state information is extracted.
  • S3005 Applying a measure plan for starting the implementation of the condition change specified by the measure candidate information n from the first predictive state information by the measure effect prediction unit 140, using the first predictive state information and the measure candidate information n as input. A third congestion prediction result is created.
  • the countermeasure required congestion detector 130 determines whether the third congestion prediction result includes countermeasure required congestion.
  • the prediction intermediate state information recorded at the time before the first prediction intermediate state information is recorded is set as the second prediction intermediate state information.
  • the prediction intermediate state information recorded at the time after the first prediction intermediate state information is recorded is set as the second prediction intermediate state information.
  • the countermeasure proposal for starting the implementation of the condition change specified by the countermeasure candidate information n from the second predicted intermediate state information by the countermeasure effect prediction unit 140 is applied.
  • the fourth congestion prediction result is created.
  • FIG. 15 shows an example in which the third congestion prediction result does not include countermeasure required congestion, and the predicted intermediate state information recorded next to the first predicted intermediate state information is used as the second predicted intermediate state information. is there.
  • the countermeasure required congestion detection unit 130 determines whether the fourth congestion prediction result includes countermeasure required congestion.
  • S3009 It is determined that only one of the third congestion prediction result and the fourth congestion prediction result includes countermeasure required congestion, and it is determined whether the termination condition is satisfied.
  • the termination condition that is, when countermeasure required congestion is included in only one of the third congestion prediction result and the fourth congestion prediction result
  • the process proceeds to S3010.
  • the fourth congestion prediction result is set as a new third congestion prediction result. In this case, as viewed from the second prediction intermediate state information, the first prediction intermediate state information and the prediction intermediate state information in the direction opposite to the time axis are set as new second prediction intermediate state information, and the process proceeds to S3007.
  • S4001 The congestion prediction result output from the congestion prediction unit 120 or the countermeasure effect prediction unit 140 is input.
  • the prediction target time may be expanded until a candidate value for the countermeasure cancelable time is found.
  • a congestion index value at the start of countermeasures is used as the release threshold.
  • the latest time among the candidate values of the countermeasure releasable time of all the partial spaces e is calculated as the candidate value of the countermeasure releasable time of the entire space.
  • the candidate value of the home countermeasure releasable time is calculated as the candidate value of the congestion releasable time of the entire space.
  • the intermediate state information 280 is output as the third predicted intermediate state information.
  • the countermeasure required congestion detection unit 130 determines whether or not countermeasure required congestion is included. If countermeasure required congestion is included, the process proceeds to S4008. If the countermeasure required congestion is not included, the process proceeds to S4009.
  • S4008 Recorded next to the time when the third prediction intermediate state information is recorded from the prediction intermediate state information 280 recorded when the congestion prediction result 250 that is input to the countermeasure cancellation possible time calculation unit 160 is predicted.
  • the prediction intermediate state information is newly set as third prediction intermediate state information, and the process returns to S4006.
  • the candidate value of the countermeasure releasable time for the entire space may be directly output as the countermeasure releasable time without performing the processing from S4006 to S4008. Further, instead of the candidate value of the countermeasure cancelable time for the entire space output in S4004, the candidate value of the countermeasure cancelable time for each partial space calculated in S4003 is output, and the countermeasure countermeasure related to the partial space can be canceled. It may be used as time. As a result, when a plurality of countermeasures are applied, it is possible to support the phased cancellation of each countermeasure.
  • a countermeasure candidate database 113 that stores information on countermeasure candidates that can be implemented for congestion in a predetermined space, and a congestion prediction unit that calculates a transition of a congestion index value at a future time for the predetermined space.
  • a countermeasure-necessary congestion detector 130 for detecting a countermeasure-necessary congestion whose congestion index value exceeds a threshold value
  • a countermeasure-effect prediction unit 140 for calculating a transition of a congestion index value at a future time under a condition in which countermeasure candidates are applied
  • a countermeasure cancellation time calculation unit 160 for calculating a countermeasure cancellation possible time is provided.
  • the countermeasure cancellation time calculation unit 160 includes an output unit that outputs a countermeasure cancellation possible time.
  • the countermeasure cancellation possible time calculation unit receives a transition of the congestion index value calculated under the condition to which the countermeasure candidate is applied as an input for the countermeasure candidate. Even if the application of is canceled, a countermeasure releasable time is calculated, which is a time at which countermeasure congestion is not detected for a predetermined time or more.
  • the countermeasure cancellation time calculation unit targets a partial space obtained by dividing a predetermined space, and the transition of predicted congestion index values in all the partial spaces is below a threshold for determining the predetermined countermeasure cancellation, and then exceeds a predetermined time or more.
  • the time that does not occur is calculated and output as the measure cancelable time for the entire space.
  • a countermeasure start limit time calculation for identifying a countermeasure start limit time that is the latest time among countermeasure start times at which the candidate countermeasures can be started at a timing at which the occurrence of the countermeasure required congestion understood by the system can be avoided. Part.

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Abstract

Provided is a feature with which it is possible to determine an appropriate countermeasure against a congestion that requires countermeasures and an appropriate execution time in managing the congestion state of a prescribed space. A congestion countermeasure assist system having: a countermeasure candidate database in which information pertaining to candidates for congestion countermeasures executable for the congestion of a prescribed space is stored; a congestion prediction unit for calculating the transition of a congestion index value in a future time regarding the prescribed space; a countermeasure-required congestion detection unit for detecting a countermeasure-required congestion, specified by space and time, which requires countermeasures when the congestion index value exceeds a threshold; and an output unit for outputting the application start time and releasable time of a countermeasure in which the predicted value of a congestion index value calculated under a condition where a countermeasure candidate applicable to the space in which the countermeasure-required congestion was detected is applied does not exceed the threshold for a prescribed time or longer.

Description

混雑対策支援システムCongestion countermeasure support system
　本発明は，混雑予測情報および混雑対策情報を提供する混雑対策支援システムに関する。 The present invention relates to a congestion countermeasure support system that provides congestion prediction information and congestion countermeasure information.
　鉄道駅にはホームやコンコースなど旅客が出入りし、中を移動する様々な空間がある。そのような鉄道駅の空間では、通勤時間帯などの日常的な混雑の発生に加えて、鉄道の輸送障害やイベントの影響を受けて混雑が増大することがしばしば発生する。こうした混雑の増大は，列車への旅客の乗降に要する時間の増大による列車遅延の発生，将棋倒しなどの群集事故の発生を引き起こすと懸念される。 There are various spaces where passengers come in and out of the railway station and move inside. In such a space of a railway station, in addition to the occurrence of daily congestion such as commuting hours, congestion often occurs due to the influence of railway transportation failures and events. There is a concern that this increase in congestion will cause the occurrence of train accidents such as train delays due to increased time required for passengers to get on and off trains, and shogi defeats.
　特許文献１では，イベント会場や通路など警備対象区域の入出の時系列データを用いて，以降の入出と人流を予測する機能と，予測した入出が予め定めた危険警報設定値を超えた場合に警報を通知する警報機能と，危険警報設定値を超えた時間帯の入出を危険警報設定値よりも少なくするための通行規制の地点と時間帯を入力して，通行規制を行った場合の人流を予測する機能を備えたイベント警備監視装置が開示されている。 In Patent Document 1, using time-series data of entry / exit of guarded areas such as event venues and passages, a function for predicting subsequent entry / exit and human flow, and when the predicted entry / exit exceeds a predetermined danger alarm set value. The alarm function that notifies the alarm and the traffic flow when traffic restriction is performed by entering the traffic restriction point and time zone to make the entry and exit of the time zone that exceeds the danger alarm setting value less than the danger alarm setting value An event security monitoring device having a function of predicting the above is disclosed.
特開２００４－１７８３５８JP2004-178358
　しかし，特許文献１に記載されたイベント警備監視装置には，通行規制を行った場合の混雑状況を予測する機能はあるが，通行規制を開始すべき時刻および解除してもよい時刻は監視員が判断する必要がある。 However, although the event security monitoring device described in Patent Document 1 has a function of predicting the congestion situation when the traffic restriction is performed, the time when the traffic restriction should be started and the time when the traffic restriction may be canceled Need to judge.
　本発明の目的は，混雑対策支援システムにおいて，対策の解除時刻を提供することである。 An object of the present invention is to provide a countermeasure release time in a congestion countermeasure support system.
　本発明の一つの実施形態に従う混雑対策支援システムは，所定の空間の混雑に対して実施可能な対策候補の情報を格納する対策候補データベースと，前記所定の空間について将来の時間における混雑指標値の遷移を算出する混雑予測部と，前記混雑指標値が閾値を超える要対策混雑を検知する要対策混雑検知部と，前記対策候補を適用した条件で将来の時間における前記混雑指標値の遷移を算出する対策効果予測部と、対策解除可能時刻を算出する対策解除時刻計算部を有する。 A congestion countermeasure support system according to an embodiment of the present invention includes a countermeasure candidate database that stores information on candidate countermeasures that can be implemented for congestion in a predetermined space, and a congestion index value at a future time for the predetermined space. A congestion prediction unit for calculating a transition, a countermeasure required congestion detection unit for detecting a countermeasure required congestion for which the congestion index value exceeds a threshold, and a transition of the congestion index value at a future time under the condition to which the countermeasure candidate is applied A countermeasure effect predicting section for calculating the countermeasure cancellation time and a countermeasure cancellation time calculating section for calculating the countermeasure cancellation possible time.
　本発明によれば，混雑対策支援システムにおいて，対策の解除時刻を提供できる。 According to the present invention, the countermeasure release time can be provided in the congestion countermeasure support system.
本発明の実施例である混雑対策支援システムの構成の一例を示した図である。It is the figure which showed an example of the structure of the congestion countermeasure assistance system which is an Example of this invention. 予測人数情報のデータの一例を示した図である。It is the figure which showed an example of the data of estimated number information. 空間情報のデータの一例を示した図である。It is the figure which showed an example of the data of spatial information. 部分空間情報のデータの一例を示した図である。It is the figure which showed an example of the data of partial space information. 部分空間の範囲の一例を示した図である。It is the figure which showed an example of the range of the partial space. 対策候補情報のデータの一例を示した図である。It is the figure which showed an example of the data of countermeasure candidate information. 混雑予測結果のデータの一例を示した図である。It is the figure which showed an example of the data of a congestion prediction result. 要対策混雑情報のデータの一例を示した図である。It is the figure which showed an example of the data of countermeasure required congestion information. 対策案情報のデータの一例を示した図である。It is the figure which showed an example of the data of countermeasure plan information. 予測途中状態情報のデータの一例を示した図である。It is the figure which showed an example of the data of the prediction middle state information. 混雑対策支援システムの処理の一例を示したフローチャートである。It is the flowchart which showed an example of the process of a congestion countermeasure support system. 対策案情報作成処理の一例を示したフローチャートである。It is the flowchart which showed an example of the countermeasure plan information creation process. 対策開始限度時刻計算部の処理の一例を示したフローチャートである。It is the flowchart which showed an example of the process of a countermeasure start limit time calculation part. 対策開始限度時刻の概算値の算出方法の一例を示した図である。It is the figure which showed an example of the calculation method of the rough value of countermeasure start time limit. 対策開始限度時刻の算出方法の一例を示した図である。It is the figure which showed an example of the calculation method of countermeasure start limit time. 対策解除可能時刻計算部の処理の一例を示したフローチャートである。It is the flowchart which showed an example of the process of a countermeasure cancellation | release possible time calculation part. 対策解除可能時刻の算出方法の一例を示した図である。It is the figure which showed an example of the calculation method of countermeasure cancellation | release possible time.
　以下，本発明の混雑対策支援システムの実施形態について図面を用いて説明する。 Hereinafter, embodiments of the congestion countermeasure support system of the present invention will be described with reference to the drawings.
　＜発明の実施例の構成＞
図１は，本実施形態による混雑対策支援システムの構成例を示す図である。混雑対策支援システムは，鉄道駅などの空間内における旅客の混雑状況を監視し，必要に応じて監視員による混雑対策を支援するコンピュータシステムである。混雑対策支援システムは，１つまたは複数の相互に通信可能なコンピュータにより構成する。 <Configuration of Example of Invention>
FIG. 1 is a diagram illustrating a configuration example of a congestion countermeasure support system according to the present embodiment. The congestion countermeasure support system is a computer system that monitors the congestion situation of passengers in a space such as a railway station, and supports congestion countermeasures by a supervisor if necessary. The congestion countermeasure support system is composed of one or a plurality of mutually communicable computers.
　混雑対策支援システムは，予測人数情報データベース１１１，空間情報データベース１１２，対策候補データベース１１３，予測途中状態データベース１１４，混雑予測部１２０，要対策混雑検知部１３０，対策効果予測部１４０，対策開始限度時刻計算部１５０，対策解除可能時刻計算部１６０，対策案管理部１７０，入出力部１８０を有する。 The congestion countermeasure support system includes a predicted number of people information database 111, a spatial information database 112, a countermeasure candidate database 113, a prediction intermediate state database 114, a congestion prediction section 120, a countermeasure countermeasure congestion detection section 130, a countermeasure effect prediction section 140, and a countermeasure start limit time. It has a calculation unit 150, a measure releasable time calculation unit 160, a measure plan management unit 170, and an input / output unit 180.
　混雑対策支援システムは，図２に示す予測人数情報２１０，図３に示す空間情報２２０，図４に示す部分空間情報２３０，図６に示す対策候補情報２４０，図７に示す混雑予測結果２５０，図８に示す要対策混雑情報２６０，図９に示す対策案２７０，図１０に示す予測途中状態情報２８０をデータとして用いる。 The congestion countermeasure support system includes the predicted number information 210 shown in FIG. 2, the spatial information 220 shown in FIG. 3, the partial space information 230 shown in FIG. 4, the countermeasure candidate information 240 shown in FIG. 6, the congestion prediction result 250 shown in FIG. The countermeasure required congestion information 260 shown in FIG. 8, the countermeasure plan 270 shown in FIG. 9, and the predicted midway state information 280 shown in FIG. 10 are used as data.
　＜機能の説明＞
予測人数情報データベース１１１は，予測人数情報２１０を記録するデータベースであり，記録，検索，および読出の機能を有する。予測人数情報データベースに保存される予測人数情報２１０は，例えば，空間内に設置されたカメラなどの映像分析によって得られる通行人数や改札機の通行ログなどのデータを用いて，公知の路線レベルでの旅客シミュレーションを用いて予測する。 <Description of functions>
The predicted number of people information database 111 is a database that records the predicted number of people information 210, and has functions of recording, searching, and reading. The predicted number of people information 210 stored in the predicted number of people information database is, for example, at a known route level using data such as the number of people who pass through the video analysis of cameras installed in the space and the traffic logs of ticket gates. Using the passenger simulation.
　空間情報データベース１１２は，空間情報２２０および部分空間情報２３０を記録するデータベースであり，記録，検索，および読出の機能を有する。空間情報２２０および部分空間２３０は，混雑予測を行う対象の駅ごとに予め作成する。空間情報２２０は，対象とする空間（例えば鉄道駅）の各位置の設備の属性を含む構造化された地図データである。部分空間情報２３０は，対象とする空間に包含される所定範囲に定義された部分空間について，その範囲および属性を付与した情報である。 The spatial information database 112 is a database that records the spatial information 220 and the partial spatial information 230, and has functions of recording, searching, and reading. The spatial information 220 and the partial space 230 are created in advance for each target station for which congestion prediction is performed. The spatial information 220 is structured map data including attributes of equipment at each position in a target space (for example, a railway station). The partial space information 230 is information in which a range and attributes are given to a partial space defined in a predetermined range included in the target space.
　対策候補データベース１１３は，対策候補情報２４０を記録するデータベースであり，記録，検索，および読出の機能を有する。対策候補情報２４０は，部分空間毎にその混雑に対して適用可能な対策の候補を対応付けて記録するデータである。対策候補情報２４０は，対象とする空間毎に予め作成する。 The countermeasure candidate database 113 is a database that records the countermeasure candidate information 240, and has recording, searching, and reading functions. The countermeasure candidate information 240 is data in which candidate countermeasures applicable to the congestion are recorded in association with each partial space. The countermeasure candidate information 240 is created in advance for each target space.
　予測途中状態データベース１１４は，予測途中状態情報２８０を記録するデータベースであり，記録，検索，および読出の機能を有する。予測途中状態情報２８０は，混雑予測部１２０あるいは対策効果予測部１４０が，予測実施中の途中状態を保存したデータである。混雑予測部１２０あるいは対策効果予測部１４０は，予測途中状態２８０を入力として，予測途中状態情報２８０が記録された時刻から予測を再開できる。 The prediction halfway state database 114 is a database that records the prediction halfway state information 280, and has recording, searching, and reading functions. Prediction intermediate state information 280 is data in which the congestion prediction unit 120 or the countermeasure effect prediction unit 140 stores the intermediate state during the prediction. The congestion prediction unit 120 or the countermeasure effect prediction unit 140 can resume prediction from the time when the prediction intermediate state information 280 is recorded, with the prediction intermediate state 280 as an input.
　混雑予測部１２０は，予測人数情報２１０，空間情報２２０，部分空間情報２３０を入力として，空間情報で表される空間に，予測人数情報２１０に従って旅客の移動を発生させたときのその空間内での混雑状況を公知の歩行者シミュレーション装置を用いて逐次予測し，各部分空間の混雑指標値の時系列の遷移である混雑予測結果２５０を逐次出力する。混雑指標値は，例えば空間内または空間内の特定の範囲の滞在人数，群集密度，動線密度を用いる。また，混雑予測部１２０は逐次予測を行う途中状態を予測途中状態情報２８０として出力でき，予測人数情報２１０，空間情報２２０，部分空間情報２３０の替わりに予測途中状態情報２８０を入力として予測を行うことができる。本実施形態では，公知のセルオートマトンの歩行者シミュレーション装置を混雑予測部２８０の一例として用いる。 The congestion prediction unit 120 receives the predicted number of people information 210, the spatial information 220, and the partial space information 230 as input, and in the space when the passenger is moved according to the predicted number of people information 210 in the space represented by the spatial information. Is sequentially predicted using a known pedestrian simulation device, and a congestion prediction result 250 that is a time-series transition of the congestion index value of each partial space is sequentially output. As the congestion index value, for example, the number of people staying in the space or a specific range in the space, the crowd density, and the flow line density are used. In addition, the congestion prediction unit 120 can output an intermediate state in which sequential prediction is performed as the predicted intermediate state information 280, and performs prediction using the predicted intermediate state information 280 as input instead of the predicted number information 210, the spatial information 220, and the partial space information 230. be able to. In this embodiment, a known cellular automaton pedestrian simulation device is used as an example of the congestion prediction unit 280.
　要対策混雑検知部１３０は，混雑予測部１２０が出力する混雑予測結果２５０を入力として，各部分空間に対して，混雑予測結果に対策が必要な混雑状況であるか否かを判定する対策閾値を超える混雑指標値が含まれているか否かを判定する。対策閾値を超える混雑指標値が含まれている場合には，当該混雑が発生する部分空間および時間の情報を含む要対策混雑情報２６０を出力する。対策閾値は，例えば用いる混雑指標において，群集事故のリスクが所定値以上に高いとされる値あるいは監視者が予め定めた値を用いる。 The countermeasure required congestion detection unit 130 receives the congestion prediction result 250 output from the congestion prediction unit 120 as an input, and measures threshold values for determining whether or not each of the partial spaces is in a congestion situation that requires countermeasures for the congestion prediction result. It is determined whether or not a congestion index value exceeding 1 is included. When a congestion index value exceeding the countermeasure threshold is included, countermeasure required congestion information 260 including information on the partial space where the congestion occurs and time is output. As the countermeasure threshold, for example, a value that indicates that the risk of a crowd accident is higher than a predetermined value or a value that is set in advance by a monitor is used in the congestion index to be used.
　対策効果予測部１４０は，予測人数情報２１０と空間情報２２０と部分空間情報２３０に加えて，対策候補情報２４０を入力として，対策候補を適用した条件で混雑予測部１２０と同様の方法で空間内の混雑状況を予測し，対策候補を適用した条件下での混雑予測結果２５０を出力する。また，混雑予測部１２０と同様に逐次予測を行う途中状態を予測途中状態情報２８０として出力でき，予測人数情報２１０，空間情報２２０，部分空間情報２３０の替わりに予測途中状態情報２８０を入力として予測を行うことができる。 The countermeasure effect prediction unit 140 receives the countermeasure candidate information 240 in addition to the predicted number of people information 210, the spatial information 220, and the partial space information 230, and uses the same method as the congestion prediction unit 120 under the condition that the countermeasure candidate is applied. The congestion prediction result 250 under the condition where the countermeasure candidate is applied is output. Further, in the same way as the congestion prediction unit 120, it is possible to output an intermediate state in which sequential prediction is performed as prediction intermediate state information 280, and predict using prediction intermediate state information 280 as an input instead of the predicted number information 210, space information 220, and partial space information 230. It can be performed.
　対策開始限度時刻計算部１５０は，要対策混雑情報２６０と，要対策混雑情報２６０が検知された部分空間に関連付けられた対策候補情報２４０と，当該対策候補を入力として対策効果予測部１４０が出力する混雑予測結果２５０を入力として，混雑指標値が検知閾値を超えないために遅くとも当該対策候補を実施しなければならない時刻である対策開始限度時刻を出力する。 The countermeasure start limit time calculation unit 150 receives the countermeasure required congestion information 260, the countermeasure candidate information 240 associated with the partial space in which the countermeasure required congestion information 260 is detected, and the countermeasure effect prediction unit 140 outputs the countermeasure candidate as input. The congestion prediction result 250 is input, and the countermeasure start limit time, which is the time when the countermeasure candidate must be executed at the latest because the congestion index value does not exceed the detection threshold, is output.
　対策解除可能時刻計算部１６０は，部分空間情報２３０と，要対策混雑情報２６０と，要対策混雑情報２６０が検知された部分空間に関連付けられた対策候補情報２４０と，混雑予測部１２０と対策効果予測部１４０の少なくとも一方が出力する混雑予測結果２５０を入力として，適用済みあるいは適用候補となる対策を解除可能な時刻である対策解除可能時刻を出力する
　対策案管理部１７０は，要対策混雑検知部１３０が出力する要対策混雑情報２６０と，対策開始限度時刻計算部１５０が出力する対策開始限度時刻と，対策解除可能時刻計算部１６０が出力する対策解除可能時刻を入力として，要対策混雑情報２６０に対して関連する対策候補の混雑緩和効果，対策開始限度時刻，対策解除可能時刻などを含む対策案情報２７０を出力する。また，実施中の対策案情報２７０を保持する。 The measure cancelable time calculation unit 160 includes the partial space information 230, the countermeasure necessary congestion information 260, the countermeasure candidate information 240 associated with the partial space in which the countermeasure necessary congestion information 260 is detected, the congestion predicting unit 120, and the countermeasure effect. Based on the congestion prediction result 250 output by at least one of the prediction units 140, the countermeasure proposal management unit 170 outputs a countermeasure releasable time that is a time at which a countermeasure that has already been applied or applied can be cancelled. The countermeasure required congestion information 260 output by the unit 130, the countermeasure start limit time output by the countermeasure start limit time calculating unit 150, and the countermeasure cancelable time output by the countermeasure cancelable time calculating unit 160 are input. The countermeasure proposal information 270 including the congestion alleviation effect of the countermeasure countermeasures related to 260, the countermeasure start limit time, the countermeasure cancelable time, etc. The Also, the countermeasure plan information 270 being implemented is held.
　入出力部１８０は，混雑予測結果２５０および対策案情報２７０を出力して表示装置に出力する機能を有する。表示装置は，例えば監視員が保持するコンピュータの表示装置，利用者への情報提供を行うデジタルサイネージ装置，利用者が保持するコンピュータの表示装置などを用いる。また，監視者などの操作者あるいは改札機などの機器から対策案の実施を行うか否か，実施中であるか否かを入力として受け付ける。 The input / output unit 180 has a function of outputting the congestion prediction result 250 and the countermeasure plan information 270 and outputting them to the display device. As the display device, for example, a computer display device held by a supervisor, a digital signage device that provides information to the user, a computer display device held by the user, or the like is used. In addition, it accepts as input whether or not to implement a countermeasure plan from an operator such as a supervisor or a device such as a ticket gate.
　以上に示した本実施形態の混雑対策支援システムの各部の機能は，演算装置，制御装置，記録装置，および入出力装置を有する一つ以上のコンピュータと各々のコンピュータ上で動作するソフトウェアとして実現される。 The functions of each part of the congestion countermeasure support system of the present embodiment described above are realized as one or more computers having an arithmetic device, a control device, a recording device, and an input / output device, and software operating on each computer. The
　＜データの説明＞
続いて，混雑対策支援システムで用いるデータについて説明する。 <Explanation of data>
Next, data used in the congestion countermeasure support system will be described.
　図２は，予測人数情報データベース１１１に記録する予測人数情報２１０の一例を示す図である。予測人数情報２１０は，出発地と目的地の組合せごとに，その間を移動する人数を時間別に記録した時系列データである。駅構内では，駅の出入口およびホーム（列車）が出発地および目的地である。出発地がホーム（列車）の場合，列車の到着時刻を開始時刻，列車出発時刻を終了時刻としてデータを作成する。 FIG. 2 is a diagram illustrating an example of the predicted number of people information 210 recorded in the predicted number of people information database 111. The predicted number of people information 210 is time-series data in which the number of people moving between the departure place and the destination is recorded by time. In the station yard, the station entrance and platform (train) are the departure and destination. When the departure place is a home (train), data is created with the arrival time of the train as the start time and the train departure time as the end time.
　図２では、出入口１を出発地として、時刻７：００から時刻７：１０の間に、ホーム１を目的地として移動する人数が１０人である。また、ホーム１を出発地として、時刻７：０１から時刻７：０２の間に、ホーム２を目的地として移動する人数が３０人である。これは時刻７：０１にホーム１に到着し、時刻７：０２に出発する列車１から降車する旅客のうち３０人がホーム２を目的地として移動することを意味する。 In FIG. 2, the number of people moving from the doorway 1 to the starting point and from the time 7:00 to the time 7:10 with the home 1 as the destination is ten. In addition, the number of people who move from home 7 as the destination and from home 7 as the destination between time 7:01 and time 7:02 is 30. This means that 30 passengers who arrive at the home 1 at the time 7:01 and get off from the train 1 leaving at the time 7:02 travel with the home 2 as the destination.
　図３は，空間情報データベースに記録する空間情報２２０の一例を示す図である。空間情報は、駅の構造および設備の情報を記録するデータであり、駅構造を元に予め作成する。本実施形態では、公知のセルオートマトンの歩行者シミュレーション装置で用いられる空間を格子状に分割した空間情報を用いる。空間情報２２０は，空間を構成する要素の単位である単位格子の組合せとして構成する。単位格子とは，例えば，通路格子，壁格子あるいは通行不可格子，階段格子，改札機格子，出入口格子，乗車位置格子などである。単位格子は各々属性として，例えば，通行の可否，通行可能な速度，通行可能な方向，通行に要する距離コスト，歩行者の空間への流出入の可否などを有する。図３は、改札口が１つ、ホームが２つある駅構造を、単位格子を用いた空間情報として表した一例である。 FIG. 3 is a diagram showing an example of the spatial information 220 recorded in the spatial information database. Spatial information is data for recording station structure and facility information, and is created in advance based on the station structure. In the present embodiment, spatial information obtained by dividing a space used in a known cellular automaton pedestrian simulation apparatus into a lattice shape is used. The spatial information 220 is configured as a combination of unit lattices that are units of elements constituting the space. The unit grid is, for example, a passage grid, a wall grid or an impassable grid, a stair grid, a ticket gate grid, an entrance grid, a boarding grid. Each unit grid has, as attributes, for example, whether or not it can pass, speed that can pass, direction that can pass, distance cost required for passing, whether or not it is possible to enter and exit the pedestrian space. FIG. 3 shows an example of a station structure having one ticket gate and two platforms as spatial information using a unit grid.
　図４は，空間情報データベース１１２に記録されている部分空間情報２３０の一例を示す図である。部分空間情報２３０は、混雑支援を行う対象の空間に包含される範囲で定義される部分空間の属性を格納するデータである。部分空間は，例えば通行量の規制や通行方向の制限が実施可能な地点，経路の分岐が生じる地点などで空間を分割した範囲で定義する。部分空間情報２３０は，例えば部分空間名，範囲，部分空間優先度，接続地点，接続部分空間名で構成する。 FIG. 4 is a diagram showing an example of the partial space information 230 recorded in the spatial information database 112. The partial space information 230 is data that stores the attributes of the partial space defined in the range included in the space to be subjected to the congestion support. For example, the partial space is defined as a range obtained by dividing the space at a point where the traffic volume can be restricted and the direction of the traffic can be restricted, or where a branch of the route occurs. The subspace information 230 includes, for example, a subspace name, a range, a subspace priority, a connection point, and a connection subspace name.
　部分空間名は，各部分空間を一意的に特定するための名称である。 The partial space name is a name for uniquely identifying each partial space.
　範囲は，部分空間の定義範囲を表し，空間内の座標あるいは単位格子の集合として保持する。図５は，図４の部分空間情報２３０に含まれる部分空間の範囲を平面図に示した一例である。図５に示す範囲Ａ００１～Ａ００４はそれぞれ図４の部分空間の範囲に対応し，地点Ａ０１１～Ａ０１４（階段）およびＡ０２１（改札機）によって分離されている。部分空間の範囲は，要対策混雑の検知を行う単位を基本とし，より細かい単位で設定してもよい。例えば，部分空間名「ホーム１」の部分空間を複数の部分空間に分割して定義してもよい。 The range represents the definition range of the subspace and is held as a set of coordinates in the space or unit cell. FIG. 5 is an example in which a partial space range included in the partial space information 230 of FIG. 4 is shown in a plan view. Ranges A001 to A004 shown in FIG. 5 correspond to the partial space ranges of FIG. 4 and are separated by points A011 to A014 (stairs) and A021 (ticket gates). The range of the subspace is basically based on the unit for detecting the countermeasure congestion, and may be set in finer units. For example, the partial space with the partial space name “Home 1” may be defined by being divided into a plurality of partial spaces.
　部分空間優先度は，複数の部分空間で要対策混雑情報２６０が検知されたときに対策を行う優先度を判定するときに用いる予め設定する評価値である。例えば，ホーム上の混雑は，退避が難しく，転落，将棋倒しなどの危険な事故が発生するリスクがコンコースよりも高いため，ホーム上の部分空間に対してコンコースの部分空間よりも高い優先度を設定する。 The partial space priority is an evaluation value that is set in advance to determine the priority for taking countermeasures when countermeasure required congestion information 260 is detected in a plurality of partial spaces. For example, congestion on the platform is difficult to evacuate and the risk of dangerous accidents such as falls and shogi falls is higher than the concourse, so the subspace on the home has a higher priority than the concourse subspace. Set.
　接続地点は，部分空間が他の部分空間と接続する地点を記録する。例えば，ホーム１（範囲Ａ００１）は，階段１（地点Ａ０１１）および階段２（地点Ａ０１２）を接続地点として，他の部分空間と接続する。接続地点は，範囲と同様に空間内の座標あるいは単位格子の集合として保持する。 The connection point records the point where the partial space is connected to another partial space. For example, the home 1 (range A001) is connected to another partial space using the staircase 1 (point A011) and the staircase 2 (point A012) as connection points. The connection point is held as a set of coordinates in the space or unit cell like the range.
　　接続部分空間名は，部分空間が接続地点を介して接続する他の部分空間の部分空間名を記録する。例えば，ホーム１（範囲Ａ００１）は，階段１（地点Ａ０１１）および階段２（地点Ａ０１２）を介してコンコース内（範囲Ａ００３）と接続する。 The connection subspace name records the subspace name of the other subspace to which the subspace is connected via the connection point. For example, home 1 (range A001) is connected to the inside of the concourse (range A003) via stairs 1 (point A011) and stairs 2 (point A012).
　図６は，対策候補情報データベース１１３に記録する対策候補情報２４０の一例を示す図である。対策候補情報２４０は，部分空間毎に実施可能な対策の候補を予め入力して保持するデータであり，例えば対象部分空間名，対象地点，対策状態で構成する。 FIG. 6 is a diagram showing an example of the countermeasure candidate information 240 recorded in the countermeasure candidate information database 113. The countermeasure candidate information 240 is data that is inputted and held in advance candidate countermeasures that can be implemented for each partial space, and is composed of, for example, a target partial space name, a target point, and a countermeasure state.
　対象部分空間名は，当該対策の実施対象となる部分空間を特定するためのデータである。 The target subspace name is data for specifying the subspace to be subjected to the countermeasure.
　対象地点とは，対策として状態を変更する対象を特定するデータである。部分空間の接続地点と同一であることが多いが，それに限定されない。また，同一地点内をさらに細分化して定義する場合もある。例えば，改札機の集合を表す地点ではなく，改札機１台１台に細分化して定義してもよい。 The target point is data for specifying a target whose state is changed as a countermeasure. It is often the same as the connection point of the partial space, but is not limited to this. In addition, the same point may be further subdivided and defined. For example, instead of a point representing a set of ticket gates, it may be defined by subdividing into one ticket gate.
　対策状態とは，対象地点が取り得る状態を表す。取り得る状態は，改札機のように機械の取り得る状態と，階段の通行方向規制のように監視員が交通整理を行うなどによって生じる状態のことを言う。 Measure state indicates the state that the target point can take. The states that can be taken are the state that can be taken by a machine such as a ticket gate, and the state that occurs when an observer conducts traffic control, such as regulation of the direction of stairs.
　図７は，混雑予測部１２０あるいは対策効果予測部１４０が出力する混雑予測結果２５０の一例を示す図である。混雑予測結果２５０は，各部分空間の混雑指標値の時系列の予測値で構成する。部分空間の混雑指標値としては，例えば部分空間内の滞在人数，群集密度，あるいは動線密度を用いる。図７の例は滞在人数を表している。 FIG. 7 is a diagram illustrating an example of the congestion prediction result 250 output from the congestion prediction unit 120 or the countermeasure effect prediction unit 140. The congestion prediction result 250 is composed of time series prediction values of the congestion index value of each partial space. As the congestion index value of the subspace, for example, the number of people staying in the subspace, the crowd density, or the flow line density is used. The example of FIG. 7 represents the number of visitors.
　図８は，要対策混雑検知部１３０が要対策混雑を検知した場合に出力する要対策混雑情報２６０の一例を示す図である。検知部分空間名は，要対策混雑が発生すると検知された部分空間の名称である。予測混雑時間は，要対策混雑が発生する時間帯である。最大混雑は，予測された要対策混雑における混雑指標値の最大値である。 FIG. 8 is a diagram illustrating an example of countermeasure required congestion information 260 that is output when the countermeasure required congestion detection unit 130 detects countermeasure required congestion. The detection subspace name is the name of the subspace detected when the countermeasure required congestion occurs. The predicted congestion time is a time zone in which countermeasures are required. The maximum congestion is the maximum value of the congestion index value in the predicted countermeasure required congestion.
　図９は，対策案管理部１７０が出力・保持する対策案情報２７０の一例を示す図である。混雑対策案情報２７０は，要対策混雑情報２６０の検知部分空間に対応する対策候補情報２４０に含まれる対象地点と対策状態と，対応する対策開始限度時刻計算部１５０が出力する対策開始限度時刻と，対策解除可能時刻計算部１６０が出力する対策解除可能時刻と，対策開始限度時刻から対策解除可能時刻まで当該対策候補情報２４０を適用した混雑予測の混雑緩和効果で構成する。図９は，要対策混雑が部分空間ホーム１で検知された場合の対策案情報の例である。 FIG. 9 is a diagram showing an example of the measure plan information 270 output / held by the measure plan management unit 170. The congestion countermeasure plan information 270 includes target points and countermeasure states included in the countermeasure candidate information 240 corresponding to the detection subspace of the countermeasure countermeasure congestion information 260, the countermeasure start limit time output by the corresponding countermeasure start limit time calculation unit 150, and The countermeasure cancelable time calculation unit 160 outputs the countermeasure cancelable time and the congestion mitigation effect of the congestion prediction using the countermeasure candidate information 240 from the countermeasure start limit time to the countermeasure cancelable time. FIG. 9 is an example of countermeasure plan information when countermeasure required congestion is detected in the partial space home 1.
　図１０は，混雑予測部１２０あるいは混雑効果予測部１４０が予測実行時に出力する予測途中状態情報２８０の一例を示す図である。予測途中情報２８０は，混雑予測部１２０あるいは混雑効果予測部１４０を任意の予測時間から再度予測を開始するために必要な情報を全て保存したデータである。例えば，予測途中状態情報２８０の一例として，予測途中状態情報を保存した記録予測時刻，予測空間内の歩行者エージェント毎に出発地，目的地，現在位置座標，進行方向，次に行動する行動時刻などを保持する歩行者エージェントの状態情報２８１，予測人数情報を予測空間にどこまで反映したか判別する目的地毎の残流入人数を保持する流入人数の状態情報２８２，混雑予測部１２０および混雑効果予測部１４０を構成する歩行者シミュレーション装置の内部パラメータ２８３，空間の状態を保持する空間情報２２０などで構成する。 FIG. 10 is a diagram illustrating an example of the prediction intermediate state information 280 output by the congestion prediction unit 120 or the congestion effect prediction unit 140 when the prediction is executed. Prediction in-progress information 280 is data that stores all information necessary for the congestion prediction unit 120 or the congestion effect prediction unit 140 to start prediction again from an arbitrary prediction time. For example, as an example of the predicted intermediate state information 280, the predicted recording time when the predicted intermediate state information is stored, the departure point, the destination, the current position coordinates, the traveling direction, and the next action time for each pedestrian agent in the prediction space The state information 281 of the pedestrian agent that holds the information, etc., the state information 282 of the inflowing number that holds the remaining inflowing number for each destination that determines how much the predicted number of information is reflected in the prediction space, the congestion prediction unit 120, and the congestion effect prediction The internal parameter 283 of the pedestrian simulation apparatus which comprises the part 140, and the space information 220 etc. which hold | maintain the state of space are comprised.
　＜処理の説明＞
続いて，混雑対策支援システムの処理フローについて説明する。 <Description of processing>
Next, the processing flow of the congestion countermeasure support system will be described.
　混雑対策支援システムは，逐次保存される予測人数情報２１０を入力として，対象空間内の以降の混雑状況を逐次予測し，対策が必要な混雑を検知した場合に，適用可能な対策内容および対策時間を含む対策案を提示することによって混雑対策を支援するシステムである。 The congestion countermeasure support system inputs the predicted number of people information 210 that is stored sequentially, predicts the subsequent congestion status in the target space, and detects the congestion that requires countermeasures, and the countermeasure contents and countermeasure time that can be applied. It is a system that supports congestion countermeasures by presenting countermeasure plans including
　まず，図１１のフローチャートを用いて混雑対策システム全体の処理フローの一例について説明する。その後，混雑対策システムを構成する要素の処理フローについて詳記する。以下，ステップをＳと略記する。例えば，ステップ１００１はＳ１００１と表記する。 First, an example of the processing flow of the entire congestion countermeasure system will be described using the flowchart of FIG. After that, the processing flow of the elements constituting the congestion countermeasure system will be described in detail. Hereinafter, step is abbreviated as S. For example, step 1001 is expressed as S1001.
　Ｓ１００１：混雑対策支援システムの処理対象時間をＴとすると，混雑対策支援システムは，時間インターバル S1001: When the processing target time of the congestion countermeasure support system is T, the congestion countermeasure support system
Figure JPOXMLDOC01-appb-M000001
 毎に周期的にＳ１００２からＳ１００９の処理を行う。
Figure JPOXMLDOC01-appb-M000001
 Periodically, the processing from S1002 to S1009 is performed.
　Ｓ１００２：混雑対策支援システムは，予測人数情報データベース１１１から現在時刻以降の予測人数情報２１０と，空間情報データベース１１２から空間情報２２０および部分空間情報２３０を読み込み，混雑予測部１２０に入力する。 S1002: The congestion countermeasure support system reads the predicted number information 210 after the current time from the predicted number information database 111 and the spatial information 220 and the partial space information 230 from the spatial information database 112 and inputs them to the congestion prediction unit 120.
　Ｓ１００３：混雑予測部１２０は，混雑対策支援システムに入力された予測人数情報２１０，空間情報２２０，部分空間情報２３０に基づいて混雑予測を実施し，混雑予測結果２５０を出力する。また，所与の時間周期で予測途中状態情報２８０を予測途中状態データベース１１３に格納する。 S1003: The congestion prediction unit 120 performs congestion prediction based on the predicted number of people information 210, the spatial information 220, and the partial space information 230 input to the congestion countermeasure support system, and outputs a congestion prediction result 250. Further, the predicted intermediate state information 280 is stored in the predicted intermediate state database 113 at a given time period.
　Ｓ１００４：要対策混雑検知部１３０は，混雑予測部１２０が出力する混雑予測結果２５０を入力として，対策を要する混雑の発生の有無を判定する。対策を要する混雑が検知される場合は，要対策混雑情報２６０を出力し，Ｓ１００５に進む。対策を要する混雑が検知されない場合，Ｓ１００６に進む。 S1004: The countermeasure-necessary congestion detection unit 130 receives the congestion prediction result 250 output from the congestion prediction unit 120 and determines whether or not congestion requiring countermeasures has occurred. If congestion requiring countermeasures is detected, countermeasure necessary congestion information 260 is output, and the process proceeds to S1005. If congestion requiring countermeasures is not detected, the process proceeds to S1006.
　Ｓ１００５：要対策混雑検知部１３０が出力する要対策混雑情報２６０を入力として，対策案情報２７０を作成し，対策案管理部１７０に対策案を追加する。 S 1005: The countermeasure required congestion information 260 output from the countermeasure required congestion detection unit 130 is input, the countermeasure plan information 270 is created, and the countermeasure plan is added to the countermeasure plan management unit 170.
　Ｓ１００６：対策案管理部１７０で，入出力部１８０から受け取った入力情報を基に実施中の対策案を更新する。 S1006: The countermeasure plan management unit 170 updates the currently implemented countermeasure plan based on the input information received from the input / output unit 180.
　Ｓ１００７：対策案管理部１７０は，現在実施中の対策案があるか否かを判定する。現在実施している対策がある場合は，Ｓ１００８に進む。現在実施している対策がない場合は，Ｓ１００９に進む。 S1007: The countermeasure plan management unit 170 determines whether there is a countermeasure plan that is currently being implemented. If there is a measure currently being implemented, the process proceeds to S1008. If there is no currently implemented measure, the process proceeds to S1009.
　Ｓ１００８：対策解除可能時刻計算部１６０は，混雑予測部１２０が出力する混雑予測結果を入力として，対策解除可能時刻を再計算し，適用中の対策案を更新する。 S1008: The measure cancelable time calculation unit 160 receives the congestion prediction result output from the congestion prediction unit 120 as an input, recalculates the measure cancelable time, and updates the currently applied measure plan.
　Ｓ１００９：混雑対策支援システムは，以降の混雑予測結果，要対策混雑が検知された場合は対策案，実施中の対策案に対する対策解除可能時刻を出力する。 S1009: The congestion countermeasure support system outputs a subsequent congestion prediction result, and when a countermeasure necessary congestion is detected, a countermeasure plan and a countermeasure releasable time for the currently implemented countermeasure plan are output.
　以上の処理により，対策解除可能時刻を含む混雑対策案を出力可能となる。また，実施中の対策案の対策解除可能時刻を再度計算することで，より精緻に対策解除可能時刻を算出可能となる。加えて，予測途中状態情報２８０を周期的に格納することによって，任意の時刻から再計算を行う処理負荷を減らすことが可能となる。 By the above processing, it is possible to output a congestion countermeasure plan including the time when countermeasures can be released. In addition, by calculating again the countermeasure releasable time of the countermeasure plan being implemented, the countermeasure releasable time can be calculated more precisely. In addition, by periodically storing the predicted intermediate state information 280, it is possible to reduce the processing load for performing recalculation from an arbitrary time.
　続いて，図１２のフローチャートを用いて対策案情報作成処理（Ｓ１００５）の処理フローの一例について説明する。 Subsequently, an example of the processing flow of the measure plan information creation process (S1005) will be described using the flowchart of FIG.
　Ｓ２００１：要対策混雑検知部１３０が出力する要対策混雑情報２６０が検知される部分空間を対象とする対策候補情報２４０を対策候補情報データベース１１３から抽出する。 S2001: Countermeasure candidate information 240 for the partial space in which the countermeasure required congestion information 260 output by the countermeasure required congestion detection unit 130 is detected is extracted from the countermeasure candidate information database 113.
　Ｓ２００２：抽出され対策候補情報の集合をＮとして，対策候補情報 S2002: Countermeasure candidate information, where N is a set of extracted countermeasure candidate information
Figure JPOXMLDOC01-appb-M000002
 についてＳ２００３～Ｓ２００５の処理を繰り返す。
Figure JPOXMLDOC01-appb-M000002
 The processes from S2003 to S2005 are repeated.
　Ｓ２００３：対策開始限度時刻計算部１５０は，対策候補情報nが特定する対策による状態変化を反映した空間情報２２０および部分空間情報２３０と，人数予測情報２１０を入力として，対策候補情報nを適用した場合の対策開始限度時刻を算出する。 S2003: The countermeasure start limit time calculation unit 150 applies the countermeasure candidate information n with the spatial information 220 and the partial space information 230 reflecting the state change caused by the countermeasure specified by the countermeasure candidate information n and the number of people prediction information 210 as inputs. In this case, the countermeasure start time limit is calculated.
　Ｓ２００４：対策終了可能時刻計算部１６０は，対策候補情報nが特定する対策による状態変化を反映した空間情報２２０および部分空間情報２３０と，人数予測情報２１０，対策候補情報ｎに対応する対策開始限度時刻を入力として，対策候補情報nを対応する対策開始限度時刻から適用した場合の対策終了可能時刻を算出する。 S2004: The measure end possible time calculation unit 160 sets the measure start limit corresponding to the spatial information 220 and the partial space information 230 reflecting the change in state due to the measure specified by the measure candidate information n, the number of people prediction information 210, and the measure candidate information n. Taking the time as an input, calculate the possible countermeasure end time when countermeasure candidate information n is applied from the corresponding countermeasure start limit time.
　Ｓ２００５：対策効果予測部１２０は，対策候補情報nに対応する条件を対策開始限度時刻から対策解除時刻まで適用した条件の空間情報２２０および部分空間情報２３０と，人数予測情報２１０を入力として，対策案を実施したときの混雑予測結果を算出し，その混雑指標値の最大値と，要対策混雑情報２６０の最大混雑との差分を混雑緩和効果として算出する。 S2005: The countermeasure effect prediction unit 120 inputs the spatial information 220 and the partial space information 230 of the condition in which the condition corresponding to the countermeasure candidate information n is applied from the countermeasure start limit time to the countermeasure cancellation time, and the number prediction information 210 as an input. The congestion prediction result when the plan is implemented is calculated, and the difference between the maximum value of the congestion index value and the maximum congestion of the countermeasure required congestion information 260 is calculated as the congestion mitigation effect.
　Ｓ２００６：対策案管理部１７０は，対策候補情報ｎと，対策候補情報ｎについて対応する対策開始限度時刻と，対策解除可能時刻と，混雑緩和効果を用いて対策案情報を生成し，保持する。 S2006: The countermeasure plan management unit 170 generates and holds countermeasure plan information using the countermeasure candidate information n, the countermeasure start limit time corresponding to the countermeasure candidate information n, the countermeasure releasable time, and the congestion mitigation effect.
　Ｓ２００７：要対策混雑情報２６０に対する対策案情報２７０を出力する。 S2007: The countermeasure plan information 270 for the countermeasure required congestion information 260 is output.
　以上の処理により，対策案情報２７０の作成が可能となる。混雑対策支援システムでは，予測を行う時間周期毎に全ての要対策混雑情報２６０および対策候補情報２４０に対して対策案の作成および更新を行うのを基本とするが，必ずしも全ての対策案の作成および更新を行う必要はない。例えば，計算資源の制約により全ての対策案を毎周期再評価することが難しい場合，各対策案を再評価する周期と予測を行う時間周期とは別に設定してもよい。各対策案を再評価する周期は，例えば対策開始限度時刻までの時間が短いほど短い周期となるように定める。これにより，対策開始限度時刻が近い対策案ほど高頻度に情報を更新することができる。 By the above processing, the countermeasure plan information 270 can be created. In the congestion countermeasure support system, it is basic to create and update countermeasure plans for all the necessary countermeasure congestion information 260 and countermeasure candidate information 240 for each prediction time period, but not necessarily all countermeasure plans are created. And there is no need to update. For example, when it is difficult to re-evaluate every countermeasure plan every cycle due to the limitation of computing resources, the period for re-evaluating each countermeasure plan and the time period for prediction may be set separately. The cycle for re-evaluating each countermeasure plan is determined so that, for example, the shorter the time until the countermeasure start limit time, the shorter the cycle. As a result, it is possible to update the information more frequently as the countermeasure plan has a shorter countermeasure start time limit.
　続いて，図１３のフローチャートと図１４，図１５を用いて対策開始限度時刻計算部１５０の処理フローの一例について説明する。 Subsequently, an example of the processing flow of the countermeasure start limit time calculation unit 150 will be described with reference to the flowchart of FIG. 13 and FIGS. 14 and 15.
　Ｓ３００１：混雑予測部１２０が出力する要対策混雑情報２７０が出力された部分空間の混雑予測結果２５０である第１の混雑予測結果と，対策開始限度時刻を算出する対象の対策候補情報ｎを入力する。 S3001: Input the first congestion prediction result, which is the congestion prediction result 250 of the partial space in which the countermeasure required congestion information 270 output by the congestion prediction unit 120 is output, and the countermeasure candidate information n for which the countermeasure start limit time is calculated. To do.
　Ｓ３００２：対策候補情報nの特定する条件変更を現時刻から要対策混雑情報が検知されるまでの範囲内の任意の時刻から実施開始する対策案を適用した第２の混雑予測結果を対策効果予測部１４０で算出し，第２の混雑予測結果とする。 S3002: Predicting the effect of countermeasures based on the second congestion prediction result to which a countermeasure plan for starting the change of conditions specified by the countermeasure candidate information n from an arbitrary time within the range from the current time until the detection of countermeasure congestion information is detected. Calculated by the unit 140 and used as the second congestion prediction result.
　Ｓ３００３：第１の混雑予測結果と第２の混雑予測結果から対策開始限度時刻の概算値を算出する。対策開始限度時刻の概算値の算出方法は，例えば，図１４に示すように第１の混雑予測結果の混雑指標値の最大値をとる時刻ｔとそれ以前に極小値をとる時刻ｓを算出し，時刻ｓから時刻ｔまでの第２の混雑予測結果の混雑指標値の変化を直線近似し，近似した直線と同一の傾きで時刻ｔに検知閾値となる点を通る直線と，第１の混雑予測結果の混雑指標値のグラフとの時刻ｔ以前の交点を算出し，該交点の時刻を対策開始限度時刻とする。 S3003: An approximate value of the countermeasure start limit time is calculated from the first congestion prediction result and the second congestion prediction result. For example, as shown in FIG. 14, the approximate value of the countermeasure start limit time is calculated by calculating a time t at which the maximum congestion index value of the first congestion prediction result is taken and a time s at which the minimum value is taken before that. , A change in the congestion index value of the second congestion prediction result from time s to time t is linearly approximated, a straight line passing through the point that becomes the detection threshold at time t with the same slope as the approximated straight line, and the first congestion The intersection before the time t with the graph of the congestion index value of the prediction result is calculated, and the time of the intersection is set as the countermeasure start limit time.
　Ｓ３００４：予測途中状態データベース１１４から第１の混雑予測結果を予測時に保存した予測途中状態情報の集合から，対策開始限度時刻の概算値の直前の時刻の予測途中状態情報を第１の予測途中状態情報として抽出する。例えば，図１５に示すように第１の予測途中状態情報を抽出する。 S3004: From the set of prediction intermediate state information in which the first congestion prediction result is stored at the time of prediction from the prediction intermediate state database 114, the prediction intermediate state information at the time immediately before the approximate value of the countermeasure start limit time is obtained as the first prediction intermediate state. Extract as information. For example, as shown in FIG. 15, the first prediction intermediate state information is extracted.
　Ｓ３００５：第１の予測途中状態情報と対策候補情報nを入力として，対策効果予測部１４０で第１の予測途中状態情報から対策候補情報nが特定する条件変更を実施開始する対策案を適用した第３の混雑予測結果を作成する。 S3005: Applying a measure plan for starting the implementation of the condition change specified by the measure candidate information n from the first predictive state information by the measure effect prediction unit 140, using the first predictive state information and the measure candidate information n as input. A third congestion prediction result is created.
　Ｓ３００６：第３の混雑予測結果に要対策混雑が含まれているか否かを要対策混雑検知部１３０で判定する。 S3006: The countermeasure required congestion detector 130 determines whether the third congestion prediction result includes countermeasure required congestion.
　Ｓ３００７：第３の混雑予測結果に要対策混雑が含まれている場合，第１の予測途中状態情報が記録された以前の時刻に記録された予測途中状態情報を第２の予測途中状態情報とする。第３の混雑予測結果に要対策混雑が含まれていない場合，第１の予測途中状態情報が記録された以降の時刻に記録された予測途中状態情報を第２の予測途中状態情報とする。続いて，第２の予測途中状態情報と対策候補情報nを入力として，対策効果予測部１４０で第２の予測途中状態情報から対策候補情報nが特定する条件変更を実施開始する対策案を適用した第４の混雑予測結果を作成する。図１５では，第３の混雑予測結果に要対策混雑が含まれておらず，第１の予測途中状態情報の次に記録された予測途中状態情報を第２の予測途中状態情報とした例である。 S3007: When the countermeasure required congestion is included in the third congestion prediction result, the prediction intermediate state information recorded at the time before the first prediction intermediate state information is recorded is set as the second prediction intermediate state information. To do. If the countermeasure required congestion is not included in the third congestion prediction result, the prediction intermediate state information recorded at the time after the first prediction intermediate state information is recorded is set as the second prediction intermediate state information. Subsequently, with the second predicted intermediate state information and the countermeasure candidate information n as inputs, the countermeasure proposal for starting the implementation of the condition change specified by the countermeasure candidate information n from the second predicted intermediate state information by the countermeasure effect prediction unit 140 is applied. The fourth congestion prediction result is created. FIG. 15 shows an example in which the third congestion prediction result does not include countermeasure required congestion, and the predicted intermediate state information recorded next to the first predicted intermediate state information is used as the second predicted intermediate state information. is there.
　Ｓ３００８：第４の混雑予測結果に要対策混雑が含まれているか否かを要対策混雑検知部１３０で判定する。 S3008: The countermeasure required congestion detection unit 130 determines whether the fourth congestion prediction result includes countermeasure required congestion.
　Ｓ３００９：第３の混雑予測結果と第４の混雑予測結果のいずれか一方のみに要対策混雑が含まれていることを終了条件とし、終了条件を満たしているか否かを判定する。終了条件を満たしている場合、すなわち第３の混雑予測結果と第４の混雑予測結果のいずれか一方のみに要対策混雑が含まれている場合，Ｓ３０１０に進む。終了条件を満たさない場合，第４の混雑予測結果を新しい第３の混雑予測結果とする。この場合，第２の予測途中状態情報から見て，第１の予測途中状態情報と時間軸の反対方向にある予測途中状態情報を新しい第２の予測途中状態情報として，Ｓ３００７に進む。 S3009: It is determined that only one of the third congestion prediction result and the fourth congestion prediction result includes countermeasure required congestion, and it is determined whether the termination condition is satisfied. When the termination condition is satisfied, that is, when countermeasure required congestion is included in only one of the third congestion prediction result and the fourth congestion prediction result, the process proceeds to S3010. If the end condition is not satisfied, the fourth congestion prediction result is set as a new third congestion prediction result. In this case, as viewed from the second prediction intermediate state information, the first prediction intermediate state information and the prediction intermediate state information in the direction opposite to the time axis are set as new second prediction intermediate state information, and the process proceeds to S3007.
　Ｓ３０１０：第３の混雑予測結果と第４の混雑予測結果の内，要対策混雑を含まない一方の予測結果の入力となった予測途中状態情報が記録された予測時刻を対策開始限度時刻とする。 S3010: Among the third congestion prediction result and the fourth congestion prediction result, the prediction start time when the prediction intermediate state information that is the input of one of the prediction results not including the countermeasure required congestion is recorded is set as the countermeasure start limit time. .
　以上の処理により，概算値を用いて対策開始限度時刻の探索時間範囲を限定しつつ，より厳密な対策開始限度時刻を算出することが可能となる。 Through the above processing, it is possible to calculate a more precise countermeasure start time limit while limiting the search time range of the countermeasure start limit time using the approximate value.
　続いて，図１６のフローチャートと図１７を用いて対策解除可能時刻計算部１６０の処理フローについて説明する。 Subsequently, the processing flow of the measure cancelable time calculation unit 160 will be described with reference to the flowchart of FIG. 16 and FIG.
　Ｓ４００１：混雑予測部１２０あるいは対策効果予測部１４０が出力する混雑予測結果を入力する。 S4001: The congestion prediction result output from the congestion prediction unit 120 or the countermeasure effect prediction unit 140 is input.
　Ｓ４００２：部分空間の集合をＥとして，全ての部分空間 S4002: All subspaces with a set of subspaces as E
Figure JPOXMLDOC01-appb-M000003
 についてＳ４００３の処理を繰り返す。
Figure JPOXMLDOC01-appb-M000003
 The process of S4003 is repeated.
　Ｓ４００３：部分空間ｅの混雑予測結果から予測対象時間内に予測される混雑指標値が予め定めた解除閾値を最後に下回り，かつその後解除閾値を所定の時間（例えば，駅構内を対象とするとき列車到着あるいは発車の時間間隔を用いる）以上上回ることがないと予測される時刻を部分空間ｅの対策解除可能時刻の候補値として算出する。現時刻以降に予測される混雑指標値が解除閾値を超えることがない場合は，現時刻を部分空間ｅの対策解除可能時刻の候補値とする。予測対象時間内に予測される混雑指標値が所定の時間以上解除閾値を上回ることがない時刻が発見できない場合，予測対象時間内には部分空間ｅの対策解除可能時刻の候補値がないと判断する。このとき対策解除可能時刻の候補値が見つかるまで予測対象時間を拡大してもよい。解除閾値は，例えば対策開始時の混雑指標値を用いる。例えば，部分空間がコンコース改札外，コンコース改札内，ホームの３つある空間を対象とした場合，図１７のように、３つある部分空間それぞれで、混雑指標値が解除閾値を下回る時刻を対策解除可能時刻の候補値として算出する。 S4003: When the congestion index value predicted within the prediction target time from the congestion prediction result of the partial space e is finally below a predetermined release threshold, and then the release threshold is set for a predetermined time (for example, in a station premises) The time that is predicted not to exceed the train arrival or departure time interval is calculated as a candidate value for the time when the countermeasure can be canceled in the subspace e. If the congestion index value predicted after the current time does not exceed the cancellation threshold, the current time is set as a candidate value for the countermeasure cancellation possible time of the partial space e. If it is not possible to find a time when the congestion index value predicted within the prediction target time does not exceed the release threshold for a predetermined time or more, it is determined that there is no candidate value for the time at which the countermeasure can be released within the prediction target time. To do. At this time, the prediction target time may be expanded until a candidate value for the countermeasure cancelable time is found. For example, a congestion index value at the start of countermeasures is used as the release threshold. For example, in the case where the partial space is a space outside the concourse ticket gate, inside the concourse ticket gate, and home, as shown in FIG. 17, the time when the congestion index value falls below the release threshold in each of the three partial spaces as shown in FIG. Is calculated as a candidate value for the time at which countermeasures can be canceled.
　Ｓ４００４：全ての部分空間ｅの対策解除可能時刻の候補値の中から最も遅い時刻を空間全体の対策解除可能時刻の候補値として算出する。例えば，図１７の例では，ホームの対策解除可能時刻の候補値を空間全体の混雑解除可能時刻の候補値として算出する。 S4004: The latest time among the candidate values of the countermeasure releasable time of all the partial spaces e is calculated as the candidate value of the countermeasure releasable time of the entire space. For example, in the example of FIG. 17, the candidate value of the home countermeasure releasable time is calculated as the candidate value of the congestion releasable time of the entire space.
　Ｓ４００５：対策解除可能時刻算出部１６０の入力となる混雑予測結果２５０を予測した際に記録された予測途中状態情報２８０から，空間全体の対策解除可能時刻を以降で最も早い時刻に記録された予測途中状態情報２８０を第３の予測途中状態情報として出力する。 S4005: Prediction recorded as the earliest time after which the countermeasure releasable time of the entire space is recorded from the prediction intermediate state information 280 recorded when the congestion prediction result 250 that is input to the countermeasure releasable time calculation unit 160 is predicted. The intermediate state information 280 is output as the third predicted intermediate state information.
　Ｓ４００６：第３の予測途中状態情報から混雑対策案を解除した条件で予測対象時間の混雑予測を実施し，その混雑予測結果２５０を出力する。 S4006: Congestion prediction of the prediction target time is performed under the condition that the congestion countermeasure plan is canceled from the third prediction intermediate state information, and the congestion prediction result 250 is output.
　Ｓ４００７：Ｓ４００６で出力した混雑予測結果２５０を入力として，要対策混雑検知部１３０で要対策混雑が含まれているか否かを判定する。要対策混雑が含まれている場合，Ｓ４００８に進む。要対策混雑が含まれていない場合，Ｓ４００９に進む。 S4007: With the congestion prediction result 250 output in S4006 as an input, the countermeasure required congestion detection unit 130 determines whether or not countermeasure required congestion is included. If countermeasure required congestion is included, the process proceeds to S4008. If the countermeasure required congestion is not included, the process proceeds to S4009.
　Ｓ４００８：対策解除可能時刻算出部１６０の入力となる混雑予測結果２５０を予測した際に記録された予測途中状態情報２８０から，第３の予測途中状態情報が記録された時刻の次に記録された予測途中状態情報を新たに第３の予測途中状態情報として，Ｓ４００６に戻る。 S4008: Recorded next to the time when the third prediction intermediate state information is recorded from the prediction intermediate state information 280 recorded when the congestion prediction result 250 that is input to the countermeasure cancellation possible time calculation unit 160 is predicted. The prediction intermediate state information is newly set as third prediction intermediate state information, and the process returns to S4006.
　Ｓ４００９：第３の予測途中状態情報が記録された時刻を対策解除可能時刻として出力する。 S4009: The time at which the third predicted intermediate state information is recorded is output as the countermeasure releasable time.
　以上の処理により，要対策混雑状態が再び検知されないように対策解除可能時刻の算出が可能となる。Ｓ４００６からＳ４００８の処理を行わず，空間全体の対策解除可能時刻の候補値を直接対策解除可能時刻として出力してもよい。また，Ｓ４００４で出力する空間全体の対策解除可能時刻の候補値の替わりにＳ４００３で算出する部分空間毎の対策解除可能時刻の候補値を出力し，該部分空間に関連する対策案の対策解除可能時刻として用いてもよい。これにより，複数の対策案が適用されているときに，対策案毎に段階的に解除することを支援可能となる。 By the above processing, it is possible to calculate the countermeasure releasable time so that the countermeasure necessary congestion state is not detected again. The candidate value of the countermeasure releasable time for the entire space may be directly output as the countermeasure releasable time without performing the processing from S4006 to S4008. Further, instead of the candidate value of the countermeasure cancelable time for the entire space output in S4004, the candidate value of the countermeasure cancelable time for each partial space calculated in S4003 is output, and the countermeasure countermeasure related to the partial space can be canceled. It may be used as time. As a result, when a plurality of countermeasures are applied, it is possible to support the phased cancellation of each countermeasure.
　以上説明した実施例では、所定の空間の混雑に対して実施可能な対策候補の情報を格納する対策候補データベース１１３と，所定の空間について将来の時間における混雑指標値の遷移を算出する混雑予測部１２０と，混雑指標値が閾値を超える要対策混雑を検知する要対策混雑検知部１３０と，対策候補を適用した条件で将来の時間における混雑指標値の遷移を算出する対策効果予測部１４０と、対策解除可能時刻を算出する対策解除時刻計算部１６０を有する。このような構成により、所定の空間の混雑状況の管理において，対策を要する混雑に対して，適切な対策の解除時刻を提供することができる。 In the embodiment described above, a countermeasure candidate database 113 that stores information on countermeasure candidates that can be implemented for congestion in a predetermined space, and a congestion prediction unit that calculates a transition of a congestion index value at a future time for the predetermined space. 120, a countermeasure-necessary congestion detector 130 for detecting a countermeasure-necessary congestion whose congestion index value exceeds a threshold value, a countermeasure-effect prediction unit 140 for calculating a transition of a congestion index value at a future time under a condition in which countermeasure candidates are applied, A countermeasure cancellation time calculation unit 160 for calculating a countermeasure cancellation possible time is provided. With such a configuration, it is possible to provide an appropriate countermeasure release time for congestion that requires countermeasures in managing the congestion status of a predetermined space.
　この対策解除時刻計算部１６０は、対策解除可能時刻を出力する出力部を有し、対策解除可能時刻計算部は、対策候補を適用した条件で算出する混雑指標値の遷移を入力として，対策候補の適用を解除しても所定の時間以上要対策混雑が検知されない時刻である対策解除可能時刻を算出する。 The countermeasure cancellation time calculation unit 160 includes an output unit that outputs a countermeasure cancellation possible time. The countermeasure cancellation possible time calculation unit receives a transition of the congestion index value calculated under the condition to which the countermeasure candidate is applied as an input for the countermeasure candidate. Even if the application of is canceled, a countermeasure releasable time is calculated, which is a time at which countermeasure congestion is not detected for a predetermined time or more.
　対策解除時刻計算部は、所定の空間を分割した部分空間を対象として，全ての部分空間の予測される混雑指標値の遷移が所定の対策解除を判定する閾値を下回り，その後所定の時間以上上回ることがない時刻を空間全体の対策解除可能時刻として算出して出力する。全ての部分空間の代わりに、特定の部分区間毎の対策解除可能時間として算出、出力することもできる。
また本実施例は、システムが把握した前記要対策混雑の発生を回避できるタイミングで対策候補を開始可能な対策開始時刻のうち、最も遅い時刻である対策開始限度時刻を特定する対策開始限度時刻計算部を有する。このような構成により、要対策混雑が発生する空間および時間と共に，その混雑に対して適用可能な混雑対策候補と，要対策混雑を解消するために混雑対策候補を遅くとも実行しなければならない対策開始限度時刻に加えて，混雑対策候補を安全に解除できる時刻である対策解除可能時刻を出力することで，要対策混雑に対して適切な対策の実施が容易となる。 The countermeasure cancellation time calculation unit targets a partial space obtained by dividing a predetermined space, and the transition of predicted congestion index values in all the partial spaces is below a threshold for determining the predetermined countermeasure cancellation, and then exceeds a predetermined time or more. The time that does not occur is calculated and output as the measure cancelable time for the entire space. Instead of all the partial spaces, it is also possible to calculate and output the countermeasure cancelable time for each specific partial section.
Also, in this embodiment, a countermeasure start limit time calculation for identifying a countermeasure start limit time that is the latest time among countermeasure start times at which the candidate countermeasures can be started at a timing at which the occurrence of the countermeasure required congestion understood by the system can be avoided. Part. With such a configuration, along with the space and time in which countermeasures are required, congestion countermeasure candidates that can be applied to the congestion, and countermeasures that must be executed at the latest to eliminate the countermeasure congestion are started. In addition to the time limit, by outputting a countermeasure releasable time that is a time when the congestion countermeasure candidate can be safely canceled, it is easy to implement an appropriate countermeasure against the countermeasure congestion.
１１１　予測人数情報データベース
１１２　空間情報データベース
１１３　対策候補データベース
１１４　予測途中状態データベース
１２０　混雑予測部
１３０　要対策混雑検知部
１４０　対策効果予測部
１５０　対策開始限度時刻計算部
１６０　対策解除可能時刻計算部
１７０　対策案管理部
１８０　入出力部
２１０　予測人数情報
２２０　空間情報
２３０　部分空間情報
２４０　対策候補情報
２５０　混雑予測結果
２６０　要対策混雑情報
２７０　対策案
２８０　予測途中状態情報 111 Predicted Number Information Database 112 Spatial Information Database 113 Countermeasure Candidate Database 114 Prediction State Database 120 Congestion Prediction Unit 130 Countermeasure Congestion Detection Unit 140 Countermeasure Effect Prediction Unit 150 Countermeasure Start Limit Time Calculation Unit 160 Countermeasure Removable Time Calculation Unit 170 Countermeasure Proposal Management unit 180 Input / output unit 210 Predicted number of people information 220 Spatial information 230 Partial space information 240 Countermeasure candidate information 250 Congestion prediction result 260 Countermeasure congestion information 270 Countermeasure plan 280 Prediction state information

Claims (15)

  1. 　所定の空間の混雑に対して実施可能な対策候補の情報を格納する対策候補データベースと，前記所定の空間について将来の時間における混雑指標値の遷移を算出する混雑予測部と，前記混雑指標値が閾値を超える要対策混雑を検知する要対策混雑検知部と，前記対策候補を適用した条件で将来の時間における前記混雑指標値の遷移を算出する対策効果予測部と、対策解除可能時刻を算出する対策解除時刻計算部を有することを特徴とする混雑対策支援システム。 A countermeasure candidate database that stores information on candidate countermeasures that can be implemented for congestion in a predetermined space, a congestion prediction unit that calculates a transition of a congestion index value at a future time for the predetermined space, and the congestion index value A countermeasure required congestion detection unit for detecting countermeasure required congestion exceeding a threshold value, a countermeasure effect prediction unit for calculating a transition of the congestion index value at a future time under the condition to which the countermeasure candidate is applied, and calculating a countermeasure releasable time A congestion countermeasure support system comprising a countermeasure cancellation time calculation unit.
  2. 　請求項１に記載の混雑対策支援システムであって、前記対策解除可能時刻を出力する出力部を有し、前記対策解除可能時刻計算部は、前記対策候補を適用した条件で算出する混雑指標値の遷移を入力として，前記対策候補の適用を解除しても所定の時間以上要対策混雑が検知されない時刻である対策解除可能時刻を算出することを特徴とする混雑対策支援システム。 2. The congestion countermeasure support system according to claim 1, further comprising an output unit that outputs the countermeasure releasable time, wherein the countermeasure releasable time calculation unit calculates a congestion index value under a condition to which the countermeasure candidate is applied. An anti-congestion support system characterized in that a countermeasure cancelable time, which is a time at which no countermeasure countermeasure congestion is detected for a predetermined time or more even when the application of the countermeasure candidate is cancelled, is input.
  3. 　請求項１に記載の混雑対策支援システムであって，前記対策解除時刻計算部は、所定の空間を分割した部分空間を対象として，全ての部分空間の予測される混雑指標値の遷移が所定の対策解除を判定する閾値を下回り，その後所定の時間以上上回ることがない時刻を空間全体の対策解除可能時刻として算出して出力することを特徴とする混雑対策支援システム。 2. The congestion countermeasure support system according to claim 1, wherein the countermeasure cancellation time calculation unit is configured to perform predetermined congestion index value transitions in all partial spaces for a predetermined partial space. A congestion countermeasure support system characterized in that a time that falls below a threshold for determining countermeasure cancellation and then does not exceed a predetermined time is calculated and output as a countermeasure cancellation possible time for the entire space.
  4. 　請求項１に記載の混雑対策支援システムであって，前記対策解除時刻計算部は、所定の空間を分割した部分空間を対象として，部分空間の予測される混雑指標値の遷移が所定の対策解除を判定する閾値を下回り，その後所定の時間以上上回ることがない時刻を部分空間毎の対策解除可能時刻として算出して出力することを特徴とする混雑対策支援システム。 2. The congestion countermeasure support system according to claim 1, wherein the countermeasure cancellation time calculation unit is configured to cancel a predetermined countermeasure when a transition of a predicted congestion index value of a partial space is targeted for a partial space divided into a predetermined space. A congestion countermeasure support system that calculates and outputs a time that falls below a threshold value for determining whether or not it exceeds a predetermined time thereafter as a countermeasure releasable time for each partial space.
  5. 　請求項３または請求項４に記載の混雑対策支援システムであって，前記対策解除時刻計算部は、対策解除可能時刻の算出に用いる所定の時間に列車到着あるいは列車発車の時間間隔を用いることを特徴とする混雑対策支援システム。 5. The congestion countermeasure support system according to claim 3, wherein the countermeasure cancellation time calculation unit uses a time interval of train arrival or train departure at a predetermined time used for calculating the countermeasure cancellation possible time. A featured congestion countermeasure support system.
  6. 　請求項１に記載の混雑対策支援システムであって，前記混雑予測部が予測対象時間に対して時間方向に逐次計算によって混雑を予測し，予測対象時間内の任意の時刻の計算状態を該時刻から予測を再開可能な情報である予測途中状態情報として、出力する、あるいは予測途中状態データベースに格納することを特徴とする混雑対策支援システム。 2. The congestion countermeasure support system according to claim 1, wherein the congestion prediction unit predicts congestion by sequential calculation in a time direction with respect to a prediction target time, and indicates a calculation state at an arbitrary time within the prediction target time. A congestion countermeasure support system, characterized in that it is output as prediction intermediate state information, which is information capable of resuming prediction from, or stored in a prediction intermediate state database.
  7. 　請求項６に記載の混雑対策支援システムであって，予測実行時に所定の時間周期毎に予測途中状態情報を予測途中状態データベースに格納することを特徴とする混雑対策支援システム。 7. The congestion countermeasure support system according to claim 6, wherein the predicted intermediate state information is stored in the predicted intermediate state database for each predetermined time period at the time of prediction execution.
  8. 　請求項６に記載の混雑対策支援システムであって，特定の時刻から対策を適用あるいは解除した条件で混雑予測を行う場合に，前記予測途中状態データベースから前記時刻とは別の時刻に記録された予測途中状態情報を用いて，前記時刻から予測を開始する機能を有することを特徴とする混雑対策支援システム。 The congestion countermeasure support system according to claim 6, wherein when the congestion prediction is performed under a condition in which the countermeasure is applied or canceled at a specific time, the congestion is recorded at a time different from the time from the prediction intermediate state database. A congestion countermeasure support system characterized by having a function of starting prediction from the time using prediction intermediate state information.
  9. 　請求項６に記載の混雑対策支援システムであって、システムが把握した前記要対策混雑の発生を回避できるタイミングで前記対策候補を開始可能な対策開始時刻のうち、最も遅い時刻である対策開始限度時刻を特定する対策開始限度時刻計算部を有することを特徴とする混雑対策支援システム。 7. The congestion countermeasure support system according to claim 6, wherein a countermeasure start limit that is the latest time among countermeasure start times at which the candidate countermeasures can be started at a timing at which the occurrence of the countermeasure countermeasure congestion grasped by the system can be avoided. A congestion countermeasure support system comprising a countermeasure start limit time calculation unit for specifying a time.
  10. 　請求項９に記載の混雑対策支援システムであって，前記対策開始限度時刻計算部は、前記対策開始限度時刻の概算値を算出し，前記概算値とは別の時刻に記録された予測途中状態情報を始点として，前記始点以外の時刻に記録された前記予測途中状態情報から対策案を適用した混雑予測を実施した場合に前記要対策混雑が発生するか否かを順次評価することで，前記要対策混雑が発生する以前に前記要対策混雑が発生しない最も遅く記録された前記予測途中状態情報を探索し、該予測途中状態情報が記録された時刻を対策開始限度時刻として出力することを特徴とする混雑対策支援システム。 10. The congestion countermeasure support system according to claim 9, wherein the countermeasure start limit time calculation unit calculates an approximate value of the countermeasure start limit time, and is in a predicted halfway state recorded at a time different from the approximate value. By sequentially evaluating whether or not the countermeasure congestion is required when performing the congestion prediction to which the countermeasure plan is applied from the prediction midway state information recorded at a time other than the starting point, using the information as a starting point, Searching for the latest predicted midway state information recorded before the countermeasure required congestion does not occur, and outputting the time when the predicted midway state information was recorded as the countermeasure start limit time Congestion countermeasure support system.
  11. 　請求項８に記載の混雑対策支援システムであって，前記対策開始限度時刻計算部は、対策解除可能時刻の概算値を算出し，前記概算値とは別の時刻に記録された前記予測途中状態情報を始点として，前記始点以外の時刻に記録された前記予測途中状態情報から対策案を適用した混雑予測を実施した場合に前記要対策混雑が発生するか否かを順次評価することで，前記要対策混雑が発生する以降に前記要対策混雑が発生しない最も早く記録された前記予測途中状態情報を探索し、該予測途中状態情報が記録された時刻を対策解除可能時刻として出力することを特徴とする混雑対策支援システム。 9. The congestion countermeasure support system according to claim 8, wherein the countermeasure start limit time calculation unit calculates an approximate value of the countermeasure cancelable time, and the predicted intermediate state recorded at a time different from the approximate value By sequentially evaluating whether or not the countermeasure congestion is required when performing the congestion prediction to which the countermeasure plan is applied from the prediction midway state information recorded at a time other than the starting point, using the information as a starting point, Searching the earliest predicted intermediate state information that does not cause the countermeasure required congestion after the countermeasure required congestion occurs, and outputs the time when the predicted intermediate state information was recorded as the countermeasure cancelable time Congestion countermeasure support system.
  12. 　請求項９に記載の混雑対策支援システムであって，前記対策開始限度時刻を再度計算して出力する計算周期を，対策案毎に該対策案の優先順位に基づいて定めることを特徴とする混雑対策支援システム。 10. The congestion countermeasure support system according to claim 9, wherein a calculation cycle for recalculating and outputting the countermeasure start limit time is determined for each countermeasure plan based on the priority order of the countermeasure plans. Countermeasure support system.
  13. 請求項１２に記載の混雑対策支援システムであって，対策案毎の計算および出力の優先順位を現時刻から対策開始限度時刻までの残り時間に基づいて定めることを特徴とする混雑対策支援システム。 13. The congestion countermeasure support system according to claim 12, wherein priority of calculation and output for each countermeasure plan is determined based on a remaining time from the current time to the countermeasure start limit time.
  14. 　請求項１に記載の混雑対策支援システムであって，前記対策解除可能時刻を再度計算して出力する計算周期を，対策案毎に該対策案の優先順位に基づいて定めることを特徴とする混雑対策支援システム。 2. The congestion countermeasure support system according to claim 1, wherein a calculation cycle for recalculating and outputting the countermeasure releasable time is determined for each countermeasure plan based on the priority order of the countermeasure plans. Countermeasure support system.
  15. 請求項１４に記載の混雑対策支援システムであって，対策案毎の計算および出力の優先順位を現時刻から対策解除可能時刻までの残り時間に基づいて定めることを特徴とする混雑対策支援システム。 15. The congestion countermeasure support system according to claim 14, wherein the priority of calculation and output for each countermeasure plan is determined on the basis of the remaining time from the current time to the countermeasure releasable time.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005165676A (en) * 2003-12-02 2005-06-23 Mitsubishi Heavy Ind Ltd Facility management system and facility management method
JP2015215766A (en) * 2014-05-12 2015-12-03 株式会社Ｓｃｒｅｅｎホールディングス Evacuation route providing system, evacuation route providing method, and evacuation route providing program
JP2016057354A (en) * 2014-09-05 2016-04-21 株式会社ナビタイムジャパン Information processing system, information processor, inductive information output system, method for processing information, and information processing program

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005165676A (en) * 2003-12-02 2005-06-23 Mitsubishi Heavy Ind Ltd Facility management system and facility management method
JP2015215766A (en) * 2014-05-12 2015-12-03 株式会社Ｓｃｒｅｅｎホールディングス Evacuation route providing system, evacuation route providing method, and evacuation route providing program
JP2016057354A (en) * 2014-09-05 2016-04-21 株式会社ナビタイムジャパン Information processing system, information processor, inductive information output system, method for processing information, and information processing program

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YAMASHITA TOMOHISA: "Proposal of complex hierarchical model and exhaustive analysis for urban area simulation", RESEARCH REPORT OF INFORMATION PROCESSING SOCIETY OF JAPAN, vol. 2012-ICS-166, no. 4, 15 April 2012 (2012-04-15), pages 5 - 6, ISSN: 2186-2583 *

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