WO2016075932A1 - モデル集約装置、避難予測システム、集約モデル生成装置、集約方法及びコンピュータ読み取り可能記録媒体 - Google Patents
モデル集約装置、避難予測システム、集約モデル生成装置、集約方法及びコンピュータ読み取り可能記録媒体 Download PDFInfo
- Publication number
- WO2016075932A1 WO2016075932A1 PCT/JP2015/005613 JP2015005613W WO2016075932A1 WO 2016075932 A1 WO2016075932 A1 WO 2016075932A1 JP 2015005613 W JP2015005613 W JP 2015005613W WO 2016075932 A1 WO2016075932 A1 WO 2016075932A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- evacuation
- information
- model
- aggregating
- route
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims description 10
- 238000011084 recovery Methods 0.000 claims abstract description 56
- 230000002776 aggregation Effects 0.000 claims description 67
- 238000004220 aggregation Methods 0.000 claims description 67
- 230000004931 aggregating effect Effects 0.000 claims description 52
- 238000004458 analytical method Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 3
- 230000007704 transition Effects 0.000 description 39
- 238000010586 diagram Methods 0.000 description 17
- 238000003860 storage Methods 0.000 description 7
- 238000010304 firing Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 4
- 230000010365 information processing Effects 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000001186 cumulative effect Effects 0.000 description 3
- 238000005315 distribution function Methods 0.000 description 3
- 239000003112 inhibitor Substances 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000010845 search algorithm Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- 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
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- 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
- G06Q90/00—Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
- G06Q90/20—Destination assistance within a business structure or complex
- G06Q90/205—Building evacuation
Definitions
- the present invention relates to a model aggregation device, an evacuation prediction system, an aggregate model generation device, an aggregation method, and a computer-readable recording medium.
- Patent Document 1 describes an evacuation plan evaluation system and the like.
- the number of persons requiring assistance calculates the number of persons requiring assistance during evacuation based on the attribute information of the user of the mobile device.
- the number-of-evacuation destination calculation unit calculates the number of evacuees who evacuate to their homes and evacuation centers.
- the simulation unit performs a simulation when an evacuee evacuates from each polygon area to the home and evacuation site.
- a score calculation part calculates the score for an evacuation plan based on the number of persons requiring assistance, the number of evacuees, and a simulation result.
- Patent Document 2 describes a data processing apparatus that can predict a destination even when there is a lack in data on the current location acquired in real time.
- Patent Document 3 describes an evacuation time predicting device that predicts an evacuation time from a multi-level building having stairs.
- the present invention has been made to solve the above-described problems, and has as its main object to provide a model aggregation device and the like that make it easy to predict the time required for an evacuee to finish evacuation.
- the model aggregating apparatus is a model that accepts an evacuation model that includes evacuation information about an evacuee's evacuation route, restoration information about the restoration time of a location where a failure has occurred on the evacuation route, and a relationship between the evacuation information and the evacuation information.
- the aggregation method includes an evacuation model including evacuation information about an evacuee's evacuation route, restoration information about a restoration time of a place where a failure occurred in the evacuation route, and a relationship between the evacuation information and the evacuation information.
- Information included in the evacuation model is collected based on predetermined conditions regarding acceptance, evacuation information, or recovery information.
- the computer-readable recording medium includes a computer, evacuation information about an evacuation route of an evacuee, recovery information about a recovery time of a place where a failure occurs in the evacuation route, and a relationship between the evacuation information and the evacuation information.
- a program for executing a process for receiving an evacuation model including, and a process for aggregating information included in the evacuation model based on a predetermined condition regarding evacuation information or recovery information is stored non-temporarily.
- each component of each device represents a functional unit block.
- Each component of each device can be realized by any combination of an information processing device 500 and software as shown in FIG. 14, for example.
- the information processing apparatus 500 includes the following configuration as an example.
- each device can be realized as a dedicated device.
- Each device can be realized by a combination of a plurality of devices.
- the direction of the arrow in a drawing shows an example and does not limit the direction of the signal between components.
- FIG. 1 is a diagram illustrating a configuration of a model aggregation device according to the first embodiment of the present invention.
- FIG. 2 is a diagram illustrating a configuration of an evacuation prediction system including the model aggregation device according to the first embodiment of the present invention.
- FIG. 3 is a diagram illustrating an example of an evacuation model received by the model aggregation device according to the first embodiment of the present invention.
- FIG. 4 is a diagram illustrating an example of an evacuation route and the like represented by an evacuation model received by the model aggregation device according to the first embodiment of the present invention.
- FIG. 1 is a diagram illustrating a configuration of a model aggregation device according to the first embodiment of the present invention.
- FIG. 2 is a diagram illustrating a configuration of an evacuation prediction system including the model aggregation device according to the first embodiment of the present invention.
- FIG. 3 is a diagram illustrating an example of an evacuation model received by the model aggregation device according to the first embodiment of the present invention.
- FIG. 5 is a diagram illustrating an example of an evacuation route and the like represented by an evacuation model received by the model aggregation device according to the first embodiment of the present invention.
- FIG. 6 is a diagram illustrating an example of the relationship information of the evacuation model received by the model aggregation device according to the first embodiment of the present invention.
- FIG. 7 is a diagram illustrating another example of an evacuation model received by the model aggregation device according to the first embodiment of the present invention.
- FIG. 8 is a diagram illustrating an example of evacuation models that are aggregated in the model aggregating apparatus according to the first embodiment of the present invention.
- FIG. 9 is a diagram illustrating another example of evacuation models that are aggregated in the model aggregating apparatus according to the first embodiment of the present invention.
- FIG. 10 is a diagram illustrating another example of evacuation models that are aggregated in the model aggregating apparatus according to the first embodiment of the present invention.
- FIG. 11 is a flowchart showing the operation of the model aggregating apparatus in the first embodiment of the present invention.
- FIG. 12 is a diagram illustrating a configuration of a modified example of the evacuation prediction system according to the first embodiment of the present invention.
- FIG. 13 is a diagram illustrating a configuration of the modified example of the evacuation prediction system and the aggregate model generation device according to the first embodiment of the present invention.
- the model aggregation device 100 includes a model reception unit 110 and an aggregation unit 120.
- the model reception unit 110 receives an evacuation model including evacuation information regarding the evacuation route of the refugee, recovery information regarding the recovery time of the location where the failure occurred in the evacuation route, and a relationship between the evacuation information and the evacuation information.
- the aggregating unit 120 aggregates evacuation models based on predetermined conditions regarding evacuation information or recovery information.
- an evacuation prediction system 10 including the model generation unit 20, the above-described model aggregation device 100, and the analysis unit 30 is configured.
- the model generation unit 20 generates an evacuation model based on the above-described evacuation information and recovery information.
- the analysis unit 30 uses the evacuation model aggregated by the model aggregating apparatus 100 to predict the time required for the evacuee to evacuate.
- the model reception unit 110 receives an evacuation model including evacuation information, recovery information, and a relationship between them.
- the evacuation model is generated by the model generation unit 20 included in the evacuation prediction system 10.
- the evacuation model may be generated by a person other than the model generation unit 20 such as a person who directly seeks the time required for the evacuee to evacuate.
- the model reception unit 110 can receive an evacuation model in which the evacuation information, the recovery information, and their relationship are represented as one model.
- the model receiving unit 110 can receive an evacuation model in which evacuation information, recovery information, and their relationship are divided into a plurality of submodels.
- the evacuation model is represented as evacuation submodel representing evacuation information, a restoration submodel representing restoration information, and relation information representing a relationship between the evacuation submodel and the restoration submodel.
- the model reception unit 110 receives a model described in, for example, a stochastic time Petri net (hereinafter referred to as “sTPN”).
- sTPN stochastic time Petri net
- STPN is represented as a set of ⁇ P, T, A-, A +, A ⁇ , m0, EFT, LFT, F, C, E, L> as an example. Each of these elements is represented by a predetermined figure (not shown).
- P is a set of places.
- the place is represented by a white circle.
- T is a set of transitions.
- transitions are represented by white squares or bars.
- A- is an input arc that connects a place and a transition in the direction from the place to the transition.
- a + is an output arc that connects the place and the transition in the direction from the transition to the place.
- the input arc and the output arc may be simply referred to as an arc.
- the arc In the diagram representing sTPN, the arc is represented by an arrow.
- A. is an inhibitor arc that connects places and transitions in the direction from place to transition.
- the inhibitor arc In the diagram representing sTPN, the inhibitor arc is represented by an arrow with a round tip.
- M0 is an initial marking that represents the number of non-negative tokens in each place.
- the token is represented by a black circle arranged inside the place.
- EFT and LFT are the minimum and maximum firing times for each transition included in T.
- EFT is a non-negative real number including zero.
- LFT is a non-negative real number including zero and infinity. Further, the value of LFT is equal to or greater than the value of EFT.
- F is a cumulative distribution function relating to the firing time between EFT and LFT for each transition included in T.
- C is a weight representing the ease of firing for each of a plurality of transitions that can be fired when a plurality of transitions can fire simultaneously.
- C is assigned for transitions that can be fired simultaneously.
- E is an enabling function associated with the marking for each transition included in T.
- L (flushed function) is assigned to the transition. When the transition to which L is assigned is ignited, the token on the place associated with L is erased regardless of the connection relationship by arc with the transition.
- a transition can be ignited in the following cases.
- the transition fires, one token is reduced from the places connected via the input arc, and one token is added to the place connected via the output arc.
- the time is larger than the EFT value and smaller than the LFT value. • The enabling function is true.
- the evacuation model is represented by sTPN divided into an evacuation submodel, a restoration submodel, and related information.
- FIG. 3A shows an example of an evacuation submodel.
- the evacuation submodel shown in FIG. 3A is generated based on the evacuation information related to the evacuee E1 shown in FIG.
- Evacuation information includes, for example, geographic information related to evacuation routes, such as road networks that evacuees may pass during evacuation, evacuation sources, evacuation destinations, and evacuation status according to the recovery status of evacuation routes
- the information regarding the evacuation route that is the route through which is passed is included.
- the geographic information is represented in a form such as a directed graph shown in FIG.
- areas and places where evacuees may stay are represented as nodes.
- the area indicated by the node numbered 1 or 2 is a disaster area.
- the area indicated by the node with the number 6 or 7 is an area that is a candidate for an evacuation destination.
- road networks that can connect to areas where evacuees may stay and can serve as evacuation routes are represented as arrows.
- the direction of the arrow is determined according to the direction in which the evacuees evacuate.
- the location where the fault has occurred when there is a location where a fault has occurred in the road network (hereinafter referred to as “the location where the fault has occurred”), information indicating that is located at the position corresponding to the location where the fault has occurred as necessary. Attached.
- FIG. 4 (A) it is assumed that a road network has failed at two locations f1 and f2 and cannot pass.
- FIG. 4B shows information regarding the evacuation source, the evacuation destination, and the evacuation route regarding the evacuee E1.
- the evacuee E1 evacuates from the area corresponding to the node assigned number 1 in FIG. 4A to the area corresponding to the node assigned number 6.
- evacuation takes place on the evacuation route indicated by (i) to (iv) in FIG. 4B according to the restoration status of f1 and f2, which are the places where the failure occurred.
- places p1 to p7 correspond to nodes 1 to 7 having the same numbers shown in FIG.
- transitions t0 to t3, t5 to t8, and arcs connected to the transitions correspond to the arrows shown in FIG.
- FIG. 3B shows another example of the evacuation submodel.
- the evacuation submodel shown in FIG. 3B is generated based on the evacuation information related to the evacuee E2 shown in FIG.
- the geographic information is the same as the example related to the refugee E1.
- the evacuee E2 evacuates from the area corresponding to the node assigned number 3 in FIG. 5A to the area corresponding to the node assigned number 7. It is assumed that Then, it is assumed that evacuation is performed on the evacuation route indicated by (i) to (iv) in FIG. 5B according to the restoration status of f1 and f2, which are the places where the failure occurred.
- the evacuation information requires transit time when passing through the evacuation route, the number of people accommodated in each area, the capacity of the evacuation route (for example, the number of people who can pass per unit time), etc. Contains information about time.
- FIG. 3C shows an example of the recovery submodel.
- the restoration submodel shown in FIG. 3C is generated with respect to the evacuation route of the refugee E1 in FIG. 4 and the refugee E2 shown in FIG.
- the evacuation submodel shown in FIG. 3 (C) the evacuation submodel is generated assuming that f2 is restored first and f1 is restored for the failure locations f1 and f2 shown in FIG. 4 or FIG. ing.
- the recovery submodel is generally generated based on recovery information regarding the evacuation route of the evacuees.
- the recovery information includes, for example, operations for recovery and their order.
- the operation for restoration includes, for example, the restoration work itself at the location where the failure occurred and the movement of the restoration resource for restoration.
- the order of recovery includes a case where one recovery resource performs recovery in order, and a case where a plurality of recovery resources perform recovery in parallel.
- the recovery information includes information related to a change in time required for evacuation (not shown) such as a change in time required for passing through the evacuation route and a change in capacity of the evacuation route in accordance with the failure information.
- FIG. 6 shows an example of relation information when sTPN is used for the evacuation submodel and the recovery submodel.
- the relationship information shown in FIG. 6 is generated for the refugee E1 in FIG. 4 and the refugee E2 shown in FIG.
- This relation information is generated as an enabling function of the sTPN so that the transition that can be ignited in the recovery sub-model changes according to the recovery status of f1 and f2, which are the locations where the failure occurred, for the evacuee E1.
- this relation information is generated as an enabling function of sTPN so that the transition that can be ignited in the recovery sub-model changes according to the recovery status of f1, which is the location of failure, for the evacuee E2. .
- model reception unit 110 can receive an evacuation model in which the evacuation information, the recovery information, and the relationship thereof are represented as one model, as described above.
- the model receiving unit 110 can also receive such a model.
- the aggregation unit 120 aggregates the evacuation models received by the model reception unit 110 based on a predetermined condition.
- to collect the evacuation model means to change the evacuation model so as to exclude from the evacuation model information or the like that is unnecessary when obtaining the time required for the evacuee to evacuate.
- the aggregation unit 120 can delete the unnecessary information described above from the evacuation model.
- the information to be aggregated is determined under predetermined conditions described later.
- the aggregating unit 120 determines that the unnecessary information is not necessary when obtaining the time required for evacuation of the evacuees with respect to other information. Can be aggregated from the evacuation model.
- the aggregating unit 120 can aggregate geographical information related to evacuation information included in the evacuation model, for example.
- the geographical information to be aggregated includes an area where the evacuees may stay and a route through which the evacuees may pass.
- the aggregating unit 120 can aggregate, for example, elements having a small relationship with the evacuation route (locations or roads that do not become an evacuation route) among the above-described geographical information.
- the aggregating unit 120 can delete, for example, a place corresponding to the area targeted for aggregation.
- the aggregation unit 120 can delete, for example, a transition, an input arc, and an output arc corresponding to a route that is an aggregation target.
- FIG. 8 shows an example in which the above-described evacuation submodels related to the evacuee E1 are collected.
- FIG. 8A is an example of an evacuation submodel for the refugee E1. This evacuation model is the same as the evacuation submodel shown in FIG.
- FIG. 8B shows an evacuation submodel collected for the failure occurrence location f2 in FIG. In the evacuation submodel shown in FIG. 8B, the transition t5 corresponding to the failure occurrence location f2 and the arc connected to the transition t5 are deleted.
- FIG. 9 shows an example in which the above-described evacuation models related to the evacuee E2 are collected.
- FIG. 9A is an example of an evacuation submodel for the refugee E2. This evacuation model is the same as the evacuation submodel shown in FIG.
- FIG. 9B shows an evacuation submodel collected for the route from region 2 to region 4 in FIG. In the evacuation submodel shown in FIG. 9B, the transition t2 corresponding to the route and the arc connected to the transition t2 are deleted.
- the paths corresponding to the transitions t4 and t7, the places p2 and p4, and the arcs connecting these are redundant. That is, the evacuees do not travel along the route when evacuating in this case. Therefore, these elements may be deleted as in the sub model shown in FIG.
- the aggregating unit 120 can aggregate information related to recovery information included in the evacuation model, for example.
- the information to be aggregated includes an operation for restoring the location where the failure occurred, the order of restoration, and the like.
- the aggregation unit 120 can delete sTPN elements such as places and transitions corresponding to each.
- the aggregation unit 120 does not have to delete the elements to be aggregated from the evacuation model.
- the aggregating unit 120 may add additional information to the evacuation model so that the above-described elements to be aggregated are not analyzed when the evacuation model is analyzed. In this case, the elements to be aggregated may be left in the evacuation model.
- the aggregating unit 120 may appropriately add a place and an input arc connected to the transition, for example, so that a transition subject to aggregation is not ignited. .
- FIG. 10 shows an example in which the recovery submodels related to the evacuees E1 and E2 described above are aggregated.
- FIG. 10A is an example of a recovery submodel. This evacuation model is the same as the evacuation submodel shown in FIG.
- the evacuation submodel shown in FIG. 10A is generated assuming that f2 is restored first and then f1 is restored for the failure locations f1 and f2 in FIG. 4 or FIG.
- FIG. 10B shows a restoration submodel in which related information is collected assuming that the failure occurrence location f2 is not restored.
- the restoration of f2 and the transition t11 representing the state and the places p19 and p15 are deleted.
- transitions, places, and arcs related to these are also deleted.
- the aggregating unit 120 can aggregate the evacuation models based on any predetermined condition.
- the aggregating unit 120 can aggregate evacuation models based on conditions related to evacuation information.
- the aggregating unit 120 can aggregate, for example, information on areas and routes away from the evacuation routes of refugees, that is, information on regions and routes where the refugees may not pass during evacuation. Areas and routes that may not be included in the evacuation route of the evacuees can pass through the evacuee's evacuation source, evacuation destination, and areas, routes, and the number of refugees that are a certain distance from the evacuation route Includes small capacity areas and routes. In this case, the aggregation unit 120 can change the area to be aggregated and the range of the route according to the distance from the evacuation source to the evacuation destination.
- the aggregating unit 120 may aggregate the evacuation model by setting predetermined conditions so that regions and routes that are relatively close to the evacuation source and the evacuation destination are also subject to aggregation. it can. Further, the aggregating unit 120 can aggregate the evacuation models by setting a predetermined condition so that a route with a small capacity is not targeted for aggregation. In other words, the aggregation unit 120 limits the evacuation route to an area or route that is relatively close to the evacuation source or the evacuation destination, but defines a predetermined condition so that a route with a small capacity is included in the evacuation route.
- the aggregating unit 120 may aggregate the evacuation models by setting predetermined conditions so that the areas and routes that are relatively far away from the evacuation source and the evacuation destination are subject to aggregation. it can. Further, the aggregating unit 120 can aggregate the evacuation models by setting a predetermined condition so that a route with a small capacity is targeted for aggregation. That is, the aggregating unit 120 includes a region or a route that is relatively far away from the evacuation source or the evacuation destination in the evacuation route, but defines a predetermined condition so that the evacuation route is limited to a route with a large capacity.
- the aggregating unit 120 can aggregate evacuation models based on conditions related to recovery information. For example, the aggregating unit 120 can aggregate information related to faulty locations that are not related to evacuation of evacuees. For example, there is a failure occurrence location that is not included in the evacuation route of the evacuees.
- the aggregating unit 120 can aggregate information about the failure location based on the time required for recovery at each failure location.
- the aggregating unit 120 can specify a target failure occurrence location based on information on the transition time related to failure recovery.
- the information related to the transition time includes the value of the minimum firing time and the maximum firing time of the transition, and the firing probability represented by a cumulative distribution function.
- the model receiving unit 110 of the model aggregating apparatus 100 accepts an evacuation model to be aggregated (step S101).
- the model receiving unit 110 can receive such information via any input means or the like.
- the model reception unit 110 may use information stored in advance in an arbitrary storage unit such as a memory or a disk.
- the model receiving unit 110 may receive such information via a network.
- the model receiving unit 110 can also receive predetermined conditions that are used later by the aggregation unit 120.
- the accepted evacuation model or the like is stored in a memory or storage device (not shown).
- the aggregation unit 120 of the model aggregation device 100 aggregates the evacuation model (step S102).
- the evacuation models aggregated by the aggregation unit 120 are output in an arbitrary form. Further, the evacuation models aggregated by the aggregation unit 120 are stored in a memory, a storage device, or the like (not shown).
- the output evacuation model is used in the analysis unit 30 of the evacuation prediction system 10, for example.
- the analysis unit 30 predicts the time required for the evacuee to evacuate using the evacuation model.
- the analysis unit 30 determines, for example, the time required for the evacuee to evacuate the time until the token in the evacuation submodel reaches the place representing the evacuation destination from the initial state. can do.
- the analysis unit 120 can use any state search algorithm of sTPN including a known method.
- the result predicted by the analysis unit 30 is output in an arbitrary method or format.
- the time required for the evacuation of the evacuees is represented by, for example, a cumulative distribution function of the time required for the evacuation to be completed.
- a location corresponding to the information aggregated by the aggregation unit 120 may be applied to the evacuation route.
- the model aggregating apparatus 100 aggregates evacuation models used for predicting the time required for an evacuee to evacuate based on a predetermined condition.
- the time required to analyze the evacuation model is shortened when the evacuation model is used to determine the time required for the evacuation of the evacuees.
- the analysis can be performed even if the evacuation model is complicated. Therefore, the model aggregating apparatus 100 according to the present embodiment can easily predict the time required for the evacuees to finish evacuation.
- the evacuation prediction system 10 including the model aggregating apparatus 100 in the present embodiment can easily predict the time required for an evacuee to finish evacuation in a short time, for example.
- the model aggregation device 100 and the evacuation prediction system 10 in this embodiment use sTPN as a model.
- the model handled by the model aggregation device 100 and the evacuation prediction system 10 is not limited to sTPN.
- the model aggregation device 100 and the evacuation prediction system 10 can handle models of a format other than sTPN that can represent the above-described evacuation information, recovery information, and the like.
- each component in the evacuation prediction system 10 may be different from the example shown in FIG.
- the model generation unit 21 generates an evacuation model so that the evacuation model can be aggregated by the aggregation unit 120.
- the aggregate model generation apparatus 101 including the model generation unit 21 and the aggregation unit 120 may be configured.
- the aggregate model generation device 101 and the aggregation unit 120 may be realized as a single system, or may be realized as a single device.
- the aggregation model generation device 101 and the aggregation unit 120 are connected via, for example, a wired or wireless communication network.
- data representing each sub model or the like may be exchanged between the model aggregating apparatus 101 and the aggregating unit 120 via a file.
- Model aggregation device 101
- Aggregation model generation device 110
- Model reception unit 120
- Aggregation unit 500
- Information processing device 501 CPU 502 ROM 503 RAM 504
- Program 505 Storage device
- Storage medium 506
- Communication interface 509 Network 510
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Computer Security & Cryptography (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Alarm Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
・ROM(Read Only Memory)502
・RAM(Random Access Memory)503
・RAM503にロードされるプログラム504
・プログラム504を格納する記憶装置505
・記憶媒体506の読み書きを行うドライブ装置507
・ネットワーク509と接続する通信インターフェース508
・データの入出力を行う入出力インターフェース510
・各構成要素を接続するバス511
各装置の実現方法には様々な変形例がある。例えば、各装置は、専用の装置として実現することができる。各装置は、複数の装置の組み合わせにより実現することができる。
また、各装置や各システム等の構成を示す図において、図面中の矢印の方向は一例を示すものであり、構成要素間の信号の向きを限定するものではない。
まず、本発明の第1の実施形態について説明する。図1は、本発明の第1の実施形態におけるモデル集約装置の構成を示す図である。図2は、本発明の第1の実施形態におけるモデル集約装置を含む避難予測システムの構成を示す図である。図3は、本発明の第1の実施形態におけるモデル集約装置が受付ける避難モデルの一例を示す図である。図4は、本発明の第1の実施形態におけるモデル集約装置が受付ける避難モデルにて表される避難経路等の例を示す図である。図5は、本発明の第1の実施形態におけるモデル集約装置が受付ける避難モデルにて表される避難経路等の例を示す図である。図6は、本発明の第1の実施形態におけるモデル集約装置が受付ける避難モデルの関係情報に関する一例を示す図である。図7は、本発明の第1の実施形態におけるモデル集約装置が受付ける避難モデルの別の例を示す図である。図8は、本発明の第1の実施形態におけるモデル集約装置において集約される避難モデルの一例を示す図である。図9は、本発明の第1の実施形態におけるモデル集約装置において集約される避難モデルの別の一例を示す図である。図10は、本発明の第1の実施形態におけるモデル集約装置において集約される避難モデルの別の一例を示す図である。図11は、本発明の第1の実施形態におけるモデル集約装置の動作を示すフローチャートである。図12は、本発明の第1の実施形態における避難予測システムの変形例の構成を示す図である。図13は、本発明の第1の実施形態における避難予測システムの変形例及び集約モデル生成装置の構成を示す図である。
・入力アークを介して接続されるプレースの全てに1つ以上のトークンが存在する。
・インヒビタ―・アークを介して接続されるプレースの全てにトークンが存在しない。
・時刻がEFTの値より大きく、LFTの値より小さい。
・エネーブリングファンクションが真となる。
図10(A)に示す避難サブモデルは、図4又は図5における障害発生個所f1及びf2について、f2が先に復旧され、続いてf1が復旧されるとして生成されている。この場合において、障害発生個所f2が復旧されないとして、関連する情報が集約された復旧サブモデルが図10(B)に示されている。図10(B)に示される復旧サブモデルでは、f2の復旧及びその状態を表すトランジションt11並びにプレースp19及びp15が削除されている。また、これらに関連するトランジション、プレース、アークが併せて削除されている。
本実施形態におけるモデル集約装置100及び避難予測システム10には、変形例が考えられる。
20、21 モデル生成部
30 解析部
100 モデル集約装置
101 集約モデル生成装置
110 モデル受付部
120 集約部
500 情報処理装置
501 CPU
502 ROM
503 RAM
504 プログラム
505 記憶装置
506 記憶媒体
507 ドライブ装置
508 通信インターフェース
509 ネットワーク
510 入出力インターフェース
511 バス
Claims (11)
- 避難者の避難経路に関する避難情報、前記避難経路において障害が発生した個所の復旧時期に関する復旧情報及び前記避難情報と前記避難情報との関係を含む避難モデルを受付けるモデル受付手段と、
前記避難情報又は前記復旧情報に関する所定の条件に基づいて、前記避難モデルに含まれる情報を集約する集約手段とを備える、モデル集約装置。 - 前記集約手段は、前記避難経路に関連する地理情報を集約する、請求項1に記載のモデル集約装置。
- 前記集約手段は、避難経路からの距離に基づいて、前記避難経路に関連する地理情報を集約する、請求項2に記載のモデル集約装置。
- 前記集約手段は、前記障害が発生した個所の復旧の操作に関する情報を集約する、請求項1から3のいずれか一項に記載のモデル集約装置。
- 前記集約手段は、前記障害が発生した個所に対する復旧の操作に関する時期に基づいて、前記復旧の操作に関する情報を集約する、請求項4に記載のモデル集約装置。
- 前記集約手段は、前記集約するとされた情報を、前記避難モデルから削除する、請求項1から5のいずれか一項に記載のモデル集約装置。
- 前記モデル受付手段は、確率時間ペトリネットにて表されている前記避難モデルを受付ける、請求項1から6のいずれか一項に記載のモデル集約装置。
- 前記避難情報、前記復旧情報及び前記避難情報と前記復旧情報との関係に基づいて、前記避難モデルを生成するモデル生成手段と、
請求項1から7のいずれか一項に記載のモデル集約装置と、
前記モデル集約装置にて集約された前記避難モデルを用いて、前記避難者の避難に要する時間を予測する解析手段とを備える、避難予測システム。 - 避難者の避難経路に関する避難情報、前記避難経路において障害が発生した個所の復旧時期に関する復旧情報及び前記避難情報と前記復旧情報との関係に基づいて、避難モデルを生成するモデル生成手段と、
前記避難情報又は前記復旧情報に関する所定の条件に基づいて、前記避難モデルに含まれる情報を集約する集約手段とを備える、避難モデル生成装置。 - 避難者の避難経路に関する避難情報、前記避難経路において障害が発生した個所の復旧時期に関する復旧情報及びに前記避難情報と前記避難情報との関係を含む避難モデルを受付け、
前記避難情報又は前記復旧情報に関する所定の条件に基づいて、前記避難モデルに含まれる情報を集約する、集約方法。 - コンピュータに、
避難者の避難経路に関する避難情報、前記避難経路において障害が発生した個所の復旧時期に関する復旧情報及び前記避難情報と前記避難情報との関係を含む避難モデルを受付ける処理と、
前記避難情報又は前記復旧情報に関する所定の条件に基づいて、前記避難モデルに含まれる情報を集約する処理とを実行させるプログラムを格納したコンピュータ読み取り可能記録媒体。
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2016558891A JP6665786B2 (ja) | 2014-11-14 | 2015-11-10 | モデル集約装置、避難予測システム、集約モデル生成装置、集約方法及びコンピュータ読み取り可能記録媒体 |
EP15858223.9A EP3220324A4 (en) | 2014-11-14 | 2015-11-10 | Model summarizing device, evacuation prediction system, summarized model generating device, summarizing method, and computer-readable recording medium |
US15/526,144 US20170316532A1 (en) | 2014-11-14 | 2015-11-10 | Model summarizing device, evacuation prediction system, summarized model generating device, summarizing method, and computer-readable recording medium |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2014-231391 | 2014-11-14 | ||
JP2014231391 | 2014-11-14 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2016075932A1 true WO2016075932A1 (ja) | 2016-05-19 |
Family
ID=55954035
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/JP2015/005613 WO2016075932A1 (ja) | 2014-11-14 | 2015-11-10 | モデル集約装置、避難予測システム、集約モデル生成装置、集約方法及びコンピュータ読み取り可能記録媒体 |
Country Status (4)
Country | Link |
---|---|
US (1) | US20170316532A1 (ja) |
EP (1) | EP3220324A4 (ja) |
JP (1) | JP6665786B2 (ja) |
WO (1) | WO2016075932A1 (ja) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114818360B (zh) * | 2022-05-10 | 2022-11-01 | 煤炭科学研究总院有限公司 | 人群应急疏散场景下的疏散出口设置方法及装置 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH10312217A (ja) * | 1997-05-12 | 1998-11-24 | Shinko Electric Co Ltd | 運行管理制御装置および運行管理制御方法 |
JP2007220030A (ja) * | 2006-02-20 | 2007-08-30 | Chubu Consultant:Kk | 地理情報システムを用いた防災・災害復旧支援システム |
JP2007269240A (ja) * | 2006-03-31 | 2007-10-18 | Railway Technical Res Inst | プログラム、列車運行適応ペトリネットモデル生成装置、列車抑止手配支援装置、列車運行適応ペトリネットモデル生成方法及び列車抑止手配支援方法 |
JP2013089224A (ja) * | 2012-03-26 | 2013-05-13 | Ism Corp | 津波避難支援システム、津波避難支援方法、津波避難支援装置およびその制御方法と制御プログラム |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7349768B2 (en) * | 2005-04-25 | 2008-03-25 | The Boeing Company | Evacuation route planning tool |
-
2015
- 2015-11-10 JP JP2016558891A patent/JP6665786B2/ja active Active
- 2015-11-10 EP EP15858223.9A patent/EP3220324A4/en not_active Withdrawn
- 2015-11-10 US US15/526,144 patent/US20170316532A1/en not_active Abandoned
- 2015-11-10 WO PCT/JP2015/005613 patent/WO2016075932A1/ja active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH10312217A (ja) * | 1997-05-12 | 1998-11-24 | Shinko Electric Co Ltd | 運行管理制御装置および運行管理制御方法 |
JP2007220030A (ja) * | 2006-02-20 | 2007-08-30 | Chubu Consultant:Kk | 地理情報システムを用いた防災・災害復旧支援システム |
JP2007269240A (ja) * | 2006-03-31 | 2007-10-18 | Railway Technical Res Inst | プログラム、列車運行適応ペトリネットモデル生成装置、列車抑止手配支援装置、列車運行適応ペトリネットモデル生成方法及び列車抑止手配支援方法 |
JP2013089224A (ja) * | 2012-03-26 | 2013-05-13 | Ism Corp | 津波避難支援システム、津波避難支援方法、津波避難支援装置およびその制御方法と制御プログラム |
Non-Patent Citations (3)
Title |
---|
See also references of EP3220324A4 * |
TAKASHI MINAMOTO ET AL.: "DEVELOPMENT OF TSUNAMI EVACUATION SIMULATION SYSTEM AND ITS APPLICATION TO ASSESSMENT OF AREA REFUGE SAFETY", PROCEEDINGS OF THE JAPAN SOCIETY OF CIVIL ENGINEERS A1 (STRUCTURAL ENGINEERING & EARTHQUAKE ENGINEERING, vol. 65, no. 1, 30 April 2011 (2011-04-30), pages 757 - 767, XP003034338, ISSN: 2185-4653, Retrieved from the Internet <URL:https://www.jstage.jst.go.jp/article/jscejseee/65/1/65_1_757/_pdf> [retrieved on 20160115] * |
TORU FUTAGAMI ET AL.: "Application Study of Petri Net Simulator for Human Evacuation Behavior Simulation", 2002, XP003034339, Retrieved from the Internet <URL:http://www.jsce.or.jp/library/open/proc/maglist2/00039/200211_no26/pdf/330.pdf> [retrieved on 20160115] * |
Also Published As
Publication number | Publication date |
---|---|
EP3220324A4 (en) | 2018-04-04 |
JPWO2016075932A1 (ja) | 2017-08-24 |
US20170316532A1 (en) | 2017-11-02 |
EP3220324A1 (en) | 2017-09-20 |
JP6665786B2 (ja) | 2020-03-13 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhou et al. | Resilience of transportation systems: concepts and comprehensive review | |
Cavallaro et al. | Assessment of urban ecosystem resilience through hybrid social–physical complex networks | |
Ulusan et al. | Restoration of services in disrupted infrastructure systems: A network science approach | |
Wang et al. | Evacuation planning for disaster responses: A stochastic programming framework | |
Karamlou et al. | Sequencing algorithm with multiple-input genetic operators: Application to disaster resilience | |
Kaviani et al. | Improving regional road network resilience by optimised traffic guidance | |
CN112665601A (zh) | 一种路径规划方法、装置、电子设备及可读存储介质 | |
WO2022142013A1 (zh) | 基于人工智能的ab测试方法、装置、计算机设备及介质 | |
Cai et al. | Vulnerability analysis of metro network incorporating flow impact and capacity constraint after a disaster | |
JP6683129B2 (ja) | 避難予測システム、モデル生成装置、予測装置、避難予測方法及びコンピュータ読み取り可能記録媒体 | |
JP6665785B2 (ja) | 避難予測システム、避難予測方法及びコンピュータ読み取り可能記録媒体 | |
Liang et al. | A two-level agent-based model for hurricane evacuation in new orleans | |
WO2016075932A1 (ja) | モデル集約装置、避難予測システム、集約モデル生成装置、集約方法及びコンピュータ読み取り可能記録媒体 | |
TWI555356B (zh) | 具有學習效應之多元流動網路之可靠度計算方法及其系統 | |
Zhou et al. | An adaptation of reference class forecasting for the assessment of large-scale urban planning vision, a SEM-ANN approach to the case of Hong Kong Lantau tomorrow | |
Jin et al. | Maximum flow-based resilience analysis: From component to system | |
JP6410965B2 (ja) | 計算機システムの管理システム及び管理方法 | |
Huang et al. | Exact algorithms on reliable routing problems under uncertain topology using aggregation techniques for exponentially many scenarios | |
KR102342929B1 (ko) | 사고 발생패턴 분석을 통한 사고 취약지 분석 장치 및 방법 | |
Hwang et al. | Meta-heuristic approach for high-demand facility locations considering traffic congestion and greenhouse gas emission | |
KR101995885B1 (ko) | 건축물 건설관리 통합 시스템 및 이를 이용하여 건축물 건설관리 통합 정보를 생성하는 방법 | |
Jafari et al. | Resilience-Based Optimal Seismic Retrofit and Recovery Strategies of Bridge Networks under Mainshock–Aftershock Sequences | |
Baker et al. | Local measures of disruption for quantifying seismic risk and reliability of complex networks | |
Testa et al. | Seismic resilience assessment of highway networks: A topology-based approach | |
Ulusan | Optimizing post-disruption response and recovery operations to improve resilience of critical infrastructure systems |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 15858223 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2016558891 Country of ref document: JP Kind code of ref document: A |
|
WWE | Wipo information: entry into national phase |
Ref document number: 15526144 Country of ref document: US |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
REEP | Request for entry into the european phase |
Ref document number: 2015858223 Country of ref document: EP |