JP6622777B2 - Route search device, route search method, and program - Google Patents

Description

  The present invention relates to a route search device, a route search method, and a program.

  There is a known route search method that can support the selection of a physical route that is relatively less affected by a disaster for a link added to a network (Non-Patent Document 1). In Non-Patent Document 1, a grid (grid) is set at an appropriate interval between the start point and end point to be searched for, and equipment (nodes) near the vertex (grid point) of each grid is used as a representative. select. Then, by setting a partial problem to search for a path with the minimum path length between nodes selected as representatives, and combining multiple paths (partial paths) between the lattices obtained from these partial problems, the entire path Looking for.

  Since the influence of a disaster depends on the length of the path length included in the disaster area, Non-Patent Document 1 states that the influence of the disaster becomes relatively smaller as the path length of the path becomes shorter.

PN Tran and H. Saito, "Enhancing Physical Network Robustness Against Earthquake Disasters With Additional Links," in Journal of Lightwave Technology, vol. 34, no. 22, pp. 5226-5238, Nov.15, 15 2016. doi: 10.1109 /JLT.2016.2607171

  Here, in Non-Patent Document 1, since the route evaluation index is the route length, only direct influences due to a single earthquake or disaster can be considered. For this reason, in the event of a secondary disaster (for example, a tsunami or landslide disaster caused by an earthquake) that has a significant impact on network equipment in an actual earthquake, it is easy to recover and avoid secondary disasters. However, it was not possible to perform route extraction considering ease of use.

  For example, FIG. 1 shows a partial problem 1 for performing a minimum path search between the start node S and the representative node and a minimum path search between the representative node and the end node D by the path search method of Non-Patent Document 1. In the example, the partial routes obtained in the partial problem 2 to be performed are combined to extract the route between the start node S and the end node D. In the route search method of Non-Patent Document 1, since the route evaluated only by the route length is extracted, for example, whether or not it passes through the tsunami area is not evaluated. For this reason, as shown in FIG. 1, a route passing through the tsunami area may be extracted, and an optimal route is not necessarily extracted when a secondary disaster is considered.

  Therefore, when considering secondary disasters, 1 or more evaluation indexes (for example, the number of links included in the tsunami area, the link damage rate, etc.) can be calculated from one or more objective functions to search for a route. It is preferable to perform a route search by a route search algorithm corresponding to the above objective function.

  In general, a route search algorithm corresponding to an evaluation index other than the route length optimizes an objective function value and does not consider the cost such as the route length. For this reason, generally, the cost such as the upper limit route length is given as a constraint condition for route search, separately from the objective function for calculating the evaluation index. Therefore, the constraint condition is a condition indicating how much cost may be applied in order to optimize the objective function (that is, how far the object function may be rotated).

  However, when the route search algorithm as described above is applied to a subproblem, it is necessary to set a constraint condition for the subproblem. However, if the constraint condition is too large, a route that goes more than necessary (a long route or a route with high cost) Etc.) is calculated. On the other hand, if the constraint condition is too small, the search may be terminated due to the constraint condition, and the route may not be found.

  For this reason, when the above route search algorithm is applied to a partial problem, it is necessary to set appropriate constraints. In addition, since there are a large number of such subproblems, it is necessary to determine appropriate constraint conditions within a practical calculation time.

  The present invention has been made in view of the above points, and when extracting a route combining a partial route obtained from a partial problem having one or more evaluation indices, there are few appropriate restriction conditions for the partial problem. The purpose is to calculate with a calculation amount.

  In order to solve the above problem, a shortest path search unit that searches for a shortest path from a start point node to an end point node in the network, a first cost of the shortest path searched by the shortest path search unit, and the start point node, Based on the first cost and the second cost calculated by the cost calculation unit, a cost calculation unit that calculates a second cost of a straight line connecting the end point node, a predetermined first A determination unit that determines whether or not the approximate expression is valid; and a first node and a second node that are included in the network when the determination unit determines that the first approximate expression is valid A determination unit that determines a constraint condition in a partial problem for searching for a partial path indicating a path between and a partial problem having one or more evaluation indices using the first approximate expression; Have .

  When extracting a route in which partial routes obtained from a partial problem having one or more evaluation indexes are extracted, an appropriate constraint condition for the partial problem can be calculated with a small amount of calculation.

It is a figure explaining an example of the path | route extracted by a prior art. It is a figure which shows an example of a structure of the route search apparatus in embodiment of this invention. It is a figure which shows an example of the hardware constitutions of the route search apparatus in embodiment of this invention. It is a figure which shows an example of a function structure of the calculating part in embodiment of this invention. It is a figure explaining an example when it is necessary to use a 2nd approximation. It is a flowchart which shows an example of the validity determination process of the 1st approximate expression in embodiment of this invention. It is a flowchart which shows an example of the determination process of the cost constraint condition in embodiment of this invention. It is a flowchart which shows the other example of the determination process of the cost constraint condition in embodiment of this invention. It is a figure explaining the cost constraint conditions determined by the constraint condition determination processing unit. It is a flowchart which shows an example of the route search process in embodiment of this invention. It is a figure which shows an example of the node which starts the installation which is not a search object. It is a flowchart which shows an example of the search area | region definition process in embodiment of this invention. It is a figure which shows an example of the distance of the node through which the shortest path passes, and the straight line SD. It is a figure which shows an example of a rectangular area. It is a figure which shows an example of the correction | amendment which rounds up the length of the edge | side of a rectangular area. It is a figure which shows an example of the correction | amendment which adds the margin M with respect to the length of the edge | side of a rectangular area.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

<Configuration of route search apparatus 10>
First, the configuration of the route search apparatus 10 according to the embodiment of the present invention will be described with reference to FIG. FIG. 2 is a diagram illustrating an example of the configuration of the route search apparatus 10 according to the embodiment of the present invention.

  The route search apparatus 10 shown in FIG. 2 searches for a partial route by a partial problem having one or more evaluation indices, and combines the searched partial routes to output a route that also considers the influence of a secondary disaster. Computer. Further, the route search apparatus 10 determines a constraint condition (hereinafter referred to as “cost constraint condition”) C indicating a cost such as an upper limit route length for each partial problem.

  The route search device 10 illustrated in FIG. 2 includes an input unit 100, a calculation unit 200, and an output unit 300. Each of these units is realized by processing that one or more programs installed in the route search apparatus 10 cause a (Central Processing Unit) 507 described later to execute.

  Further, the route search apparatus 10 shown in FIG. The data storage unit 400 can be realized by using an auxiliary storage device 508, a RAM (Random Access Memory) 505, and the like which will be described later. Note that the data storage unit 400 may be realized using, for example, a storage device connected to the route search apparatus 10 via a network.

The input unit 100 inputs various information. Information input to the input unit 100 includes, for example, network information, start / end node information, evaluation index information, priority information, a first approximate expression f ₁ , a second approximate expression f ₂ , a safety factor F _s , and a detour. There is an allowable amount R and the like. Among these pieces of information, the first approximate expression f ₁ , the second approximate expression f ₂ , the safety coefficient F _s , and the roundabout allowable amount R will be described later.

  The network information is information related to nodes and links constituting the network. Examples of the node include a router, a building, a manhole, and a utility pole. Examples of the link include a cable, a roadway, a common groove, and a pipeline. In addition to the telecommunication network, the network may be various networks such as a road network, a railroad network, a power network, a water network, and a gas network.

  Attribute information can be added to the network information. The attribute information includes, for example, position information (latitude and longitude information), link length (cable length, path length, joint groove length, conduit length, etc.), equipment (building, (Such as utility poles, pipelines), and disaster areas (tsunami, landslide disaster, flood, liquefaction, avalanche, lightning, lightning, volcanic eruption, etc.).

  The start / end node information is information indicating the start and end nodes in the route search of the network. Hereinafter, the starting point node in the route search of the network is represented as “start point node S”, and the end point node in the route search is represented as “end point node D”.

  The evaluation index information is information relating to one or more objective functions (multipurpose functions) for calculating the evaluation index. Examples of the objective function include the cost of the network equipment (cost of the equipment itself, installation cost, maintenance cost, etc.), the damage rate of the equipment, the number and length of equipment included in the disaster area, and the like. The priority information is information related to the priority of one or more objective functions.

  The calculation unit 200 determines the cost constraint condition C, calculates a partial route based on the partial problem in which the determined cost constraint condition C is set, and a route combining the partial routes (route from the start node S to the end node D). ). A detailed functional configuration of the arithmetic unit 200 will be described later.

  The output unit 300 outputs the route created by the calculation unit 200 to, for example, the display device 502 described later.

  The data storage unit 400 stores various information input by the input unit 100. Further, the data storage unit 400 also stores, for example, calculation data in the middle of partial path search and intermediate data such as a cache.

  The configuration of the route search apparatus 10 illustrated in FIG. 2 is an example, and other configurations may be used. For example, the route search apparatus 10 illustrated in FIG. 2 may be configured by a plurality of computers.

<Hardware Configuration of Route Search Device 10>
Next, the hardware configuration of the route search apparatus 10 will be described with reference to FIG. FIG. 3 is a diagram illustrating an example of a hardware configuration of the route search apparatus 10 according to the embodiment of the present invention.

  As illustrated in FIG. 3, the route search apparatus 10 includes an input device 501, a display device 502, an external I / F 503, a communication I / F 504, a RAM 505, a ROM (Read Only Memory) 506, a CPU 507, And an auxiliary storage device 508. Each of these hardware is connected via a bus B so as to be able to communicate.

  The input device 501 is, for example, a keyboard, a mouse, a touch panel, or the like, and is used for a user to input various operations. The display device 502 is, for example, a display and displays the processing result of the route search device 10. Note that at least one of the input device 501 and the display device 502 may be connected to the route search device 10 when necessary.

  The external I / F 503 is an interface with an external device. The external device includes a recording medium 503a and the like. The route search apparatus 10 can read and write the recording medium 503a and the like via the external I / F 503.

  Examples of the recording medium 503a include a flexible disk, a CD (Compact Disc), a DVD (Digital Versatile Disk), an SD memory card (Secure Digital memory card), and a USB (Universal Serial Bus) memory card.

  The communication I / F 504 is an interface for connecting the route search apparatus 10 to a network.

  The RAM 505 is a volatile semiconductor memory that temporarily stores programs and data. The ROM 506 is a non-volatile semiconductor memory that can retain programs and data even when the power is turned off. The ROM 506 stores, for example, OS (Operating System) settings, network settings, and the like.

  The CPU 507 is an arithmetic device that reads a program and data from the ROM 506, the auxiliary storage device 508, and the like onto the RAM 505 and executes processing.

  The auxiliary storage device 508 is, for example, a hard disk drive (HDD) or a solid state drive (SSD), and is a non-volatile storage device that stores programs and data. Examples of programs and data stored in the auxiliary storage device 508 include an OS, application software that implements various functions on the OS, and a program that implements various processes according to the present embodiment.

  The route search apparatus 10 according to the present embodiment has the hardware configuration shown in FIG.

<Functional Configuration of Calculation Unit 200>
Next, the functional configuration of the arithmetic unit 200 will be described with reference to FIG. FIG. 4 is a diagram illustrating an example of a functional configuration of the arithmetic unit 200 according to the embodiment of the present invention.

  As illustrated in FIG. 4, the calculation unit 200 includes a constraint condition determination processing unit 210 and a route search processing unit 220. The constraint condition determination processing unit 210 performs a process for determining the cost constraint condition C for each partial problem. The route search processing unit 220 uses the cost constraint condition C determined for each partial problem by the constraint condition determination processing unit 210 to calculate a partial route and combine the partial routes (from the start node S to the end node D). Route).

  Here, the constraint condition determination processing unit 210 includes a shortest path type route search unit 211, a cost calculation unit 212, an approximate expression validity determination unit 213, a partial node set straight line distance calculation unit 214, and a constraint condition determination unit 215. And have.

  The shortest path type path search unit 211 searches for the shortest path between the start node S and the end node D. The shortest path type path search unit 211 searches for the shortest path using, for example, the k-shortest-path algorithm, the Dijkstra method using the path length as an objective function, or the like.

The cost calculation unit 212 calculates the minimum cost L _c of the route searched by the shortest route type route search unit 211. Further, the cost calculation unit 212 calculates a straight line cost L _d when the start point node S and the end point node D are connected in a geospatial line. The minimum cost L _c is not limited to the distance from the start point node S to the end point node D on the route, and may be, for example, the cost from the start point node S to the end point node D on the route.

The approximate expression validity determination unit 213 determines whether or not the first approximate expression f ₁ is valid based on the linear cost L _d , the minimum cost L _c, and the safety factor F _s .

First approximation formula f ₁ is a function for determining the cost constraints C, and regardless of the search area of the route, is an approximate expression that generally can be used to determine the cost constraints C. On the other hand, if the first approximate expression f ₁ is not valid, the second approximate expression f ₂ is used to determine the cost constraint condition C. Second approximation formula f _2, like the first approximation formula f _1, is a function for determining the cost constraints C.

The safety factor F _s is a value of 0 or more that is arbitrarily set to set the strictness when determining the validity of the first approximate expression f ₁ .

Furthermore, a case where the first approximation formula f ₁ is not valid, the search area for searching a route from a start node S to the destination node D, for example, when such local detour occurs. On the other hand, as for the first approximation formula f ₁ is appropriate is when local circuitous as described above that does not occur.

The partial node set straight line distance calculation unit 214 connects the nodes included in the node set selected by the route search processing unit 220 (hereinafter, referred to as “partial node set”) with a straight line in geospatial terms. In this case, the linear cost L _p is calculated. The solution of the partial problem is a path (partial path) between the respective nodes of the partial node set. Hereinafter, among the nodes included in the partial node set, the node that is the starting point of the partial path is expressed as “starting point node s”, and the node that is the end point of the partial path is expressed as “ending point node d”. An area for searching for a partial route is referred to as a “partial search area”. The partial search area is a predetermined area included in the search area for searching for a route from the start point node S to the end point node D.

Constraint determination unit 215, the determination result by the approximate expression appropriateness determination unit 213 (the first approximation formula f ₁ Relevance), and the straight line cost L _p which is calculated by the partial node set straight distance calculation unit 214, circuitous tolerance Based on R, the cost constraint condition C is determined.

When the validity of the first approximate expression f ₁ is “valid”, the constraint condition determining unit 215 determines the cost constraint condition C using the first approximate expression f ₁ . On the other hand, if the validity of the first approximate expression f ₁ is not “valid”, the constraint condition determining unit 215 determines the cost constraint condition C using the second approximate expression f ₂ .

  The roundabout allowable amount R is, for example, a value set in advance by the user of the route search device 10, and in order to avoid a disaster area (for example, a tsunami area or a landslide disaster area) due to a secondary disaster, It is a value indicating whether to allow. The roundabout allowable amount R is determined, for example, before the start / end node information is input. The roundabout allowable amount R is determined in consideration of, for example, how much cost is required to reduce the damage rate.

<First approximate expression f ₁ and second approximate expression f ₂ >
Here, specific examples of the first approximate expression f ₁ and the second approximate expression f ₂ will be described.

The first approximate expression f ₁ can be represented by f ₁ (L _p ) = A × L _p + B. Here, A and B are, for example, A = 1.628 and B = 10.00.0. However, specific values of A and B differ depending on the network characteristics and the like.

The second approximate expression f ₂ can be expressed by f ₂ (L _p ) = A × L _p + R _c . Here, R _c is required circuitous distance, for example, is that the distance at which the minimum detour required by locally there is a mountain or the like. R _c can be represented by R _c = L _c -L _d .

Here, a case where it is necessary to use a second approximation formula f ₂ for the determination of the cost constraints C, and described with reference to FIG. Figure 5 is a diagram illustrating an example of a case where it is necessary to use a second approximation formula f _2.

  As shown in FIG. 5, a case will be described in which a route is searched within a search area from the start point node S to the end point node D. Each rectangular area in the search area is a partial search area.

As shown in FIG. 5, since there is a mountain in the search area, a local circuitous circuit occurs. In this case, the first approximation formula f _1, can not be searched partial path in each partial search range (i.e., not out solutions of subproblems) in some cases. In this case, the solution of the partial problem can be calculated by using the second approximate expression f ₂ in which the term B in the first approximate expression f ₁ is replaced with R _c . Note that R _c is the minimum cost L _c on the route (ie, for example, the sum of the distances along the route) to the straight line cost L _d (ie, from the start node S to the end node D, for example), as described above. Is a value obtained by subtracting the linear distance).

<Appropriateness determination processing of the first approximation formula f _1>
First, in the search region for searching for a route from the start node S to the end point node D, for using the first approximation formula f ₁ can determine the cost constraint C to determine whether it is appropriate The processing will be described with reference to FIG. FIG. 6 is a flowchart showing an example of the validity determination process of the first approximate expression f ₁ in the embodiment of the present invention.

  First, the shortest path type route searching unit 211 acquires the start point node S and the end point node D from the data storage unit 400 (step S101). Note that the start point node S and the start point node D are included in the start / end node information input by the input unit 100.

  Next, the shortest path type route search unit 211 searches for one or more routes that are the shortest route between the start node S and the end node D (step S102).

Next, the cost calculation unit 212, the shortest path type route search unit 211 calculates the minimum cost _{L c} of the one or more routes searched (step S103). The minimum cost L _c is the minimum cost among the costs of one or more routes searched by the shortest route type route search unit 211.

Next, the cost calculation unit 212 calculates a straight line cost L _d when the start point node S and the end point node D are connected in a geospatial line (step S104). The straight line cost L _d is, for example, the Euclidean distance between the start point node S and the end point node D in the Euclidean space.

  Note that the processing order of the processing in step S103 and the processing in step S104 may be reversed.

Next, the approximate expression validity determination unit 213 acquires the first approximate expression f ₁ and the safety coefficient F _s from the data storage unit 400 (step S105). The first approximate expression f ₁ and the safety factor F _s are input by the input unit 100 and stored in the data storage unit 400.

Next, the approximate expression validity determination unit 213 determines whether or not the first approximate expression f ₁ satisfies f ₁ (L _d ) −L _c > F _s (step S106).

When it is determined in step S105 that the first approximate expression f ₁ satisfies f ₁ (L _d ) −L _c > F _s , the approximate expression validity determination unit 213 determines that the first approximate expression f ₁ is valid. It is determined that there is (step S107). In this case, the approximate expression validity determination unit 213 outputs the determination result (that is, the first approximate expression f ₁ validity = “valid”) to the constraint condition determination unit 215.

On the other hand, when it is determined that the first approximate expression f ₁ does not satisfy f ₁ (L _d ) −L _c > F _s (that is, when f ₁ (L _d ) −L _c ≦ F _s ). The approximate expression validity determination unit 213 determines that the first approximate expression f ₁ is not valid (step S108). In this case, the approximate expression validity determination unit 213 outputs the determination result (that is, the first approximate expression f ₁ validity = “invalid”) to the constraint condition determination unit 215.

As described above, it is determined whether or not it is appropriate to determine the cost constraint condition C using the first approximate expression f ₁ in the search area for searching for the route from the start node S to the end node D. .

<Process for determining cost constraint condition C>
Next, the cost constraint C in a partial problem (that is, the cost used when searching for a partial path between a certain starting point node s in a certain partial search region and a certain end point node d in the partial search region) The process for determining the constraint condition C) will be described with reference to FIG. FIG. 7 is a flowchart showing an example of a process for determining the cost constraint condition C according to the embodiment of the present invention.

  First, the partial node set linear distance calculation unit 214 acquires the start point node s and the end point node d included in the partial node set (step S201). The partial node set is output by the route search processing unit 220.

Next, the partial node set straight line distance calculation unit 214 calculates a straight line cost L _p when the start point node s and the end point node d are connected by a geospatial line (step S202).

Next, the constraint condition determination unit 215 determines whether or not the validity of the first approximate expression f ₁ output by the approximate expression validity determination part 213 is “valid” (step S203).

If it is determined in step S203 that the validity of the first approximate expression f ₁ is “valid”, the constraint condition determination unit 215 acquires the first approximate expression f ₁ and the permitted roundabout amount R from the data storage unit 400. (Step S204). Note that the first approximate expression f ₁ and the permitted roundabout R are input by the input unit 100 and stored in the data storage unit 400.

Next, the constraint condition determination unit 215 sets L = f ₁ (L _p ), where L is the approximate value based on the first approximate expression f ₁ (step S205).

On the other hand, when it is determined in step S203 that the validity of the first approximate expression f ₁ is not “valid”, the constraint condition determination unit 215 obtains the second approximate expression f ₂ and the permitted detour amount R from the data storage unit 400. Obtain (step S206). Incidentally, the first approximation formula f ₂ and circuitous allowance R is input by the input unit 100, is stored in the data storage unit 400.

Next, the constraint condition determination unit 215 sets L = f ₂ (L _p ), where L is an approximate value based on the second approximate expression f ₂ (step S207).

Subsequent to step S205 or step S207, the constraint condition determination unit 215 outputs the cost constraint condition C to the route search processing unit 220 as C = L + R (step S208). As described above, the constraint condition determination unit 215 determines the cost constraint condition C using the first approximate expression f ₁ when the first approximate expression f ₁ is valid. On the other hand, the constraint condition determination unit 215 determines the cost constraint condition C using the second approximate expression f ₂ when the first approximate expression f ₁ is not valid.

<Another example of processing for determining cost constraint condition C>
Here, in the process of determining the cost constraint condition C in FIG. 7 described above, when the first approximate expression f ₁ is not valid, the cost constraint condition C using the second approximate expression f ₂ is uniformly determined. That is, when the first approximation formula f ₁ is not valid, cost constraints C as determined by the second approximation formula f ₂ in the search of the partial path in all partial search region is used.

However, for example, even when the first approximate expression f ₁ is not valid, when it is known in advance that no detour occurs in a partial search area, the search for a partial path in the partial search area is not performed. It is considered that the cost constraint condition C determined by the first approximate expression f ₁ may be used.

Therefore, hereinafter, when the first approximate expression f ₁ is not valid, it is determined whether the partial search area is a “detour generation area” in which a detour occurs, and then the cost constraint condition C is determined. A process for determining which one of the approximate expression f _{1 and} the second approximate expression f ₂ is used will be described with reference to FIG. FIG. 8 is a flowchart showing another example of the process for determining the cost constraint condition C according to the embodiment of the present invention. Note that the processing in steps S201 to S205 in FIG. 8 is the same as that in FIG.

In step S203, if the first approximation formula f ₁ validity is determined not "valid", the constraint condition determining unit 215, partial search region is determined whether a detour generation region (step S301). Whether or not the partial search area is a detour generation area is determined by using, for example, additional information input by the input unit 100 to determine whether or not a detour occurs in an area around the partial node set. The additional information includes, for example, attribute information included in the network information.

  In general, in the network information of infrastructure such as a power network, a water network, and a communication network, nodes (manholes, utility poles, etc.) and links (pipes connecting the nodes, paths, overhead cables, pipes, etc.) Attribute information indicating what kind of geographical situation is present). Specific examples of the attribute information include, for example, the height from the ground, the depth from the ground surface, whether it is near a river, whether it is an urban area, whether it is a mountain area, or the like.

  Based on the attribute information described above, the constraint condition determination unit 215 calculates how much the area around the partial node set belongs to a mountainous area or a river, and whether the partial search area is a detour generation area based on the ratio What is necessary is just to determine.

  Further, the constraint condition determination unit 215 uses the map information as additional information to calculate how much area the mountainous area or river occupies in the area around the partial node set, and the partial search area based on the ratio It may be determined whether or not is a detour generation region.

  In addition to the above, the constraint condition determination unit 215 may determine whether or not the partial search region is a detour generation region, for example, using the methods shown in the following (1) to (7).

  (1) Using geographical information, map information, national land numerical information, etc., if the proportion of mountainous areas and forests (broadleaf forest, coniferous forest, bamboo forest, etc.) in the partial search area is T% or more, the partial search It is determined that the area is a circuitous generation area. Here, T is an arbitrary value.

  (2) Using the geographical information, map information, national land numerical information, etc., if the ratio of the river part or the water area in the partial search area is equal to or greater than T%, the partial search area is determined to be the circumvention area. . Here, T is an arbitrary value.

  (3) Using the geographic information, map information, national land numerical information, etc., when the area of the area where the inclination angle is D degrees or more is T% or more in the partial search area, the partial search area is generated by a detour. It is determined that the area. Here, D and T are arbitrary values.

  (4) When T% or more of the nodes included in the partial search area belong to a mountainous area or a water area, it is determined that the partial search area is a roundabout generation area. Here, T is an arbitrary value.

  (5) When T% or more of the links included in the partial search area belong to a mountainous area or a water area, it is determined that the partial search area is a detour generation area. Here, T is an arbitrary value.

  (6) When T% or more of the nodes and links included in the partial search area belong to a mountainous area or a water area, it is determined that the partial search area is a roundabout generation area. Here, T is an arbitrary value.

  (7) Among the determination results obtained by the above methods (1) to (6), if it is determined that the region is a circuitous route generation region by N or more methods, the partial search region is determined to be a circuitous region generation region. Here, N is an arbitrary value.

In step S301, if the partial search region is determined to be a circuitous generation region, the constraint condition determining unit 215 obtains a second approximation formula f ₂ and circuitous allowable amount R from the data storage unit 400 (Step S302) .

Next, the constraint condition determination unit 215 sets L = f ₂ (L _p ), where L is an approximate value based on the second approximate expression f ₂ (step S303).

On the other hand, in step S301, if the partial search region is determined not to be circuitous generation region, the constraint condition determining unit 215 obtains a first approximation formula f ₁ and circuitous allowable amount R from the data storage unit 400 (step S304 ).

Next, the constraint condition determination unit 215 sets L = f ₁ (L _p ), where L is an approximate value based on the first approximate expression f ₁ (step S305).

Subsequent to step S205, step S303, or step S305, the constraint condition determination unit 215 outputs the cost constraint condition C to the route search processing unit 220 as C = L + R (step S306). Thus, even if the first approximate expression f ₁ is not valid, the constraint condition determination unit 215 determines the cost constraint condition C using the first approximate expression f ₁ when the partial search region is not a circuitous generation region. decide. On the other hand, the constraint condition determination unit 215 determines the cost constraint condition C using the second approximate expression f ₂ when the first approximate expression f ₁ is not valid and the partial search area is a detour generation area. decide.

<Cost constraint C>
Here, with respect to the cost constraint condition C determined in the cost constraint condition determination process of FIG. 6 or FIG. 7 (that is, the cost constraint condition C determined by the constraint condition determination processing unit 210), refer to FIG. While explaining. FIG. 9 is a diagram for explaining cost constraint conditions determined by the constraint condition determination processing unit 210.

  If the cost constraint condition C is too large, a partial path (solution of the partial problem) that goes around more than necessary is calculated, or the search takes time. On the other hand, if the cost constraint condition C is too small, the search is terminated, and the partial path May not be calculated.

That is, as shown in FIG. 9, the cost constraints C is 0 or more, when it is less than the minimum cost L _c is not obtained solution subproblem (i.e., it is impossible to search the partial path). Therefore, the minimum cost L _c is a lower limit value of the cost constraint condition C.

In the embodiment of the present invention, the first approximate expression f ₁ is _defined as f ₁ (L _p ) = A × L _p + B, and the first approximate expression f ₂ is defined as f ₂ (L _p ) = A × L _p + R _c . by approximate value L may be so as not to fall below the L _c.

  In the embodiment of the present invention, the value obtained by adding the allowable roundabout amount R to the approximate value L is set as the cost constraint condition C, so that a certain degree of roundabout is permitted in order to bypass the secondary disaster area. Cost constraint condition C can be determined.

  Moreover, in determining the cost constraint condition C, in the embodiment of the present invention, the search for the shortest path is performed only once between the start node S and the end node D (step S102 in FIG. 6). In other words, the search for the shortest path is not performed between the start node s and the end node d included in the partial node set. Therefore, according to the embodiment of the present invention, even if the number of partial problems is enormous, the cost constraint condition C can be determined with a small amount of calculation.

<Route search process>
Next, a process for creating a route from the start point node S to the end point node D by calculating a partial route due to a partial problem and combining these partial routes will be described with reference to FIG. FIG. 10 is a flowchart showing an example of route search processing according to the embodiment of the present invention.

First, the input unit 100 inputs various types of information related to a search target (Step S401).
That is, the input unit 100 includes, for example, network information, start / end node information, evaluation index information, priority information, a first approximate expression f ₁ , a second approximate expression f ₂ , a safety coefficient F _s , and a detour allowable amount R. Enter.

  Next, the route search processing unit 220 defines a search area on the network from the start point node S and the end point node D included in the start / end point node information (step S402). The route search processing unit 220 may set an arbitrary convex area between the start point node S and the end point node D as a search area in the network. As will be described later, a partial problem is defined in each partial search area included in the search area.

Next, the constraint condition determination processing unit 210 performs the validity determination process in the first approximation formula _{f 1} in FIG. 6 (step S403).

  Next, the route search processing unit 220 divides the search area into one or more partial search areas (the partial search area is also referred to as “mesh”) (step S404). The size of the partial search area is arbitrary. The partial search area is preferably a lattice shape with variable length and width, but is not limited to this.

  Next, the route search processing unit 220 creates a plurality of mesh sets in which one or more meshes are combined into one (step S405). The mesh combinations are preferably combined in the direction from the start node S to the end node D, for example, from the viewpoint of route search and calculation efficiency. However, the method of combining meshes is not limited to this. Further, since the route search accuracy improves as the number of mesh sets is increased, it is preferable to create all combinations of mesh sets that can be combined in the direction from the start node S to the end node D.

  Next, the route search processing unit 220 selects one mesh set from the plurality of mesh sets created in step S405 (step S406).

  Next, the route search processing unit 220 selects two meshes from the mesh set selected in step S406 described above, and sets the selected two meshes as partial mesh sets (step S407).

  Next, the route search processing unit 220 selects one node from each mesh of the partial mesh set, and sets the selected two nodes as the partial node set (step S408). The partial node set is output to the constraint condition determination processing unit 210.

  Next, the constraint condition determination processing unit 210 performs the cost constraint condition C determination process of FIG. 7 or FIG. 8 for the partial node set (step S409). Thus, the cost constraint condition C is determined for each partial node set created in step S408.

  Next, the route search processing unit 220 searches for a route (partial route) between the start node s and the end node d included in the partial node set by a route search algorithm according to the multi-objective function (step S410). At this time, the route search processing unit 220 performs a route search using the cost constraint condition C determined in step S409.

  Next, the route search processing unit 220 performs a route search for all node combinations included in the partial mesh set selected in step S407 (that is, all partial node sets included in the partial mesh set). It is determined whether or not it has been performed (step S411).

  In step S411, when there is a partial node set for which the route search is not performed among the partial node sets included in the partial mesh set, the route search processing unit 220 returns to step S408.

  On the other hand, when the route search is performed for all the partial node sets included in the partial mesh set in step S411, the route search processing unit 220 applies to all the partial mesh sets selected in the above step S406. It is then determined whether a route search has been performed (step S412).

  In step S412, when there is a partial mesh set for which a route search has not been performed among the partial mesh sets included in the mesh set, the route search processing unit 220 returns to step S407.

  On the other hand, in step S412, when the route search is performed for all the partial mesh sets included in the mesh set, the route search processing unit 220 combines the partial routes to provide a route from the start node S to the end node D. Is created (step S413). That is, the route search processing unit 220 creates a route from the start point node S to the end point node D by connecting the partial routes in the partial mesh set included in a certain mesh set between adjacent partial mesh sets. Note that it may be impossible to reach the end node D from the start point node S even if the partial paths are connected. In this case, a route from the start node S to the end node D is not created.

  Next, the route search processing unit 220 determines whether or not routes have been created for all mesh groups created in step S405 (step S414).

  In step S414, when there is a mesh set for which a route has not been created among the plurality of mesh sets, the route search processing unit 220 returns to step S406.

  On the other hand, when a route is created for all mesh groups, the output unit 300 outputs the route created by the route search processing unit 220 (step S415). At this time, the output unit 300 may output, for example, only the M routes having good evaluation indexes based on one or more evaluation indexes (multipurpose function values), for example.

<Search area definition processing>
As described in step S402 of FIG. 10, the route search processing unit 220 defines a search area on the network from the start point node S and the end point node D included in the start / end point node information. At this time, as the search area increases, the number of nodes, links, and partial search areas included in the search area increases, and the partial node set input to the constraint condition determination processing unit 210 has a linear cost L _p. Bigger things are more. As a result, as shown in FIG. 11, a node of a facility group starting from a different representative facility (that is, a facility not to be searched (for example, a building)) is easily selected as a partial node set. For this reason, the approximation accuracy of the first approximate expression f ₁ and the second approximate expression f ₂ may deteriorate.

  On the other hand, as the search area becomes smaller, the number of routes from the start node s to the end node d of the partial node set decreases, and it may be impossible to find a route that is resistant to disaster.

  Therefore, in order to improve the accuracy of the route search, it is necessary to define a search area of an appropriate size. A process for defining a search area having such an appropriate size will be described with reference to FIG. FIG. 12 is a flowchart showing an example of search area definition processing according to the embodiment of the present invention.

  First, the route search processing unit 220 searches for a route that is the shortest route between the start point node S and the end point node D (step S501).

  Next, the route search processing unit 220 determines the distance between all the nodes through which the shortest route searched in step S501 passes and the straight line SD connecting the start point node S and the end point node D (from the node to the straight line SD). A perpendicular distance indicating the length of the perpendicular is calculated (step S502).

  For example, as shown in FIG. 13, it is assumed that the nodes through which the shortest path from the start node S to the end node D passes are “node 1” to “node 7”. In this case, the route search processing unit 220 calculates a perpendicular distance 1 from the node 1 to the straight line SD. Similarly, the route search processing unit 220 calculates a perpendicular distance 2 from the node 2 to the straight line SD. Similarly, the route search processing unit 220 calculates a perpendicular distance 3 to a perpendicular distance 7 to the straight line SD for each of the nodes 3 to 7.

  Next, the route search processing unit 220 calculates the minimum rectangular area including the two nodes having the longest perpendicular distance on both sides of the straight line SD, the start point node S, and the end point node D (step S503).

  For example, as shown in FIG. 13, the maximum perpendicular distance among the perpendicular distances on one side of the straight line SD is “perpendicular distance 5”. The maximum perpendicular distance among the perpendicular distances on the other side of the straight line SD is “perpendicular distance 1”. Therefore, as illustrated in FIG. 14, the route search processing unit 220 calculates a minimum rectangular area including the node 1, the node 5, the start point node S, and the end point node D.

  Next, the route search processing unit 220 performs correction for rounding up the length of the side of the rectangular area so that the rectangular area calculated in step S503 is an integral multiple of the mesh (partial search area) (step S503). S504).

  That is, as shown in FIG. 15, the route search processing unit 220 sets the lengths of the two sides of the sides of the rectangular area so that the length of two sides perpendicular to the straight line SD is an integral multiple of the mesh size. Perform correction to round up the length.

  Next, the route search processing unit 220 performs correction for adding a margin M to the rectangular region corrected in step S504 (step S505).

  That is, as shown in FIG. 16, the route search processing unit 220 performs correction by adding a predetermined margin M for the length of two sides perpendicular to the straight line SD among the sides of the rectangular area. The margin M is preferably an integer multiple of the mesh size.

  Next, the route search processing unit 220 defines the rectangular area corrected in step S505 as a search area (step S506). Thereby, in the process after step S403 of FIG. 10, the process is executed using the search area defined in step S506.

In addition, when the process of said step S501-step S506 is performed in step S402 of FIG. 10, step S403 of FIG. 10 (the validity determination process of a 1st approximation formula) is not performed, but step The processing after S404 may be executed. In this case, the process of step S409 (processing for determining the cost constraints), always using the first approximation formula f ₁ may be determined cost constraints C.

  In FIG. 12, a case has been described in which both the correction for rounding up the side length of the rectangular area and the correction for adding the margin M are performed, but the present invention is not limited to this. Only one of these corrections may be performed. Further, both of these corrections may not be performed.

<Summary>
As described above, the route search device 10 according to the embodiment of the present invention calculates a partial route when a route obtained by combining partial routes obtained from a partial problem having one or more evaluation indexes is extracted. The cost constraint condition C used for the subproblem can be determined. In addition, the route search device 10 according to the embodiment of the present invention determines the cost constraint condition C for each subproblem by performing the shortest route search between the start node S and the end node D only once. Can do. Therefore, for example, even when the number of partial problems is enormous, the cost constraint condition C can be determined within a practical time.

  The present invention is not limited to the specifically disclosed embodiments, and various modifications and changes can be made without departing from the scope of the claims.

DESCRIPTION OF SYMBOLS 10 Path | route search apparatus 100 Input part 200 Operation part 210 Constraint condition determination process part 211 Shortest path | route type route | root search part 212 Cost calculation part 213 Approximate expression validity determination part 214 Partial node set linear distance calculation part 215 Constraint condition determination part 220 Path search Processing unit 300 Output unit 400 Data storage unit

Claims (10)

A shortest path search unit that searches for a shortest path from a start node to an end node in the network;
A cost calculation unit that calculates a first cost of the shortest route searched by the shortest route search unit and a second cost of a straight line connecting the start point node and the end point node;
A determination unit that determines whether or not a predetermined first approximate expression is appropriate based on the first cost and the second cost calculated by the cost calculation unit;
A partial problem for searching for a partial path indicating a path between the first node and the second node included in the network when the determination unit determines that the first approximate expression is valid. A determination unit that determines a constraint condition in a subproblem having one or more evaluation indices using the first approximate expression;
A route search device having A partial route search unit that searches for the partial route by solving the partial problem using the constraint condition determined by the determination unit;
A route search unit for searching for a route from the start node to the end node by combining the partial routes searched by the partial route search unit;
The route search device according to claim 1, comprising: The route search unit
Based on the distance between a node on the shortest path between the start point node and the end point node and a straight line connecting the start point node and the end point node, a search region used for determination by the determination unit is defined,
The determination unit
3. The constraint condition for searching for the partial route between the first node and the second node included in the search area defined by the route search unit is determined according to claim 2. Route search device. The route search device
When the search area is defined by the route search unit, the calculation of the first cost and the second cost by the cost calculation unit and the first approximate expression by the determination unit are valid. Without judging
The determination unit
The route search device according to claim 3, wherein the constraint condition is determined using the first approximate expression. The determination unit
The constraint condition is determined by adding a predetermined setting cost set in advance to a value obtained by approximating the linear distance between the first node and the second node by the first approximate expression. 5. The route search device according to any one of claims 1 to 4.   The route search device according to claim 5, wherein the set cost is a cost that is allowed when a detour is made in the search for the partial route. The determination unit
When the determination unit determines that the first approximate expression is not valid, a constraint condition in the partial problem is determined using a predetermined second approximate expression different from the first approximate expression. Item 7. The route search device according to any one of Items 1 to 6. The determination unit
By determining whether or not a value obtained by subtracting the first cost from a value obtained by approximating the second cost with the first approximate expression is greater than a preset reference value, the first approximation The route search device according to any one of claims 1 to 7, which determines whether or not an expression is valid. A shortest path search procedure for searching for the shortest path from the start node to the end node in the network;
A cost calculation procedure for calculating a first cost of the shortest route searched by the shortest route search procedure and a second cost of a straight line connecting the start point node and the end point node;
A determination procedure for determining whether or not a predetermined first approximate expression is valid based on the first cost and the second cost calculated by the cost calculation procedure;
A partial problem for searching for a partial route indicating a route between the first node and the second node included in the network when the first approximate expression is determined to be valid by the determination procedure. A determination procedure for determining a constraint condition in a subproblem having one or more evaluation indices using the first approximate expression;
Route search method in which the computer executes. A shortest path search procedure for searching for the shortest path from the start node to the end node in the network;
A cost calculation procedure for calculating a first cost of the shortest route searched by the shortest route search procedure and a second cost of a straight line connecting the start point node and the end point node;
A determination procedure for determining whether or not a predetermined first approximate expression is valid based on the first cost and the second cost calculated by the cost calculation procedure;
A partial problem for searching for a partial route indicating a route between the first node and the second node included in the network when the first approximate expression is determined to be valid by the determination procedure. A determination procedure for determining a constraint condition in a subproblem having one or more evaluation indices using the first approximate expression;
A program that causes a computer to execute.