JP2007187584A - Navigation system having patrol route retrieving function, route retrieval server, and patrol route retrieving method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To retrieve a patrol route capable of meeting an order and/or time constraint when the constraint exists at a plurality of points to be patrolled. <P>SOLUTION: This navigation system comprises an operation/input means 170 for inputting positional information of points to be patrolled and/or constraint condition of the points to be patrolled, a two-point route retrieving means 153 for retrieving the route between two points, and a patrol route retrieving means 150 for retrieving a patrol route through which a plurality of points to be patrolled are patrolled. The two-point route retrieving means 153 retrieves and stores the shortest routes between any two points to be patrolled. The patrol route retrieving means 150, in determining the optimum patrol route using heredity algorithm, calculates an evaluation value by adding a constraint cost according to the constraint condition of the points to be patrolled to the sum of the route cost according to the patrol order. When the evaluation value is better than that of heredity in an existing group, the patrol route retrieving means 150 adds it to the heredity group and continues the patrol route retrieval. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention provides a navigation system that searches for and guides an optimal route from a desired point to a desired point, and searches for an efficient route for traveling through a plurality of predetermined points. A navigation system and a route search server having a function, and in particular, when there is an orderly or temporal restriction condition at a place to be visited, a navigation system that searches for an efficient route that takes the restriction condition into consideration, and The present invention relates to a route search server and a cyclic route search method.

2. Description of the Related Art Conventionally, a navigation device and a navigation system that guide a user by searching for a route from a desired departure place to a destination using map data and road data are known.
As such a navigation device and navigation system, a car navigation device (hereinafter referred to as “car navigation”) that is installed in an automobile and guides a route to a driver, a route search request is sent to a route search server using a mobile phone as a navigation terminal. A communication type navigation system that receives the result and receives route guidance has been put into practical use.

  The car navigation system uses a GPS (Global Positioning System), receives GPS signals transmitted from a plurality of GPS satellites orbiting the earth with a GPS antenna, and transmits the GPS signals to the GPS signals. The position is specified by analyzing the satellite position and clock information included. At least four GPS satellites are required. The single positioning accuracy of GPS is generally over 10 m, but it is improved to 5 m or less by adopting DGPS (Differential GPS). In particular, a positioning unit that is conventionally mounted only on some mobile phones, such as a GPS receiver that receives a signal from a GPS (Global Positioning System) satellite and performs positioning, is called a third-generation mobile phone. There is a tendency to be installed in all models of telephones.

  A general navigation device, a route search device and a route search method used in a communication navigation system are disclosed in, for example, the following Patent Document 1 (Japanese Patent Laid-Open No. 2001-165681). This navigation system is configured to send information of departure and destination from a portable navigation terminal to a route search server, and search and guide a route that matches a search condition from road network and traffic network data by the route search server. Has been. As the search condition, there are means for moving from the departure place to the destination, for example, walking, automobile, combined use of railroad and walking, and the route is searched as one of the search conditions.

  The route search server uses roads (routes) of map data as nodes and node positions as the nodes, links connecting the nodes as links, and cost information (distance and required time) for all links as a database. ing. Then, the route search server refers to the database, sequentially searches for links from the departure node to the destination node, and traces the node and link with the smallest cost information of the link as a guide route. The shortest route can be guided to the portable navigation terminal. As such a route search method, a method called label determination method or Dijkstra method is used. Patent Document 1 also discloses a route search method using this Dijkstra method.

  By the way, in addition to searching for an optimal route between two desired points in route search, there is a cyclic route search for searching for a route that goes around a predetermined plurality of points from a desired starting point to a destination. It may be necessary. Such a route search is necessary, for example, in an article delivery work or a work in which a salesman visits a plurality of customer locations. Such a route search is called a cyclic route search.

  The cyclic route search starts from a vertex s ∈ V, where du, v is the cost of edge (u, v) ∈ E, given a complete graph G (V, E) with n vertices. The goal is to visit the other vertices all at once and find a route to reach destination e at the end. Here, considering the efficiency of operations such as general delivery, it is desirable that the total cost of the route to be circulated is as small as possible. This problem of searching for a patrol route is known as a problem that is difficult to NP.

An NP-hard problem is a problem in which the time complexity of the algorithm is exponential time, that is, the asymptotic limit on the number of steps required in the algorithm to solve the problem is O (cn) (where c is greater than 1) A large real number, where n is the scale of the problem).
For example, when the number of patrol points not including the starting point and the destination is n, the number of times of searching the patrol route for obtaining the optimum patrol route is n! It becomes. That is, the number of searches in the case of visiting 10 points is (10 × 9 × 8 × 7 × 6 × 5 × 4 × 3 × 2 × 1) = 362880.
By the way, if you include your departure point in the number of patrols and return to the departure point, the number of times of patrol route search is (n-1)! / 2.

  FIG. 15 shows the total number of cyclic routes and the calculation for each number of rounds when the above round round trips are performed using a supercomputer of 10 TFlops (1013 floating point operations per second are possible). Show time. The 10 TFlops supercomputer has, for example, a computing capacity of about 5000 Pentium4 (registered trademark) -1.8 GHz personal computers.

  As shown in FIG. 15, the cyclic route search has a problem that, when the number of circulation points increases, if a brute force calculation is performed with a current general arithmetic processing device, a huge amount of calculation is required, which is difficult in practice. Therefore, generally, an approximate solution is obtained using an algorithm called “genetic algorithm”.

  For example, the following Patent Document 2 (Japanese Patent Laid-Open No. 2000-172664) and Patent Document 3 (Japanese Patent Laid-Open No. 8-202675) disclose a technique for searching for a circular route using a genetic algorithm.

  The genetic algorithm is a method of probabilistically searching for a solution using a model of a biological evolution process (crossover, mutation, etc.). First, here is a description of the genetic algorithm. Details of genetic algorithms are disclosed in, for example, Non-Patent Document 1 (David E. Gold, “Genetic Algorithms in Search, Optimization and Machine Learning” (Addison Wesley)).

  In the genetic algorithm, a set P of x bit strings B = (B1, B2,..., BX) (or simply abbreviated as B1B2... BX) is given as input values. The number of elements (x bit strings) of the set P is n, each element is called a gene, and the set P is called an individual group. Each element of the set P expresses a solution of a given problem, and the genetic algorithm tries to obtain an optimal solution by adding an iterative process following the evolution process of the organism to the set P.

  The outline of the process in the genetic algorithm is as follows. First, a population is constructed as a set of randomly generated genes. This is called the initial population. Next, according to the evaluation scale (evaluation function) given according to the problem, the goodness as a solution is evaluated for each gene in the individual group, and the result is expressed as an evaluation value for each gene. Then, according to the obtained evaluation value, the number of genes with low evaluation is reduced, and the number of genes with good evaluation is increased accordingly. As a result, genes with better evaluation have a higher proportion of the population P. Such a phenomenon is called “淘汰”. This gene selection process is performed as follows.

  First, two genes are selected from a randomly generated individual group by a method such as a rank method or a roulette method, and a part of a bit string forming these genes is exchanged. This operation is called “crossover”. The number of times this crossover is repeated is determined by a preset crossover rate. The number of genes newly generated by crossover is obtained by multiplying the crossover rate by the number of genes n. Subsequently, the gene obtained by crossover is slightly changed according to the mutation rate that is usually set with a low probability. This operation is called “mutation”. As a mutation method, for example, a method of bit-inverting a part of a bit string of a gene is known.

  When operations such as crossover and mutation are completed, the goodness as a solution is evaluated for each gene in the population, and the next population is generated according to the evaluation result. A new population is generated by extracting genes having a good evaluation value by the number n of genes in the population created by the above operation and the previous population. This one-time selection, that is, one cycle of crossover / mutation and generation of a new population corresponds to “generation”. By repeating the above-described processing, the generation of the population advances, and each gene in the population is deceived, so that each gene in the population reaches the optimal solution or sub-optimal solution of the given problem. .

  When the above genetic algorithm is used for a route search that goes around a plurality of points, one gene becomes one round route including a starting point and a destination. For example, when searching for a patrol route from a departure point to all destinations once to reach the destination, first, a plurality of routes from the starting point to each destination through the patrol points are randomly generated. These plural genes are stored as a population, and a set P0 is formed. The evaluation value of each gene in the set P0 is the sum of route costs (time and / or distance) between the set points.

  Next, a new gene is generated from two genes selected from the set P0, and an evaluation value of the gene is obtained. For example, when a new set P1 is created by the roulette method, a gene having a good evaluation value is selected from the set P0 with a higher probability. In other words, genes with better evaluation values are more likely to be seeds for generating new genes. A new gene is created by exchanging a part of the circulation order in the two genes (circulation route) selected here, and mutation is performed with a low probability, and the evaluation value is obtained. If the evaluation value of this new gene has a better evaluation value than the gene with the worst evaluation value in the set P0, this gene is replaced. The set P1 is obtained by performing such an operation for one generation. This process is repeated to perform generation change, and if the generation change reaches a predetermined number of times set in advance or the evaluation value converges, the process ends, and the gene having the best evaluation value in the finally obtained set PN ( (Circular route) is an optimal solution or a sub-optimal solution.

JP 2001-165681 A (FIGS. 1 and 2) JP 2000-172664 A JP-A-8-202675 "Genetic Algorithms in Search, Optimization and Machine Learning" by David E. Gold (published in 1989 by Addison Wesley)

  In general, the route search is aimed at improving the efficiency of delivery and pick-up of goods, and therefore an optimal solution is not necessarily required. In other words, a suboptimal solution is sufficiently useful. In general business, it is only necessary to derive a better result than a route considered by humans without obtaining an optimal patrol route. Alternatively, since it is possible to reduce the labor of a person thinking about a route by using a cyclic route search device, it is possible to improve the efficiency of business by itself. These facts are also one factor that the approximate solution is applied to the cyclic route search.

The calculation to find a patrol route is extremely computationally intensive and extremely difficult, so there are cases where compromise is made by relying on human intuition or by finding a route that only goes around the current location. In addition, a simple route search is intended to obtain a short route as much as possible, and may not be suitable for the actual use situation.
For example,
1) When there is a priority condition for the order at the traveling point 2) When there is a time condition for stopping at the traveling point 3) When there is a time condition for stopping at the traveling point and the priority condition for the order.

  In route determination in the collection and delivery of goods, there are order restrictions (priority conditions) between points to be visited. In other words, when an article is delivered, there is a collection and a delivery. Therefore, when searching for a patrol route, the order of the collection and delivery must be satisfied. Therefore, there is a problem that a satisfactory result cannot be obtained by a simple cyclic route search using the genetic algorithm. Such a problem is also disclosed as a problem in Japanese Patent Laid-Open No. 2005-263447.

As another example, this corresponds to a case where a bus is operated in a depopulated area in order to satisfy the desire of movement of a plurality of residents at the same time with one vehicle. The desired boarding location and the desired getting-off location for each inhabitant are the patrol points, but since the order of getting on and getting off is a condition, a pure patrol route search does not require a solution. (If the order of getting on and getting off is reversed, the residents cannot use it.)
In such a case, if the number of places that need to be visited is 10, the place c is the place where the circuit R R {a, b, c, d, e, f, g, h, i, j}. It is required to derive a circulation route that simultaneously satisfies the condition that the circuit travels after point a or after points a, d, c after points h, i, j.

The case where there is a time restriction (priority condition) at the patrol point is, for example, a case where the patrol point is in a building that can enter only at a certain time. An example of delivery of goods is a case where goods are delivered to a vending machine installed in a building where access time is limited.
That is, in such a route search, it is required to derive a tour route that satisfies conditions such as a desired arrival time at a tour point based on the departure time of the departure place or the arrival time of the arrival place.

  In addition, there are cases where the above-described order restriction and time restriction exist simultaneously. In such a case, for example, in the above described route search for shared bus operation in a depopulated area, the residents in the depopulated area can set conditions up to a desired ride position, a desired get-off position, and a desired time to get on or get off. It develops into a theme.

  The inventor of the present application has conducted various studies to solve the above-described problems, and as a result, has provided means for inputting the restriction conditions of the points to be visited and searched for the shortest path between all two points to be visited. The order search means considers not only the sum of the route costs according to the order of the tour, but also the evaluation value for the constraint conditions of the places to be visited when determining the optimum tour route using the genetic algorithm. If this evaluation value is a better evaluation value than the gene in the existing population, the present invention has been completed by conceiving that the above problem can be solved if the cyclic route search is advanced in addition to the gene population. It has come to be.

  That is, the present invention aims to solve the above problems, and when there are order and / or time restrictions at a plurality of points to be visited, it is possible to search for a circulation route that can satisfy the restrictions. An object of the present invention is to provide a navigation system and a route search server.

In order to solve the above-mentioned problem, the invention according to claim 1 of the present application is
A navigation system having a patrol route search function that patrols a plurality of points,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route,
The point-to-point route search means searches all shortest routes between two points to be visited and stores the optimum route and route cost.
The cyclic route searching means, when obtaining an optimal cyclic route using a genetic algorithm, adds a constraint cost according to the constraint condition of the target point to be added to the sum of the route costs according to the cyclic order, and an evaluation value If the evaluation value is an evaluation value better than that of the gene in the existing population, the route search is advanced in addition to the gene population.

The invention according to claim 2 of the present application is the navigation system according to claim 1,
The restriction condition of the point to be visited is the priority of the order of visiting the point.

The invention according to claim 2 of the present application is the navigation system according to claim 1,
The restriction condition for the point to be visited is a tour time or a tour time zone of the spot.

The invention according to claim 4 of the present application is the navigation system according to claim 1,
The route cost according to the patrol order is a distance or time calculated considering at least one of date / time, traffic jam information, road type, cruise speed, congestion degree, and loading capacity.

The invention according to claim 5 of the present application is the navigation system according to claim 1,
The point-to-point route search means treats a route between two points as a directed link when searching for the shortest route between two points to be visited, and calculates and stores a route cost in both directions. To do.

The invention according to claim 6 is:
A route search server having a patrol route search function for patroling a plurality of points,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route, and
The point-to-point route search means searches all shortest routes between two points to be visited and stores the optimum route and route cost.
The cyclic route searching means, when obtaining an optimal cyclic route using a genetic algorithm, adds a constraint cost according to the constraint condition of the target point to be added to the sum of the route costs according to the cyclic order, and an evaluation value If the evaluation value is an evaluation value better than that of the gene in the existing population, the route search is advanced in addition to the gene population.

The invention according to claim 7 of the present application is the navigation server according to claim 6,
The restriction condition of the point to be visited is the priority of the order of visiting the point.

The invention according to claim 8 of the present application is the navigation server according to claim 6,
The restriction condition for the point to be visited is a tour time or a tour time zone of the spot.

The invention according to claim 9 of the present application is the navigation server according to claim 6,
The route cost according to the patrol order is a distance or time calculated considering at least one of date / time, traffic jam information, road type, cruise speed, congestion degree, and loading capacity.

The invention according to claim 10 of the present application is the navigation server according to claim 6,
The point-to-point route search means treats a route between two points as a directed link when searching for the shortest route between two points to be visited, and calculates and stores a route cost in both directions. To do.

The invention according to claim 11 of the present application is
A patrol route search method that patrols a plurality of points,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route, and
The point-to-point route search means searching for all shortest routes between two points to be visited and storing the optimum route and route cost;
When the traveling route search means obtains an optimum traveling route using a genetic algorithm, an evaluation value is added by adding a constraint cost according to the constraint condition of the site to be visited to the sum of the route costs according to the circulation order. And a step of adding to the gene population if the evaluation value is an evaluation value better than that of the gene in the existing population.

The invention according to claim 12 of the present application is the cyclic route search method according to claim 11,
The step of searching for the shortest route and storing the optimum route and route cost by the point-to-point route search means handling the route between the two points as a directed link, and calculating and storing a route cost in both directions. It is characterized by including.

In the invention according to claim 1,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route, and the route search means between two points searches for the shortest route between all two points to be visited and stores the optimum route and route cost. When calculating an optimal tour route using a genetic algorithm, an evaluation value is calculated by adding a constraint cost according to the constraint condition of the target site to be toured to the sum of route costs according to the tour order, If the value is an evaluation value that is better than the gene in the existing population, in addition to the gene population, the cyclic route search is advanced.

  According to such a configuration, even when there is a temporal restriction condition or an order restriction condition between the circulation points when searching for a circulation route that circulates a plurality of circulation points using the genetic algorithm. Thus, it is possible to search and provide an efficient patrol route that satisfies the constraint conditions.

In the invention according to claim 2, in the navigation system according to claim 1, the restriction condition of the point to be visited is the priority of the order of the points to be visited.
According to such a configuration, when searching for a circulation route that circulates a plurality of circulation points using a genetic algorithm, the restriction condition is satisfied even when there are sequential restriction conditions between the circulation points. It becomes possible to search and provide an efficient patrol route.

According to a third aspect of the present invention, in the navigation system according to the first aspect, the restriction condition of the point to be visited is a tour time or a tour time zone of the point.
According to such a configuration, when searching for a tour route that travels through a plurality of tour locations using a genetic algorithm, even if there is a time constraint condition at the tour location, it is efficient to satisfy the constraint conditions. It is possible to search for and provide a simple patrol route.

In the invention according to claim 4, in the navigation system according to claim 1, the route cost corresponding to the patrol order is at least one of date / time, traffic jam information, road type, cruise speed, congestion degree, and loading capacity. Is a distance or time calculated in consideration of
According to such a configuration, even when there is a temporal restriction condition or an order restriction condition between the circulation points when searching for a circulation route that circulates a plurality of circulation points using the genetic algorithm. In addition, it is possible to search and provide an efficient patrol route that satisfies one of the constraints, and takes into consideration one of date / time, traffic jam information, road type, cruise speed, congestion degree, and load capacity.

Further, in the invention according to claim 5, in the navigation system according to claim 1, the point-to-point route searching means searches for the shortest route between the two points to be visited, and the route between the two points. Is treated as a directed link, and the route cost in both directions is calculated and stored.
According to such a configuration, it is possible to search for an efficient traveling route even when there are many one-way streets as in urban areas, or when there are routes with significantly different costs depending on directions.

  In the invention according to claims 6 to 10 of the present application, a route search server constituting the navigation system according to claims 1 to 5 can be provided, respectively, and the invention according to claims 11 and 12 is provided. In the above, it is possible to provide a cyclic route search method in the route search server according to claims 6 and 10, respectively.

  Hereinafter, specific examples of the present invention will be described in detail with reference to examples and drawings. However, the embodiments shown below illustrate a navigation system for embodying the technical idea of the present invention, and are not intended to specify the present invention for this navigation system. The present invention can be equally applied to navigation systems of other embodiments included in the scope.

  The navigation system according to the embodiment of the present invention includes a route search server 10 as shown in FIG. The route search server 10 receives a route search request from a terminal device (not shown), searches for an optimum route from the departure point to the destination, and distributes it to the requesting terminal device.

  The route search server 10 includes a control unit 110, a communication unit 120, an output / distribution unit 130, a route search unit 140, a cyclic route search unit 150, a preprocessing unit 151, a point-to-point route search unit 153, a GA processing unit 155, a display. Means 160, operation / input means 170, map data 180, road network database 190, guidance database 200, and the like are provided.

  The control unit 110 is a microprocessor having a RAM, a ROM, and a processor (not shown), and controls the operation of each unit by a control program stored in the ROM. The communication means 120 is for communicating with a terminal device or another server via a network. The output / distribution means 130 outputs a route search result or distributes it to a terminal device, and edits route data and guidance data into data for distribution to the terminal device. The operation / input means 170 is for making desired inputs and operations to the route search server 10, and the display means 160 is composed of a liquid crystal display unit or the like and displays a predetermined input screen.

  The map data 180 stores the map information to be provided to the terminal device, and the road network database 190 uses the roads (routes) of the map data as nodes and the positions of the inflection points as nodes, and routes connecting the nodes. As links, cost information (distance and required time) of all links is stored as a database. The guidance database 200 stores voice data and guidance display image data for direction guidance at intersections and the like.

  The route search means 140 performs a normal route search with reference to the road network database 190. The traveling route search means 150 searches for a traveling route in this embodiment. The traveling route search means 150 includes a preprocessing means 151, a point-to-point route search means 153, and a GA processing means 155. Details of these will be described later.

  In the present embodiment, a model for searching for a traveling route will be described, for example, with an example of baggage collection and delivery in remote areas. In remote areas, since the number of deliveries per unit area is small, it is efficient to collect and deliver with one vehicle. In addition, the fact that the number of deliveries per unit area is small means that the mileage per delivery is also long, so that it cannot be established as a business unless it is delivered as efficiently as possible in terms of profit.

  FIG. 2 is an input screen for a tour point in the terminal device when the route search server 10 performs a tour route search. In implementation, it should be customized to the optimum user interface (UI) according to each business condition, but only the function will be briefly described. In this embodiment, the departure place is registered as the distributor's sales office, and the final destination is registered as returning to the departure place. Further, the route search server 10 can be replaced with a personal computer in which the route search engine of this embodiment is installed.

In order to make a collection / delivery plan for a certain day, a patrol point is first input from the input screen shown in FIG. A patrol point can be input as a free word by selecting an address, a store / facility name, a telephone number, etc. (It is also possible to search and input by a search function without selecting it). If there is a time condition to stop at the patrol point, select from the time designation. The default is “None”, but the pull-down menu
11:00 to 13:00
13:00 to 15:00
15:00 to 17:00
...
It is possible to specify a time constraint condition.
In the case of performing from package collection to delivery, order conditions are specified by inputting a collection point in the left column and a delivery destination in the right column.

  When checking the location, you can check the location on the map by pressing the map button. It can also be corrected on the map. If you make a mistake, you can use the delete button to clear the line. If there are more patrol points, enter them sequentially on the next line. If you run out of lines, you can press the add button to add more input lines. Since the tour route is obtained, the operator only has to input the points sequentially (there is no need to be aware of the input order).

  When the search button is pressed after the input is completed, the process proceeds to a cyclic route search process. The cyclic route search process is roughly divided into steps shown in the flowchart of FIG. That is, after performing the preprocessing in the process of step S11, the optimal route search of all combinations between the two traveling points is performed in the process of step S12. (Note that if the route between two points does not include a one-way route, the route cost due to the order between the two points may not be considered, but if there are many one-way routes, such as in urban areas, or the direction (If there are routes with significantly different costs, it is better to search for a bidirectional route.) Once the optimal route search is performed and the route cost is stored, only the route cost needs to be handled in the cyclic route search. .

  For this reason, the cost of all the routes searched in the process of step S13 is stored. Next, a cyclic route search is performed in the process of step S14, and the result is output in the process of step S15. Each process will be described in more detail below.

[Preprocessing]
First, the preprocessing unit 151 performs input data arrangement and error checking as preprocessing. As shown in FIG. 4A, for example, when the order from the same point (A) to a plurality of points (B, C, D) is input, a plurality of time designations are made at the same point (A). If there is not, the same point (A) is visited only once, and A, B, C, D are regarded as one group in the internal representation, and A (1), B (2), C as shown in FIG. 4B. (2), D (2) ... (in parentheses are group priorities)
Make a group called
Conversely, as shown in FIG. 4C, the delivery destination is a single point (A). In addition, if the time designation is reversed in time between collection and delivery, processing is performed to change the delivery time designation so that it is after collection or to display an error and prompt re-input. .

[Route search between two points]
Next, the point-to-point route search means 153 performs an optimum route search between two points in all combinations between the two points of the tour points registered through the preprocessing. Once the optimal route search is performed and the route cost is stored, only the route cost needs to be handled in the search for the cyclic route. This search may be performed using the route search means 140 that performs a normal route search.

[Circuit route search]
Next, a cyclic route search that is the center of the present invention is performed. This processing is performed by the GA processing means 155. The GA processing means 155 searches for a route using genetic algorithms. The genetic algorithm is one of the search methods inspired by the theory of evolution as described above. Gene-like solutions imitate the natural evolution process in order to efficiently and systematically search the solution space and find the optimal solution.

A gene with certain advantageous characteristics, i.e., a solution close to the optimal solution, can survive longer and leave many offspring. In other words, the theory of natural selection is used. In this process, the solution is expressed as a bit string gene, and is performed by operations such as gene selection and crossover / mutation.
Among these, a more characteristic part is a gene evaluation method. Genetic evaluation is an important indicator for good solutions. This is because it is determined whether or not to leave the gene for the next generation based on the evaluation value obtained by this function.

  In this processing, as described above, one cyclic route including the starting point and the destination is one gene, and an individual group and a set P0 are formed by a plurality of genes. For example, when searching for a round route from the departure point to the destination through all the round points only once, a plurality of routes from the departure point to the destination through the round points are created at random. These plural genes are stored as a population, and a set P0 is formed. The evaluation value of each gene in the set P0 is the sum of route costs (time and / or distance) between the set points.

  Next, a new gene is generated from two genes selected from the set P0, and an evaluation value of the gene is obtained. For example, when a new set P1 is created by the roulette method, a gene having a good evaluation value is selected from the set P0 with a higher probability. In other words, genes with better evaluation values are more likely to be seeds for generating new genes. A new gene is created by exchanging a part of the circulation order in the two genes (circulation route) selected here, and mutation is performed with a low probability, and the evaluation value is obtained. If the evaluation value of this new gene has a better evaluation value than the gene with the worst evaluation value in the set P0, this gene is replaced. The set P1 is obtained by performing such an operation for one generation. This process is repeated to perform generation change, and if the generation change reaches a predetermined number of times set in advance or the evaluation value converges, the process ends, and the gene having the best evaluation value in the finally obtained set PN ( The cyclic route) is the optimal solution or the suboptimal solution.

  In the present invention, the order constraint and the time constraint condition are input at each traveling point, and in addition to the route cost when calculating the evaluation value using the genetic algorithm, the order constraint set at each traveling point. It is characterized in that it is calculated by adding a constraint cost based on a temporal constraint condition. The cost based on this constraint varies depending on the number of patrol places that violate the order constraint and the time constraint. As a result, it is possible to calculate a patrol route when there are restrictions on the patrol points.

  5 and 6 are schematic diagrams for explaining the concept of this processing. FIGS. 5 and 6 show an example of searching for a round route from the departure point ST to the destination points GL through the round trip points X1 to X6. The round trip points X3 and X6 have an order condition (X6 round trip order). The priority is higher than X3: a condition that X6 goes around before X3) is added.

  As the evaluation value of the cyclic route represented by one gene G0 in the randomly generated initial population shown in FIG. 5, first, the total route cost is added. The evaluation value is the total route cost. The route cost of the optimum route between all two points is searched in the point-to-point route search, and the result is stored. Here, the traveling points X3 and X6 have an order restriction, and since the priority of the traveling order of X6 is higher than X3, the number of traveling points that violate the order restriction is counted up. According to Equation 1 described later, the constraint cost is obtained by dividing the total number of constraint violations by the weight of the preset order constraint and multiplying it by the route cost. Here, the order constraint weight is normally set to 1, but a value of about 0.1 to 0.9 is set to increase the order-related constraint, and a value of about 1.1 to 1.9 is set to weaken. . The evaluation value of this gene is determined by adding the constraint cost and the route cost obtained in advance. The same applies when there are time restrictions at the patrol points.

  Next, FIG. 6 shows a cyclic route represented by the newly generated gene G1 by selecting two genes from the initial population. In this gene, the tour points X2 and X5 in the tour route represented by the above gene are exchanged. The evaluation value of this gene is calculated in the same manner as described above. If the evaluation value obtained here is an evaluation value better than the gene in the initial population, a new gene is stored in addition to the gene population, and if it is a bad evaluation value, it is discarded without being added to the gene population.

  If this process is repeated and all the genes have the same evaluation value, or the evaluation value of the best gene converges to a state that is almost the same as the average evaluation value of the population, the gene with the best evaluation value Becomes an optimal solution or a sub-optimal solution that travels around the tour points X1 to X6.

  FIG. 7 is a conceptual diagram showing a processing procedure in the GA processing unit 153. Information input to the GA processing unit 153 includes the number of tour points for which preprocessing has been completed, tour location information, and the route cost of each two-point route. In the process of step S21, an initial gene generation process is performed. The initial population P0 is created by randomly generating the number of genes specified here. The initial gene is a process of drawing a route that passes through each round point at random and obtaining the total cost of the route between the two points that has already been saved. At this time, the evaluation value of each gene is calculated in consideration of the constraint cost due to the constraint condition of each tour point added in the preprocessing.

  Subsequently, two genes are selected as parent genes from the initial population P0 generated as the initial gene in the process of step S22, and an arbitrary circulation point of the two genes is selected and replaced in the process of step S23. Crossover and mutation which is an operation for changing the circulation point in one gene is performed, and a new gene, that is, a child gene is generated in step S24. At the same time, genetic manipulation is performed so that all the tour points are passed only once, that is, the same point is not visited a plurality of times and there is no point that does not visit once.

  Next, an evaluation value of a newly generated child gene in the process of step S25 is calculated and compared with an evaluation value of each gene in the population P0. If the generated child gene is a value that is better than the evaluation value of the gene in the population P0, this child gene is adopted, the evaluation value and the gene representing the circulation order (root) are stored, and the child gene is stored. If it is worse than the evaluation value of the gene of the population P0, this child gene is not adopted and the result is not stored.

  Thereafter, in step S26, it is determined whether or not to end the process. If the process has not ended, the process returns to step S22, and the above genetic operation is repeated.

[End processing]
The search process by the GA processing unit 153 ends when the number of trials satisfies the designated number, or when the evaluation value in the gene population converges. For example, assuming that the process of selecting two genes and generating a child gene is one trial, when the number of cycles is n, in this case, the maximum number of trials should be about 50n2. Moreover, the state where the evaluation values have converged means a state where the evaluation values of all the genes are the same or the evaluation value of the best gene is almost the same as the average evaluation value of the population. If it becomes such a state, it will determine with the process having been complete | finished, and the number of patrol spots and the patrol order will be output. If the circulation order is known, the optimum route between the two points stored can be arranged in the circulation order to provide the circulation route.

The objective function F in the cyclic route search means 150 is as follows.
F = min (eval (n))
Here, the evaluation function eval (n) for obtaining the evaluation value of the gene is defined as follows.
Where n is the number of patrol points
dist (i): Route cost between i-th patrol points
Ddist: Weight for route cost
Dtime: Weight for time constraint
Dorder: Weight for order constraints
Ptime: Total number of patrol points that violate time constraints
[Porder]: The total number of patrol points that violate the order constraint.

  If the evaluation value obtained by this evaluation is an evaluation value better than that of the gene in the existing population, it is added to the gene population (see the processing in steps S22 to S25 in FIG. 7). At this time, the gene with the lowest evaluation value in the existing gene population is excluded from the population.

  A result of obtaining a patrol route that patrols the patrol points 1 to 5 shown in FIG. 8 using the patrol route searching means 150 having the above configuration will be described below. FIG. 9 is a diagram illustrating a map of an area and a delivery point that are targets of the delivery operation of the specific example of FIG. In the delivery business in this area, there are a departure point, a destination, and delivery points from point 1 to point 28. The specific example of FIG. 8 shows a case where a patrol route from the departure point to the destination point 28 to the destination is searched.

  In FIG. 8, Specific Example 1 shows a case in which a tour route having no restrictions on arrival time and priority order is found at tour points 1 to 5 among the tour points. Specific example 2 shows a case where there are restrictions on arrival times as shown in the circuit points 1 and 3 among the circuit points. The arrival time is shown in elapsed time (seconds) from the departure time.

  Specific example 3 is the first group (group 1) in which the order of priority is set in each of the tour points 1 and 2, and the order of priority between the tour points 3 and 5 A case where two groups (group 2) exist is shown. It is assumed that the priority of the order is higher as the numerical value (1 to 3) described after the priority is smaller. Specific example 4 shows a case where the constraint conditions of specific example 3 and specific example 4 are combined.

  FIG. 10 shows a traveling route obtained as a result of searching for a traveling route under the condition of the first specific example of FIG. In FIG. 10, the traveling points shown in FIG. 9 are represented by waypoints + numerical values (circulating order) according to the traveling order. For example, patrol point 1 (see FIG. 9) is indicated by waypoint 8 (see FIG. 10), indicating that the patrol order is eighth. In the case of the specific example 1, the gene calculated in the process of the GA processing unit 155 is the total of the route cost connecting each two points of the tour points, and as shown in FIG. The smallest optimal solution was obtained.

  FIG. 11 is a diagram illustrating a circular route obtained as a result of searching for a circular route under the condition of the second specific example. In this patrol route, the time-constrained patrol point 1 becomes the transit point 2, the patrol point 3 becomes the transit point 6, the patrol order of the patrol point 1 is the second, and the patrol order of the patrol point 3 is the sixth. A patrol route is required.

  FIG. 12 is a diagram illustrating a traveling route obtained as a result of searching for a traveling route under the condition of the third specific example. In this patrol route, the traveling point 1 of the group 1 with the order restriction becomes the transit point 6, the traveling point 2 becomes the transit point 7, the traveling order of the traveling point 1 is the sixth, and the traveling order of the traveling point 2 is 7th. The traveling point 3 of the group 2 with the order restriction becomes the transit point 9, the traveling point 4 becomes the transit point 11, the traveling point 5 becomes the transit point 22, the traveling order of the traveling point 3 is the ninth, and the traveling of the traveling point 4 There is a demand for a tour route in which the order is the eleventh and the tour order of the tour point 5 is the twenty-second.

  FIG. 13 is a diagram illustrating a traveling route obtained as a result of searching for a traveling route under the condition of the fourth specific example. In this patrol route, the traveling point 1 of the group 1 with the order restriction becomes the transit point 2, the traveling point 2 becomes the transit point 15, the tour order of the tour point 1 is second, and the tour order of the tour point 2 is 15th. The traveling point 3 of the group 2 with the order restriction becomes the transit point 4, the traveling point 4 becomes the transit point 6, the traveling point 5 becomes the transit point 10, the traveling order of the traveling point 3 is fourth, and the traveling point 4 The traveling order is sixth, and the traveling order of the traveling point 5 is tenth. Further, with regard to the traveling point 1 and the traveling point 3 having a time constraint, the traveling order of the traveling point 1 is the second, the traveling order of the traveling point 3 is the fourth, and a traveling route that satisfies the time constraint is required. .

  As described above, it is possible to obtain different cyclic routes for all the conditions in the specific examples 1 to 4, and the cyclic routes are cyclic routes that satisfy the respective conditions (time constraint and order constraint). It has become. The execution time of the arithmetic processing required for this processing is about 10 seconds in all cases, and most (99%) is the processing time until the point-to-point path search.

  FIG. 14 is a graph showing the calculation time required for the above-described cyclic route search. In FIG. 14, the horizontal axis represents the number of patrol points, the vertical axis represents the processing time (seconds) of the processor, and each graph represents the processor as Pentium 1.8G (512M), Pentium 2.4G (1.0G), Pentium 3 The calculation time with respect to the number of traveling points when 0.0 G (1.0 G) is used is shown.

  As can be seen from FIG. 14, the processing time required for the patrol route search when the number of patrol points is about 30 is very short, and sufficient results are obtained as shown in the first to fourth examples. It can be seen that it is suitable for practical use.

  In the above embodiment, when the point-to-point route search means 153 searches for the optimum route cost between two points, a network that considers the date and time, congestion information, road type, cruise speed, congestion degree, loading capacity, etc. Using data, when searching for a round route that travels through multiple round trip points using a genetic algorithm, even if there are temporal or sequential constraints between the round trip points, It is possible to search for an efficient route that satisfies the conditions and takes into account the date and time, traffic information, road type, cruise speed, congestion degree, and load capacity. The route search server 10 collects data from other information distribution servers such as a road traffic information server and replaces the data in the road network database 190 with respect to the date / time, traffic jam information, road type, cruise speed, congestion degree, and load capacity data. Can be implemented.

  As described above, the present invention can be used in a wide range of fields such as logistics, sales, and patrol surveys because it can perform a patrol route search that efficiently patrols patrol points that have various constraints that are practical. It is.

It is a block diagram which shows the structure of the route search server in the navigation system concerning the Example of this invention. It is a figure which shows the structure of the input screen at the time of inputting the patrol point in a patrol route search. It is a flowchart which shows the process sequence of the cyclic route search concerning the Example of this invention. It is a figure which shows the concept of the pre-processing of the cyclic route search concerning the Example of this invention, FIG. 4A is a schematic diagram which shows the state in which the order from a departure place to several points was input, FIG. FIG. 4B is a schematic diagram showing a state in which a group of patrol points having a destination A is created. It is a schematic diagram for demonstrating the concept of the cyclic route search process concerning the Example of this invention. It is a schematic diagram for demonstrating the concept of the cyclic route search process concerning the Example of this invention. It is a conceptual diagram which shows the process sequence in the GA process part using a genetic algorithm. It is a figure which shows the specific example which implemented the cyclic route search by applying the cyclic route search by this invention, and is a figure which shows various restrictions conditions of a cyclic point. It is a figure which shows the map of the area used as the delivery work object of the specific example shown in FIG. 8, and a delivery point. It is a figure which shows the patrol route calculated | required on the conditions of the specific example 1 shown in FIG. It is a figure which shows the patrol path | route calculated | required on the conditions of the specific example 2 shown in FIG. It is a figure which shows the patrol path | route calculated | required on the conditions of the specific example 3 shown in FIG. It is a figure which shows the patrol path | route calculated | required on the conditions of the specific example 4 shown in FIG. It is a graph which shows the calculation time required for the cyclic route search concerning the Example of this invention. It is a figure which shows the total number of circulation paths and calculation time with respect to each number of rounds when a roundabout path search is performed using the supercomputer of 10TFlops (1013 times floating point arithmetic is possible per second).

Explanation of symbols

10. Route search server 110 ... Control means 120 ... Communication means 130 ... Output / distribution means 140 ... Route search means 150 ... Traveling route search means 151 ... Pre-processing means 153... Point-to-point route search means 155... GA processing means 160... Display means 170 .. operation / input means 180... Map data 190.

Claims (12)

A navigation system having a patrol route search function that patrols a plurality of points,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route,
The point-to-point route search means searches all shortest routes between two points to be visited and stores the optimum route and route cost.
The cyclic route searching means, when obtaining an optimal cyclic route using a genetic algorithm, adds a constraint cost according to the constraint condition of the target point to be added to the sum of the route costs according to the cyclic order, and an evaluation value If the evaluation value is an evaluation value better than the gene in the existing population, the navigation system having a cyclic route search function is characterized in that the cyclic route search is advanced in addition to the gene population.   The navigation system having a tour route search function according to claim 1, wherein the restriction condition of the tour target point is a priority of a tour order of the tour point.   The navigation system having a traveling route search function according to claim 1, wherein the restriction condition of the traveling target point is a traveling time or a traveling time zone of the point.   The route cost according to the patrol order is a distance or time calculated considering at least one of date and time, traffic jam information, road type, cruise speed, congestion degree, and loading capacity. A navigation system having the patrol route search function described in 1.   The point-to-point route search means treats a route between two points as a directed link when searching for the shortest route between two points to be visited, and calculates and stores a route cost in both directions. A navigation system having a traveling route search function according to claim 1. A route search server having a patrol route search function for patroling a plurality of points,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route, and
The point-to-point route search means searches all shortest routes between two points to be visited and stores the optimum route and route cost.
The cyclic route searching means, when obtaining an optimal cyclic route using a genetic algorithm, adds a constraint cost according to the constraint condition of the target point to be added to the sum of the route costs according to the cyclic order, and an evaluation value A route search server having a cyclic route search function characterized in that, if the evaluation value is an evaluation value better than the gene in the existing population, the cyclic route search is advanced in addition to the gene population.   The route search server having a tour route search function according to claim 6, wherein the restriction condition of the tour target point is a priority of a tour order of the tour point.   The route search server having a tour route search function according to claim 6, wherein the restriction condition of the tour target point is a tour time or a tour time zone of the tour point.   The route cost according to the patrol order is a distance or time calculated considering at least one of date and time, congestion information, road type, cruise speed, congestion degree, and loading capacity. A route search server having a cyclic route search function described in 1.   The point-to-point route search means treats a route between two points as a directed link when searching for the shortest route between two points to be visited, and calculates and stores a route cost in both directions. A route search server having a cyclic route search function according to claim 6. A patrol route search method that patrols a plurality of points,
An input means for inputting position information of a tour target point and / or a restriction condition for a tour target point, a two-point route search means for searching for a route between two points, and a tour for visiting a plurality of tour target points A route search means for searching for a route, and
The point-to-point route search means searching for all shortest routes between two points to be visited and storing the optimum route and route cost;
When the traveling route search means obtains an optimum traveling route using a genetic algorithm, an evaluation value is added by adding a constraint cost according to the constraint condition of the site to be visited to the sum of the route costs according to the circulation order. And a step of adding to the gene population if the evaluation value is an evaluation value better than that of the gene in the existing population.   The step of searching for the shortest route and storing the optimum route and route cost by the point-to-point route search means handling the route between the two points as a directed link, and calculating and storing a route cost in both directions. The method for searching for a traveling route according to claim 11, further comprising: