Travelling route acquisition system

Abstract

To provide a technology that acquires a travelling route covering desired roads.SOLUTION: A travelling route acquisition system is configured that comprises: an object road acquisition unit acquiring a plurality of object roads targeted for monitoring; a constraint condition acquisition unit acquiring a constraint condition corresponding to an allowable maximum movement distance; and a travelling route acquisition unit acquiring, when a vehicle travels on a plurality of travelling routes meeting the constraint condition, the travelling route in which all of the object road is travelled more than once.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a driving route acquisition system.

Background Art

[0002] Conventionally, a technique for identifying the driving route of a delivery vehicle by solving the delivery planning problem is known. For example, Patent Document 1 discloses a technique for creating a route for a vehicle to travel between all logistics nodes at each specified time period within a certain period.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, with the conventional technology, it was not possible to obtain a driving route that covers all the planned roads. That is, in the conventional technology, a driving route that covers all nodes has been obtained, and covering all roads has not been considered. For example, when monitoring roads for road maintenance or the like, it is necessary to cover all the roads to be monitored, and conventionally, it has not been possible to obtain a driving route that covers the desired roads. The present invention has been made in view of the above problems, and an object thereof is to provide a technique for obtaining a driving route that covers desired roads.

Means for Solving the Problems

[0005] To achieve the above object, a travel route acquisition system includes a target road acquisition unit that acquires a plurality of target roads that are roads to be monitored, a constraint condition acquisition unit that acquires constraint conditions corresponding to a maximum allowable travel distance, and a travel route acquisition unit that acquires a plurality of travel routes that satisfy the constraint conditions and in which all of the target roads are traveled at least once when a vehicle travels along the plurality of travel routes.

[0006] That is, in the travel route acquisition system, a plurality of travel routes that satisfy the constraint conditions are searched, and the search is performed such that all of the target roads are traveled at least once when all of the searched travel routes are traveled. As a result, a travel route that covers the desired roads can be obtained.

Brief Description of the Drawings

[0007]

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Embodiments for Carrying Out the Invention

[0008] Here, embodiments of the present invention will be described in the following order. (1) Configuration of the travel route acquisition system: (2) Travel route acquisition process: (3) Other embodiments:

[0009] (1) Configuration of the travel route acquisition system: FIG. 1 is a block diagram showing the configuration of a travel route acquisition system according to the first embodiment. In this embodiment, the travel route acquisition system 10 can be realized by a general-purpose computer or the like. In this embodiment, the travel route acquisition system 10 is used by a road administrator or the like. In this embodiment, the travel route acquisition system 10 is used to acquire a travel route when a local government monitors a target road to be maintained.

[0010] The road administrator in the local government monitors all of the target roads. In this embodiment, by a vehicle equipped with a sensor such as a camera traveling on the target road, the degree of deterioration of the target road is specified, and a target road in which deterioration exceeding the standard is found becomes a repair target. In order to perform such monitoring, it is preferable to create a travel route that covers all of the target roads and monitor all of the target roads by traveling a vehicle along the travel route.

[0011] The travel route acquisition system 10 is a system that generates a plurality of travel routes so as to cover all of the target roads. The travel route acquisition system 10 includes a control unit 20 including a CPU, a RAM, a ROM, etc., a recording medium 30, and a user I / F unit 40. The user I / F unit 40 includes an input unit such as a keyboard, a mouse, a touch panel, etc. for receiving an input by an administrator, and an output unit such as a display for providing various information to the administrator.

[0012] In addition, map information 30a is recorded on the recording medium 30. The map information 30a is information indicating a road network on which the vehicle can travel, and includes node data indicating the positions of nodes set on the road on which the vehicle travels, shape interpolation point data indicating the positions of shape interpolation points for specifying the shape of the road between nodes, link data indicating the connection between nodes, and feature data indicating the positions and attributes of features existing on the road and its surroundings, etc. In the present embodiment, information indicating the road attribute of the road section indicated by the link data is associated with the link data. In the present embodiment, the road attribute is an attribute corresponding to the administrative division that is the subject of managing each road. For example, it is an attribute such as a prefectural road whose management subject is a prefecture, or a municipal road whose management subject is a city. Note that the attribute is not limited to such examples, and may be an attribute corresponding to the scale or regulation of the road, such as an expressway, a general road, or a narrow street road.

[0013] The control unit 20 can execute the programs stored in the recording medium 30 and the ROM by the control unit 20. In the present embodiment, a travel route acquisition program 21 can be executed as this program. When the travel route acquisition program 21 is executed, the control unit 20 functions as a target road acquisition unit 21a, a constraint condition acquisition unit 21b, and a travel route acquisition unit 21c.

[0014] By the function of the target road acquisition unit 21a, the control unit 20 acquires a plurality of target roads that are the roads to be monitored. In the present embodiment, the target roads are designated according to the attributes of the road sections. Specifically, the administrator designates the administrative division of the management subject via the input unit of the user I / F unit 40. The control unit 20 acquires all the road sections to be managed existing within the designated administrative division according to the designation. For example, the administrator of City A designates City A as the management subject. The control unit 20 refers to the map information 30a and acquires, as target roads, the road sections existing within City A and having the attribute of a municipal road associated therewith.

[0015] By the function of the constraint condition acquisition unit 21b, the control unit 20 acquires the constraint conditions corresponding to the maximum allowable travel distance. In the present embodiment, the constraint conditions can be specified by the maximum travel distance that each vehicle can travel in a day. It is assumed that the vehicle can be dispatched once a day. The administrator operates the input unit of the user I / F unit 40 to input the maximum travel distance that each vehicle can travel in a day. The control unit 20 acquires the input information as the constraint conditions.

[0016] By the function of the travel route acquisition unit 21c, the control unit 20 acquires a plurality of travel routes that satisfy the constraint conditions, and all target roads are traveled at least once when the vehicle travels on the plurality of travel routes. The travel route acquisition unit 21c only needs to be able to search for a travel route that satisfies the constraint conditions and travels on at least one of the plurality of travel routes so that the target road is traveled at least once.

[0017] Such a search can be realized, for example, by defining a search policy for searching for a travel route connected to a node from the node of the departure place, and repeating the process of searching for the travel route of one vehicle according to the search policy within the range that satisfies the constraint conditions. The process is repeated until all target roads are covered by a plurality of travel routes. An example of the process will be described later.

[0018] When the control unit 20 acquires a plurality of travel routes by the function of the travel route acquisition unit 21c, the desired roads can be covered by the acquired plurality of travel routes. That is, all target roads are traveled at least once when the vehicle travels on the plurality of travel routes. Therefore, all of the target roads can be monitored without omission.

[0019] (2) Travel Route Acquisition Process: Next, the travel route acquisition process executed by the control unit 20 will be described with reference to an actual example. FIG. 2 is a flowchart showing the travel route acquisition process executed by the control unit 20. When the travel route acquisition process is started, the control unit 20 acquires the overall map by the function of the target road acquisition unit 21a (step S100). Specifically, the administrator designates the administrative division of the management entity via the input unit of the user I / F unit 40. The control unit 20 refers to the map information 30a and acquires information on road sections existing within the administrative division. That is, the control unit 20 refers to the map information 30a and acquires node data and link data indicating nodes and links existing within the designated administrative division.

[0020] FIG. 3A is a diagram showing an example of the overall map. In FIG. 3A, nodes are schematically shown as black circles and links are shown as straight lines. Note that thick links indicate road sections that are municipal roads, and thin links indicate road sections other than municipal roads. Also, hereinafter, an example will be described in which the administrator is a city employee and the road sections to be managed are municipal roads.

[0021] Next, the control unit 20 acquires the target road by the function of the target road acquisition unit 21a (step S105). Specifically, the control unit 20 acquires road sections with attributes corresponding to the management entity designated by the administrator as the target road. In the example shown in FIG. 3A, an example is assumed in which the administrator is a city employee and the road sections to be managed are municipal roads. In this case, the control unit 20 extracts node data and link data indicating the nodes and links of the municipal roads under the management of the city designated by the administrator from the overall map and regards them as the target road.

[0022] FIG. 3B is a diagram showing the target road acquired in the case of the example shown in FIG. 3A. In FIG. 3B, each road section is called link a to link j, and each node is called node N1 to node N7. Also, here, it is assumed that there is a vehicle base at node N1, and each vehicle departs from node N1, travels along each road section while monitoring each road section according to the travel route, and returns to node N1.

[0023] Next, the control unit 20 acquires constraint conditions by the function of the constraint condition acquisition unit 21b (step S110). Specifically, the administrator operates the input unit of the user I / F unit 40 to input the maximum travel distance that can be traveled in one day for each vehicle. The maximum travel distance is the upper limit of the travel distance allowed in one day for each vehicle, and may be specified from, for example, the working hours of the driver of the vehicle. The control unit 20 acquires the input information as constraint conditions.

[0024] Next, the control unit 20 acquires a travel route that covers the target road by the function of the travel route acquisition unit 21c (step S115). The process for acquiring the travel route can be executed by an algorithm created in advance. The algorithm can be defined to acquire travel routes in various ways. The algorithm can be defined, for example, as the following algorithm that minimizes a specific value.

[0025] The variables for realizing the algorithm are as follows. l: Number of links n: Number of nodes J: Connection matrix C: Route coverage matrix m: Number of travel routes r i : Travel route (i is an integer from 1 to m)

[0026] Here, the number of links l is the number of road sections that are the target roads. For example, in the example shown in FIG. 3B, the number of links l is 10. The number of nodes n is the number of nodes that constitute the endpoints of the target road. For example, in the example shown in FIG. 3B, the number of nodes n is 7.

[0027] The connection matrix J is a matrix showing the connection relationship between nodes and links. In the vertical direction, links are arranged, and in the horizontal direction, nodes are arranged, and the numerical value of the position of the node connected to a certain link becomes 1.

[0028] In the example shown in FIG. 3B, the connection matrix J is expressed, for example, by the following formula (1).

Number

[0029] The route coverage matrix C is a matrix for counting the number of times each link is traveled by a driving route. In the present embodiment, the route coverage matrix C is a 1-row matrix having components with the same number of columns as the number l of links. In the example shown in FIG. 3B, the route coverage matrix C is expressed as, for example, C = (1, 0, 0, 0, 0, 0, 0, 0, 0, 0), etc. The numerical values of the route coverage matrix C indicate the number of times links a to j are traveled in order from the left. If C = (1, 0, 0, 0, 0, 0, 0, 0, 0, 0), it indicates a state where link a is traveled once and the other links are not traveled at all.

[0030] The number m of driving routes indicates the number of driving routes obtained as a result of the search. That is, in the process of executing the algorithm, when a daily driving route by one vehicle is searched, m is incremented. The driving route r i indicates the searched driving route. The driving route may be shown in various ways, but here, an example is assumed in which the driving route is shown by a permutation of nodes. For example, the driving route r that traverses the nodes in the order of nodes N1, N2, N3, N2, N1 i is, r i =(12321). The above route coverage matrix C, the number m of driving routes, and the driving route r i are sequentially updated as the search for the driving route progresses.

[0031] In this example, the constraint conditions are expressed as follows. S: The maximum travel distance in one travel route Also, in this embodiment, since the departure point and the final arrival point are the same (node N1), the condition that the departure point and the final arrival point are the same can also be regarded as a constraint condition. Generally, in order to perform monitoring efficiently, it is preferable to minimize the number of travel routes. For this reason, here, the travel routes are explored so that the number m of travel routes becomes as small as possible.

[0032] In this case, in this embodiment, it can be said that it is an optimization algorithm that solves the problem of minimizing the number m of travel routes while satisfying the following 1. to 3. 1. The distance of travel route r i ≤ S 2. The departure point and the arrival point of travel route r i are the same 3. All components of the route coverage matrix C are greater than 0

[0033] When solving such a problem, for example, the travel routes may be explored randomly, or known methods such as the simulated annealing method and the dynamic programming method may be used, and the travel routes may be explored with various search policies. That is, when exploring the travel routes, the exploration is performed according to the search policy. The search policy may be various policies, and according to each policy, the travel routes that satisfy the constraint conditions are explored. Note that the algorithm for exploring the travel routes may be an algorithm that approximately minimizes the number m of travel routes pseudo, and in this case, the number m of travel routes may not be strictly minimized.

[0034] Here, first, an example of exploration with the following search policy will be described. (A) Randomly explore until the maximum reachable point and return the same way (B) Select the link with the fewest number of trips from the selectable links In the following, an example of exploration will be described using FIG. 3B. Also, for simplicity, an example where the distance of all links is 1 and the maximum travel distance S = 6 will be described.

[0035] Figure 4 is a flowchart of the travel route search process according to this example. The control unit 20 starts searching for a travel route from the node N1 which is the starting point. At this time, the control unit 20 searches for a travel route by sequentially changing the current node to be ahead of the starting point. For this reason, the control unit 20 sets the starting point as the current node (step S200).

[0036] Next, the control unit 20 selects the next link of the current node (step S205). At this time, the control unit 20 refers to the connection matrix J and the route coverage matrix C, and randomly selects a link with the minimum number of runs from among the selectable links. For example, links a and g are connected to the node N1, and since the value of the route coverage matrix C is 0 initially, links a and g are randomly selected. When link a is selected, the control unit 20 increments the component corresponding to link a in the route coverage matrix C to make C = (1, 0, 0, 0, 0, 0, 0, 0, 0, 0). Also, the control unit 20 inputs the values indicating the nodes to the first and second components of the travel route r1 to make r1 = (1 2). The values of the third and subsequent components are undetermined.

[0037] Next, the control unit 20 updates the current node (step S210). That is, the control unit 20 sets the node on the opposite side of the current node among the nodes connected to the link selected in step S205 as the new current node. For example, when the link a connected to the node N1 is selected, the current node becomes the node N2 which is the end point on the opposite side of the link a as the new current node.

[0038] Next, the control unit 20 determines whether the current node is the maximum reachable point (step S215). In this example, since the same path is returned after reaching the maximum reachable point, the distance from the starting point to the maximum reachable point is half of the maximum moving distance S. For example, when the maximum moving distance S is 6, when reaching a node with a moving distance of 3, by returning the same path, it is possible to return to the starting point with a movement distance of 6, which is the maximum moving distance S. Therefore, when the control unit 20 extends the driving route up to a moving distance of 3, it obtains one driving route by returning the same path.

[0039] Therefore, when the control unit 20 determines in step S215 that the current node is not the maximum reachable point, it repeats the processes after step S205. On the other hand, when it is determined in step S215 that the current node is the maximum reachable point, the control unit 20 obtains the searched route as one driving route (step S220). That is, the control unit 20 stores information indicating a driving route that returns along the route up to the maximum reachable point in a storage medium such as a RAM. When one driving route is obtained, the control unit 20 increments the number m of driving routes and sets m = 1. For example, when the driving route up to the maximum reachable point is the route of nodes N1, N2, N3, N4, from the above processes, C=(2,2,2,0,0,0,0,0,0,0), r1=(1234321), and m = 1.

[0040] When one driving route is obtained, the control unit 20 refers to the route coverage matrix C and determines whether all components are greater than 0 (that is, whether all target roads are traveled at least once) (step S225). If it is not determined that all components of the route coverage matrix C are greater than 0, the control unit 20 repeats the processes after step S200. That is, it searches for a new driving route starting from the node N1 at the starting point. The control unit 20 repeats the above processes until it is determined in step S225 that all components of the route coverage matrix C are greater than 0.

[0041] As a result of the above processing, it is possible to search for a driving route that covers all road sections. FIGS. 5A to 5E are examples of searching for a plurality of driving routes. In these figures, the latest searched driving route is indicated by a dashed line, and the existing driving routes searched in the past are indicated by a one-dot chain line. When the latest driving route overlaps with the existing driving routes searched in the past, the latest driving route is preferentially indicated by a dashed line. Also, after the characters of links a to j, the number of times the road section of the link is traveled is shown.

[0042] In FIG. 5A, an example is shown in which, as a result of randomly searching for the link with the minimum number of travels, the driving route r1 = (1234321) is searched. Since the driving route r1 is a route that follows links such as links a, b, c, c, b, a, in FIG. 5A, links a, b, c are shown as the latest searched driving route by a dashed line. Also, the number of times each of links a, b, c is traveled is 2 times for the round trip. At this stage, the route coverage matrix C becomes C = (2, 2, 2, 0, 0, 0, 0, 0, 0, 0).

[0043] Next, when another driving route is searched, since link a is traveled 2 times and link g is not traveled, link g is selected next to node N1. The routes after node N7 are specified by randomly searching for the link with the minimum number of travels. FIG. 5B shows an example in which, as a result of such a search, the driving route r2 = (1765671) is searched. In this case, since it is a route that follows links g, f, e, e, f, g, the route coverage matrix C becomes C = (2, 2, 2, 0, 2, 2, 2, 0, 0, 0).

[0044] Next, when another driving route is searched, since link a is traveled twice and link g is traveled twice, the links after node N1 are randomly searched. In FIG. 5C, it is assumed that link a is selected after node N1. At node N2, which is an endpoint of link a, since the number of times that links b, h, and i connected to node N2 are traveled is 2, 0, and 0 respectively as shown in FIG. 5B, a link is randomly selected from links h and i. FIG. 5C shows an example in which link h is selected. Although links f and j are connected to node N7, which is an endpoint of link h, since the number of times that links f and j are traveled is 2 and 0 respectively as shown in FIG. 5B, link j is selected. As a result, the driving route r3=(1275721) is searched. In this case, the route coverage matrix C is C=(4, 2, 2, 0, 2, 2, 2, 2, 0, 2).

[0045] FIG. 5D shows an example in which the driving route r4=(1754571) is searched as another driving route. In this case, the route coverage matrix C is C=(4, 2, 2, 2, 2, 2, 4, 2, 0, 4). FIG. 5E shows an example in which the driving route r5=(1254521) is searched as another driving route. In this case, the route coverage matrix C is C=(6, 2, 2, 4, 2, 2, 4, 2, 2, 4). At this stage, since all components of the route coverage matrix C are greater than 0, the search ends. When randomly searching for links after a node, the latest driving route may be the same as an existing driving route. In such a case, after the search ends, duplicate driving routes may be deleted and only one driving route may be left.

[0046] As described above, when a plurality of driving routes that cover all road sections are searched, the control unit 20 displays the driving routes (step S120). That is, the control unit 20 controls the output unit of the user I / F unit 40 to display information indicating the plurality of searched driving routes. The display mode may be various modes. For example, r iEach of them may be shown, or each driving route may be shown on a map. According to the above configuration, a driving route covering a desired road can be obtained, and the administrator can recognize the obtained driving route.

[0047] (3) Other embodiments: The above embodiments are examples for implementing the present invention, and various other embodiments can also be adopted. For example, the driving route acquisition system 10 may be composed of a plurality of systems. Also, some functions of the driving route acquisition system 10 may be realized by a terminal used by the administrator, or some functions of the driving route acquisition system 10 may be realized by a cloud server. Furthermore, in the above-described embodiments, a driving route that only travels on the target road is searched, but a driving route that passes through a road section other than the target road may also be searched.

[0048] The target road acquisition unit only needs to be able to acquire a plurality of target roads that are roads to be monitored. That is, the target roads to be covered are determined in advance and presented to the driving route acquisition system. In the above-described embodiment, the target road is a road to be monitored for road maintenance, but it is not limited to such a configuration. For example, for the maintenance of street trees, the road where street trees exist may be set as the target road, or the road for which various statistics such as traffic jams are to be acquired may be set as the target road, and various roads may be the target roads.

[0049] The target road may be specified in various ways and is not limited to a configuration in which the target road is acquired based on the road type. For example, a configuration in which a designated person such as a road administrator designates the target road using a map, identification information, etc. may be adopted, or a configuration in which the target road is specified based on various conditions such as the number of years elapsed after the latest repair may be adopted, and various configurations can be adopted.

[0050] The constraint condition acquisition unit only needs to be able to acquire a constraint condition corresponding to the maximum allowable moving distance. That is, a constraint condition is imposed to acquire a driving route. A driving route that satisfies the constraint condition may be searched by any search method.

[0051] The constraint condition is a condition corresponding to the maximum allowable travel distance. That is, a travel route within a range that does not exceed the maximum travel distance is obtained. The constraint condition may be defined in various manners. Therefore, the constraint condition may be defined as the maximum travel distance for each of a plurality of vehicles as in the above-described embodiment, or may be the cumulative travel distance that can be traveled by a plurality of vehicles. Further, the number of vehicles, the number of trips per vehicle, the cumulative number of trips by the vehicle, etc. may be included in the constraint condition. The maximum travel distance may be directly defined by distance or may be defined by time. That is, generally speaking, since the longer the maximum operating time is, the longer the maximum travel distance is, it can be said that the constraint condition defined by the maximum operating time is a condition corresponding to the maximum travel distance.

[0052] The travel route acquisition unit only needs to be able to acquire a plurality of travel routes that satisfy the constraint condition and in which all target roads are traveled at least once when the vehicle travels a plurality of travel routes. That is, the travel route acquisition unit only needs to be able to search for a plurality of travel routes so as to cover all of the target roads under the condition of imposing the constraint condition.

[0053] The method for searching for a travel route may be various methods. For example, within a range that satisfies the constraint condition, the search can be performed by an algorithm or the like that sequentially selects a road that is not a travel route from among the target roads along the road network starting from the starting point. After searching for one travel route, if there are target roads that are not included in any of the travel routes, further searching can be continued to search for a plurality of travel routes that satisfy the constraint condition and cover the target roads. Of course, when there are no roads that are not travel routes at a certain node, the road next to that node may be a road that is traveled more than once.

[0054] (Second Embodiment) Furthermore, the above algorithm for exploring the driving route is just an example, and the driving route may be explored in various other ways. For example, the above (A) may be modified as follows (A1) as the exploration guideline. (A1) Randomly explore until the maximum reachable point and then return along the shortest path (B) Select a link with fewer driving times from among the selectable links Here too, an example of the exploration will be described using Fig. 3B. Also, for simplicity, an example where the distance of all links is 1 and the maximum moving distance S = 6 will be described.

[0055] In this algorithm, in addition to the variables (the number of links l, the number of nodes n, the connection matrix J, the route coverage matrix C, the number of driving routes m, the driving route r i ) used in the above algorithms (A) and (B), a distance matrix D shown in the following formula (2) is used.

Equation

[0056] Each component in the same row corresponds to the node to which the starting node moves next. For example, the fifth row shows the distance when node N5 is the starting point. And the second component from the left in the fifth row shows the shortest distance when returning to the starting node N1 after moving from node N5 to node N2. That is, in the example shown in Fig. 3B, links d, e, i, j are connected to node N5, and it is possible to move from node N5 to nodes N2, N4, N6, N7 next. Therefore, in the fifth row from the top, the values indicating the shortest distances are shown in the second, fourth, sixth, and seventh components from the left.

[0057] In this embodiment, the one with the smallest distance among these options is shown as the fifth value from the left. Therefore, in this embodiment, by referring to the value in the x-th row and x-th column, the shortest distance from node Nx can be determined. To move the shortest distance, among the values in the x-th row other than the x-th column, it is sufficient to move to the node corresponding to the value that is the shortest distance. For example, in the example shown in FIG. 3B, the shortest distance from node N5 to the starting point is the distance 2, which is the value in the 5th row and 5th column. It can be seen that by moving from node N5 to either node N2 or N7, the return can be made at the shortest distance.

[0058] Using the distance matrix D, in a configuration similar to FIG. 1, by executing the same processing as in FIG. 4, a plurality of driving routes that cover the target road can be searched. However, in step S220 of FIG. 4, the control unit 20 determines a driving route from the maximum reachable point based on the distance matrix D. That is, the control unit 20 stores information indicating a driving route composed of a route to the maximum reachable point and a route returning from the maximum reachable point to the starting node N1 at the shortest distance in a storage medium such as a RAM.

[0059] The driving route returning from the maximum reachable point to the starting node N1 at the shortest distance is specified based on the distance matrix D. For example, when the maximum reachable point is node N5, the control unit 20 specifies that the value in the 5th row and 5th column of the distance matrix D is 2, and specifies nodes N2 and N7 indicating the distance 2 from among the components in the 5th row. Then, the control unit 20 refers to the route coverage matrix C and selects any one of the routes following the links connected to these nodes to obtain a driving route. The processing after one driving route is obtained is the same as the above-described processing.

[0060] As a result of the above processing, a driving route that covers all road sections can be searched. FIGS. 6A to 6E are examples of searching for a plurality of driving routes. In these figures, the notation method of the figures is the same as that in FIG. 5A and the like.

[0061] In Fig. 6A, an example is shown where, as a result of randomly searching for the link with the minimum number of runs, links a, b, and c are traced and node N4 becomes the maximum reachable point. In this case, it is found from the distance matrix D that either node N3 or N5 can be selected next to node N4. However, in Fig. 6A, node N3 is selected and an example where the travel route r1 = (1234321) is searched is shown. At this stage, the route coverage matrix C becomes C = (2, 2, 2, 0, 0, 0, 0, 0, 0, 0).

[0062] Next, when another travel route is searched, since link a is traveled twice and link g is not traveled, link g is selected next to node N1. After node N7, the route to the maximum reachable point is randomly selected from the links with the fewest number of runs. Fig. 6B shows an example where, as a result of such a search, node N5 becomes the maximum reachable point. In this case, it is found from the distance matrix D that either node N2 or N7 can be selected next to node N5 to return at the shortest distance. However, in Fig. 6B, node N2 is selected and an example where the travel route r2 = (176521) is searched is shown. At this stage, the route coverage matrix C becomes C = (3, 2, 2, 0, 1, 1, 1, 0, 1, 0).

[0063] Next, when another travel route is searched, since link a is traveled three times and link g is traveled once, link g is selected next to node N1. Next to node N7, either link h or j, which has been traveled zero times, is selected. In Fig. 6C, an example where link h is selected next to node N7 is shown. Since the number of times that links b, h, and i connected to node N2, which is the endpoint of link h, have been traveled is 2, 1, and 1 at this stage, either link h or i is selected next to node N2. In Fig. 6C, an example where link h is selected next to node N2 is shown. As a result, node N7 is reached again, and the node N7 becomes the maximum reachable point. In this case, according to the distance matrix D, a route that returns to the starting point with the shortest distance is selected next to node N7. As a result, the travel route r3=(17271) is searched. At this stage, the route coverage matrix C becomes C=(3,2,2,0,1,1,3,2,1,0).

[0064] Here, as shown in Fig. 6D, an example where the travel route r4=(1254321) is searched as another travel route is assumed. In this case, the route coverage matrix C becomes C=(5,3,3,1,1,1,3,2,2,0). Fig. 6E shows an example where the travel route r5=(1754321) is searched as another travel route. In this case, the route coverage matrix C becomes C=(6,4,4,2,1,1,4,2,2,1). At this stage, since all components of the route coverage matrix C are greater than 0, the search ends. According to the above processing, compared with the first embodiment, the possibility that the average travel distance of the vehicle becomes shorter can be increased.

[0065] (Third Embodiment) Furthermore, by using the distance matrix D, it is also possible to search for routes other than the route that can return in the shortest way. For example, it becomes possible to obtain a travel route that returns to the starting point after going around more links than returning in the shortest way from the maximum reachable point. When performing such processing, for example, the above (A) may be changed to the following (A2) as a search policy. (A2) Randomly proceed with the search until the maximum reachable point, and return to the route with the minimum number of times a link is traveled among the routes that can return to the starting point. (B) Select a link with a small number of travel times from among the selectable links. Here too, an example of the search will be described using Fig. 3B. Also, for simplicity, the distance of all links is set to 1, and an example with a maximum movement distance S = 6 will be described.

[0066] Using the distance matrix D, a plurality of travel routes that cover the target road can be searched by performing the same processing as in Fig. 4 in the same configuration as in Fig. 1. However, in step S220 of Fig. 4, the control unit 20 determines a travel route from the maximum reachable point based on the distance matrix D. That is, the control unit 20 searches for a travel route composed of a route to the maximum reachable point and a route that returns from the maximum reachable point to the node N1 of the starting point. Then, in the search for the return route, the control unit 20 selects the route with the least number of times traveled by the searched travel route. The control unit 20 stores the information indicating the travel route searched in this way in a storage medium such as a RAM.

[0067] The travel route that selects the route with the least number of times traveled from the maximum reachable point and returns to the node N1 of the starting point is specified based on the distance matrix D. For example, when the maximum reachable point is node N5, the control unit 20 refers to the values excluding the value of the 5th row and 5th column among the components of the 5th row. As a result, if the next node is node N4, it is not possible to return to the starting point within the maximum movement distance S = 6, but if it is any of nodes N2, N6, or N7, it can be seen that it is possible to return to the starting point within the maximum movement distance S = 6. Therefore, the control unit 20 refers to the route coverage matrix C and selects the route with the smaller number of travel times among the routes that follow the links connected to these nodes to obtain the travel route. The processing after one travel route is obtained is the same as the above-described processing.

[0068] As a result of the above processing, it is possible to search for a driving route that covers all road sections. FIGS. 7A and 7B are examples of searching for a plurality of driving routes. In these figures, the notation method of the figures is the same as that of FIG. 5A and the like.

[0069] In FIG. 7A, as a result of randomly searching for the link with the minimum number of driving times, an example is shown in which links a, b, and c are traversed and node N4 becomes the maximum reachable point. In this case, it is found from the distance matrix D that either node N3 or N5 can be selected next to node N4. When the route extends to node N4, link c for reaching node N3 is in a state of being driven once, and link d for reaching node N5 is in a state of not being driven. Therefore, link d is selected as the next link of node N4.

[0070] Since the distance traveled until reaching node N5 is distance 4, when the distance matrix D is also referred to when the next link of node N5 is selected, it is found that either node N2 or N7 can be selected as the next selectable node. When the route extends to node N5, links i and j for reaching these nodes are both in a state of not being driven. Therefore, the next link of node N5 is randomly selected. In FIG. 7A, an example is shown in which node N2 is selected at this time and the driving route r1 = (1234521) is searched. At this stage, the route coverage matrix C becomes C = (2, 1, 1, 1, 0, 0, 0, 0, 1, 0).

[0071] Next, when another driving route is searched, since link a is driven twice and link g is not driven, link g is selected next to node N1. After node N7, the route to the maximum reachable point is randomly selected from the links with the fewest number of driving times. FIG. 7B shows an example in which node N5 becomes the maximum reachable point via node N6 as a result of such a search.

[0072] In this case, when the distance matrix D is referred to when selecting the next link of node N5, it is found that any of nodes N2, N6, and N7 can be selected as the next node. When the route extends to node N5, the number of times the links i, e, and j are traveled to reach these nodes is 1, 1, and 0, respectively. Therefore, link j is selected as the next link of node N5. After that, for node N7, the untraveled link h is selected next, and r2=(1765721) is searched. At this stage, the route coverage matrix C becomes C=(3,1,1,1,1,1,1,1,1,1). According to the above processing, compared with the first and second embodiments, the possibility that the number m of travel routes is reduced can be increased.

[0073] (Fourth Embodiment) Furthermore, as constraint conditions, various conditions can be adopted. For example, a condition that the travel frequency of a predetermined target road is higher than that of other target roads can be used to search for travel routes subject to constraint conditions, and this can be realized in various ways. Here, in addition to the variables (the number l of links, the number n of nodes, the connection matrix J, the route coverage matrix C, the number m of travel routes, the travel route r i , the distance matrix D) used in the above-described first to third embodiments, an example in which a weight matrix W is used will be described.

[0074] The weight matrix W is a matrix indicating the priority of each link. In this embodiment, the weight matrix W is a one-row matrix having components with the same number of columns as the number l of links. The weight matrix W is expressed, for example, as W=(3,3,3,3,3,3,3,1,1,3), etc. The numerical values of the weight matrix W indicate the priorities of links a to j in order from the left. The smaller the numerical value, the higher the priority. For example, when monitoring a target road, due to reasons such as a large number of years elapsed after repair, a high likelihood of complaints, and a large traffic volume, the degree of priority for monitoring each road section may be different. Therefore, by using the weight matrix W, the priority for each road section can be defined, and the travel frequency of the target road with a higher priority can be made higher than that of other target roads.

[0075] When using the weight matrix W, a plurality of driving routes that cover the target road can be searched by performing the same processing as in FIG. 4 in the same configuration as in FIG. 1. However, in step S205 of FIG. 4, the control unit 20 selects the next link of the current node using the weight matrix W. Specifically, the control unit 20 obtains the product of the same components by the route coverage matrix C × the weight matrix W, and regards it as a reference value for selecting each link. Of course, the reference value may be calculated only for the necessary links. Then, the control unit 20 refers to the connection matrix J and selects the link with the smallest reference value from among the selectable links. When there are a plurality of links with the smallest reference value, the control unit 20 randomly selects a link from those links.

[0076] Even if the above processing is executed, a driving route that covers all road sections can be searched. FIGS. 8A to 8E are examples of searching for a plurality of driving routes. In these figures, the notation method of the figures is the same as that in FIGS. 5A and the like. Also in this embodiment, various search policies can be adopted, and the various search policies used in the first to third embodiments can be applied. In the following, an example of the same search policy as in the first embodiment will be described. Here, it is assumed that W = (3, 3, 3, 3, 3, 3, 3, 1, 1, 3).

[0077] When searching for the first driving route r1, since the route coverage matrix C is the initial value and all components are 0, the reference values are 0 for all links even when the weight matrix W is multiplied. Therefore, the search for the first driving route r1 is executed in the same manner as in the first embodiment described above. In FIG. 8A, similar to FIG. 5A, an example in which the driving route r1 = (1234321) is searched by random search is shown. At this stage, the route coverage matrix C becomes C = (2, 2, 2, 0, 0, 0, 0, 0, 0, 0).

[0078] Next, when another driving route is searched, the reference value becomes (6, 6, 6, 0, 0, 0, 0, 0, 0, 0) according to C×W. Therefore, the reference values of links a and g are 6 and 0, and link g is selected next to node N1. The routes after node N7 are also selected based on the sequentially calculated reference values. In FIG. 8B, an example in which the driving route r2 = (1765671) is searched as a result of such search is shown. In this case, the route coverage matrix C is C = (2, 2, 2, 0, 2, 2, 2, 0, 0, 0). Note that C×W represents the product of the same components.

[0079] Next, when another driving route is searched, the reference value becomes (6, 6, 6, 0, 6, 6, 6, 0, 0, 0) according to C×W. Therefore, the reference values of links a and g are 6 and 6, and the links after node N1 are searched randomly. In FIG. 8C, a case where link a is selected after node N1 is assumed. At node N2, which is the end point of link a, the reference value becomes (9, 6, 6, 0, 6, 6, 6, 0, 0, 0). Therefore, the reference values of links b, h, and i connected to node N2 are 6, 0, and 0, and a link is randomly selected from links h and i. In FIG. 8C, an example in which link h is selected is shown. In FIG. 8C, an example in which the driving route r3 = (1275721) is searched as a result of such search is shown. In this case, the route coverage matrix C is C = (4, 2, 2, 0, 2, 2, 2, 2, 0, 2).

[0080] Next, when another driving route is searched, the reference values become (12, 6, 6, 0, 6, 6, 6, 2, 0, 6) according to C×W. Therefore, the reference values of links a and g are 12 and 6, and link g is selected next to node N1. At node N7, which is the end point of link g, the reference values become (12, 6, 6, 0, 6, 6, 9, 2, 0, 6). Therefore, the reference values of links f, h, and j connected to node N7 are 6, 2, and 6, and link h is selected. At node N2, which is the end point of link h, the reference values become (12, 6, 6, 0, 6, 6, 9, 3, 0, 6). Therefore, the reference values of links b, h, and i connected to node N2 are 6, 3, and 0, and link i is selected. Since node N5, which is the end point of link i, is the maximum reachable point, the searched route is the driving route r4 = (1725271). In this case, the route coverage matrix C is C = (4, 2, 2, 0, 2, 2, 4, 4, 2, 2).

[0081] Next, when another driving route is searched, the reference values become (12, 6, 6, 0, 6, 6, 12, 4, 2, 6) according to C×W. Therefore, the reference values of links a and g are 12 and 12, and the next link of node N1 is randomly selected. Here, assume that link a is selected. At node N2, which is the end point of link a, the reference values become (15, 6, 6, 0, 6, 6, 12, 4, 2, 6). Therefore, the reference values of links b, h, and i connected to node N2 are 6, 4, and 2, and link i is selected. At node N5, which is the end point of link i, the reference values become (15, 6, 6, 0, 6, 6, 12, 4, 3, 6). Therefore, the reference values of links d, e, and j connected to node N5 are 0, 6, and 6, and link d is selected. Since node N4, which is the end point of link d, is the maximum reachable point, the searched route is the driving route r5 = (1254521). In this case, the route coverage matrix C is C = (6, 2, 2, 2, 2, 2, 4, 4, 4, 2). At this stage, since all components of the route coverage matrix C are greater than 0, the search ends. According to the above processing, compared with the first to third embodiments, a desired road section can be more easily incorporated into the driving route.

[0082] (Fifth Embodiment) Furthermore, among the selectable driving routes, a condition for selecting a driving route with a lower cost during driving may be included in the constraint conditions. The search for a driving route with such constraint conditions can be realized in various ways. Here, in addition to the variables (the number of links l, the number of nodes n, the connection matrix J, the route coverage matrix C, the number of driving routes m, the driving route r i , the distance matrix D) used in the above-described first to fourth embodiments, an example in which a cost matrix P is used will be described.

[0083] The cost matrix P is a matrix showing the cost of each link. In the present embodiment, the cost matrix P is a one-row matrix having components with the same number of columns as the number of links l. The cost matrix P is expressed, for example, as P = (5, 15, 20, 15, 10, 5, 10, 15, 20, 15), etc. The numerical values of the cost matrix P indicate the costs of links a to j in order from the left. For example, the required time, fuel consumption, distance, etc. when driving on the target road can be represented as costs. By using the cost matrix P, the cost for each road section can be defined, and a driving route can be searched so that the cost becomes as small as possible.

[0084] Note that since the cost of each link is information corresponding to the distance of each link, it is possible to define a constraint condition corresponding to the maximum movement distance by the cost. In the present embodiment, as a constraint condition corresponding to the maximum movement distance, a cumulative maximum cost is introduced. For example, if the cumulative maximum cost is 100, a driving route is searched such that the cumulative cost when driving according to the driving route is 100 or less. Hereinafter, in the present embodiment, an example in which the cumulative maximum cost is 100 is assumed.

[0085] When using the cost matrix P, it is possible to search for a plurality of driving routes that cover the target road by executing the same processing as in FIG. 4 in the same configuration as in FIG. 1. However, in step S205 of FIG. 4, the control unit 20 selects the next link of the current node using the cost matrix P. Specifically, the control unit 20 obtains the product of the same components by the route coverage matrix C × the cost matrix P, and regards it as a reference value for selecting each link. Of course, the reference value may be calculated only for the necessary links. Then, the control unit 20 refers to the connection matrix J and selects the link with the smallest reference value from among the selectable links. When there are a plurality of links with the smallest reference value, the control unit 20 randomly selects a link from those links.

[0086] Also, the farthest point that does not exceed half of the cumulative maximum cost 100, that is, 50, of the searched driving route is the maximum reachable point. For this reason, in step S215, the control unit 20 can adopt a configuration in which, for example, when the value obtained by adding the maximum cost 20 of a single road section to the cumulative cost to the current node exceeds 50, it is determined that the current node is the maximum reachable point.

[0087] Even if the above processing is executed, it is possible to search for a driving route that covers all road sections. FIGS. 9A to 9E are examples of searching for a plurality of driving routes. In these figures, the notation method of the figures is the same as that in FIGS. 5A and the like. Also in this embodiment, various search policies can be adopted, and the various search policies used in the first to fourth embodiments can be applied. In the following, an example of the same search policy as in the first embodiment will be described. Here, it is assumed that P = (5, 15, 20, 15, 10, 5, 10, 15, 20, 15).

[0088] When searching for the first travel route r1, the route coverage matrix C is an initial value with all components being 0, so even when multiplied by the cost matrix P, the reference values are 0 for all links. Therefore, the search for the first travel route r1 is performed in the same manner as in the first embodiment described above. FIG. 9A shows an example in which a travel route r1=(1234321) is searched for by random search, similar to FIG. 5A. At this stage, the route coverage matrix C becomes C=(2,2,2,0,0,0,0,0,0,0,0).

[0089] Next, when another driving route is searched, the reference values become (10, 30, 40, 0, 0, 0, 0, 0, 0) by C × P. Therefore, the reference values of links a and g become 10, 0, and link g is selected after node N1. At this stage, the cumulative cost is 10. At node N7, which is the endpoint of link g, the reference values become (10, 30, 40, 0, 0, 0, 10, 0, 0, 0). Therefore, the reference values of links f, h, and j connected to node N7 become 0, 0, 0, and a link is selected randomly from these links. Here, we consider an example in which link f is selected. At this stage, the cumulative cost is 15.

[0090] At node N6, which is the end point of link f, the reference value is (10, 30, 40, 0, 0, 5, 10, 0, 0, 0). Therefore, the reference value of link e connected to node N6 is 0, and link e is selected. At this stage, the cumulative cost is 25. At node N5, which is the end point of link e, the reference value is (10, 30, 40, 0, 10, 5, 10, 0, 0, 0). Therefore, the reference values of links d, i, and j connected to node N5 are 0, 0, 0, and a link is randomly selected from these links. Here, we assume an example in which link d is selected. At this stage, the cumulative cost is 40. Since the cumulative cost of 40 is 40 plus the maximum cost of a single road section, 20, exceeds 50, node N4, which is the end point of link d, is the most reachable point. Figure 9B shows an example in which a driving route r2 = (176545671) is found as a result of such a search. In this case, the root coverage matrix C is C=(2,2,2,2,2,2,2,2,0,0,0).

[0091] Next, when another driving route is searched, the reference values become (10, 30, 40, 30, 20, 10, 20, 0, 0, 0) according to C×P. Therefore, the reference values of links a and g are 10 and 20, and link a is selected next to node N1. At this stage, the cumulative cost is 5. At node N2, which is the end point of link a, the reference values become (15, 30, 40, 30, 20, 10, 20, 0, 0, 0). Therefore, the reference values of links b, h, and i connected to node N2 are 30, 0, and 0, and a link is randomly selected from links h and i. Here, an example where link h is selected is assumed. At this stage, the cumulative cost is 20.

[0092] At node N7, which is the end point of link h, the reference values become (15, 30, 40, 30, 20, 10, 20, 15, 0, 0). Therefore, the reference values of links f and j connected to node N7 are 10 and 0, and link j is selected. At this stage, the cumulative cost is 35. Since the value obtained by adding the maximum cost 20 of a single road section to the cumulative cost 35 exceeds 50, node N5, which is the end point of link j, is the maximum reachable point. In FIG. 9C, an example where the driving route r3 = (1275721) is searched as a result of such a search is shown. In this case, the route coverage matrix C is C = (4, 2, 2, 2, 2, 2, 2, 2, 0, 2).

[0093] Next, when another driving route is searched, the reference values become (20, 30, 40, 30, 20, 10, 20, 30, 0, 30) according to C×P. Therefore, the reference values of links a and g are 20 and 20, and a link is randomly selected from links a and g. Here, an example where link a is selected is assumed. At this stage, the cumulative cost is 5. At node N2, which is the end point of link a, the reference values become (25, 30, 40, 30, 20, 10, 20, 30, 0, 30). Therefore, the reference values of links b, h, and i connected to node N2 are 30, 30, and 0, and link i is selected. At this stage, the cumulative cost is 25.

[0094] At node N5 which is an endpoint of link i, the reference values are (25, 30, 40, 30, 20, 10, 20, 30, 20, 30). Therefore, the reference values of links d, e, and j connected to node N5 are 30, 20, and 30, and link e is selected. At this stage, the cumulative cost is 35. Since adding the maximum cost 20 of a single road section to the value 35 exceeds 50, node N6 which is an endpoint of link e is the maximum reachable point. In FIG. 9D, as a result of such search, the driving route r4 = (1256521) is obtained. In this case, the route coverage matrix C is C = (6, 2, 2, 2, 4, 2, 2, 2, 2, 2). At this stage, since all components of the route coverage matrix C are greater than 0, the search ends. According to the above processing, compared with the first to fourth embodiments, it is possible to search for a driving route that covers road sections while suppressing the cost. Here, after reaching the maximum reachable point and searching for a driving route back to the starting point, it is determined whether all components of the route coverage matrix C are greater than 0. However, if all components of the route coverage matrix C become greater than 0 before reaching the maximum reachable point, the search may end.

[0095] (Sixth Embodiment) Furthermore, the constraint condition may include the condition that all the deliveries to be delivered to the delivery points existing along the target road are made deliverable during the driving process of the driving route. That is, in the vehicle, when delivering goods to the delivery points along the target road while covering the target road, the search for the driving route may be applied. FIG. 10A shows an example where delivery points exist along the same target road as in FIG. 3B. In FIG. 10A, the delivery points along the target road are shown by attaching rectangular icons beside the links indicating the road sections. That is, in the example shown in FIG. 10A, delivery points exist along links c, e, f, h, i, and j.

[0096] Searching for a driving route that imposes a constraint condition to make all the deliveries to be delivered to the delivery points deliverable during the driving process of the driving route can be realized in various ways. Here, in addition to the variables (the number of links l, the number of nodes n, the connection matrix J, the route coverage matrix C, the number of driving routes m, the driving route r i , the distance matrix D) used in the above-described first to fifth embodiments, an example in which the package information DL is used will be described.

[0097] The package information DL is a matrix indicating the number of packages to be delivered to each delivery point. In this embodiment, since the delivery points exist along the target road, the package information DL is a one-row matrix having the same number of columns as the number of links l. Each component indicates the number of packages. The package information DL is expressed, for example, as DL = (0, 0, 1, 0, 1, 1, 0, 1, 1, 1), etc. The numerical values of the package information DL indicate the number of packages to be delivered to the delivery points along links a to j in order from the left. In this example, it means that there are no delivery points (or no packages to be delivered to the delivery points) along links a, b, d, g, and it means that one package should be delivered to each of the delivery points along links c, e, f, h, i, j. Of course, the package information DL may be defined by the size, weight, etc. of the packages.

[0098] Using the package information DL, it is possible to search for a driving route that makes all the deliveries to be delivered to the delivery points existing along the target road deliverable during the driving process of the driving route. Such processing can be realized by various methods. In a configuration similar to that of FIG. 1, the same processing as in FIG. 4 may be executed while imposing a constraint condition to deliver all the packages, or after executing the same processing as in FIG. 4, the driving route may be corrected so as to satisfy the constraint condition to deliver all the packages.

[0099] Here, the latter aspect will be described. Assume that multiple driving routes are searched under conditions not considering the delivery of packages, and a state is assumed where two driving routes, r1 = (1234521) and r2 = (1765721), are searched. In this case, C = (3, 1, 1, 1, 1, 1, 1, 1, 1, 1), and all target roads are covered. In FIG. 10A, the driving routes r1 and r2 are shown by a broken line and a one-dot chain line (the overlapping sections of both are represented by a broken line). The control unit 20 determines whether all packages can be delivered based on the multiple driving routes obtained in this way. If it is not determined that all packages can be delivered, the control unit 20 corrects the acquired driving route.

[0100] This correction is executed, for example, so as to satisfy the following constraint conditions. (a) When the number of packages when delivering to all delivery points on the driving route ri is within the loading capacity of the vehicle, it is determined as the driving route for delivering to all delivery points along the route. (b) When the number of packages when delivering to all delivery points on the driving route ri exceeds the loading capacity of the vehicle, the packages to be delivered on the driving route ri are divided and loaded onto multiple vehicles.

[0101] As a method for dividing packages among multiple vehicles in (b), various methods may be adopted. For example, when distributing the packages on the driving route ri to two vehicles, the first vehicle will load the maximum number of packages within the loading capacity and drive along the original driving route ri. According to this configuration, it is guaranteed that the target roads are covered by the driving of the first vehicle. In the case of this configuration, the second vehicle will take the shortest route for delivering the packages that could not be loaded onto the first vehicle. Note that if the packages cannot be carried by two vehicles, they may be distributed to three or more vehicles.

[0102] When correcting to meet the above conditions based on two driving routes r1 = (1234521) and r2 = (1765721), the control unit 20 sequentially makes judgments for each of the driving routes. Regarding the driving route r1, the control unit 20 specifies that the driving order of the links is a, b, c, d, i, a based on the permutation of the nodes. The control unit 20 refers to the cargo information DL and specifies that there are delivery points along links c and i among these links, and it is necessary to deliver a total of two cargos. Furthermore, the control unit 20 specifies the loading capacity of the vehicle. Here, it is assumed that the loading capacity is 3. In this case, the control unit 20 determines that the number of cargos when delivering to all the delivery points on the driving route r1 is within the loading capacity of the vehicle. Therefore, the control unit 20 determines the driving route r1 as a driving route for delivering to all the delivery points along the route.

[0103] On the other hand, regarding the driving route r2, the control unit 20 specifies that the driving order of the links is g, f, e, j, h, a based on the permutation of the nodes. The control unit 20 refers to the cargo information DL and specifies that there are delivery points along links e, f, h, and j among these links, and it is necessary to deliver a total of four cargos. Furthermore, based on the loading capacity 3 of the vehicle, the control unit 20 determines that the number of cargos when delivering to all the delivery points on the driving route r2 exceeds the loading capacity of the vehicle.

[0104] In this case, the control unit 20 distributes the four cargos to multiple vehicles. The first vehicle loads the maximum number of cargos within the loading capacity and travels on the original driving route r2. Therefore, the control unit 20 determines that the driving route of the first vehicle is determined as the driving route r2, and three cargos will be loaded. The delivery points may be determined by various methods, but here, it is assumed that deliveries are made in order from the delivery points that appear first along the driving route. For this reason, the first vehicle is loaded with the cargos to be delivered to the delivery points along links f, e, and j. In FIG. 10B, the driving route r2 of the first vehicle is shown by a dashed line, and the delivery points delivered by the first vehicle are shown by black rectangles. The delivery points of the white rectangles are not the delivery points delivered by the first vehicle.

[0105] In the second vehicle, the shortest route for delivering the luggage that could not be loaded on the first vehicle becomes the driving route. Specifically, the shortest driving route r3 that covers the delivery points not delivered by the first vehicle is identified. Such a driving route can be identified by various methods. For example, the control unit 20 can identify the driving route by tracing the nodes from the rear of the driving route r2 until all the delivery points are covered.

[0106] Specifically, when tracing the nodes from the rear of the driving route r2, it is possible to trace the nodes N1, N2, and N7. When reaching the node N7, it can be seen that the link h can be traveled from the node N7, and the delivery points can be covered by delivering to the delivery points along the link h. Therefore, the control unit 20 refers to the above-mentioned distance matrix D, for example, and identifies that in order to reach the node N7 from the node N1 which is the starting point, it is only necessary to pass through the link g after the starting point. Then, the control unit 20 short-circuits the route that selects the link g and the route after the node N7 in the driving route r2 to identify the driving route r3=(1721). In FIG. 10C, the driving route r3 of the second vehicle is shown by a broken line, and the delivery points delivered by the second vehicle are shown by white rectangles. According to the above processing, it is possible to search for a driving route for delivering luggage to all the delivery points while covering all of the target roads.

[0107] Note that the technical ideas according to the above-described embodiments are not mutually exclusive, and the technical idea of any one of the embodiments may be realized in a single embodiment by at least two technical ideas selected from the fourth to sixth embodiments.

[0108] Furthermore, the present invention is also applicable as a program or a method. Also, the above-described system, program, and method may be realized as a single device or may be realized by using components shared with other devices, and include various aspects. For example, it is possible to provide a method or a program realized by the above-described system. Also, it can be appropriately changed, such as being partly software and partly hardware. Furthermore, the invention is also established as a recording medium for a program that controls a device. Of course, the software recording medium may be a magnetic recording medium, a semiconductor memory, or any recording medium developed in the future, and can be considered in exactly the same way.

Explanation of Signs

[0109] 10…Travel route acquisition system, 20…Control unit, 21…Quantity prediction program, 21a…Business hours acquisition unit, 21b…Quantity prediction unit, 21c…Delivery plan acquisition unit, 30…Recording medium, 30a…Store information, 30b…Quantity actual results, 30c…Calendar information, 30d…Trained model, 40…Communication unit, 100…Administrator terminal, 200…Weather information server, 300…VRP server

Claims

1. A target road acquisition unit that acquires a plurality of target roads that are roads to be monitored; A constraint condition acquisition unit that acquires constraint conditions corresponding to the maximum allowable travel distance; A travel route acquisition unit that acquires a plurality of travel routes that satisfy the constraint conditions, and when a vehicle travels on the plurality of travel routes, all the target roads are traveled at least once; A travel route acquisition system comprising the above.

2. The constraint conditions are: Including a condition that makes the travel frequency of the predetermined target road higher than the travel frequencies of the other target roads; The travel route acquisition system according to Claim 1.

3. The constraint conditions are: Including a condition that selects a travel route with a lower cost when traveling from among the selectable travel routes; The travel route acquisition system according to Claim 1.

4. The constraint conditions are: Including a condition that enables all of the deliveries to be delivered to the delivery points existing along the target road during the travel of the travel route; The travel route acquisition system according to Claim 1.

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