JP6170743B2 - Information processing system, information processing apparatus, information processing method, and information processing program - Google Patents

Description

  The present invention relates to an information processing system, an information processing apparatus, an information processing method, and an information processing program for evaluating a traffic service level.

  In order to improve transportation services, it is necessary to evaluate existing transportation service levels. However, since there are various transportation facilities such as trains and buses for transportation services, and there are various viewpoints such as required time and cost, the evaluation method has not been established. For example, Patent Document 1 discloses an apparatus for searching for a route from a departure place to a destination, but has not yet evaluated the traffic service level.

JP 2012-58889 A

  The present invention has been made in view of the above problems, and an object thereof is to provide an information processing system, an information processing apparatus, an information processing method, and an information processing program for evaluating a traffic service level.

  According to one aspect of the present invention, the optimal route from the departure place to the destination in consideration of one or more search conditions, while changing the departure time based on the timetable of the nearest station of the departure place, or Obtaining an optimum route group composed of a plurality of optimum routes, which are the results of searching while changing the arrival time based on the timetable of the nearest station of the destination, the departure time or the arrival time being different from each other An optimum route group acquisition unit; an evaluation unit that evaluates a traffic service level from the departure place to the destination based on the optimum route group; and an output unit that outputs an evaluation result of the traffic service level. An information processing system characterized by this is provided.

  According to the present invention, the traffic service level can be appropriately evaluated.

1 is a block diagram showing a schematic configuration of an information processing system according to an embodiment of the present invention. The figure explaining the processing operation of the optimal route group acquisition part 2 which searches a some optimal route. The figure which shows an optimal route group typically. The figure explaining the processing operation of the optimal route group acquisition part 2 which searches a some optimal route. The figure which shows an optimal route group typically. The figure explaining the counting method of a route. The figure which shows an example of the relationship between each optimal route group and the number of kinds of a route. The graph which shows an example of the relationship between each optimal route group and the number of kinds of route. The graph which shows typically an example of the relationship between the average value of waiting time before departure, and the maximum time interval ratio of each optimal route group. The figure which shows an example of the conversion table for calculating generalization expense. The graph which shows typically an example of the relationship between the average value of generalization cost, and the maximum generalization cost ratio of each optimal path group. The figure explaining the method of specifying the evaluation item which characterizes the generalization cost of an optimal path group.

  Embodiments according to the present invention will be specifically described below with reference to the drawings.

  FIG. 1 is a block diagram showing a schematic configuration of an information processing system according to an embodiment of the present invention. This information processing system evaluates the level of transportation services. The information processing system includes a storage unit 1, an optimum route group acquisition unit 2, an evaluation unit 3, and an output unit 4. Each of these units may be included in one device, or may be included in a plurality of devices connected to each other via a network.

  The optimum route group acquisition unit 2 searches for a route from the departure point to the destination using the data stored in the storage unit 1, and generates an optimum route group (described later). And the evaluation part 3 evaluates the traffic service level from a departure place to the destination based on the optimal route group.

  The output unit 4 outputs an evaluation result, and is, for example, a display or a printer. Alternatively, the output unit 4 may generate and output a signal for displaying the evaluation result on a display connected to the outside of the information processing system, or may be a printer connected to the outside. On the other hand, information for printing out the evaluation result may be generated and output.

  Hereinafter, each part will be described in detail.

  The storage unit 1 stores route network data 11 and timetable data 12. The route network data 11 is information indicating a traffic network or a road network related to transportation such as railroads, buses, ferries, and airplanes.

  The information on the transportation network includes route information and toll information for transportation. Specifically, the information on the transportation network may include station positions, required time between stations, costs, and the like. In this specification, the term “station” includes not only a train station but also a bus stop, a ferry stop, an airport, and the like.

  The information on the road network is expressed by a combination of a node that is a node on the road network expression such as an intersection and a link that is a road section between the nodes. The information on the road network may include a type such as whether each link is a general road or a toll road, time required for movement between nodes, and the like.

  The timetable data 12 is information on a station timetable included in the route network data 11. More specifically, the timetable data 12 includes information on the time at which a train departs (and / or arrives) at each station.

  Search conditions including at least a departure place, a destination, and priority conditions are set in the optimum route group acquisition unit 2. The starting point and the destination are two points to be evaluated for the traffic service level. Then, the optimum route group acquisition unit 2 searches for the optimum route using the route network data 11 and the timetable data 12 based on the set search condition.

  The optimum route is one route selected according to the priority condition when a plurality of routes from the starting point to the destination are obtained as a result of the route search. For example, when the priority condition is “required time”, the optimum route group acquisition unit 2 sets the route having the shortest required time as the optimum route regardless of the number of transfers and the cost. When the priority condition is “cost”, the optimum route group acquisition unit 2 sets the route having the lowest cost as the optimum route. When a plurality of priority conditions are defined and there are a plurality of paths having the same first priority condition, one path may be selected according to the second priority condition. Moreover, you may select an optimal path | route based on the generalization expense mentioned later. If only one route is obtained as a result of the route search, the route is determined as the optimum route.

  In the present embodiment, it is assumed that each route includes at least one transportation facility such as a train that operates according to a timetable.

  The optimum route group acquisition unit 2 searches for a plurality of optimum routes over the time zone subject to evaluation of the traffic service level. For example, the optimum route group acquisition unit 2 searches for a plurality of optimum routes while changing the departure time within the time zone based on the timetable of the nearest station of the departure place.

FIG. 2 is a diagram illustrating the processing operation of the optimum route group acquisition unit 2 that searches for a plurality of optimum routes. Here, the following is assumed.
・ The time zone subject to the evaluation of the traffic service level shall be 7: 00-20: 00.
-It takes 5 minutes to get on foot from the departure point O1 to the nearest station S1.
-At station S1, there are 7:10, 20:35 and 35:35 trains.

  In this case, the start time of the traffic service level evaluation target is 7 o'clock. Therefore, the optimal route group acquisition unit 2 first performs a route search with a departure time of 7:00. However, the train departs at 7:10 at station S1. In order to get on this train, it is only necessary to depart from O1 at 7: 5, 5 minutes before. Therefore, the optimal route group acquisition unit 2 sets the departure time as 7:05, and sets the route for boarding at 7:10 at the station S1 as one optimal route.

  Next, the optimum route group acquisition unit 2 delays the departure time by 1 minute and searches for a route at 7: 6. However, at the station S1, the next train departs at 7:20. Therefore, next, the optimum route group acquisition unit 2 sets the departure time as 7:15, and the route on board at 7:20 as the second optimum route at the station S2. Thereafter, the optimum route group acquisition unit 2 obtains a plurality of optimum routes in the same manner until the departure time reaches 20:00.

  As described above, a plurality of optimum routes based on the timetable of the nearest station can be obtained. Hereinafter, the plurality of optimum routes obtained are also referred to as “optimum route group” or “route timetable”. The output unit 4 may output the optimum route group, or may output it in a timetable format.

  FIG. 3 is a diagram schematically showing the optimum route group, and shows an example in which the optimum route group is configured from the optimum routes 1 to 3. As shown in the figure, even if the starting point and the destination are the same, not all the optimum routes are the same route.

  In the optimum route 1, the vehicle departs from the departure point O1, travels by train from the station S1 to the station S2, and arrives at the destination D1. On the other hand, in the optimal route 2, the vehicle departs from the departure point O1, travels by train from the station S1 to the station S3, changes trains at the station S3, travels to the station S2, and arrives at the destination D1. The optimum route 3 also uses a bus. Since the number of transportation facilities and destinations differ depending on the time zone, the transportation facilities used differ depending on the departure time. As a result, as shown in FIG. 3, the optimum route group often includes a plurality of routes.

  Moreover, in FIG. 2 and FIG. 3 mentioned above, the example in which the optimal path | route is obtained with respect to each departure time of the station S1 is shown. However, there may be no optimal route for a part of the departure time.

  FIG. 4 is a diagram for explaining the processing operation of the optimum route group acquisition unit 2 that searches for a plurality of optimum routes. In order to simplify the explanation, it is assumed in the figure that the departure point O1 to the station S1 is a 5-minute walk, and there is no need to transfer from the station S1 to the destination station D1. In addition, it is assumed that the charges are the same for each stop, express and limited express.

  The route (1) is a route for taking an express train departing at 7:10 at the station S1, and arrives at the station D1 at 8:10. Here, when there is no route that arrives at the station D1 before 8:10 at the station D1, the route (1) is the optimum route.

  Route (2) is a route for getting on each stop at 7:15 at station S1 and arrives at station D1 at 8:45. On the other hand, the route (3) is a route on the express train departing at 7:20 at the station S1, and arrives at the station D1 at 8:15.

  Comparing the route (2) and the route (3), the route (2) has a later arrival time than the route (3) although the departure time is earlier than the route (3). Therefore, the route (2) is not adopted as the optimum route. Similarly, by comparing the route (4) and the route (5), the route (4) is not adopted as the optimum route.

  As a result, the optimum route group is as shown in FIG.

  In addition, when the departure place is a station, the station is the nearest station of the departure place. One station closest to the departure point may be the nearest station, or one or more stations within a predetermined range from the departure point may be the nearest station. In the latter case, when the nearest station of the departure point is two stations, Station A and Station B, the optimum route group acquisition unit 2 for the one departure time, the route arriving at the destination via the station A And a route arriving at the destination via the station B. Then, the optimum route group acquisition unit 2 sets one of these routes as the optimum route based on the priority condition.

  The optimum route group acquisition unit 2 may generate one optimum route group for one destination from one departure point. In this case, the evaluation unit 3 can absolutely evaluate the traffic service level from one departure place to one destination based on the one optimum route group.

  The optimum route group acquisition unit 2 generates a plurality of optimum route groups for a plurality of departure point and destination sets (hereinafter also referred to as OD pairs) in which at least one of the departure point and the destination is different from each other. May be. In this case, the evaluation unit 3 may evaluate the relative traffic service level by comparing each OD pair with another OD pair. Thus, it can be seen that the traffic service between OD pairs with relatively low traffic service levels should be improved with priority.

  In order to perform relative evaluation, the evaluation unit 3 first generates an evaluation value of the traffic service level for each of the plurality of OD pairs. Subsequently, the evaluation unit 3 performs statistical processing such as calculating an average value for the plurality of generated evaluation values. And the evaluation part 3 should just evaluate the traffic service level of the said evaluation object OD pair by comparing the result of a statistical process, and the evaluation value of the traffic service level of the evaluation object OD pair.

  Further, the evaluation unit 3 may perform evaluation so that a plurality of optimum route groups are grouped according to route characteristics and the traffic service level can be compared for each group. Variations in grouping may be performed in consideration of the transportation facility to be used, such as whether or not a ferry is used. The group variation may be increased or decreased based on the evaluation result. For example, in the evaluation process, when it becomes clear that there is a characteristic common to routes using the ferry, the route using the ferry may be grouped into a route not using the ferry.

  Here, the level of transportation service indicates how well the transportation system is enhanced from various viewpoints such as the number and types of transportation facilities, station positions, operation intervals, fees, and required time. The evaluation unit 3 may evaluate the traffic service level qualitatively or may quantitatively evaluate using some index.

  Hereinafter, three examples of (1) route diversity analysis, (2) waiting time analysis before departure, and (3) analysis based on generalized costs will be described in detail as examples of quantitative evaluation methods by the evaluation unit 3.

  First, route diversity analysis will be described.

  If the optimal route from a specific departure point to a specific destination is all the same route, the user may use that route regardless of the departure time and arrival time. For example, it is always the same route and safe for elderly people and people unfamiliar with the land. It can be said that this is convenient for the user and the traffic service level is high.

  Conversely, when the optimum route group includes various routes, the user must use different routes according to the departure time and arrival time. This increases the information necessary for the user to select a route, which may cause the user to feel troublesome or possibly make a wrong route selection. Therefore, it can be said that the traffic service level is low. Alternatively, from the viewpoint that there are various routes and there are more choices, it can be considered that the traffic service level is high for young people and those who are used to the land, for example.

  In any case, the evaluation unit 3 can evaluate the traffic service level based on the diversity of how many types of routes are included in the optimum route group. The route type counting method is arbitrary, and rules can be determined as appropriate. As the index value of diversity, for example, the route type may be counted according to the “traffic mode pattern”, or the route type may be counted according to the “route pattern”.

  In the “traffic mode pattern”, combinations of stations used for getting on and off and transfer, and modes of transportation between stations (routes are not distinguished) are counted. On the other hand, in the “route pattern”, the mode of transportation between the stations is counted while distinguishing the route.

  FIG. 6 is a diagram for explaining a route counting method and shows an example of the optimum route included in the optimum route group. When counting with the “traffic mode pattern”, the optimum route 1 and the optimum route 2 are counted as the same type since the route from the station C to the station B is different, but the transportation is a bus. Therefore, there are a total of four types of routes in the “traffic mode pattern”. On the other hand, when counting by the “route pattern”, the optimum route 1 and the optimum route 2 are counted as different types since the route from the station C to the station B is different. Accordingly, there are a total of five types of routes in the “route pattern”.

  The evaluation unit 3 may use the number of route types as an evaluation value of the traffic service level. Alternatively, the evaluation unit 3 may evaluate the traffic service level by comparing the number of types of routes with a predetermined reference value. For example, the traffic service level can be “high” if the route is two or less, “medium” if the route is 3 to 5, and “low” if the route is six or more.

  Moreover, when the optimal route group acquisition part 2 produces | generates several optimal route groups, the evaluation part 3 may evaluate a traffic service level relatively about each OD pair. 7 and 8 are diagrams showing an example of the relationship between each optimum route group and the number of route types. In the figure, the number of types of routes in the optimum route group for the starting point O1 and the destination Dk (k = 1 to 5) is illustrated. The average value of the number of types of the five optimum route groups is 3, while the route of the optimum route group 5 is seven types. Therefore, it can be seen that the traffic service level from the departure point O1 to the destination D5 is relatively low.

  The output unit 4 may display or print the table shown in FIG. 7 or the graph shown in FIG. 8, or may generate and output a signal necessary for display or printing.

  Moreover, when comparing a traffic service level for every group, the evaluation part 3 may calculate the average value and the maximum value of the number of kinds of the route of each group, and may enable it to compare these values for every group.

  7 and FIG. 8 show examples in which the starting point is common O1 and the destinations are different D1 to D5, but of course the destinations may be common and the starting points may be different from each other. Both the destination and the departure point may be different from each other.

  Next, the waiting time analysis before departure will be described.

  The waiting time before departure is the difference between the departure times of two optimum routes that are included in one optimum route group and that have consecutive departure times. In the example of FIG. 3, the waiting time before departure between the optimum route 1 and the optimum route 2 is 10 minutes, and the waiting time before departure between the optimum route 2 and the optimum route 3 is 15 minutes.

  As an example, the evaluation unit 3 may calculate an average value of waiting times before departure of a plurality of optimum routes constituting the optimum route group or obtain a maximum value as an evaluation value of the traffic service level. In FIG. 3, if there are only three optimum routes included in the optimum route group, the average value of the waiting time before departure is 12.5 minutes, and the maximum value is 15 minutes.

  The waiting time before departure is a blank time that may occur before departure, and corresponds to the operation interval of transportation. It can be said that the smaller the average value of the waiting time before departure, the shorter the operation interval of transportation and the higher the level of transportation service. In addition, the smaller the maximum waiting time before departure, the more uniform the operation intervals and the higher the level of traffic service.

  Moreover, the evaluation part 3 is good also considering the ratio (henceforth the maximum time interval ratio) of the maximum value of waiting time before departure with respect to the average value of waiting time before departure as an evaluation value of a traffic service level. It can be said that the smaller the maximum time interval ratio, the smaller the variation in waiting time before departure depending on the departure time, and the higher the traffic service level.

  The evaluation unit 3 may output the traffic service level by comparing the average value, the maximum value, or the maximum time interval ratio with a predetermined reference value as a value based on the waiting time before departure. For example, when the maximum time interval ratio is greater than or equal to a certain threshold, it may be evaluated that there is a problem with the traffic service level.

  This threshold value may be different depending on the time zone. For example, the threshold value may be set to 10 minutes in the commuting / commuting rush hour period such as 7 to 8 o'clock in the morning or 18 to 20 o'clock in the evening, and 15 minutes may be set in other time periods. This is because the shorter the waiting time before departure when traveling by means of transportation is, the higher the level of transportation service is.

  Moreover, when the optimal route group acquisition part 2 produces | generates several optimal route groups, the evaluation part 3 may evaluate a traffic service level relatively about each OD pair as follows.

  FIG. 9 is a graph schematically showing an example of the relationship between the average value of the waiting time before departure and the maximum time interval ratio for each optimum route group. In the figure, for each of the optimum route group 1 to the optimum route group 5, the average value of the waiting time before departure is plotted on the horizontal axis and the maximum time interval ratio is plotted on the vertical axis. In order to relatively evaluate the traffic service level, the average value of the waiting time before departure of the optimum route group 1 to the optimum route group 5 and the average value of the maximum time interval ratio are calculated. .

  The graph of FIG. 9 makes it possible to visually compare the evaluation value of the optimum route group related to a plurality of OD pairs with the evaluation value of the optimum route group related to the OD pair that is the evaluation target of the traffic service level. For example, in the optimum route group 3, the average value of the waiting time before departure is the longest and greatly exceeds the average value of the waiting time before departure. Therefore, from the viewpoint of the average value of the waiting time before departure, it can be seen that the traffic service level from the departure point O1 to the destination D3 is relatively low. Moreover, the maximum time interval ratio of the optimum route group 5 greatly exceeds the average value of the maximum time interval ratio. Therefore, from the viewpoint of the maximum time interval ratio, it can be seen that the traffic service level from the departure point O1 to the destination D5 is relatively low.

  The output unit 4 may display or print a graph as shown in FIG. 9, or may generate and output a signal necessary for display or printing.

  Next, an analysis based on generalized costs will be described. The generalized cost is a value quantified in consideration of one or a plurality of evaluation items at the same time. More specifically, generalized costs can be calculated by converting evaluation items other than costs such as walking time and number of transfers to costs for convenience.

  FIG. 10 is a diagram illustrating an example of a conversion table for calculating the generalized cost. Costs such as train charges are used as they are. The time required for boarding time and transfer time is converted to 30 yen per minute with a conversion factor of 30. This conversion coefficient can be determined based on, for example, the time value of the local residents who evaluate the traffic service level. Conversion factors for other items can be determined, for example, by multiplying the time value by a predetermined coefficient.

For example, the generalization cost of the optimal route 2 in FIG. 3 is 10 minutes waiting time before departure, 5 minutes on foot, 40 minutes of transit time / transfer time, 3 minutes on foot, so the evaluation unit 3 generalizes as follows. Calculate the cost.
10 [minutes] * 25 [yen / minute] +5 [minutes] * 40 [yen / minute] +40 [minutes] * 30 [yen / minute] +5 [minutes] * 40 [yen / minute] = 1850 [yen]

  Note that the conversion coefficient in FIG. 10 can be arbitrarily determined. For example, an evaluation item that should be emphasized may have a large conversion coefficient. In addition, for the evaluation items that need not be considered, the conversion coefficient may be lowered, or in some cases, 0 may not be considered at all. In FIG. 10, the conversion factor other than the waiting time before departure can be considered as the above-described waiting time analysis before departure. Furthermore, an evaluation item different from the evaluation items shown in FIG. 10 may be added.

  The above-mentioned waiting time before departure corresponds to the maximum waiting time before departure. On the other hand, the expected value of the time actually waiting before departure may be calculated and used as an evaluation item instead of the waiting time before departure. For example, as the time corresponding to the waiting time before departure, ½ of the waiting time before departure corresponding to the average value of the waiting time before departure may be used as the evaluation item.

  As an example, the evaluation unit 3 may calculate an average value of generalized costs of a plurality of optimum routes constituting the optimum route group or obtain a maximum value as an evaluation value of the traffic service level. It can be said that the lower the average or maximum generalized cost, the higher the level of transportation service.

  Moreover, the evaluation part 3 is good also considering the ratio (henceforth the maximum generalized cost ratio) of the maximum value of generalization cost with respect to the average value of generalization cost as an evaluation value of a traffic service level. It can be said that the smaller the maximum generalized cost ratio is, the smaller the variation in generalized cost due to departure time and the higher the level of traffic service.

  The evaluation unit 3 may output the traffic service level by comparing the average value, the maximum value, or the maximum generalized cost ratio with a predetermined reference value as a value based on the generalized cost. For example, when the maximum generalized cost ratio is equal to or greater than a certain threshold, it may be evaluated that there is a problem with the traffic service level.

  Moreover, when the optimal route group acquisition part 2 produces | generates several optimal route groups, the evaluation part 3 may evaluate a traffic service level relatively about each OD pair as follows.

  FIG. 11 is a graph schematically showing an example of the relationship between the average value of generalized costs and the maximum generalized cost ratio for each optimum route group. In the figure, for each of the optimum route group 1 to the optimum route group 5, the average value of generalized costs is plotted on the horizontal axis, and the maximum generalized cost ratio is plotted on the vertical axis. In addition, in order to relatively evaluate the traffic service level, the average value of the generalized costs of the optimal route group 1 to the optimal route group 5 and the average value of the maximum generalized cost ratio are calculated. .

  With the graph of FIG. 11, it is possible to visually compare the evaluation value of the optimum route group related to a plurality of OD pairs and the evaluation value of the optimum route group related to the OD pair that is the evaluation target of the traffic service level. For example, the optimum route group 3 has the longest average value of generalization costs, and greatly exceeds the average value of generalization costs. Therefore, from the viewpoint of the average value of generalized costs, it can be seen that the traffic service level from the starting point O1 to the destination D3 is relatively low. Moreover, the maximum generalized cost ratio of the optimum route group 5 greatly exceeds the average value of the maximum generalized cost ratio. Therefore, from the viewpoint of the maximum generalized cost ratio, it can be seen that the traffic service level from the starting point O1 to the destination D5 is relatively low.

  The output unit 4 may display or print a graph as shown in FIG. 11, or may generate and output data necessary for display or printing.

  Further, the evaluation unit 3 may specify an evaluation item that characterizes the generalized cost of the optimum route group. Hereinafter, as an example, a method for identifying which evaluation item has caused the generalization cost to increase for the optimal route having the maximum generalization cost in the optimal route group will be described.

  FIG. 12 is a diagram for explaining a method for specifying an evaluation item that characterizes the generalized cost of the optimum route group. First, the evaluation unit 3 calculates an average value of costs for each evaluation item for a plurality of optimized routes constituting the optimum route group. For example, if the average waiting time before departure is 10 minutes, referring to FIG. 12, the average cost of waiting time before departure is 250 yen.

  And the evaluation part 3 compares the cost of the optimal path | route with the largest generalization cost with an average cost for every evaluation item. In the example shown in FIG. 12, there is almost no difference in cost, boarding time / transfer time, walking time around the OD, and number of transfers, but the waiting time before departure is greatly different. From this, the evaluation unit 3 can specify that the cost of the optimum route is increased due to the waiting time before departure.

  Thus, in this embodiment, the traffic service level can be appropriately evaluated by generating an optimum route group including a plurality of optimum routes having different departure times.

  In the embodiment described above, a plurality of optimum routes are searched while changing the departure time within the range of the time zone subject to evaluation of the traffic service level based on the timetable of the nearest station of the departure place, and the optimum route group However, the optimum route group may be generated by another method.

  For example, the optimum route group acquisition unit 2 changes the arrival time within the time zone subject to the evaluation of the traffic service level based on the timetable of the nearest station of the destination instead of the nearest station of the departure place. Alternatively, a plurality of optimum routes may be searched to generate an optimum route group.

  Further, the optimum route group acquisition unit 2 does not change the departure time and arrival time based on the timetable, but shifts the departure time (or arrival time) at predetermined time intervals for each departure time (or arrival time). A plurality of optimum routes may be searched to generate an optimum route group. Alternatively, the optimum route group acquisition unit 2 may search for a plurality of optimum routes under arbitrary conditions such as a plurality of departure times (or arrival times) designated by the user, and generate an optimum route group.

  Furthermore, the optimum route group acquisition unit 2 may generate an optimum route group by searching for the optimum route in consideration of a plurality of different search conditions. For example, the optimum route group acquisition unit 2 generates an optimum route group based on the search condition A and an optimum route group based on the search condition B, and merges these optimum route groups to evaluate the traffic service level. The optimal route group may be generated.

  In the present embodiment, the optimum route group acquisition unit 2 performs route search to generate the optimum route group. However, instead of the optimum route group acquisition unit 2, another device performs route search to generate the optimum route group. The traffic service level may be evaluated using the set of optimum routes.

  That is, the optimum route group acquisition unit 2 is a result of searching for the optimum route, and it is only necessary to obtain an optimum route group composed of a plurality of optimum routes having different departure times or arrival times. For example, the optimal route group acquisition unit 2 may acquire an already-searched or generated optimal route group. Then, the optimum route group acquisition unit 2 may obtain an optimum route group stored in advance in the information processing system, or may obtain an optimum route group from another device such as an external server.

  In addition, as one of the evaluation methods, an example in which the traffic service level is evaluated based on the waiting time before departure has been shown. Good. The waiting time after arrival is a difference between the arrival times of two optimum routes that are included in one optimum route group and that have successive arrival times.

  When the optimum route group acquisition unit 2 generates the optimum route group based on the timetable of the nearest station of the departure place, the evaluation unit 3 may evaluate the traffic service level based on the waiting time before departure. On the other hand, when the optimal route group acquisition unit 2 generates an optimal route group based on the timetable of the nearest station of the destination, the evaluation unit 3 may evaluate the traffic service level based on the waiting time after arrival.

  At least a part of the information processing system described in the above-described embodiment may be configured by hardware or software. When configured by software, a program for realizing at least a part of the functions of the information processing system may be stored in a recording medium such as a flexible disk or a CD-ROM, and read and executed by a computer. The recording medium is not limited to a removable medium such as a magnetic disk or an optical disk, but may be a fixed recording medium such as a hard disk device or a memory.

  Further, a program that realizes at least a part of the functions of the information processing system may be distributed via a communication line (including wireless communication) such as the Internet. Further, the program may be distributed in a state where the program is encrypted, modulated or compressed, and stored in a recording medium via a wired line such as the Internet or a wireless line.

  Based on the above description, those skilled in the art may be able to conceive additional effects and various modifications of the present invention, but the aspects of the present invention are not limited to the individual embodiments described above. Absent. Various additions, modifications, and partial deletions can be made without departing from the concept and spirit of the present invention derived from the contents defined in the claims and equivalents thereof.

DESCRIPTION OF SYMBOLS 1 Memory | storage part 11 Route network data 12 Timetable data 2 Optimal route group acquisition part 3 Evaluation part 4 Output part

Claims (9)

The optimal route from the starting point to the destination, taking into account one or more search conditions,
While changing the departure time based on the timetable of the nearest station of the departure place, or
While changing the arrival time based on the timetable of the nearest station of the destination,
An optimum route group obtaining means for obtaining an optimum route group composed of a plurality of the optimum routes that are the results of the search, the departure time or the arrival time being different from each other;
An evaluation means for evaluating a traffic service level from the departure place to the destination based on the optimum route group;
And an output means for outputting the evaluation result of the traffic service level. The optimum route group obtaining means obtains the optimum route group for each of a plurality of sets of starting points and destinations, wherein at least one of the starting point and the destination is different from each other,
The evaluation means includes
For each of the plurality of sets, generate an evaluation value of the traffic service level,
Statistically processing the plurality of generated evaluation values,
The result of the statistical processing is compared with the evaluation value of the traffic service level of the set of the starting point and the destination to be evaluated to evaluate the traffic service level of the set of the starting point and the destination of the evaluation target. The information processing system according to claim 1.   3. The information processing system according to claim 1, wherein the evaluation unit evaluates the traffic service level based on diversity of optimal routes constituting the optimal route group. The evaluation means includes
Based on the difference between the departure times of two optimal routes that are included in the optimal route group,
Based on the difference between the arrival times of two optimal routes that are included in the optimal route group and that have consecutive arrival times,
The information processing system according to claim 1, wherein the traffic service level is evaluated.   The evaluation means calculates a generalized cost for each of the optimum routes in consideration of one or a plurality of evaluation items, and evaluates the traffic service level based on the generalized cost. Item 3. The information processing system according to item 1 or 2.   The information processing system according to claim 5, wherein the evaluation unit specifies an evaluation item that characterizes the traffic service level among the plurality of evaluation items. The optimal route from the starting point to the destination, taking into account one or more search conditions,
While changing the departure time based on the timetable of the nearest station of the departure place, or
While changing the arrival time based on the timetable of the nearest station of the destination,
An optimum route group obtaining means for obtaining an optimum route group composed of a plurality of the optimum routes that are the results of the search, the departure time or the arrival time being different from each other;
An evaluation means for evaluating a traffic service level from the departure place to the destination based on the optimum route group;
And an output means for outputting an evaluation result of the traffic service level. An information processing method executed by a computer,
The optimal route from the starting point to the destination, taking into account one or more search conditions,
While changing the departure time based on the timetable of the nearest station of the departure place, or
While changing the arrival time based on the timetable of the nearest station of the destination,
A result of the search, obtaining an optimum route group composed of a plurality of the optimum routes having different departure times or arrival times; and
Evaluating a traffic service level from the departure point to the destination based on the optimum route group;
Outputting the evaluation result of the traffic service level. An information processing method comprising:

The optimal route from the starting point to the destination, taking into account one or more search conditions,
While changing the departure time based on the timetable of the nearest station of the departure place, or
While changing the arrival time based on the timetable of the nearest station of the destination,
A result of the search, obtaining an optimum route group composed of a plurality of the optimum routes having different departure times or arrival times; and
Evaluating a traffic service level from the departure point to the destination based on the optimum route group;
An information processing program causing a computer to execute the step of outputting the evaluation result of the traffic service level.

Images