Simulation data determination method and simulation data determination program

Abstract

This makes it possible to perform simulations in a short amount of time without reducing the accuracy of the simulation. [Solution] The simulation data determination method is a method for determining a portion of a set of source data, which is a collection of past data, as data to be used for simulation. This method involves performing a process multiple times to randomly select a portion of past data from the source data to generate a dataset, identifying a first distribution information that shows the distribution of data focusing on at least one of the location information and date and time information of the past data contained in each dataset, identifying a second distribution information that corresponds to the first distribution information of the past data contained in the source data, and determining that the dataset among the multiple datasets in which the first distribution information is most similar to the second distribution information is the dataset to be used for simulation.

Description

[Technical Field]

[0001] This invention relates to a method for determining simulation data and a program for determining simulation data. [Background technology]

[0002] In local governments and other organizations, the number of emergency calls is increasing year by year due to factors such as the growing demand for emergency transport as the elderly population increases, and the rise in heatstroke cases. Local governments and other organizations may review their troop deployments, including increasing the number of troops, in order to shorten the time it takes for emergency teams (paramedics and firefighters) to arrive at the scene. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2010-67224 [Overview of the project] [Problems that the invention aims to solve]

[0004] When local governments simulate troop deployments, they sometimes use data from past dispatch records to calculate how long it would take for any of the troop deployments to reach a given location (site) included in those past dispatch records, calculating the travel time for each possible combination of troop and site.

[0005] However, if too much historical dispatch data is used in the above simulation, the simulation time will increase. Conversely, if the amount of dispatch data is reduced, there is a risk that highly accurate simulation results cannot be obtained.

[0006] In one aspect, the present invention aims to provide a simulation data determination method and a simulation data determination program that enable simulations to be performed in a short time without reducing the accuracy of the simulation. [Means for solving the problem]

[0007] In one embodiment, the simulation data determination method is a simulation data determination method in which a computer performs a process to determine which data to be used for simulation from a set of source data which is a collection of past data, and to determine which data to be used for simulation from which data to be used, and for each of the multiple datasets generated, a first distribution information is identified which shows the distribution of data that focuses on at least one of the location information and date and time information of the past data contained in each dataset, and a second distribution information is identified which corresponds to the first distribution information of the past data contained in the source data, and the dataset in which the first distribution information is most similar to the second distribution information is determined to be used for simulation from which data to be used from which data to be used from which data [Effects of the Invention]

[0008] This allows simulations to be performed in a shorter time without reducing their accuracy. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a schematic diagram showing the configuration of a troop deployment decision support system according to one embodiment. [Figure 2] Figure 2(a) shows an example of the hardware configuration of a user terminal, and Figure 2(b) shows an example of the hardware configuration of a server. [Figure 3] Figure 3 is a functional block diagram of the server. [Figure 4] Figure 4 shows the data structure of the dispatch history database. [Figure 5] Figure 5 shows information on potential deployment locations. [Figure 6] Figure 6 is a map that shows the deployment information for each of the deployment candidates in Figure 5 at a specific point in time. [Figure 7]FIG. 7 is a flowchart showing the process of reducing dispatch history by the server. [Figure 8] FIG. 8(a) is a diagram schematically showing the outline of the processing content of step S14 in FIG. 7, and FIG. 8(b) is a diagram schematically showing the outline of the processing content of step S16 in FIG. 7. [Figure 9] FIG. 9 is a diagram showing the state of dividing the municipality into meshes. [Figure 10] FIGS. 10(a) and 10(b) are diagrams showing an example of a graph representing the distribution of all dispatch history information by district and the distribution of a certain dataset candidate by district. [Figure 11] FIG. 11 is a diagram schematically showing the outline of the processing content of step S18 in FIG. 7. [Figure 12] FIGS. 12(a) to 12(c) are diagrams for explaining the processing of step S20 in FIG. 7. [Figure 13] FIG. 13 is a flowchart showing the simulation process by the server. [Figure 14] FIG. 14 is a flowchart showing the specific processing content of step S36 in FIG. 13. [Figure 15] FIGS. 15(a) and 15(b) are diagrams for explaining the processing of FIG. 14. [Figure 16] FIG. 16 is a diagram for explaining the information output by the output unit. [Figure 17] FIG. 17 is a diagram for explaining another example of the information output by the output unit. [Figure 18] FIG. 18 is a diagram for explaining a modified example.

MODE FOR CARRYING OUT THE INVENTION

[0010] Hereinafter, an embodiment of the team deployment decision support system will be described in detail based on FIGS. 1 to 16.

[0011] Figure 1 schematically shows the configuration of a troop deployment decision support system 100 according to one embodiment. The troop deployment decision support system 100 of this embodiment is a system that provides the necessary information and supports consideration when a local government considers how many troops (ambulance teams and fire teams) should be deployed to each location within the local government (existing fire stations, new fire stations, etc.). In this embodiment, the troops to be deployed to each location are ambulance teams, and each ambulance team will dispatch the same type of vehicle (ambulance).

[0012] As shown in Figure 1, the troop deployment decision support system 100 comprises a user terminal 70 and a server 10. The user terminal 70 and the server 10 are connected to a network 80 such as the internet or a mobile phone network.

[0013] The user terminal 70 is a PC (Personal Computer), tablet, smartphone, etc., and is operated by local government officials. On the user terminal 70, local government officials input information on potential deployment locations (information that specifies how many units to deploy to which locations). The user terminal 70 also collects information on past deployment history from a database that manages deployment history. The user terminal 70 transmits the input information on potential deployment locations and the collected information on past deployment history to the server 10. The user terminal 70 also receives and displays information from the server 10 that supports the consideration of unit deployment.

[0014] Figure 2(a) shows the hardware configuration of the user terminal 70. As shown in Figure 2(a), the user terminal 70 includes a CPU (Central Processing Unit) 190, ROM (Read Only Memory) 192, RAM (Random Access Memory) 194, storage (SSD (Solid State Drive), HDD (Hard Disk Drive), etc.) 196, a network interface 197, a display unit 193, an input unit 195, and a portable storage medium drive 199, etc. Each of these components of the user terminal 70 is connected to the bus 198.

[0015] Returning to Figure 1, the server 10 generates information to support the consideration of troop deployment based on the information obtained from the user terminal 70 (information on deployment candidates and past deployment history), and sends it to the user terminal 70.

[0016] Figure 2(b) shows the hardware configuration of server 10. As shown in Figure 2(b), server 10 includes a CPU 90, ROM 92, RAM 94, storage 96, network interface 97, and a portable storage medium drive 99, etc. Each of these components of server 10 is connected to bus 98. In server 10, the CPU 90 executes programs (including travel time prediction programs) stored in ROM 92 or storage 96, or programs read from the portable storage medium 91 by the portable storage medium drive 99, thereby realizing the functions of each component shown in Figure 3. Note that the functions of each component in Figure 3 may be realized by integrated circuits such as ASICs (Application Specific Integrated Circuits) or FPGAs (Field Programmable Gate Arrays).

[0017] Figure 3 shows a functional block diagram of server 10. In server 10, the CPU 90 executes programs to realize the functions of dispatch history acquisition unit 20, reduction unit 22, deployment candidate acquisition unit 24, simulation unit 26, and output unit 28. Figure 3 also illustrates the dispatch history DB 40, reduced dispatch history DB 42, deployment candidate DB 44, and simulation result DB 46 stored in storage 96, etc.

[0018] The dispatch history acquisition unit 20 acquires dispatch history information collected by the user terminal 70 and stores it in the dispatch history DB 40. The dispatch history DB 40 stores dispatch history information as shown in Figure 4. Dispatch history information is information obtained when an ambulance (ambulance crew) is actually dispatched to a scene (dispatch location). Specifically, as shown in Figure 4, the dispatch history information has data for the following items: "Dispatch ID", "Dispatch location latitude", "Dispatch location longitude", "Date", "Day of the week", "Fire station latitude", "Fire station longitude", "Dispatch time", "Arrival time", and "Departure time". "Dispatch ID" is identification information assigned to each dispatch history piece in order of dispatch date and time. "Dispatch location latitude" and "Dispatch location longitude" are the latitude and longitude information of the dispatch location (such as the location of the patient using the ambulance). "Date" and "Day of the week" are the date and day of the week when the dispatch request was made. "Fire station latitude" and "fire station longitude" are the location information (latitude and longitude) of the fire station where the dispatched team was actually stationed. "Dispatch time" is the time when the ambulance actually started its dispatch after the dispatch request was received, "arrival time" is the time when the ambulance actually arrived at the location, and "return time" is the time when the dispatched team returned to the fire station.

[0019] Returning to Figure 3, the reduction unit 22 executes a process to reduce the dispatch history information stored in the dispatch history DB 40. The dispatch history information is expected to be large, exceeding 200,000 entries per year depending on the municipality. Even if the simulation described later using one dispatch history (one row of data in Figure 4) takes 1 msec, if the simulation is performed using one year's worth of dispatch history information (200,000 entries), it will take 1 msec × 200,000 = 200 seconds. In this embodiment, the reduction unit 22 reduces a portion of the dispatch history stored in the dispatch history DB 40 to create a dataset for use in the simulation, without reducing the accuracy of the simulation. The reduction unit 22 stores the dispatch history information (dataset) after partial reduction in the reduced dispatch history DB 42. Details of the processing of the reduction unit 22 will be described later.

[0020] The deployment candidate acquisition unit 24 acquires deployment candidate information for teams created by local government officials on user terminals 70 and stores it in the deployment candidate DB 44. Deployment candidate information for teams is, for example, the information shown in Figure 5. Specifically, for each deployment candidate 1, 2...M, it is defined which team operates at which fire station from what time to what time, and as shown in Figure 5, it includes information such as "team ID," "fire station," "fire station latitude," "fire station longitude," and "operating hours." "Team ID" is identification information assigned to each team, "fire station" is the name of the fire station, and "fire station latitude" and "fire station longitude" are the location information of the fire station. "Operating hours" is the operating hours for each team. When the deployment information for each deployment candidate at a certain point in time is represented on a map, it looks like Figure 6. The circled numbers on the map in Figure 6 indicate the locations of fire stations, and the values ​​of the circled numbers indicate the number of teams deployed at each fire station. For example, local government officials use the current deployment information (information on deployments actually in operation) as a basis to increase or decrease the number of units, add or add fire stations, etc., in the table in Figure 5, set the number of units for each fire station, and create multiple (M) candidate unit deployments. Note that the candidate unit deployments may be automatically generated (for example, randomly under predetermined conditions) by the user terminal 70 or server 10.

[0021] The simulation unit 26 simulates the ambulance travel time to each scene (dispatch destination) assuming that a dispatch request similar to the dispatch history stored in the dispatch history DB 40 occurs when each of the team deployment candidates is adopted. Specific examples of the simulation will be described later. The simulation unit 26 stores the simulation results for each of the team deployment candidates in the simulation results DB 46. If the simulation yields an average travel time to each scene for each deployment candidate, the simulation results DB 46 will store the average travel time linked to the deployment candidate.

[0022] The output unit 28 reads the simulation results from the simulation results DB 46 and outputs them to the user terminal 70. For example, if the simulation result is the average travel time, the output unit 28 outputs the average travel time for each deployment candidate to the user terminal 70. As a result, the display unit 193 of the user terminal 70 displays the average travel time for each deployment candidate (see Figure 16). This allows local government officials to refer to the average travel time for each deployment candidate and consider which deployment candidate should be adopted.

[0023] (Regarding the processing on Server 10) Next, the processing of server 10 will be explained in detail, following the flowcharts in Figures 7, 13, and 14, and referring to other diagrams as appropriate. Server 10 performs the following: dispatch history reduction processing (Figure 7) and simulation processing (Figures 13 and 14).

[0024] (Processing to reduce dispatch history) Figure 7 shows a flowchart illustrating the dispatch history reduction process performed by server 10.

[0025] When the process shown in Figure 7 begins, in step S10, the dispatch history acquisition unit 20 waits until dispatch history information is input from the user terminal 70. Once dispatch history information is input from the user terminal 70, the process moves to step S12, where the dispatch history acquisition unit 20 acquires the dispatch history information and stores it in the dispatch history DB 40 (Figure 4). The dispatch history information can be described as a collection of past data.

[0026] Next, in step S14, the reduction unit 22 creates candidate datasets from the dispatch history information (original data group) stored in the dispatch history DB 40. Figure 8(a) schematically shows an overview of the processing content in step S14. As shown in Figure 8(a), the reduction unit 22 creates P candidate datasets by randomly sampling n data points from the dispatch history information (which contains N data points). In other words, the reduction unit 22 performs the process of randomly selecting some data from the dispatch history information to generate candidate datasets multiple times (P times). Note that n can be determined based on the time to be reduced from the time required for the simulation. For example, if N = 200,000 and the time required for the simulation is to be reduced to 1 / 4, then n = 200,000 / 4 = 50,000.

[0027] Returning to Figure 7, in the next step S16, the reduction unit 22 calculates the distance between all dispatch history information and each of the dataset candidates. Here, as an example, the reduction unit 22 divides the entire municipality into meshes as shown in Figure 9 and assigns a district number to each mesh (district). Note that each district may be divided based on city, town, or village, or street address, instead of using meshes. The reduction unit 22 then aggregates the number of data points for each district in the all dispatch history information and plots the distribution for each district (second distribution information) on the graph. The reduction unit 22 also aggregates the number of data points for each district in the dataset candidates and plots the distribution for each district (first distribution information) on the graph.

[0028] For example, suppose the distribution of all dispatch history information by district and the distribution of a candidate dataset by district are as shown in Figure 10(a). In this case, the reduction unit 22 uses methods such as KL divergence or JS divergence to determine the distance d (a value indicating how statistically close) between the graphs. As shown in Figure 10(a), when the shapes of the two graphs are similar, the value of the distance d becomes small. On the other hand, as shown in Figure 10(b), when the shapes of the two graphs are not very similar, the value of the distance d becomes large.

[0029] In this embodiment, the reduction unit 22 measures the distances d1, d2, ..., d between the total dispatch history information and each of the dataset candidates 1, 2, ..., P, as schematically shown in Figure 8(b). P Calculate.

[0030] Next, in step S18 of Figure 7, the reduction unit 22 reduces the distances d1, d2, ..., d calculated in step S16. P Based on this, select the dataset to be used for the simulation. For example, distances d1, d2, ..., d P If the distance with the smallest value is d2, the reduction unit 22 selects candidate dataset 2 as the dataset to be used for the simulation, as shown by the thick outline in Figure 11.

[0031] Next, in step S20 of Figure 7, the reduction unit 22 corrects the data density. Since the selected dataset is randomly sampled from all dispatch history information, the number of dispatches per day will be reduced, which is expected to affect the simulation. Therefore, the reduction unit 22 corrects the data density by correcting the dates of the reduced data so that the number of dispatches per day does not decrease. For example, suppose the number of entries (data points) per day of all dispatch history information is distributed as shown in Figure 12(a). In this case, if, for example, 1 / 4 of the total dispatch history information is randomly sampled in the dataset, the distribution of the number of entries (data points) per day will be roughly as shown in Figure 12(b). In this case, the reduction unit 22 corrects the dates so that, for example, four weeks' worth of data is compressed into one week's worth. For example, the dates from the 1st to the 7th of a given month are not changed, and the dates from the 8th to the 14th, 15th to the 21st, and 22nd to the 29th are changed to the 1st to the 7th. As a result, as shown in Figure 12(c), the number of data points can be reduced while maintaining the same number of events per day (data density). Note that if days of different days of the week are treated as the same day, it may affect the simulation results, so it is preferable to correct days of the same day of the week to be treated as the same day. In step S18, the reduction unit 22 performs a process to bring the difference between the number of data points (density) corresponding to a unit of time (e.g., one day) of all dispatch history information and the number of data points (density) corresponding to a unit of time of the dataset used for the simulation within a predetermined range.

[0032] The reduction unit 22 stores the dataset, after correcting the data density, in the reduced dispatch history DB 42.

[0033] As described above, once the process up to step S20 in Figure 7 is completed, all the processes in Figure 7 are finished.

[0034] (Simulation process) Next, the simulation process performed by server 10 will be explained in detail, following the flowcharts in Figures 13 and 14, and referring to other diagrams as appropriate.

[0035] When the process shown in Figure 13 begins, first, in step S30, the deployment candidate acquisition unit 24 waits until information on M deployment candidates is entered. Once information on M deployment candidates is entered from the user terminal 70, the process moves to step S32, where the deployment candidate acquisition unit 24 acquires information on the M deployment candidates and stores it in the deployment candidate DB 44. The deployment candidate DB 44 stores deployment candidates 1, 2, ..., M as shown in Figure 5.

[0036] Next, in step S34, the simulation unit 26 selects an unselected deployment candidate. For example, the simulation unit 26 selects deployment candidate 1.

[0037] Next, in step S36, the simulation unit 26 performs a simulation using the dataset stored in the reduced deployment history DB 42 and the selected deployment candidates. In step S36, the simulation unit 26 performs the process shown in Figure 14.

[0038] (Processing in step S36 (Figure 14)) When the process shown in Figure 14 begins, first, in step S50, the simulation unit 26 retrieves the first dispatch history from the dataset stored in the reduced dispatch history DB 42. The simulation unit 26 retrieves the oldest dispatch history (the one with the most recent corrected date and time) from the dataset.

[0039] Next, in step S52, the simulation unit 26 updates the unit deployment information in accordance with the acquired deployment history. Specifically, the simulation unit 26 refers to the corrected date and time of deployment in the acquired deployment history and adjusts the unit deployment to correspond to the corrected date and time of deployment. For example, suppose the simulation unit 26 deploys the unit based on the corrected date and time of deployment in the acquired deployment history, taking into account the operating time in Figure 5, and as a result, the deployment information shown in Figure 15(a) is obtained.

[0040] Next, in step S54, the simulation unit 26 selects one unselected team from among the teams that have not yet been dispatched. The simulation unit 26 selects one team from each fire station, and does not select more than one team from a single fire station.

[0041] Next, in step S56, the simulation unit 26 determines the travel time for the selected team to arrive at the destination of the dispatch history obtained in step S50. In this case, the simulation unit 26 uses, for example, a general route search method to determine the travel time from the fire station to the dispatch destination of the selected team. However, if a general route search method is used, there is a high possibility that the travel time of a private car will be determined. Therefore, the simulation unit 26 may determine the travel time of an ambulance by multiplying the determined travel time of a private car by a predetermined percentage.

[0042] Next, in step S58, the simulation unit 26 determines whether or not it has finished selecting all the units that have not yet been deployed. If the determination in step S58 is negative, the process returns to step S54. After that, the processing and determination in steps S54, S56, and S58 are repeated to determine the time required for each unit that has not yet been deployed to travel to its destination (travel time). If the determination in step S58 is positive, the simulation unit 26 proceeds to step S60.

[0043] When the program moves to step S60, the simulation unit 26 resets the squad selection. That is, the simulation unit 26 sets all squads to "unselected".

[0044] Next, in step S62, the simulation unit 26 determines which team to dispatch. For example, if the travel times to the destination for each fire station's team are as shown in Figure 15(b), the simulation unit 26 will determine which team will dispatch, for example, the team with the shortest travel time (in Figure 15(b), the team with a travel time of 3 minutes). However, the simulation unit 26 is not limited to this, and may, for example, determine teams within a predetermined time from the shortest travel time (3 minutes in Figure 15(b)) as dispatch candidates. The simulation unit 26 may then determine which team to dispatch, based on the number of teams waiting at the same fire station among the dispatch candidates. Alternatively, the simulation unit 26 may determine which team to dispatch by taking other criteria and travel time into consideration.

[0045] Next, in step S64, the simulation unit 26 determines the withdrawal time of the dispatched unit. In this case, the simulation unit 26 may use the difference between the withdrawal time and the arrival time included in the currently acquired dispatch history as the withdrawal time, or it may use a predetermined time that is set in advance as the withdrawal time.

[0046] Next, in step S66, the simulation unit 26 determines whether it has already acquired all dispatch history included in the dataset stored in the reduced dispatch history DB 42. If the determination in step S66 is negative, the process proceeds to step S68, where the simulation unit 26 acquires the next (next oldest) dispatch history. After that, the process returns to step S52.

[0047] Returning to step S52, the simulation unit 26 updates the unit deployment information according to the acquired dispatch history. In this case, units that have already been dispatched and whose return time has not yet elapsed, based on the corrected date and time of dispatch in the acquired dispatch history, are treated as not having returned to the fire station.

[0048] Subsequently, the simulation unit 26 repeatedly executes steps S52 to S68. That is, for each deployment history, the simulation unit 26 calculates the travel time required for each unit to move to the deployment destination and decides which unit to deploy. Once processing for all deployment histories is complete, the decision in step S66 is affirmed, and the system proceeds to step S70. At the stage of proceeding to step S70, the travel times for all deployment histories have been calculated assuming that the deployment candidate selected in step S34 of Figure 13 is adopted.

[0049] Next, in step S70, the simulation unit 26 performs aggregation processing. In this case, as an example, the simulation unit 26 calculates the average travel time for all dispatch history (average travel time for ambulances). The simulation unit 26 stores the average travel time in the simulation results DB 46, linked to the deployment candidate number.

[0050] Therefore, once the process in Figure 14 is completed, the process proceeds to step S38 in Figure 13.

[0051] When the program proceeds to step S38 in Figure 13, the simulation unit 26 determines whether all deployment candidates have been selected. If the determination in step S38 is negative, the program returns to step S34. If the program returns to step S34, the simulation unit 26 selects any unselected deployment candidates (S34) and performs a simulation for the selected deployment candidates to calculate the average travel time (S36).

[0052] On the other hand, if the simulations for all deployment candidates are completed and the judgment in step S38 is affirmed, the process proceeds to step S40.

[0053] When the system moves to step S40, the output unit 28 outputs optimal deployment information. Here, as an example, the output unit 28 reads the average travel time for each deployment candidate from the simulation results DB 46 and outputs the deployment candidate with the shortest average travel time to the user terminal 70. For example, as shown in Figure 16, if the average travel time for deployment candidate 2 is the shortest after calculating the average travel time for each deployment candidate, deployment candidate 2 is output. In this case, the display unit 193 of the user terminal 70 displays information indicating that deployment candidate 2 is the optimal deployment.

[0054] In step S40, the output unit 28 may output a screen to the user terminal 70 displaying the deployment candidates in order of shortest average travel time. In this case, the local government officials may actually adopt the deployment candidate with the shortest average travel time, or they may select a deployment candidate to actually adopt by comprehensively considering the average travel time and the costs and effort required to change from the current troop deployment.

[0055] As described in detail above, according to this embodiment, the reduction unit 22 performs the process of randomly selecting some data from the total dispatch history information to generate candidate datasets multiple times (P times) (S14 in Figure 7). The reduction unit 22 also generates a distribution of data for each district (first distribution information) for each of the generated candidate datasets, as well as a distribution of data for each district in the total dispatch history information (second distribution information) (S16 in Figure 7). The reduction unit 22 then determines the candidate dataset whose distribution is most similar to the total dispatch history information (the candidate dataset with the closest distance d) to be used for the simulation (S18 in Figure 7). This makes it possible to use a dataset in the simulation that enables the simulation to be performed in a short time without reducing the simulation accuracy.

[0056] Furthermore, in this embodiment, the reduction unit 22 corrects the data density in step S20 of Figure 7. This makes it possible to obtain appropriate simulation results even when using a dataset with a reduced number of data points in the simulation.

[0057] In the above embodiment, as shown in Figures 10(a) and 10(b), the case in which the distribution of the number of data points for each district (for each district number) is used when determining the distance between the dataset used for simulation and the total dispatch history information was described. However, this is not the only method, and the distribution of the number of data points focusing on other elements may also be used. For example, in Figures 10(a) and 10(b), the distribution of the number of data points when the horizontal axis is the day of the week may be used. Also, in Figures 10(a) and 10(b), the distribution of the number of data points when the horizontal axis is the time of day may be used. Furthermore, in Figures 10(a) and 10(b), the horizontal axis may be a combination of district and time of day, a combination of district and day of the week, a combination of day of the week and time of day, or a combination of district, day of the week, and time of day.

[0058] In the above embodiment, the output unit 28 was described as outputting information on the optimal deployment candidate based on the average travel time of each deployment candidate, or outputting a screen that sorts and displays each deployment candidate based on the average travel time, but it is not limited to this. For example, the output unit 28 may divide the local government into regions, calculate the average travel time to the deployment destinations included in each region, and output a screen that color-codes each region based on the calculated average value, as shown in Figure 17. Local government officials can check the average travel time for each region for each deployment candidate, as shown in Figure 17, to see if there are any differences in average travel time between regions. This allows them to select a more appropriate deployment candidate.

[0059] (modified version) In the above embodiment, the case where the number of data points included in the candidate dataset is predetermined (as in Figure 8(a), where it is set to n points) was described. However, the invention is not limited to this, and the server 10 may perform processing according to the flowchart in Figure 18 instead of the dispatch history reduction processing in Figure 7.

[0060] In the flowchart of FIG. 18, the processes and judgments indicated by the thick-line frames are the processes and judgments added to the process of FIG. 7. Therefore, in the following, the processes and judgments indicated by the thick-line frames will be mainly described.

[0061] In the process of FIG. 18, when steps S10 and S12 are performed and the activation history information is stored in the activation history DB 40, in the next step S13, the reduction unit 22 sets the number of data of the dataset candidate to the initial value n.

[0062] Thereafter, the processes of steps S14, S16, S18, and S20 are performed in the same manner as in the above-described embodiment (FIG. 7). As a result, one dataset is selected and the density of the selected dataset is corrected.

[0063] Next, in step S21, the reduction unit 22 performs a simulation using all the activation history information (second simulation) and a simulation using the density-corrected dataset (first simulation) using the current deployment information and the like. This simulation is executed in the same manner as step S36 in FIG. 13. Note that, as a result of the simulation using all the activation history information, the average travel time t all is obtained, and it is assumed that the average travel time t sample is obtained as a result of the simulation using the density-corrected dataset.

[0064] Next, when moving to step S22 in FIG. 18, the reduction unit 22 determines whether or not the difference in the simulation results is less than or equal to a predetermined value. Here, the reduction unit 22 obtains |t all -t sample |, and if |t all -t sample | is greater than the predetermined value TH (|t all -t sample |>TH), the determination in step S22 is negative.

[0065] If the judgment in step S22 is rejected, it means that the accuracy of the simulation is not very high when the number of data points in the dataset is n. In this case, the process moves to step S23, where the reduction unit 22 increases the number of data points in the candidate dataset by α. That is, the number of data points is set to (n+α) to improve the accuracy of the simulation using the dataset. After that, the process returns to step S14, and the processing and judgment in steps S14 to S23 are repeated until the judgment in step S22 is affirmed.

[0066] If the determination in step S22 is affirmed, the process proceeds to step S24, where the reduction unit 22 stores the density-corrected dataset obtained as a result of step S20 into the reduced dispatch history DB 42. After that, all processing shown in Figure 18 is completed.

[0067] Thus, according to this modified example, when the accuracy of a simulation using a dataset is not very high, the number of data points in the dataset can be increased to improve the accuracy of the simulation.

[0068] In the above embodiments and modifications, the unit deployment decision support system 100 shown in Figure 1 was used when deciding on the deployment of ambulances and fire engines at a fire station, but this is not the only option. For example, when deploying people or units to multiple locations, such as a security company, taxi company, or delivery company, the unit deployment decision support system 100 shown in Figure 1 may also be used.

[0069] In the above embodiment, the case in which the server 10 performs the processes shown in Figures 7, 13, and 14 has been described. However, the embodiment is not limited to this, and each user terminal 70 may also perform the processes shown in Figures 7, 13, and 14. Furthermore, the devices that perform each process may differ, such as the server 10 performing the process shown in Figure 7 and the user terminals 70 performing the processes shown in Figures 13 and 14.

[0070] The above processing functions can be implemented by a computer. In this case, a program describing the processing content of the functions that the processing unit should have is provided. By executing this program on a computer, the above processing functions are implemented on the computer. The program describing the processing content can be recorded on a computer-readable recording medium (excluding carrier waves).

[0071] When distributing a program, it may be sold in the form of a portable recording medium such as a DVD (Digital Versatile Disc) or CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. Alternatively, the program can be stored in the storage device of a server computer and transferred from the server computer to other computers via a network.

[0072] A computer executing a program stores the program, for example, on a portable storage medium or transferred from a server computer, in its own memory. The computer then reads the program from its memory and executes the processing according to the program. Alternatively, the computer can directly read the program from the portable storage medium and execute the processing according to that program. Furthermore, the computer can sequentially execute the processing according to the program received each time it is transferred from a server computer.

[0073] The embodiments described above are preferred examples of the present invention. However, the invention is not limited thereto, and various modifications are possible without departing from the spirit of the invention.

[0074] Furthermore, the following additional information is disclosed regarding the above description of the embodiments. (Note 1) A simulation data determination method in which a portion of the original data set, which is a collection of past data, is selected as the data to be used for the simulation, The process of randomly selecting some historical data from the aforementioned original data set to generate a new dataset is performed multiple times. For each of the generated datasets, a first distribution information is identified that shows the distribution of data focusing on at least one of the location information and date / time information of the historical data contained in each dataset, and a second distribution information corresponding to the first distribution information of the historical data contained in the original data set is identified. Of the aforementioned multiple datasets, the dataset whose first distribution information is most similar to the second distribution information is determined to be the dataset used for the simulation. A method for determining simulation data, characterized in that the processing is performed by a computer. (Note 2) The simulation data determination method according to Note 1, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each region when the past data is allocated to each of the pre-divided regions. (Note 3) The simulation data determination method according to Note 1, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each day of the week when the past data is allocated to each day of the week. (Note 4) The simulation data determination method according to Note 1, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each time period when the past data is allocated to each time period. (Note 5) The simulation is performed using the data to be used in the simulation determined in the decision process to obtain a first simulation result, and the simulation is performed using the original data set to obtain a second simulation result. A simulation data determination method according to any one of the appendices 1 to 4, characterized in that, if the difference between the first simulation result and the second simulation result exceeds a predetermined range, the process of generating the dataset by randomly selecting past data is executed again, and the process of identifying and the process of determining are executed again. (Note 6) A method for determining simulation data according to any one of Notes 1 to 5, characterized in that the computer performs a process to correct the date and time information contained in the past data included in the dataset used for the simulation, so that the difference between the number of past data corresponding to a unit time in the dataset used for the simulation and the number of past data corresponding to a unit time in the original data group is within a predetermined range. (Note 7) The simulation data determination method according to Note 6, characterized in that when correcting the date and time information, the correction is made to a different day of the same week. (Note 8) A simulation data determination method in which a portion of the original data set, which is a collection of past data, is selected as the data to be used for the simulation, The process of randomly selecting some historical data from the aforementioned original data set to generate a new dataset is performed multiple times. For each of the generated datasets, a first distribution information is identified that shows the distribution of data focusing on at least one of the location information and date / time information of the historical data contained in each dataset, and a second distribution information corresponding to the first distribution information of the historical data contained in the original data set is identified. Of the aforementioned multiple datasets, the dataset whose first distribution information is most similar to the second distribution information is determined to be the dataset used for the simulation. A simulation data determination program characterized by having a computer perform the processing. (Note 9) The simulation data determination program according to Note 8, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each region when the past data is allocated to each of the pre-divided regions. (Note 10) The simulation data determination program according to Note 8, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each day of the week when the past data is allocated to each day of the week. (Note 11) The simulation data determination program according to Note 8, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each time period when the past data is allocated to each time period. (Note 12) The simulation is performed using the data to be used in the simulation determined in the determination process to obtain a first simulation result, and the simulation is performed using the original data set to obtain a second simulation result. A simulation data determination program according to any one of the appendices 8 to 11, characterized in that, if the difference between the first simulation result and the second simulation result exceeds a predetermined range, the program repeats the process of generating the dataset by randomly selecting past data, and also repeats the process of identifying and the process of determining. (Note 13) A simulation data determination program according to any one of Notes 8 to 12, characterized in that the computer performs a process to correct the date and time information contained in the past data included in the dataset used for the simulation, so that the difference between the number of past data corresponding to a unit time in the dataset used for the simulation and the number of past data corresponding to a unit time in the original data group is within a predetermined range. (Note 14) The simulation data determination program described in Note 13, characterized in that when correcting the date and time information, the correction is performed on different days of the same week. [Explanation of symbols]

[0075] 10 servers 20 Dispatch History Acquisition Unit 22 Reduction Department 24 Deployment candidate acquisition unit 26 Simulation Department 28 Output section 40 Dispatch History Database 42. Dispatch History Database after Reduction 44 Deployment candidate DB 46 Simulation Results Database 70 User terminals 100 Unit Deployment Decision Support System

Claims

1. A simulation data determination method that selects a portion of the original data set, which is a collection of past data, as the data to be used in the simulation, The process of randomly selecting some historical data from the aforementioned original data set to generate a new dataset is performed multiple times. For each of the generated datasets, a first distribution information is identified that shows the distribution of data focusing on at least one of the location information and date / time information of the historical data contained in each dataset, and a second distribution information corresponding to the first distribution information of the historical data contained in the original data set is identified. Among the aforementioned multiple datasets, the dataset whose first distribution information is most similar to the second distribution information is determined to be the dataset used for the simulation. A method for determining simulation data, characterized in that the processing is performed by a computer.

2. The simulation data determination method according to claim 1, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each region when the past data is allocated to each of the pre-divided regions.

3. The simulation data determination method according to claim 1, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each day of the week when the past data is allocated to each day of the week.

4. The simulation data determination method according to claim 1, characterized in that the first and second distribution information is information showing the distribution of the number of data points for each time period when the past data is allocated to each time period.

5. The simulation is performed using the data to be used in the simulation determined in the decision-making process to obtain a first simulation result, and the simulation is performed using the original data set to obtain a second simulation result. The simulation data determination method according to claim 1, characterized in that, if the difference between the first simulation result and the second simulation result exceeds a predetermined range, the process of generating the dataset by randomly selecting past data is executed again, and the process of identifying and the process of determining are executed again.

6. The simulation data determination method according to claim 1, characterized in that the computer performs a process to correct the date and time information contained in the historical data included in the dataset used for the simulation, so that the difference between the number of historical data corresponding to a unit time in the dataset used for the simulation and the number of historical data corresponding to a unit time in the original data group is within a predetermined range.

7. The simulation data determination method according to claim 6, characterized in that when correcting the aforementioned date and time information, the correction is performed on different days of the same week.

8. A simulation data determination method that selects a portion of the original data set, which is a collection of past data, as the data to be used in the simulation, The process of randomly selecting some historical data from the aforementioned original data set to generate a new dataset is performed multiple times. For each of the generated datasets, a first distribution information is identified that shows the distribution of data focusing on at least one of the location information and date / time information of the historical data contained in each dataset, and a second distribution information corresponding to the first distribution information of the historical data contained in the original data set is identified. Among the aforementioned multiple datasets, the dataset whose first distribution information is most similar to the second distribution information is determined to be the dataset used for the simulation. A simulation data determination program characterized by having a computer perform the processing.

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