JP6261475B2 - Communication traffic prediction method, program, and apparatus - Google Patents

Description

  The present invention relates to a communication traffic prediction method, program, and apparatus, and more particularly to a communication traffic prediction method, program, and apparatus for predicting a future communication traffic volume based on the number of calls of a communication terminal.

  Patent Documents 1-4 disclose techniques for calculating future communication traffic based on actual values of traffic characteristics.

  In Patent Document 1, as a method for predicting the traffic volume of a communication area that fluctuates depending on the date and time, location registration information in a mobile / mobile communication network is monitored and measured, and the call status of a mobile terminal that has made a call is also monitored. A technique for measuring and predicting the future date and time and the call situation for each region based on these past performance values is disclosed.

  In Patent Document 2, as a method for predicting traffic in a fluctuating communication region, traffic depending on each position (unit region) on a map is calculated from traffic in a telecommunication facility and a service providing region of the telecommunication facility. A technique for predicting future position-dependent traffic from actual values of position-dependent traffic is disclosed. The amount of traffic in each unit area is calculated based on the area ratio between the service providing area of the telecommunication equipment and the observation unit area as the traffic between the telecommunication equipment.

  In Patent Document 3, as a method for predicting the communication traffic volume of a fixed network such as a backbone line or an INET line, the time and the size of a packet that flow on the network are measured, and the traffic volume and its fluctuation are affected A technique for managing external factors (calendar information, event status, etc.) and predicting the future traffic volume by applying a statistical analysis method to such information is disclosed.

  In Patent Document 4, as a method for predicting the amount of communication traffic that will occur when a new service is spread, the number of service subscribers affecting the traffic volume of the new service, and the trend of the traffic amount at the time when the service is spread in the past are disclosed.・ Technology is disclosed that manages and measures performance, etc., and predicts traffic volume when new services are spread in the future.

JP 2004-80315 A JP 2001-168985 A JP 2012-253445 JP JP 2012-182677 A Japanese Patent Application No. 2013-203895

  Patent Document 1 predicts the number of calls by measuring signal information related to location registration and signaling, and predicts the amount of communication traffic based on the prediction result of the number of calls. Therefore, if the terminal user repeatedly moves in the boundary region of the location area related to location registration (region / area unit managed by location registration), location registration information is generated more than necessary, and the number of location registration information becomes the number of users. It is not proportional. As a result, the correlation between the number of location registration information and the traffic amount is also lowered, and the prediction accuracy of the traffic amount is lowered.

  Patent Documents 2-4 directly calculate the predicted traffic volume by applying a time-series prediction method to the actual value of the traffic volume. However, the accuracy of time-series prediction decreases as the total amount of communication traffic and the fluctuation range of the traffic amount per communication increase, so traffic that includes a large amount of traffic per communication and a small amount of communication like today. There was a technical problem that the prediction accuracy decreased under the environment.

  Furthermore, in Patent Document 2, although geographical-dependent traffic is measured for each unit area, the traffic amount of each unit area is the total traffic regardless of whether each area is a residential area, a shopping area, an office area, or the like. Calculated as area ratio to quantity. However, since the area ratio and traffic ratio of each unit region are not necessarily equivalent, the traffic volume cannot be predicted with high accuracy.

  Thus, in the prior art, when predicting the future traffic volume, a time series prediction method based on the past or current traffic volume or the like is used. However, with the diversification of contents and services, there has been a technical problem that sufficient prediction accuracy cannot be ensured because the traffic amount deviation per communication increases.

  In addition, since the prediction is performed for each communication device or each line, when the communication device or line is added / removed, the traffic amount changes nonlinearly, and it is difficult to make the traffic prediction follow the change. There was a problem.

  On the other hand, in order to solve such a technical problem, the inventors of the present invention measure the traffic characteristics for each unit area obtained by dividing the monitoring area into a mesh shape depending on the geography, and based on the measurement result, A technology for predicting the future number of calls and the traffic volume per call for each region and calculating the total predicted traffic volume in the future was invented and a patent application was filed (Patent Document 5).

  However, in Patent Document 5, since the size and shape of the unit area are fixed, even if the terminal user is physically and spatially located at the same position, there is a problem of the calculation processing accuracy of GPS and three-point positioning. The calculation result of the terminal position may vary.

  In particular, when the terminal position is calculated by three-point positioning, the terminal and the terminal are connected even if the terminal user is physically and spatially located at the same position and there is no variation in the calculation processing accuracy of the three-point positioning. The calculation result of the terminal position may vary depending on the radio wave propagation environment between the base station and the base station and the load status of the base station (such as instructing connection to an adjacent base station according to the load).

  Furthermore, even if the terminal user's daily activity place itself is the same and the action range and the movement range are the same (routine), it is not always located at the exact same position every day, Variations of several tens of meters can occur normally.

  Therefore, especially for terminals located near the boundaries of each unit area, the traffic characteristics cannot be accurately associated with each unit area, which has the technical problem of causing an error in the traffic prediction accuracy. .

  An object of the present invention is to solve the above-mentioned problems of the prior art and to predict a future communication traffic total amount accurately based on the number of calls regardless of the presence or absence of various events that fluctuate terminal demand. And providing an apparatus.

  In order to achieve the above object, the present invention is characterized in that a communication traffic prediction apparatus for predicting the amount of communication traffic has the following configuration.

  (1) Traffic characteristic measurement means for measuring traffic characteristics including the number of calls and traffic volume per call for each unit area where the monitoring area is divided depending on geography, and a part of the unit area based on the traffic characteristics Unit area reorganization means that integrates and reorganizes into unit areas depending on traffic characteristics, and traffic prediction means that predicts at least the number of calls in the future number of calls and traffic volume per call for each unit area after the reorganization, Predicted traffic total amount calculating means for calculating a predicted traffic total amount for each unit area after the reorganization based on a prediction result of the number of calls.

  (2) Unit area reorganization means integrated multiple unit areas with similar traffic characteristics into one unit area.

  (3) The unit area reorganization means expands each unit area into a similar shape in multiple stages, and unit units within the extended range where the standard deviation for a given traffic characteristic is reduced and minimized from that unit area as one unit. Integrated into the region.

  (4) The unit region reorganization means clusters all unit regions based on predetermined traffic characteristics, and unites unit regions of the same cluster into one unit region.

According to the present invention, the following effects are achieved.
(1) In calculating the future daily forecast of the total traffic calculated as the total product of the number of calls (C) and the traffic volume per call (V / C), Since each unit region depends on traffic characteristics, not every unit region, it is possible to suppress variations in traffic characteristics and results within the prediction range, and to improve prediction accuracy. In addition, since the number of unit areas is reduced as compared with the case of being geographically dependent, calculation processing associated with prediction is reduced.

  (2) For example, in the vicinity of the boundary between two unit areas, a large number of terminal users frequently move, or a large number of terminal users are located, and due to variations in calculation results of each terminal position, traffic of a large number of terminals However, in each case, even when linked to one unit area or linked to the other unit area, it is linked to each unit area by combining the two unit areas as one unit area. Traffic variation can be prevented.

  (3) In calculating the future daily forecast value of the total traffic volume, the number of calls with little sudden or temporary fluctuation due to the difference or change of communication contents such as contents and services is predicted. While calculating the future daily forecast value of the total traffic volume as the sum of the products of the representative traffic volume per call, it is not suitable for daily forecasts due to many sudden or temporary fluctuation components and fluctuation factors. Since the traffic amount per call is not used for the prediction, the total daily communication traffic amount in the future can be accurately predicted.

  (4) If changes in traffic volume per call, such as regularity and selectivity, can be expected and high prediction accuracy can be expected, predict not only the number of calls but also the traffic volume per call. Therefore, it becomes possible to predict the future daily communication traffic total amount more accurately.

  (5) Since the number of calls and the traffic volume per call can be predicted separately, independent prediction with different numbers of samples and sample conditions becomes possible. as a result,

  (4a) For example, if a schedule for higher resolution (for example, full HD) of a distribution video is given separately, this is reflected in the traffic volume per call, so that the future traffic volume can be further increased. Be able to predict accurately.

  (4b) For example, when a popular device is newly released or a new customer acquisition campaign is planned, the number of new customers expected from the implementation of the campaign is reflected in the number of calls, so the total amount of future traffic can be calculated. It becomes possible to predict more accurately.

  (4c) Geographic dependence and time dependence in predicting traffic volume can be separated into the number of calls and traffic volume per call and reflected independently, so geographical dependence and time dependence can be reflected in the total traffic volume. It becomes possible to accurately reflect the predicted value.

It is the functional block diagram which showed the structure of the principal part of the communication traffic prediction apparatus which concerns on 1st Embodiment of this invention. It is the flowchart which showed the operation | movement of 1st Embodiment of this invention. It is the flowchart which showed the procedure (the 1) of area reorganization. It is the figure which showed the example of integration of the unit area (the 1). It is the figure which showed the criteria of determination of whether integration of a unit area is possible. It is the flowchart which showed the procedure (the 2) of area reorganization. It is the figure which showed the example of integration of the unit area (the 2). It is the figure which showed an example of the prediction result. It is the functional block diagram which showed the structure of the principal part of the communication traffic prediction apparatus which concerns on 2nd Embodiment of this invention. It is the functional block diagram which showed the structure of the principal part of the communication traffic prediction apparatus which concerns on 3rd Embodiment of this invention. It is the flowchart which showed the flow of the prediction process which calculates 1st prediction traffic total amount for every unit area by applying 1st Embodiment for every communication system.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a functional block diagram showing the configuration of the main part of the communication traffic prediction apparatus according to the first embodiment of the present invention. Here, the configuration unnecessary for the description of the present invention is omitted. Such a communication traffic prediction apparatus can be configured by mounting an application (program) for realizing each function on a general-purpose computer or server. Alternatively, a part of the application may be configured as a dedicated machine or a single-function machine in which hardware or ROM is implemented.

  The communication log collection unit with position information 5 was seen as the communication log of each communication terminal when time information, transmission source information, destination information, and each communication terminal communicated for each call (communication trial unit) (communication is possible). Various types of information including communication delay times between the base station and each base station were collected, and positioning results using each communication delay time (for example, three-point positioning) or installed in each communication terminal The position estimated by the GPS function is recorded as position information.

  The communication log DB1 with location information defines a number of geographically dependent unit areas (for example, a rectangle of 100 m × 100 m) by virtually dividing the monitoring area into a mesh shape based on division information given separately, Each communication log is associated with one of the unit areas based on the position information and is databased. The division information includes an area to be divided and information on a mesh standard / unit (size) defined by the Geographical Survey Institute or the Ministry of Internal Affairs and Communications, for example.

  The traffic characteristic measurement unit 2 includes a call number measurement unit 21, a traffic amount measurement unit 22, and a unit area reorganization unit 23. Each measurement unit 21, 22 is based on a communication log associated with each unit area. The traffic characteristics including the number of calls (number of attempts) C and the traffic volume V / C per call are measured for each region. The unit region reorganization unit 23 reorganizes the geographically dependent region organization into the traffic property dependent region organization by integrating the unit regions having similar traffic trends based on the traffic characteristic measurement results obtained for each unit region. To do.

  The traffic prediction unit 3 predicts the future number of calls Cf for each unit region and unit time by applying an appropriate prediction method to the measurement results of the number of calls C up to the present for each unit region after the reorganization. As the prediction method, for example, a time series prediction method such as Autoregressive (autoregressive), Moving Average (moving average), or ARIMA (Autoregressive Integrated Moving Average) can be applied.

  Alternatively, a time series prediction method such as Seasonal Auto ARIMA that considers the Seasonal component (seasonal or periodic fluctuation) and automatically calculates a unique parameter from past performance data and reflects it in the prediction may be applied. Furthermore, in addition to the time series prediction method described above, prediction methods such as SVM (support vector machine), NN (neural network), genetic algorithm, machine learning algorithm, etc. may be applied.

  The unit time may be a fixed time (for example, one hour cycle), or a unit region with a large amount of traffic is set to a shorter time cycle, while a unit region with a small amount of traffic is set to a longer time cycle. For example, it may be changed dynamically according to the traffic characteristics of each unit area.

  The predicted traffic total amount calculation unit 4 multiplies the prediction result Cf of the number of calls C and the statistical value (actual value) M (= V / C) of the traffic amount per call for each unit time. The predicted total traffic Vf (= Cf x V / C) is calculated for each unit area.

  FIG. 2 is a flowchart showing the operation of the present embodiment. In step S1, the traffic characteristic measuring unit 2 determines, for each unit area, based on the communication log stored in the communication log DB1 with position information. The actual value of the number C of calls is measured, and the statistical value of the traffic volume V / C per call is calculated.

  In step S2, the unit area reorganization unit 23 determines the traffic characteristics obtained for each unit area, that is, the number of calls C (actual value), the traffic volume per call V / C (statistical value), or these statistical values. Thus, a regional reorganization is performed in which a plurality of unit regions having similar traffic trends are integrated into one unit region.

  FIG. 3 is a flowchart showing an example of the area reorganization procedure in step S2. In step S201, one unit area in the monitoring area is selected as the current attention unit area. In step S202, as shown in FIG. 4A, it is determined whether or not the unit area of interest can be expanded once in a similar manner in a predetermined direction.

  In the present embodiment, as shown in FIG. 5, the monitoring area is classified into areas A and C where the base station or its sector is unique, and area B where the coverage of a plurality of base stations or its sectors overlaps, Unit areas belonging to A and unit areas belonging to area C are allowed to be integrated, while unit areas belonging to area B, and unit areas belonging to areas A and C and unit areas belonging to area B are allowed to be integrated. I try not to. Therefore, if all the unit areas of the expansion range belong to the area A or C, it is determined that expansion is possible, and if any unit area of another area is included, it is determined that expansion is inappropriate.

  Alternatively, paying attention to a higher-level network device (for example, a base station control device) that manages and controls a plurality of base stations or sectors thereof, the unit area can be expanded and determined as a unit. Also good. In this way, areas where the coverage of the base station or its sector overlaps can be made an area that can be integrated.

  If it is determined in step S202 that expansion is possible, the process proceeds to step S205, and all unit regions (here, four regions) within the expansion range are provisionally integrated into one unit region. In step S206, the standard deviation of the traffic characteristics of the temporarily integrated unit area is calculated.

  In the next step S202, as shown in FIG. 4B, it is determined whether or not the unit area of interest can be expanded twice in a similar manner in a predetermined direction. If it is determined that expansion is possible, the process proceeds to step S205, and all unit areas (here, nine areas) within the expansion range are provisionally integrated into one unit area. In step S206, the standard deviation of the traffic characteristics of the temporarily integrated unit area is calculated.

  In the next step S202, as shown in FIG. 4C, it is determined whether or not the unit area of interest can be expanded three times in a similar manner in a predetermined direction. If it is determined that expansion is possible, the process proceeds to step S205, and all unit areas within the expansion range are provisionally integrated into one unit area. In step S206, the standard deviation of the traffic characteristics of the temporarily integrated unit area is calculated.

  In the next step S202, as shown in FIG. 4 (d), it is determined whether or not the unit area of interest can be expanded four times in a similar manner in a predetermined direction. If it is determined that expansion is impossible, the process proceeds to step S203. . In step S203, the standard deviation obtained for each of the extended ranges with respect to the current attention unit area is referred to. Then, an extended range that is smaller than the standard deviation of the target unit area and gives the minimum standard deviation is selected, and all the unit areas included in the extended range are integrated into one unit area.

  In step S204, it is determined whether all unit areas have been noticed. If there is a unit area that has not been noticed, the process returns to step S201, and the above-described processes are repeated while switching the noticed unit area to another unit area other than the unit areas that have been integrated.

  Note that the traffic characteristic calculated in step S206 is not limited to the standard deviation as an index for determining the extended range in step S203, and a part of the actual data period is supervised and learned for each extended range. Data and other parts are separated into answering data, and applied time-series prediction (eg ARIMA) prediction accuracy, that is, errors and differences between forecasting and answering results are less than the target unit area, and the minimum You may make it integrate in the extended range which becomes.

  FIG. 6 is a flowchart showing another example of the area reorganization procedure in step S2. In step S251, all unit areas are clustered based on their traffic characteristics.

  For example, if you want to focus on the absolute value of traffic volume per call as the traffic characteristics, the first cluster whose traffic volume per call is 100kByte / call or less, the second cluster of 100k-200Byte / call, 200k-300Byte A third cluster of / calls is prepared, and each unit area is classified into one of the clusters.

  Note that the traffic characteristics of interest are not limited to the absolute value of the traffic volume per call, but may be focused on a distribution graph (histogram) of the traffic volume per call. In this case, a cluster with a distribution% ile value of 10-20%, a cluster of 20-30%, a cluster of 30-40%, etc. are prepared, and each unit area is classified into one of the clusters. Further, as the traffic characteristics to be noticed, an absolute value or an increase / decrease rate of the actual number of calls may be adopted.

  However, the amount of traffic per call can be an indicator of how the communication is used, such as a messaging application if the value is small, video playback if the value is large, and Web browsing if the value is medium. Therefore, if each unit area is clustered using the traffic volume per call, the area integration based on the dominant communication service or application used in each unit area becomes possible.

  In step S252, attention is focused on one of the clusters, for example, the first cluster of 100 kByte / call or less. In step S253, attention is paid to one unit area belonging to the cluster of interest. In step S254, only the unit area in which the current unit area of interest and the base station or the same sector are the same are extracted from the cluster of interest and set as integration candidates.

  In step S255, the current unit area of interest and all of the extracted unit areas are integrated into a new unit area. In step S256, it is determined whether all unit areas belonging to the cluster of interest have been noticed. If there is a unit area that has not been noticed, the process returns to step S253, and the above processes are repeated while switching the unit area of interest to another unit area.

  In step S257, it is determined whether all clusters have been noticed. If there is a cluster that has not been noticed, the process returns to step S252, and the above processes are repeated while switching the noticed cluster to another cluster.

  FIG. 7 is a diagram showing an example of unit area integration by the above reorganization procedure. In FIG. 7A, four unit areas are integrated, and a new unit area similar to the unit area before integration is formed. Has been. In FIG. 5B, three unit areas are integrated, and a new unit area having a different shape from the unit area before integration is formed. In FIG. 5C, three unit areas are integrated to form unit areas that are not geographically continuous (separated).

  Note that the traffic characteristics to be noted in step S251 are not limited to the absolute value of the traffic volume per call, but may be focused on a distribution graph (histogram) of traffic volume per call. In this case, a cluster with a distribution% ile value of 10-20%, a cluster of 20-30%, a cluster of 30-40%, etc. are prepared, and each unit area is classified into one of the clusters. Further, as the traffic characteristics to be noticed, an absolute value or an increase / decrease rate of the actual number of calls may be adopted.

  In the above embodiment, it has been described that all unit areas that can be integrated for each cluster are integrated. However, the present invention is not limited to this, and all unit areas that can be integrated for each cluster are described. Calculate the standard deviation of the traffic characteristics after integration, and select only the unit area of the combination that gives the smallest standard deviation, and the standard deviation after integration is less than the standard deviation of each unit area before integration. , They may be integrated.

  Returning to FIG. 2, in step S <b> 3, the traffic prediction unit 3 applies an appropriate prediction method to the actual value of the number of calls C to calculate the predicted number of future calls Cf for each unit area after reorganization. . In step S4, the predicted traffic total amount calculation unit 4 calculates the predicted traffic total amount Vf by multiplying the predicted value Cf of the number of calls C by the statistical value V / C of the traffic amount per call for each unit area.

  At this time, if the number of calls C is an hourly predicted value, the predicted traffic total amount Vf is also calculated for each unit area and every hour. As for the statistical value V / C, an average value, a median value, a predetermined percentage value (% ile, for example, 99% ile) or the like may be used to remove abnormal values and noise components.

  FIG. 8 is a diagram showing an example of a prediction result according to the present embodiment, and future traffic characteristics are predicted for each unit area depending on the traffic characteristics based on the actual performance of the traffic characteristics up to now.

  According to this embodiment, in calculating the future daily predicted value of the total traffic calculated as the total product of the number of calls (C) and the traffic volume per call (V / C), the prediction range is In addition, since each unit region depends on traffic characteristics, not every unit region depending on geography, variation in traffic characteristics and results within the prediction range can be suppressed, and prediction accuracy is improved. In addition, since the number of unit areas is reduced as compared with the case of being geographically dependent, calculation processing associated with prediction is reduced.

  In addition, according to the present embodiment, while predicting the number C of calls with less sudden or temporary fluctuations, which is the effect of differences or changes in communication contents such as contents and services, sudden or temporary fluctuation components and fluctuations are predicted. Since there are many factors, the traffic amount per call that is not suitable for daily prediction is not used for prediction, so that the total daily communication traffic amount in the future can be accurately predicted.

  Furthermore, according to the present embodiment, since the number of calls and the traffic amount per call can be predicted separately, for example, a schedule for increasing the resolution (for example, full HD) of a distribution video is given separately. By reflecting this on changes in traffic volume per call (for example, 1000kB / Call will increase to 1500kB / Call in 6 months, etc.), it will be possible to predict future traffic volume more accurately. Become.

  Furthermore, according to the present embodiment, the geographical dependency and time dependency in predicting the traffic amount can be separated into the number of calls and the traffic amount per call and reflected independently. Even if the communication traffic volume increases on a daily basis, if this is due to an increase in the number of calls and is not related to the traffic volume per call, this time dependency is reflected only in the number of calls. It is possible not to reflect the amount of traffic per call. As a result, the geographical dependency and the time dependency can be more accurately reflected in the predicted value of the total traffic.

  FIG. 9 is a functional block diagram of the second embodiment of the present invention, and the same reference numerals as those described above represent the same or equivalent parts.

  In the first embodiment, only the number of calls C is predicted based on the measurement result of the traffic characteristics, and the future daily life is calculated by multiplying the prediction result Cf by the statistical value of the traffic volume V / C per call. The total amount of communication traffic Vf was predicted.

  On the other hand, in the second embodiment of the present invention, the traffic prediction unit 3 predicts both the future number of calls Cf and the traffic amount Mf per call based on the measurement result of the traffic characteristics, and the predicted total traffic amount. The calculation unit 4 calculates the future predicted traffic total amount Vf (Cf × Mf) for each unit region by multiplying the prediction results Cf and Mf for each unit region and unit time after the reorganization.

  According to this embodiment, in calculating the future daily predicted value of the total traffic calculated as the total of the product of the number of calls (C) and the traffic volume per call (V / C), the contents and services The amount of traffic per call as well as the number of calls C, which has very little sudden or temporary fluctuations regardless of the change in communication content, the trend of being out of fashion, etc. was predicted, so the change in traffic per call In addition, for example, when regularity and selectivity are recognized and high prediction accuracy can be expected, the future daily communication traffic total amount can be predicted more accurately.

  Although the above embodiments do not mention the difference in communication method and the like, if multiple types of communication are mixed for the communication method (3G method and LTE method) and the frequency band (band class), FIG. As shown in Fig. 5, the traffic characteristics may be further analyzed and predicted for each communication method and frequency band.

  FIG. 11 is a flowchart showing a flow of a prediction process for calculating the predicted traffic volume Vf for each unit area by applying the first embodiment for each communication method.

  In step S21, the number of calls C_3g of 3G lines and the traffic volume V / C_3g per call are measured and calculated for each unit area, and the number of calls C_lte of LTE lines and the traffic volume V / C_lte per call are measured and calculated. Calculated.

  In step S22, the communication system ratios R_3g and R_lte of the 3G line and the LTE line for each unit area are calculated as, for example, the ratio of the total number of calls of each line within a predetermined period.

  In step S23, the total (total number of calls) C_sum of the actual number of calls for each 3G line and LTE line for each unit area is calculated. In step S24, the predicted number of calls Cf is calculated by applying the actual value of the total number of calls C_sum to, for example, a time series prediction method.

  In step S25, the total traffic volume per call is calculated by multiplying the average traffic volume per call {(V / C_3g + V / C_lte) / 2} and the predicted number of calls Cf. The At this time, the average value of the traffic volume per call may be a weighted average according to the communication method ratios R_3g and R_lte.

  In step S26, the predicted total traffic amount Vf_3g of the 3G line and the predicted traffic volume Vf_lte of the LTE line are calculated by multiplying the sum of the predicted traffic totals by the communication method ratios R_3g and R_lte.

  DESCRIPTION OF SYMBOLS 1 ... Communication log DB with position information, 2 ... Traffic characteristic measurement part, 3 ... Traffic prediction part, 4 ... Predicted traffic total amount calculation part, 5 ... Communication log collection part with position information, 21 ... Call number measurement part, 22 ... Traffic Quantity measuring part, 23 ... Unit area reorganization part

Claims (16)

In a communication traffic prediction device that predicts the amount of communication traffic,
Traffic characteristic measuring means for measuring traffic characteristics including the number of calls and the traffic volume per call for each unit area obtained by dividing the monitoring area according to geography,
Unit area reorganization means for integrating a part of unit areas divided according to geography based on traffic characteristics and reorganizing the unit areas depending on traffic characteristics;
Traffic prediction means for predicting at least the number of future calls and the traffic volume per call for each unit area after the reorganization,
A communication traffic prediction apparatus, comprising: predicted traffic total amount calculation means for calculating a predicted traffic total amount for each unit area after the reorganization based on a prediction result of the number of calls.   The communication traffic prediction apparatus according to claim 1, wherein the unit area reorganization unit integrates a plurality of unit areas having similar traffic characteristics into one unit area.   The unit area reorganization means expands each unit area into a similar shape in multiple stages, and unit areas within the extended range in which the standard deviation related to a predetermined traffic characteristic is reduced and minimized from the unit area into one unit area. The communication traffic prediction apparatus according to claim 1, wherein the communication traffic prediction apparatus is integrated.   3. The communication traffic according to claim 1, wherein the unit area reorganization unit clusters all unit areas based on a predetermined traffic characteristic, and integrates the unit areas of the same cluster into one unit area. Prediction device.   5. The unit area reorganization unit integrates, for each cluster, a unit area of a combination in which a standard deviation after integration is smaller than a standard deviation of each unit area before integration and is minimized. The communication traffic prediction apparatus according to 1.   6. The communication traffic prediction apparatus according to claim 1, wherein the unit area reorganization means reorganizes the unit area every predetermined time period.   7. The traffic characteristic according to claim 1, wherein the traffic characteristic is at least one of the number of calls, the traffic amount per call, the rate of increase / decrease in the number of calls, and the rate of increase / decrease in the traffic amount per call. The communication traffic prediction apparatus according to 1.   The predicted traffic total amount calculating means calculates a predicted future traffic total amount for each unit area based on a prediction result of the number of calls and a statistical value of the traffic amount per call. The communication traffic prediction apparatus according to any one of 1 to 7.   9. The predicted total traffic calculation unit calculates a predicted total future traffic based on prediction results of the number of calls and the traffic volume per call for each unit area. Communication traffic prediction device.   The communication traffic prediction apparatus according to claim 1, wherein the predicted traffic total amount calculation unit predicts the predicted traffic total amount for each communication method.   11. The predicted traffic total amount calculation unit predicts the predicted traffic total amount as a sum value of all communication methods, and then divides the sum value based on a ratio of each communication method. Communication traffic prediction device. In a communication traffic prediction program for predicting the amount of communication traffic by a computer,
A procedure for measuring traffic characteristics including the number of calls and the traffic volume per call for each unit area obtained by dividing the monitoring area according to geography.
A procedure for integrating a part of unit areas divided according to geography based on their traffic characteristics and reorganizing them into unit areas depending on traffic characteristics,
For each unit area after the reorganization, a procedure for predicting at least the number of calls in the future and the traffic volume per call;
A communication traffic prediction program for causing a computer to execute a procedure for calculating a predicted traffic total amount for each unit area after the reorganization based on a prediction result of the number of calls.   13. The communication traffic prediction program according to claim 12, wherein, in the procedure of reorganizing the unit areas, a plurality of unit areas having similar traffic characteristics are integrated into one unit area.   In the procedure for reorganizing the unit regions, each unit region is expanded in a multi-stage to a similar shape, and unit regions within the expanded range in which the standard deviation regarding a predetermined traffic characteristic is reduced and minimized from the unit region are combined into one unit. 14. The communication traffic prediction program according to claim 12, wherein the communication traffic prediction program is integrated into a region.   The procedure for reorganizing the unit areas includes clustering all the unit areas based on predetermined traffic characteristics, and integrating the unit areas of the same cluster into one unit area. Communication traffic prediction program. In a communication traffic prediction method for predicting the amount of communication traffic by a computer,
A procedure for measuring traffic characteristics including the number of calls and the traffic volume per call for each unit area obtained by dividing the monitoring area according to geography.
A procedure for integrating a part of unit areas divided according to geography based on their traffic characteristics and reorganizing them into unit areas depending on traffic characteristics,
For each unit area after the reorganization, a procedure for predicting at least the number of calls in the future and the traffic volume per call;
A communication traffic prediction method including: calculating a predicted traffic total amount for each unit area after the reorganization based on a prediction result of the number of calls.