Patent Publication Number: US-11398113-B2

Title: Toll control system, toll control apparatus, toll control method, and computer-readable recording medium

Description:
This application is a National Stage Entry of PCT/JP2018/017677 filed on May 7, 2018, the contents of all of which are incorporated herein by reference, in their entirety. 
     TECHNICAL FIELD 
     The present invention relates to a toll control system for controlling road traffic volume and road toll, a toll control apparatus that are used in the system, a toll control method, and to a computer-readable recording medium that includes a program recorded thereon for realizing the system, apparatus and method. 
     BACKGROUND ART 
     In recent years, toll control systems that vary the road toll on toll roads for the purpose of adjusting the traffic volume according to traffic conditions have been proposed in order to alleviate traffic congestion in urban areas (e.g., refer to Patent Document 1). Various countries are looking at introducing such a toll control system, and some countries have already introduced such a system. 
     Specifically, Patent Document 1 discloses a toll control system that sets the road toll using past data. The toll control system disclosed in Patent Document 1, first, estimates the traffic demand on a target day for every combination of an origin and a destination from past traffic data, and predicts traffic volume from the traffic volume distribution for each route and the estimated traffic demand. The toll control system disclosed in Patent Document 1 then sets a target value for distribution of the traffic volume on each route based on the predicted traffic volume, and corrects the initial road toll such that the set target value is achieved. 
     According to the toll control system disclosed in Patent Document 1, the traffic volume on each route can be controlled to achieve an appropriately volume based on past data, enabling traffic congestion in urban areas to be alleviated, and, thus, also conceivably allowing for improvement in the surrounding environment. 
     LIST OF RELATED ART DOCUMENTS 
     Patent Document 
     
         
         Patent Document 1: Japanese Patent Laid-Open Publication No. 2008-9639 
       
    
     SUMMARY OF INVENTION 
     Problems to be Solved by the Invention 
     Incidentally, given the many costs that are incurred in maintaining and managing roads, it is important to secure a financial return particularly with toll roads. In setting the road toll on toll roads, it is thus necessary to take into consideration not only traffic volume but also the financial return. 
     However, with the toll control system disclosed in Patent Document 1, only control aimed at optimizing the traffic volume distribution on each route is performed, and control aimed at securing a financial return from toll roads is not performed, thus possibly making it difficult to secure a financial return. 
     Furthermore, in the toll control system disclosed in Patent Document 1, traffic volume is predicted assuming that the road environment changes in a regular pattern, and thus it is difficult to respond to dynamic environmental changes on the road, such as sudden traffic congestion caused by an accident, for example. 
     An example object of the invention is to provide a toll control system, a toll control apparatus, a toll control method and a computer-readable recording medium that solves the above problems and make it possible to respond to dynamic environmental changes on the road and to secure a financial return through road tolls. 
     Means for Solving the Problems 
     A toll control apparatus according to an example aspect of the invention is for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road, the apparatus including: 
     a traffic volume prediction means that predicts a future overall traffic volume on the first road and the second road; 
     a toll control means that outputs, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     a toll optimization means that sets the road toll on the second road, 
     the toll optimization means setting one or more road toll candidates, selecting a road toll candidate for which a predicted traveling speed obtained by inputting the road toll candidate to the toll control means is greater than or equal to a threshold value, and setting, as the road toll on the second road, the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output by the toll control means, among the selected road toll candidates. 
     A toll control system according to an example aspect of the invention is for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road, the system including: 
     a toll control apparatus that predicts a future traffic volume on the second road; 
     a toll display device that displays, on a screen, a set road toll on the second road; and 
     a traffic sensor that outputs sensor data for detecting a number and a speed of vehicles traveling on the second road, 
     the toll control apparatus including: 
     a traffic volume prediction means that predicts a future overall traffic volume on the first road and the second road; 
     a toll control means that outputs, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     a toll optimization means that sets the road toll on the second road, and 
     the toll optimization means setting one or more road toll candidates, selecting a road toll candidate for which a predicted traveling speed obtained by inputting the road toll candidate to the toll control means is greater than or equal to a threshold value, and setting, as the road toll on the second road, the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output by the toll control means, among the selected road toll candidates. 
     Also, a toll control method according to an example aspect of the invention is for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road, the method including: 
     (a) a step of predicting a future overall traffic volume on the first road and the second road; 
     (b) a step of outputting, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     (c) a step of setting the road toll on the second road, 
     in the step (c), one or more road toll candidates being set, a road toll candidate for which a predicted traveling speed obtained by executing the step (b) using the road toll candidate as an input of the step (b) is greater than or equal to a threshold value being selected, and the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output in the executed step (b) being set as the road toll on the second road, among the selected road toll candidates. 
     Furthermore, a computer-readable recording medium according to an example aspect of the invention includes a program recorded thereon for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road with a computer, the program including instructions that cause a computer to carry out: 
     (a) a step of predicting a future overall traffic volume on the first road and the second road; 
     (b) a step of outputting, with a predicted traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     (c) a step of setting the road toll on the second road, 
     in the step (c), one or more road toll candidates being set, a road toll candidate for which a predicted traveling speed obtained by executing the step (b) using the road toll candidate as an input of the step (b) is greater than or equal to a threshold value being selected, and the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output in the executed step (b) being set as the road toll on the second road, among the selected road toll candidates. 
     Advantageous Effects of the Invention 
     As described above, according to the invention, it becomes possible to respond to dynamic environmental changes on the road and to secure a financial return through road tolls. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a configuration diagram showing a schematic configuration of a toll control system and a toll control apparatus in a first example embodiment of the invention. 
         FIG. 2  is a configuration diagram more specifically showing the configuration of the toll control apparatus in the first example embodiment of the invention. 
         FIG. 3  is a block diagram more specifically showing the configuration of a traffic volume prediction unit shown in  FIG. 2 . 
         FIG. 4  is a block diagram more specifically showing the configuration of a toll control unit shown in  FIG. 3 . 
         FIG. 5  is a diagram showing an example of an upper limit and a lower limit of a road toll that are set in the example embodiment of the invention. 
         FIG. 6  is a flow diagram showing operations of the toll control apparatus in the first example embodiment of the invention. 
         FIG. 7  shows an example of the result of executing steps A 1  to A 11  shown in  FIG. 6  every 10 minutes. 
         FIG. 8  is a diagram for describing processing that is performed by a toll optimization unit of a toll control apparatus in a second example embodiment of the invention. 
         FIG. 9  is a flow diagram showing operations of the toll control apparatus in the second example embodiment of the invention. 
         FIG. 10  is a diagram showing a specific example of the contents of processing performed in step B 8  shown in  FIG. 9 . 
         FIG. 11  is a block diagram showing an example of a computer that realizes the toll control apparatus in the first and second example embodiments of the invention. 
     
    
    
     EXAMPLE EMBODIMENTS 
     First Exemplary Embodiment 
     Hereinafter, a toll control system, a toll control apparatus, a toll control method and a program in a first example embodiment of the invention will be described with reference to  FIGS. 1 to 6 . 
     [System Configuration] 
     Initially, schematic configurations of the toll control system and the toll control apparatus in this first example embodiment will be described using  FIGS. 1 and 2 .  FIG. 1  is a configuration diagram showing schematic configurations of the toll control system and the toll control apparatus in the first example embodiment of the invention. 
     A toll control system  400  in this first example embodiment shown in  FIG. 1  is a system for a toll control apparatus  100  to control the road toll on a second road  402 , in the case where the second road  402  which is a toll road that bypasses a first road  401  has been established. Note that, in the following, the first road  401  will be described as a “public road”  401  and the second road  402  will be described as a “toll road”, for convenience of description. 
     As shown in  FIG. 1 , the toll control system  400  is provided with the toll control apparatus  100 , a toll display device  200 , and a traffic sensor  300 . The toll control apparatus  100  is an apparatus for executing the abovementioned road toll control, and sets the road toll on the second road. The configuration of the toll control apparatus  100  is shown in  FIG. 2 . The toll display device  200  displays the road toll set for the second road on a screen. The traffic sensor  300  outputs sensor data for detecting the number and speed of vehicles  403  traveling on the public road  401  and the toll road  402 . 
     Also, as shown in  FIG. 2 , the toll control apparatus  100  is provided with a traffic volume prediction unit  10 , a toll control unit  20 , and a toll optimization unit  30  that sets the road toll on the toll road  402 . Also, the traffic volume prediction unit  10  functions as traffic volume prediction means, the toll control unit  20  functions as toll control means, and the toll optimization unit  30  functions as toll optimization means. 
     The traffic volume prediction unit  10  predicts a future overall traffic volume on the public road  401  and the toll road  402 , based on the number and speed of the vehicles  403  that are detected from sensor data output by the traffic sensor  300 . The toll control unit  20  outputs, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for the case where the toll on the second road is set to the predetermined road toll. 
     The toll optimization unit  30 , first, sets one or more road toll candidates, and selects a road toll candidate for which the predicted traveling speed obtained by inputting the set road toll candidate to the toll control unit  20  is greater than or equal to a threshold value. Next, the toll optimization unit  30  sets, as the road toll on the second road, the road toll candidate that maximizes the toll revenue for the second road as calculated using the future traffic volume output by the toll control unit  20 , among the selected road toll candidates. 
     In this way, in this first example embodiment, prediction of overall traffic volume (i.e., traffic demand) that changes from one moment to the next is performed. Furthermore, in this first example embodiment, a toll control unit  20  is used that, upon receiving input of the predicted overall traffic volume and the road toll candidate, outputs a future traffic volume and a predicted traveling speed according to the input values. Thus, according to this first example embodiment, a road toll that raises the toll revenue can be set, while maintaining the traveling speed at or above a threshold value, making it possible to respond to dynamic environmental changes on the road and to secure a financial return through road tolls. 
     Also, in this first example embodiment, the toll display device  200  shown in  FIG. 1  is installed on the public road  401  near an entrance to the toll road  402 . The drivers of the vehicles  403  determine whether to travel on the second road after checking the road toll that is displayed on the toll display device  200 . Also, the toll road  402  bypasses the public road  401 , but does not necessarily run parallel to the public road  401 . Furthermore, in this first example embodiment, the first road  401  may also be a toll road rather than a public road. 
     In addition, in this first example embodiment, the traffic sensor  300  is installed on the public road  401  and the toll road  402 . Also, the traffic sensor  300  need only be a sensor capable of detecting the number and speed of the vehicles  403 , specific examples of which include a camera and a depth sensor. 
     Next, the configuration of the toll control apparatus  100  in this first example embodiment will be more specifically described, using  FIGS. 2 to 6 , in addition to  FIG. 1 .  FIG. 2  is a configuration diagram more specifically showing the configuration of the toll control apparatus in the first example embodiment of the invention.  FIG. 3  is a block diagram more specifically showing the configuration of the traffic volume prediction unit shown in  FIG. 2 .  FIG. 4  is a block diagram more specifically showing the configuration of the toll control unit shown in  FIG. 3 . 
     First, as shown in  FIG. 2 , in this first example embodiment, sections are set on the public road  401  and the toll road  402  by the entrances and the exits. Thus, as will be described later, processing by the traffic volume prediction unit  10 , the toll control unit  20  and the toll optimization unit  30  is performed for every section. Also, in the example of  FIG. 2 , three sections  1  to  3  are illustrated, but the number of the section is not limited in this first example embodiment. Furthermore, section  1  on the public road  401  and section  1  in the toll road  402  correspond to each other. This similarly applies to section  2  and section  3 . 
     Also, in this example embodiment, the toll control apparatus  100  acquires sensor data output by the traffic sensor  300 , and detects the number and speed of the vehicles  403  at the current time on the public road  401  and the toll road  402  using the acquired sensor data. Furthermore, the toll control apparatus  100  calculates the traffic volume and traffic density on the public road  401  and the toll road  402  at the current time using the detected number and speed. 
     The traffic volume prediction unit  10 , in this first example embodiment, applies the calculated traffic volume at the current time to a predictive model, and predicts a future overall traffic volume tflow Si  for every section S i  set on the public road  401  and the toll road  402 , as shown in  FIG. 2 . 
     Specifically, the toll control apparatus  100  is provided with traffic volume prediction units  10 - 1  to  10 - 3  for every section. Furthermore, as shown in  FIG. 3 , the traffic volume prediction units  10 - 1  to  10 - 3  are each provided with traffic volume predictors  11 - 1  to  11 - n  (n: arbitrary natural number) for every elapsed time period (+T1 min., +T2 min., . . . , +Tn min.) from a reference time. 
     Note that, in the example in  FIG. 3 , the elapsed time period is set at 10 minute intervals such as +10 minutes, +20 minutes, and so on. Also, the following description will refer to “traffic volume prediction unit  10 ”, in the case where a specific traffic volume prediction unit is not indicated. Similarly, description will refer to “traffic volume predictor  11 ”, in the case where a specific traffic volume predictor is not indicated. 
     In the traffic volume prediction units  10 - 1  to  10 - 3 , the traffic volume predictors  11 - 1  to  11 - n  each predict the overall traffic volume for the corresponding elapsed time period in the corresponding section, using the abovementioned predictive model. 
     Also, in this first example embodiment, the predictive model is built by machine learning that utilizes data specifying past traffic conditions, past meteorological data, and information specifying the day of week and time at which this data was acquired. Past traffic volume, past traffic density and past traveling speed are given as examples of data specifying the past traffic conditions. Climate, rainfall, humidity and visibility conditions are given as examples of past meteorological data. Also, deep learning is given as an example of a machine learning technique. Furthermore, machine learning techniques such as supervised learning can also be utilized as a machine learning technique. Support vector machines, naive Bayes classifiers and DNN (deep neural network) classifiers are given as examples of supervised learning techniques. 
     Furthermore, a linear regression model, an autoregressive model and an autoregressive moving average model are given as examples of predictive models. Specifically, a linear regression model defined in the following equation 1 is given as an example of a predictive model. 
     
       
         
           
             
               
                 
                   
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     In the above equation 1, t is the current time and t+n is the prediction target time (n=10, 20, . . . , Tn). tflow t+n  is the predicted traffic volume at the prediction target time. X t,i  represents a factor (feature amount) affecting the traffic volume on a prediction target section S i . As described above, information such as the number of vehicles  403  at the current time, speed, day of week and weather is utilized as X t, i . Note that although only information on the current time is used here for simplification, past information prior to the current time and highly reliable future information on the weather and the like may also be used. Also, at is a weight parameter indicating the relationship between tflow t+n , and X t, i , and b is an intercept indicating the contribution of factors not included in learning data. The values of at and b are determined by the abovementioned machine learning. 
     Note that, in  FIG. 2 , in order to distinguish the traffic volume for every section predicted by the respective traffic volume prediction units  10 , the overall traffic volume predicted by the traffic volume prediction unit  10 - 1  is denoted as tflow S1 , the overall traffic volume predicted by the traffic volume prediction unit  10 - 2  is denoted as tflow S2 , and the overall traffic volume predicted by the traffic volume prediction unit  10 - 3  is denoted as tflow S3 . 
     The toll control unit  20 , in this example embodiment, as shown in  FIG. 2 , outputs a future traffic volume q si  and a predicted traveling speed v si  of a specific section S i , with the overall traffic volume tflow Si  predicted for the specific section S i  and a predetermined road toll as inputs. The toll control unit  20  is also able to output a traffic density k Si  of the specific section S i . 
     Specifically, the toll control apparatus  100  is provided with toll control units  20 - 1  to  20 - 3  for every section. Furthermore, as shown in  FIG. 4 , the toll control units  20 - 1  to  20 - 3  are each provided with toll controllers  21 - 1  to  21 - n , for every elapsed time period (+10 min., +20 min., . . . , +Tn min.) from a reference time. Also, the following description will refer to “toll control unit  20 ”, in the case where a specific toll control unit is not indicated. Similarly, the following description will refer to “toll controller  21 ”, in the case where a specific toll controller is not indicated. 
     In the toll control units  20 - 1  to  20 - 3 , the toll controllers  21 - 1  to  21 - n  each input the overall traffic volume tflow Si  predicted for the corresponding section and elapsed time period and a predetermined road toll (road toll candidate) p t  into a state model. The toll controllers  21 - 1  to  21 - n  each thereby acquires, from the state model, a future traffic volume q t , a predicted traveling speed v t  and a traffic density k t  of the corresponding section and elapsed time period for the case where the toll of the toll road  402  is set to the predetermined road toll p t , and outputs these values to the toll optimization unit  30 . 
     Also, the state model is a model defining the relationship between overall traffic volume, road toll and predicted traveling speed on the public road  401  and the toll road  402 . More specifically, the state model defines the relationship between overall traffic volume, road toll and predicted traveling speed for every section. The model defined in the following equations 2 to 5 is given as a specific example of the state model. 
     In the following equations 2 to 5, a state model that is used by the toll control unit  20  is represented. Here, y t  denotes an output vector (or matrix) and u t  denotes an input vector (or matrix). x t  is a parameter matrix indicating a space state model. A is a parameter matrix indicating the relationship between space state models of time t and time (t+1), B is a parameter matrix indicating the relationship between the input and the space state model of time (t+1), and C is a parameter matrix indicating the relationship between the output y t  and the space state model at time t. The values of A, B, C and x t  are determined by machine learning, based on past data. Specifically, these values are determined by a least squares method or the like that uses past traffic volume, past road toll, past traveling speed, past traffic density and the like as learning data, for example. 
     
       
         
           
             
               
                 
                   
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     The toll optimization unit  30 , in this first example embodiment, first, sets a road toll candidate, for every section, and then inputs the set road toll candidate to the toll control unit  20 , and, in the case where the output predicted traveling speed is greater than or equal to a threshold value, selects this road toll candidate. 
     In this first example embodiment, the toll optimization unit  30  is also able to select a road toll candidate for which the predicted traveling speed output by the toll control unit  20  is greater than or equal to a threshold value (minimum guaranteed speed), and that satisfies upper and lower limits that are determined by the traffic density output by the toll control unit  20 . 
     In this first example embodiment, as shown in  FIG. 5 , the upper and lower limits are set based on a reference toll.  FIG. 5  is a diagram showing an example of the upper and lower limits of the road toll that are set in the example embodiment of the invention. In the example in  FIG. 5 , the reference toll is set to increase monotonically when the traffic density increases. This is in order to avoid the traffic volume increasing too much, by reducing the number of vehicles entering to the toll road  402  as the traffic density increases. Also, the upper and lower limits of the road toll are preset on the basis of this reference toll. In the example in  FIG. 5 , the upper and lower limits are also set to increase monotonically when traffic density increases. 
     Specifically, the toll optimization unit  30 , upon the road toll candidate p t  being set for every section, inputs the predicted overall traffic volume tflow Si  and the set road toll candidate p t  to the respective toll controllers  21 - 1  to  21 - n . Each toll controller thereby outputs a future traffic volume q t , a predicted traveling speed v t  and a traffic density k t  of the toll road  402 . 
     The toll optimization unit  30  selects the input road toll candidate p t , in the case where the predicted traveling speed v t  output by each toll controllers is greater than or equal to the threshold value, and, furthermore, the input road toll candidate p t  is in a range of the upper and lower limits specified from the traffic density k t . Also, setting and selection of the road toll candidate p t  may be performed once, or may be performed a plurality of times within the range of the upper and lower limits. 
     Next, the toll optimization unit  30 , upon the selection of road toll candidates ending, calculates, for every section, the toll revenue for that section, by multiplying one selected road toll candidate by the future traffic volume output by the toll control unit  20 . The toll optimization unit  30  then specifies, for every section, the road toll candidate that maximizes the toll revenue, and takes the specified road toll candidate as the road toll for that section. 
     Specifically, the toll optimization unit  30  calculates the toll revenue, for every section, using the following equation 6, and specifies the road toll candidate that maximizes the toll revenue. In the following equation 6, Total Revenue on the left side shows the overall toll revenue for the toll road  402 . Also, on the right side of equation 6, the first term indicates the toll revenue for the section  1 , the second term indicates the toll revenue for the section  2 , and the third term indicates the toll revenue for the section  3 . H indicates the time period from the current time to the last prediction target time.
 
Total Revenue=argmax p     S2     ∈P     S2   Σ t   t+H   p   S1 ( t ) q   S1 ( t )+argmax p     S2     ∈P     S2   Σ t   t+H   p   S2 ( t ) q   S2 ( t )+argmax p     S2     ∈P     S2   Σ t   t+H   p   S3 ( t ) q   S3 ( t )  [Equation 6]
 
     Note that, in this first example embodiment, as shown in  FIG. 5 , a restriction is placed on the road toll by setting upper and lower limits with respect to a reference toll, and the road toll is stably controlled on the basis of that restriction on the road toll, although this first example embodiment is not limited to this mode. This first example embodiment may be a mode in which the road toll is determined by solving equation 6, rather than placing a restriction of the upper and lower limits. 
     [System Operations] 
     Next, operations of the toll control system  400  and the toll control apparatus  100  in this first example embodiment will be described using  FIG. 6 .  FIG. 6  is a flow diagram showing operations of the toll control apparatus in the first example embodiment of the invention. In the following description,  FIGS. 1 to 5  will be referred to as appropriate. Also, in this first example embodiment, the toll control method is implemented by operating the toll control apparatus. Therefore, description of the toll control method in this first example embodiment will be replaced with the following description of the operations of the toll control apparatus  100 . 
     As shown in  FIG. 6 , initially, the toll optimization unit  30  selects one section of the public road  401  and the toll road  402  (step A 1 ). Also, the toll optimization unit  30  instructs the traffic volume prediction unit  10  corresponding to the selected section to start processing. 
     Next, the traffic volume prediction unit  10  instructed to start processing uses the traffic volume predictors  11 - 1  to  11 - n  to predict the overall traffic volume tflow t+n , using the predictive model, for every elapsed time period (+T1 min., +T2 min., . . . , +Tn min.) from a reference time (step A 2 ). 
     Next, the toll optimization unit  30  sets an initial value p 0  of the road toll candidate for the section selected in step A 1  (step A 3 ). Specifically, the toll optimization unit  30  collates the traffic density calculated from the sensor data output by the traffic sensor  300  with the curve indicating the lower limit shown in  FIG. 5 , and sets the value of the collated lower limit as the initial value. 
     Next, the toll optimization unit  30  inputs the overall traffic volume tflow t+n  for every elapsed time period predicted in step A 2  and the road toll candidate p t  to the respective toll controllers  21 - 1  to  21 - n , and causes the toll controllers to predict the future traffic volume q t , the predicted traveling speed v t , and the traffic density k t  (step A 4 ). 
     Next, the toll optimization unit  30  determines whether all the predicted traveling speeds v t  predicted in step A 4  are greater than or equal to a threshold value (step A 5 ). If the determination of step A 5  indicates that all the predicted traveling speeds v t  are not greater than or equal to the threshold value, the toll optimization unit  30  raises the road toll candidate p t  (step A 11 ), and executes step A 4  again using the raised road toll candidate p t . 
     On the other hand, if the determination of step A 5  indicates that all the predicted traveling speeds v t  are greater than or equal to the threshold value, the toll optimization unit  30  derives an upper limit for every traffic density k t  predicted in step A 4 , and determines whether the road toll candidate p t  exceeds any of the upper limits (step A 6 ). 
     If the determination of step A 6  indicates that the road toll candidate p t  exceeds none of the upper limits, the toll optimization unit  30  selects the set road toll candidate p t  (step A 12 ). The toll optimization unit  30  then, furthermore, executes step A 11 , and thereafter executes step A 4  again. 
     On the other hand, if the determination of step A 6  indicates that the road toll candidate p t  exceeds any one of the upper limits, the toll optimization unit  30  specifies the road toll candidate that maximizes the toll revenue in the section selected in step A 1 , among the road toll candidates p t  selected in step A 12 . The toll optimization unit  30  then determines the specified road toll candidate as the road toll for that section (step A 7 ). 
     Next, the toll optimization unit  30  determines whether processing has ended for all the sections (step A 8 ). If the determination of step A 8  indicates that processing for all the sections has not ended, the toll optimization unit  30  executes step A 1  again. 
     On the other hand, if the determination of step A 8  indicates that processing for all the sections has ended, the toll optimization unit  30  calculates the road toll in the case of straddling a plurality of sections, and, if the calculated road toll exceeds an upper limit, corrects the road toll (step A 9 ). For example, assume that, in the case where the road toll is set to a maximum of $20 in the case of straddling a plurality of sections, the road toll for section  1  is $7 and the road toll for section  2  is $15. In this case, the road toll in the case of utilizing section  1  and section  2  is originally $22, but the road toll in this case is reduced to $20 as a result of the above step A 9  (refer to  FIG. 10  described later). 
     Thereafter, the toll optimization unit  30  displays, on the toll display device  200 , the road toll finally determined after the end of step A 9  (step A 10 ). 
     Also, although processing in the processing in the toll control apparatus  100  ends with the execution of step A 10 , step A 1  is executed again after a set time period (e.g., 10 min.) has elapsed. The road toll displayed on the toll display device  200  will thereby be updated as required. 
     Here, a specific example of the toll control by the toll control system  400  in this first example embodiment will be described using  FIG. 7 .  FIG. 7  shows a specific example of the result in the case where steps A 1  to A 12  shown in  FIG. 6  are executed every 10 minutes. 
     As shown in  FIG. 7 , the road toll changes over time. Also, in the example in  FIG. 7 , the reference toll is included for reference purposes. Also, with regard to traffic volume, speed and density, the values inside the parentheses are the values predicted by the toll control unit  20  from initial value of the road toll candidate, and the values outside the parentheses are values obtained after processing by the toll optimization unit  30 . 
     Also, in the example in  FIG. 7 , an increase in traffic volume is achieved by reducing the toll after 10 minutes, and the financial return increases. Also, although the traffic volume decreases as a result of increasing the toll after 20 minutes, the financial return increases. Furthermore, the toll is increased after 30 minutes, in order to maintain the minimum guaranteed speed. 
     Effects of First Example Embodiment 
     As described above, according to this first example embodiment, a road toll that maximizes the toll revenue can be set, while maintaining the traveling speed at or above a threshold value, according to road conditions that change from one moment to the next. In other words, according to this first example embodiment, it becomes possible to respond to dynamic environmental changes on the road and to secure a financial return through road tolls. 
     [Program] 
     A program in this first example embodiment need only be a program that causes a computer to execute steps A 1  to A 12  shown in  FIG. 6 . The toll control apparatus  100  and the toll control method in this first example embodiment can be realized by this program being installed on a computer and executed. In this case, a processor of the computer performs processing while functioning as the traffic volume prediction unit  10 , the toll control unit  20 , and the toll optimization unit  30 . 
     Also, the program in this example embodiment may be executed by a computer system built from a plurality of computers. In this case, for example, the computers may each function as one of the traffic volume prediction unit  10 , the toll control unit  20 , and the toll optimization unit  30 . 
     Second Example Embodiment 
     Next, a toll control apparatus, a toll control method and a program in a second example embodiment of the invention will be described, with reference to  FIGS. 8 to 10 . 
     [System Configuration] 
     First, the toll control apparatus in this second example embodiment is constituted similarly to the toll control apparatus  100  in the first example embodiment shown in  FIGS. 1 to 5 . Therefore, in the following description,  FIGS. 1 to 5  will be referred to as appropriate. In this second example embodiment, however, the processing in the toll optimization unit  30  differs from the first example embodiment. Hereinafter, description will be given focusing on the differences from the first example embodiment. 
     In this second example embodiment, the toll optimization unit  30 , first, calculates, for every combination of an origin and a destination obtained using the origin and the destination of the respective sections, a weight using the traffic volume on the toll road  402  in the section corresponding to the combination. 
       FIG. 8  is a diagram for describing processing performed by the toll optimization unit of the toll control apparatus in the second example embodiment of the invention. In the example in  FIG. 8 , first, OD 12 , OD 13 , OD 22  and OD 23  are set from the respective origins and destinations of sections  1  to  3 , as combinations of an origin and a destination (hereinafter, “OD pairs”). 
     First, the traffic volumes on the toll road  402  for the sections corresponding to the OD pairs (hereinafter, “traffic volumes of the OD pairs”) are totaled from records of entries and exits by the vehicles  403  at the entrances and exits, by a toll collection system (not shown in  FIGS. 1 to 8 ) of the toll road  402 , for example. The toll optimization unit  30  calculates a weight OD w   ij  for each OD pair, using the totaled traffic volumes of the OD pairs. For example, if the number of cars that enter the toll road from the entrance of the section  1  and leave from the exit of the section  3  in a given time slot is 50, OD 13  will be 50. Similarly, assuming that OD 12  is OD 22  is 30 and OD 23  is 40, the weights of the respective OD pairs in this time slot will respectively be 1, 5, 3 and 4 for OD w   12 , OD w   13 , OD w   22  and OD w   23 , where the weight OD w   12  of OD w   12  is 1. 
     The toll optimization unit  30  then calculates the toll revenue for the entirety of the toll road  402 , using each calculated weight and the road toll candidate selected for every section, and specifies the road toll candidate that maximizes the toll revenue. Specifically, the toll optimization unit  30  calculates the toll revenue for the entirety of the toll road  402 , using the following equation 7. 
     
       
         
           
             
               
                 
                   
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     [System Operations] 
     Next, operations of the toll control system and the toll control apparatus in this second example embodiment will be described using  FIG. 9 .  FIG. 9  is a flow diagram showing operations of the toll control apparatus in the second example embodiment of the invention. In the following description,  FIGS. 1 to 5  will be referred to as appropriate. Also, in this second example embodiment, the toll control method is implemented by operating the toll control apparatus. Therefore, description of the toll control method in this second example embodiment will be replaced with the following description of the operations of the toll control apparatus. 
     As shown in  FIG. 9 , the toll optimization unit  30 , first, selects one section of the toll road  402  (step B 1 ). Step B 1  is similar to step A 1  shown in  FIG. 6 . 
     Next, the traffic volume prediction unit  10  instructed to start processing uses the traffic volume predictors  11 - 1  to  11 - n  to predict the overall traffic volume tflow t+n  using the predictive model, for every elapsed time period (+T1 min., +T2 min., . . . , +Tn min.) from a reference time (step B 2 ). Step B 2  is similar to step A 2  shown in  FIG. 6 . 
     Next, the toll optimization unit  30  sets the initial value p 0  of the road toll candidate for the section selected in step B 1  (step B 3 ). Step B 3  is similar to step A 3  shown in  FIG. 6 . 
     Next, the toll optimization unit  30  inputs the overall traffic volume tflow t+n  for every elapsed time period predicted in step B 2  and the road toll candidate p t  to the respective toll controllers  21 - 1  to  21 - n , and causes the toll controllers to predict the future traffic volume q t , the predicted traveling speed v t , and the traffic density k t  (step B 4 ). Step B 4  is similar to step A 4  shown in  FIG. 6 . 
     Next, the toll optimization unit  30  determines whether all the predicted traveling speeds v t  predicted in step B 4  are greater than or equal to a threshold value (step B 5 ). Step B 5  is similar to step A 5  shown in  FIG. 6 . 
     If the determination of step B 5  indicates that all the predicted traveling speeds v t  are not greater than or equal to the threshold value, the toll optimization unit  30  raises the road toll candidate p t  (step B 12 ), and executes step B 4  again using the raised road toll candidate p t . Step B 12  is similar to step A 10  shown in  FIG. 6 . 
     On the other hand, if the determination of step B 5  indicates that all predicted traveling speed v t  are greater than or equal to the threshold value, the toll optimization unit  30  derives an upper limit for every traffic density k t  predicted in step B 4 , and determines whether the road toll candidate p t  exceeds any of the upper limits (step B 6 ). Step B 6  is similar to step A 6  shown in  FIG. 6 . 
     If the determination of step B 6  indicates that the road toll candidate p t  exceeds none of the upper limits, the toll optimization unit  30  selects the set road toll candidate p t  (step B 13 ). Step B 13  is similar to step A 11  shown in  FIG. 6 . The toll optimization unit  30  then, furthermore, executes step B 12 , and thereafter executes step B 4  again. 
     On the other hand, if the determination of step B 6  indicates that the road toll candidate p t  exceeds any one of the upper limits, the toll optimization unit  30  determines whether processing has ended for all the sections (step B 7 ). Step B 7  is similar to step A 8  shown in  FIG. 6 . 
     If the determination of step B 7  indicates that processing for all the sections has not ended, the toll optimization unit  30  executes step B 1  again. 
     On the other hand, if the determination of step B 7  indicates that processing for all the sections has ended, the toll optimization unit  30  specifies an OD pair straddling a plurality of sections, and calculates a road toll for the specified OD pair, using the road toll candidate for every section selected in step B 13 . The toll optimization unit  30  then, if the calculated road toll exceeds an upper limit, corrects the road toll (step B 8 ). 
     Here, step B 8  will be described using  FIG. 10 .  FIG. 10  is a diagram showing a specific example of the content of processing that is performed in step B 8  shown in  FIG. 9 . In the example in  FIG. 10 , $20 is set the upper limit for the road toll of the OD pairs. Also, OD 12 , OD 13 , OD 22 , and OD 23  are illustrated as OD pairs. 
     In the example in the upper part of  FIG. 10 , the road toll does not exceed the upper limit for any of the OD pairs, but in the example in the lower part, the road toll exceeds the upper limit for OD 12  and OD 13  straddling a plurality of sections. Thus, the toll optimization unit  30  reduces the road tolls for OD 12  and OD 13  to the upper limit. 
     Also, since the determination of whether the upper limit is exceeded is performed for the road toll candidate of every section in step B 6 , the toll optimization unit  30 , in step B 8 , may perform processing on only the OD pairs straddling a plurality of sections. 
     Next, the toll optimization unit  30  calculates, for every OD pair, a weight for the OD pair, using the totaled traffic volume of the OD pair (step B 9 ). Note that the traffic volume of the OD pairs is, as described above, totaled from the records of the toll collection system, for example. 
     Next, the toll optimization units  30  calculate the toll revenue for the entirety of the toll road  402 , by applying the respective weights calculated in step B 9 , the road toll candidate selected for every section and the traffic volume predicted in step B 4  to the above equation 7. The toll optimization unit  30  then specifies, for every section, the road toll candidate that maximizes the toll revenue, and determines the specified road toll candidate as the road toll for that section (step B 10 ). 
     Thereafter, the toll optimization unit  30  causes the toll display device  200  to display the road toll determined in step B 10  (step B 11 ). Step B 11  is similar to step A 10  shown in  FIG. 6 . 
     Also, although processing in the processing in the toll control apparatus ends with the execution of step B 11 , step B 1  is executed again after a set time period (e.g., 10 min.) has elapsed. The road toll displayed on the toll display device  200  will thereby be updated as required. 
     Effects of Second Example Embodiment 
     As described above, in this second example embodiment, it becomes possible to respond to dynamic environmental changes on the road and to secure a financial return through road tolls, similarly to the first example embodiment. Also, in this second example embodiment, given that the road toll is determined using a weight for every OD pair, it becomes possible to induce long distance users to use the toll road  402 . Furthermore, as a result, according to this second example embodiment, substantial toll revenue can be expected from the sum of maximizing the toll revenue of every section according to the first example embodiment. 
     [Program] 
     A program in this second example embodiment need only be a program that causes a computer to execute steps B 1  to B 13  shown in  FIG. 9 . The toll control apparatus and the toll control method in this second example embodiment can be realized by this program being installed on a computer and executed. In this case, a processor of the computer performs processing while functioning as the traffic volume prediction unit  10 , the toll control unit  20 , and the toll optimization unit  30 . 
     Also, the program in this example embodiment may be executed by a computer system built from a plurality of computers. In this case, for example, the computers may each function as one of the traffic volume prediction unit  10 , the toll control unit  20 , and the toll optimization unit  30 . 
     [Physical Configuration] 
     Here, an example of a computer capable of realizing a toll control apparatus, by executing a program according to the first and second example embodiments will be described using  FIG. 11 .  FIG. 11  is a block diagram showing an example of a computer that realizes the toll control apparatus according to the first and second example embodiments of the invention. 
     As shown in  FIG. 11 , a computer  110  includes a CPU (Central Processing Unit)  111 , a main memory  112 , a storage device  113 , an input interface  114 , a display controller  115 , a data reader/writer  116 , and a communication interface  117 . These constituent elements are connected to each other in a manner that enables data communication, via a bus  121 . Note that the computer  110  may include a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array), in addition to the CPU  111  or instead of the CPU  111 . 
     The CPU  111  implements various computational operations, by extracting a program (codes) according to the example embodiments that are stored in the storage device  113  to the main memory  112 , and executing these codes in predetermined order. The main memory  112 , typically, is a volatile storage device such as a DRAM (Dynamic Random Access Memory). Also, programs in the example embodiment are provided in a state of being stored in a computer-readable recording medium  120 . Note that programs according to the example embodiments may be distributed over the Internet connected via the communication interface  117 . 
     Also, a semiconductor storage device such as a flash memory is given as a specific example of the storage device  113 , other than a hard disk drive. The input interface  114  mediates data transmission between the CPU  111  and input devices  118  such as a keyboard and a mouse. The display controller  115  is connected to a display device  119  and controls display by the display device  119 . 
     The data reader/writer  116  mediates data transmission between the CPU  111  and the recording medium  120 , and executes readout of programs from the recording medium  120  and writing of processing results of the computer  110  to the recording medium  120 . The communication interface  117  mediates data transmission between the CPU  111  and other computers. 
     Also, a general-purpose semiconductor storage device such as a CF (Compact Flash (registered trademark)) card or an SD (Secure Digital) card, a magnetic recording medium such as a flexible disk, and an optical recording medium such as a CD-ROM (Compact Disk Read Only Memory) are given as specific examples of the recording medium  120 . 
     Note that a toll control apparatus according to the example embodiments is also realizable by using hardware corresponding to the respective constituent elements, rather than by a computer on which programs are installed. Furthermore, the toll control apparatus may be realized in part by programs, and the remaining portion may be realized by hardware. 
     The example embodiments described above can be partially or wholly realized by supplementary notes 1 to 22 described below, but the invention is not limited to the following description. 
     (Supplementary Note 1) 
     A toll control apparatus for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road, the apparatus including: 
     a traffic volume prediction means that predicts a future overall traffic volume on the first road and the second road; 
     a toll control means that outputs, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     a toll optimization means that sets the road toll on the second road, 
     the toll optimization means setting one or more road toll candidates, selecting a road toll candidate for which a predicted traveling speed obtained by inputting the road toll candidate to the toll control means is greater than or equal to a threshold value, and setting, as the road toll on the second road, the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output by the toll control means, among the selected road toll candidates. 
     (Supplementary Note 2) 
     The toll control apparatus according to supplementary note 1, in which 
     the toll control means further outputs a traffic density on the second road for the case where the toll on the second road is set to the predetermined road toll, and 
     the toll optimization means selects a road toll candidate for which the predicted traveling speed output by the toll control means is greater than or equal to the threshold value, and that satisfies an upper limit and a lower limit that are determined by the traffic density output by the toll control means. 
     (Supplementary Note 3) 
     The toll control apparatus according to supplementary note 1 or 2, in which 
     the traffic volume prediction means predicts the overall traffic volume for every section set on the first road and the second road, 
     the toll control means outputs, with a traffic volume predicted for a specific section and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the specific section, and 
     the toll optimization means performs the setting of one or more road toll candidates and the selection of road toll candidates, for every section, and takes, as the road toll on the section, the road toll candidate that maximizes the toll revenue for an entirety of the second road or for the section as calculated using the future traffic volume output by the toll control means, among the selected road toll candidates. 
     (Supplementary Note 4) 
     The toll control apparatus according to supplementary note 3, in which 
     the toll optimization means multiplies, for every section, the future traffic volume output by the toll control means by the road toll candidate input to the toll control means to calculate the toll revenue for the section. 
     (Supplementary Note 5) 
     The toll control apparatus according to supplementary note 3, in which 
     the toll optimization means calculates, for every combination of an origin and a destination obtained using the origin and the destination of each of the sections, a weight using the traffic volume on the second road in the section corresponding to the combination, and 
     calculates the toll revenue for the entirety of the second road, using the calculated weights and the road toll candidate selected for every section. 
     (Supplementary Note 6) 
     The toll control apparatus according to any of supplementary notes 1 to 5, in which 
     the traffic volume prediction means predicts the overall traffic volume, using a predictive model built by machine learning that utilizes data specifying past traffic conditions on the first road and the second road and past meteorological data. 
     (Supplementary Note 7) 
     The toll control apparatus according to any of supplementary notes 1 to 6, in which 
     the toll control means, by inputting the predicted overall traffic volume and a predetermined road toll into a state model defining a relationship between overall traffic volume on the first road and the second road, road toll and predicted traveling speed, outputs a future traffic volume and a predicted traveling speed on the second road for the case where the toll on the second road is set to the predetermined road toll. 
     (Supplementary Note 8) 
     A toll control system for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road, the system including: 
     a toll control apparatus that predicts a future traffic volume on the second road; 
     a toll display device that displays, on a screen, a set road toll on the second road; and 
     a traffic sensor that outputs sensor data for detecting a number and a speed of vehicles traveling on the second road, 
     the toll control apparatus including: 
     a traffic volume prediction means that predicts a future overall traffic volume on the first road and the second road; 
     a toll control means that outputs, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     a toll optimization means that sets the road toll on the second road, and 
     the toll optimization means setting one or more road toll candidates, selecting a road toll candidate for which a predicted traveling speed obtained by inputting the road toll candidate to the toll control means is greater than or equal to a threshold value, and setting, as the road toll on the second road, the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output by the toll control means, among the selected road toll candidates. 
     (Supplementary Note 9) 
     A toll control method for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road, the method including: 
     (a) a step of predicting a future overall traffic volume on the first road and the second road; 
     (b) a step of outputting, with the predicted overall traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     (c) a step of setting the road toll on the second road, 
     in the step (c), one or more road toll candidates being set, a road toll candidate for which a predicted traveling speed obtained by executing the step (b) using the road toll candidate as an input of the step (b) is greater than or equal to a threshold value being selected, and the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output in the executed step (b) being set as the road toll on the second road, among the selected road toll candidates. 
     (Supplementary Note 10) 
     The toll control method according to supplementary note 9, in which 
     in the step (b), a traffic density on the second road for the case where the toll on the second road is set to the predetermined road toll is further output, and 
     in the step (c), a road toll candidate for which the predicted traveling speed output in the executed step (b) is greater than or equal to the threshold value, and that satisfies an upper limit and a lower limit that are determined by the traffic density output in the executed step (b) is selected. 
     (Supplementary Note 11) 
     The toll control method according to supplementary note 9 or 10, in which 
     in the step (a), the overall traffic volume is predicted for every section set on the first road and the second road, 
     in the step (b), with a traffic volume predicted for a specific section and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the specific section are output, and 
     in the step (c), the setting of one or more road toll candidates and the selection of road toll candidates are performed for every section, and the road toll candidate that maximizes the toll revenue for an entirety of the second road or for the section as calculated using the future traffic volume output in the executed step (b) is taken as the road toll on the section, among the selected road toll candidates. 
     (Supplementary Note 12) 
     The toll control method according to supplementary note 11, in which 
     in the step (c), the future traffic volume output in the executed step (c) is multiplied by the road toll candidate used as an input in the executed step (b), for every section, to calculate the toll revenue for the section. 
     (Supplementary Note 13) 
     The toll control method according to supplementary note 11, in which 
     in the step (c), for every combination of an origin and a destination obtained using the origin and the destination of each of the sections, a weight is calculated using the traffic volume on the second road in the section corresponding to the combination, and 
     the toll revenue for the entirety of the second road is calculated, using the calculated weights and the road toll candidate selected for every section. 
     (Supplementary Note 14) 
     The toll control method according to any of supplementary notes 9 to 13, in which 
     in the step (a), the overall traffic volume is predicted, using a predictive model built by machine learning that utilizes data specifying past traffic conditions on the first road and the second road and past meteorological data. 
     (Supplementary Note 15) 
     The toll control method according to any of supplementary notes 9 to 14, in which 
     in the step (b), by inputting the predicted overall traffic volume and a predetermined road toll into a state model defining a relationship between traffic volume on the second road, road toll and predicted traveling speed, a future traffic volume and a predicted traveling speed on the second road for the case where the toll on the second road is set to the predetermined road toll are output. 
     (Supplementary Note 16) 
     A computer-readable recording medium that includes a program recorded thereon for, in a case where a second road that is a toll road bypassing a first road is established, controlling a road toll on the second road with a computer, the program including instructions that cause a computer to carry out: 
     (a) a step of predicting a future overall traffic volume on the first road and the second road; 
     (b) a step of outputting, with a predicted traffic volume and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the second road for a case where the toll on the second road is set to the predetermined road toll; and 
     (c) a step of setting the road toll on the second road, 
     in the step (c), one or more road toll candidates being set, a road toll candidate for which a predicted traveling speed obtained by executing the step (b) using the road toll candidate as an input of the step (b) is greater than or equal to a threshold value being selected, and the road toll candidate that maximizes a toll revenue for the second road as calculated using the future traffic volume output in the executed step (b) being set as the road toll on the second road, among the selected road toll candidates. 
     (Supplementary Note 17) 
     The computer-readable recording medium according to supplementary note 16, in which 
     in the step (b), a traffic density on the second road for the case where the toll on the second road is set to the predetermined road toll is further output, and 
     in the step (c), a road toll candidate for which the predicted traveling speed output in the executed step (b) is greater than or equal to the threshold value, and that satisfies an upper limit and a lower limit that are determined by the traffic density output in the executed step (b) is selected. 
     (Supplementary Note 18) 
     The computer-readable recording medium according to supplementary note 16 or 17, in which 
     in the step (a), the overall traffic volume is predicted for every section set on the first road and the second road, 
     in the step (b), with a traffic volume predicted for a specific section and a predetermined road toll as inputs, a future traffic volume and a predicted traveling speed on the specific section are output, and 
     in the step (c), the setting of one or more road toll candidates and the selection of road toll candidates are performed for every section, and the road toll candidate that maximizes the toll revenue for an entirety of the second road or for the section as calculated using the future traffic volume output in the executed step (b) is taken as the road toll on the section, among the selected road toll candidates. 
     (Supplementary Note 19) 
     The computer-readable recording medium according to supplementary note 18, in which 
     in the step (c), the future traffic volume output in the executed step (c) is multiplied by the road toll candidate used as an input in the executed step (b), for every section, to calculate the toll revenue for the section. 
     (Supplementary Note 20) 
     The computer-readable recording medium according to supplementary note 18, in which 
     in the step (c), for every combination of an origin and a destination obtained using the origin and the destination of each of the sections, a weight is calculated using the traffic volume on the second road in the section corresponding to the combination, and 
     the toll revenue for the entirety of the second road is calculated, using the calculated weights and the road toll candidate selected for every section. 
     (Supplementary Note 21) 
     The computer-readable recording medium according to any of supplementary notes 16 to 20, in which 
     in the step (a), the future traffic volume is predicted, using a predictive model built by machine learning that utilizes data specifying past traffic conditions on the second road and past meteorological data. 
     (Supplementary Note 22) 
     The computer-readable recording medium according to any of supplementary notes 16 to 21, in which 
     in the step (b), by inputting the predicted overall traffic volume and a predetermined road toll into a state model defining a relationship between traffic volume on the second road, road toll and predicted traveling speed, a future traffic volume and a predicted traveling speed on the second road for the case where the toll on the second road is set to the predetermined road toll are output. 
     Although the invention of the present application has been described above with reference to example embodiments, the invention is not limited to the foregoing example embodiments. Various modifications apparent to those skilled in the art can be made to the configurations and details of the invention of the present application within the scope of the invention. 
     INDUSTRIAL APPLICABILITY 
     As described above, according to the invention, it becomes possible to respond to dynamic environmental changes on the road and to secure a financial return through road tolls. The invention is useful in toll control systems that vary the road toll on toll roads. 
     LIST OF REFERENCE SIGNS 
     
         
         
           
               10 ,  10 - 1 - 10 - 3  Traffic volume prediction unit 
               11 ,  11 - 1 - 11 - n  Traffic volume predictor 
               20 ,  20 - 1 - 20 - 3  Toll control unit 
               21 ,  21 - 1 - 21 - n  Toll controller 
               30  Toll optimization unit 
               100  Toll control apparatus 
               110  Computer 
               111  CPU 
               112  Main memory 
               113  Storage device 
               114  Input interface 
               115  Display controller 
               116  Data reader/writer 
               117  Communication interface 
               118  Input device 
               119  Display device 
               120  Recording medium 
               121  Bus 
               200  Toll display device 
               300  Traffic sensor 
               400  Toll control system 
               401  First road (public road) 
               402  Second road (toll road) 
               403  Vehicle