JP5376465B2 - Congestion situation prediction program, computer-readable recording medium recording congestion situation prediction program and congestion situation prediction apparatus, navigation program, computer-readable recording medium recording navigation program, and navigation apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a new traffic congestion prediction program and a navigation program using the same, which always accurately predicts the future traffic congestion one step ahead, which changes with time, and achieves proper navigation based on the predicted traffic congestion information. <P>SOLUTION: To predict a traffic condition, a computer functions as a current traffic congestion information input means for inputting current traffic congestion information at an arbitrary place, a similarity calculation means for calculating similarity between the input current traffic congestion information and each of a plurality of past traffic congestion information of the place at the same time as the current time, and a traffic congestion prediction means for assuming past traffic information at a predetermined time ahead and following the past traffic information having the highest similarity in the calculation, as predicted traffic congestion information at the place. <P>COPYRIGHT: (C)2011,JPO&amp;INPIT

Description

  The invention of this application records a new congestion situation prediction program that can be used for various navigations for route guidance of cars and people, can predict accurate congestion information one step ahead, and realize accurate navigation, and the program. The present invention relates to a computer-readable recording medium and a congestion situation prediction apparatus, a new navigation program, a computer-readable recording medium recording the program, and a navigation apparatus.

Recent vehicle-mounted navigation devices often have a function to obtain traffic information related to congestion, etc. in real time using a road traffic information communication system (VICS: Vehicle Information and Communication System) and to perform route guidance based on the traffic information. (For example, refer to Patent Document 1).
JP-A-6-147907

  However, since the current navigation devices simply obtain congestion information at each real time, that is, because the route is guided only by real-time information, When a different situation occurs, it may happen that the route originally selected is not preferable. For example, based on the congestion information at the current time, avoiding a busy road and selecting a road that is not crowded to guide the route, the road starts to get crowded while moving, eventually the original road It was a situation that was faster or another way earlier. That is, even with real-time traffic information support, navigation that accurately responds to changes in road conditions has not been realized.

  Therefore, in view of the circumstances as described above, the invention of this application can always accurately predict one step ahead of the constantly changing congestion situation, and can realize accurate navigation based on the congestion prediction information. To provide a new congestion situation prediction program, a computer-readable recording medium and a congestion situation prediction apparatus that record the congestion situation prediction program, and a new navigation program and a computer-readable recording medium and navigation apparatus that record the navigation program It is an issue.

  In order to solve the above-mentioned problems, the invention of this application is as follows. First, a computer is used to predict the congestion situation, and congestion current information input means for inputting congestion current information at the current time regarding an arbitrary point, input The similarity calculation means for calculating the similarity between the current congestion information and the plurality of congestion past information at the same time as the current time for the point stored in advance, and the congestion history with the largest calculated similarity Provided is a congestion status prediction program that causes congestion past information at a predetermined time ahead of information to function as congestion prediction means that regards as congestion prediction information regarding the point.

  Secondly, a computer for predicting the congestion situation, a mobile unit number input means for inputting the number of mobile units at the current time flowing into the arbitrary branch point from the arbitrary mobile path, and the number of input mobile units The predicted inflow amount is calculated by multiplying the branch point stored in advance by the probability that the mobile body that has flowed in from the travel path branches to another travel path, and calculates the predicted inflow amount to the other travel path. Provided is a congestion situation prediction program characterized by functioning as a means.

  Thirdly, in order to predict the congestion situation, the computer is provided with a movement plan information input means for inputting movement plan information at the current time of each moving body, and at the current time relating to the planned movement point included in the input movement plan information. Congestion current information input means for inputting current congestion information, travel required time calculation means for calculating the required travel time of each mobile body to the planned travel point based on the input travel plan information and congestion current information, and the same movement Provided is a congestion situation prediction program that functions as a movement prediction information calculating unit that calculates movement prediction information related to a scheduled movement point by adding the number of moving bodies having the same required travel time to the scheduled point. .

  Fourth, the computer is used to predict the congestion situation, the movement plan information input means for inputting the movement plan information at the current time of each moving body, the input moving plan information, the moving speed and the moving distance of each moving body. Based on the above, the required travel time calculation means for calculating the required travel time to the planned travel point included in the travel plan information of each mobile body, and the number of mobiles having the same required travel time to the same planned travel point are added. In addition, there is provided a congestion situation prediction program that functions as movement prediction information calculation means for calculating movement prediction information related to the planned movement point.

  Fifthly, in order to predict the congestion situation, the computer, the congestion current information input means for inputting the congestion current information at the current time regarding an arbitrary point, the input congestion current information, and the point related to the point stored in advance Similarity calculation means for calculating the degree of similarity with each of a plurality of congestion past information at the same time as the current time, and congestion past information at a predetermined time ahead following the congestion past information with the highest calculated similarity Congestion prediction means to be regarded as congestion prediction information regarding a point, mobile number input means for inputting the number of mobile bodies at the current time flowing from an arbitrary movement path to an arbitrary branch point, and the number of input mobile bodies The predicted inflow amount that calculates the predicted inflow amount to the other travel path by multiplying the accumulated branch point by the probability that the mobile body that has flowed in from the travel path will branch to the other travel path. It means out, and, by combining with the inflow predicted amount and said congestion prediction information, provides a congestion prediction program for causing to function as the final congestion prediction information calculating means for calculating a final congestion prediction information.

  Sixth, in order to predict the congestion situation, the computer is input with congestion current information input means for inputting the congestion current information at the current time regarding an arbitrary point, the inputted congestion current information, and the point regarding the point stored in advance. Similarity calculation means for calculating the similarity with each of a plurality of congestion past information at the same time as the current time, and congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity, Congestion prediction means regarded as congestion prediction information related to the point, movement plan information input means for inputting movement plan information at the current time of each moving body, input movement plan information and input based on the congestion current information Based on the travel plan information and the travel speed and travel distance of each mobile object, the time required to travel to the planned travel point included in the travel plan information for each mobile object is calculated. The required travel time calculation means, the movement prediction information calculation means for calculating the movement prediction information related to the planned movement point by adding the number of moving bodies having the same required movement time to the same planned movement point, and the congestion Provided is a congestion situation prediction program characterized by combining prediction information and the movement prediction information to function as final congestion prediction information calculation means for calculating final congestion prediction information.

  Seventh, a computer for predicting the congestion situation, mobile unit number input means for inputting the number of mobile units at the current time flowing into the arbitrary branch point from the arbitrary mobile path, and the number of input mobile units The predicted inflow amount is calculated by multiplying the branch point stored in advance by the probability that the mobile body that has flowed in from the travel path branches to another travel path, and calculates the predicted inflow amount to the other travel path. Means, and movement plan information input means for inputting movement plan information at the current time of each moving body, congestion current information input for inputting congestion current information at the current time regarding a planned movement point included in the input movement plan information Based on the means, the input movement plan information and the congestion current information, or based on the input movement plan information and the movement speed and movement distance of each mobile object, the time required for each mobile object to travel is calculated. The required travel time calculation means to be issued, the number of mobile bodies having the same required travel time to the same planned travel point, and the travel prediction information calculation means for calculating the travel prediction information related to the planned travel point, and Provided is a congestion status prediction program characterized by combining an expected inflow amount and the movement prediction information to function as final congestion prediction information calculation means for calculating final congestion prediction information.

  Eighth, in order to predict the congestion situation, the computer is input with the congestion current information input means for inputting the congestion current information at the current time regarding the arbitrary point, the input congestion current information, and the point regarding the point stored in advance. Similarity calculation means for calculating the similarity with each of a plurality of congestion past information at the same time as the current time, and congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity, Congestion prediction means regarded as congestion prediction information related to the point, mobile number input means for inputting the number of moving objects at an arbitrary branch point at the current time flowing from an arbitrary movement path, and the number of input moving objects; Inflow prediction that calculates the inflow prediction amount to the other travel path by multiplying the branch point accumulated in advance by the probability that the mobile body that has flowed in from the travel path branches to the other travel path Based on the calculation means, the movement plan information input means for inputting movement plan information at the current time of each mobile body, the input movement plan information and the congestion current information, or the input movement plan information and each mobile body Based on the moving speed and the moving distance, the moving time calculating means for calculating the required moving time to the moving planned point included in the moving plan information of each moving body, and the moving having the same moving required time to the same moving planned point The movement prediction information calculation means for calculating movement prediction information related to the planned movement point by adding the number of bodies, and the congestion prediction information, the inflow prediction amount, and the movement prediction information are combined to obtain the final congestion prediction. Provided is a congestion situation prediction program characterized by functioning as final congestion prediction information calculation means for calculating information.

  Ninth, in order to predict the congestion situation, the computer is input with congestion current information input means for inputting the congestion current information at the current time regarding an arbitrary point, the input congestion current information, and the point regarding the point stored in advance. Similarity calculation means for calculating the degree of similarity with each of a plurality of congestion past information at the same time as the current time, and congestion past information at a predetermined time ahead following the congestion past information with the highest calculated similarity Congestion prediction means to be regarded as congestion prediction information regarding a point, mobile number input means for inputting the number of mobile bodies at the current time flowing from an arbitrary movement path to an arbitrary branch point, and the number of input mobile bodies The predicted inflow amount that calculates the predicted inflow amount to the other travel path by multiplying the accumulated branch point by the probability that the mobile body that has flowed in from the travel path will branch to the other travel path. And a combination congestion prediction information calculation unit that calculates the combined congestion prediction information by combining the congestion prediction information and the inflow prediction amount, and movement plan information at the current time of each mobile unit. Based on the movement plan information input means, the input movement plan information and the combined congestion prediction information, the required movement time calculation means for calculating the required movement time to the planned movement point included in the movement plan information of each moving body, and the same Provided is a congestion situation prediction program that functions as a movement prediction information calculation means for calculating movement prediction information related to a planned movement point by adding together the number of moving bodies having the same required travel time to the planned movement point To do.

  Tenth, a computer is used to predict the congestion situation, congestion current information input means for inputting congestion current information at the current time relating to an arbitrary point, input congestion current information, and Similarity calculation means for calculating the degree of similarity with each of a plurality of congestion past information at the same time as the current time, and congestion past information at a predetermined time ahead following the congestion past information with the highest calculated similarity Congestion prediction means to be regarded as congestion prediction information regarding a point, mobile number input means for inputting the number of mobile bodies at the current time flowing from an arbitrary movement path to an arbitrary branch point, and the number of input mobile bodies Multiplying the accumulated branch point by the probability that a mobile body that has flowed in from the travel path will branch to another travel path, and calculating an inflow prediction amount to the other travel path The calculation means, the combined congestion prediction information calculating means for calculating the combined congestion prediction information by combining the congestion prediction information and the inflow prediction amount, and the movement plan information at the current time of each mobile unit are input. Based on the movement plan information input means, the inputted movement plan information, the congestion current information and the combined congestion prediction information, the movement required time for calculating the movement required time to the planned movement point included in the movement plan information of each moving body A congestion functioning as a calculation means and a movement prediction information calculation means for calculating movement prediction information related to the planned movement point by adding together the number of moving bodies having the same required travel time to the same planned movement point Provide situation prediction programs.

  Eleventh, the computer further functions as a synthesis parameter learning unit that learns and updates a synthesis parameter used in the synthesis process by the final congestion prediction information calculation unit or the synthesis congestion prediction information calculation unit in order to predict a congestion situation. A congestion situation prediction program is provided.

  12thly, the computer-readable recording medium which recorded the said congestion condition prediction program is provided.

  13thly, it is an apparatus which predicts a congestion situation, The congestion present information receiving means which receives the congestion present information at the present time regarding arbitrary points, The congestion past information storage which memorize | stores the congestion past information regarding arbitrary points previously Means for calculating the similarity between the congestion current information received by the congestion current information receiving means and a plurality of congestion past information at the same time as the current time for the point stored in the congestion past information storage means And a congestion prediction unit that reads out congestion past information at a predetermined time ahead following the congestion past information having the highest calculated similarity degree from the congestion past information storage unit and regards it as congestion prediction information regarding the point. The present invention provides a congestion situation prediction apparatus.

  14thly, it is the apparatus which estimates a congestion condition, Comprising: The moving body number receiving means which receives the number of moving bodies in the present time which flows in from an arbitrary moving path to an arbitrary branch point, Arbitrary moving path to an arbitrary branch point Branch probability storage means for storing in advance the probability that a mobile body that has flowed in from other branches will branch, and the number of mobile bodies received by the mobile body number reception means and the branch stored in the branch probability storage means It is characterized by having an inflow predicted amount calculating means for calculating a predicted amount of inflow into the other moving path by multiplying the point by the probability that the moving body flowing in from the moving path branches to the other moving path. A congestion situation prediction device is provided.

  Fifteenth, an apparatus for predicting the congestion situation, the movement plan information receiving means for receiving movement plan information at the current time of each mobile body, the movement included in the movement plan information received by the movement plan information receiving means Based on the congestion current information receiving means for receiving the current congestion information at the current time regarding the planned location, the movement plan information received by the movement plan information receiving means, and the congestion current information received by the congestion current information receiving means, Calculate the travel prediction information for the planned travel point by adding the required travel time calculation means to calculate the travel time to the planned travel point and the number of mobiles having the same travel time to the same planned travel point. There is provided a congestion situation prediction apparatus characterized by having movement prediction information calculation means for performing the following.

  Sixteenth, an apparatus for predicting the congestion situation, a movement plan information receiving means for receiving movement plan information at the current time of each mobile unit, the received movement plan information, and the moving speed and moving distance of each mobile unit Based on the above, the required travel time calculation means for calculating the required travel time to the planned travel point included in the travel plan information of each mobile body, and the number of mobiles having the same required travel time to the same planned travel point are added. In addition, the present invention provides a congestion state prediction apparatus characterized by having movement prediction information calculation means for calculating movement prediction information related to the planned movement point.

  17thly, it is an apparatus which estimates a congestion condition, The congestion present information receiving means which receives the congestion present information at the present time regarding arbitrary points, The congestion past information storage which memorize | stores the congestion past information regarding arbitrary points previously Means for calculating the similarity between the congestion current information received by the congestion current information receiving means and a plurality of congestion past information at the same time as the current time for the point stored in the congestion past information storage means A congestion degree calculation means, and a congestion prediction means that reads out congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity degree from the congestion past information storage means, and regards it as congestion prediction information related to the point, and , Mobile number receiving means for receiving the number of moving bodies at the current time flowing into the arbitrary branch point from the arbitrary moving path, flowing into the arbitrary branch point from the arbitrary moving path Branch probability storage means for storing in advance the probability that a moving object will branch to another moving path, and the number of moving bodies received by the moving body number receiving means, and the movement to the branch point stored in the branch probability storage means The inflow prediction amount calculation means for calculating the inflow prediction amount to the other movement path by multiplying the probability that the mobile body flowing in from the road branches to the other movement path, and the congestion prediction information and the inflow prediction There is provided a congestion situation prediction apparatus characterized by comprising final congestion prediction information calculation means for calculating final congestion prediction information by combining amounts.

  Eighteenth is an apparatus for predicting the congestion situation, a congestion current information receiving means for receiving congestion current information at the current time regarding an arbitrary point, and a congestion past information storage for previously storing congestion past information regarding an arbitrary point. Means for calculating the similarity between the congestion current information received by the congestion current information receiving means and each of a plurality of congestion past information at the same time as the current time for the point stored by the congestion past information storage means A congestion degree calculation means, and a congestion prediction means that reads out congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity degree from the congestion past information storage means, and regards it as congestion prediction information related to the point, and The movement plan information receiving means for receiving the movement plan information at the current time of each moving body, the movement plan information received by the movement plan information receiving means, and Based on the congestion current information received by the congestion current information receiving means, or based on the received movement plan information and the moving speed and moving distance of each moving object, the moving planned point included in the moving plan information of each moving object The required travel time calculating means for calculating the required travel time, and the predicted motion information calculating means for calculating the predicted motion information for the planned travel point by adding together the number of moving bodies having the same required travel time to the same planned travel point. In addition, the present invention provides a congestion situation prediction apparatus comprising final congestion prediction information calculation means for calculating final congestion prediction information by combining the congestion prediction information and the movement prediction information.

  Nineteenth, an apparatus for predicting a congestion situation, a mobile body number receiving means for receiving the number of mobile bodies at the current time flowing into an arbitrary branch point from an arbitrary mobile path, and an arbitrary mobile path at an arbitrary branch point The branch probability storage means for storing in advance the probability that the mobile body that has flowed in from the other path will branch, and the number of mobile bodies received by the mobile body number reception means, and the movement stored in the branch probability storage means Inflow prediction amount calculation means for calculating the inflow prediction amount to the other movement path by multiplying the planned branch point by the probability that each of the mobile bodies flowing in from the planned movement path will branch to the other movement path, and , Movement plan information receiving means for receiving movement plan information at the current time of each mobile unit, congestion for receiving current congestion information at the current time regarding a planned movement point included in the movement plan information received by the movement plan information receiving means Currently receiving information Means, based on the movement plan information received by the movement plan information receiving means and the congestion current information received by the congestion current information receiving means, or based on the received movement plan information and the moving speed and moving distance of each moving body, The movement required time calculation means for calculating the required time for moving the moving body to the planned moving point and the number of moving bodies having the same moving required time to the same planned moving point are added together to predict movement related to the planned moving position. Congestion status prediction, comprising: movement prediction information calculation means for calculating information; and final congestion prediction information calculation means for calculating final congestion prediction information by combining the predicted inflow amount and the movement prediction information Providing equipment.

  20thly, it is a device for predicting the congestion situation, a congestion current information receiving means for receiving the congestion current information at the current time regarding an arbitrary point, and a congestion past information storage for previously storing the congestion past information regarding an arbitrary point. Means for calculating the similarity between the congestion current information received by the congestion current information receiving means and a plurality of congestion past information at the same time as the current time for the point stored in the congestion past information storage means A congestion degree calculation means, and a congestion prediction means that reads out congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity degree from the congestion past information storage means, and regards it as congestion prediction information related to the point, and , Mobile number receiving means for receiving the number of moving bodies at the current time flowing into the arbitrary branch point from the arbitrary moving path, flowing into the arbitrary branch point from the arbitrary moving path Branch probability storage means for storing in advance the probability that a moving object will branch to another moving path, and the number of moving bodies received by the moving body number receiving means, and the movement to the branch point stored in the branch probability storage means The inflow prediction amount calculating means for calculating the inflow prediction amount to the other moving path by multiplying the probability that the moving body flowing in from the road branches to the other moving path, and the current time of each moving body Based on the movement plan information received by the movement plan information receiving means, the movement plan information received by the movement plan information receiving means and the congestion current information received by the congestion current information receiving means, or the received movement plan information and each mobile unit Based on the moving speed and moving distance, the required moving time calculating means for calculating the required moving time to the moving planned point included in the moving plan information of each moving body, and the same moving planned point The movement prediction information calculation means for calculating the movement prediction information related to the scheduled movement point by adding the number of moving bodies having the same movement required time, and the congestion prediction information, the inflow prediction amount, and the movement prediction information. There is provided a congestion situation prediction apparatus characterized by comprising final congestion prediction information calculation means for combining and calculating final congestion prediction information.

  21st is a device for predicting the congestion situation, a congestion current information receiving means for receiving the congestion current information at the current time regarding an arbitrary point, and a congestion past information storage for previously storing the congestion past information regarding an arbitrary point Means for calculating the similarity between the congestion current information received by the congestion current information receiving means and a plurality of congestion past information at the same time as the current time for the point stored in the congestion past information storage means A congestion degree calculation means, and a congestion prediction means that reads out congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity degree from the congestion past information storage means, and regards it as congestion prediction information related to the point, and , Mobile number receiving means for receiving the number of moving bodies at the current time flowing into the arbitrary branch point from the arbitrary moving path, flowing into the arbitrary branch point from the arbitrary moving path Branch probability storage means for storing in advance the probability that a moving object will branch to another moving path, and the number of moving bodies received by the moving body number receiving means, and the movement to the branch point stored in the branch probability storage means The inflow prediction amount calculation means for calculating the inflow prediction amount to the other movement path by multiplying the probability that the mobile body flowing in from the road branches to the other movement path, and the congestion prediction information and the inflow prediction Combined congestion prediction information calculation means for calculating combined congestion prediction information, movement plan information reception means for receiving movement plan information at the current time of each mobile body, and movement plan information reception means Based on the travel plan information and the combined congestion prediction information, the travel time calculation means for calculating the travel time to the planned travel point included in the travel plan information of each mobile unit, and the same travel plan By adding the number of mobile body having the same travel time to the point, provides a congestion status prediction apparatus characterized by having a movement prediction information calculating means for calculating a movement prediction information about the moving prediction place.

  Twenty-second device is a device for predicting the congestion situation, a congestion current information receiving means for receiving the congestion current information at the current time regarding an arbitrary point, and a congestion past information storage for previously storing the congestion past information regarding an arbitrary point. Means for calculating the similarity between the congestion current information received by the congestion current information receiving means and a plurality of congestion past information at the same time as the current time for the point stored in the congestion past information storage means A congestion degree calculation means, and a congestion prediction means that reads out congestion past information at a predetermined time ahead of the congestion past information with the highest calculated similarity degree from the congestion past information storage means, and regards it as congestion prediction information related to the point, and , Mobile number receiving means for receiving the number of moving bodies at the current time flowing into the arbitrary branch point from the arbitrary moving path, flowing into the arbitrary branch point from the arbitrary moving path Branch probability storage means for storing in advance the probability that a moving object will branch to another moving path, and the number of moving bodies received by the moving body number receiving means, and the movement to the branch point stored in the branch probability storage means The inflow prediction amount calculation means for calculating the inflow prediction amount to the other movement path by multiplying the probability that the mobile body flowing in from the road branches to the other movement path, and the congestion prediction information and the inflow prediction Combined congestion prediction information calculation means for calculating combined congestion prediction information, movement plan information reception means for receiving movement plan information at the current time of each mobile body, and movement plan information reception means Travel time calculation means for calculating the required travel time to the planned travel point included in the travel plan information of each mobile unit based on the travel plan information, the congestion current information, and the combined congestion prediction information And a congestion situation prediction device comprising movement prediction information calculation means for calculating movement prediction information relating to the scheduled movement point by adding together the number of moving bodies having the same required travel time to the same planned movement point. provide.

  Twenty-third, a congestion situation prediction apparatus is provided, further comprising synthesis parameter learning means for learning and updating a synthesis parameter used for the synthesis processing by the final congestion prediction information calculation means or the synthesized congestion prediction information calculation means To do.

  In a twenty-fourth aspect, there is provided a navigation program characterized by causing a computer to function as means for performing navigation based on the congestion situation predicted by the congestion situation prediction program.

  25thly, the computer-readable recording medium which recorded the navigation program characterized by operating a computer as a means to perform navigation based on the congestion condition estimated by the said congestion condition prediction program is provided.

  According to a twenty-sixth aspect of the present invention, there is provided a navigation apparatus characterized in that navigation is performed based on the congestion situation predicted by the congestion situation prediction apparatus.

  According to the first congestion status prediction program, not only simply providing congestion information at the present time, but also by performing a similarity analysis between the current real-time congestion status and the past accumulated congestion status, It is possible to accurately predict the congestion situation when a certain amount of time has passed, that is, the congestion situation that may occur in the future, and to appropriately provide the prediction information to the user. From the user's point of view, it is possible to accurately grasp the congestion situation one step ahead, and it is possible to change the movement plan and the movement route more flexibly and appropriately based on the prediction situation.

  According to the second to fourth congestion situation prediction programs, the amount of moving body inflow in real time, that is, the number of moving bodies flowing into a certain branch point from a certain moving path, and the same movement obtained based on past accumulated data By performing predictive analysis based on the probability of branching from one road to another, it is also possible to use the movement plan information for each moving object and each movement planned point included in the plan information, that is, a point on the movement route or a movement. Predictive analysis based on real-time congestion information about the destination location, etc., and combined with the movement plan information of each mobile object and the moving speed and distance of each mobile object As in the first prediction program, it is possible not only to provide congestion information at the present time but also to accurately predict the congestion situation one step ahead and provide it to the user as appropriate.

  According to the fifth to eighth congestion situation prediction programs, the data relating to the similarity, the inflow amount, and the movement plan are used in various combinations, that is, based on each of the first to fourth prediction programs. By synthesizing various prediction data obtained in the same manner as described above, it is possible to improve the prediction accuracy of the congestion situation and provide the user with a more accurate congestion situation.

  According to the ninth congestion situation prediction program, the congestion situation prediction process using the movement plan information is performed as in the third prediction program, but instead of the congestion information (congestion current information) at the current time. In addition, based on the combined congestion prediction information obtained by using the similarity and the inflow amount in combination, that is, the congestion information one step ahead, separately, the congestion status prediction processing is performed. The used congestion prediction can be realized with higher accuracy.

  According to the tenth congestion state prediction program, the congestion state prediction process is performed based on the combined congestion prediction information separately predicted using the similarity and the inflow amount in the same manner as the ninth prediction program. Since congestion information at the current time is also used, it is possible to realize congestion prediction using even more accurate movement plan information.

  According to the eleventh congestion status prediction program, the same effects as those of the fourth to tenth prediction programs can be obtained, and various prediction data obtained based on the similarity, the inflow amount, and the movement plan can be obtained. Thus, it is possible to perform more accurate congestion situation prediction by learning and updating the synthesis parameters used when combining in combination.

  According to the computer-readable recording medium on which the twelfth congestion status prediction program is recorded, a flexible disk or a CD-recording the first to eleventh prediction programs capable of excellent congestion status prediction as described above. A recording medium such as a ROM or a DVD-ROM is realized.

  According to the thirteenth to twenty-third congestion status prediction apparatuses, similarly to the first to eleventh prediction programs, the user can accurately predict the congestion status that may occur one step ahead from the current time, and Thus, a prediction device that can be appropriately provided is realized.

  According to the twenty-fourth navigation program, the same effects as those of the first to eleventh congestion status prediction programs can be obtained, and accurate navigation for a moving body such as a car or a person based on the obtained congestion prediction information. Can be realized.

  According to the computer-readable recording medium in which the 25th navigation program is recorded, it is possible to perform excellent congestion situation prediction as in the first to 11th congestion situation prediction programs, and the obtained congestion prediction information. Based on the above, a recording medium such as a flexible disk, a CD-ROM, and a DVD-ROM on which a navigation program capable of accurate navigation with respect to a moving body such as a car or a person is recorded is realized.

  According to the twenty-sixth navigation device, the same effects as in the thirteenth to twenty-third navigation devices can be obtained, and accurate navigation with respect to a moving body such as a car or a person is realized based on the obtained congestion prediction information. become able to.

It is a functional block diagram for demonstrating one Embodiment of invention of this application which performs the congestion condition prediction process using a similarity degree. It is a figure for demonstrating the congestion condition prediction process using a similarity degree. It is a functional block diagram for demonstrating one Embodiment of invention of this application which performs the congestion condition prediction process using inflow amount. It is a figure for demonstrating the congestion condition prediction process using the inflow amount. It is a functional block diagram for demonstrating one Embodiment of invention of this application which performs the congestion condition prediction process using a movement plan. It is a functional block diagram for demonstrating one Embodiment of this invention of this application which performs the congestion condition prediction process which used the similarity degree, inflow amount, and the movement plan together. It is a functional block diagram for demonstrating another embodiment of this invention of this application which performs the congestion condition prediction process using a movement plan. It is a functional block diagram for demonstrating another one Embodiment of this invention of this application which performs the congestion condition prediction process which used the similarity, the inflow amount, and the movement plan together. It is a functional block diagram for demonstrating another one Embodiment of invention of this application which performs the congestion condition prediction process using a movement plan. It is a figure for demonstrating the central management navigation which is one Embodiment of this invention. It is a functional block diagram for demonstrating the central management navigation which is one Embodiment of this invention.

  Hereinafter, an embodiment of the invention of this application will be described in detail with appropriate reference to FIGS.

<Congestion situation prediction processing using similarity>
First, the congestion status prediction process using the similarity will be described. In the embodiment of FIG. 1, a congestion information receiving circuit (100) that receives the congestion current information at the current time regarding an arbitrary point from the outside (the congestion current information input). This congestion information receiving circuit (100) receives congestion information typified by VICS information from the road traffic information center in real time and stores it in an internal memory or the like. input.

  On the other hand, a congestion situation database (200) (corresponding to the congestion past information storage means) that stores past congestion information on an arbitrary point is preliminarily constructed and received by the congestion situation database (200). The congestion information such as the VICS information is accumulated in advance for each point.

  Then, by comparing the current congestion information received by the congestion information receiving circuit (100) with the past congestion information stored in the congestion status database (200), a predetermined time ahead at the target point is obtained. Predict the congestion situation.

  More specifically, in the embodiment of FIG. 1, the similarity for calculating the degree of similarity between the congestion current information at the current time of an arbitrary point and each of a plurality of congestion past information at the same time as the current time for the arbitrary point. The congestion degree information (201) (corresponding to the similarity degree calculation means), the congestion past information at a predetermined time after the congestion past information with the largest calculated similarity degree, and congestion prediction information related to the arbitrary point The most similar situation retrieval unit (202) to be regarded (corresponding to the congestion prediction means) is provided. For example, as illustrated in FIG. 2, the similarity calculation unit (201) performs the current time t at the point A to be predicted. = I, current data at time i, and time-series data sets a, b, c at point A stored in the congestion status database (200), and past data at the same time as the current time t = i of The similarity is calculated, and the set with the highest similarity is searched by the most similar situation search unit (202), and the past data of n unit time ahead (t = i + 1) following the past data at time t = i in the set. , T = i + 2,..., T = i + n), and this is regarded as congestion prediction information that may occur at the point A by time t = i + n, and is displayed on the display (600) or transmitted to the user. It is provided by transmitting data from the circuit (601).

Here, as congestion information, data consisting of attribute information and congestion information in unit time can be considered, and attribute information includes data related to date, day of the week, time, specific situation (continuous vacation, stay of a key person, etc.), and congestion information Can be data relating to the congestion status of each target point (traveling path, branching point, etc.). In this case, the similarity is calculated and compared for each of these element items. For example, relevance numbers representing the item x, i.e. the current value of the items x at the point A data p _x and relevance r _x with previous numerical data _{n x = | p x -n x} | / p x , And the sum R of the relevance r ₁ , r ₂ ,..., R _x ,. This calculation is performed for each data set a, b, c..., It is determined that the one with the smallest R value has the highest similarity, and t = i + n in the data set as described above. The past data until is provided to the user as congestion prediction information.

  According to the above processing, it is possible to accurately predict how the congestion situation will change from the current time by analyzing the current situation obtained in real time and the past situation accumulated in advance. It can be provided to the user, and more accurate navigation based on the prediction information can be realized.

  Note that, in order to further improve the accuracy of the congestion status prediction process, the congestion status database (200) may be capable of sequentially storing and updating the current congestion information received by the congestion information receiving circuit (100). preferable.

<Congestion situation prediction process using inflow volume>
Next, the congestion state prediction process using the inflow amount will be described. In the embodiment of FIG. 3, in the embodiment of FIG. 3, a flow rate information receiving circuit that receives the number of moving bodies flowing in from an arbitrary movement path to an arbitrary branch point ( 300) (corresponding to the moving body number input means and the moving body number receiving means) is provided, and the flow rate information receiving circuit (300) is provided for each branch point from the outside such as an external measuring device or a measurement counting center. In addition, the moving body number data is received in real time for each moving path connected to each branch point and input to the internal memory or the like.

  On the other hand, a branch information database (301) (corresponding to the branch probability storage means) that stores the probability that a mobile body that has flowed into an arbitrary branch point from an arbitrary travel path branches to another travel path is preliminarily constructed and provided. In the branch information database (301), branch probability data calculated in the past is stored in advance for each branch point and for each movement path.

  Then, the inflow prediction amount is calculated by multiplying the current number of moving bodies received by the flow rate information receiving circuit (300) and the past branch probability stored in the branch information database (301).

More specifically, in the embodiment of FIG. 3, the number of moving bodies at the current time flowing into the arbitrary branching point from the arbitrary moving path, and the moving body flowing into the branching point from the moving path to other moving points A branch flow rate calculation unit (302) (corresponding to the inflow prediction amount calculation means) that calculates the inflow prediction amount to the other travel path by multiplying by the probability of branching to the road is provided, for example, FIG. As illustrated in FIG. 5, the branch flow rate calculation unit (302) moves the moving object number data Q1 _in at the current time t = i flowing into the prediction target branch point X from the movement path 1 and the branch point X. Probabilities P ₁₂ , P ₁₃ , P ₁₄ ... That the mobile body that has flowed in from the path 1 branches to other moving paths 2, 3, 4,. , the probability P ₁₂ of the mobile in other words flows into the moving path 2 from moving path 1 at the branch point X, it flows into the movement path 3 That the probability P _13, by multiplying the respective probabilities P ₁₄ that flows into the moving path 4, the moving path 2,3,4 inflow predicted amount Q1 _in · P ₁₂ to ···, Q1 _in · P _13, Q1 _in · P ₁₄ ... Is calculated and provided to the user by displaying it on the display (600) or by transmitting data from the prediction information transmitting circuit (601).

  Here, with regard to the branching probability, for example, the actual number of branches or how many of the mobile objects that have passed through the branching point branch to which traveling path is actually measured and calculated as the average value of the number of branches. These branch numbers and branch probabilities are stored in advance in the branch information database (301) for each branch point and each travel path. Then, as described above, the flow rate prediction value for each moving path is calculated by multiplying the number of real-time moving bodies at the corresponding branch point and the accumulated branch probability.

  According to the above processing, for example, a route from the moving path 1 to the moving path 2 at the branch point X and a route from the moving path 1 to the moving path 3 can be selected as a route to a certain destination. At this time, when it is possible to predict that the amount of flow to the moving path 3 is smaller by taking into account the flow rate prediction value calculated as described above, the branch to the moving path 3 is selected as the recommended route and navigated. become able to. Therefore, how to change the flow rate at the bifurcation point from the current time by combining the inflow amount of the moving body, which is the current situation obtained in real time, and the branch probability, which is the past situation accumulated in advance. It is possible to predict exactly what to do, and to achieve accurate navigation based on it.

<Congestion situation prediction process using travel plan information>
Next, the congestion state prediction process using the movement plan information of each moving body will be described. In the embodiment of FIG. 5, first, the movement plan information receiving circuit (400) that receives the movement plan information at the current time of the moving body. ) (Corresponding to the movement plan information input means and the movement plan information reception means), and the movement plan information receiving circuit (400) receives movement plan information data uploaded from each moving body by wireless transmission or the like. Receive and input to internal memory.

  On the other hand, a congestion information reception circuit (100) (corresponding to the congestion current information input means and the congestion current information reception means) for receiving the congestion current information at the current time regarding the planned movement point included in the movement plan information is also provided. The congestion information receiving circuit (100) receives the congestion current information data at the current time according to the movement plan information and inputs it to the internal memory or the like. As for the congestion current information, VICS information related to the planned moving point can be considered.

  Then, the congestion situation is predicted based on the movement plan information and congestion current information for each moving object.

  More specifically, in the embodiment of FIG. 5, a required travel time calculation unit (401) (corresponding to the travel required time calculation unit) and a travel prediction information calculation unit (402) (corresponding to the travel prediction information calculation unit). First, the travel time calculation unit (401) calculates the travel time to the planned travel point for each mobile body based on the received travel plan information and congestion current information of each mobile body. calculate. This calculation process can be executed by using, for example, an existing calculation method capable of calculating the required time to a moving point such as a destination by incorporating VICS information.

  The calculated required travel time is temporarily stored in a storage means (not shown) such as an internal memory or a database together with information related to the planned travel point and the moving body. As the information about the moving object, identification information of each moving object received and input together with the movement plan information can be considered.

  Then, the movement prediction information calculation unit (402) calculates the movement prediction information related to the scheduled movement point by adding together the number of moving objects having the same required movement time to the same scheduled movement point from the stored data. To do. That is, the total number of moving bodies predicted to be present at the same scheduled movement point A at a certain future time (future time t = i + k with the same travel time k added to the current time t = i) is calculated, This is provided to the user as movement prediction information.

  For example, by calculating and analyzing that there are 100 cars planned to leave the Tokyo Metropolitan Expressway Odaiba exit around 1 pm, the number of cars at the Tokyo Metropolitan Expressway Odaiba exit as of 12:00 pm It is possible to predict the future situation where the number of vehicles will increase to about 100 around 1 pm, and based on this prediction, it will be possible to navigate through the general road below instead of the capital high.

  According to the above processing, it is possible to analyze in real time when and how many mobile objects are scheduled to flow at each point, and to accurately determine how the congestion situation at each point changes. Therefore, more accurate navigation based on the prediction can be realized.

<Congestion situation prediction process using the above three processes together>
In the invention of this application, the above-described “congestion situation prediction process using similarity”, “congestion situation prediction process using inflow amount”, and “congestion situation prediction process using movement plan” are variously combined. Thus, it is also possible to execute congestion situation prediction that is used together.

  The embodiment of FIG. 6 assumes these three types of combined processing, and each processing is as described above (the congestion information receiving circuit (100) is used for three types of processing). Before the prediction information is provided to the user, the congestion prediction results are synthesized.

More specifically, in the embodiment of FIG. 6, a prediction information synthesis unit (500) (corresponding to the final congestion prediction information calculation unit) is provided, and congestion by “congestion status prediction processing using similarity” is performed. and prediction information x _1, the inflow predictor x ₂ by the "congestion prediction process using the inflow amount", a movement prediction information x ₃ by the "congestion prediction process using the moving schedule", for example, the weighted average a ₁ x ₁
Provided by calculating final congestion prediction information by combining with + a ₂ x ₂ + a ₃ x ₃ and displaying this on the display (600) to the user or transmitting data from the prediction information transmission circuit (601) To do.

Here, the synthesis parameters a ₁ , a ₂ , and a ₃ can provide more appropriate prediction information.
It is preferable to update by learning. In the embodiment of FIG. 6, a synthesis method learning unit (501) (corresponding to the synthesis parameter learning means) for this purpose is provided. More specifically, the synthesis method learning unit (501) compares the predicted result with the actual congestion situation, and obtains a synthesis result closer to the actual congestion situation so that the synthesis parameters a ₁ , a ₂ , to update the value of a _3. For this update, a known learning method such as a linear programming method, a neural network, or a genetic algorithm can be used.

More specifically, for example, a composite prediction that a certain intersection is very crowded at 1 pm is obtained by the initial composition parameters a ₁ , a ₂ , and a ₃ , but the intersection when it actually becomes 1 pm If the level of congestion is less than predicted, the initial synthesis parameters can be used to eliminate or minimize the difference between these congestion data values using learning methods such as linear programming, neural networks, and genetic algorithms. a ₁ , a ₂ , a ₃ are adjusted to be updated synthesis parameters a ′ ₁ , a ′ ₂ , a ′ _3, and in the next synthesis prediction process, the updated synthesis parameters a ′ ₁ , a ′ ₂ , a ′ ₃ are Use. This learning update makes it possible to provide more appropriate prediction information.

  According to the above combined processing, more accurate congestion prediction and more accurate navigation based on it can be realized.

In addition, although this embodiment is a thing of the combined process which synthesize | combines all the said 3 types of prediction information, the combined use which combined two types, such as similarity and inflow, similarity and movement plan, inflow, and movement plan Needless to say, processing can be performed, and excellent congestion prediction and navigation can be realized in any case. For example, the weighted average composition is a ₁ x ₁ + a ₂ x _{2 in} the combined processing of similarity and inflow, and a ₁ x ₁ + a ₃ x _{3 in} the combined processing of similarity and movement plan. In the combined treatment, a ₂ x ₂ + a ₃ x ₃ is obtained.

<Congestion situation prediction process 2 using movement plan information>
By the way, regarding the prediction process using the movement plan information, in the above-described embodiment of FIG. 5, in addition to the movement plan information received and input by the movement plan information receiving circuit (400), the congestion information receiving circuit (100). The congestion status is also predicted using the congestion input information received and input. For example, as shown in FIG. 7, instead of the congestion current information, the moving speed and moving distance of the moving body, more preferably the average movement Speed and average travel distance can be used.

  More specifically, in the embodiment of FIG. 7, a mobile body information receiving circuit (700) that receives and inputs a moving speed and a moving distance of the mobile body is provided, and the mobile body information receiving circuit (700) The moving speed / movement distance data is received by wireless transmission from each moving body and input to the internal memory or the like. The same applies to the average moving speed and average moving distance.

  After that, similarly to the above-described embodiment of FIG. 5, first, the movement required time calculation unit (401) first calculates each moving object based on the movement plan information and moving speed / movement distance information of each moving object received and input. Calculate the required travel time to the planned travel point from the travel speed and travel distance, and add the number of moving objects having the same required travel time to the same planned travel point by the travel prediction information calculation unit (402). The movement prediction information related to the planned movement point is calculated.

  According to the above processing, when and how many mobile objects are flowing to each point can be calculated and analyzed in real time from the actual moving speed and distance of each moving member. Change can be predicted accurately.

  Of course, the prediction process of this embodiment can be used in combination with the above-described other prediction processes using the similarity and the inflow amount as illustrated in FIG. The synthesis process of each prediction result is executed in the same manner as in the embodiment of FIG.

<Congestion situation prediction process 3 using movement plan information>
Furthermore, for the prediction process using the movement plan information, for example, as shown in FIG. 9, the process based on the combined prediction result of the prediction process using the above-described similarity and the prediction process using the inflow amount; It is also possible to do. That is, in the embodiment of FIG. 5, prediction is performed based on congestion current information received and input by the congestion information receiving circuit (100), that is, congestion information at the current time, but in the embodiment of FIG. The information synthesizing unit (500) (corresponding to the synthetic congestion prediction information calculating means) performs prediction based on the synthetic congestion prediction information obtained in the future, that is, congestion information in the future.

More specifically, the congestion information receiving circuit (100), congestion database (200), calculates congestion prediction information x ₁ by the similarity calculation section (201) and most similar situation search unit (202), as well as flow rate information The calculation of the inflow prediction amount x ₂ by the receiving circuit (300), the branch information database (301) and the branch flow rate calculation unit (302), and the synthesis of the calculation results by the prediction information synthesis unit (500) are shown in FIGS. 3. Same as the processing in the embodiment of FIG. 6, but in the calculation of the required travel time to the predicted travel point by the required travel time calculation unit (401), the combined result by the prediction information combining unit (500) is used. .

This calculation can be executed, for example, by calculating the required time taking into account the combined prediction information a ₁ x ₁ + a ₂ x ₂ by weighted average.

First, the current time is set to t _0, and the time required for passing the current moving route (road) r ₀ is calculated using the composite prediction information a ₁ x ₁ + a ₂ x ₂ , and this is added to t ₀ . Let t ₁ be the target. For this calculation, the actual moving speed and moving distance of the received moving body, more preferably the average moving speed and average moving distance may be used.

Since t ₁ is one time point in the future, the route (road) scheduled to move at t ₁ is next extracted from the movement plan information, which is defined as r _1, and the time required for passage of r ₁ is the combined prediction information a Calculation is performed using ₁ x ₁ + a ₂ x ₂ . The required time obtained is added to t ₁ to be t ₂ . The moving speed and moving distance can also be used for this calculation.

  By repeating the above calculation, the required travel time to each predicted travel point is calculated.

  After that, similarly to the above, the movement prediction information calculation unit (402) calculates the movement prediction information related to the planned movement point by adding the number of moving bodies having the same required movement time to the same planned movement point. The information is provided to the user via the display (600) and the prediction information transmission circuit (601).

  According to this, since the mobile flow rate at each point is predicted based on the future congestion information that has been predicted and synthesized in advance using the similarity and the inflow amount, it is more than the case where the congestion information at the current time is used. More accurate congestion situation prediction can be realized.

  And of course, the travel prediction information calculation unit (402) calculates the required time by using the congestion information at the current time by the congestion information receiving circuit (100) and the future congestion information by the prediction information synthesis unit (500) together. Needless to say, the prediction accuracy can be further improved.

  Calculation in the case of this combined use is performed as follows, for example.

First, the time required for passing the current moving route (road) r ₀ is calculated using congestion current information such as VICS information relating to r ₀ , preferably using the moving speed and moving distance, and this is calculated. In addition to the current time t ₀ , the scheduled passage time t ₁ is used.

Subsequently, the time required for passage of the route (road) r _{1 to} be moved at t ₁ is preferably calculated by using the combined prediction information a ₁ x ₁ + a ₂ x ₂ as congestion future information. Is also calculated and added to t ₁ to obtain t ₂ .

  After that, the same calculation may be repeated.

<Central management navigation form>
The invention of this application as described above can be configured as a system form that performs centralized management navigation by wireless communication between the central information server and the mobile unit, as illustrated in FIGS. 10 and 11, for example.

In FIG. 11 illustrating this central management system, the congestion information receiving circuit (100), the flow rate information receiving circuit (300), the movement plan information receiving circuit (400), and the prediction information transmitting circuit (601) are added to the central information server S. Although not shown for convenience of explanation, the congestion status database (200), similarity calculation unit (201), most similar status search unit (202), branch information database (301), branch flow rate calculation unit (302), A required moving time calculation unit (401), a movement prediction information calculation unit (402), a prediction information synthesis unit (500), a synthesis method learning unit (501), and a mobile body information reception circuit (700) are provided, and the mobile body C ₁ , C _2, C _3, the ... (such as a navigation device moving body is mounted or portable), mobile plan information transmitting circuit (801) and congestion prediction information Shin circuit (802), and the movement planning unit (800), is provided with a movement plan correction unit (803) and a display (804).

The central information server S centrally manages all the congestion information, flow rate information, and movement plan information, and calculates congestion prediction information suitable for each of the mobile bodies C ₁ , C ₂ , C ₃ ,. Is provided to each mobile unit, and each mobile unit C ₁ , C ₂ , C ₃ ,.
800) creates an initial movement plan by internal processing, transmits it to the central information server S by the movement plan information transmission circuit (801), and receives congestion prediction information data from the central information server S as a congestion prediction information reception circuit ( 802) and a recommended movement plan in which the initial movement plan is corrected by the movement plan correction unit (803) based on the received movement plan is displayed on the display (804). Do.

  As described above, more efficient central management navigation can be realized. Of course, the configuration of the central management system shown in FIG. 11 is merely an example, and various modes are possible for the distribution of each function.

<Individual navigation form>
Of course, the invention of this application can also be in the form of an on-board or portable navigation device for each mobile unit. In this case, all of the processing functions, circuits, and databases shown in FIGS. 1 to 9 may be constructed so as to be integrated together with a navigation function including functions such as movement plan creation / correction. However, since it is necessary to receive congestion information and flow rate information from the outside of the mobile body in real time, for example, a communication function such as VICS information with the traffic information communication center is provided. Needless to say, the congestion status database (200) and the branch information database (301) may be constructed externally so that necessary data can be transmitted and received at any time.

  Of course, the invention of this application is not limited to the above embodiments, and various aspects are possible for details.

  As described above in detail, according to the invention of this application, a new congestion situation prediction program and a computer readable recording medium on which a congestion situation prediction program is recorded, which can always accurately predict one step ahead of the constantly changing congestion situation In addition, a new navigation program, a computer-readable recording medium recording the navigation program, and a navigation device capable of realizing more accurate automatic navigation based on the congestion prediction information are provided. Is done.

  According to this, not only efficient navigation of cars but also indoors such as event venues and offices, or street corners, it is possible to accurately predict the congestion situation of each pavilion and each landmark and to provide accurate human navigation. It can be realized, and the efficiency of human flow can be improved.

  In addition, it is possible to improve the efficiency of the flow of the mobile transport device for moving and transporting articles in factories, warehouses, ships, aircraft, spacecrafts, and the like.

DESCRIPTION OF SYMBOLS 100 Congestion information receiving circuit 200 Congestion situation database 201 Similarity calculation part 202 The most similar situation search part 300 Flow rate information receiving circuit 301 Branch information database 302 Branch flow rate calculation part 400 Movement plan information reception circuit 401 Movement required time calculation part 402 Movement prediction information Calculation unit 500 Prediction information combining unit 501 Combining method learning unit 600 Display 601 Congestion prediction information transmission circuit 700 Moving body information reception circuit 800 Movement plan creation unit 801 Movement plan information transmission circuit 802 Congestion prediction information reception circuit 803 Movement plan correction unit 804 Display

Claims (6)

Computers to predict congestion,
The moving body number input means for inputting the moving body number data Q1 _in at the current time t = 1 flowing into the arbitrary branch point X from the arbitrary moving path 1, the input moving body number data Q1 _in, and the moving path 1 From the moving path 1 to the pre-stored branching point X, which is the average value of the number of actually measured branches branched to other moving paths 2, 3, 4... Multiplying the probabilities P ₁₂ , P ₁₃ , P ₁₄ ... Of the mobile body branching to the other movement paths 2, 3, 4. inflow predictor _{x 1 = Q1 in · P 12} , Q1 in · P 13, Q1 in · P 14 flows predicted amount calculating means for calculating a ... to,
And
Movement plan information input means for inputting movement plan information at the current time of each moving object,
Congestion current information input means for inputting current congestion information at the current time regarding the planned travel point included in the input travel plan information;
A movement that calculates the required travel time of each moving body to the planned moving point based on the input moving plan information and congestion current information, or based on the input moving plan information and the moving speed and moving distance of each moving body Time required calculation means, and
By adding the number of mobile body having the same travel time to the same moving prediction place, movement prediction information calculating means for calculating a movement prediction information x ₂ about the moving prediction place,
And
Final congestion prediction information calculation means for calculating final congestion prediction information by combining the expected inflow amount x ₁ and the movement prediction information x ₂ with a weighted average a ₁ x ₁ + a ₂ x ₂ ;
The synthesis parameters a ₁ and a ₂ used in the synthesis process by the final congestion prediction information calculation means are adjusted by adjusting the initial synthesis parameter so as to reduce the difference between the congestion synthesis predicted value based on the initial synthesis parameter and the actual congestion value. A congestion condition prediction program that functions as a synthetic parameter learning means for learning and updating so as to obtain an updated synthetic parameter . The computer-readable recording medium which recorded the congestion condition prediction program of Claim 1 . A device for predicting the congestion situation,
Moving body number receiving means for receiving the moving body number data Q1 _in at the current time t = 1 flowing into the arbitrary branch point X from the arbitrary moving path 1,
The movement that has flowed from the arbitrary movement path 1 to the branch point X, which is the average value of the actually measured number of branches branched from the movement path 1 to the other movement paths 2, 3, 4,. Branch probability storage means for storing in advance probabilities P ₁₂ , P ₁₃ , P ₁₄ ... For branching to another moving path, and the number of moving bodies received by the moving body number receiving means, and branch probability storage means Are multiplied by the probabilities P ₁₂ , P ₁₃ , P ₁₄ ... Stored _{in the above,} and the estimated inflow x ₁ = Q1 _in · P ₁₂ into each of the other travel paths 2, 3, 4. , Q1 _in · P ₁₃ , Q1 _in · P ₁₄ ...
Movement plan information receiving means for receiving movement plan information at the current time of each moving object,
Congestion current information receiving means for receiving current congestion information at the current time relating to the planned movement point included in the movement plan information received by the movement plan information receiving means;
Based on the movement plan information received by the movement plan information receiving means and the congestion current information received by the congestion current information receiving means, or based on the received movement plan information and the moving speed and moving distance of each moving body The travel required time calculation means for calculating the required travel time to the planned travel point and the number of mobiles having the same travel required time to the same planned travel point are added together to obtain the motion prediction information x regarding the planned travel point x Movement prediction information calculating means for calculating ₂ ,
And
Final congestion prediction information calculation means for calculating final congestion prediction information by combining the expected inflow amount x ₁ and the movement prediction information x ₂ with a weighted average a ₁ x ₁ + a ₂ x ₂ ;
The synthesis parameters a ₁ and a ₂ used in the synthesis process by the final congestion prediction information calculation means are adjusted by adjusting the initial synthesis parameter so as to reduce the difference between the congestion synthesis predicted value based on the initial synthesis parameter and the actual congestion value. A congestion condition prediction device comprising : a synthesis parameter learning means for learning and updating so as to obtain an update synthesis parameter . A navigation program for causing a computer to function as means for performing navigation based on the congestion situation predicted by the congestion situation prediction program according to claim 1 . The computer-readable recording medium which recorded the navigation program of Claim 4 . A navigation device that performs navigation based on a congestion situation predicted by the congestion situation prediction apparatus according to claim 3 .

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