JP4495746B2 - Traffic jam travel time prediction database creation device, traffic jam travel time prediction database creation method, traffic jam travel time prediction database creation program implementing the method and recording medium recording the program, traffic jam travel time prediction device, traffic jam travel time prediction method, Program for predicting traffic jam travel time and a recording medium recording the program - Google Patents

Description

  The present invention relates to a traffic information prediction technique based on traffic information accumulated for each road section.

  Considering correspondence to basic day type information that has been known so far (for example, days of the week, weekdays or holidays, fifty days, and year-end and New Year periods, Bon holidays, Golden Week, etc.) There are several traffic information prediction methods that have been proposed (for example, see Patent Document 1).

  The traffic information prediction method stores all of the past traffic information after adding information that is a search key such as a road name, time, date, day of the week, etc. As the predicted traffic information of the specific date and time, the past traffic information that meets or is closest to the conditions of the prediction target date and time of the road is retrieved and presented.

In addition, the definition (for example, refer patent document 2) etc. regarding the traffic congestion area in each road area are known as related technology regarding the traffic information prediction method.
JP 2001-118188 A (paragraphs [0012] to [0019] and the like). JP 2004-164373 A (paragraphs [0035] to [0037] etc.).

  The traffic information prediction method described above requires a large capacity information storage means for storing all past traffic information. Furthermore, when calculating prediction information, it takes a very long processing time to calculate prediction information because it sequentially searches for cases that match or are similar to the prediction conditions from a large amount of past traffic information when the prediction request occurs. It becomes. For this reason, for example, it has been practically difficult to mount on a car navigation apparatus having limited storage capacity and arithmetic processing capability.

  The present invention has been made on the basis of the above-mentioned problem, and a traffic jam travel time prediction database creation device and traffic jam travel time prediction that create highly accurate predicted traffic information that takes into account day type information such as days of the week, weekdays, or holidays. Database creation method, traffic jam travel time prediction database creation program implementing the method, recording medium recording the program, traffic jam travel time prediction device for predicting traffic information at high speed using the created forecast traffic information, traffic jam travel The object is to provide a time prediction method, a traffic jam travel time prediction program in which the method is implemented, and a recording medium on which the program is recorded.

In order to solve the above-mentioned problem, the invention according to claim 1 is characterized in that the traffic information storage means stores traffic information and travel time information which are past traffic conditions collected and accumulated for each road section, and one or a plurality of traffic information storage means. Corresponding to the road section group consisting of the road sections, and for each date on which the congestion information and travel time information are collected, a combination of basic day type information including information indicating the day of the week, weekday or holiday, season, A day identifier definition section that defines a day identifier that is uniquely determined according to one of the periods in which traffic conditions occur, and a day defined in the day identifier definition section on each day of the month including the prediction target date A day section identifier calendar information having a calendar section made up of information associated with a kind identifier, and a road section group day kind identifier calendar information. A road section group corresponding day class identifier calendar information storing means for storing loop day class identifier calendar information, and a combination of a road section group day class identifier calendar name and road section name for designating the road section group day class identifier calendar information. And a road segment group-corresponding day type identifier calendar name storage means that constitutes a road section group by the combination, and a prediction information storage means for storing prediction information, based on the past traffic jam information and travel time information A traffic jam travel time prediction database creation device for creating data for making predictions regarding future traffic conditions in the road section, wherein the traffic information stored in the traffic information storage means is a road corresponding to the road section. Sector group day type identifier Corresponds to a specific day type identifier included in the calendar information Based on the result of statistical analysis using basic day type information, the degree of congestion in each time period of the day corresponding to a specific day type identifier according to the road partial section that determines the degree of congestion in the road section The traffic time degree stored in the traffic information storage means is converted into a specific day type identifier included in the road section group day type identifier calendar information corresponding to the road section. Based on the result of statistical analysis using the relevant basic day type information, the day type identifier corresponding travel time for calculating the travel time information of each time zone of the day corresponding to the specific day type identifier of the road section The traffic congestion degree calculated by the calculation means and the day type identifier correspondence congestion degree calculation means is used to calculate the traffic jam prediction information for each time zone of the day on the date corresponding to the day type identifier of the road section in the future. And the day type identifier corresponding predicted traffic information storage unit for storing the day type identifier, the predicted traffic jam information, and the road section name in the prediction information storage unit, and the day type identifier corresponding travel time calculation unit. Is regarded as predicted travel time information for each time zone of a day on a date corresponding to the future day type identifier of the road section, and the predicted travel time information, the day type identifier , A day type identifier-corresponding predicted travel time information storage unit that stores the road section name in combination in the prediction information storage unit.

According to a second aspect of the present invention, in the first aspect of the invention, the day type identifier-corresponding traffic congestion degree calculating means is based on a traffic congestion degree that is traffic congestion information stored in the traffic information storage means. For each road partial section that determines the degree of traffic congestion, the mode value of the degree of traffic congestion in the relevant time zone of the past date corresponding to the day type identifier is calculated, and the mode value is used as the future day type identifier of the road section. Means for calculating the estimated traffic jam information for each time slot in the day.

According to a third aspect of the present invention, in the first aspect of the invention, the day type identifier corresponding travel time calculating unit is configured to calculate the day type of the road section based on the travel time information stored in the traffic information storing unit. Either the average value, mode value, or median value of the travel time information of the relevant time zone in the past corresponding to the identifier is used as the predicted travel of each time zone of the day in the future day type identifier of the road section. And means for calculating time information.

Invention of claim 4, claims 1 to predicted congestion information and date seed identifier corresponding to the predicted congestion information calculated generated by congestion travel time pre Hakade database creating apparatus according to any one of 3 a day factor identifier corresponding traffic information storage means for storing, according to claim 1 to congested travel time pre Hakade database creating apparatus predicted travel time is generated is calculated by the information and the predicted travel time of any one of 3 and species identifier corresponding travel time storage means day for storing the corresponding day type identifier information, claims 1 to 3 of any one congested travel time prediction database creation device road segment group date species stored by the described From the information that associates the day type identifier definition section of the identifier calendar information with the day type identifier defined in the day type identifier definition section for each year and month including the forecast target A road section group corresponding day type calendar information storing means for storing and managing road section group day type identifier calendar information for each road section group consisting of one or a plurality of road sections, A road section group date type identifier calendar name for specifying road section group date type identifier calendar information, a combination of a road section name and a road section group, and a road section group corresponding day type identifier calendar name storing means constituting a road section group by the combination; the provided, a predicted traffic jam information and the predicted travel time astringency information you get a residence travel time prediction system in the future traffic conditions in the road section to be predicted, prediction target date, the prediction target time zone, the predicted target road Accepts a search request including a road section name indicating the section, and the road of the road section to be predicted The road segment group Jan species identification calendar name corresponding to the section name retrieved from the road segment group correspond Jan species identifier Calendar name storage means, the road segment group Jan species identifier determined from the retrieved road segment groups Jan species identification calendar name Calendar information is specified using the road section group day type identifier corresponding calendar information storage means, and the day type identifier of the prediction target day of the road section to be predicted is searched from the specified road section group day type identifier calendar information. The combination of the retrieved day type identifier, the prediction target time zone, and the prediction target road section name is used as a search key, and the predicted traffic jam information corresponding to the search key is stored in the day type identifier corresponding traffic information storage unit. From the predicted traffic jam information corresponding to the day type identifier, and the estimated travel time information corresponding to the search key is Road segment group-corresponding traffic jam travel time searching means for searching from predicted travel time information corresponding to the day type identifier stored in the bespoke-corresponding travel time storage means.

The invention according to claim 5 includes a traffic information storing means for storing traffic information and travel time information, which are past traffic conditions collected and accumulated for each road section, and a road section group comprising one or a plurality of road sections. Correspondingly, for each date on which the congestion information and travel time information are collected, any of the day of the week, weekdays or holidays, a combination of basic day type information including information indicating the season, or a period during which a unique traffic situation occurs A day type identifier definition section that defines a day type identifier that is uniquely determined according to the date, and information that associates the day type identifier defined in the day type identifier definition section with each day of the month including the prediction target date. A day section identifier calendar information having a calendar section and road section group day class identifier calendar information, and the road section group day class identifier calendar Storing road segment group correspond Jan species identifier calendar information storage means for storing distribution, the combination of the road segment group Jan species that specifies the road segment group Jan species identifier calendar information identifier calendar name and road section name, the A road section group corresponding day type identifier calendar name storage means constituting a road section group by combination, and a prediction information storage means for storing prediction information, and based on the past traffic jam information and travel time information, the road A traffic jam travel time prediction database creation method used in a device for creating data for forecasting future traffic conditions in a section, wherein the traffic information stored in the traffic information storage means corresponds to the road section Road section group day type identifier Basic day type information corresponding to a specific day type identifier included in the calendar information The day type for calculating the degree of congestion in each time zone of the day corresponding to the specific day type identifier according to the road partial section that determines the degree of congestion in the road section based on the result of statistical analysis using The basic day type corresponding to the specific day type identifier included in the road section group day type identifier calendar information corresponding to the road section, the travel time information stored in the traffic information storage means and the identifier-corresponding congestion degree calculating step Based on a result of statistical analysis using information, a day type identifier corresponding travel time calculation step for calculating travel time information of each time zone of a day corresponding to a specific day type identifier of the road section; The traffic jam degree calculated in the day type identifier corresponding traffic degree calculation step is calculated based on the predicted traffic jam information for each time zone of the day on the date corresponding to the day type identifier of the road section in the future. The day type identifier corresponding predicted traffic jam information storing step for storing the day type identifier, the predicted traffic jam information, and the road section name in the predicted information storage unit in combination, and the day type identifier corresponding travel time calculating step The travel time information calculated in this way is regarded as predicted travel time information for each time zone of the day on the date corresponding to the future day type identifier of the road section, and the predicted travel time information, the day type identifier, And a day type identifier corresponding predicted travel time information storing step of storing the road section name in combination in the predicted information storing means.

According to a sixth aspect of the present invention, in the fifth aspect of the invention, the day identifier-corresponding congestion degree calculating step is based on a congestion degree that is congestion information stored in the traffic information storage means. For each road partial section that determines the degree of traffic congestion, the mode value of the degree of traffic congestion in the relevant time zone of the past date corresponding to the day type identifier is calculated, and the mode value is used as the future day type identifier of the road section. And calculating the estimated traffic congestion information for each time slot in a day.

The invention according to claim 7 is the invention according to claim 5 , wherein the date type identifier corresponding travel time calculating step is based on the travel time information stored in the traffic information storage means. Either the average value, mode value, or median value of the travel time information of the relevant time zone in the past corresponding to the identifier is used as the predicted travel of each time zone of the day in the future day type identifier of the road section. And calculating the time information.

The invention of claim 8, wherein the species day corresponding to claims 5 to 7 predicted traffic jam information and the predicted traffic jam information calculated generated by congestion travel time pre Hakade database creation method according to any one of the identifier a day factor identifier corresponding traffic information storage means for storing, claims 5 to 7 congested travel time pre Hakade database created predicted travel time is calculated to produce by the method information and the predicted travel time of any one of and species identifier corresponding travel time storage means day for storing the corresponding day type identifier information, claims 5 to 7 any one congested travel time prediction database creation method road segment group date species stored by according to the From the information that associates the day type identifier definition section of the identifier calendar information with the day type identifier defined in the day type identifier definition section for each year and month including the forecast target A road section group corresponding day type calendar information storing means for storing and managing road section group day type identifier calendar information for each road section group consisting of one or a plurality of road sections, A road section group date type identifier calendar name for specifying road section group date type identifier calendar information, a combination of a road section name and a road section group, and a road section group corresponding day type identifier calendar name storing means constituting a road section group by the combination; the provided, a congestion travel time prediction method to use an apparatus for obtaining a predicted traffic jam information and the predicted travel time information is a future traffic conditions in the road section to be predicted, prediction target date, the prediction target time zone , Accepts a search request including the road section name indicating the prediction target road section, Find the road segment group Jan species identifier calendar name corresponding to the road section name of a road segment from the road segment group correspond Jan species identifier calendar name storing means, the retrieved road segment groups Jan species identifier road segment determined from calendar name The group day type identifier calendar information is specified using the road section group day type identifier corresponding calendar information storage means, and the prediction target date of the road section to be predicted from the specified road section group day type identifier calendar information A search is made for a species identifier, a combination of the searched day identifier, a prediction target time zone, and a prediction target road section name is used as a search key, and predicted traffic jam information corresponding to the search key is stored in the traffic jam information corresponding to the day identifier. Search from predicted traffic jam information according to the day type identifier stored in the means, and predicted travel time information according to the search key The road segment group-corresponding traffic jam travel time searching step is searched from the predicted travel time information corresponding to the day type identifier stored in the day type identifier corresponding travel time storage means.

The invention described in claim 9 is a traffic jam travel time prediction database creation program, wherein each means in the traffic jam travel time prediction database creation device according to any one of claims 1 to 3 is a computer program executable by a computer. Characterized by the description.

The invention described in claim 10 is a traffic jam travel time prediction program, wherein each means in the traffic jam travel time prediction device according to claim 4 is described as a computer program executable by a computer.

The invention described in claim 11 is a recording medium, wherein each means in the traffic jam travel time prediction database creating apparatus according to any one of claims 1 to 3 is described as a computer program executable by a computer, and the computer The program is recorded.

The invention described in claim 12 is a recording medium, wherein each means in the traffic jam travel time prediction device according to claim 4 is described as a computer program executable by a computer, and the computer program is recorded. And

In the first and fifth aspects of the present invention, the basic day type information and the day type identifier corresponding to the period in which the unique traffic situation occurs can be used.

According to the first and fifth aspects of the present invention, the predicted traffic jam information and the predicted travel time information according to the day type identifier can be acquired.

According to the second and sixth aspects of the invention, the mode value of the degree of congestion can be acquired.

In the inventions according to the third and seventh aspects, an average value, a mode value, or a median value of travel times can be selected.

According to the first and fifth aspects of the present invention, predicted traffic jam information and predicted travel information can be acquired corresponding to a road segment group.

According to the fourth and eighth aspects of the present invention, it is possible to search the generated predicted traffic jam information and the predicted travel time information according to the day type identifier corresponding to the road section group.

According to the ninth aspect of the present invention, each means in the traffic jam travel time prediction database creating apparatus according to any one of the first to third aspects can be described as a computer program.

In the invention described in claim 10, each means in the traffic jam travel time predicting device described in claim 4 can be described as a computer program.

The invention of the eleventh aspect can record, on a recording medium, a computer program that implements each means in the traffic jam travel time prediction database creation device according to any one of the first to third aspects.

The invention according to the twelfth aspect can record, on a recording medium, a computer program that implements each means in the traffic jam travel time predicting apparatus according to the fourth aspect .

  The prediction information calculation / generation means in the invention according to claim 1 will be described in detail. The prediction information calculation / generation means includes the traffic information stored in the traffic information storage means, including the basic day type information. Based on the result of the analysis, the degree of congestion corresponding to the day identifier that calculates the degree of congestion in each time zone of the day corresponding to the specific day type identifier according to the road partial section that determines the degree of congestion in the road section You may provide only a calculation means. Further, in accordance with this, the prediction information storage means calculates the traffic congestion degree calculated by the day type identifier corresponding traffic degree calculation means for each hour of the day on the date corresponding to the future day type identifier of the road section. It may be provided with only the day identifier-corresponding traffic information storage means for storing the predicted traffic jam information in association with the day type identifier, considering the band predicted traffic jam information.

  In addition, the prediction information calculation and generation unit may include a specific day type identifier of the road section based on a result of statistical analysis of travel time information stored in the traffic information storage unit including the basic day type information. Only the day type identifier corresponding travel time calculating means for calculating the travel time of each time slot corresponding to the day may be provided. Further, accordingly, the prediction information storage means sets the travel time calculated by the day type identifier corresponding travel time calculation means to each time of the day on the date corresponding to the future day type identifier of the road section. It may be assumed that the estimated travel time information of the belt is provided, and only the day type identifier corresponding predicted travel time information storage means for storing the predicted travel time information in association with the day type identifier may be provided.

As described above, according to the first and fifth aspects of the invention, it is possible to obtain predicted traffic information corresponding to the day type identifier.

According to the first and fifth aspects of the invention, it is possible to provide predicted traffic jam information and predicted travel time information according to the day type identifier.

According to the second and sixth aspects of the invention, it is possible to provide predicted traffic jam information based on the mode value of the traffic jam degree.

According to the invention of claim 3, 7, can provide a predicted travel time information having a value of either an average value or mode value or the median value of travel time.

According to the first and fifth aspects of the present invention, it is possible to provide predicted traffic jam information and predicted travel time information corresponding to a road segment group.

According to the fourth and eighth aspects of the present invention, the traffic jam information and the travel time can be predicted at high speed by searching the prepared traffic jam information and the predicted travel time information corresponding to the road section group.

According to the invention of claim 9 , it is possible to provide a computer program in which each means in the traffic jam travel time prediction database creation device is mounted.

According to the invention of claim 10 , it is possible to provide a computer program in which each means in the traffic jam travel time prediction device is mounted.

According to invention of Claim 11 , the recording medium which recorded the computer program which mounted each means in a traffic jam travel time prediction database preparation apparatus can be provided.

According to invention of Claim 12 , the recording medium which recorded the computer program which mounted each means in a traffic jam travel time prediction apparatus can be provided.

  These can contribute to the traffic information processing field.

  Hereinafter, first and second embodiments of the present invention will be described in detail with reference to the drawings.

[First Embodiment]
A traffic jam travel time prediction database creation device and a traffic jam travel time prediction device using prediction data created by the traffic jam travel time prediction database creation device in the first embodiment will be described in detail with reference to the drawings.

  In addition, the day type identifier calendar information (for example, the day type identifier past calendar information, the day type identifier future calendar information) used in the traffic jam travel time prediction database creation device and the traffic jam travel time prediction device in the first and second embodiments, It is configured to use day type identifier calendar information in which day type identifiers for all past, present, and future dates are defined, and both congestion prediction and travel time prediction are executed.

  The traffic travel time information handled in the first and second embodiments includes the Japan Road Traffic Information Center (JARTIC) and the Road Traffic Information Communication System Center (VICS (registered trademark) ( Vehicle Information and Communication System))) provided VICS (registered trademark) traffic information.

  The traffic jam travel time prediction database creation device in the first and second embodiments may include a control unit (for example, a CPU (Central Processing Unit)) that controls each unit. The traffic jam travel time prediction apparatus in the first and second embodiments may include a control unit (for example, a CPU) that controls each unit.

  Each storage unit of the congestion travel time prediction database creation device in the first and second embodiments may include a general storage device (for example, a memory or a hard disk device) for storing each information. Each storage means of the traffic jam travel time prediction device in the first and second embodiments may include a general storage device for storing each information.

  An overall configuration of a traffic jam travel time prediction database creation apparatus according to the first embodiment will be described with reference to FIG.

  Reference numeral 1 in FIG. 1 is a traffic jam travel time prediction database creation apparatus main body for creating data (database data) for predicting traffic jam travel time, and reference numeral 111 stores past traffic information (congestion information and travel time information). The past traffic information storage means 101 is a day type identifier calendar information storage means for storing day type identifier calendar information defining day type identifiers for all past, present and future dates. For example, the day type identifier calendar information storage unit 111 determines whether the date corresponds to a day of the week, a weekday, a holiday, or a fifty day for each past date of collecting traffic jam information and travel time information. A calendar of date identifiers that are uniquely determined by a combination of basic date information such as whether it falls under the autumn / winter), large consecutive holidays (that is, periods during which special traffic conditions such as the year-end and New Year holidays, Bon holidays, and Golden Weeks occur) The day type identifier calendar information corresponding to each date is stored. The day type identifier is, for example, 11 = Monday of weekday of spring (March to May), 82 = First half of year-end and New Year, and the like.

  Reference numerals 102 and 103 are defined in the day type identifier and the day type identifier stored in the day type identifier calendar information storage unit 101 from the past traffic jam information and travel time information stored in the past traffic information storage unit 111. Based on the result of statistical analysis with reference to basic day type information (day of the week, weekday, holiday, etc.), the degree of congestion corresponding to the day type identifier that calculates the degree of congestion of the day corresponding to the day type identifier It is a day type identifier corresponding travel time calculation means for calculating a calculation means and a travel time.

  Reference numerals 104 and 105 denote the degree of congestion of the day corresponding to the day type identifier calculated by the day type identifier corresponding traffic degree calculating unit 102 and the day type identifier corresponding travel time calculating unit 103 (predicted traffic jam information). And a day type identifier-corresponding congestion degree (congestion information) storing means and a day type identifier corresponding travel time storing means for storing travel time (predicted travel time information), respectively.

  The traffic information and travel time information for each road section accumulated in the past traffic information storage means 111 in FIG. 1 will be described with reference to FIG. FIG. 2 is an example of past traffic jam information and travel time information for each road section.

  In the traffic jam information and travel time information Tj11 to Tj13 for each road section in FIG. 2, the traffic jam information and travel time information are recorded with the time information (date and time) at which the information was observed for each road section. Shall be. In addition, the congestion information is a numerical value indicating the degree of congestion at the time of observation for each congestion section in which the degree of congestion is defined in the road section (for example, in FIG. 2, the congestion degree is a three-stage numerical value, 0 = congestion None, 1 = congested, 2 = congested numerical value). As described in Patent Document 2, the congestion section in each road section is checked for traffic information at each time in a certain period for each road section, and the road section appears as the beginning and end of the congestion section at each time. A section defined by a point where the upper points are arranged from the end point of the road section toward the start point is defined as a traffic jam definition section. The traffic jam section is a partial section (road partial section) in the road section.

  One example of the day identifier calendar information recorded in the day identifier calendar information storage means 101 in FIG. 1 will be described with reference to FIG. The day type identifier calendar 301 in FIG. 3 includes a day type identifier definition section Sc11 that defines basic day type information (day of the week, season, weekday, holiday, etc.) of each day type identifier, and past, current, and future dates. The calendar section Sc12 describes the relevant day type identifier.

  “30: Autumn / Weekday / Monday” in the day type identifier definition section Sc11 is a definition indicating Monday in the fall (September to November), which is not a holiday or a transfer holiday. “/” (Slash) is a delimiter in the day type identifier definition section.

  In the calendar section Sc12, the day type identifier of each date for one month is described in one line, the beginning of each line indicates the year and month, and after the “:” (colon) is separated, The day type identifiers for each day are separated by “,” (comma). The order of the day type identifier indicates the day of the month. For example, the day type identifier indicated by the symbol d11 is “12” because it is “Spring / Weekday / Wednesday” on March 1, 2006. The day type identifiers indicated by the symbols d12 to d17 are also interpreted as described above.

  A recording image of information on the degree of traffic jam and travel time at each time of day corresponding to the day type identifier stored in the day type identifier corresponding traffic degree storage unit 104 and the day type identifier corresponding travel time storage unit 105 in FIG. This will be described with reference to FIG.

  In creating a traffic jam travel time prediction database, first, the travel time calculating means 103 in FIG. 1 sequentially searches past travel time information from the past traffic information storage means 111 for the travel time. Next, the date type identifier and basic date type information corresponding to the date of the searched past travel time information are searched from the date type identifier calendar information storage means 101 using the date as a key (search key). Then, a statistical analysis is performed on the travel time information of the same time zone on the same date as the specific day type identifier of the road section, and the travel time is calculated for each road section, for each day type identifier, and for each time zone. The travel time is stored by the day type identifier corresponding travel time storage means 105. For example, the calculated travel time (predicted travel time information) is stored as shown in the third column in the congestion degree and travel time information Tj21 to Tj23 in FIG.

  For example, the travel time calculation means 103 corresponding to the day type identifier in FIG. 1 shows the past corresponding to the day type identifier of the road section as the travel time of each time zone of the day in the specific day type identifier of the road section. The average value, mode or median value of the travel time in the time zone of the date is calculated as the travel time in the time zone of the road section.

  In addition, the calculation method of the travel time for each specific road section, each day type identifier, and each time zone is the average value or the most frequent travel time for each road section and the time zone for the same day type identifier date. The value or the median can be calculated, but the calculation method is not limited to this.

  Further, as with the travel time, the traffic congestion degree calculation means 102 in FIG. 1 sequentially searches for past traffic information from the past traffic information storage means 111. Next, the day type identifier and basic day type information corresponding to the date of the past traffic information that has been sequentially searched are searched from the day type identifier calendar information storage unit 101 using the date as a key. Then, statistical analysis is performed on the traffic information of the same time zone on the same date as the specific day type identifier of the road section, and the degree of traffic congestion is calculated for each road section, for each day type identifier, and for each time zone. The degree of traffic congestion is stored by the day type identifier corresponding traffic congestion degree storage means 104. The calculated traffic jam degree (predicted traffic jam information) is stored as the fourth and subsequent columns in the traffic jam degree and travel time information Tj21 to Tj23 in FIG.

  For example, for each road partial section that determines the congestion degree in the road section, the day type identifier corresponding congestion degree calculation means 102 in FIG. In addition, the mode value of the degree of congestion in the time zone of the past date corresponding to the day type identifier is calculated as the degree of congestion in the time zone of the road partial section.

  In addition, as a method of calculating the degree of congestion for each specific road section, day type identifier, and time zone, the mode value of the degree of congestion for each road section and time zone for the same day type identifier date is calculated. However, the calculation method is not limited to this.

  The overall configuration of the traffic jam travel time prediction apparatus according to the first embodiment will be described with reference to FIG. Reference numeral 5 in FIG. 5 is a traffic jam travel time prediction apparatus main body, and reference numeral 501 is a day type identifier calendar information storing means for storing day type identifier calendar information defining day type identifiers for all past, present and future dates. .

  Reference numerals 504 and 505 denote day-identifier-corresponding congestion degree storage means for storing the degree of congestion in the time zone of the day corresponding to the day-identifier created by the congestion travel time prediction database creating apparatus 1 in FIG. It is a day type identifier corresponding travel time storage means for storing travel time.

  Reference numeral 502 indicates a predicted traffic jam and predicted travel time search request (prediction target date, prediction target time (or prediction target time zone), prediction target road section from the outside (for example, a user interface device including a communication line and a keyboard). A search request including a road section name indicating the day type), and by referring to the day type identifier stored in the day type identifier calendar information storage unit 501, the degree of congestion in the time zone of the day corresponding to the day type identifier and It is a predicted traffic jam travel time search unit that searches the travel time from the day type identifier corresponding traffic degree storage unit 504 and the day type identifier corresponding travel time storage unit 505. The road section name includes a mesh number, a link category, and a link number.

  In the prediction of the traffic jam travel time, when the prediction traffic search time information search unit 502 in FIG. 5 receives a request for search for prediction information designating the name of the road to be predicted and the date and time of the prediction, the traffic jam travel time information search unit 502 Searches the date type identifier calendar information storage unit 501 for the day type identifier of the prediction target date using the date in the prediction target date and time as a key. Then, as a next step, the day type identifier corresponding congestion degree storage means 504 and the day type identifier corresponding travel time storage are stored using the prediction target road section, the day type identifier of the prediction target day, and the prediction target time (or the prediction target time zone) as keys. The means 505 or the day identifier-corresponding congestion degree storage means 504 or the day-identifier-identified travel time storage means 505 is searched, and the travel time information (the day-identifier-identified congestion) corresponding to the road segment and the day-identifier of the time Travel time information), and responds to the prediction request source as predicted traffic jam travel time information.

  As described above, a traffic jam travel time prediction (result) database that takes into account basic day type information such as day of the week, weekday, or holiday is created using the day type identifier as a key, as in the traffic jam travel time prediction database creation device in the first embodiment. By doing so, the traffic jam travel time prediction process can be executed by only two-step data retrieval as in the traffic jam travel time prediction apparatus in the first embodiment, so that the calculation processing capability of a car navigation device is limited. Even with this device, it is possible to predict the travel time for traffic jams taking into account day type information.

[Second Embodiment]
A traffic jam travel time prediction database creation device and a traffic jam travel time prediction device using the forecast data created by the traffic jam travel time prediction database creation device according to the second embodiment will be described in detail with reference to the drawings.

  The overall configuration of the traffic jam travel time prediction database creation apparatus according to the second embodiment will be described with reference to FIG. Reference numeral 6 in FIG. 6 is a traffic jam travel time prediction database creating apparatus main body for creating data (database data) for predicting traffic jam travel time, and reference 611 is past traffic information in which past traffic information and travel time information are accumulated. Storage means, reference numeral 601 indicates date identifier calendar information (road segment group date identifier calendar information; date identifier for all future dates) corresponding to the past, present, road section (or road section county or road section group). Road section correspondence (road section group) day kind identifier calendar information storage means storing the day kind identifier calendar information defining), reference numeral 606 denotes a road section correspondence (road section group) day kind identifier calendar information corresponding to each road section. Road section correspondence (road section group) day type identifier Karen recording the name corresponding to It is over name storage means.

  Reference numerals 602 and 603 denote past congestion information and travel time information of each road section stored in the road section corresponding day type identifier calendar information storage means 611, and the corresponding road section corresponding day type identifier calendar name storage means 606. Based on the road section corresponding day type identifier calendar name corresponding to the road, the day type identifier corresponding to the road stored in the road section corresponding day type identifier calendar information storage means 601 and the basic day type defined in the day type identifier Referring to information (day of the week, weekday, holiday, etc.), calculate the degree of congestion for the day type identifier that calculates the degree of congestion for the day of the day corresponding to the day type identifier of the road section, and calculate the travel time. It is a day type identifier corresponding travel time calculation means.

  Reference numerals 604 and 605 denote day-identifier-corresponding congestion degree (congestion information) storage means for storing the congestion degree of the day time zone corresponding to the day-identifier calculated by the day-identifier-specific congestion degree calculating means 602. And day type identifier corresponding travel time storing means for storing the travel time calculated by the day type identifier corresponding travel time calculating means 603.

  An example of road section corresponding day type identifier calendar information recorded in the road section corresponding day type identifier calendar information storage means 601 in FIG. 6 will be described with reference to FIG. The road section corresponding day type identifier calendars A 701 and B 702 in FIG. 7 include a day type identifier definition section Sc21 that defines basic day type information (day of the week, season, weekday, holiday, etc.) of each day type identifier, A calendar section Sc22 describing a day type identifier to which each future date corresponds is configured.

  “30: Autumn / Weekday / Monday” in the day type identifier definition section Sc21 indicates Monday of autumn (September to November), which is not a holiday or a transfer holiday. “/” (Slash) is a delimiter in the day type identifier definition section.

  In the calendar section Sc22, a day type identifier for each date for one month is described in one line, and the beginning of each line indicates the year and month, and after the “:” (colon) is separated, The format in which the day type identifiers for each day are separated by “,” (comma) is the same as the day type identifier calendar information Sc12 shown in FIG. The difference is that specific road sections such as “91: Miura Kaigan Fireworks Festival” and “92: Uraga Minato Festival” described in the day identifier definition section Sc21 of the road section corresponding day identifier calendars A 701 and B 702 and A unique day type identifier (for example, road day specific day type identifier Dk2 in FIG. 7) is defined for each road section group, and each calendar section has a road day specific day type identifier (for example, in FIG. 7). There is a date for which a day type identifier indicated by a symbol d28 is set. Further, day type identifiers other than the road section specific day type identifier Dk2 in FIG. 7 (for example, the day type identifier in the day type identifier calendar information Sc12) can be distinguished as the road section common day type identifier Dk1.

  The road segment corresponding day type identifier calendar name information recorded in the road segment corresponding day type identifier calendar name storage unit 606 in FIG. 6 and the road recorded in the road section corresponding day type identifier calendar information storage unit 601 in FIG. An example of the correspondence relationship with the section corresponding day type identifier calendar information will be described with reference to FIG. The road section corresponding day type identifier calendar is not necessary for each road section, but only one road section group (road section group) sharing the same road section specific day type identifier. That is, a road section county (or road section group) is defined and configured by a correspondence table (combination table) of road section names and road section corresponding day type identifier calendar names. For example, in the road section name and road section corresponding day type identifier calendar name correspondence table 801 in FIG. 8, road section names “533904, 2,128”, “533904, 2,129”, “533904, 2,130” are displayed. Corresponding to the road section corresponding day type identifier calendar A 821, the road section name “533905, 2, 35” corresponds to the road section corresponding day type identifier calendar B 822. Further, the road section corresponding day type identifier calendars A 821 and B 822 belong to the road section corresponding day type identifier calendar group 802.

  Corresponds to the congestion level at each time of day corresponding to the day type identifier stored in the day type identifier corresponding traffic level storage unit 604 in FIG. 6 and the day type identifier stored in the day type identifier corresponding travel time storage unit 605. A recording image showing travel time information at each time of day will be described with reference to FIG. In road sections that use different road section corresponding day type identifier calendars, entries of different road section specific day type identifiers exist in the day type identifier corresponding congestion travel time information. For example, the entry e1 in the day type identifier corresponding traffic jam travel time information 901 and the entry e2 in the day type identifier corresponding traffic jam travel time information 902 in FIG. 9 are different.

  In creating the traffic jam travel time prediction database (database data), first, the travel time calculation means 603 in FIG. 6 sequentially calculates past travel time information from the past traffic information storage means 611. Search for. Next, the date type identifier and basic date type information corresponding to the date of the searched past travel time information are obtained from the date type identifier calendar information storage unit 601, the date and the road section corresponding day type identifier calendar name storage unit 606. The search is performed using the road section corresponding day type identifier calendar name corresponding to the road section obtained by the search as a key. Then, a statistical analysis is performed on the travel time information for the same time zone on the same date as the specific day type identifier of the road section, and the travel time for each road section, for each day type identifier, and for each time zone is calculated. It stores in the species identifier corresponding travel time storage means 605.

  The calculation method of the travel time for each specific road section, each day type identifier, and each time zone is the average value or mode value of the travel time for each road section, the time zone of the same day type identifier date, Although the median value can be calculated, the calculation method is not limited to this.

  As with the travel time, first, the day identifier-corresponding traffic degree calculation unit 602 in FIG. 6 sequentially searches past traffic information from the past traffic information storage unit 611. Next, the date type identifier and basic date type information corresponding to the date of the past congestion information that has been sequentially searched are displayed from the date type identifier calendar information storage unit 601 to the date and the road section corresponding day type identifier calendar name storage unit 606. The search is performed using the road section corresponding day type identifier calendar name corresponding to the road section obtained by the search as a key. Then, statistical analysis is performed on the traffic information of the same time zone on the same date as the specific day type identifier of the road section, and the degree of traffic congestion is calculated for each road section, for each day type identifier, and for each time zone. It is stored in the identifier correspondence congestion degree storage means 604.

  As a specific method of calculating the degree of congestion for each road section, day type identifier, and time zone, the mode value of the degree of congestion for each road section and time zone for the date of the same day type identifier is calculated. However, the calculation method is not limited to this.

  The overall configuration of the traffic jam travel time prediction apparatus according to the second embodiment will be described with reference to FIG. Reference numeral 10 in FIG. 10 is a traffic jam travel time prediction apparatus main body, and reference numeral 1001 is a road section correspondence that stores day identifier calendar information defining day identifiers for all past, present, and future dates corresponding to road sections. It is a day type identifier calendar information storage means.

  Reference numerals 1004 and 1005 denote day identifier-corresponding congestion degree storage means for storing the degree of congestion of the day time zone corresponding to the day identifier created by the congestion travel time prediction database creating apparatus 6 in FIG. It is a day type identifier corresponding travel time storage means for storing travel time.

  Reference numeral 1002 accepts a search request for predicted traffic jam and predicted travel time from the outside (for example, a user interface device including a communication line and a keyboard), searches the road section corresponding day type identifier calendar name storage means 1006, and performs prediction. A road section corresponding day type identifier calendar name corresponding to the road section is obtained, and a day type identifier of the prediction target date and time is retrieved from the day section identifier calendar information corresponding to the road section from the road section corresponding day type identifier calendar information storage means 1001. Then, the degree of traffic congestion in the prediction target time zone of the date corresponding to the obtained day type identifier is searched from the day type identifier correspondence traffic degree storage means 1004, and the travel time in the prediction target time zone is determined as the day type identifier correspondence congestion degree. Predicted traffic jam travel time search searched from storage means 1004 (predicted traffic jam travel time corresponding to road section group) ) Is a means.

  In the prediction of the traffic jam travel time, when the forecast traffic jam travel time information search means 1002 in FIG. 10 accepts a search request for the forecast information specifying the forecast target road section and the forecast date and time, the forecast jam travel time information search means 1002 The road segment corresponding day type identifier calendar name corresponding to the prediction target road section is searched from 1006, and the day type identifier of the prediction target date is set as the road section using the road section corresponding day type identifier calendar name and the date in the prediction target date and time as a key. The corresponding day type identifier calendar information storage means 1001 is searched. Then, as a next step, the day type identifier corresponding traffic congestion degree storage means 1004 and the day type identifier corresponding travel time storage means 1005 or the day type using the prediction target road section, the day type identifier of the prediction target day, and the prediction target time as keys. The identifier correspondence congestion degree storage means 1004 or the day type identifier correspondence travel time storage means 1005 is searched to obtain the day type identifier correspondence traffic travel time information of the road section and the time, and this is used as the prediction traffic jam travel time information. Respond to the original.

  As described above, a traffic jam travel time prediction (result) database including basic day type information such as day of the week, weekday, or holiday is created using the day type identifier as a key, as in the traffic jam travel time prediction database creation device in the second embodiment. Thus, as shown in the traffic jam travel time prediction device in the second embodiment, the traffic jam travel time prediction processing can be executed by only three-step data search, and the arithmetic processing like a car navigation device is performed. Even with devices with limited capabilities, it is possible to predict traffic jam travel times taking into account day type information.

  Note that some or all of the functions of each means in the traffic jam travel time prediction database creation device and traffic jam travel time prediction device of the first and second embodiments are configured by a computer program, and the program is executed using the computer. The present invention can be realized, the traffic jam travel time prediction database creation method (procedure) and the traffic jam travel time prediction method according to the first and second embodiments are configured by a computer program, and the program is executed by the computer. Needless to say, a program for realizing the functions of the computer can be recorded on a computer-readable recording medium (storage medium) such as FD (Floppy (registered trademark) Disk) or MO (Magneto-Optical disk). ROM (Rea It can be recorded on a d Only Memory), a memory card, a CD (Compact Disk), a DVD (Digital Versatile Disk), a removable disk, etc., and stored or distributed. It is also possible to provide the above program through a network such as the Internet or e-mail.

  Further, the method requires a computer program that describes the above-described traffic jam travel time prediction database creation device and the method related to the traffic jam travel time prediction device or the traffic jam travel time prediction database creation method (procedure) and the traffic jam travel time prediction method. You may mount so that the memory which stored the input-output data, the external memory | storage part, etc. may be accessed.

  As described above, in the first and second embodiments, the predicted traffic jam travel time information of each road section is created based on the past traffic information and the basic day type information defined in the day type identifier in advance. A search request for traffic information, such as a car navigation device with limited arithmetic processing capability, by searching for predicted traffic information that has been created from a uniquely defined day type identifier and information that identifies a road segment to be predicted However, it is possible to search for predicted traffic information at high speed.

  In addition, in the calculation of the day type identifier compatible congestion degree and the day type identifier corresponding travel time, the day type information corresponding to the day type identifier defined in the road segment corresponding day type identifier past calendar information corresponding to each road section In the search for the congestion level corresponding to the day type identifier and the search for the travel time corresponding to the day type identifier, the day corresponding to the day type identifier defined in the future calendar information for the road section corresponding to each road section By using the seed information, it is possible to create and search predicted traffic information that takes into account the day information related only to a specific road section or road section county.

  A traffic jam travel time prediction database creation device and prediction device, a traffic jam travel time prediction database creation method and prediction method, a traffic jam travel time prediction database creation program and a prediction program, and a recording medium thereof are car navigation systems. It can be used for traffic information provision services such as systems.

  Although the embodiments of the present invention have been described above, the present invention is not limited to the described embodiments, and various modifications can be made within the scope described in each claim.

  For example, each storage unit of the congestion travel time prediction database creation device in the first and second embodiments may store each information using a general database device (or database program). Similarly, each storage unit of the traffic jam travel time prediction apparatus in the first and second embodiments may store each information using a general database device.

The lineblock diagram of the traffic jam travel time prediction database creation device in a 1st embodiment. The figure which shows an example of the traffic jam information and travel time information for every past road area. The figure which shows an example of day type identifier calendar information. The figure which shows an example of the information of the congestion degree and travel time in each time corresponding to a day type identifier. The block diagram of the traffic congestion travel time prediction apparatus in 1st Embodiment. The block diagram of the traffic jam travel time prediction database preparation apparatus in 2nd Embodiment. The figure which shows an example of road section corresponding day type identifier calendar information. The figure which shows the correspondence of a road area name and road area corresponding | compatible day type identifier calendar information. The figure which shows an example of the traffic congestion degree of each time corresponding to a day type identifier, and the information of travel time. The block diagram of the traffic congestion travel time prediction apparatus in 2nd Embodiment.

DESCRIPTION OF SYMBOLS 1 ... Traffic jam travel time prediction database preparation apparatus main body 101 ... Day type identifier calendar information storage means 102 ... Day type identifier corresponding | compatible traffic degree calculation means 103 ... Day type identifier corresponding | compatible travel time calculation means 104 ... Day type identifier corresponding | compatible traffic degree storage means 105 ... day type identifier corresponding travel time storage means 111 ... past traffic information storage means 301 ... day type identifier calendar 5 ... traffic jam travel time prediction device body 501 ... day type identifier calendar information storage means 502 ... predicted traffic jam travel time search means 504 ... Day type identifier-corresponding traffic degree storage means 505... Day type identifier corresponding travel time storage means 6... Traffic jam travel time prediction database creation device main body 601. 603... Day type identifier corresponding travel time calculation means 60 ... day type identifier corresponding traffic jam degree storing means 605 ... day type identifier corresponding travel time storage means 606 ... the road section corresponding Date species identifier calendar name storage means 611 ... past traffic information storage means 701 ... the road section corresponding Date species identifier calendar A
702 ... Road section correspondence day type identifier calendar B
801 ... Road section name and road section corresponding day type identifier calendar name correspondence table 821 ... Road section corresponding day type identifier calendar A
822 ... Road section correspondence day type identifier calendar B
802... Road section corresponding day type identifier calendar group 901, 902. Day type identifier corresponding traffic jam travel time information 10. Traffic jam travel time prediction device main body 1001. Road section corresponding day class identifier calendar information storage unit 1002. 1004... Day type identifier correspondence congestion degree storage means 1005. Day type identifier correspondence travel time storage means 1006. Road section correspondence day kind identifier calendar name storage means Dk1. Road section common day kind identifier Dk2 ... Road section specific day kind identifier Sc11. Day type identifier definition section Sc12 ... Calendar section Sc21 ... Day type identifier definition section Sc22 ... Calendar section Tj11-Tj13 ... Past congestion information and travel time information for each road section Tj21-Tj23 ... Congestion degree and travel time for each road section Affection d11~d17 ... day type identifier d21~d28 ... day species identifier e1, e2 ... entry of road section specific date species identifier

Claims (12)

Traffic information storage means for storing traffic information and travel time information, which are past traffic conditions collected and accumulated for each road section,
Corresponds to a road segment group consisting of one or more road segments,
Depending on the date on which the congestion information and travel time information were collected, depending on either the day of the week, weekdays or holidays, a combination of basic day information including information indicating the season, or the period during which a unique traffic situation occurs A day identifier definition section for defining a day identifier uniquely determined by
A calendar section comprising information associated with the day type identifier defined in the day type identifier definition section on each year and month including the prediction target date, and the day type identifier calendar information having road section group day type identifier calendar information And
Road segment group corresponding day type identifier calendar information storing means for storing the road segment group day type identifier calendar information;
A road section group day type identifier calendar name for designating the road section group day type identifier calendar information is stored, and a road section group corresponding day type identifier calendar name storage means constituting a road section group by the combination is stored. When,
Prediction information storage means for storing prediction information;
With
A traffic jam travel time prediction database creation device that creates data for making predictions regarding future traffic jam conditions in the road section based on the past traffic jam information and travel time information,
Congestion information stored in the traffic information storage means,
Based on the result of statistical analysis using the basic day type information corresponding to the specific day type identifier included in the road section group day type identifier calendar information corresponding to the road section,
A day type identifier corresponding traffic degree calculating means for calculating a degree of traffic jam in each time zone of a day corresponding to a specific day type identifier according to a road partial section that determines the degree of traffic jam in the road section;
Travel time information stored in the traffic information storage means,
Based on the result of statistical analysis using the basic day type information corresponding to the specific day type identifier included in the road section group day type identifier calendar information corresponding to the road section,
A day type identifier corresponding travel time calculating means for calculating travel time information of each time zone of a day corresponding to a specific day type identifier of the road section;
The traffic level calculated by the day type identifier corresponding traffic level calculation means is regarded as the predicted traffic information for each time zone of the day on the date corresponding to the future day type identifier of the road section, and the date type A day-type identifier-corresponding predicted traffic jam information storing means for storing the identifier, the predicted traffic jam information, and the road section name in the predicted information storage means;
The travel time information calculated by the day type identifier corresponding travel time calculation means is regarded as predicted travel time information for each time zone of the day on the date corresponding to the future day type identifier of the road section, Predicted travel time information, the day type identifier, the day type identifier corresponding predicted travel time information storage means for storing the road section name in the predicted information storage means,
A traffic jam travel time prediction database creation device characterized by comprising: In the traffic jam travel time prediction database creation device according to claim 1 ,
The day type identifier-compatible congestion degree calculating means
Based on the degree of traffic jam that is traffic jam information stored in the traffic information storage means,
For each road segment that defines the degree of congestion in the road section, calculate the mode value of the degree of congestion in the time zone for the past date corresponding to the day type identifier,
Means for calculating the mode value in consideration of the predicted traffic information of each time zone of the day in the future day type identifier of the road section;
A traffic jam travel time prediction database creation device characterized by comprising: In the traffic jam travel time prediction database creation device according to claim 1 ,
The day type identifier corresponding travel time calculating means,
Based on travel time information stored in the traffic information storage means,
Either the average value, the mode value, or the median value of the travel time information in the time zone in the past corresponding to the day type identifier of the road section is set as one day in the future day type identifier of the road section. Means to calculate the estimated travel time information for each time zone,
A traffic jam travel time prediction database creation device characterized by comprising: Any one in traffic travel time pre Hakade database creating apparatus calculated generated prediction traffic information and the predicted traffic jam species day for storing the corresponding day type identifier information identifier corresponding congested by the description of claims 1 to 3 Information storage means;
Congested travel time pre Hakade database creating apparatus predicted travel time is calculated to generate the information and date seed identifier for storing date seed identifier corresponding to the predicted travel time information according to any one of claims 1 to 3 Corresponding travel time storage means,
And day type identifier definition section of the road section group Jan species identifier calendar information stored by the congestion travel time prediction database creation apparatus according to any one of claims 1 to 3,
A calendar section composed of information in which the day type identifier defined in the day type identifier definition section is associated with each year and month including the forecast target;
Road segment group day type identifier calendar information having
Road segment group corresponding day type identifier calendar information storage means for storing and managing each road segment group consisting of one or a plurality of road segments;
A road section group day type identifier calendar name for specifying the road section group day type identifier calendar information is stored, and a road section group corresponding day type identifier calendar name storing means for constituting a road section group by the combination is stored. When,
With
A congestion travel time prediction system you get a prediction traffic information and the predicted travel time information is a future traffic conditions in the road section to be predicted,
Accept a search request that includes a prediction target date, a prediction target time zone, and a road section name indicating a prediction target road section,
Search the road section group day type identifier calendar name corresponding to the road section name of the road section to be predicted from the road section group corresponding day type identifier calendar name storage means,
The road segment group Jan species identifier calendar information determined from the retrieved road segment groups Jan species identifier calendar name identified using the road segment group corresponding date type identifier calendar information storage unit,
Search for the day identifier of the prediction target day of the road section to be predicted from the identified road section group day identifier calendar information,
A combination of the retrieved day type identifier, prediction target time zone and prediction target road section name is used as a search key,
Searching for the predicted traffic jam information corresponding to the search key from the predicted traffic jam information corresponding to the day type identifier stored in the day type identifier corresponding traffic jam information storage means;
Predicted travel time information, the date type identifier corresponding travel time stored road segment group corresponding predicted traffic congestion travel time retrieval means for retrieving from the predicted travel time information corresponding to the day factor identifier stored in the means in accordance with the retrieval key,
A traffic jam travel time predicting device comprising: Traffic information storage means for storing traffic information and travel time information, which are past traffic conditions collected and accumulated for each road section,
Corresponds to a road segment group consisting of one or more road segments,
Depending on the date on which the congestion information and travel time information were collected, depending on either the day of the week, weekdays or holidays, a combination of basic day information including information indicating the season, or the period during which a unique traffic situation occurs A day identifier definition section for defining a day identifier uniquely determined by
A calendar section comprising information associated with the day type identifier defined in the day type identifier definition section on each year and month including the prediction target date, and the day type identifier calendar information having road section group day type identifier calendar information And
And the road segment group correspondence Date species identifier calendar information storage means for storing the road segment groups Date species identifier calendar information,
A road section group day type identifier calendar name for designating the road section group day type identifier calendar information is stored, and a road section group corresponding day type identifier calendar name storage means constituting a road section group by the combination is stored. When,
Prediction information storage means for storing prediction information;
With
Based on the past traffic jam information and travel time information, a traffic jam travel time prediction database creation method used for an apparatus for creating data for forecasting a future traffic jam situation in the road section,
Congestion information stored in the traffic information storage means,
Based on the result of statistical analysis using the basic day type information corresponding to the specific day type identifier included in the road section group day type identifier calendar information corresponding to the road section,
A day type identifier corresponding traffic degree calculating step for calculating a degree of traffic jam in each time zone of a day corresponding to a specific day type identifier according to a road partial section that determines the degree of traffic jam in the road section;
Travel time information stored in the traffic information storage means,
Based on the result of statistical analysis using the basic day type information corresponding to the specific day type identifier included in the road section group day type identifier calendar information corresponding to the road section,
A day type identifier corresponding travel time calculating step for calculating travel time information for each time zone of the day corresponding to the specific day type identifier of the road section;
The traffic degree calculated in the day type identifier corresponding traffic degree calculation step is regarded as the predicted traffic information of each time zone of the day on the date corresponding to the future day type identifier of the road section, and the day type A day-type identifier-corresponding predicted traffic jam information storing step of storing the identifier, the predicted traffic jam information, and the road section name in the predicted information storage means;
The travel time information calculated in the day type identifier corresponding travel time calculation step is regarded as predicted travel time information for each time zone of the day on the date corresponding to the future day type identifier of the road section, Predicted travel time information, the day type identifier, the day type identifier-corresponding predicted travel time information storing step for storing in the predicted information storage means in combination with the road section name,
A method for creating a traffic jam travel time prediction database. In the traffic jam travel time prediction database creation method according to claim 5 ,
The day identifier compatible traffic congestion degree calculating step includes:
Based on the degree of traffic jam that is traffic jam information stored in the traffic information storage means,
For each road segment that defines the degree of congestion in the road section, calculate the mode value of the degree of congestion in the time zone for the past date corresponding to the day type identifier,
Calculating the mode value in consideration of the predicted congestion information for each time zone of the day in the day type identifier in the future of the road section;
A method for creating a traffic jam travel time prediction database. In the traffic jam travel time prediction database creation method according to claim 5 ,
The day type identifier corresponding travel time calculating step includes:
Based on travel time information stored in the traffic information storage means,
Either the average value, the mode value, or the median value of the travel time information in the time zone in the past corresponding to the day type identifier of the road section is set as one day in the future day type identifier of the road section. Calculating the estimated travel time information for each time zone,
A method for creating a traffic jam travel time prediction database. Congested travel time pre Hakade database creation method is calculated by the generated predicted congestion information and the predicted traffic jam information corresponding day type identifier storage to date seed identifier corresponding congestion to according to any one of claims 5 to 7 Information storage means;
Congested travel time pre Hakade database creating predicted travel time is calculated to generate the information and date seed identifier for storing date seed identifier corresponding to the predicted travel time information according to any one of claims 5 to 7 Corresponding travel time storage means,
And day type identifier definition section of the road section group Jan species identifier calendar information stored by the congestion travel time prediction database creation method according to any one of claims 5 to 7,
A calendar section composed of information in which the day type identifier defined in the day type identifier definition section is associated with each year and month including the forecast target;
Road segment group day type identifier calendar information having
Road segment group corresponding day type identifier calendar information storage means for storing and managing each road segment group consisting of one or a plurality of road segments;
A road section group day type identifier calendar name for specifying the road section group day type identifier calendar information is stored, and a road section group corresponding day type identifier calendar name storing means for constituting a road section group by the combination is stored. When,
With
A congestion travel time prediction method to use an apparatus for obtaining a predicted traffic jam information and the predicted travel time information is a future traffic conditions in the road section to be predicted,
Accept a search request that includes a prediction target date, a prediction target time zone, and a road section name indicating a prediction target road section,
Search the road section group day type identifier calendar name corresponding to the road section name of the road section to be predicted from the road section group corresponding day type identifier calendar name storage means,
The road segment group Jan species identifier calendar information determined from the retrieved road segment groups Jan species identifier calendar name identified using the road segment group corresponding date type identifier calendar information storage unit,
Search for the day identifier of the prediction target day of the road section to be predicted from the identified road section group day identifier calendar information,
A combination of the retrieved day type identifier, prediction target time zone and prediction target road section name is used as a search key,
Searching for the predicted traffic jam information corresponding to the search key from the predicted traffic jam information corresponding to the day type identifier stored in the day type identifier corresponding traffic jam information storage means;
Predicted travel time information, the date type identifier corresponding road section group travel Search time storage predicted travel time information corresponding to the day factor identifier stored in the unit corresponding predicted traffic congestion travel time retrieval step in accordance with the retrieval key,
A traffic jam travel time prediction method characterized by comprising: 4. A traffic jam travel time prediction database creation program, wherein each means in the traffic jam travel time prediction database creation device according to claim 1 is described as a computer program executable by a computer. 5. A traffic jam travel time prediction program, wherein each means in the traffic jam travel time prediction device according to claim 4 is described as a computer program executable by a computer. 4. A recording medium in which each means in the traffic jam travel time prediction database creating apparatus according to claim 1 is described as a computer program executable by a computer, and the computer program is recorded. 5. A recording medium in which each means in the traffic jam travel time prediction apparatus according to claim 4 is described as a computer program executable by a computer and the computer program is recorded.

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