JP2010014556A - Apparatus for predicting traffic jam, apparatus for searching route, method for predicting traffic jam, method for searching route, program for predicting traffic jam, program for searching route, and computer-readable recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an apparatus for predicting traffic jam intended to predict a traffic jam more precisely. <P>SOLUTION: The apparatus 110 for predicting traffic jam is equipped with a traffic jam DB 111, an input section 112, a first traffic jam predicting section 113 and a second traffic jam predicting section 114. The traffic jam DB 111 stores past traffic jam information about a past traffic jam. The input section 112 receives an input of information (factor information) about a factor having a potential of generating the traffic jam. The first traffic jam predicting section 113 predicts the traffic jam arising from the factor shown by the factor information. The second traffic jam predicting section 114 predicts the traffic jam by using the traffic jam DB 111 and a predicted result (first traffic jam prediction result) of the first traffic jam predicting section 113. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention is mounted on a moving body such as a vehicle, for example, a traffic jam prediction device that predicts traffic jam, a route search device provided with the traffic jam prediction device, a traffic jam prediction method, a route search method, a traffic jam prediction program, a route search program, And a computer-readable recording medium.

  Conventionally, for example, there has been a navigation device that searches for an optimum route from a current point to a destination point and guides the searched route to a user. In addition, there has been a search for an optimum route (for example, a route that requires a minimum time to reach a destination point) based on past traffic jam information (see, for example, Patent Document 1 below).

JP 2005-106610 A

  However, in the above prior art, since the traffic jam is predicted using the past traffic information obtained by statistically processing the past traffic information, even if the actual traffic situation changes due to a change in the factor of the traffic jam, etc. One example is the problem that it was difficult to predict traffic jams with high accuracy by responding quickly.

  In order to solve the above-described problems and achieve the object, the traffic jam prediction apparatus according to the present invention includes a traffic jam database that accumulates past traffic jam information related to past traffic jams, and information about factors that can cause traffic jams (hereinafter referred to as “factor information”). Using the prediction results of the traffic jam database and the first traffic jam forecasting means, the first traffic jam forecasting means for forecasting the traffic jam caused by the factor indicated by the factor information, and the traffic jam database and the first traffic jam forecasting means. And second traffic jam prediction means for predicting traffic jam.

  Further, a route search device according to the present invention includes the traffic jam prediction device described above, storage means for storing road data and cost data quantitatively indicating ease of movement with respect to each road, 2 Based on the prediction result of the traffic jam prediction means, based on the change means for changing the cost stored in the storage means, the road data stored in the storage means and the changed cost data, Route search means for searching for a route to the destination.

  The traffic jam prediction method according to the present invention is a traffic jam prediction method in a traffic jam forecasting apparatus that forecasts traffic jam using a traffic jam database that accumulates past traffic jam information related to past traffic jams, and relates to factors that may cause traffic jams ( (Hereinafter referred to as “factor information”), a first traffic jam prediction step for predicting a traffic jam caused by the factor indicated by the factor information, the traffic jam database, and the first traffic jam prediction step. And a second traffic jam prediction step of predicting traffic jam using the prediction result.

  Further, the route search method according to the present invention stores a traffic jam database that accumulates past traffic jam information related to past traffic jams, road data, and cost data that quantitatively indicates the ease of movement with respect to each road. A route search method in a route search device for searching for a route to a destination, wherein an input step of receiving input of information relating to factors that may cause traffic congestion (hereinafter referred to as “factor information”); A first traffic jam prediction step for predicting traffic jams caused by factors indicated by the information, and a second traffic jam prediction step for predicting traffic jams using the traffic jam database and the prediction results of the first traffic jam prediction step; Based on the prediction result of the second traffic jam prediction step, the changing step for changing the cost stored in the storage unit, and the road stored in the storage unit Based on the data and the cost of data after the change, characterized in that it comprises a route searching step of searching for a route to a destination, a.

  A traffic jam prediction program according to the present invention causes a computer to execute the traffic jam prediction method described above.

  A route search program according to the present invention causes a computer to execute the route search method described above.

  In addition, a computer-readable recording medium according to the present invention records the above-described traffic jam prediction program or route search program.

  With reference to the accompanying drawings, preferred embodiments of a traffic jam prediction device, a route search device, a traffic jam prediction method, a route search method, a traffic jam prediction program, a route search program, and a computer-readable recording medium according to the present invention will be described below. A form is demonstrated in detail.

(Functional configuration of traffic jam prediction device)
First, the functional configuration of the traffic jam prediction apparatus according to the embodiment of the present invention will be described. FIG. 1 is an explanatory diagram showing a functional configuration of a traffic jam prediction apparatus according to an embodiment of the present invention. As shown in FIG. 1, a traffic jam prediction device 110 according to an embodiment of the present invention includes a traffic jam database (hereinafter referred to as “traffic jam DB”) 111, an input unit 112, a first traffic jam prediction unit 113, and a second traffic jam. A prediction unit 114.

  The traffic jam DB 111 stores past traffic jam information related to past traffic jams. Here, the past traffic information is information obtained by statistically processing a traffic jam that has occurred in the past for each day of the week or each time zone. As a result, the traffic jam prediction device 110 can predict that a traffic jam will occur when a road (eg, a certain intersection) satisfies a specific condition (eg, time zone) based on past traffic jam information. . Note that the prediction of traffic jam using the past traffic jam information is a known technique, and thus detailed description thereof is omitted.

  The input unit 112 has a function of accepting input of information related to factors that may cause traffic congestion (hereinafter referred to as “factor information”). Here, factors that may cause traffic congestion include public transportation such as a route bus, a garbage truck, and a vehicle whose position does not change (for example, a narrow road). The factor information includes information such as a route (hereinafter referred to as “operation route”) and a time zone during which the factor operates (hereinafter referred to as “operation time zone”).

  In addition, the factor information may be new information or change information on routes of public transportation. The new information is information indicating that a new route for public transportation has been newly established, and includes information such as an operation route and an operation time zone of the newly established route. The change information is information indicating that the operation route or operation time zone of the existing route has changed, and includes information such as the changed operation route or operation time zone.

  For example, the factor information is input based on a user operation. The factor information may be obtained by acquiring factor information for a factor specified by the user from an external computer device via a network (not shown) and inputting the acquired factor information.

  The first traffic jam prediction unit 113 has a function of predicting the traffic jam that occurs due to the factor indicated by the factor information. For example, it is assumed that route bus information is input as factor information. At this time, the first traffic jam prediction unit 113 predicts that a predetermined traffic jam will occur on the operating route of the route bus during the operating time zone. The predetermined traffic jam may be a traffic jam that occurs in the entire operation route, or may be a traffic jam that occurs in a part of the operation route. For example, it may be generated in a part of the operation route with a narrow road width.

  The second traffic jam prediction unit 114 has a function of predicting traffic jams using the prediction results of the traffic jam DB 111 and the first traffic jam prediction unit 113 (hereinafter referred to as “first traffic jam prediction results”). For example, the second traffic jam prediction unit 114 uses the traffic jam DB 111 and the first traffic jam prediction result on a road around an arbitrary point (for example, the current location) at an arbitrary time (for example, a time specified by the user). Predict the traffic that will occur. The prediction result by the second traffic jam prediction unit 114 (hereinafter referred to as “second traffic jam prediction result”) may be guided to the user via an output unit (not shown) (for example, a display unit).

  Moreover, the traffic jam prediction device 110 may further include a comparison unit 115. Here, the comparison unit 115 has a function of comparing whether the first traffic jam prediction result is reflected in the traffic jam DB 111 by comparing the past traffic jam information accumulated in the traffic jam DB 111 with the first traffic jam prediction result. Have. For example, the comparison unit 115 compares the traffic jam state (position, time zone, size) indicated by the past traffic jam information with the traffic jam status (position, time zone, size) of the first traffic jam prediction result. Then, if the traffic jam indicated by the past traffic jam information and the traffic jam of the first traffic jam prediction result are the same or similar (within a predetermined difference), it is determined that it has already been reflected. If they are not the same or similar (within a predetermined difference), it is determined that they are not reflected.

  In the case where the comparison unit 115 is provided, the second traffic jam prediction unit 114 uses the traffic jam DB 111 and the first traffic jam prediction result if the prediction result of the first traffic jam prediction unit 113 is not reflected in the traffic jam DB 111. Predict. On the other hand, if the first traffic jam prediction result is reflected in the traffic jam DB 111, the traffic jam is predicted using only the traffic jam DB 111 without using the first traffic jam prediction result.

  Further, the traffic jam prediction device 110 may further include an update unit 116. Here, the update unit 116 has a function of updating the traffic jam DB 111 based on the first traffic jam prediction result when the first traffic jam prediction result is not reflected in the traffic jam DB 111. That is, the update unit 116 adds the traffic jam of the first traffic jam prediction result to the traffic jam DB 111 as past traffic jam information.

  Further, the traffic jam prediction device 110 may be configured as a part of the route search device 120 as shown in FIG. Here, the route search device 120 includes a traffic jam prediction device 110, a storage unit 121, a change unit 122, and a route search unit 123.

  The storage unit 121 has a function of storing road data and cost data. For example, the storage unit 121 stores a map database (hereinafter referred to as “map DB”). Map data is accumulated in the map DB, and the map data includes data representing a point object such as a building or the surface of a point, and road data representing the shape of a road.

  Although detailed description is omitted because it is a known technique, the road data includes a link representing a road and a node that joins the links. Each link has link information, and the link information includes cost data of the link. Here, the cost is one that quantitatively indicates the ease of movement (for example, the time required for passing through the link). Thereby, the traffic jam prediction device 110 can calculate the time required for movement from a certain point A to another point B, and the like.

  The changing unit 122 has a function of changing the cost stored in the storage unit 121 based on the traffic jam prediction result by the second traffic jam prediction unit 114. For example, the changing unit 122 increases the cost for the portion where the traffic jam is predicted by the second traffic jam prediction unit 114, and decreases the cost for the portion that is not predicted (no longer predicted).

  The route search unit 123 has a function of searching for a route to the destination based on the road data stored in the storage unit 121 and the changed cost data. Here, the destination is an arbitrary point designated by the user. For example, the route search unit 123 searches for a route that minimizes the cost from the current location to the destination by using the Dijkstra method or the like although a detailed description is omitted because it is a known technique. As a result, the user can reach the destination in a shorter time.

(Processing contents of the traffic jam prediction device)
Next, processing contents executed by the traffic jam prediction device 110 will be described. FIG. 2 is a flowchart showing the processing contents executed by the traffic jam prediction apparatus. As shown in FIG. 2, first, the traffic jam prediction device 110 waits until an input of factor information is accepted (step S201: No loop). As described above, the traffic jam prediction device 110 receives input of factor information through the input unit 112.

  When accepting the input of the factor information (step S201: Yes), the traffic jam prediction device 110 predicts the traffic jam that occurs due to the factor indicated by the factor information (step S202). As described above, the traffic jam prediction device 110 predicts the traffic jam that occurs due to the factor indicated by the factor information by the first traffic jam prediction unit 113. For example, when the factor indicated by the factor information is a route bus, a predetermined traffic jam is predicted on the route of the route bus.

  In step S202, after predicting the traffic jam caused by the factor, the traffic jam prediction device 110 predicts the traffic jam using the traffic jam DB 111 and the first traffic jam prediction result (the prediction result of step S202) (step S203). The process is terminated. As described above, the prediction result in step S203 may be guided to the user via an output unit (not shown).

  As described above, according to the traffic jam prediction apparatus 110 according to the embodiment of the present invention, traffic jam based on this factor information is predicted by inputting target factor information that can be a factor of traffic jam in advance (first step). (1 traffic jam prediction result), the traffic jam considering the first traffic jam prediction result (second traffic jam prediction result) can be predicted. Accordingly, it is possible to predict a traffic jam that is not reflected in the past traffic jam information, so that the accuracy of the traffic jam can be improved.

  Examples of the present invention will be described below. In addition, a present Example is an example at the time of applying said traffic jam prediction apparatus 110 and said route search apparatus 120 to the navigation apparatus mounted in mobile bodies, such as a vehicle (a four-wheeled vehicle and a two-wheeled vehicle are included), for example. .

(Hardware configuration of navigation device)
First, the hardware configuration of the navigation apparatus of the present embodiment will be described with reference to FIG. FIG. 3 is a block diagram illustrating a hardware configuration of the navigation apparatus according to the present embodiment.

  As shown in FIG. 3, the navigation apparatus 300 of the present embodiment includes a CPU 301, a ROM 302, a RAM 303, a magnetic disk drive 304, a magnetic disk 305, an optical disk drive 306, an optical disk 307, and an audio I / F ( Interface) 308, speaker 309, input device 310, video I / F 311, display 312, communication I / F 313, GPS unit 314, and various sensors 315. Each component 301 to 315 is connected by a bus 320.

  The CPU 301 governs overall control of the navigation device 300. The ROM 302 stores various programs such as a boot program, a current location specifying program, a route search program, a route guidance program, a voice generation program, a map data display program, and a traffic jam prediction program. In addition to the ROM 302, these various programs may be recorded in a nonvolatile memory such as a magnetic disk 305 and an optical disk 307 described later. The RAM 303 is used as a work area for the CPU 301.

  That is, the CPU 301 controls the entire navigation apparatus 300 by executing various programs recorded in the ROM 302 and the like while using the RAM 303 as a work area. The current location specifying program, for example, specifies the current location of the navigation device 300 based on output information from a GPS unit 314 and various sensors 315 described later.

  The route search program uses the map data recorded on the magnetic disk 305 or the optical disc 307, which will be described later, or the optimal route from the departure place (for example, the current location) to the destination, or when the optimum route is deviated Let's search for alternative routes.

  Here, the optimum route is a route with the minimum cost (for example, required time) to the destination or a route that best meets the conditions specified by the user. Since the route search program is a known technique, a detailed description thereof is omitted, but an optimal route is searched using the Dijkstra method or the like. The route information of the route searched for by executing the route search program is output to the audio I / F 308 and the video I / F 311 via the CPU 301.

  The route guidance program is read from the route information of the route searched by executing the route search program, the current location information of the navigation device 300 specified by executing the current location specifying program, the magnetic disk 305 or the optical disc 307. Based on the map data thus obtained, real-time route guidance information is generated. The route guidance information generated by executing the route guidance program is output to the audio I / F 308 and the video I / F 311 via the CPU 301.

  The voice generation program generates tone and voice information corresponding to the pattern. That is, based on the route guidance information generated by executing the route guidance program, the virtual sound source corresponding to the guidance point is set and the voice guidance information is generated. Voice guidance information includes, for example, a warning that a right / left turn point should turn right and left according to the route, a warning that the vehicle should decelerate before the right / left turn point, information about a detour route when a right / left turn fails, Guidance information to the effect that it should be turned back if it fails to turn right or left is included. The generated voice guidance information is output to the voice I / F 308 via the CPU 301.

  The map data display program causes the display 312 to display map data read from the magnetic disk 305 or the optical disk 307 by the video I / F 311. The map data display program causes the display 312 to display map data around the current location of the navigation device 300, for example. Further, the map data display program may display map data around an arbitrary point designated by the user on the display 312, for example.

  The traffic jam prediction program makes a traffic jam prediction using a traffic jam database (hereinafter referred to as “traffic jam DB”) (refer to FIG. 4), factor information, and the like stored in the magnetic disk 305 or the optical disc 307. The traffic jam predicted by executing the traffic jam prediction program is used when the route search program is executed. That is, when it is predicted that a traffic jam will occur on the route to the destination, the navigation device 300 searches for a route with the lowest cost while avoiding the traffic jam.

  The magnetic disk drive 304 controls the reading / writing of the data with respect to the magnetic disk 305 according to control of CPU301. Data written under the control of the magnetic disk drive 304 is recorded on the magnetic disk 305. As the magnetic disk 305, for example, HD or FD (flexible disk) can be used.

  The optical disk drive 306 controls the reading / writing of the data with respect to the optical disk 307 according to control of CPU301. The optical disk 307 is a detachable recording medium from which data is read according to the control of the optical disk drive 306. As the optical disk 307, for example, a CD (Compact Disc) or a DVD can be used. As the optical disc 307, a writable recording medium can be used. In addition to the optical disk 307, the removable recording medium may be an MO (Magneto Optical Disk), a memory card, or the like.

  An example of information recorded on the magnetic disk 305 or the optical disk 307 is a map database (hereinafter referred to as “map DB”) in which map data used for route search / route guidance is stored. The map data includes background data representing features (features) such as buildings, rivers, and surface points, and road data representing the shape of the road. The map data is drawn in two or three dimensions on the display screen of the display 312. Is done.

  Although not shown, the road data includes links and nodes that join the links. Here, a link corresponds to a road, and a node corresponds to a node between links such as an intersection, a bending point, a branch point, and a junction. Although a detailed description is omitted because it is a known technique, each link has link information.

  The link information quantitatively indicates the link ID unique to each link, the position (absolute coordinates) of the link (the center) on the map data, the length of each link, and the ease of movement of each link. Information indicating the cost (for example, the time required for passing through the link) and the joining nodes located at both ends of the link are included. The navigation device 300 can calculate the distance from the current location to the destination point, the required time, and the like based on the link information.

  The road data further includes traffic condition data. Traffic condition data includes, for example, the presence / absence of signals and pedestrian crossings for each node, the presence / absence of highway doorways and junctions, the direction of travel for each link, and road type (eg, highways, toll roads, general roads) Information is included.

  The magnetic disk 305 or the optical disk 307 records a traffic jam DB that stores past traffic information obtained by statistically processing past traffic jams based on seasons, days of the week, large holidays, and times. The navigation device 300 obtains information on the currently occurring traffic jam based on road traffic information received by the communication I / F 313 described later, but at an arbitrary time (for example, a designated time) based on past traffic jam information in the traffic jam DB. It is possible to predict traffic conditions.

  In this embodiment, the map data is stored in the magnetic disk 305 or the optical disk 307. However, the present invention is not limited to this. The map data is not limited to the one provided integrally with the hardware of the navigation device 300, and may be provided outside the navigation device 300. In this case, the navigation apparatus 300 acquires map data from an external computer device connected via the communication I / F 313, for example. The acquired map data is recorded in the RAM 303, the magnetic disk 305, etc., and read out as necessary.

  The audio I / F 308 is connected to an audio output speaker 309, and various audio is output from the speaker 309. Examples of the input device 310 include a remote controller having a plurality of keys for inputting characters, numerical values, various instructions, a keyboard, a mouse, a touch panel, and the like. The input device 310 inputs data corresponding to the key selected by the user into the apparatus.

  The video I / F 311 is connected to the display 312. Specifically, the video I / F 311 includes, for example, a graphic controller that controls the entire display 312, a buffer memory such as a VRAM (Video RAM) that temporarily records image information that can be displayed immediately, and a graphic controller. Based on the output image data, the display 312 is configured by a control IC or the like.

  The display 312 displays icons, cursors, menus, windows, or various data such as characters and images. As the display 312, for example, a CRT, a TFT liquid crystal display, a plasma display, an organic EL display, or the like can be used.

  The communication I / F 313 is connected to a network via wireless and functions as an interface between the navigation device 300 and the CPU 301. The communication I / F 313 is further connected to a communication network such as the Internet via wireless, and also functions as an interface between the communication network and the CPU 301. Further, the communication I / F 313 receives a television broadcast or a radio broadcast.

  Communication networks include LANs, WANs, public line networks and mobile phone networks. Specifically, the communication I / F 313 includes, for example, an FM tuner, a VICS / beacon receiver, a wireless navigation device, and other navigation devices. The communication I / F 313 receives road traffic information such as traffic congestion and traffic regulations distributed from the VICS center. get. VICS is a registered trademark.

  The GPS unit 314 receives radio waves from GPS satellites and calculates information indicating the current location of the vehicle. The output information of the GPS unit 314 is used when the current location of the vehicle is specified by the CPU 301 together with output values of various sensors 315 described later. The information indicating the current location is information for specifying one point on the map data, such as latitude / longitude and altitude.

  The various sensors 315 output information that can determine the position and behavior of the vehicle, such as a vehicle speed sensor, an acceleration sensor, and an angular velocity sensor. The output values of the various sensors 315 are used by the CPU 301 for specifying the current location of the vehicle, measuring the amount of change in speed and direction, and the like.

  Note that, for example, the traffic jam DB 111 of the traffic jam prediction device 110 is the magnetic disk 305 or the optical disc 307, the input unit 112 is the input device 310, the CPU 301, and the ROM 302, and the first traffic jam prediction unit 113 is the CPU 301 and the ROM 302. 2 The traffic jam prediction unit 114 can realize each function by the CPU 301 and the ROM 302.

(Congestion DB)
Next, the contents of the traffic jam DB stored in the magnetic disk 305 or the optical disk 307 described above will be described. FIG. 4 is an explanatory diagram showing the contents of the traffic jam DB. As shown in FIG. 4, past traffic jam information 410 obtained by statistically processing traffic jams that occurred in the past is accumulated in the traffic jam DB 400. The past traffic information 410 includes information indicating a traffic jam ID unique to each traffic jam, a position / size of each traffic jam, and conditions for occurrence of each traffic jam, and the like.

  The traffic jam ID is an identification number of each traffic jam that has a one-to-one correspondence with each traffic jam. The traffic jam ID is composed of a combination of numbers and alphabets, for example. The navigation apparatus 300 identifies each traffic jam based on the traffic jam ID, and performs a traffic jam prediction using each traffic jam.

  In the illustrated example, the traffic jam with the traffic jam ID “J01” (hereinafter referred to as “traffic jam 1”) indicates a traffic jam that occurs on links L11, L12, L13,. The size of the traffic jam 1 is the sum of the sizes of the links L11 to La.

  The occurrence condition indicates a condition in which each traffic jam occurs. Date and time (may include the day of the week) or the like is set as the generation condition. For example, when the occurrence condition is “Tuesday 8:00 to 10:00” as in the case of traffic jam 1, the navigation apparatus 300 predicts that traffic jam 1 will occur in the time zone of 8:00 to 10:00 on Tuesday.

  Further, the traffic jam DB 400 may include information representing the traffic jam level. Here, the traffic jam level represents the degree of congestion due to each traffic jam. For example, the navigation apparatus 300 changes the cost of the section (link group) where traffic congestion is predicted according to the traffic congestion level. For example, when the traffic congestion level is “high”, the cost of the section is tripled. When the traffic level is “medium”, the cost of the section is doubled. When the traffic level is “low”, the cost of the section is increased by 1.5 times.

  For example, it is assumed that the total cost (required time required for passing through this section) is 10 minutes during normal times (when there is no traffic jam). If it is predicted that a traffic jam with a traffic jam level “high” will occur in this section, the cost will be tripled, resulting in 30 minutes. By using not only the size of the traffic jam but also the traffic jam level, the navigation apparatus 300 can more accurately predict the time required for passing through the section where the traffic jam is predicted.

  For example, when the latest past traffic information is acquired from an external computer device via a network (not shown), the navigation device 300 updates the past traffic information 410 stored in the traffic jam DB 400. In addition, the navigation device 300 can update the past traffic jam information 410 using the first traffic jam prediction result predicted when the factor information is input.

(Processing contents of the navigation device of this embodiment)
Next, processing contents executed by the navigation device 300 of the present embodiment will be described. FIG. 5 is a flowchart showing the processing contents executed by the navigation device of this embodiment. As shown in FIG. 5, the navigation apparatus 300 first waits until an input of factor information is accepted (step S501: No). For example, the factor information is input when the input device 310 is operated by the user. The factor information is obtained from an external computer device via the communication I / F 313 as factor information of a factor (for example, a specific route bus) specified by the input device 310 being operated by the user. It may be input.

  In step S501, when factor information is input (step S501: Yes), the navigation apparatus 300 predicts a traffic jam that occurs due to the factor indicated by the factor information (first traffic jam prediction) (step S502). For example, when the factor information is information on a certain route bus, the navigation device 300 predicts that traffic congestion will occur on the route during the route bus operation time. In this case, it is predicted that traffic congestion will occur in the entire factor position (operation route) included in the factor information. In addition, it is good also as estimating that traffic congestion will generate | occur | produce in a part on a service route.

  In step S502, after performing the first traffic jam prediction, the navigation device 300 refers to the traffic jam DB 400, and the traffic jam at the same position (and size) as the traffic jam of the first traffic jam prediction result in the past traffic jam information 410. It is determined whether there is any (step S503). For example, it is assumed that the traffic jam of the first traffic jam prediction result is a traffic jam located on the links La, Lb, and Lc. At this time, the navigation apparatus 300 determines whether there is a traffic jam located on the links La, Lb, and Lc in the past traffic jam information 410. At this time, the position (size) of each traffic jam does not have to be completely the same. For example, the navigation device 300 may determine whether there is a traffic jam located on a part of the traffic jam of the first traffic jam prediction result, for example, the links Lb and Lc.

  In step S503, when there is no traffic jam at the same position (size) as the traffic jam of the first traffic jam prediction result in the past traffic jam information 410 (step S503: No), the process proceeds to step S506 described later.

  In step S503, when there is a traffic jam at the same position as the traffic jam of the first traffic jam prediction result (step S503: Yes), the navigation device 300 determines the time zone in which the traffic jam of the first traffic jam prediction result occurs and the first traffic jam prediction. It is determined whether the resulting traffic jam is the same as the time zone in which the traffic jam at the same position (size) occurs (step S504).

  For example, it is assumed that the time zone in which the traffic jam of the first traffic jam prediction result occurs is from 8:00 to 10:00. At this time, the navigation apparatus 300 determines whether it is a time zone 8:00 to 10:00 when the traffic jam at the same position as the traffic jam of the first traffic jam prediction result occurs. At this time, the respective time zones do not have to be completely the same. For example, the traffic jam at the same position as the traffic jam of the first traffic jam prediction result is a traffic jam that occurs in a part of the time zone (for example, 9:00 to 10:00) in which the traffic jam of the first traffic jam prediction result occurs. There may be.

  In step S504, when the time zone in which the traffic jam of the first traffic jam prediction result occurs and the time zone in which the traffic jam at the same position as the traffic jam of the first traffic jam prediction result is not the same (step S504: No) Then, the process proceeds to step S506 described later.

  In step S504, when the time zone in which the traffic jam occurs in the first traffic jam prediction result is the same as the time zone in which the traffic jam at the same position as the traffic jam in the first traffic jam forecast result occurs (step S504: Yes), navigation is performed. The apparatus 300 predicts a traffic jam using the traffic jam DB 400 (second traffic jam prediction) (step S505), and ends a series of processes. For example, the navigation device 300 predicts a traffic jam that occurs around an arbitrary point at an arbitrary time. In addition, since it is a well-known technique about the traffic jam prediction using the past traffic jam information 410 of traffic jam DB400, detailed description is abbreviate | omitted.

  On the other hand, in step S506, a traffic jam is predicted using the traffic jam DB 400 and the first traffic jam prediction result (second traffic jam prediction), and a series of processing ends. Specifically, in step S506, the navigation apparatus 300 first updates the traffic jam DB 400 based on the first traffic jam prediction result. For example, the traffic jam of the first traffic jam prediction result is added to the traffic jam DB 400 as past traffic jam information, and the traffic jam is predicted using the traffic jam DB 400 after the addition. As described above, the navigation apparatus 300 basically performs the traffic jam prediction using only the traffic jam DB 400. However, when there is a traffic jam that is not included in the traffic jam DB 400 (the traffic jam corresponding to the factor information), the traffic jam is generated. A traffic jam is predicted using the DB 400 and the first traffic jam prediction result.

(An example of traffic jam prediction by the navigation device of this embodiment)
Next, an example of traffic jam prediction by the navigation device 300 of this embodiment will be described. FIG. 6 is an explanatory diagram illustrating an example of traffic jam prediction by the navigation device of the present embodiment. The illustrated example is an example of traffic jam prediction when the route bus A is input as factor information.

  As illustrated, the factor information 610 includes the name of the factor (hereinafter referred to as “factor name”), the position / size of the factor, and the existence condition in which the factor exists. As shown in the factor information 610, in the route bus A, the links L1, L2, L3 to La, L1 are the operation routes (positions). In addition, the route bus A travels on the above-described operation route between 8:00 and 10:00 (existing conditions) on weekdays.

  For this reason, the navigation apparatus 300 predicts that a traffic jam will occur on the links L1, L2, L3, -La, L1 at 8:00 to 10:00 on weekdays (first traffic jam prediction). At this time, it may be predicted that the traffic congestion will occur not only in the links L1, L2, L3 to La, L1, but only in some sections (for example, only in a section with a narrow road width). . Reference numeral 620 in FIG. 6 represents a first traffic jam prediction result 620 based on the first traffic jam prediction. When the first traffic jam prediction result 620 is obtained, the navigation apparatus 300 refers to the traffic jam DB 400 and compares the past traffic jam information 410 stored in the traffic jam DB 400 with the first traffic jam prediction result 620.

  After comparing the past traffic jam information 410 and the first traffic jam prediction result 620, the navigation apparatus 300 determines whether there is a traffic jam that matches the first traffic jam prediction result 620 in the past traffic jam information 410. For example, it is determined whether there is a traffic jam whose first traffic jam prediction result 620 matches the position, size, time zone, and the like in the past traffic jam information 410.

  If they match, the first traffic jam prediction result 620 is discarded as it is, and the traffic jam around an arbitrary point is predicted using the traffic jam DB 400. If they do not match, the first traffic jam prediction result 620 is registered in the traffic jam DB 400 (added to the past traffic jam information 410), and traffic jam around an arbitrary point at an arbitrary time is predicted using the traffic jam DB 400 after registration. Note that when the first traffic jam prediction result 620 is registered in the traffic jam DB 400, only a part of the first traffic jam prediction result 620 may be registered. For example, when the first traffic jam prediction result 620 is a traffic jam on the links L1, L2, L3 to La, L1, it is registered as a traffic jam on the links L1, L2, and L3.

  When the first traffic jam prediction result 620 is registered in the traffic jam DB 400, the factor information 610 and the first traffic jam prediction result 620 are associated with each other and registered in the traffic jam DB 400. Thereby, for example, when the route bus A is abolished, the unnecessary first traffic jam prediction result 620 can be searched from the traffic jam DB 400 and deleted.

(Specific display example by the navigation device of this embodiment)
FIG. 7 is an explanatory diagram (part 1) illustrating a specific display example by the navigation device of the present embodiment. As shown in FIG. 7, map data 700 around the A station 701 designated by the user is displayed on the display 312 of the navigation device 300. Although illustration is omitted, the road data in the map data 700 has a large number of links and nodes that join the links.

  In the example shown in FIG. 7, since past traffic information indicating traffic around the A station 701 is not stored in the traffic jam DB 400, it is predicted that no traffic jam will occur around the A station 701. For this reason, the display 312 does not display a traffic jam icon or the like representing a prediction result that a traffic jam will occur.

  FIG. 8 is an explanatory diagram (part 2) illustrating a specific display example by the navigation device of the present embodiment. The example shown in FIG. 8 is an example in the case where the information on the route bus A of the operation route that departs from the A station 701, travels a predetermined route and returns to the A station 701 is input as the factor information.

  As described above, when the factor information is input, the navigation apparatus 300 predicts a traffic jam that occurs due to the factor (first traffic jam prediction). If the first traffic jam prediction result is not reflected in the traffic jam DB 400, the traffic jam DB 400 is updated based on the first traffic jam prediction result, and the traffic jam is predicted using the updated traffic jam DB 400.

  For this reason, as shown in FIG. 8, it is predicted that traffic congestion will occur on the route of the route bus A, and a traffic congestion icon 710 is displayed on the route of operation. In addition, the example shown in FIG. 8 is a traffic jam prediction result in the operation time zone of a route bus. If it is a traffic jam prediction result outside the operation time zone, it is predicted that no traffic jam will occur, so that the same thing as in FIG. 7 is displayed on the display 312.

  As described above, according to the navigation apparatus 300 of the present embodiment, since the traffic jam is predicted using the traffic jam DB 400 and the first traffic jam prediction result, it is possible to predict a traffic jam that is not reflected in the traffic jam DB 400 as past traffic jam information. . As a result, the navigation device 300 can perform highly accurate traffic jam prediction even when the actual traffic jam situation changes due to a change in the factor of the traffic jam. And since a user can go to the destination using the optimal route, the effort and time concerning a movement are reduced and the convenience improves.

  Further, according to the navigation device 300 of the present embodiment, when the first traffic jam prediction result is reflected in the traffic jam DB 400, the traffic jam can be predicted using the traffic jam DB 400. As a result, the navigation device 300 predicts a traffic jam using the more accurate traffic jam DB 400 except when necessary (when the first traffic jam prediction result is not reflected in the traffic jam DB 400). Can be done.

  Furthermore, according to the navigation apparatus 300 of the present embodiment, if the first traffic jam prediction result is not reflected in the traffic jam DB 400, the traffic jam DB 400 can be updated based on the first traffic jam prediction result. As a result, it is possible to increase the past traffic information that becomes a prediction material at the time of traffic jam prediction, so that it is possible to perform traffic jam prediction with higher accuracy.

  Note that when the first traffic jam prediction result is registered in the traffic jam DB 400, the conditions for eliminating the cause of the traffic jam in the first traffic jam prediction result may also be registered. Here, the cancellation condition is a condition for eliminating the traffic jam of the first traffic jam prediction result. Information indicating the resolution condition for each factor is included in the factor information and is acquired by inputting the factor information. When the cancellation condition is satisfied for a certain factor, the first traffic jam prediction result for this factor is deleted from the traffic jam DB 400. For example, the cancellation conditions include route bus operation cancellation, operation route change, road repair (road width expansion), and the like.

  The congestion prediction method and route search method described in this embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. This program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. The program may be a medium that can be distributed via a network such as the Internet.

It is explanatory drawing which shows the functional structure of the traffic congestion prediction apparatus concerning embodiment of this invention. It is a flowchart which shows the processing content which a traffic jam prediction apparatus performs. It is a block diagram which shows the hardware constitutions of the navigation apparatus of a present Example. It is explanatory drawing which shows the content of traffic jam DB. It is a flowchart which shows the processing content which the navigation apparatus of a present Example performs. It is explanatory drawing which shows an example of the traffic congestion prediction by the navigation apparatus of a present Example. It is explanatory drawing (the 1) which shows the specific example of a display by the navigation apparatus of a present Example. It is explanatory drawing (the 2) which shows the specific example of a display by the navigation apparatus of a present Example.

Explanation of symbols

110 Congestion prediction device 111 Congestion DB
112 Input Unit 113 First Congestion Prediction Unit 114 Second Congestion Prediction Unit 115 Comparison Unit 116 Update Unit 120 Route Search Device 121 Storage Unit 122 Change Unit 123 Route Search Unit

Claims (14)

A traffic jam database that stores past traffic information about past traffic,
Input means for accepting input of information related to factors that may cause traffic congestion (hereinafter referred to as “factor information”);
A first traffic jam prediction means for predicting traffic jam caused by the factor indicated by the factor information;
Second traffic jam prediction means for predicting traffic jam using prediction results of the traffic jam database and the first traffic jam prediction means;
A traffic jam prediction device comprising: Comparison comparing past traffic information accumulated in the traffic jam database with the prediction result of the first traffic jam prediction means to determine whether the prediction result of the first traffic jam prediction means is reflected in the traffic jam database. Further comprising means,
The second traffic jam prediction unit predicts traffic jam using the traffic jam database and the prediction result of the first traffic jam prediction unit when the prediction result of the first traffic jam prediction unit is not reflected in the traffic jam database. The traffic jam prediction apparatus according to claim 1.   When the prediction result of the first traffic jam prediction unit is reflected in the traffic jam database, the second traffic jam prediction unit uses the traffic jam database to determine the traffic jam without using the prediction result of the first traffic jam prediction unit. The traffic jam prediction apparatus according to claim 2, wherein prediction is performed.   The comparison unit compares the traffic jam position indicated by the past traffic jam information with the traffic jam position indicated by the prediction result of the first traffic jam prediction unit, so that the prediction result of the first traffic jam prediction unit is the traffic jam. The traffic jam prediction apparatus according to claim 2 or 3, wherein it is determined whether or not it is reflected in the database.   The comparison unit compares the occurrence time zone of the traffic jam indicated by the past traffic jam information with the occurrence time zone of the traffic jam indicated by the prediction result of the first traffic jam prediction unit, thereby predicting the first traffic jam prediction unit. It is judged whether the result is reflected in the said traffic jam database, The traffic jam prediction apparatus as described in any one of Claims 2-4 characterized by the above-mentioned.   The comparison unit compares the size of the traffic jam indicated by the past traffic jam information with the size of the traffic jam indicated by the prediction result of the first traffic jam prediction unit, so that the prediction result of the first traffic jam prediction unit is the traffic jam. It is judged whether it is reflected in the database, The congestion prediction apparatus as described in any one of Claims 2-5 characterized by the above-mentioned.   The update means which updates the said traffic jam database based on the prediction result of the said 1st traffic jam prediction means when the prediction result of the said 1st traffic jam prediction means is not reflected in the said traffic jam database. The traffic jam prediction device according to any one of Items 2 to 6.   The congestion information prediction apparatus according to any one of claims 1 to 7, wherein the factor information is new establishment information or change information of a public transportation route. The traffic jam prediction device according to any one of claims 1 to 8,
Storage means for storing road data and cost data quantitatively indicating ease of movement with respect to each road;
A changing unit that changes the cost stored in the storage unit based on a prediction result of the second traffic jam prediction unit;
Route search means for searching for a route to a destination based on the road data stored in the storage means and the cost data after the change;
A route search apparatus comprising: A traffic jam prediction method in a traffic jam prediction device that forecasts traffic jam using a traffic jam database that accumulates past traffic jam information related to past traffic jams,
An input process for accepting input of information related to factors that may cause traffic congestion (hereinafter referred to as “factor information”);
A first traffic jam prediction step for predicting traffic jam caused by the factor indicated by the factor information;
A second traffic jam prediction step for predicting traffic jam using the traffic jam database and the prediction result of the first traffic jam prediction step;
Congestion prediction method characterized by including. Search for a route to the destination with a traffic jam database that stores past traffic information on past traffic jams, storage data that stores road data, and cost data that quantitatively indicates the ease of movement for each road A route search method in a route search device,
An input process for accepting input of information related to factors that may cause traffic congestion (hereinafter referred to as “factor information”);
A first traffic jam prediction step for predicting traffic jam caused by the factor indicated by the factor information;
A second traffic jam prediction step for predicting traffic jam using the traffic jam database and the prediction result of the first traffic jam prediction step;
Based on the prediction result of the second traffic jam prediction step, a changing step for changing the cost stored in the storage means;
A route search step of searching for a route to a destination based on the road data stored in the storage means and the cost data after the change;
A route search method comprising:   A traffic jam prediction program for causing a computer to execute the traffic jam prediction method according to claim 10.   A route search program causing a computer to execute the route search method according to claim 11.   A computer-readable recording medium, wherein the traffic jam prediction program according to claim 12 or the route search program according to claim 13 is recorded.

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