JP2010026803A - Traffic jam prediction device, route search device, traffic jam prediction method, route search method, traffic jam prediction program, route search program, and computer-readable recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enhance the accuracy in traffic jam prediction. <P>SOLUTION: The traffic jam prediction device 110 includes: a traffic jam database 111; an event information acquisition part 112; a weather information acquisition part 113 and a prediction part 114. Past traffic jam information related to the past traffic jams is accumulated in the traffic jam database 111. The event information acquisition part 112 acquires event information for the holding content of an optional event. The weather information acquisition part 113 acquires weather information for the weather of the holding area of the event. The prediction part 114 predicts a traffic jam resulting from the event based on the traffic jam database 111, the event information and the weather information. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a traffic jam prediction device that predicts traffic jams, a route search device including 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. However, the use of the present invention is not limited to the traffic jam prediction device, the route search device, the traffic jam prediction method, the route search method, the traffic jam prediction program, the route search program, and the computer-readable recording medium.

  Conventionally, there has been a navigation device that predicts congestion due to an event based on real-time holding information related to the event (see, for example, Patent Document 1 below). According to this navigation device, it is possible to make a traffic jam prediction according to the event situation.

JP 2006-226977 A

  By the way, some events that are held outdoors are canceled due to the weather. If an event is canceled, there should be no traffic jams due to the event. However, with the above-mentioned conventional technology, even when the target event is not held due to rain, there is a possibility of predicting a traffic jam (that is, an unnecessary traffic jam) caused by the event. An example of such a problem is that it is difficult to predict a heavy traffic jam.

  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 event information that acquires event information related to held contents of an arbitrary event. Obtaining means; weather information obtaining means for obtaining weather information relating to the weather in a region where the event is held; prediction means for predicting traffic jams caused by the event based on the traffic jam database, the event information, and the weather information; It is characterized by providing.

  The 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, and the prediction Based on the prediction result of the means, the change means for changing the cost stored in the storage means, and the road data stored in the storage means and the cost data after the change to the destination And a route search means for searching for the route.

  The traffic jam prediction method according to the present invention is 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, and includes event information relating to the contents of an arbitrary event. An event information acquisition step for acquiring the weather information, a weather information acquisition step for acquiring weather information relating to the weather in the area where the event is held, and the traffic jam caused by the event based on the traffic jam database, the event information and the weather information. And a prediction step for prediction.

  The route search method according to the present invention includes a traffic jam database, storage means for storing road data and cost data quantitatively indicating ease of movement with respect to each road, and a route to the destination. A route search method in a route search device for searching for an event, an event information acquisition step for acquiring event information related to the held contents of an arbitrary event, and a weather information acquisition step for acquiring weather information about the weather in the event hosting region, , Based on the traffic jam database, the event information, and the weather information, a prediction step for predicting traffic jam due to the event, and the cost stored in the storage unit based on a prediction result in the prediction step On the basis of the road data stored in the storage means and the cost 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.

  Exemplary 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 with reference to the accompanying drawings. Will be described 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 a block 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 event information acquisition unit 112, a weather information acquisition unit 113, and 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. .

  The event information acquisition unit 112 has a function of acquiring event information regarding the contents of an arbitrary event. The event information includes information such as the date and time of the event, the location of the event, and the condition for canceling the event (for example, “cancelled when raining”). For example, the event information acquisition unit 112 acquires event information distributed via a network (not shown).

  The weather information acquisition unit 113 has a function of acquiring weather information regarding the weather in the event hosting area. The weather information includes information such as the type of weather (for example, “sunny”), the probability of precipitation, and the amount of precipitation. Furthermore, the weather information may include information such as wind speed and wave height. For example, the weather information acquisition unit 113 acquires the weather information distributed via a network (not shown).

  The prediction unit 114 has a function of predicting a traffic jam caused by an event based on the traffic jam DB 111, the event information acquired by the event information acquisition unit 112 and the weather information acquired by the weather information acquisition unit 113. For example, the traffic jam prediction device 110 includes a determination unit 115 as illustrated, and the determination unit 115 determines whether an event is held. Specifically, the determination unit 115 compares the event cancellation condition with the weather, and determines that the event is canceled if the cancellation condition matches the weather.

  Then, when the determination unit 115 determines that the event is to be canceled, the prediction unit 114 predicts that no traffic jam associated with the event will occur. Further, when an event is held, it is predicted that a traffic jam associated with the event will occur.

  Also, for example, the traffic jam DB 111 may store past traffic jam information of two patterns of a traffic jam that occurs when a certain event is held and a traffic jam that occurs when the event is canceled. Good. In this case, when the determination unit 115 determines that the event is to be canceled, the prediction unit 114 predicts the traffic jam using the pattern of the traffic jam that occurs when the event is canceled. When it is determined that the event is held, the traffic jam is predicted using the pattern of the traffic jam that occurs when the event is held.

  Further, as shown in FIG. 1, 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 event information and the prediction result of the prediction unit 114. For example, when the prediction unit 114 predicts a traffic jam due to a certain event, the update unit 116 updates the traffic jam prediction result in association with the event information. Thereby, for example, the traffic jam prediction device 110 can use the prediction result of the previous year when forecasting the traffic jam due to the event in the following year.

  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 prediction unit 114. For example, the changing unit 122 increases the cost for a portion where traffic congestion is predicted by the prediction unit 114 and decreases the cost for a 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, the contents of processing executed by the traffic jam prediction device 110 according to the present embodiment will be described. FIG. 2 is a flowchart showing a processing procedure of the traffic jam prediction apparatus according to the present embodiment. For example, the process shown in FIG. 2 is started at an arbitrary timing instructed by the user of the traffic jam prediction device 110. In the flowchart shown in FIG. 2, the traffic jam prediction device 110 first acquires event information (step S201). As described above, the event information is acquired by the event information acquisition unit 112.

  After acquiring event information, the traffic jam prediction device 110 acquires weather information (step S202). The weather information is acquired by the weather information acquisition unit 113. After acquiring the weather information, the traffic jam caused by the event is predicted based on the traffic jam DB 111, the event information acquired in step S201 and the weather information acquired in step S202 (step S203), and the process ends. . The prediction of the traffic jam is performed by the prediction unit 114.

  The traffic jam prediction device 110 may output the traffic jam prediction result when the traffic jam is predicted in step S203. For example, the traffic jam prediction result is displayed on a display unit (not shown). Thus, the user can know the traffic jam prediction result.

  As described above, according to the traffic jam prediction device 110 of the present embodiment, traffic jams can be predicted using the traffic jam DB 111, event information, and weather information. In other words, it is possible to determine whether or not an event is held depending on the weather, and to predict a traffic jam based on the determination result, so that a highly accurate traffic jam can be predicted. Therefore, since the user can know the optimum route according to the weather, convenience is 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 disk 307, which will be described later, and the optimum route from the departure point (for example, the current location) to the destination, or when the optimum route is deviated. Search for a detour route.

  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 includes an event information database (hereinafter referred to as “event information DB”) (see FIG. 4) and a traffic jam database (hereinafter referred to as “congestion DB”) recorded on the magnetic disk 305 or the optical disc 307 (see FIG. 5). Uses weather information to predict traffic congestion on the road. 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) and the junction 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 or absence of signals and pedestrian crossings for each node, the presence or absence of highway entrances and junctions, the direction of travel for each link, and the type of road (highway, toll road, general road), etc. Contains information.

  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 the traffic jam at an arbitrary time (for example, a specified time) is determined based on the past traffic jam information in the traffic jam DB. The situation can be predicted.

  In addition, the magnetic disk 305 or the optical disk 307 stores an event information DB that stores event information related to the event contents. The event information DB will be described later with reference to FIG.

  In this embodiment, the map data is recorded on 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, and various instructions, a keyboard, a mouse, and a touch panel. 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 disk 307, the event information acquisition unit 112 is the communication I / F 313, the CPU 301, and the ROM 302, and the weather information acquisition unit 113 is the communication I / F 313. With the CPU 301 and the ROM 302, the prediction unit 114 can realize the respective functions with the CPU 301 and the ROM 302.

(Event DB)
Next, the contents of the event information DB described above will be described. FIG. 4 is an explanatory diagram showing the contents of the event information DB. As shown in FIG. 4, the event information DB 400 stores event information for each event. The event information includes an event ID unique to each event, the name of the event (hereinafter referred to as “event name”), the date and time of the event (hereinafter referred to as “the event date”), and the location (hereinafter referred to as “the event location”). ), The contents of the event (hereinafter referred to as “event contents”), and a stop condition for stopping the event, and the like.

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

  Conditions for using the weather, wind speed, and precipitation are set as cancellation conditions. For example, if the cancellation condition is “rainy” due to weather, the event is canceled if it is raining on the date of the event. Similarly, if the cancellation condition is “20 m / s or more” due to the wind speed, the event is canceled if the wind speed is 20 m / s or more at the date of the event. Although illustration is omitted, the cancellation condition may include things such as wave height and temperature.

  Further, the event information may include information on the date and time when the event is postponed when the event does not stop when the event satisfies the stop condition. In this way, even if the event is postponed, the navigation device 300 can predict the traffic jam at that time (at the postponed date and time).

  For example, the event information is distributed from an external computer device (for example, a server computer managed by the event organizer). And the navigation apparatus 300 will update the event information memorize | stored in event information DB400, if the newest event information is acquired from an external computer apparatus via a network not shown.

(Congestion DB)
Next, the contents of the aforementioned traffic jam DB will be described. FIG. 5 is an explanatory diagram showing the contents of the traffic jam DB. As shown in FIG. 5, the traffic jam DB 500 stores past traffic jam information obtained by statistically processing traffic jams that occurred in the past. The past traffic information includes information indicating a traffic jam ID unique to each traffic jam, a position / size of each traffic jam, a traffic jam level, 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 the link L1, link L2, link L3,. The size of the traffic jam 1 is the sum of the sizes of the links L1 to La.

  The congestion level represents the degree of congestion due to each congestion. The congestion level is set according to the scale of each event. For example, if the number of visitors to the event is 10,000 or more, the traffic level is “High”. If the level is 5,000 or more and less than 10,000, the traffic level is “Medium”. If the level is less than 5,000, The traffic level is set to “low”.

  The navigation device 300 changes the cost of the section (link group) where the traffic jam is predicted according to the traffic jam 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. By using not only the size of the traffic jam but also the traffic jam level, the navigation apparatus 300 can accurately predict the time required for passing through the section in which the traffic jam is predicted.

  The occurrence condition indicates a condition in which each traffic jam occurs. In the generation condition, date and time (may include a day of the week), weather, event, and the like are set. For example, when the occurrence condition of a certain traffic jam is the weather “rain”, the navigation apparatus 300 predicts that this traffic jam will occur if the weather at the time of traffic jam prediction is rain.

  In the traffic jam 1 described above, the event “I01” is set as an occurrence condition. As a result, the navigation device 300 predicts that a traffic jam 1 will occur when an event with an event ID “I01” is held. That is, the navigation device 300 predicts that the traffic jam 1 will not occur when the event with the event ID “I01” is not held.

  Further, for example, the traffic jam DB 500 stores, for a certain event, past traffic jam information of two patterns of a traffic jam that occurs when the event is held and a traffic jam that occurs when the event is canceled. Also good. In this case, if it is determined that the event is to be canceled, the traffic jam is predicted using the pattern of the traffic jam that occurs when the event is canceled. When it is determined that the event is held, the traffic jam is predicted using the pattern of the traffic jam that occurs when the event is held.

  For example, the past traffic jam information is distributed from an external computer device (for example, a server computer of the VICS center). And the navigation apparatus 300 will update the past traffic congestion information memorize | stored in traffic congestion DB500, if the latest past traffic congestion information is acquired from an external computer apparatus via a network not shown.

(Processing contents of the navigation device)
Next, processing contents executed by the navigation device 300 of the present embodiment will be described. FIG. 6 is a flowchart showing the processing contents executed by the navigation device of this embodiment.

  As shown in FIG. 6, the navigation apparatus 300 first acquires event information (step S601). For example, the navigation device 300 acquires event information of an event that is held on the day when traffic congestion is predicted, for example. When event information is acquired, the navigation apparatus 300 acquires weather information (step S602). For example, the navigation apparatus 300 acquires the weather information of the host area where the event is held.

  After acquiring the weather information, the navigation device 300 determines whether an event is held using the event information acquired in step S601 and the weather information acquired in step S602 (step S603). Specifically, the navigation device 300 compares the cancellation condition included in the acquired event information with the weather information, and determines that the event is canceled when the weather information matches the cancellation condition.

  If it is determined in step S603 that an event is not held (step S603: No), the navigation apparatus 300 refers to the traffic jam DB 500 to determine whether there is traffic associated with the event to be canceled (step S604). . If it is determined in step S603 that an event is to be held (step S603: Yes), the process proceeds to step S606.

  If it is determined in step S604 that there is a traffic jam associated with the event to be canceled (step S604: Yes), the traffic jam level of the corresponding traffic jam is lowered (step S605). Further, the navigation apparatus 300 may determine that the traffic jam does not occur without lowering the traffic jam level. In this case, the traffic jam is not predicted at all when the traffic jam is predicted in step S606 described later. If it is determined in step S604 that there is no traffic jam associated with the event to be canceled (step S604: No), the process directly proceeds to step S606.

  In step S606, a traffic jam is predicted. Specifically, the navigation device 300 predicts a traffic jam that satisfies the generation condition at the time of prediction based on the traffic jam DB 500. For example, if the weather at the time of prediction is rain, a traffic jam whose occurrence condition is set to “rain” is predicted. When not only the weather but also other generation conditions are set, the navigation apparatus 300 predicts only traffic jams that satisfy all the generation conditions at the time of prediction.

  In step S606, when a traffic jam is predicted, the navigation apparatus 300 outputs a traffic jam prediction result and ends the process. For example, the traffic jam prediction result is output to the video I / F 311 and displayed on the display 312. Thus, the user can know the traffic jam prediction result. In addition, when the destination is input, the navigation device 300 searches for a route that minimizes the cost to the destination. At this time, the route is searched using the cost in consideration of the traffic jam prediction result. .

(Specific display example of navigation device)
Next, a specific display example of the navigation device 300 according to the present embodiment will be described. FIG. 7 is an explanatory diagram (part 1) illustrating a specific display example of the navigation device according to the present embodiment. In the illustrated example, map data 700 is displayed on the display 312. The map data 700 is map data around the A park 701 where the A fireworks display (event ID “I01”) is held. 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 the weather is fine, it is predicted that the A fireworks display will be held, and accordingly, a traffic jam 710 (congestion prediction result) is predicted. Here, the traffic jam 710 is the traffic jam 1 shown in FIG. 5 (the traffic jam with the traffic jam ID “J01”). That is, the size of the traffic jam 710 is the links L1 to La, and the traffic jam level is “high”.

  FIG. 8 is an explanatory diagram (part 2) illustrating a specific display example of the navigation device according to the present embodiment. In the example shown in FIG. 8, it is predicted that the A fireworks display will be canceled because the weather is rain, and accordingly, a traffic jam 720 is predicted. Here, the traffic jam 720 is smaller than the traffic jam 1 and has a low traffic jam level. For example, the size of the traffic jam 720 is links L1 to L3, and the traffic jam level is “low”. Thus, in the navigation apparatus 300 of the present embodiment, the traffic jam prediction result varies depending on whether or not an event is held.

  By the way, due to changes in the weather, the event may be canceled just before the event is held. At this time, a person who has been heading to the venue will turn back on the way, or a person who has been waiting for the start of the event at the venue will be pulled up from the venue. Therefore, in this case, the navigation apparatus 300 increases the cost of roads around the venue from the venue. Note that this means that the closer the time when the event is determined to be canceled is closer to the start time of the event, the more people are waiting at the venue and the people who are heading to the venue, so the cost is greatly increased. Thereby, it is possible to further improve the accuracy of the traffic jam prediction.

  As described above, according to the navigation device 300 of the present embodiment, it is determined whether or not an event is held depending on the weather, and a traffic jam according to the determination result is predicted, so an unnecessary traffic jam is predicted. This makes it possible to predict traffic congestion with high accuracy. As a result, the user can travel to the destination by an optimal route, and thus the time and effort required to reach the destination are reduced, and convenience is improved.

  Note that the traffic jam 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. Further, this program may be a medium that can be distributed via a network such as the Internet.

It is a block diagram which shows the functional structure of the traffic congestion prediction apparatus concerning embodiment of this invention. It is a flowchart which shows the process sequence of the traffic congestion prediction apparatus of this Embodiment. 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 event information DB. 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 (the 1) which shows the specific example of a display of the navigation apparatus of a present Example. It is explanatory drawing (the 2) which shows the specific example of a display of the navigation apparatus of a present Example.

Explanation of symbols

110 Congestion prediction device 111 Congestion DB
DESCRIPTION OF SYMBOLS 112 Event information acquisition part 113 Weather information acquisition part 114 Prediction part 115 Judgment part 116 Update part 120 Route search device 121 Storage part 122 Change part 123 Route search part

Claims (9)

A traffic jam database that stores past traffic information about past traffic,
Event information acquisition means for acquiring event information related to the contents of an arbitrary event;
Weather information acquisition means for acquiring weather information relating to the weather in the event hosting area;
Prediction means for predicting traffic jam caused by the event based on the traffic jam database, the event information and the weather information;
A traffic jam prediction device comprising: Based on the event information and the weather information, further comprising determination means for determining whether or not the event is held,
The traffic prediction apparatus according to claim 1, wherein the prediction unit predicts a traffic jam caused by the event according to a determination result of the determination unit.   The traffic jam prediction apparatus according to claim 1, further comprising an update unit configured to update the traffic jam database based on the event information and a prediction result of the prediction unit. The traffic jam prediction device according to any one of claims 1 to 3,
Storage means for storing road data and cost data quantitatively indicating ease of movement with respect to each road;
Based on the prediction result of the prediction means, changing means for changing the cost stored in the storage means;
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,
An event information acquisition process for acquiring event information related to the contents of an arbitrary event;
A weather information acquisition step for acquiring weather information regarding the weather in the area where the event is held;
Based on the traffic jam database, the event information, and the weather information, a prediction step for predicting traffic jam due to the event;
Congestion prediction method characterized by including. A route search method in a route search device that includes a traffic jam database and storage means for storing road data and cost data that quantitatively indicates ease of movement with respect to each road. There,
An event information acquisition process for acquiring event information related to the contents of an arbitrary event;
A weather information acquisition step for acquiring weather information regarding the weather in the area where the event is held;
Based on the traffic jam database, the event information, and the weather information, a prediction step for predicting traffic jam due to the event;
Based on the prediction result in the prediction step, a change 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 5.   A route search program causing a computer to execute the route search method according to claim 6.   A computer-readable recording medium in which the traffic jam prediction program according to claim 7 or the route search program according to claim 8 is recorded.

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