JP3215389B2 - Vehicle route guidance device and traffic congestion prediction method usable for the same - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicle route guidance apparatus for displaying a recommended route between a current position and a destination on a map, and a traffic congestion prediction method that can be used in the apparatus.

[0002]

2. Description of the Related Art FIG.
FIG. 1 is a block diagram showing a device configuration of a conventional vehicle route guidance device disclosed in Japanese Patent Application Publication No. H10-115,026. In the figure, reference numeral 1 denotes a processing unit for executing control processing in the vehicle route guidance device, and includes a central processing unit (hereinafter referred to as a CPU) 11,
It comprises a microcomputer in which a read-only memory (hereinafter referred to as ROM) 12, a write / read memory (hereinafter referred to as RAM) 13, and an input / output interface 14 are connected by a bus 15. 2 is a traffic information receiver for receiving traffic information and the like transmitted from a traffic information center via a transmitter installed on a road information board, a signal pillar, or the like;
Reference numeral 3 denotes a compact disc player which operates as a read-only external storage device for reading road map data and the like described on the compact disc. Reference numeral 4 denotes a cathode ray tube (hereinafter, referred to as CRT) display for performing display based on various data converted into display signals by the processing device 1, and 5 denotes an operation unit including input devices such as key switches and light pens. Reference numeral 6 denotes a self-contained navigation type position sensor which includes a geomagnetic sensor, a vehicle speed sensor, and the like, and detects its own current position.

Next, the operation will be described. When searching for a recommended route to the destination, first, the road map data read by the compact disc player 3 is displayed on the CRT display 4 and the destination is displayed on the road map data by using a light pen or the like of the operation unit 5. Is specified. When the destination is specified, the processing device 1 first determines a candidate route from the current position detected by the self-contained navigation type position sensor 6 to the destination in the first stage, and determines the candidate route in the second stage. A route within a predetermined restriction condition is detected. Next, in a third stage, a recommended route is extracted from the routes detected in the second stage based on the traffic information received by the traffic information receiver 2 and a predetermined route selection condition. Is displayed on the road map data displayed on the CRT display 4.

[0004] Other documents that describe the technology related to such a conventional route guidance device for a vehicle include, for example, JP-A-63-148115 and JP-A-6-148115.
JP-A-3-171377, JP-A-1-161111, JP-A-2-224200 and the like.

[0005]

Since the conventional route guidance device for a vehicle is configured as described above, no response to traffic congestion has been made, and while traveling on the recommended route, the vehicle is located in a place unknown to the occupants. In addition, when traffic congestion occurs in a place where traffic information cannot be obtained, it is inconvenient to predict how far the traffic congestion continues.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and when a traffic jam occurs while traveling on a recommended route, a vehicle route guide capable of predicting a congested road. It is an object to obtain a device and a traffic congestion prediction method that can be used for the device.

[0007]

According to the present invention, there is provided a vehicle route guidance apparatus comprising: road data storage means for storing road data on which a vehicle travels and information relating to the road data; and a current position of the vehicle. A current position detecting means for detecting, a destination setting means for setting a destination of the vehicle on the road data, and a route searching means for searching a recommended route from the current position to the destination on the road data; When a traffic jam occurs, the road type of the road in which the traffic jam is detected is detected, and the road in which the traffic is jammed is predicted from the detected road type. Traffic congestion prediction means for predicting a section that is a factor.

[0008] A vehicle route guidance apparatus according to the present invention includes a road data storage unit that stores road data on which a vehicle travels and information related to the road data, and a current position detection unit that detects a current position of the vehicle. A destination setting means for setting the destination of the vehicle on the road data; a route search means for searching a recommended route from the current position to the destination on the road data; Traffic congestion statistics data storage means for storing traffic congestion occurrence time as traffic congestion data for each road connecting the intersection and the intersection; and It predicts the road has a traffic jam based on the statistical data, traffic jam prediction hand to predict a section that is the cause of traffic congestion in the recommended route on the basis of the prediction result It is those with a door.

In the vehicle route guidance apparatus according to the present invention, the traffic jam statistical data storage means stores, for each road connecting the intersection, a plurality of traffic jam statistical data corresponding to the upward direction and the downward direction. The traffic congestion predicting means selects and uses one of the plurality of traffic congestion statistical data when predicting a traffic congested road.

[0010] The traffic congestion prediction method according to the present invention, traffic
When traffic congestion occurs, check the type of road that is congested.
And the traffic congestion based on the detected road type.
Step of estimating the road that is
Is the cause of traffic congestion on the recommended route based on
Predicting the section where the traffic congestion occurs, and using the information as the predicted traffic congestion information
It is those with a door.

In the traffic congestion prediction method according to the present invention, the step of detecting the time of the traffic congestion is compared with the detected time and the statistical congestion occurrence time at each intersection. At a given time, and a step of generating predicted traffic congestion information from the extracted intersection.

In the traffic congestion prediction method according to the present invention, the step of detecting a time of traffic congestion is compared with the detected time and a statistical congestion occurrence time of each road. At a given time, and a step of generating predicted traffic congestion information from the extracted road.

[0013]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiment 1 Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram illustrating a functional configuration of a vehicle route guidance device according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating a device configuration thereof. In FIG. 1, reference numeral 21 denotes a current position detecting means for detecting a current position of the vehicle, and reference numeral 22 denotes a destination setting means for setting a destination of the vehicle. 23 is a traffic congestion detecting means for detecting the presence or absence of traffic congestion, and 24 is a route searching means for searching for a recommended route between the current position and the destination. Reference numeral 25 denotes congestion weighting means for weighting traffic congested roads, and reference numeral 26 denotes congestion prediction means for predicting traffic congested roads on the recommended route. 27
Is a presenting means for presenting a recommended route to the occupant and presenting a congested road, 28 is road data storing means for storing road data and information relating to road data, and 29 is a road congestion distance. It is a congestion distance calculating means for calculating.

In FIG. 2, reference numeral 31 denotes a control unit which controls and processes the entire route guidance apparatus for a vehicle to realize each unit shown in FIG. Circuit 42 and output circuit 43
It is constituted by a microcomputer connected to the PU44. Reference numeral 32 denotes a GPS receiver that receives radio waves from a satellite of the Global Positioning System (GPS) and sends received information to the control unit 31. Reference numeral 33 denotes a key switch, a light pen, and an infrared ray. The operation unit includes an input device such as a touch switch and sends a control signal for input information to the control unit 31. Numeral 34 denotes a map data storage unit which is constituted by, for example, a ROM and stores road data and information on road data. It is. 36 is a brake sensor that detects braking during driving of the vehicle,
37 is a gear sensor for detecting the selected gear, and 38 is a vehicle speed sensor for detecting the running speed of the vehicle.

The current position detecting means 21 shown in FIG.
Can be of any type, for example, FIG.
Or a device that detects the position of the vehicle from the output of the distance sensor and the output of the bearing sensor and the map data, and may use the beacon receiver. Or a Loran-C receiver may be used. Its function is to obtain the current position of the vehicle and the direction in which the vehicle is facing at regular intervals (for example, every one second). The destination setting means 22 may be of any type. For example, the destination setting means 22 may use an operating device for the occupant to set the destination, such as the operating unit 33 in FIG. The point may be set by a place name, or the point may be set by latitude and longitude coordinates. The function is to transmit the information to the route searching means 24 when the occupant sets the destination.

The congestion detecting means 23 calculates the running speed of the vehicle based on the running state of the vehicle, for example, the instantaneous change of the current position obtained from the current position detecting means 21, and detects the presence or absence of congestion from the running speed of the vehicle. Things. The route searching means 24 sets the intersection closest to the current position from the current position detecting means 21 as the departure intersection, the intersection closest to the destination from the destination setting means 22 as the destination intersection, Is searched for an optimal recommended route with the shortest travel distance or the shortest travel time from the departure point intersection to the destination intersection.

The traffic congestion weighting means 25 includes a traffic congestion prediction means 2
The traffic data corresponding to the congested road obtained from 6 is weighted for congestion. In addition, when “congestion is detected” is detected by the traffic congestion detection means 23, the traffic congestion prediction means 26 estimates the intersection that is the source of the traffic congestion on the recommended route based on the recommended route and road data based on the road form. After predicting traffic congested roads,
The traffic congestion weighting means 25 and the traffic congestion distance calculating means 2 use the road on which the traffic congestion is predicted as the predicted traffic congestion information.
9 to pass. The presenting means 27 further includes a current position of the vehicle from the current position detecting means 21 and a heading of the vehicle, road data from the road data storing means 28, a recommended route searched by the route searching means 24, and a congestion distance. The congestion information from the calculating means 29 is presented to the occupant. For example, the display unit 35 shown in FIG. 2 and its display control circuit include an enlargement / reduction display of a road map, a display of a route superimposed on a map, a selection display of a map for inputting a destination, and a display of a destination. , Display of the current position of the vehicle, display of the direction of the vehicle, and the like.

The road data storage means 28 stores the road data and information on the road data in the map data storage unit 3 shown in FIG.
4 and data as shown in FIG. 3 is stored. In FIG. 3, the intersections of the lines marked with “O” indicate intersections, and each intersection has one
Numbers from 01 to 116 are assigned. The straight line connecting the “○” marks indicates the road between the intersections, the numerical value on the straight line is the distance (unit: km), and the value in parentheses () is the average traveling speed (unit: km / h). ).
The numbers in parentheses [] indicate road types, for example, [1] is an expressway, [2] is a national road, [3] is a prefectural road, [4] is a major local road, and [5] is another road. Indicates the width of the road. For example, the width of the road is 13.0 m or more, and the width of the road is 5.5 m or more.
When the width of the road is less than 13.0 m, the width of the road is 3.0 m or more to 5.
Less than 5 m indicates that the width of the road is less than 3.0 m. Note that a road where a tunnel exists is indicated by "tunnel", and a road where a bridge exists is indicated by "bridge".

The congestion distance calculating means 29 calculates the congestion distance from the predicted congestion information from the congestion prediction means 26, and transmits the predicted congestion information and the congestion distance to the presentation means 27.

Next, the operation will be described. First, in FIG. 1, a recommended route from the current position to the destination is searched,
Recommended route If you encounter traffic congestion while driving, automatically search for a route to avoid traffic congestion, calculate the distance of traffic congestion, and avoid traffic congestion and traffic congested roads and traffic congestion Of presenting the distance of the vehicle to the occupant FIG.
This will be described with reference to the flowchart of FIG.

First, the destination desired by the occupant is set by the destination setting means 22 in FIG. 1 (step ST).
1). Next, the current position of the vehicle is fetched from the current position detection means 21 (step ST2), road data is fetched from the road data storage means 28 by the route search means 24 (step ST3), and the intersection closest to the current position is determined as the departure point. As an intersection, an intersection closest to the destination is set as a destination intersection, and a recommended route from the departure intersection to the destination intersection is searched (step ST4). For example, FIG. 5 shows road data fetched from the road data storage means 28. In FIG. 5, "destination" indicates the destination set in step ST1, and "current position A" indicates the step ST1.
Since the current position of the vehicle captured in 2 is shown, the destination intersection is the intersection 107, and the departure intersection is the intersection 101. Therefore, in this case, the route search means 24 determines the intersections 101, 102, and 10 having the shortest distance and the shortest time.
In step ST4, a search is made for intersection number strings 3, 104, 105, 106, and 107 (shown by thick lines in FIG. 5) as recommended routes.

The recommended route searched in step ST4 is presented to the occupant by the presentation means 27 together with the road data from the road data storage means 28, the current position of the vehicle and the direction of the vehicle from the current position detection means 21. Is performed (step ST5). Thereafter, the current position of the vehicle is fetched from the current position detecting means 21 (step ST6). If the current position of the vehicle has reached the destination, the process is terminated. If not, the process is passed to step ST8. (Step ST7). Next, the congestion detecting means 23 detects whether or not the vehicle has entered a traffic congestion based on the traveling state of the vehicle, for example, the traveling speed (step ST8). If "congested" is detected in step ST8, the process proceeds to step ST10; otherwise, the process proceeds to step ST10.
The process returns to T5 (step ST9). Congestion detection means 2
3, when "congestion is detected" is detected by the traffic congestion prediction means 26, the traffic congested road is predicted, and the information of the predicted traffic congested road is used as the predicted traffic congestion information. It transmits to the means 25 (step ST10).

Next, the traffic congestion distance calculating means 29 calculates the traffic congestion distance (step ST11). (Step ST12). For example, in the road data of FIG. 5, the predicted traffic congestion information predicted in step ST10 by the traffic congestion prediction means 26 is used for the intersections 103, 104, 105, 1
If the intersection number sequence is 06, the congestion distance is the sum of the distances of the roads connecting the intersections 103, 104, 105, and 106, and the congestion distance is 0.8 + 0.8 + 0.8.
= 2.4, and the congestion distance is about 2.4 km.

Next, in the traffic congestion weighting means 25, the traffic data is weighted based on the predicted traffic congestion information from the traffic congestion prediction means 26. (Step ST13), and the process returns to Step ST4. Then, in step ST4, the route searching means 24 again transmits the vehicle current position from the current position detecting means 21 and the destination setting means 22 to step S4.
Based on the destination set in T1, the intersection closest to the current position is set as the departure intersection, the intersection closest to the destination is set as the destination intersection, and the road data weighted in step ST13 is used. Searching and presenting a recommended route from the departure intersection to the destination intersection
Send to

FIG. 6 shows road data obtained by weighting the traffic congestion in step ST13. "Current position B" (indicated by "x" in FIG. 6) is the current vehicle position when the traffic congestion is detected by the traffic congestion detecting means 23. It is. Based on the predicted congestion information predicted by the congestion prediction means 26, the intersections 103, 104, 10
Assuming that the road connecting 5,106 is congested, for example, weighting is performed so that the distance of the road is 5 times and the traveling speed is 1/5 times. Based on the thus-weighted road data shown in FIG. 6, the intersection 103, which is the closest intersection to the current position, is set as the departure intersection, and the intersection, which is the intersection closest to the destination set in step ST1, is set. When the route search means 24 performs a route search from the departure point intersection to the destination intersection with 107 as the destination intersection, the travel distance,
Intersections 103, 108, 10 with the shortest running times
The intersection number sequence of 9, 110, 111, 107 (FIG. 6)
Is indicated by a bold line).

The presenting means 27 determines in step S
The route searched in T4 is presented to the occupant (step ST
5). Here, FIG. 7 shows steps ST4 and ST1.
2, the road data, the current position of the vehicle and the direction the vehicle is facing, the distance of traffic congestion, the predicted traffic congestion information, and the recommended route are
This is an example of presentation when displayed on the display unit 35 of FIG. 2, where road data is displayed in the figure, and “×” is shown in the figure.
Indicates the current position of the vehicle and the direction the vehicle is facing, indicates a congested road by a series of "◇", indicates the distance of the congestion below the display, and presents a recommended route to avoid the congestion by a thick line I have.

Next, the detailed operation of step ST8 in FIG. 4 will be described. FIG. 8 shows an operation procedure of the traffic congestion detecting means 23 for detecting the traffic congestion by calculating the traveling speed of the vehicle based on the instantaneous change of the current position obtained from the current position detecting means 21.
This will be described with reference to the flowchart of FIG. “Point 1”, “Point 2”, “Count”, and “Detection start position” shown in the flowchart of FIG. 8 are variables. First, the current position is input from the current position detecting means 21 (step ST2).
1) The input current position is set to "point 2" (step ST22), "count" is set to "0" (step ST23), and "detection start position" is cleared (step ST24). The current position is again input from the current position detecting means 21 (step ST25),
The current position input at 25 is set to "point 1" (step ST26). The current position detecting means 21 detects the current position at regular intervals, for example, at one-second intervals. The speed (hourly speed) can be calculated (step ST27). For example, a point indicated by “point 1” is indicated by coordinates of (X1, Y1), and “point 2”
Is represented by the coordinates (X2, Y2), the traveling speed of the vehicle can be calculated by the following equation.

[0028]

(Equation 1)

The traveling speed calculated in step ST27 is compared with a predetermined traffic congestion determination speed v, for example, 20 km / h (step ST28). If the traffic speed exceeds the traffic congestion determination speed v, the process returns to step ST23. If so, the process proceeds to step ST29 to start the traffic jam detection. Next, the value of "count" is compared with 0 (step ST2).
9) If it is not 0, the process proceeds to step ST31. Also, if the value of “count” is 0, traffic jam detection starts,
The point where the traffic jam detection is started is set as the “detection start position” (step ST30), and the process proceeds to step ST31. Next, "1" is added to "count" (step ST3).
1) The point set as “point 1” is set as “point 2” (step ST32). If the value of “count” is less than a preset traffic jam count number n, for example, 600, the process returns to step ST25 to continue detecting traffic jams. Step S
The process proceeds to T34 (step ST33).

Next, the value of "count" is the traffic congestion count number n.
"Point 1" where the current position at the time of the above is set
Is compared with the "detection start position" at which the point where the traffic jam detection has been started set at step ST30 is set (step ST34). If "point 1" and "detection start position" are the same point, the vehicle stops. It is determined that the
Return to 23. If the “point 1” and the “detection start position” are not the same, it is determined that there is a traffic jam, and the presence of a traffic jam is transmitted to the traffic jam prediction means 26 (step ST35).

Next, the detailed operation of step ST10 in FIG. 4 will be described. The road data read from the road data storage unit 28 and the route search unit 24
The operation procedure of the traffic congestion prediction means 26 for predicting a congested road based on the recommended route searched at T4 will be described with reference to the flowchart of FIG. First, "congestion exists" by the congestion detection means 23
Is extracted from the recommended route, m traffic congestion determination intersections set in advance from the current position of the vehicle to the destination at the time of detecting the traffic congestion, for example, 10 intersections. If the number of intersections to the destination is smaller than the number m of traffic congestion determination intersections, all intersections existing at the recommended distance to the destination are extracted (step ST41). For example, in FIG.
The current vehicle position from the vehicle is “current position B” (“×” in FIG. 5).
When "congestion is present" is detected by the traffic congestion detecting means 23 when the traffic jam is detected, the intersections 103, 104, 105, 106, and 107 are extracted as intersections for determining traffic congestion.

Next, based on the road data read from the road data storage means 28, the type of the road connected to each intersection extracted in step ST41 is checked and compared with the congestion prediction coefficient table shown in Table 1 below. Then, a traffic jam prediction coefficient is added to each intersection (step ST42).

[0033]

[Table 1]

Table 1 shows an example of setting a coefficient of occurrence of traffic congestion according to the type of road connected to the intersection. In Table 1, [1] to [5] indicate road types such as [1]. Indicates an expressway, [2] indicates a national road, [3] indicates a prefectural road, [4] indicates a main local road, and [5] indicates other roads.
Since there are many congestion factors such as "there is an interchange" and "the traffic volume is large", a high value is set for the congestion prediction coefficient. On the other hand, the traffic congestion prediction coefficient is set to a low value because there are few elements of traffic congestion on main local roads and other roads. The traffic congestion prediction coefficient of each intersection extracted in step ST41 from FIG.
The congestion prediction coefficient of the intersection 3 is 3.0, the congestion prediction coefficient of the intersection 104 is 4.0, the congestion prediction coefficient of the intersection 105 is 3.0, the congestion prediction coefficient of the intersection 106 is 5.0, and the congestion prediction coefficient of the intersection 107 is 3.0.

Next, in step ST42, an intersection having the highest traffic jam prediction coefficient is selected from the intersections to which the traffic jam prediction coefficient has been added. At this time, if there are a plurality of intersections having the highest traffic congestion prediction coefficients, an intersection farthest from the current position of the vehicle is selected (step ST43). For example, FIG.
Intersections 103, 104, 105, 106, 10 at
The intersection having the highest congestion prediction coefficient among the seven is the intersection 106 having the congestion prediction coefficient of 5.0.

Next, from the current position of the vehicle, step ST4
The intersection number sequence up to the intersection with the highest traffic jam prediction coefficient selected in step 3 is extracted and used as predicted traffic jam information (step ST
44), and transmits the predicted congestion information to the congestion distance calculating means 29 and the congestion weighting means 25 (step ST4).
5). For example, the intersection selected in step ST43 in FIG. 5 is the intersection 106, and the "current position B" (FIG.
(Indicated by “x”) is the current position of the vehicle, the intersection number sequence of intersections 103, 104, 105, and 106 connecting the intersection 103 and the intersection 106 closest to the current position of the vehicle is the predicted congestion information.

Embodiment 2 In the first embodiment, the case where the traffic jam detection is determined by detecting the traveling state of the vehicle, for example, the traveling speed, has been described. The vehicle speed may be input, and the detection of the traveling speed may be detected by a vehicle speed sensor 38 provided in the vehicle as shown in FIG.

Embodiment 3 Further, in the first embodiment, the state in which the traveling speed of the vehicle is equal to or less than the traffic congestion determination speed v is determined to be congested when the traffic congestion count number n is continuously detected. If the state where the speed is equal to or less than the traffic jam determination speed v is continued for a preset traffic jam determination time t, it may be determined that there is a traffic jam. Next, FIG. 10 shows a procedure of an operation for detecting a congestion when the state where the traveling speed of the vehicle is equal to or less than the congestion determination speed v (for example, 20 km / h) is continued for a congestion determination time t (for example, 5 minutes). This will be described with reference to the flowchart of FIG. Note that this can be realized by changing the operation procedure of FIG. 8 describing the operation of the traffic congestion detecting means 23 in the first embodiment as shown in FIG.

First, the current position is inputted from the current position detecting means 21 (step ST51), the inputted current position is set to "point 2" (step ST52), and the congestion detection timer (TM) is reset (step ST52). ST53),
Clear "detection start position" (step ST5)
4). The current position is input again from the current position detecting means 21 (step ST55), and the current position input in step ST55 is set to "point 1" (step ST55).
56). The current position detecting means 21 detects the current position at fixed time intervals, for example, at one-second intervals, and multiplies the distance between “point 1” and “point 2” by time, for example, 3600 seconds, thereby obtaining the traveling speed (hours per hour) of the vehicle. Can be calculated (step ST57). For example, when the point indicated by “point 1” is indicated by coordinates of (X1, Y1) and the point indicated by “point 2” is indicated by coordinates of (X2, Y2), the traveling speed of the vehicle is calculated by the following equation. it can.

[0040]

(Equation 2)

The traveling speed calculated in step ST57 is equal to a predetermined traffic jam determination speed v (for example, 20 km /
If it exceeds h), the process returns to step ST53, and if it is equal to or less than the traffic jam determination speed v, the process proceeds to step ST59 (step ST58). Next, a traffic jam detection timer (TM)
It is determined whether or not is activated (step ST5).
9) If it is running, the process proceeds to step ST62; if it is not running, the process proceeds to step ST60. If the congestion detection timer (TM) is not running, the current position of the vehicle input in step ST55 is set to the “detection start position” (step ST60), and the congestion detection timer (TM) is started ( Step ST61).

Next, the point set as "point 1" is set as "point 2" (step ST62), and the congestion detection timer (TM) sets a predetermined congestion determination time t (for example, 5 minutes). If it is smaller, the process returns to step ST55, and if it is longer than the traffic jam determination time t, the process proceeds to step ST64 (step ST63). Next, "point 1" where the current position is set when the congestion detection timer (TM) is equal to or longer than the congestion determination time t, and the point where the congestion detection is started set in step ST60 are set. The “detection start position” is compared (step ST64). If “point 1” and “detection start position” are the same point, it is determined that the vehicle is stopped, and the process returns to step ST53. If the “point 1” and the “detection start position” are not the same, it is determined that there is a traffic jam, and “congested” is transmitted to the traffic congestion prediction means 26 (step ST65).

Embodiment 4 FIG. In the first and third embodiments, when a state in which the traveling speed of the vehicle is equal to or less than the traffic jam determination speed v is continuously detected as the traffic jam count number n times,
Alternatively, a case in which the vehicle is determined to be congested when the traffic is continued during the traffic congestion determination time t has been described. Otherwise, it may be determined that there is traffic congestion. Next, a state in which the average speed of the vehicle at every average speed extraction interval t (for example, 1 minute) is equal to or less than the traffic jam determination speed v (for example, 20 km / h) is continuously detected m times (for example, 5 times). The procedure of the operation for detecting traffic congestion in such a case will be described with reference to the flowchart of FIG.
Note that this can be realized by changing the operation procedure of FIG. 8 describing the operation of the traffic congestion detecting means 23 in the first embodiment as shown in FIG.

First, the current position is inputted from the current position detecting means 21 (step ST71), the inputted current position is set to "point 2" (step ST72), and "count" is set to "0" (step ST73). Then, the traffic jam detection timer (TM) is reset (step ST74), and the traffic jam detection timer (TM) is started (step ST7).
5). Next, "0" is set to the vehicle movement distance (Dist) (step ST76), and the current position is again input from the current position detecting means 21 (step ST77). (Step ST78). Next, "Point 1" and "Point 2"
Is calculated as the moving distance of the vehicle (step S
T79). For example, the point indicated by “point 1” is (X1,
Y1), and the point indicated by “Point 2” is (X
In terms of (2, Y2) coordinates, the traveling distance of this vehicle can be calculated by the following equation.

[0045]

(Equation 3)

The travel distance calculated in step ST79 is added to the vehicle travel distance (Dist) (step ST79).
80), and is set to “point 2” set to “point 1” (step ST81). Next, if the traffic jam detection timer is shorter than the preset average speed extraction interval t (for example, 60 seconds), the process proceeds to step ST77. If the timer is equal to or longer than the average speed extraction interval t, the process proceeds to step ST83 (step ST82). ). Since the vehicle movement distance (Dist) km during the average speed extraction interval t (for example, 60 seconds) is obtained by the processing from step ST74 to step ST82, the average speed (Dist) during the average speed extraction interval t is calculated by the following equation. Per hour) (step ST83).

Average speed [km / h] = Dist [km]
/ (1/60) [h]

The average speed during the average speed extraction interval t calculated in step ST83 is compared with a predetermined traffic jam determination speed v (for example, 20 km / h) (step ST8).
4) If the average speed exceeds the traffic jam determination speed v, the process proceeds to step ST86; if the average speed is equal to or less than the traffic jam determination speed v, the process proceeds to step ST85. That is, if the average speed during the average speed extraction interval t exceeds the traffic congestion determination speed v in the comparison in step ST84, “0” is set to “count”, and the process returns to step ST74 (step ST86). . If the average speed during the average speed extraction time t is equal to or less than the traffic jam determination speed v in the comparison in step ST84, “1” is added to “count” (step ST85), and the “count” is set in advance. It is compared with the set traffic jam determination number m (for example, 5) (step ST87), and if less than the traffic jam determination number m, the process returns to step ST74. If the “count” is equal to or greater than the number of times of traffic congestion determination m, it is determined that traffic is congested, and “congested” is transmitted to the traffic congestion predicting means 26 (step ST88).

Embodiment 5 Further, in the fourth embodiment, when the average speed of the vehicle at the average speed extraction interval t is equal to or less than the traffic congestion determination speed v is continuously detected for the traffic congestion determination count m or more, it is determined that the traffic congestion is determined. As shown, the average speed of the vehicle at each average speed extraction interval t was calculated, and the congestion determination speed v
The congestion may be determined when the ratio of the following states is equal to or greater than the congestion occurrence ratio r. Next, the average speed of the vehicle at each average speed extraction interval t (for example, 1 minute) is calculated, and the average of the past congestion determination times m (for example, 10) is equal to or less than the congestion determination speed v (for example, 20 km / h). The procedure of the operation of detecting traffic congestion when the ratio of the state is not less than the traffic congestion occurrence ratio r (for example, 80%) will be described with reference to the flowchart of FIG. This can be realized by changing the operation procedure of FIG. 8 describing the operation of the traffic congestion detecting means 23 in the fourth embodiment as shown in FIG.

First, the current position is inputted from the current position detecting means 21 (step ST91), the inputted current position is set to "point 2" (step ST92), and the congestion detection rate (Rate) is set to "0%". (Step ST93),
Reset the congestion detection timer (TM) (step ST
94), and starts a congestion detection timer (TM) (step ST95). Next, "0" is set for the vehicle travel distance (Dist).
Is set (step ST96), and the current position is again input from the current position detecting means 21 (step ST97), and the current position input in step ST97 is referred to as "point 1".
(Step ST98). Next, the distance between “point 1” and “point 2” is calculated as the moving distance of the vehicle (step ST99). For example, if a point indicated by "point 1" is indicated by coordinates of (X1, Y1) and a point indicated by "point 2" is indicated by coordinates of (X2, Y2), the movement distance is calculated by the following equation. Can be.

[0051]

(Equation 4)

The travel distance calculated in step ST99 is added to the vehicle travel distance (Dist) (step ST1).
00), the point set as “Point 1” is “Point 2”
(Step ST101). Next, if the traffic jam detection timer (TM) is shorter than the preset average speed extraction interval t (for example, 60 seconds), the process proceeds to step ST97.
The process proceeds to T103 (step ST102). Since the vehicle movement distance (Dist) km during the average speed extraction interval t (for example, 60 seconds) is obtained by the processing from step ST94 to step ST102, the average speed (Dist) during the average speed time interval t is calculated by the following equation. Per hour) (step ST103).

Average speed [km / h] = Dist [km]
/ (1/60) [h]

The average speed during the average speed extraction interval t calculated in step ST103 is compared with a preset congestion determination speed v (step ST104). If the average speed exceeds the congestion determination speed v, the process proceeds to step ST104. The process proceeds to ST106, and if the speed is equal to or less than the traffic jam determination speed v, the process proceeds to step ST105.
The process proceeds to (Step ST104). That is, step ST1
If the average speed during the average speed extraction interval t exceeds the traffic congestion determination speed v in 04, "10%" is subtracted from the traffic congestion detection rate (Rate), and the process returns to step ST94 (step ST106). However, the value of this traffic congestion detection ratio is “0%”
If it is less than, it shall not be subtracted. If the average speed during the average speed extraction interval t is equal to or less than the traffic jam determination speed v in step ST104, the traffic jam detection rate (Rat
"10%" is added to e) (step ST105).
However, the value of this traffic congestion detection ratio (Rate) is “100%”.
If it exceeds, it shall not be added. This congestion detection rate (Rate) is compared with a preset congestion occurrence rate r (for example, 80%) (step ST107), and if it is less than the congestion occurrence rate r, the process returns to step ST94.
If the traffic congestion detection rate (Rate) is equal to or greater than the traffic congestion occurrence rate r, it is determined that there is traffic congestion, and "congested" is transmitted to the traffic congestion prediction means 26 (step ST108).

Embodiment 6 FIG. Further, in the first embodiment, the case where the traffic congestion determination speed v is set to, for example, 20 km / h or less for all roads is determined as the speed at which traffic is determined to be congested. Alternatively, the value of the traffic congestion determination speed v may be changed. For example, the value of the traffic congestion determination speed v used for the traffic congestion detection in the first embodiment is set to, for example, v = 30 km / h when the road type of the traveling road is an expressway, and If the road type is national road, for example, v =
20 km / h, and if the road type of the traveling road is a prefectural road, a main local road or other roads, for example, v = 10 km / h.

Embodiment 7 FIG. Further, in the first embodiment, the road type of the road connected to each intersection is set as a target of traffic congestion prediction. However, for example, a road congestion prediction coefficient table as shown in Table 2 is used and the road connecting to the intersection is determined. The traffic congestion prediction may be performed based on the number of roads. Further, for example, a list of two-dimensional traffic congestion prediction coefficients as shown in Table 3 is used, and both the number of roads connected to the intersection and the road type are used. Traffic jam prediction may be performed.

[0057]

[Table 2]

[0058]

[Table 3]

Embodiment 8 FIG. Further, in the first embodiment, the road type of the road connected to each intersection is set as the target of the traffic congestion. May be made based on the width of the traffic jam.

[0060]

[Table 4]

Embodiment 9 FIG. Further, in the first embodiment, the road type of the road connected to each intersection is set as the target of the traffic congestion prediction. The traffic congestion prediction may be performed based on whether or not the traffic congestion factor item is connected to the road on which the traffic congestion factor item exists.

[0062]

[Table 5]

Embodiment 10 FIG. Embodiment 1
In the above, only the road type of the road connected to each intersection was targeted for traffic congestion prediction, but the road type of the road connected to the intersection, the number of roads connected to the intersection, the width of the road connected to the intersection, The traffic congestion may be estimated by comprehensively evaluating a plurality of factors such as whether a tunnel or a bridge exists on a road connected to the intersection. Next, the type of road connecting to the intersection, the number of roads connecting to the intersection,
The procedure of an operation for performing traffic congestion prediction by comprehensively evaluating a plurality of factors such as a width of a road connecting to an intersection and whether a tunnel or a bridge exists on a road connecting to the intersection will be described with reference to the flowchart of FIG. . Note that this corresponds to the first embodiment.
In FIG. 9, the operation of step ST42 in the operation procedure of FIG. 9 which describes the operation of the traffic congestion prediction means 26 can be realized by changing the procedure shown in FIG.

First, based on the road data read from the road data storage means 28, the road type of the road connected to each intersection extracted in step ST41 of FIG. 9 is checked. Then, the traffic congestion prediction coefficient of each intersection is added (step ST111). For example, the intersection extracted in step ST41 of FIG.
Are the intersections 103, 104, 105, 106, and 107, the traffic congestion prediction coefficient of the intersection 103 is 3.0 and the traffic congestion of the intersection 104 is based on the type of road connected to each intersection and the traffic congestion prediction coefficient table shown in Table 1. The prediction coefficient is 4.0, the congestion prediction coefficient of the intersection 105 is 3.0, the congestion prediction coefficient of the intersection 106 is 5.0, and the congestion prediction coefficient of the intersection 107 is 3.0.
Becomes

Next, based on the road data read from the road data storage means 28, the number of roads connected to each intersection extracted in step ST41 of FIG. 9 is checked. Then, the traffic congestion prediction coefficient is added to each intersection (step ST112). For example,
The traffic congestion prediction coefficients set in step ST111 are as follows: the traffic congestion prediction coefficient at the intersection 103 is 3.0; the traffic congestion prediction coefficient at the intersection 104 is 4.0;
0, traffic congestion prediction coefficient of intersection 106 is 5.0, intersection 10
7 is 3.0, the traffic congestion prediction coefficient of each intersection from the traffic congestion prediction coefficient table shown in Table 2 and the number of roads connected to each intersection, and the traffic congestion prediction coefficient of the intersection 103 is 3.0. 0, the congestion prediction coefficient of the intersection 104 is 4.0, the congestion prediction coefficient of the intersection 105 is 3.0, the congestion prediction coefficient of the intersection 106 is 5.0, and the congestion prediction coefficient of the intersection 107 is 3.0.
It becomes 0.

Next, based on the road data read from the road data storage means 28, the width of the road connected to each intersection extracted in step ST41 of FIG. 9 is checked. Then, the traffic congestion prediction coefficient is added to each intersection (step ST113). For example,
The traffic congestion prediction coefficients set at step ST112 are as follows: the traffic congestion prediction coefficient at the intersection 103 is 3.0; the traffic congestion prediction coefficient at the intersection 104 is 4.0;
0, traffic congestion prediction coefficient of intersection 106 is 5.0, intersection 10
If the traffic congestion prediction coefficient of the intersection 103 is 3.0 and the traffic congestion prediction coefficient of the intersection 103 is 7.0 based on the width of the road connected to each intersection and the traffic congestion prediction coefficient table shown in Table 4. 0, the congestion prediction coefficient of the intersection 104 is 9.0, the congestion prediction coefficient of the intersection 105 is 8.0, the congestion prediction coefficient of the intersection 106 is 10.0, and the congestion prediction coefficient of the intersection 107 is 7.0.

Next, based on the road data read from the road data storage means 28, the traffic congestion factor items existing on the road connected to each intersection extracted in step ST41 of FIG. The traffic congestion prediction coefficient is added to each intersection in comparison with the prediction coefficient table (step ST
114). For example, the traffic congestion prediction coefficient set in step ST113 indicates that the traffic congestion prediction coefficient of the intersection 103 is 7.0,
If the congestion prediction coefficient of the intersection 104 is 9.0, the congestion prediction coefficient of the intersection 105 is 8.0, the congestion prediction coefficient of the intersection 106 is 10.0, and the congestion prediction coefficient of the intersection 107 is 7.0, From the congestion factor items existing on the road connected to the road and the congestion prediction coefficient table shown in Table 5, the congestion prediction coefficient for each intersection is 7.0 for the intersection 103, and 9.3 for the intersection 104. , The congestion prediction coefficient of the intersection 105 is 8.0, and the congestion prediction coefficient of the intersection 106 is 1
0.0, the traffic congestion prediction coefficient at the intersection 107 is 7.0.

Embodiment 11 FIG. Embodiment 1
In the above, the road from the current position to the intersection estimated to be the cause of the traffic congestion is weighted as congestion, but the road from the current position to the intersection estimated to be the cause of the traffic congestion and the May be weighted for traffic congestion on the roads connected to the intersections existing in.

Embodiment 12 FIG. Embodiment 1
Now, 5 times the distance of the predicted congested road,
The case where the traveling speed is reduced to 1/5 and the recommended route to reach the destination while avoiding the traffic congestion is described, but the weight may be changed according to the type of the road to be weighted. Good. For example, if the road to be weighted is a highway, the road distance is increased by a factor of 10 and the traveling speed is reduced to 1/10. If the weighted road is a national road, the distance of the road is increased by a factor of eight. When the speed is 1/8 times and the road to be weighted is a prefectural road, the distance of the road is 6 times, the traveling speed is 1/6 times, and the road to be weighted is a main local road. Makes the road distance four times and the traveling speed 1/4, and if the road to be weighted is another road, the road distance is doubled and the traveling speed is 1/2.

Embodiment 13 FIG. Further, in each of the first embodiments, the presenting unit 27 is described as visually presenting using a CRT display. However, auditory presentation may be performed by voice.

Embodiment 14 FIG. Next, a fourteenth embodiment of the present invention will be described with reference to the drawings. In the fourteenth embodiment, as the driving state of the vehicle, the timing at which the occupant applies the brake is detected by using the brake sensor 36 of FIG. The operation procedure of the means 23 will be described with reference to the flowchart of FIG.

First, the congestion detection timer (TM2) is reset (step ST121), and the brake interval detection timer (TM1) is reset (step ST122).
A brake interval detection timer (TM1) is started (step ST123). After that, it waits for the brake to be applied (step ST124). For example, the brake sensor 36 in FIG. 2 generates an interrupt to the control unit 31 when the occupant applies a brake, and the congestion detecting unit 23 in FIG. It receives the signal of "braking".

Next, when it is detected in step ST124 that the brake is applied, it is determined whether or not the brake interval detection timer (TM1) at that time is equal to or less than a preset traffic jam brake interval time t1 (for example, 30 seconds). (Step ST125) If the result is less than or equal to t1, step ST125
The process proceeds to 126, and if it is equal to or longer than t1, the process returns to step ST121. Brake interval detection timer (T
If M1) is equal to or less than the traffic jam brake interval time t1, it is next determined whether or not the traffic jam detection timer (TM2) has been activated (step ST126). Step ST
Proceed to 127. That is, if the congestion detection timer (TM2) is not running, the congestion detection timer is started and the process returns to step ST122 (step ST127). On the other hand, if the congestion detection timer (TM2) is running, it is determined whether or not the congestion detection timer (TM2) is longer than a preset congestion detection time t2 (for example, 10 minutes) (step ST1).
28), the congestion detection timer (TM2) sets the congestion detection time t
If less than 2, the process returns to step ST122. If the congestion detection timer (TM2) is equal to or longer than the congestion detection time t2, it is determined that a congestion has occurred, and "congested" is transmitted to the congestion prediction means 26 (step ST12).
9).

Embodiment 15 FIG. In the first embodiment,
In No. 4, the case where the state in which the brake interval is equal to or less than the congestion brake interval time t1 is determined to be a congestion when the congestion detection time t2 or more is continued is described. If the following braking occurs continuously more than a preset number n (for example, 20) of traffic jam detection times, it may be determined that there is a traffic jam.

Embodiment 16 FIG. Embodiment 1
In No. 4, on all roads, the brake interval for judging that there is traffic congestion is set to be equal to or less than the congestion brake interval time t1 (for example, 30 seconds), and the time for judging that traffic is congested is set to the congestion detection time t2 (for example, 10 minutes) or more, but the values of the traffic jam brake interval time t1 and the traffic jam detection time t2 may be changed according to the type of the road on which the vehicle is traveling. For example, the values of the congestion brake interval time t1 and the congestion detection time t2 used for the congestion detection in the fourteenth embodiment are determined by the following equation.
0 seconds, the traffic jam detection time is t2 = 5 minutes, and if the road type of the traveling road is a national road, for example, the traffic jam brake interval time is t1 = 40 seconds, the traffic jam detection time is t2 = 10 minutes, and the driving is performed. If the road type of the road is a prefectural road, a main local road or another road, for example, the traffic jam brake interval time is t1 = 20 seconds, and the traffic jam detection time is t2.
= 15 minutes.

Embodiment 17 FIG. Next, a seventeenth embodiment of the present invention will be described with reference to the drawings. In the seventeenth embodiment, the gear change position of the transmission gear mounted on the vehicle and the duration of the gear change position are detected by using the gear sensor 37 shown in FIG. The traffic congestion detection means 2 shown in FIG.
The operation procedure 3 will be described with reference to the flowchart in FIG.

First, the congestion gear position total time TL is set to 0 seconds (step ST131), and the congestion gear position timer (T
M1) is reset (step ST132), and a gear change position is extracted (step ST133). For example, the gear sensor 37 in FIG. 2 is for extracting the gear change position of the transmission gear mounted on the vehicle. For example, if the gear change position is the “low gear” position, “1” is set, "2" if position
"3" for the "third gear" position, "4" for the "top gear" position, "-1" for the "reverse gear" position, and "neutral In the case of the "gear" position, "0" is extracted by a digital signal.

Next, the gear change position extracted in step ST133 is "3" (third gear) or "4".
If it is (top gear) or "-1" (reverse gear), it is determined that there is no traffic jam, and the process returns to step ST131 (step ST134). Also,
If it is "1" (low gear) or "2" (second gear), the process proceeds to step ST13 to start detection of traffic jam.
6, and if "0" (neutral gear), the process proceeds to step ST138 (step ST135).
If it is determined in step ST135 that the gear change position detected in step ST133 is "1" (low gear) or "2" (second gear), the congestion gear position timer (TM1) is started. It is determined whether the operation is in progress (step ST136). If the result is in operation, the process proceeds to step ST133. If the result is not in operation, the process proceeds to step ST137. Step ST13
If it is determined in step 6 that the congestion gear position timer (TM1) is not running, the congestion gear position timer (TM1) is started (step ST137).

On the other hand, if the determination in step ST135 is negative (N), the congestion gear position timer (TM
1) is added to the congestion gear position total time (TL) (step ST138), and it is determined whether the congestion gear position total time (TL) is equal to or longer than a preset congestion detection time t1 (for example, 3 minutes) ( (Step ST139) If the result is negative (N), the process returns to step ST132. On the other hand, if the result is affirmative (Y), the gear position is “1” (low gear) or “2” (second gear)
Is longer than the traffic congestion detection time t1, it is determined that there is a traffic congestion, and "congested" is transmitted to the traffic congestion prediction means 26 (step ST140).

Embodiment 18 FIG. In the first embodiment,
In FIG. 7, the specified time for detecting traffic congestion is set to the traffic congestion detection time t1 (for example, 3 minutes) on all roads, but the traffic congestion detection time t1 varies depending on the road type of the road on which the vehicle is traveling. You may make it do. For example, when the traffic congestion detection time t1 of the seventeenth embodiment is set to t
1 = 1 minute, when the road type of the traveling road is the national road, the congestion detection time is set to t1 = 2 minutes, and the road type of the traveling road is a prefectural road, a main local road, or another road. If there is, the congestion detection time is set to t1 = 3 minutes

Embodiment 19 FIG. Next, a nineteenth embodiment of the present invention will be described with reference to the drawings. In the nineteenth embodiment, the number of times of acceleration / deceleration of the vehicle speed is detected during the traffic jam detection time t (for example, 60 seconds) using the vehicle speed sensor 38 in FIG. (For example, 5 times)
And the vehicle speed is the traffic jam speed V (for example, 20 km /
h) Detect traffic congestion when:

First, the number of times of acceleration / deceleration (CNT) is set to “0” (step ST141), the acceleration flag (FLG) is set to “0” (step ST142), and the congestion detection time timer (TM) is reset (step ST143). A traffic jam detection time timer (TM) is started (step ST14).
4). Next, the congestion detection time timer (TM) is compared with a preset congestion detection time t (for example, 60 seconds) (step ST145), and if it exceeds the congestion detection time t, the process returns to step ST141. If the traffic jam detection time timer (TM) is equal to or shorter than the traffic jam detection time t, the vehicle speed is detected by the vehicle speed sensor 38 in FIG. 2 and the detected vehicle speed is set to the first vehicle speed v1 (step ST146). Next, the first vehicle speed v1 detected in step ST146 is compared with a preset congestion speed V (for example, 20 km / h) (step ST147), and if it exceeds the congestion speed V, the process returns to step ST141. Also, the first vehicle speed v
If 1 is equal to or less than the congestion speed V, the traveling speed of the vehicle is detected by the vehicle speed sensor 38 in FIG.
2 (step ST148).

Next, the second vehicle speed v2 detected in step ST148 is compared with a preset congestion speed V (step ST149). If the second vehicle speed v2 exceeds the congestion detection vehicle speed V, the process proceeds to step ST141. And also
If the second vehicle speed v2 is equal to or less than the congestion speed V, the first vehicle speed v1
And the second vehicle speed v2 are determined (step ST
150). As a result of the determination, the second vehicle speed v2 is the first vehicle speed v
If it is larger than 1, it is determined that the vehicle is accelerating, and "1" is set to the acceleration flag (FLG) (step ST151). If the second vehicle speed v2 is not higher than the first vehicle speed v1, the acceleration flag (FL)
G) is determined to be “1” (step ST15)
2) As a result, if the acceleration flag is not "1", the process returns to step ST145.

On the other hand, if the result of determination in step ST152 is that the acceleration flag is "1", the result of the previous determination is "acceleration", and in step ST150 the second vehicle speed v2 is greater than the first vehicle speed v1. Since the current determination result is “no acceleration”, 1 is added to the number of times of acceleration / deceleration (CNT) (step ST15).
3). Then, it is determined whether or not the number of times of acceleration / deceleration (CNT) is equal to or greater than a predetermined number of times of traffic jam acceleration / deceleration n (for example, 5 times) (step ST154). If the result is less than the number of times of traffic jam acceleration / deceleration n, the process returns to step ST145. . On the other hand, if the result of determination in step ST154 is that the number of times of acceleration / deceleration (CNT) is equal to or greater than the number of times of congestion acceleration / deceleration n, it is determined that a traffic jam has occurred.
"Congestion is present" is transmitted to traffic congestion prediction means 26 (step ST155).

Embodiment 20 FIG. In the first embodiment,
9 describes the case where the number of times of acceleration / deceleration of the traveling speed for detecting traffic congestion is the number of times of traffic congestion acceleration / deceleration n on all roads. You may make it change. For example, if the number of times of congestion acceleration / deceleration n in the nineteenth embodiment is n, the number of times of congestion acceleration / deceleration is n = 3 if the road type of the traveling road is an expressway, and the road type of the traveling road is national road. If there is, the number of times of congestion acceleration / deceleration is set to n = 5. If the road type of the traveling road is a prefectural road, a main local road, or other roads, the number of times of congestion acceleration / deceleration is set to n = 8. .

Embodiment 21 FIG. Embodiment 1
In FIG. 9, the vehicle speed is detected by using the vehicle speed sensor 38 shown in FIG. May be detected.

Embodiment 22 FIG. Next, a twenty-second embodiment of the present invention will be described with reference to the drawings. In the twenty-second embodiment, a traffic congested road is predicted from a change in the traveling speed of a vehicle after the traffic congestion. This will be described with reference to a flowchart.

First, an average running speed for the past t minutes (for example, 10 minutes) is calculated from the moving distance of the current position from the current position detecting means 21 in FIG. 1 and set as the first average speed v1 (step ST161). ), And then in step ST1
The average running speed during the past t / 2 minutes (for example, 5 minutes) is calculated in the same procedure as 61, and is set as the second average speed v2 (step ST162). Thus, for example, for the past t minutes,
For example, when the moving distance of the current position in the past 10 minutes is 2 km, the first average speed v1 is calculated by the following equation.

V1 = 2 km / (１ ／ hour) = 12 km / h

If the moving distance of the current position in the past t / 2 minutes, for example, the past 5 minutes is 1.5 km, the second
The average speed v2 is calculated by the following equation.

V2 = 1.5 km / (1/12 hour) = 18 km / h

Next, the first average speed v1 is compared with the second average speed v2 (step ST163). As a result of the comparison in step ST163, when the second average speed v2 indicating the average speed in the past t / 2 minutes is faster than the first average speed v1 indicating the average speed in the past t minutes,
It is determined that traffic congestion is being avoided because the vehicle is accelerating, and coefficient a is set to, for example,-／, and coefficient b is set to, for example, 6 (step ST164). Step ST1
63, the first average speed v1 indicating the average speed in the past t minutes and the second average speed v1 indicating the average speed in the past t / 2 minutes.
If the average speed v2 is the same, it is determined that the vehicle is traveling near the center of traffic congestion because the vehicle is moving at a constant speed, and the coefficient a
Is set to, for example, -3/5 and the coefficient b is set to, for example, 16 (step ST165). Further, step ST163
As a result, the second average indicating the average speed in the past t / 2 minutes is larger than the first average speed v1 indicating the average speed in the past t minutes.
If the average speed v2 is slower, it is determined that the vehicle has just decelerated because the vehicle is decelerating.
Is set to, for example, -6/5 and the coefficient b is set to, for example, 32 (step ST166).

Next, the coefficients a and b set in steps ST164 to ST166 are compared with those in step ST162.
Using the average speed v2 for the past t / 2 minutes set in
The congestion distance L is calculated using the following equation (step ST1)
67).

L = a × v2 + b

For example, if the first average speed v1 is 12 km / h
When the second average speed v2 is 18 km / h, since the vehicle is accelerating, the coefficients a and b are a = −− １ and b = 6, respectively, and the congestion distance L is By substituting into the above equation, L = 2.4 km is obtained. FIG. 18 shows this as a table. Here, in FIG. 18, the horizontal axis represents the second average speed v2km / h, which is the average speed during the past t / 2 minutes (for example, 5 minutes), and the vertical axis represents the congestion distance Lkm. , A constant traffic speed, a change during acceleration, and a traffic congestion distance L corresponding to the second average speed v2.

Next, based on the traffic congestion distance L calculated in step ST167, the current position of the vehicle from the current position detecting means 21, and the recommended route searched by the route searching means 24, the current position of the vehicle and the destination are determined. Congestion distance L towards
It is determined that there is traffic congestion up to km, and predicted traffic congestion information is created.
9 (step ST168).

For example, in FIG. 5, the current position of the vehicle is “current position B” (indicated by “x” in FIG. 5) at the intersection 10.
It is 0.2km before 3, and the congestion distance L is 2.4km /
In the case of h, the predicted traffic congestion information is an intersection existing within 2.4 km from the "current position B" toward the destination, and becomes an intersection number sequence of intersections 103, 104, and 105.

Embodiment 23 FIG. Next, a twenty-third embodiment of the present invention will be described with reference to the drawings. In the twenty-third embodiment, a road type of a road in which traffic is congested is detected, and a road in which traffic is congested is predicted from the detected road type.
Hereinafter, the operation procedure of the traffic congestion prediction means 26 of FIG. 1 will be described with reference to the flowchart of FIG.

First, based on the current position of the vehicle from the current position detecting means 21 and the road data from the road data storing means 28, the type of the currently traveling road is checked (step ST1).
71). Next, it is checked whether or not the road type is an expressway (step ST172), and if it is an expressway, predicted traffic congestion information at the time of expressway congestion is created (step ST173).
The traffic jam prediction process ends. For example, traffic congestion on an expressway is likely to start at an interchange, so it is predicted that the road from the current position of the vehicle to the n-th, for example, three-way, interchange is congested, and the number of the intersection between them The column is used as predicted traffic information.

If it is determined in step ST172 that the road type is not an expressway, it is next checked whether or not the road type is a national road (step ST174). (Step ST17)
5), the congestion prediction processing ends. For example, traffic congestion on a national road is likely to be congested over a long distance on the same route, so it is predicted that the entire national road on the same route is congested from the current position of the vehicle, and the number sequence of intersections existing between them Is the predicted traffic congestion information.

If it is determined in step ST174 that the road type is not a national road, it is next checked whether or not the road is a prefectural road (step ST176). Predicted congestion information is created (step ST177), and the congestion prediction process ends. For example, traffic congestion on prefectural roads is likely to be congestion for exiting on national roads and expressways, so it is predicted that the road from the current position of the vehicle to the intersection leading to the expressway or national road is congested, The number sequence of the intersection existing between them is used as the predicted traffic congestion information.

If it is determined in step ST176 that the road type is not a prefectural road, it is checked whether or not it is a main local road (step ST178). The predicted traffic congestion information at the time is created (step ST179), and the traffic congestion prediction processing ends.
For example, traffic congestion on major local roads is likely to be congestion on national roads, expressways, and prefectural roads. It is predicted that the road is congested, and the number sequence of the intersection existing between the roads is used as predicted congestion information.

If it is determined in step ST178 that the road is not a main local road, information on the predicted traffic congestion at the time of other traffic congestion is created for the road data (step ST180), and the traffic congestion prediction processing ends. . For example, traffic congestion on other roads is likely to be a short distance, so it is predicted that the road from the current position of the vehicle to the nth intersection, for example, the intersection two ahead, is congested, and the intersection existing between them Is used as predicted traffic congestion information.

Hereinafter, a specific description will be given with reference to FIG.
FIG. 5 shows road data fetched from the road data storage unit 28 of FIG. 1. The bold line indicates a recommended route searched by the route searching unit 24 of FIG. 1, and the current position B “×” indicates the traffic congestion prediction of FIG. This shows the current position of the vehicle when the means 26 detects "congestion". For example, in the road data shown in FIG.
When "x" detects "congested" on the road connecting the intersections 102 and 103 by the congestion detecting means 23 in FIG. 1, the type of the road connecting the intersections 102 and 103 is the prefectural road [3].
Therefore, it is predicted that there is traffic congestion up to the intersection where the expressway or the national road intersects. In FIG. 5, since the road intersects with the national road at the intersection 104, it is predicted that the road from the current position to the intersection 104 is congested, and the intersection number sequence of the intersections 103 and 104 is the predicted congestion information.

Embodiment 24 FIG. Next, an embodiment 24 of the invention will be described with reference to the drawings. The twenty-fourth embodiment detects a time of traffic congestion and predicts a congested road from traffic congestion statistical data based on the detected time.
In FIG. 20, the time of traffic congestion is detected, and based on the detected time, traffic congested roads are predicted.
FIG. 35 shows a detailed block diagram of a traffic congestion prediction means 26 according to the twenty-fourth embodiment. In the figure, reference numeral 51 denotes a current time, for example, “November 30, 1992, Monday, 18:00,
It is a current time detecting means such as a clock for detecting "32 minutes, 28 seconds" or the like. Reference numeral 52 denotes a traffic congestion statistical data storage unit that stores a statistical time at which traffic congestion occurs at each intersection in association with each intersection number, and stores, for example, information shown in Table 6 below.

[0106]

[Table 6]

Table 6 is a list showing traffic congestion statistical data in which statistical times at which traffic congestion occurs correspond to each intersection. In the table, the numbers shown in the "intersection number" column are the intersection numbers of each intersection. And corresponds to, for example, the intersection number of the road data shown in FIG. The information shown in the “congestion time information” column indicates a statistical time period during which traffic congestion occurs at the intersection indicated by the corresponding intersection number. In the case of Table 6, it is considered that traffic congestion occurs at intersection 103. The time zone is “6:30 am to 8:30 am”.

When the traffic congestion detecting means 23 of FIG. 1 detects "congestion", the current time detecting means 53
1 is a predicted traffic congestion information creating unit that extracts the current time from 1 and extracts the intersection number at which traffic congestion is currently occurring from the traffic congestion statistical data storage unit 52 to create predicted traffic congestion information.

Next, the procedure of the operation when the traffic congestion predicting means 26 shown in FIG. 20 predicts a road on which traffic congestion occurs will be described with reference to the flowchart of FIG. First, the current time is fetched from the current time detecting means 51 (step ST181), and the traffic jam statistic data at the time fetched in step ST181 is fetched from the traffic jam statistical data storage means 52 (step ST182). Next, predicted traffic congestion information creating means 53 creates predicted traffic congestion information based on the traffic congestion statistical data taken in step ST182, and transmits the predicted traffic congestion information to traffic congestion weighting means 25 and traffic congestion distance calculating means 29 in FIG. (Step ST183).

For example, if the current time fetched in step ST181 is “1992, November, 30th, Monday, 8
Hour, 10 minutes, 28 seconds ", and if the traffic jam statistical data stored in the traffic jam statistical data storage means 52 is the data shown in Table 6, the traffic jam time information including the time" 8:10 "is included. All the intersection numbers are extracted, the roads connected by the extracted intersections are determined to be congested roads, and predicted congestion information is created based on the road data. The intersections extracted in this case are the intersections 103, 104, 109, 11
4 and the intersection number sequence of intersections 103, 104, 109, and 114 becomes the predicted congestion information.

Embodiment 25 FIG. The second embodiment
In FIG. 4, statistical traffic congestion time data in which statistical traffic congestion time information is stored in association with each intersection is shown. Information and statistical traffic congestion information in the down direction may be stored.

Embodiment 26 FIG. Embodiment 2
In FIG. 4, only the statistical traffic congestion time information is stored as the traffic congestion statistical data. However, as the statistical congestion period, for example, a congested month, a congested day,
Then, statistical information of the traffic jam time may be stored.

Embodiment 27 FIG. Embodiment 2
In FIG. 4, as the traffic congestion statistical data, only the information on the statistical traffic congestion time is stored. The average traveling speed may be stored together.

Embodiment 28 FIG. Embodiment 2
In FIG. 4, as the traffic congestion statistical data, only statistical traffic congestion time information is stored corresponding to each intersection. However, the traffic in the up direction is associated with each road connecting the intersections. Statistical traffic information and statistical traffic information in the down direction are stored, respectively, and as statistical traffic congestion periods, for example, statistical information of traffic congestion month, traffic congestion day, traffic congestion day, and traffic congestion time May be stored together with the degree of influence of traffic congestion, for example, the average traveling speed of the vehicle when traffic congestion occurs, as shown in Table 7 below.

[0115]

[Table 7]

Table 7 shows statistical congestion periods, such as congested months, congested days, congested days, and congested times, corresponding to the upward and downward directions of the road connecting the intersections. And the traffic congestion statistical data stored in the traffic congestion statistical data storage means 52 indicating the degree of influence of traffic congestion, for example, the average traveling speed of the vehicle when traffic congestion occurs. In Table 7, the information shown in the "road information" column indicates the ups and downs of each road. For example, the road information in the first column indicates the ups and downs of the road connecting the intersection 101 and the intersection 102. The information shown in the “Congestion period” column indicates statistical information that is likely to cause traffic congestion, and the “Month” column in the “Congestion period” column indicates the statistical month in which congestion is likely to occur. , The "Day" column contains information on statistical days with a high probability of congestion, the "Day of the Week" column contains information on statistical days with a high probability of congestion,
The “time” column indicates information on statistical times at which congestion is likely to occur. For example, in the congestion period of the road from the intersection 102 in the second column to the intersection 103,
This indicates that traffic congestion occurs from 6:30 am to 8:30 am only from Monday to Friday, from January 1 to December 1 to 31. The value shown in the “Average traffic speed during traffic jam” column is the travel speed k when traffic jam occurs.
m / h, for example, the average speed during traffic congestion on the road from the second row of intersections 102 to the intersection 103 is 10 k
m / h.

Therefore, according to Table 7, in 1992
Roads on which traffic congestion is predicted to occur at 8:10:28 am on January 30 (Monday) include a road from intersection 102 to intersection 103 and a road from intersection 103 to intersection 10
4; a road from the intersection 104 to the intersection 109; and a road from the intersection 109 to the intersection 114.

Embodiment 29 FIG. Next, a twenty-ninth embodiment of the present invention will be described with reference to the drawings. This embodiment 29 detects the time of traffic jam, predicts a congested road from the traffic jam statistical data based on the detected time, and if the detected traffic jam is not included in the statistical data. Updates the statistical data each time and learns the traffic congestion. FIG. 22 detects the time of the traffic congestion from the above,
Based on the detected time, the traffic congestion statistics are used to predict congested roads, and if the detected traffic congestion is not included in the statistics, the statistics are updated each time,
FIG. 29 shows a detailed block diagram of a traffic congestion predicting means 26 according to the twenty-ninth embodiment for learning traffic congestion.

In the figure, reference numeral 51 denotes current time detecting means;
Reference numeral 52 denotes a traffic congestion statistical data storage unit, which is the same as that shown in FIG. The road 54 detects a traffic jam on the basis of the current position of the vehicle from the current position detection means 21 and the road data from the road data storage means 28 in FIG. When there is no traffic jam statistical data, the traffic jam statistical data updating means updates the traffic jam statistical data. Reference numeral 55 denotes the updated traffic congestion statistics data from the traffic congestion statistics data updating means 54 and the current time detection means 51.
20 in that the intersection number where the traffic congestion is currently occurring is extracted from the current time detected by the system to generate the predicted traffic congestion information.
Is a means for creating predicted traffic congestion information, which is different from the one with reference numeral 53 appended.

Next, a description will be given, with reference to the flowchart of FIG. 23, of an operation procedure when the traffic congestion predicting means 26 shown in FIG. 22 predicts a road on which traffic congestion occurs and learns traffic congestion statistical data. First, the current time is fetched from the current time detecting means 51 (step ST191), and the traffic jam statistic data of the current time fetched in step ST191 is fetched from the traffic jam statistical data storage means 52 (step ST1).
92). Next, based on the current position of the vehicle from the current position detection unit 21 in FIG. 1 and the road data from the road data storage unit 28, the road on which the vehicle is traveling and the direction in which the vehicle is facing are extracted and extracted. An intersection in front of the road is extracted (step ST193). Next, it is determined whether or not the intersection extracted in step ST193 exists in the traffic congestion statistical data acquired in step ST192 (step ST194).
The process proceeds to step ST196 if it exists. That is, if the determination result in step ST194 is affirmative (Y), the traffic congestion information is added to the traffic congestion statistical data corresponding to the intersection extracted in step ST193 in units of a statistical traffic congestion information storage time unit t (for example, 30 minutes). (Step ST1
95).

For example, if the current time from the current time detection means 51 is "1992, November, 30th, Monday, 8:00,
46 minutes, 30 seconds ".
Is the information as shown in Table 6, where the road on which the vehicle is traveling is the road going from the intersection 102 to the intersection 103, and the intersection ahead of the vehicle than the direction in which the vehicle is facing is the intersection. In the case of 103, when an intersection where a traffic congestion occurs at the current time is extracted from the traffic congestion statistical data, the intersections 104, 109 and 114 are extracted. Since there is no traffic congestion statistical data of the intersection 103 indicating the detected traffic congestion in the extracted intersections, the traffic congestion time information of the intersection 103 is added to the traffic congestion statistics data in units of the statistical traffic congestion information storage time unit t. Update data. Table 8 shows the updated traffic statistics data.

[0122]

[Table 8]

Table 8 is obtained by updating the traffic jam statistical data of Table 6, and the traffic jam time information of the intersection 103 before the update is “6:30 to 8:30”, but “19”.
Monday, November 30, 1992, 8:00, 46 minutes, 30
When the traffic congestion at the intersection 103 of “second” is added in the unit of the statistical traffic congestion information storage time t, for example, in units of 30 minutes, “8: 46: 3
The time zone from “8:30 to 9:00” including “0 second” is added to the traffic jam statistical data, and the traffic jam time information of the intersection 103 is “6:30 to 9:00”.

Next, the predicted traffic congestion information creating means 55
Predicted congestion information is created based on the congestion statistical data, and the predicted congestion information is transmitted to the congestion distance calculating means 29 in FIG. 1 and transmitted to the congestion weighting means 25 to update its weight coefficient (step ST196). For example, based on the updated traffic congestion statistics data in Table 8, the current time “1992, 11
When the intersection where traffic congestion occurs on “Month, 30th, Monday, 8:00, 46 minutes, 30 seconds” is extracted, the intersections 103 and 10 are extracted.
4, 109 and 114 are extracted, and the predicted congestion information becomes an intersection number sequence of intersections 103, 104, 109 and 114.

Embodiment 30 FIG. The second embodiment
In FIG. 9, statistical traffic congestion time information corresponding to each intersection is stored as traffic congestion statistical data. Information and statistical information on traffic congestion going down,
Each may be stored.

Embodiment 31 FIG. Embodiment 2
In FIG. 9, only the statistical traffic jam time information is stored as the traffic jam statistical data. However, as the statistical traffic jam period, for example, a traffic jam month, a traffic jam day, a traffic jam day, and a traffic jam time May be stored.

Embodiment 32 FIG. Embodiment 2
In FIG. 9, only the statistical traffic congestion time information is stored as the traffic congestion statistical data. The average traveling speed may be stored.

Embodiment 33 FIG. Embodiment 2
In FIG. 9, as traffic congestion statistical data, only statistical traffic congestion time information is stored corresponding to each intersection.
Corresponding to each road connecting the intersection, the statistical traffic information in the up direction and the statistical traffic information in the down direction are stored, respectively, and as the statistical congestion period, for example, Statistical information on days of congestion, days of congestion, and times of congestion is stored, and the degree of influence due to congestion, for example, the average traveling speed of a vehicle when congestion occurs, is stored. You may make it learn.

[0129]

As described above, according to the present invention, road data storage means for storing road data on which a vehicle travels and information relating to the road data, and current position detection means for detecting the current position of the vehicle A destination setting means for setting the destination of the vehicle on the road data, a route search means for searching for a recommended route from the current position to the destination on the road data, and traffic congestion. Sometimes, a road type of a traffic jam is detected, a traffic congested road is predicted from the detected road type, and a traffic congestion factor in the recommended route is determined based on the prediction result. Therefore, even if traffic congestion occurs in a place where information from outside cannot be obtained while traveling on the recommended route, there is an effect that a congested road can be predicted.

According to the present invention, road data storage means for storing road data on which a vehicle travels and information relating to the road data, current position detection means for detecting a current position of the vehicle, and a destination of the vehicle Destination setting means for setting on the road data, a route searching means for searching for a recommended route from the current position to the destination on the road data, for each intersection or for each road connecting intersections. In addition, a traffic jam statistical data storage means for storing the time of occurrence of traffic jam as traffic jam statistical data,
The traffic jam time is detected, and the detected time, the traffic jam statistical data and the traffic congested road are predicted. Based on the prediction result, the section of the recommended route which is the cause of the traffic congestion is predicted. Therefore, the present invention has an effect of realizing a vehicle route guidance device capable of predicting a congested road when a traffic jam occurs while traveling on a recommended route.

In particular, the time of occurrence of traffic congestion is stored as traffic congestion statistical data for each intersection or for each road connecting the intersections, and the section which causes traffic congestion is predicted using this data. There is an effect that traffic congestion can be predicted in consideration of time information.

According to the present invention, the traffic jam statistical data storage means stores, for each road connecting the intersection, a plurality of traffic jam statistical data corresponding to the upward direction and the downward direction.
When the traffic congestion predicting means predicts a traffic congested road, one of the plurality of traffic congestion statistical data is selected and used. It is possible to predict a congested road, and it is possible to predict traffic congestion in consideration of more likely time information.

In the vehicle route guidance apparatus according to the present invention, the traffic jam statistical data storage means stores, for each road connecting the intersections, a plurality of traffic jam statistical data corresponding to the upward direction and the downward direction. The traffic congestion predicting means selects and uses one of the plurality of traffic congestion statistical data when predicting a traffic congested road.

According to the present invention, when traffic jam occurs
The step of detecting the type of road in which traffic is congested
And roads that are congested based on the detected road type
Predicting a road, and based on the prediction result,
Predict the section of the recommended route that causes traffic congestion.
And providing the information as the predicted traffic congestion information. Therefore, even if traffic congestion occurs in a place where information from outside cannot be obtained, it is possible to predict a road in which traffic congestion occurs. By performing prediction based on the road type in this way, there is an effect that a traffic congested road can be predicted with predetermined certainty.

In the traffic congestion prediction method according to the present invention, the step of detecting the time of traffic congestion is compared with the detected time and the statistical congestion occurrence time at each intersection. At the time of traffic jam, and a step of generating predicted traffic jam information from the extracted intersection, even if traffic jams where information from outside is not available. Thus, there is an effect that it is possible to predict a road on which congestion occurs. The prediction based on the time of the traffic congestion as described above has an effect that the traffic congested road can be predicted with predetermined certainty.

In the traffic congestion prediction method according to the present invention, the step of detecting the time of traffic congestion is compared with the detected time and the statistical congestion occurrence time of each road. At the time of the traffic jam, and a step of generating predicted traffic jam information from the extracted road, so that even if traffic jams where information from outside is not available, Thus, there is an effect that it is possible to predict a road on which congestion occurs.
Since the section of the road where the traffic is congested is appropriately predicted based on the time of the traffic congestion as described above, the traffic congested road can be predicted with a certain certainty.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a functional configuration according to a first embodiment of the present invention.

FIG. 2 is a block diagram showing a device configuration of the embodiment.

FIG. 3 is an explanatory diagram showing the contents of road data stored in road data storage means of the embodiment.

FIG. 4 is a flowchart showing a procedure of an operation of the embodiment.

FIG. 5 is an explanatory diagram showing a search result of a recommended route in the embodiment.

FIG. 6 is an explanatory diagram showing a search result of a recommended route that avoids congestion in the embodiment.

FIG. 7 is an explanatory diagram showing a display example of traffic jam information in the embodiment.

FIG. 8 is a flowchart showing a procedure of a traffic jam detection operation in the embodiment.

FIG. 9 is a flowchart showing a procedure of a traffic congestion prediction operation in the embodiment.

FIG. 10 is a flowchart showing a procedure of a traffic jam detection operation according to Embodiment 3 of the present invention.

FIG. 11 is a flowchart showing a procedure of a traffic jam detection operation according to Embodiment 4 of the present invention.

FIG. 12 is a flowchart showing a procedure of a traffic jam detection operation according to Embodiment 5 of the present invention.

FIG. 13 is a flowchart showing a procedure of an operation of adding a traffic jam prediction coefficient according to Embodiment 10 of the present invention.

FIG. 14 is a flowchart showing a procedure of a traffic jam detection operation according to Embodiment 14 of the present invention.

FIG. 15 is a flowchart showing a procedure of a traffic jam detection operation in Embodiment 17 of the present invention.

FIG. 16 is a flowchart showing a procedure of a traffic jam detection operation according to Embodiment 19 of the present invention.

FIG. 17 is a flowchart showing a procedure of a traffic congestion prediction operation according to Embodiment 22 of the present invention.

FIG. 18 is a characteristic diagram illustrating a relationship between an average speed and a congestion distance in the embodiment.

FIG. 19 is a flowchart showing a procedure of a traffic jam prediction operation according to Embodiment 23 of the present invention.

FIG. 20 is a block diagram showing a functional configuration of traffic congestion prediction means according to Embodiment 24 of the present invention.

FIG. 21 is a flowchart showing a procedure of a traffic congestion prediction operation in the embodiment.

FIG. 22 is a block diagram showing a functional configuration of traffic congestion prediction means according to Embodiment 29 of the present invention.

FIG. 23 is a flowchart showing a procedure of a traffic congestion prediction operation in the embodiment.

FIG. 24 is a block diagram showing a device configuration of a conventional vehicle route guidance device.

[Explanation of symbols]

 21 Current position detecting means 22 Destination setting means 23 Traffic congestion detecting means 24 Route searching means 25 Traffic congestion weighting means 26 Traffic congestion predicting means 28 Road data storage means 54 Traffic congestion statistical data updating means

──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Takashi Kakitani 8-1-1, Tsukaguchi Honcho, Amagasaki City Mitsubishi Electric Corporation Industrial System Research Laboratory (56) References JP-A-62-95423 (JP, A) JP-A-2-278500 (JP, A) JP-A-3-152700 (JP, A) JP-A-2-129800 (JP, A) JP-A-4-20200 (JP, U) (58) Fields surveyed ( Int.Cl. ⁷ , DB name) G01C 21/00 G08G 1/00-1/0969 G09B 29/00-29/10

Claims (6)

(57) [Claims] 1. Road data storage means storing road data on which a vehicle travels and information related to the road data; current position detection means for detecting a current position of the vehicle; A destination setting means for setting on the road data; a route searching means for searching for a recommended route from the current position to the destination on the road data; and A traffic congestion predicting means for detecting a road type, predicting a traffic congested road from the detected road type, and predicting a section of the recommended route which causes traffic congestion based on the prediction result. Vehicle route guidance device. 2. Road data storage means for storing road data on which a vehicle travels and information related to the road data; current position detection means for detecting a current position of the vehicle; Destination setting means for setting on the road data, route searching means for searching on the road data for a recommended route from the current position to the destination, for each intersection or for each road connecting the intersections,
A traffic jam statistical data storage means for storing a traffic jam occurrence time as traffic jam statistical data; and detecting a time of traffic jam when traffic jam occurs,
Traffic congestion prediction means for predicting a traffic congested road based on the detected time and the traffic congestion statistical data, and predicting a section of the recommended route which is a factor of traffic congestion based on the prediction result. Route guidance device for vehicles equipped with. 3. The traffic congestion statistical data storage means stores a plurality of traffic congestion statistics data corresponding to an up direction and a down direction for each road connecting an intersection, and the congestion prediction means comprises a traffic congestion. 3. The vehicle route guidance device according to claim 2, wherein one of the plurality of traffic congestion statistics data is selected and used when predicting a road on which the vehicle is traveling. (4)The road data on which the vehicle travels and the road data
Information related to data Road data storage means in which is stored
Current position detecting means for detecting a current position of the vehicle
And setting the destination of the vehicle on the road data.
Destination setting means and recommendation from the current position to the destination
Route search means for searching for a route on the road data;
Predict traffic congested roads and use it as predicted traffic congestion information
Traffic congestion prediction means and road data based on the predicted traffic congestion information
Traffic congestion weighting means for setting the traffic congestion weight for
Traffic used in a vehicle route guidance device equipped with a vehicle
Arrears prediction method, The road type of the road that was congested when it was congested
Detecting Based on the detected road type, the traffic congested road
Predicting, The recommendation based on this prediction result
Predict sections that cause traffic congestion on routes
And setting the predicted traffic congestion information; Based on the predicted traffic information, the traffic
Setting the find; The weight of the congestion is set by the route searching means.
From the current position to the destination using the road data
Re-searching the recommended route of Traffic congestion forecast
Measurement method. (5)The road data on which the vehicle travels and the road data
Road data storage means in which information related to data is stored
Current position detecting means for detecting a current position of the vehicle
And setting the destination of the vehicle on the road data.
Destination setting means and recommendation from the current position to the destination
Route search means for searching for a route on the road data;
Predict traffic congested roads and use it as predicted traffic congestion information
Traffic congestion prediction means and road data based on the predicted traffic congestion information
Traffic congestion weighting means for setting the traffic congestion weight for
Traffic used in a vehicle route guidance device equipped with a vehicle
Arrears prediction method,  Detecting the time of traffic congestion; and detecting the detected time and statistical congestion at each intersection.
Time and the traffic jam occurs at the detected time.
Extracting an existing intersection, and generating predicted traffic congestion information from the extracted intersection.
And, Based on the predicted traffic information, the traffic
Setting the find; The weight of the congestion is set by the route searching means.
From the current position to the destination using the road data
Re-searching the recommended route of Traffic congestion forecast
Measurement method. 6.The road data on which the vehicle travels and the road data
Road data storage means in which information related to data is stored
Current position detecting means for detecting a current position of the vehicle
And setting the destination of the vehicle on the road data.
Destination setting means and recommendation from the current position to the destination
Route search means for searching for a route on the road data;
Predict traffic congested roads and use it as predicted traffic congestion information
Traffic congestion prediction means and road data based on the predicted traffic congestion information
Traffic congestion weighting means for setting the traffic congestion weight for
Traffic used in a vehicle route guidance device equipped with a vehicle
Arrears prediction method,  Detecting the time of traffic congestion; detecting the time and the statistical congestion occurrence time for each road
And congestion occurs at the detected time
Extracting a traffic congestion information from the extracted roads.
And, Based on the predicted traffic information, the traffic
Setting the find; The weight of the congestion is set by the route searching means.
From the current position to the destination using the road data
Re-searching the recommended route of Traffic congestion forecast
Measurement method.