JP2017126122A - Traffic congestion prediction display device and traffic congestion prediction display program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To visualize congestion prediction information for each of areas obtained by dividing a map by certain section so as for a user to easily grasp the information on a client side without setting a destination.SOLUTION: There is provided an on-vehicle navigation device 1 that displays future traffic congestion to be caused by vehicles, the on-vehicle navigation device 1 comprising: a control part 10; a storage part 11 that stores a road map; a radio part 12 that receives data on prediction of the degree of future congestion in areas obtained by dividing the map by certain section; and a display part 14. The control part 10 displays, on the display part 14, the prediction data received by the radio part 12 overlapped on the map read from the storage part 11 with a color according to the degree of congestion.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a traffic jam prediction display of a vehicle on a road, and in particular, a traffic jam prediction display apparatus and traffic jam prediction using data such as position information and travel speed uploaded from a vehicle traveling on a road. It relates to the display method.

Conventionally, as a technique for predicting road traffic information caused by a vehicle, for example, the current traffic situation is grasped using probe data uploaded from a vehicle (probe car), and the current traffic situation is displayed on a map. There was a technology that can be used to set the route to the destination by displaying it. Probe data refers to data such as time-series position information (latitude and longitude), time information, traffic information, travel history, travel speed, and the like uploaded from a probe car. The probe data is also called floating car data (FCD).
However, the above-mentioned traffic jam prediction is a traffic jam situation in a linear road link unit, and the current traffic jam situation on the road on the map is displayed, for example, a heavy traffic jam is a red line, and a traffic jam that is a noronoro driving is yellow. By superimposing and displaying such as a line, the driver looked at the display and predicted the future traffic jam situation and took action to avoid the traffic jam.
However, as the vehicle moves, time elapses and the traffic situation changes with time. After the vehicle moved, there was a case where the traffic jam disappeared or a new traffic jam occurred. . For this reason, it is required not only to predict the current traffic jam situation but also to predict the future traffic jam situation.

  In addition, as described above, a congestion prediction technique for each linear road link is conventionally performed. The linear traffic jam prediction determines the state of smoothness, congestion, and traffic jam from the magnitude of the link travel time on the road. It consists of a module that calculates link travel time from probe data, for example. However, it is not currently performed to predict the traffic congestion information for each predetermined area of the map.

  Moreover, as described in Patent Document 1, as a conventional technique for predicting a future traffic situation by a computer, a road situation pattern in the same time zone in the past is similar to a road situation pattern in the current time zone. There is a technique for predicting the required travel time in the current time zone based on a change in the situation of road conditions in the same time zone in the past by searching.

JP 2004-362290 A

In the conventional technology that displays the current traffic condition on the road on the map, the prediction of the future traffic condition depends on the driver's intuition, and is influenced by experience. In addition, in the conventional technology, the predicted traffic situation is used for route calculation, but the user is only benefited when the destination is set, and when the destination is not set in the user's living area etc. There was no means to display the result of traffic jam prediction.
Generally, it is said that it is about 10% of the boarding time to set a destination and receive route guidance when a user gets on a vehicle equipped with a car navigation device. This indicates that even if the car navigation apparatus is equipped with a guidance function that avoids traffic jams by the traffic jam prediction function, most of the time is not benefited from that function.
Further, in the conventional technology, there is no function for easily determining which area is crowded, and there is no means for displaying area-specific traffic jam information.
Also, with the technology described in Patent Document 1, it is difficult to predict and display traffic jams that deviate from the pattern, for example, traffic jams caused by sudden increases in vehicles due to events, weather, or the like.

  The present invention has been made in view of such problems. The present invention provides a traffic jam prediction display device and a traffic jam prediction display program that enable a user to provide traffic jam forecast information for each mesh area that is simple and accurate without setting a destination. With the goal.

  (1) The present invention is a traffic jam prediction display device (for example, “vehicle-mounted navigation device 1” or “portable terminal 2” described later) that displays a future traffic jam due to a vehicle, and the traffic jam prediction display device includes: A wireless unit (for example, “wireless unit 12” or “wireless unit 22” described later) that receives prediction data of the future congestion degree of an area (for example, “mesh region” described later) divided into certain sections of the map. ), A storage unit (for example, “storage unit 12” or “storage unit 22” described later) that stores the road map, and a display unit (for example, “display unit 14” or “display unit 24” described later) and control (For example, “control unit 10” or “control unit 20” described later), and the control unit stores the prediction data received via the wireless unit in a color corresponding to the degree of congestion. The display unit is superimposed on the map read from the unit About traffic congestion prediction display device and displaying.

The traffic congestion prediction display device of (1) displays the prediction data received via the wireless unit on the display unit in a color corresponding to the degree of traffic congestion and superimposed on the map read from the storage unit.
As a result, it is possible to easily grasp the traffic jam prediction information for each mesh area without setting the destination on the client side.

  (2) The wireless unit (for example, “wireless unit 12” or “wireless unit 22” to be described later) further receives current congestion level data and predicted traffic level prediction data for a plurality of future time zones. Then, the control unit (for example, “control unit 10” or “control unit 20” described later) further determines the current congestion level data and the predicted congestion level prediction data for each of a plurality of future time zones. The display unit (for example, described later) is superimposed on a map read from the storage unit (for example, “storage unit 12” or “storage unit 22” described later) while sequentially switching the screen in a color corresponding to the degree. (Display part 14 "or" display part 24 "), the traffic jam prediction display device according to (1).

(2) The traffic congestion prediction display device further switches the screen of the current congestion degree data and the prediction data of the congestion degree for each of a plurality of future time zones sequentially in a color corresponding to the congestion degree. The data is displayed on the display unit so as to overlap the map read from the storage unit.
As a result, it is possible to intuitively understand in which direction the traffic jam is propagating. Further, even if a destination is not set, it is possible to drive on an empty road or avoid a time when it becomes congested.

  (3) The present invention includes a display unit (for example, “display unit 14” or “display unit 24” described later), a wireless unit (for example, “wireless unit 12” or “wireless unit 22” described later), and storage. Traffic that displays a future traffic jam caused by a vehicle including a computer having a control unit (for example, “storage unit 11” described later) and a control unit (for example, “control unit 10” or “control unit 20” described later). In a program that functions as a traffic jam prediction display device (for example, “vehicle-mounted navigation device 1” or “portable terminal 2” described later), the storage unit stores a road map and receives the road map via the wireless unit. The prediction data of the future congestion degree of the area (for example, “mesh area” described later) divided into certain sections of the map is displayed in the storage unit (described later “storage unit 12” in a color corresponding to the congestion degree. Or “storage unit 22”) It is made to function as a traffic jam information display control unit (for example, “traffic traffic information display control unit 104” or “traffic jam information display control unit 204” described later) displayed on the display unit in a superimposed manner on the displayed map. Related to the program.

  (4) As the traffic information display control unit (for example, “congestion information display control unit 104” or “congestion information display control unit 204”, which will be described later), the wireless unit (for example, “wireless unit 12”, which will be described later). ”Or“ Wireless Unit 22 ”), the screen of the current congestion level and the prediction data of the congestion level for each of several future time zones are sequentially switched in a color corresponding to the congestion level. However, the display unit (for example, “display unit 14”, which will be described later) or “display unit” which is described later is superimposed on a map read from the storage unit (for example, “storage unit 12” or “storage unit 22” which will be described later). The program according to (3), which causes the computer to function so as to be displayed in 24 ”).

  According to the programs of (3) and (4), the same effects as the traffic jam prediction display devices of (1) and (2) can be obtained, respectively.

According to the traffic jam prediction display device of the present invention, the user can specifically determine which traffic jam situation for each mesh area after a certain period of time has elapsed from the present in a time-sequential (regional) manner. Whether the area is crowded or will be crowded in the future can be grasped intuitively in a time-series manner in terms of congestion in each area, which is difficult to understand with the linear traffic jam information.
Also, even when the user has not set a destination because the road that is running is within the user's living area, etc., the traffic volume for each near-future mesh area can be viewed in a time series (regional B) Enables predictive display. As a result, it is possible for the user to easily navigate the traffic situation.
Further, it is possible to predict and display a traffic jam caused by a sudden increase in vehicles.
In addition, by displaying the current congestion degree data and the predicted congestion degree data for each of several future time zones in a time-series manner, it is possible to intuitively know in which direction the congestion is propagating. It becomes possible to grasp it. By doing so, the user can drive on an empty road or avoid a time of traffic jam without setting a destination.

1 is a diagram illustrating a system configuration of a traffic jam prediction system 100. FIG. It is a figure which shows the structure of the vehicle-mounted navigation apparatus 1 mounted in a vehicle. 2 is a diagram illustrating a configuration of a mobile terminal 2. FIG. It is a figure which shows the structure of the traffic congestion prediction server system. It is a figure which shows an example of the mesh area | region set on a map. It is a figure which shows an example of the screen for starting traffic information distribution displayed on the vehicle-mounted navigation apparatus 1 or the portable terminal 2. FIG. It is a figure which shows an example of the screen which displays the traffic information of the present time in each mesh area | region and the traffic congestion prediction information after a fixed time progress (multiple) on the vehicle-mounted navigation apparatus 1 or the portable terminal 2 in time series. It is a figure which shows an example of the screen which displays the traffic information of the present time in each mesh area | region and the traffic congestion prediction information after a fixed time progress (multiple) on the vehicle-mounted navigation apparatus 1 or the portable terminal 2 in time series. It is a figure which shows an example of the outline | summary about the course taking of a traffic congestion avoidance route. Based on the current number of traffic jam-stopped vehicles in the 5 mesh area, the traffic jam escape speed in the longitude direction (east), and the traffic escape speed in the latitude (north) direction, the number of vehicles jammed in the central mesh area after 30 minutes, the longitude direction It is a figure which shows the outline | summary which calculates the traffic escape speed of the (east) traffic jam and the traffic escape speed of the latitude (north) direction. It is a figure which shows an example of the process outline | summary which correct | amends the congestion degree of each mesh area | region with a vehicle advancing direction in the vehicle-mounted navigation apparatus 1 or the portable terminal 2. FIG. It is a figure which shows an example of the process outline | summary which correct | amends the congestion degree of each mesh area | region with a vehicle advancing direction in the vehicle-mounted navigation apparatus 1 or the portable terminal 2. FIG. It is a flowchart which shows the flow of a process of the traffic congestion prediction system. It is a flowchart which shows the flow of a process of the traffic congestion prediction system. It is a figure which shows the structure of the vehicle-mounted navigation apparatus 1 in 2nd Embodiment. It is a figure which shows the structure of the portable terminal 2 in 2nd Embodiment. It is a figure which shows the structure of the traffic congestion prediction server system 3 in 2nd Embodiment.

[First Embodiment]
Hereinafter, a preferred embodiment of the traffic jam prediction system of the present invention will be described with reference to the drawings.

[Functional configuration of traffic jam prediction system 100]
A configuration of a traffic jam prediction system 100 according to a preferred embodiment of the present invention will be described.

  As shown in FIG. 1, the traffic congestion prediction system 100 includes an in-vehicle navigation device 1 mounted on a vehicle, a mobile terminal 2, a traffic congestion prediction server system 3, and a wireless communication network 4. . Note that either the vehicle-mounted navigation device 1 or the mobile terminal 2 may be either one or both. Here, the vehicle is assumed to be a two-wheeled vehicle, a four-wheeled vehicle, or the like, for example.

The in-vehicle navigation device 1 mounted on the vehicle includes a PND (Portable Navigation Device) and the like. The in-vehicle navigation device 1 is installed with traffic jam prediction software, and displays traffic jam information based on a user request. The traffic jam information is traffic jam information predicted at the present time and after a plurality of fixed times as will be described later, and the in-vehicle navigation device 1 automatically or According to user operation, it accumulates in time series and displays it sequentially.
Here, after a lapse of a certain time (a plurality), for example, T1 after a lapse of a certain time from the current T0, T2 after a lapse of a certain time from T1, T3 after a lapse of a certain time from T2, etc. Point to multiple near futures from the present.
The traffic jam prediction software may be installed in advance. Moreover, you may download suitably as needed at the time of transmission / reception with the traffic congestion prediction server system 3. FIG.

The mobile terminal 2 is a mobile phone, a smart phone, a tablet terminal, a PDA (Personal Digital / Data Assistance), a notebook computer, or other portable electronic device, and includes an electronic device having a wireless communication function. The mobile terminal 2 is installed with traffic jam prediction software, and displays traffic jam information based on a user request.
The traffic jam prediction software may be installed in advance. Moreover, you may download suitably as needed at the time of transmission / reception with the traffic congestion prediction server system 3. FIG.

  The traffic jam prediction server system 3 transmits and receives information to and from the in-vehicle navigation device 1 and the mobile terminal 2 via the communication unit 32. The traffic jam prediction server system 3 calculates the current traffic jam information and the traffic jam forecast information after a lapse of a certain time (for a plurality of mesh areas) for each mesh area, and sends the current traffic jam information and Provide traffic jam prediction information after a certain period of time.

  In the embodiment of the present invention, the traffic jam prediction server system 3 is described as a single server, but each function of the traffic jam prediction server system 3 may be appropriately distributed to a plurality of servers. Moreover, you may implement | achieve the one part function or all the functions of the traffic congestion prediction server system 3 using a virtual server function etc. on a cloud.

  Next, each configuration will be described.

<In-vehicle navigation device 1>
As shown in FIG. 2, the in-vehicle navigation device 1 mounted on a vehicle includes at least a control unit 10, a storage unit 11, a radio unit 12, a sensor unit 13, a display unit 14, and an input unit 15. Prepare.

  The control unit 10 is constituted by a microprocessor or the like, and controls each component unit. Details will be described later.

The storage unit 11 includes a hard disk drive, a semiconductor memory, and the like, and stores various information such as an operating system (OS), each program for traffic jam prediction display, map information, and position information. In addition, as will be described later, a traffic jam prediction information storage area (not shown) that accumulates the current traffic jam information distributed from the traffic jam prediction server system 3 and the traffic jam prediction information after a predetermined time (a plurality) in time series. Is provided.
In addition, about the map information, the structure previously memorize | stored in the memory | storage part 11 or the structure acquired from the traffic congestion prediction server system 3 may be sufficient.

The wireless unit 12 includes a DSP (Digital Signal Processor) and the like, performs wireless communication through a wireless communication network represented by a mobile phone network such as 3G or LTE, and performs wireless communication with the traffic congestion prediction server system 3. It is configured to be possible.
The wireless unit 12 transmits an identification number (hereinafter also referred to as “vehicle ID”) for identifying a vehicle, a password, current position information of the vehicle, and the like to the traffic congestion prediction server system 3, and the traffic congestion prediction information and the like are predicted for traffic congestion. It can be received from the server system 3.

The sensor unit 13 includes a GPS sensor, a vehicle speed sensor, a gyro sensor, a geomagnetic sensor, and the like provided in the vehicle. The sensor unit 13 has a function as position detecting means for detecting the current position, receives a GPS satellite signal by the GPS sensor, and measures the current position (latitude and longitude) of the vehicle. In addition, by providing a vehicle speed sensor, a gyro sensor, a geomagnetic sensor, etc., the current position of the vehicle can be determined by autonomous navigation. If the GPS sensor cannot be determined from the GPS satellite signal, the current position is determined instead. be able to.
In addition, when GPS communication is impossible, the current position of the vehicle can be calculated from base station information acquired from the wireless unit 12 using AGPS (Assisted Global Positioning System) communication.

  The display unit 14 includes a display device such as a liquid crystal display or an organic EL panel, and displays an image in response to an instruction from the control unit 10. More specifically, the display unit 14 receives the instruction from the control unit 10, the map information around the current position of the vehicle read from the storage unit 11, the current position of the vehicle, and the traffic via the wireless unit 12. The current traffic jam information acquired from the traffic jam prediction server system 3 and the traffic jam forecast information after a predetermined time (plural) are automatically or sequentially displayed in time series in response to a user operation.

The input unit 15 includes a physical switch called a numeric keypad, an input device (not shown) such as a touch panel provided on the display surface of the display unit 14, and the like. Operations such as map enlargement / reduction and map scrolling can be performed by outputting to the control unit 10 a signal based on an operation input from the input unit 15, for example, pressing of a numeric keypad by a user or touch on a touch panel.
In addition, although not shown, a speaker 16 and a microphone 17 can be provided. The speaker 16 outputs a sound to the driver, and the microphone 17 collects a sound emitted by the driver.
By doing so, information can be output from the speaker 16 by voice, and various selections and instructions by the driver (or user) input by voice through the microphone 17 can be input to the control unit 10 by voice recognition technology. it can.

  The control unit 10 includes a microprocessor having a CPU, RAM, ROM, I / O, and the like. The CPU executes each program read from the ROM or the storage unit 11, reads information from the RAM, ROM, and the storage unit 11 at the time of execution, writes information to the RAM and the storage unit 11, The wireless unit 12, the sensor unit 13, the display unit 14, and the input unit 15 exchange signals.

  As shown in FIG. 2, the control unit 10 includes a connection processing unit 101, a position information update unit 102, a traffic jam information acquisition unit 103, a traffic jam information display control unit 104, and a route guidance unit 105.

The control unit 10 executes the respective programs (hereinafter, also collectively referred to as “traffic congestion prediction display application”), so that the in-vehicle navigation device 1 is connected to the connection processing unit 101, the position information update unit 102, the congestion information acquisition unit 103, Functions as the traffic jam information display control unit 104 and the route guidance unit 105 (hereinafter collectively referred to as “traffic traffic jam prediction display unit”). For example, when the traffic jam prediction display application is started, the current traffic jam information acquired from the traffic jam prediction server system 3 and the traffic jam forecast information after a certain time (plural) are communicated in time series. Display is possible.
In addition, the control unit 10 executes each program so that the in-vehicle navigation device 1 includes a connection processing step, a location information update step, a traffic jam information acquisition step, a traffic jam information display control step, and a route guidance step (hereinafter, “ "Traffic congestion prediction display step" is collectively called).

  Hereinafter, functions of the control unit 10 will be described from the viewpoint of the traffic jam prediction display unit. Note that the explanation based on the viewpoint of the traffic jam prediction display step can be explained by replacing “part” with “step”, and thus will be omitted.

<Automatic start>
The vehicle-mounted navigation device 1 can be automatically started by turning on an ignition switch (starting an engine) of a vehicle by a driver. The in-vehicle navigation device 1 is operated until the driver turns off the ignition switch of the vehicle (stops the engine). Note that the in-vehicle navigation device 1 may be activated by a user.

<Connection processing unit 101>
When the in-vehicle navigation device 1 is automatically activated or activated, the connection processing unit 101 executes login processing for the traffic jam prediction server system 3 using, for example, an identification number (vehicle ID) and a password for identifying the vehicle. Then, the current position information (latitude and longitude) of the vehicle calculated by the sensor unit 13 and the current time information acquired from the time measuring unit (not shown) are transmitted to the traffic jam prediction server system 3. At this time, the connection processing unit 101 may transmit information on the traveling direction of the vehicle calculated by the sensor unit 13. Further, the connection processing unit 101 may transmit information on each sensor such as a geomagnetic sensor and a gyro sensor. As will be described later, when a login process is requested, traffic information distribution may be turned on as a default setting.

<Location information update unit 102>
The position information update unit 102 periodically (for example, at intervals of 3 seconds) obtains the vehicle ID, the current position information of the vehicle calculated by the sensor unit 13, the current time information acquired from the time measuring unit (not shown), and the like. It transmits to the prediction server system 3. The transmitted information can include the traveling direction of the vehicle calculated by the sensor unit 13.
The position information update unit 102 can transmit the current position information and current time information of the vehicle acquired at predetermined time intervals (for example, every 3 seconds) to the traffic congestion prediction server system 3 in real time each time. In addition, the position information update unit 102 does not transmit in real time, but collectively (for example, collects the current position information and current time information of the vehicle for 5 minutes) and transmits at a time (so-called burst transmission). can do.
The position information update unit 102 sets a calculation time interval (for example, an interval of 3 seconds) of the current position information of the vehicle and the number of data to be transmitted at a time when a plurality of burst transmissions are performed in advance by user input. can do.

<Congestion information acquisition unit 103>
As shown in FIG. 6, the traffic jam information acquisition unit 103 displays, for example, a “traffic traffic information distribution start ON” button on the display unit 14, and operates to turn on traffic jam information distribution by a user touching the button. If it is, a traffic jam information distribution start request is transmitted to the traffic jam prediction server system 3 via the radio unit 12. In addition, it is possible to turn on congestion information distribution via the input unit 15.
Further, when the connection processing unit 101 makes a login processing request to the traffic congestion prediction server system 3, the traffic information distribution may be turned on as a default setting. By doing so, the user can omit the operation of transmitting the traffic information distribution start request.

  The traffic jam information acquisition unit 103 sends the current traffic jam information and a preset near-future fixed time (a plurality) (for example, 15) for each mesh area on the preset map from the traffic jam prediction server system 3. Minute, 30 minutes, 45 minutes, 60 minutes, and the like) are received at a preset time interval (for example, every 15 minutes). In addition, as the traffic jam information or the traffic jam prediction information, for example, it is possible to receive a traffic jam degree obtained by dividing the traffic jam status into n stages (n is an arbitrary natural number larger than 1). For example, when n = 3, the congestion level 1 is vacant, the congestion level 2 is the road is crowded with traveling vehicles, and the vehicle is traveling at a speed that makes a noronoro driving, and the traffic congestion level 3 is It is very crowded with traveling vehicles and represents heavy traffic. Details will be described later.

For the mesh area on the map, after the lapse of a certain time in the near future (for example, 15 minutes, 30 minutes, 45 minutes, 60 minutes, etc.) and the congestion information distribution time interval as the traffic jam prediction time, It is set in the traffic jam prediction server system 3.
For the mesh area on the map, the traffic jam information acquisition unit 103 displays a mesh area having a size that is n ² times (n ≧ 2) the size of the mesh area set in the traffic jam prediction server system 3. Can be made.
Further, after a lapse of a certain time in the near future (plural), a selection may be made after a lapse of a certain time (plural) set in the traffic jam prediction server system 3.

The traffic jam information acquisition unit 103 includes the current traffic jam information in all mesh regions and the traffic jam prediction information after a certain period of time (plural) included in a sufficiently large area including the current position of the host vehicle at the current time as a traffic jam prediction server. It is preferable to acquire from the system 3 in chronological order or at a time. More specifically, for example, at least the current traffic jam information and the traffic jam prediction information after a certain period of time (plurality) in the mesh area included in the map displayed on the display unit 14 are acquired in time series or at a time. Is preferred. The traffic jam information acquisition unit 103 displays other traffic areas not displayed on the display unit 14 in the background while the current traffic jam information and the traffic jam prediction information after a predetermined time (plural) are displayed on the display unit 14. The current traffic jam information and the traffic jam prediction information after a predetermined time (plural) may be acquired from the traffic jam prediction server system 3 in parallel.
By doing so, the traffic jam information acquisition unit 103 has the current traffic jam information in each mesh area included in a sufficiently large area area centered on the mesh area including the current position of the host vehicle at the current time and after a certain time has elapsed ( A plurality of) traffic jam prediction information is received via the wireless unit 12, and stored in the traffic jam prediction information storage area of the storage unit 11, for example, in chronological order.

  In addition, about the various inputs by the user mentioned above, it can also be comprised so that a user may input into the traffic congestion information acquisition part 103 by a voice recognition technique by carrying out the voice input through the microphone 17. FIG.

<Congestion information display control unit 104>
The traffic jam information display control unit 104 acquires the current traffic jam information in all mesh regions included in the surrounding area including the current position of the host vehicle and the traffic jam prediction information after a predetermined time (plurality) acquired by the traffic jam information acquisition unit 103. Are displayed on the display unit 14 in time series. Here, the congestion level is displayed in different colors.

Here, one of the features of the present invention is that it is possible to predict traffic congestion and display traffic congestion prediction information without the user setting a destination. Generally, it is said that it is about 10% of the boarding time to set a destination and receive route guidance when a user gets on a vehicle equipped with a car navigation device. This indicates that even if the car navigation apparatus is equipped with a guidance function that avoids traffic jams by the traffic jam prediction function, most of the time is not benefited from that function.
However, in the present invention, even if the destination is not set when boarding the vehicle, the traffic information acquisition unit 103 causes the current traffic information in the surrounding area including the current position of the host vehicle and after a certain period of time (multiple ) Traffic jam prediction information can be displayed on the display unit 14.
Such a display is particularly effective in an area where the user has some intuition, such as a user's living area where no destination is set.

FIG. 7 and FIG. 8 show an example of a screen that displays the current traffic jam information and the traffic jam prediction information after a predetermined time (plural) on the display unit 14. 7 and 8, when the predetermined time is T, the near future traffic congestion prediction (display B) after the elapse of time T from the present time, and the near future traffic congestion prediction (display B) after the time 2T elapses from the present time C) two near future are illustrated, but as will be described later, the traffic congestion prediction server system 3 (congestion stop vehicle prediction unit 307) predicted N (N ≧ 1) near future. In this case, the traffic jam information display control unit 104 displays N near-future traffic jam prediction information in chronological order.
As described above, when the traffic congestion level is divided into three levels, the traffic congestion information display control unit 104 determines the current traffic congestion level and the traffic congestion level after a certain period of time when the traffic congestion level is 1 for each mesh area. Intuitively distinguish between “blue”, “yellow” when the traffic level is 2, and “red” when the traffic level is 3, by overlaying the color corresponding to the traffic level on each mesh area on the map. Can be easier. The arrow indicates the vehicle position.
7 and 8, the traffic jam information display control unit 104 performs mesh display by so-called “heading up display” in which the traveling direction displays a map upward. The traffic jam information display control unit 104 can display “north-up display” in which the north direction is always displayed above the map by designating the display direction.

Referring to FIG. 7, the traffic jam information display control unit 104 first displays the current traffic jam status on the display unit 14 as a mesh (display A). Next to the display A, the traffic information display control unit 104 displays a display B indicating the traffic situation after a certain time (hour or minute), and after the display B, the traffic jam after a certain time (hour or minute). The display operation of displaying the status C in order so as to display the status C and then returning to the status A indicating the current traffic jam status is repeated.
In order to move the screen, the traffic information display control unit 104 displays, for example, a “move to previous screen” button, a “move to next screen” button, or a “pause movement” button, and the button by the user. By performing the touch operation, it is possible to move to the previous screen, the next screen, or to continuously display the currently displayed screen.

Referring to FIG. 7, it can be seen at a glance that the traffic jam propagates from the right front to the left rear in the traveling direction of the vehicle. In this way, the user can intuitively grasp the general situation of the traffic jam situation in each area divided by the mesh.
By doing so, for example, even if the user is involved in a traffic jam to some extent, the user can select a straight route, or can know whether to make a roundabout while avoiding the traffic jam.

  It should be noted that the above-described various inputs by the user can be configured to be input to the traffic jam information display control unit 104 after being converted into a code by a voice recognition technique by allowing the user to input a voice via the microphone 17.

<Time bar>
The traffic jam information display control unit 104 may provide a time bar in a part of the screen as shown in FIG. With the time bar, the user can easily grasp whether the current traffic jam display screen is the current time or how much the predicted screen is after the current time.

[Modification of time bar]
For the current traffic jam situation, the traffic jam information acquisition unit 103 predicts the forecast information in a mesh area of a predetermined size. After that, the traffic jam forecast information is larger than the size of the previous mesh area at a point farther than the current time. The prediction information predicted in the mesh area can be acquired, that is, the size of the mesh area at the prediction time can be increased as the prediction time becomes farther than the current time.
FIG. 8 shows a display example of traffic jam prediction information by the traffic jam information display control unit 104. As shown in FIG. 8, the traffic jam information display control unit 104 displays the current traffic jam situation with a mesh of a predetermined size, and then the mesh size increases as the traffic jam prediction information is farther from the current time. . More specifically, the size of the mesh in the display B is larger than the size of the mesh in the display A, and the size of the mesh in the display C is displayed larger than the size of the mesh in the display B.
By doing so, it is expected that the hit rate decreases (the error increases) as the traffic prediction becomes farther, so the traffic jam information display control unit 104 increases the mesh region as the traffic jam prediction time becomes farther. By increasing the size of, it is possible to intuitively display a display that allows the size of the error. In addition, it becomes easy for the user to grasp how far the traffic congestion is predicted at the point of time depending on the size of the mesh. In this way, there is an effect that the user can recognize more intuitively than the time bar shown in FIG.

<Route guide 105>
When a destination is set by the user, the route guide unit 105 can calculate route information from the current position to the destination and execute route guidance.
The route information may be received from the traffic congestion prediction server system 3 or another navigation server system (not shown).

<Course of traffic avoidance route>
FIG. 9 shows an outline of the course collection for the traffic jam avoidance route. As shown in L1 of FIG. 9, if a linear traffic jam avoidance route (L1) from the current position to the destination is drawn to a zone where there is a lot of traffic jam, the route of traffic jam can be displayed in an easy-to-understand manner.
Separately from this, as shown in L2 of FIG. 9, the user can go to the destination while selecting a mesh area with a congestion degree of 1 (L2), for example, without setting the destination.
As described above, according to the present invention, it is possible to perform navigation that allows the course to be freely selected and allows the user to freely select a zone with less traffic congestion or pass through a loophole.

<Recommendation service>
In addition to the above functions, the vehicle-mounted navigation device 1 can provide a recommendation service to the user when a traffic jam for each future mesh area is predicted.
Specifically, for example, the traffic congestion prediction server system 3 to be described later transmits to the in-vehicle navigation device 1 that the user is recommended to go out earlier than planned when the traffic congestion is predicted in the near future. . The in-vehicle navigation device 1 can display the received recommendation information on the display unit 14, thereby enabling the user to avoid traffic jams in the near future.
Conversely, when the traffic jam prediction server system 3 predicts that the traffic jam will be absolutely involved in the near future, the in-vehicle navigation device 1 is recommended to recommend the user to break or kill time. Send. The in-vehicle navigation device 1 displays the received recommendation information on the display unit 14 so that the user can avoid traffic jams in the near future.

<Mobile terminal 2>
Next, the mobile terminal 2 will be described.
As shown in FIG. 4, the mobile terminal 2 includes at least a control unit 20, a storage unit 21, a wireless unit 22, a sensor unit 23, a display unit 24, and an input unit 25.

  The control unit 20 is constituted by a microprocessor having a CPU, RAM, ROM, I / O and the like. The CPU executes each program for traffic jam prediction display read from the ROM or the storage unit 21, and when executing the program, reads information from the RAM, ROM, and the storage unit 21, and sends the information to the RAM and the storage unit 31. Information is written, and signals are exchanged with the communication unit 32, the sensor unit 33, the display unit 34, and the input unit 35.

  The storage unit 21 is composed of a hard disk, a semiconductor memory, and the like, and stores various information such as an operating system (OS), traffic congestion prediction display programs, map information, and position information. In addition, about the map information, the structure memorize | stored beforehand in the memory | storage part 21 or the structure acquired from the traffic congestion prediction server system 3 may be sufficient.

The portable device 2 is installed with a program equivalent to each traffic congestion prediction display program installed in the in-vehicle navigation device 3.
The control unit 20 executes each program (hereinafter, also collectively referred to as “traffic congestion prediction display application”), so that the mobile terminal 2 is connected to the connection processing unit 201, the position information update unit 202, the congestion information acquisition unit 203, the congestion It functions as the information display control unit 204 and the route guidance unit 205 (hereinafter collectively referred to as “traffic jam prediction display unit”).
When the program for traffic jam prediction display is started, the mobile device communicates with the traffic jam prediction server system 3 and the current traffic jam information obtained from the traffic jam prediction server system 3 and the traffic jam after a certain period of time (plural). The prediction information can be displayed in time series.
In addition, the control unit 20 executes each program to cause the mobile terminal 2 to include a connection processing step, a location information update step, a traffic jam information acquisition step, a traffic jam information display control step, and a route guidance step (hereinafter referred to as “traffic”). (Collectively referred to as “congestion prediction display step”).

  Hereinafter, functions of the control unit 20 will be described from the viewpoint of the traffic jam prediction display unit. Note that the explanation based on the viewpoint of the traffic jam prediction display step can be explained by replacing “part” with “step”, and thus will be omitted.

  As shown in FIG. 3, the control unit 20, like the in-vehicle navigation device 1, includes a connection processing unit 201, a position information update unit 202, a traffic jam information acquisition unit 203, a traffic jam information display control unit 204, and route guidance. Unit 205.

  Each constituent element included in the mobile device 2 corresponds to each constituent element included in the in-vehicle navigation device 1 and realizes the same function as each constituent element included in the in-vehicle navigation device 1. For this reason, the detailed description regarding the portable device 2 is omitted. Hereinafter, functions unique to the mobile device 2 will be described.

<Connection processing unit 201>
When the traffic jam prediction display application is activated by the user, the connection processing unit 201 turns on the GPS sensor and the like to the traffic jam prediction server system 3 to identify, for example, the service subscription status of the user ( (Hereinafter also referred to as “user ID”) and a password are used to execute login processing, current position information of the mobile terminal 2 calculated by the sensor unit 23, current time information acquired from a time measuring unit (not shown), and the like Is transmitted to the traffic jam prediction server system 3. Note that when the user terminates the operation of the traffic jam prediction display application of the mobile terminal 2, the traffic jam prediction display application stops its function.

<Location information update unit 202>
The location information update unit 202 periodically predicts the traffic jam based on the user ID, the current location information of the mobile terminal 2 calculated by the sensor unit 23, the traveling direction, and the current time information acquired from the timekeeping unit (not shown). It transmits to the server system 3.

  The location information update unit 202 is acquired from the user ID, the current location information of the mobile terminal 2 calculated by the sensor unit 13, and the time measuring unit (not shown) until the traffic jam prediction display application of the mobile terminal 2 is terminated. Current time information and the like are periodically transmitted to the traffic jam prediction server system 3.

The location information update unit 202 does not transmit the current location information and current time information of the mobile terminal 2 acquired at a predetermined time interval (for example, every 3 seconds) to the traffic congestion prediction server system 3 in real time each time. A plurality of data can be transmitted together (for example, current position information and current time information of the mobile terminal 2 for 5 minutes) and transmitted at a time (so-called burst transmission).
It should be noted that the acquisition time interval of the current position information of the mobile terminal 2 and the number of transmissions at a time when a plurality of burst transmissions are collectively transmitted can be set in advance.

The processing contents of the traffic jam information acquisition unit 203, the traffic jam information display control unit 204, and the route guidance unit 205 are the traffic jam information acquisition unit 103, the traffic jam information display control unit 104, and the route guidance of the in-vehicle navigation device 1, respectively. This is the same as the processing content of the unit 105, and the description is omitted.
Also, the process for taking a course of the traffic jam avoidance route and the recommendation service process are the same as the corresponding processes of the in-vehicle navigation device 1, and the description thereof is omitted.

Next, the traffic jam prediction server system 3 will be described.
<Traffic jam prediction server system 3>
As shown in FIG. 4, the traffic jam prediction server system 3 includes at least a control unit 30, a storage unit 31, and a communication unit 32, and further includes a display unit 34 and an input unit 35 as necessary. .

The control unit 30 includes a processor having a CPU, a RAM, a ROM, an I / O, and the like, and controls each component. The CPU executes each program for navigation read from the RAM, ROM, or storage unit 31, and reads information from the RAM, ROM, and storage unit 31 when executing the program. Information is written and signals are exchanged with the communication unit 32.
Details will be described later.

  The storage unit 31 includes a semiconductor memory, a hard disk drive, and the like, and stores various information such as an operating system (OS) and software called applications. Therefore, various storage areas such as a user information area 311, a navigation information area 312, a traveling vehicle data area 313, and a traffic jam prediction information storage area 314 are secured in the storage unit 31.

  The user information area 311 of the storage unit 31 manages vehicle information (for example, information such as vehicle type, license plate, and frame number of the vehicle body) for each “vehicle ID” and user information for each “user ID”. A subscriber information database 3111 for managing (for example, owned vehicle ID, family structure, age structure, current address, etc.) is stored.

In the navigation information area 312 of the storage unit 31, map information for navigation, road link information, and the like are stored in advance. Map information includes display map data for displaying the background of roads and road maps, position information of nodes (for example, road intersections, inflection points, end points, etc.) and their type information, and routes connecting the nodes. Road network data including link cost data and the like regarding position information and type information of a link and cost information (for example, distance, required time, etc.) of all links are included.
In addition, the traffic information received from a traffic information center etc. can be memorize | stored with the recording time which received the said traffic information. The traffic information includes elements that influence the travel cost such as the number of traveling vehicles for each link, the required travel time, traffic jam information, traffic regulation information, weather information, and the like. Also, past traffic information of the link can be recorded.

The navigation information area 312 of the storage unit 31 further includes mesh map information that is divided into mesh areas and stored as a map including a road map. Each mesh region can be configured to be identified by, for example, center position information (latitude and longitude) of the mesh region.
Here, as the size of the mesh area, for example, a section where each side is 10 km long (hereinafter referred to as “large mesh area”), an area of 1/4 of the large mesh area (each side of the large mesh area). A section (hereinafter referred to as “medium mesh area”) that is 1/2) of a length, and a section (hereinafter referred to as 1/4 of the length of each side of the large mesh area) that is 1/16 of the large mesh area A plurality of types of mesh regions can be set, such as “small mesh region”.
In addition, the size of the mesh region can be set as appropriate according to the type of road (general road, highway, etc.).
The navigation information area 312 stores mesh map information for each mesh area size.

In the traveling vehicle data area 313 of the storage unit 31, each vehicle traveling on the road, for example, the location information, the time information, and the destination that are periodically transmitted from the vehicle-mounted navigation device 1 and the portable terminal 2 are periodically transmitted. Information and traveling vehicle data such as the traveling direction of the vehicle (or portable terminal 2) are stored in time series for each user ID or vehicle ID.
Therefore, the mesh area where each vehicle is located can be determined based on the position information (latitude and longitude) of each vehicle traveling on the road.
Further, in the traveling vehicle data area 313, the position information (latitude and longitude), time information, traffic information, traveling history of the probe car sent from the probe car every certain time and every certain traveling section. Probe data such as traveling speed is also stored.

  In the traffic jam prediction information storage area 314 of the storage unit 31, as will be described later, for each mesh region, the traffic jam amount that is the number of vehicles with traffic jam stopped determined by the traffic jam stop vehicle determination unit 304 to be described later, A longitude (east) traffic escape speed and a latitude (north) traffic escape speed calculated by a traffic escape vector calculation unit 306, which will be described later, are stored in time series (calculation time) in a storage unit 31 (congestion prediction information storage area). 314).

The communication unit 32 includes a wireless unit that periodically receives position information and time information of the terminal from the in-vehicle navigation device 1 or the portable terminal 2 that is a terminal that receives the traffic jam prediction information service.
Note that the communication unit 32 may receive probe data uploaded from a plurality of vehicles (probe cars) on which probe machines are mounted. Further, the probe data may be received from another server (FCD server system) that receives the probe data periodically uploaded from each probe car traveling on the road.

Next, the control unit 30 will be described.
As shown in FIG. 4, the control unit 30 includes a login processing unit 301, a current position information processing unit 302, a vehicle movement direction estimation unit 303, a traffic jam stop vehicle determination unit 304, and a traffic jam stop vehicle number calculation unit 305. A traffic jam escape vector calculation unit 306, a traffic jam stop vehicle prediction unit 307, a traffic jam degree prediction unit 308, and a traffic jam prediction display information generation unit 309.

  The login processing unit 301, the current position information processing unit 302, and the vehicle movement direction estimation unit 303 perform processing for input from each vehicle traveling on the road, for example, the in-vehicle navigation device 1 or the portable terminal 2. For simplicity, it is referred to as a processing group A.

  A traffic jam stop vehicle determination unit 304, a traffic jam stop vehicle number calculation unit 305, a traffic jam exit vector calculation unit 306, and a traffic jam stop vehicle prediction unit 307 perform processing to generate traffic jam prediction information based on traveling vehicle data. For simplicity, it is referred to as a processing group B.

  The traffic jam degree prediction unit 308 and the traffic jam prediction display information generation unit 309 provide the current traffic jam information and the traffic jam prediction information after a certain period of time in the near future (plural) to the in-vehicle navigation device 1 or the mobile terminal 2. The processing is performed and is referred to as a processing group C for simplicity.

The control unit 30 executes each program for traffic jam prediction, thereby causing the traffic jam prediction server system 3 to function as a login processing unit 301, a current position information processing unit 302, a vehicle movement direction estimation unit 303, and a traffic jam stop vehicle determination unit 304. , A traffic jam stop vehicle number calculation unit 305, a traffic jam escape vector calculation unit 306, a traffic jam stop vehicle prediction unit 307, a traffic jam degree prediction unit 308, and a traffic jam prediction display information generation unit 309 (hereinafter collectively referred to as “traffic jam prediction control unit”). ) Make it work.
In addition, the control unit 30 executes each program for traffic jam prediction to the traffic jam prediction server system 3 to log in processing step, current position information processing step, vehicle movement direction estimation step, traffic jam stop vehicle determination step, A method (hereinafter collectively referred to as “traffic jam prediction control step”) including a jam stop vehicle number calculation step, a jam exit vector calculation step, a jam stop vehicle prediction step, a jam degree prediction step, and a jam prediction display information generation step is executed. .

  Hereinafter, functions of the control unit 30 will be described from the viewpoint of the traffic jam prediction control unit. The explanation based on the viewpoint of the traffic jam prediction control step (method) can be explained by replacing “part” with “step”, and will be omitted.

<About processing group A>
First, the function of each processing unit included in the processing group A will be described. As described above, the processing group A includes the login processing unit 301, the current position information processing unit 302, and the vehicle movement direction estimation unit 303.

<Login processing unit 301>
The login processing unit 301 executes a login process from a traveling vehicle, for example, the in-vehicle navigation device 1 or the portable terminal 2 carried by the vehicle, performs a connection process with the in-vehicle navigation device 1 or the portable terminal 2, and the in-vehicle navigation. A vehicle travel data record for managing position information, time information, traveling direction, destination information, and the like transmitted from the device 1 or the portable terminal 2 is provided for each vehicle, specifically for each user ID or vehicle ID. Created and stored in the traveling vehicle data area 313. Moreover, you may receive the information of each sensors, such as a geomagnetic sensor and a gyro sensor. When the in-vehicle navigation device 1 or the mobile terminal 2 has a navigation function and a destination is set, the destination information is received.

<Current position information processing unit 302>
The current position information processing unit 302 includes current position information, time information, a traveling direction of the vehicle, and the like that are periodically transmitted from the in-vehicle navigation device 1 and current position information, time information, and the like that are periodically transmitted from the mobile terminal 2. The traveling direction or the like of the mobile terminal 2 is additionally updated in the vehicle travel data record described above for each vehicle, specifically for each user ID or vehicle ID.
Also, from when the vehicle arrives at the destination until the ignition switch is turned off (engine stopped), or until the user starts operating the traffic jam prediction display application of the mobile terminal 2 and ends it. In the meantime, the current position information, the time information, the traveling direction of the vehicle and the like transmitted from the in-vehicle navigation device 1 or the portable terminal 2 are additionally updated in the vehicle travel data record.
The probe data uploaded from the probe car may also be stored in the traveling vehicle data area 313 of the storage unit 31 in time series for each probe car by the current position information processing unit 302.

<Vehicle moving direction estimation unit 303>
The vehicle moving direction estimation unit 303 estimates the moving direction of the vehicle based on the position information and time information of the in-vehicle navigation device 1 or the portable terminal 2 received from the in-vehicle navigation device 1 or the portable terminal 2 via the communication unit 32. .
The vehicle movement direction estimation unit 303 takes time series position information, information on each sensor such as a geomagnetic sensor, a gyro sensor, etc., and destination information, etc., into the movement direction of the in-vehicle navigation device 1 or the portable terminal 2. May be estimated.

<About processing group B>
Next, functions of the processing units included in the processing group B will be described. As described above, the processing group B includes the traffic jam stopped vehicle determination unit 304, the traffic jam stopped vehicle number calculation unit 305, the traffic jam exit vector calculation unit 306, and the traffic jam stopped vehicle prediction unit 307.

<Congestion stop vehicle determination unit 304>
The traffic jam stop vehicle determination unit 304 is involved in traffic jams in each mesh area on the map based on running vehicle data including vehicle identification information, position information, time information, etc. uploaded from each running vehicle traveling on the road. The vehicle that is currently on the road (hereinafter referred to as “congestion stop vehicle”) is periodically determined. The traffic jam stop vehicle determination unit 304 is preset with a vehicle speed threshold and a duration threshold for determining whether the vehicle is involved in the traffic jam.
There are various speed ranges in which traffic is perceived as being congested, and in general, there is a tendency for suburbs to feel traffic jam at higher speeds than in urban areas. In addition, highways tend to feel traffic jams at higher speeds than ordinary roads. Therefore, it is desirable to change the speed range for “congestion stop” for determining that the vehicle is involved in a traffic jam, depending on the region, the country, etc., or between a general road and a highway.

As described above, the vehicle speed threshold for determining a traffic jam stop vehicle (hereinafter referred to as “first vehicle speed threshold”) and the duration threshold for determining a traffic jam stopped vehicle (hereinafter referred to as “first duration threshold”) are as follows. For example, different values can be set for areas, countries, etc., and general roads and highways.
Similarly, a vehicle speed threshold value (hereinafter referred to as “second vehicle speed threshold value”) for determining that a vehicle with a traffic jam has exited the traffic jam is set to a different value depending on, for example, a region, a country, a general road, and a highway. can do.
In addition, the traffic jam stop vehicle determination unit 304 sets a cycle time in advance as a cycle for periodically determining a traffic jam stopped vehicle. In the following description, the cycle time is 15 minutes, but the cycle time is not limited to this number, and can be set arbitrarily.

  For example, on a general road, the first vehicle speed threshold can be set to 20 km / h, the first duration threshold can be set to 15 seconds, and the second vehicle speed threshold can be set to 30 km / h. On a highway, the first vehicle speed threshold can be 40 km / h, the second duration threshold can be 15 seconds, and the second vehicle speed threshold can be 60 km / h. These threshold values are not limited to these numbers, and can be arbitrarily set for each type of road, for example.

  More specifically, the jam-stopped vehicle determination unit 304 determines the traveling vehicle data (the traveling vehicle data stored in the traveling vehicle data area 313 of the storage unit 31) periodically (every 15 minutes) from a vehicle traveling on a general road. ) For each mesh area, a vehicle moving at a speed of the first vehicle speed threshold of 20 km / h or less for a first duration threshold of 15 seconds or more is recognized as being in a traffic jam stop state. The vehicle is determined as a traffic jam stop vehicle. Excludes vehicles parked in the parking lot.

  On the other hand, the congestion stop vehicle determination unit 304 periodically (every 15 minutes) determines whether the vehicle determined to be a congestion stop vehicle passes the above-described cycle time (for example, 15 minutes) for each mesh area. Or, when the speed per hour is equal to or higher than the second vehicle speed threshold of 30 km / h, it is determined that the vehicle has once left the traffic jam. However, if the vehicle is in the traffic jam stop state for more than the cycle time (for example, 15 minutes) even though the traveling vehicle data is received from the traffic jam stop vehicle, it is judged as the traffic jam stop state again and determined as the traffic jam stop vehicle. To do. If the vehicle is stopped at a speed of 0 km / h for a cycle time (for example, 15 minutes) or longer, it is treated as a pure stop and is not counted as a traffic jam stop vehicle.

In addition, the jammed vehicle determination unit 304 periodically (every 15 minutes) from a vehicle traveling on the highway based on traveling vehicle data (traveling vehicle data stored in the traveling vehicle data area 313 of the storage unit 31). For each mesh area, a vehicle moving at a speed of a first vehicle speed threshold of 40 km / h or less for a first duration threshold of 15 seconds or more is recognized as being in a traffic jam stop state, and the traffic jam is stopped. Judge as a vehicle.
On the other hand, the congestion stop vehicle determination unit 304 periodically (every 15 minutes) determines whether the vehicle determined to be a congestion stop vehicle passes the above-described cycle time (for example, 15 minutes) for each mesh area. Or, when the speed per hour is equal to or higher than the second vehicle speed threshold 60 km / h, it is determined that the vehicle has once left the traffic jam. However, if the vehicle is in the traffic jam stop state for more than the cycle time (for example, 15 minutes) even though the traveling vehicle data is received from the traffic jam stop vehicle, it is judged as the traffic jam stop state again and determined as the traffic jam stop vehicle. To do. Further, when the vehicle is stopped at a speed of 0 km / h for a cycle time (for example, 15 minutes) or more, it is treated as a pure stop and is not counted as a traffic jam stop vehicle.

By doing so, it is possible to determine a traffic jam stop vehicle existing in each mesh area at the present time. The ID of the jammed vehicle determined in this way is specified. In addition, the traffic jam stop vehicle determination unit 304 calculates the position information (latitude and longitude), the travel speed in the longitude direction, and the travel speed in the latitude direction for each vehicle determined to be a traffic jam stop vehicle.
In addition, the moving speed of the traffic jam-stopped vehicle determined by the traffic jam stopped vehicle determination unit 304 as being in a traffic jam stopped state for 15 minutes or more is calculated as 0 km / h. In this way, if the traffic jam stop state continues even after 15 minutes, the traffic jam stop vehicle forcibly sets the moving speed to zero. By doing so, the moving speed becomes slower overall as the number of vehicles whose traffic jam has stopped for 15 minutes or more increases.

  As described above, the traffic jam stopped vehicle determination unit 304 calculates the traffic jam stopped vehicle ID, position information (latitude and longitude), the travel speed in the longitude direction, and the travel speed in the latitude direction in each mesh region. A vector having a moving speed in the longitude direction in the longitude direction and a moving speed in the latitude direction in the latitude direction is referred to as a speed vector.

<Congestion Stopped Vehicle Number Calculation Unit 305>
The number-of-congested-vehicles calculation unit 305 periodically (for example, every 15 minutes) determines the number of congested vehicles that have been determined to be congested by the congested vehicle determination unit 304 (hereinafter, “congestion amount”) for each mesh area. ").

<Congestion Escape Vector Calculation Unit 306>
In each mesh area, the traffic jam escape vector calculation unit 306 moves in the longitude direction (east) and the latitude direction (north) of each traffic jammed vehicle determined by the traffic jam stop vehicle determination unit 304 as being in a traffic jam stop state. Are respectively summed to calculate the total value of the moving speed in the longitude direction (east) and the moving speed in the latitude direction (north) of the entire traffic jammed vehicle in each area.
Note that, as described above, if the traffic jam stopped vehicle continues even after 15 minutes, the moving speed of the traffic jam stopped vehicle is zero.
The value obtained by dividing the total value of the moving speed in the longitude direction (east) and the moving speed in the latitude direction (north) divided by the number of traffic jam-stopped vehicles in this way is the congestion escape speed in the longitude direction (east) from each mesh area and It is called the congestion escape speed in the latitude (north) direction.
For example, if the number of vehicles with traffic jams in a certain area is 20, the total of the moving speed in the longitude direction and the moving speed in the latitude direction of the vehicles with traffic jams divided by the number of vehicles with traffic jams is 10 km in the right (east) direction. / H, assuming 10km / h in the south (down) direction, the number of traffic jams is 20, and the escape speed is 10km / h to the right (east), 10km / h to the bottom (south) (minus 10km / h to the north) It can also be said).
The storage unit 31 (congestion prediction information storage area) correlates the traffic amount, longitude direction (east) traffic escape speed and latitude (north) direction traffic escape speed calculated in this way with each mesh region and calculation time. 314).
As the mesh area ID, for example, the latitude and longitude of the center position of each mesh area (or the position included in each mesh area) in each mesh map information can be adopted. By doing so, it is possible to identify each mesh region of each mesh map information stored in the navigation information area 312 of the storage unit 31 by combining with the mesh map information ID.

<Congestion stop vehicle prediction unit 307>
The traffic jam stop vehicle prediction unit 307 can predict the traffic jam status of each mesh after elapse of, for example, a predetermined time (or minutes) from the predicted start time, for example, using a random forest as follows.
Here, the random forest is bagging using a weak learner as a decision tree (decision tree), and is one of collective learning algorithms using the decision tree. In a random forest, a large number of decision trees are created, the answers given by each decision tree are majority, and are classified into the most popular class, that is, the output mode values of the individual decision trees are finalized. The solution is obtained by using the output value.

The traffic jam stop vehicle prediction unit 307 can employ the number of traffic jam vehicles and the traffic jam escape vector in the mesh region as variables for creating a decision tree of a random forest.
This is the number of traffic jam stop vehicles, traffic jam exit direction and traffic jam escape at the estimated start time for 5 mesh areas including 4 mesh areas (hereinafter also referred to as “adjacent mesh areas”) adjacent to each mesh area. Based on the speed, the number of vehicles with traffic jams in the mesh area (hereinafter also referred to as “central mesh area”) after a certain time (or minutes) has elapsed, the escape direction from the traffic jam, and the escape speed are predicted. .

  More specifically, the traffic jam stop vehicle predicting unit 307 determines, for each mesh area, five mesh areas of four adjacent mesh areas adjacent to the left, right, top, and bottom (hereinafter also referred to as “around 5 squares” for simplicity). ) By learning based on the data of 5 squares around (the number of traffic jam stopped vehicles and the traffic jam escape vector), randomly selected decision tree with the number of traffic jam stopped vehicles and the traffic jam escape vector as variables. A large number of decision trees are created with the number of 5 jam vehicles and the traffic escape vector as variables.

<An example of a decision tree>
For example, as an example of the decision tree created by learning the relationship with the left adjacent mesh region, the following decision tree can be cited.
First, the first branch is made depending on whether the number of vehicles with traffic jam in the left adjacent mesh area of the central mesh area is large.
When the number of jammed vehicles in the left adjacent mesh area is small, the number of jammed vehicles in the central mesh area is unchanged.
When the number of vehicles with traffic jams in the left adjacent mesh area is large, the second branch is made depending on whether or not the traffic jam escape vector in the left adjacent mesh area is directed to the central mesh area.
It is assumed that when the congestion escape vector in the left adjacent mesh area is directed to the center mesh area, the number of vehicles in which the congestion is stopped in the center mesh area is significantly increased.
When the traffic jam escape vector in the left adjacent mesh region is facing left (opposite to the center mesh region), or when it cannot be said to be in either direction, it is assumed that the number of jammed vehicles in the center mesh region slightly increases.

The traffic jam stopped vehicle prediction unit 307 can generate a large number of such decision trees by randomly learning data of 5 squares around.
By doing so, the traffic jam stop vehicle prediction unit 307 determines each decision tree of the plurality of decision trees generated based on the data of the surrounding 5 squares (the number of traffic jam stopped vehicles and the traffic jam escape vector) for every mesh region. Every time, the number of traffic jam stop vehicles in the central mesh area and the traffic jam escape vector are calculated, and the average number of traffic jam stop vehicles in the central mesh area and the traffic jam escape vector calculated for each decision tree are calculated in each central mesh area. The number of vehicles with traffic jams and the traffic jam escape vector can be obtained.
By implementing this as a central mesh area for all mesh areas, it is possible to predict the number of traffic jam-stopped vehicles and traffic jam escape vectors in each mesh area after a certain time (or minute) has elapsed. .

For example, the number of traffic jam-stopped vehicles after 30 minutes in each mesh area when the current time is assumed to be the start time, the escape direction from the traffic jam, and the escape speed will be described with reference to FIG. FIG. 10 shows 30 minutes based on the number of vehicles with traffic jams in the 5 mesh area at 15 o'clock on June 20, 2015, the traffic escape speed in the longitude direction (east), and the traffic escape speed in the latitude direction (north). The outline | summary which calculates the number of vehicles with traffic jam stop in the center mesh area | region after that, the traffic escape speed of the longitude direction (east), and the traffic congestion escape speed of the latitude (north) direction is shown.
Hereinafter, for simplicity, the mesh area located on the left side of the central mesh area is the left adjacent mesh area, the mesh area located on the right side is the right adjacent mesh area, the mesh area located on the upper side is located on the upper adjacent mesh area, and the lower side. The mesh area to be performed is referred to as a lower adjacent mesh area.
As shown in FIG. 10A, the number of vehicles with traffic jams in the central mesh area at the expected start time is 60, the traffic escape speed in the longitude direction (east) is 0 km / h, and the traffic escape speed in the latitude (north) direction. 0 km / s, 30 traffic jam-stopped vehicles in the left adjacent mesh area, 20 km / h in the traffic jam escape speed in the longitude direction (east), 0 km / s in the traffic jam escape speed in the latitude (north) direction, right adjacent mesh area 30 traffic jam stop vehicles, 20 km / h traffic escape speed in the longitude direction (west), 0 km / s traffic exit speed in the latitude (north) direction, 40 traffic jam stop vehicles in the upper adjacent mesh area, The congestion escape speed in the longitude direction (east) is 0 km / h, the congestion escape speed in the latitude (north) direction is 20 km / s, the number of congested vehicles in the lower adjacent mesh area is 20, and the congestion escape in the longitude direction (east) Speed is 10km / , Latitude and south direction of the congestion escape velocity and 30 km / s.
The traffic jam stop vehicle prediction unit 307 calculates the number of traffic jam stopped vehicles in the central mesh region calculated from each decision tree of the generated plurality of decision trees, and the number of traffic jam stopped vehicles in each central mesh region from the average value of the traffic jam escape vector, And the traffic congestion escape vector is obtained. In this way, it is possible to predict the number of traffic jam-stopped vehicles and the traffic jam escape vector in the central mesh area shown in FIG.
The traffic jam stop vehicle prediction unit 307 calculates the average value of the values calculated for each of a plurality of decision trees, with each mesh region as a central mesh region for all mesh regions, and after a predetermined time (or minutes) has elapsed. It is possible to predict the number of vehicles with traffic jams stopped in each mesh area, the direction of traffic jam escape from each mesh area, and the speed of traffic jam escape.

As described above, the traffic jam stop vehicle prediction unit 307 performs the traffic jam stop vehicle number after a certain period of time in each mesh region when the current time is the expected start time, the traffic jam escape speed in the longitude direction (east), and the latitude ( The traffic escape speed in the north direction can be predicted.
In the same manner, the traffic jam stop vehicle prediction unit 307 determines the number of traffic jam stopped vehicles after a predetermined time (or minutes) in each mesh region when the time after the predetermined time (or minutes) has been set as the predicted start time. The traffic escape speed in the direction (east) and the traffic escape speed in the latitude (north) direction can be predicted. By repeating the processing in this manner, the traffic jam stop vehicle prediction unit 307 can calculate (a plurality of) traffic jam prediction information after a certain period of time in the near future in each mesh region. The traffic jam stop vehicle prediction unit 307 is a traffic jam prediction information after a predetermined time (plural) in each mesh area (amount of traffic jam, a traffic jam escape speed in the longitude direction (east) and a traffic jam escape speed in the latitude direction (north)). Are stored in the storage unit 31 (congestion prediction information storage area 314) in association with each mesh region and the current time. Note that the number of repetitions can be set in advance.

The traffic jam stop vehicle prediction unit 307 calculates the number of traffic jam stopped vehicles, the longitude (east) traffic jam escape speed, and the latitude (north) traffic jam escape speed after a certain time (or minute) has elapsed. However, it is not limited to the random forest method.
For example, it is possible to use a VAR (Vector Auto Regressive) model which is a model that estimates by including a self-lag of a variable in a model by one of statistical methods.

<About processing group C>
Finally, the function of the processing unit included in the processing group C will be described. As described above, the processing group C includes the traffic jam degree prediction unit 308 and the traffic jam prediction display information generation unit 309.

<Congestion degree prediction unit 308>
The traffic jam degree prediction unit 308 stores the current traffic jam information in each mesh region and the traffic jam forecast information after a certain period of time in the near future (plural) stored in the storage unit 31 (traffic jam prediction information storage area 314). Based on the number of vehicles, the congestion escape speed in the longitude direction (east), and the congestion escape speed in the latitude (north) direction), the congestion degree of each mesh area is predicted.
More specifically, the traffic congestion degree prediction unit 308 transmits the vehicle travel data at the predicted start time of the number of traffic jam stopped vehicles in each mesh area after a certain time predicted by the traffic jam stop vehicle prediction unit 307. A threshold (hereinafter referred to as “congestion degree ranking threshold”) regarding the ratio (%) to the total number of vehicles (hereinafter referred to as “congestion stop vehicle ratio”) is set in advance, and the congestion degree of each mesh region is determined based on the threshold. Rank.

  Note that the congestion degree ranking threshold can be set according to the size of the mesh area (large mesh area, medium mesh area, and small mesh area). For example, in the case of a large mesh area, when the traffic jam stop vehicle ratio in the large mesh area is 15/4000 or less, the traffic jam degree is set to 1, indicating a free state, the traffic jam stop vehicle ratio is greater than 15/4000 and 150 When it becomes / 4000 or less, the congestion degree is 2, and it indicates a traffic jam that becomes a non-noro drive, and when the traffic jam stop vehicle ratio is larger than 150/4000, the congestion degree is 3, and a heavy congestion can be expressed.

  For example, in the case of the middle mesh area, when the traffic jam stop vehicle ratio in the middle mesh area is 7/4000 or less, the traffic jam degree is set to 1, indicating a free state, the traffic jam stop vehicle ratio is greater than 7/4000 and 75 When it becomes / 4000 or less, the traffic congestion degree is 2, and it indicates a traffic jam that becomes a noronoro driving, and when the traffic jam stop vehicle ratio is larger than 75/4000, the traffic congestion degree is 3 and a heavy traffic jam can be expressed.

For example, in the case of a small mesh area, when the traffic jam stop vehicle ratio in the small mesh area is 3/4000 or less, the traffic jam degree is set to 1 to indicate a vacant state, the traffic jam stop vehicle ratio is greater than 3/4000 and 30 When it becomes / 4000 or less, the degree of traffic congestion can be expressed as 2, and heavy traffic congestion can be expressed. When the percentage of vehicles with traffic jam is larger than 30/4000, the traffic congestion level can be expressed as 3 and heavy traffic congestion can be expressed.
These threshold values can be set to arbitrary values. For example, the degree of congestion may be divided into five levels.

<Congestion level correction based on vehicle direction>
The traffic congestion degree predicting unit 308 can correct by the traveling direction of the host vehicle (more precisely, the traveling direction of the in-vehicle navigation device 1 or the portable terminal 2 carried by the host vehicle) estimated by the vehicle moving direction estimating unit 303. preferable.
For example, the traffic congestion degree prediction unit 308 is based on the moving direction of the own vehicle and the escape direction from each mesh region after a predetermined time predicted by the traffic jam stop vehicle prediction unit 307, as viewed from the vehicle after a certain time. It is preferable to predict the degree of congestion in each mesh area.
More specifically, when the congestion escape direction in each mesh area deviates from a certain angle (for example, 120 degrees) including the traveling direction of the host vehicle, the certain angle including the traveling direction of the host vehicle is It is preferable that correction is made so that the degree of congestion is reduced as compared with the case where the direction of exit from the congestion is within a certain angle range including the traveling direction of the host vehicle. More specifically, it can be corrected by subtracting 1 from the degree of congestion. If the traffic level is 1, the traffic level 1 remains unchanged.

This is shown in FIGS.
FIG. 11 illustrates that both the longitude direction (east) traffic escape direction and the latitude (north) traffic exit direction are included within a certain angle range (for example, 120 degrees) including the traveling direction of the host vehicle. ing. In this case, the congestion degree prediction unit 308 does not correct the congestion degree.
On the other hand, FIG. 12 shows that the congestion escape direction in the longitude direction (east) deviates from a certain angle (for example, 120 degrees) including the traveling direction of the host vehicle. In this case, the congestion degree prediction unit 308 corrects the congestion degree, for example, by subtracting 1 from the congestion degree.
By doing so, the traffic congestion degree predicting unit 308, when moving the host vehicle in the direction opposite to the direction of the traffic jam, is compared with the case of moving the host vehicle in the same direction as the direction of the traffic jam. The degree of traffic jam can be reduced, and the possibility of being actually involved in the traffic jam of the host vehicle can be expressed more accurately.

<Congestion prediction display information generation unit 309>
The traffic jam prediction display information generation unit 309 performs coloring, for example, on each mesh region according to the traffic jam level in each mesh region after a predetermined time predicted by the traffic jam degree prediction unit 308, and then via the communication unit 32. Delivered to the in-vehicle navigation device 1 or the portable terminal 2.
In the first embodiment, the traffic jam prediction display information generation unit 309 displays a color on a map such as “blue”, “yellow”, and “red” for each mesh area with respect to the degree of congestion after a lapse of a predetermined time. To generate traffic jam display information.
By doing so, the user can intuitively grasp the traffic situation of each area divided by the mesh area, and as described above (see FIG. 9), for example, even if it is involved to some extent in a traffic jam, it is linear. You can figure out whether you should choose a route or avoid a traffic jam.
Such a display is particularly effective in an area where the user has a certain amount of land, such as within the living area. Generally, when a user gets on a vehicle equipped with a car navigation device, it is said that the destination setting and route guidance are about 10% of the boarding time. This indicates that even if the car navigation apparatus is equipped with a guidance function that avoids traffic jams by the traffic jam prediction function, most of the time is not benefited from that function. As described above, according to the present invention, it is possible to predict a traffic jam without the user setting a destination.

  The display example of the traffic jam prediction information generated by the traffic jam prediction display information generation unit 309 has been described above with reference to FIGS. Here, the description is omitted.

Further, as the function of the processing group C, the recommendation service function described above can be added.
Specifically, the traffic jam prediction server system 3 can transmit to the in-vehicle navigation device 1 that the user is recommended to go out earlier than planned when the traffic jam is predicted in the near future. By doing so, it is possible to prevent the user from encountering a traffic jam in the near future.
In addition, the traffic jam prediction server system 3 transmits, to the in-vehicle navigation device 1, a recommendation to the user to take a break or kill time when it is predicted that the traffic jam will be absolutely involved in the near future. be able to. By doing so, it will be possible for users to avoid traffic jams in the near future.

<Modification 1>
The traffic jam prediction server system 3 may include a route setting unit (not shown) and a route notification unit (not shown) as the processing group A.
In response to receiving the location information of the destination from the in-vehicle navigation device 1 or the portable terminal 2, the route setting unit sets the location information as the destination and routes from the current position of the vehicle to the destination. Calculate information. When calculating route information, use the traffic information and map information stored in the navigation area 312 of the storage unit 31 to calculate the optimum route taking into account various conditions such as arrival time and use of toll roads. To do.
The route notification unit transmits the route information calculated by the route setting unit to the in-vehicle navigation device 1 or the mobile terminal 2 via the communication unit 32.
By doing so, the in-vehicle navigation device 1 or the mobile terminal 2 performs route guidance based on the received route information by the route guidance unit 105 or the route guidance unit 205.

<Modification 2>
The embodiment of each functional unit of the traffic jam prediction system 100 of the present invention has been described based on the configuration of the in-vehicle navigation device 1, the mobile terminal 2, and the traffic jam prediction server system 3 that are mounted on the vehicle. However, the embodiment of each functional unit included in the traffic jam prediction server system 3 of the present invention is distributed even with one computer or with a large number of computers in one place or distributed in several places and interconnected by a communication network. And can be deployed to execute. It can also be configured using a plurality of virtual computers on the cloud.
Each function provided in the traffic jam prediction server system 3 can be executed by any computer. Therefore, those skilled in the art can appropriately design which computer each function of the traffic jam prediction server system 3 is assigned to.
For example, processing group A (login processing unit 301, current position information processing unit 302, vehicle movement direction estimation unit 303) and processing group C (congestion degree prediction unit 308, congestion prediction display information generation unit 309) are combined into one server ( A traffic jam prediction information providing front server), and processing group B (the jam stop vehicle determination unit 304, the jam stop vehicle number calculation unit 305, the jam exit vector calculation unit 306, and the jam stop vehicle prediction unit 307) are separate servers. For example, an FCD server system may be used.
Further, the traffic jam prediction server system 3 may be a server system including, for example, a Web server, an application server, a database server, and a plurality of servers.

(Operation of traffic jam prediction system 100)
The configuration of the traffic jam prediction system 100 has been described above. Next, the operation of the traffic jam prediction system 100 will be described using the in-vehicle navigation device 1 as an example. 13A and 13B are flowcharts illustrating an example of a process flow of the traffic jam prediction system 100.

First, a processing flow of the traffic jam prediction system 100 will be described with reference to FIGS. 13A and 13B.
In step S101, the vehicle-mounted navigation device 1 is automatically activated when the driver turns on an ignition switch of the vehicle (starts the engine), and the vehicle-mounted navigation device 1 (connection processing unit 101) is a traffic jam prediction server system. A login request (connection request) is transmitted to 3 using the vehicle ID and password, and the current position and current time of the vehicle are transmitted.

  In step S <b> 301, the traffic jam prediction server system 3 (login processing unit 301) performs connection processing with the in-vehicle navigation device 1, and acquires vehicle travel data for managing the current position and the current time received from the in-vehicle navigation device 1. Created in the traveling vehicle data area 313.

In step S102, the in-vehicle navigation device 1 (the traffic jam information acquisition unit 103) transmits a traffic jam information distribution start request to the traffic jam prediction server system 3, for example, by an operation by the user.
Note that when the connection processing unit 101 makes a default setting to turn on traffic information distribution when the login processing request is made to the traffic jam prediction server system 3, step S102 is omitted. .

  In step S103, when the in-vehicle navigation device 1 has a navigation function and a destination is set, the destination information is transmitted to the traffic congestion prediction server system 3.

  In step S104, the in-vehicle navigation device 1 (position information update unit 102) periodically (for example, at intervals of 3 seconds), the vehicle ID, the current position information of the vehicle calculated by the sensor unit 13, and a timekeeping unit (not shown). Is transmitted to the traffic jam prediction server system 3.

  In step S302, the traffic jam prediction server system 3 (current position information processing unit 302) additionally updates and updates the current position and current time received from the in-vehicle navigation device 1 in the connection history record, and the position of the vehicle in time series. Manage information.

  In step S303, the traffic jam prediction server system 3 (vehicle movement direction estimation unit 303) estimates the movement direction of the vehicle.

  In step S304, the traffic congestion prediction server system 3 (congestion stop vehicle determination unit 304) performs traveling vehicle data uploaded to the traveling vehicle data area 313 from the traveling vehicle (vehicle identification information, positional information, time information, etc. of the traveling vehicle). Based on the above, a vehicle (congestion stop vehicle) that is involved in a traffic jam is determined for each mesh area included in the mesh map information. As described above, in the determination, it is determined whether or not the vehicle is a traffic jam stop vehicle based on a threshold set in advance according to the road (general road, highway, etc.) on which the traveling vehicle is traveling.

  In step S305, the traffic jam prediction server system 3 (the jammed vehicle number calculation unit 305) calculates the number of jammed vehicles (traffic amount) for each mesh area.

In step S306, the traffic congestion prediction server system 3 (congestion escape vector calculation unit 306) is based on the moving speed in the longitude direction (east) and the moving direction in the latitude direction (north) of the traffic jam stopped vehicle for each mesh area. Then, the congestion escape speed in the longitude direction (east) and the congestion escape speed in the latitude (north) direction from each mesh area are calculated.
The traffic volume in each mesh area, the longitude direction (east) traffic escape speed, and the latitude (north) traffic escape speed calculated in this way, the mesh map information identification information, each mesh area identification information and calculation The information is stored in the storage unit 31 (navigation information area 312) in association with the time.

  In step S307, the traffic congestion prediction server system 3 (congestion stop vehicle prediction unit 307) determines, for each mesh region, the current amount of traffic in each mesh region, the congestion escape speed in the longitude direction (east), and the latitude (north) direction. The number of vehicles with traffic jams in each mesh area at the preset multiple times in the near future (for example, 15 minutes, 30 minutes, 45 minutes, 60 minutes, etc.) Estimate escape speed and congestion escape speed in the latitude (north) direction.

  In step S308, the traffic congestion prediction server system 3 (congestion degree prediction unit 308) determines, for each mesh area, the current traffic jam information and the traffic jam prediction information after a predetermined time (plural) in the near future (the number of traffic jammed vehicles, longitude). Based on the direction (east) congestion escape speed and the latitude (north) direction congestion escape speed), the current congestion degree of each mesh area and the congestion degree after a certain period of time in the near future (plural) are calculated.

  In step S309, the traffic congestion prediction server system 3 (congestion degree prediction unit 308) moves the vehicle estimated by the traffic congestion prediction server system 3 (vehicle movement direction estimation unit 303) in step S303 for each mesh region. Based on the direction or the direction of the destination, the current congestion degree of each mesh area and the congestion degree after a certain time in the near future (plural) are corrected.

In step S310, the traffic congestion prediction server system 3 (congestion prediction display information generation unit 309) determines each mesh area according to the corrected congestion degree in each mesh area after a predetermined time predicted by the congestion degree prediction unit 308. For example, after coloring, it distributes to the vehicle-mounted navigation device 1.
Thereafter, the process returns to step S302.

  In step S105, the vehicle-mounted navigation device 1 (the traffic jam information acquisition unit 103) receives the traffic jam prediction information predicted at a plurality of preset near future times from the current traffic jam information for each mesh region.

In step S106, the in-vehicle navigation device 1 (the traffic jam information display control unit 104) predicts a plurality of traffic jams predicted at a plurality of times in the near future set in advance from current traffic jam information in the area area including the current position of the host vehicle. Information (congestion prediction information predicted at preset multiple times in the near future) is displayed on the display unit 14.
Thereafter, the process returns to step S103.

  Note that the operation of the traffic congestion prediction system 100 when the mobile terminal 2 is applied instead of the in-vehicle navigation device 1 also changes the in-vehicle navigation device 1 to the mobile terminal 2 in the steps described above, except for the points described below. The operation of the traffic jam prediction system 100 in the case of the mobile terminal 2 can be explained by rereading.

<Different points>
In step S101, the vehicle-mounted navigation device 1 is automatically activated when the driver turns on the ignition switch of the vehicle (starts the engine), and transmits the current position and current time of the vehicle to the navigation server system 3. On the other hand, the portable terminal 2 is different in that the current position and the current time of the portable terminal 2 are transmitted to the navigation server system 3.
Except for these operations, the operation of the traffic jam prediction system 100 in the case of the mobile terminal 2 can be described by replacing the vehicle-mounted navigation device 1 with the mobile terminal 2 in the steps described above.

  The traffic congestion prediction system 100 according to the first embodiment has been described above. However, the present invention is not limited to the first embodiment.

[Second Embodiment]
In the first embodiment, the vehicle movement direction estimation unit 303 in the traffic jam prediction server system 3 estimates the movement direction of the host vehicle based on the time information and position information of the in-vehicle navigation device 1 or the portable terminal 2, The traffic congestion degree predicting unit 308 predicts the number of congested vehicles in each mesh region predicted by the traffic jam stopped vehicle prediction unit 307, the traffic jam escape speed in the longitude direction (east), and the traffic jam escape speed in the latitude (north) direction. The traffic jam degree of each mesh area is predicted based on the vehicle travel direction, and further, the traffic jam degree of each mesh area is corrected according to the traveling direction of the host vehicle, and the traffic jam prediction display information generation unit 309 is predicted by the traffic jam degree prediction unit 308. Each mesh area is colored according to the degree of congestion in each mesh area after a certain time, and after display A, the congestion after a certain time (or minutes) has elapsed A traffic jam prediction display information that repeats a display operation of changing to a display C showing a traffic situation after a certain time (or minute) has passed to a display C showing a traffic situation and then returning to the display A is generated, Delivered to the navigation device 1 or the portable terminal 2.
On the other hand, as another embodiment, the traffic congestion prediction server system 3 includes the vehicle navigation device 1 or the vehicle movement direction estimation unit 303, the traffic congestion degree prediction unit 308, and the traffic jam prediction display information generation unit 309 instead of including the vehicle movement direction estimation unit 303, The mobile terminal 2 may include a function of the vehicle movement direction estimation unit 303, a function of the traffic jam degree prediction unit 308, and a traffic jam prediction display information generation function.

FIG. 14, FIG. 15, and FIG. 16 are diagrams showing the configurations of the in-vehicle navigation device 1, the mobile terminal 2, and the traffic jam prediction server system 3 in the second embodiment, respectively.
As shown in FIG. 14, in the second embodiment, the in-vehicle navigation device 1 further generates the in-vehicle navigation_vehicle movement direction estimation unit 111, the in-vehicle navigation_congestion degree prediction unit 112, and the in-vehicle navigation_congestion prediction display information generation. The unit 113 is provided. Similarly, as illustrated in FIG. 15, the mobile terminal 2 further includes a mobile terminal_vehicle movement direction estimation unit 211, a mobile terminal_congestion degree prediction unit 212, and a mobile terminal_congestion prediction display information generation unit 213.

  Hereinafter, the vehicle-mounted navigation device 1 will be described. The same applies to the mobile terminal 2.

The in-vehicle navigation_vehicle movement direction estimation unit 111 estimates the movement direction of the vehicle based on time information and position information (latitude and longitude) of the in-vehicle navigation device 1.
The in-vehicle navigation_vehicle movement direction estimation unit 111 includes time-series position information (latitude and longitude), information on each sensor such as a geomagnetic sensor, a gyro sensor, and destination information, and the in-vehicle navigation device. One moving direction may be estimated.

The in-vehicle navigation_congestion degree prediction unit 112 is calculated in each mesh region calculated by the traffic congestion prediction server system 3 (particularly, a traffic jam stop vehicle number calculation unit 305, a traffic jam exit vector calculation unit 306, and a traffic jam stop vehicle prediction unit 307). The current traffic jam information and the traffic jam prediction information after a certain time in the near future (plural) are received via the wireless unit 12. More specifically, the in-vehicle navigation_congestion degree prediction unit 112 stores the current traffic jam information in each mesh region and a certain amount of time in the near future stored in the storage unit 31 (congestion prediction information storage area 314). ) Traffic jam prediction information.
By doing so, the in-vehicle navigation_congestion degree prediction unit 112 performs the same processing as the congestion degree prediction unit 308 of the traffic congestion prediction server system 3, so that the current congestion degree in each mesh region and a certain time in the near future It is possible to calculate the degree of congestion after a lapse (plural).

  The in-vehicle navigation_congestion prediction display information generating unit 113 determines the current traffic congestion level in each mesh area calculated by the in-vehicle navigation_congestion level prediction unit 112 and the traffic congestion level after a certain period of time in the near future (plural). Each mesh region can be colored, for example.

  Although the processing amount of the vehicle-mounted navigation device 1 or the portable terminal 2 increases as described above, effects such as easy customization (for example, color) according to the user's preference occur.

  According to the traffic jam prediction system 100 described above, the following effects can be obtained.

(1) In the traffic jam prediction system 100 of the first embodiment, the in-vehicle navigation device 1 (or the mobile terminal 2) receives the current traffic jam in each mesh area received via the radio unit 12 (or the radio unit 22). The information on the map having the mesh area read out from the storage unit 11 (or the storage unit 22) with the information and the traffic congestion prediction information after a certain time in the near future (plural) in a color corresponding to the congestion degree The image is displayed on the display unit 14 so as to overlap the area.
This makes it possible to visualize the congestion situation in each region, which was difficult to understand with linear traffic jam information. In addition, without setting the destination on the client side, the congestion information and the congestion prediction information for each mesh area in the surrounding area of the user's vehicle such as the user's living area, etc., in a color corresponding to the degree of congestion Can be easily grasped.

(2) In the navigation server system 3 according to the first embodiment, the in-vehicle navigation device 1 (or the portable terminal 2) further includes the current traffic jam information in each mesh region and the (multiple) after a certain time in the near future. The traffic jam prediction information is read out from the storage unit 11 (or the storage unit 22) in a color corresponding to the traffic congestion level and sequentially in time series to the mesh region on the map having the mesh region. The images are superimposed and displayed on the display unit 14 (or the display unit 24).
As a result, the user can intuitively understand the direction in which the traffic jam propagates. Further, even if a destination is not set, it is possible to drive on an empty road or avoid a time when it becomes congested.
In addition, the user can select whether to travel in an area with less traffic congestion (mesh area) or travel on a traffic jam route in an area where traffic is congested.

(3) In the traffic jam prediction system 100 of the second embodiment, the in-vehicle navigation device 1 (or the mobile terminal 2) is configured to estimate the moving direction of the vehicle, the current traffic jam level in each mesh region, and a certain time in the near future. The later (plural) congestion level is calculated, and each mesh area is colored according to the congestion level.
Thereby, in the vehicle-mounted navigation device 1 (or the portable terminal 2), customization (for example, color) according to the user's preference is facilitated.

The traffic jam prediction system 100 of the present invention can be executed by hardware or can be executed by software.
In other words, the functional configuration in FIGS. 2 to 4 or 14 to 16 is merely an example, and is not particularly limited. That is, it is only necessary that the traffic jam prediction system 100 has a function capable of executing a series of processes related to the traffic jam prediction and display function of the present invention as a whole, and what functional block is used to realize this function. Is not particularly limited to the examples of FIGS. 2 to 4 or FIGS.
In addition, one functional block may be constituted by hardware alone, software alone, or a combination thereof.

When a series of processing is executed by software, a program constituting the software is installed on a computer or the like from a network or a recording medium.
The computer may be a computer incorporated in dedicated hardware. The computer may be a computer capable of executing various functions by installing various programs.

  A recording medium including such a program is not only constituted by a removable medium distributed separately from the apparatus main body in order to provide the program to the user, but is provided to the user in a state of being preinstalled in the apparatus main body. It consists of a recording medium. The removable medium is composed of, for example, a magnetic disk (including a floppy disk), a Blu-ray disk, an optical disk, a magneto-optical disk, or the like. The optical disk is composed of, for example, a CD-ROM (Compact Disk-Read Only Memory), a DVD (Digital Versatile Disk), or the like. The magneto-optical disk is configured by an MD (Mini-Disk) or the like. In addition, the recording medium provided to the user in a state of being incorporated in advance in the apparatus main body includes, for example, the storage unit 11 in FIG. 2, the storage unit 21 in FIG. 3, the storage unit 31 in FIG. 5 of a hard disk included in the storage unit 41.

DESCRIPTION OF SYMBOLS 100 Traffic jam prediction system 1 In-vehicle navigation apparatus 10 Control part 101 Connection processing part 102 Location information update part 103 Congestion information acquisition part 104 Congestion information display control part 105 Route guidance part 111 In-vehicle navigation_vehicle movement direction estimation part 112 In-vehicle navigation_congestion Degree prediction unit 113 In-vehicle navigation_congestion prediction display information generation unit 11 Storage unit 12 Wireless unit 13 Sensor unit 14 Display unit 15 Input unit 2 Mobile terminal 20 Control unit 201 Connection processing unit 202 Location information update unit 203 Congestion information acquisition unit 204 Congestion Information display control unit 205 Route guidance unit 211 Portable terminal_vehicle movement direction estimation unit 212 Portable terminal_congestion degree prediction unit 213 Portable terminal_congestion prediction display information generation unit 21 Storage unit 22 Radio unit 23 Sensor unit 24 Display unit 25 Input unit 3 Traffic jam prediction server system 30 Control unit 30 Login processing unit 302 Current position information processing unit 303 Vehicle movement direction estimation unit 304 Congestion stop vehicle determination unit 305 Congestion stop vehicle number calculation unit 306 Congestion exit vector calculation unit 307 Congestion stop vehicle prediction unit 308 Congestion degree prediction unit 309 Congestion prediction display information Generation unit 31 Storage unit 311 User information area 312 Navigation information area 313 Traveling vehicle data area 314 Congestion prediction information storage area 32 Communication unit

Claims (4)

A traffic jam prediction display device that displays a future traffic jam caused by a vehicle,
The traffic jam prediction display device
A radio unit that receives prediction data of the future degree of traffic congestion in an area divided into certain sections of the map;
A storage unit for storing road maps;
A display unit, a control unit,
With
The control unit displays the prediction data received via the wireless unit on the display unit in a color corresponding to a degree of traffic congestion and superimposed on a map read from the storage unit. Congestion prediction display device. The wireless unit further receives current congestion degree data and future congestion degree prediction data for a plurality of time zones,
The control unit further reads out the current congestion degree data and the prediction data of the future congestion degree for each of a plurality of time zones from the storage unit while sequentially switching the screen in a color corresponding to the congestion degree. The traffic jam prediction display device according to claim 1, wherein the traffic congestion prediction display device is displayed on the display unit so as to overlap the map. In a program that causes a computer including a display unit, a radio unit, a storage unit, and a control unit to function as a traffic jam prediction display device that displays a future traffic jam due to a vehicle,
The storage unit stores a road map,
In the control unit,
The prediction data of the future congestion level of the area divided into certain sections of the map received via the wireless unit is superimposed on the map read from the storage unit in a color corresponding to the congestion level. , A traffic information display control unit to be displayed on the display unit,
A program characterized by functioning as As the traffic information display control unit,
Current traffic congestion level data received via the wireless unit and traffic congestion level prediction data for each of a plurality of time zones in the future according to the traffic congestion level from the storage unit while sequentially switching the screen To be displayed on the display unit superimposed on the read map,
The program according to claim 3, which causes the computer to function.

Images