JP2001229483A - Traffic flow simulator and navigation terminal - Google Patents

Abstract

PROBLEM TO BE SOLVED: To actualize a traffic flow simulator which automatically extracts network data inputted to the traffic simulator from map data having neither node data nor link data. SOLUTION: The traffic flow simulator is equipped with a means which selectively extracts link data, intersection data, and end-point data from digitized image map data.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a traffic flow simulator.

[0002]

2. Description of the Related Art As a conventional traffic flow simulator, McTrans of the University of Florida has been assigned by the US Federal Highway Administration (FHWA: Federa).
l There is a traffic flow simulation model NETSIM developed for Highway Administration). In this simulator, an intersection is described as a node and a line segment connecting the intersections is described as a link to represent the connection relationship of the target road. This network data is input by converting the coordinates of the point read by the human eye from the drawing into numerical values and directly inputting the numerical values in a text file format, or selecting and inputting a figure that matches the shape of the road with the human eye Is what you do.

The instruction manual of the traffic flow simulator for evaluating traffic signal control algorithms issued by the Japan Traffic Management Association contains link data and node data on map data read by an image scanner. It is described that a human selects and inputs.

Japanese Patent Application Laid-Open No. 8-83044 discloses that
A traffic flow simulator using network data is described. The network data uses a digital road map in which node data and link data are defined in advance.

"Automatic extraction of road network from map image" IEICE Transactions on Information and System II
-Pattern processing (VOL.J82-D-II NO.11 NOVEMBER 1999)
pp1990-1999) describes that network data used for car navigation is automatically extracted using a route search technique.

[0006]

The conventional method of inputting network data to a traffic flow simulator is to use a digital road map in which node data and link data are defined in advance, or to manually input the data. .

[0007] "Automatic extraction of road network from map image" IEICE Transactions on Information and System II
-Pattern processing (VOL.J82-D-II NO.11 NOVEMBER 1999)
pp1990-1999) certainly describes the automatic extraction of network data using the route search method. However, since the network data is extracted for the purpose of car navigation, the simulation target area It does not consider extracting the end points of. That is, this extraction method does not consider selectively extracting intersections and end points, and in particular, does not consider extraction of boundary points of the simulation target area among the end points. It is not possible to set the incoming vehicle and cannot use it for a traffic flow simulator.

[0008] As described above, an object of the present invention is to provide a road map in which node data and link data are not defined.
An object of the present invention is to provide a traffic flow simulator capable of selectively extracting network data (intersection information and end point information of node data) required for a simulation with high accuracy.

It is another object of the present invention to provide a method of displaying a simulation result on a display device together with a scene read from a map.

[0010]

As an aspect of the present invention, means for separating and extracting road data and landscape data from digitized image map data, and link data, intersection data, There is a traffic flow simulator configured to include a means for extracting the data and the end point data, and a traffic flow simulation means for performing a simulation using the extracted network data.

In another aspect, the present invention relates to a traffic flow simulator using network data including node data having intersection data and end point data, and link data, wherein a pattern for specifying a road shape is held. Holding means, means for matching the held pattern with the road shape of the digitized image map data, and simulate using the network data extracted as a result of the matching. There is a configuration in which a simulation unit for performing the simulation and a display unit for displaying the simulation result on a display device using an image representing a road created based on the network data and an image representing a vehicle are provided.

[0012]

DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 is a diagram for explaining a hardware configuration for realizing one embodiment of the present invention.

The traffic flow simulator used in the present invention comprises:
An image map file on a database 1 holding a digitized image map, an image map input processing unit 3 for converting a handwritten map 2 into a predetermined image file format, and a node from the converted image map file. Map image processing unit 4 for extracting data and link data, and network data conversion unit 5 for converting node data and link data into network data
And a network data editing unit 6 for editing a defect in the extracted network data, a network database 7 for storing the extracted network data and the edited network data, and parameters necessary for performing a traffic flow simulation. A traffic flow simulator 10 that simulates a vehicle flow on a computer based on a parameter database 8, a landscape data database 9 in which landscape data is held, and network data.
And an animation display unit 11 for combining and displaying a simulation result and image map data, and a display 12 for displaying an animation.

Next, the flow of processing according to this embodiment will be described with reference to FIG.

First, the image map input processing unit 3
The image file format stored in the image road map database 1 is converted into a monochrome 256-color BMP format. In the case of the drawing of the handwritten map 2, the drawing information is read by the image scanner device and the monochrome 256 color B
Convert to MP format (21).

Here, the image data file to be converted is a bitmap image format file such as GIF / JPEG / TIFF and may be either color or monochrome. The number of colors is arbitrary.

Next, the map image processing section 4 reads the converted BMP format image file, extracts node data consisting of intersection data and end point data by image processing (22), and the network data conversion section 5 Network data that defines a road connection relationship composed of node data and link data is extracted (23).

By synthesizing and displaying the road image based on the extracted network data and the image map based on the image map data, it is possible to confirm on the display screen whether the network data has been correctly extracted.

As a result, if the data is not correctly extracted, the network data editing processor 6 adds or deletes link data or node data on the display screen to create desired network data (2).
4).

Further, a network data editing processing unit 6
Adds lane data having up / down directions to link data of network data based on the number of lanes specified in advance.

If the number of added lanes is different from the actual number of lanes and is different from the actual number of lanes, desired lane data is created by individually adding or deleting the number of lanes (2).
5).

Finally, in the animation display section 11,
The processing result of the traffic flow simulator 10 is superimposed on the image map data and displayed on the display 12 (2
6).

The processing flow of the map image processing shown in FIG. 3 will be described using the image map data shown in FIG. 4 as an example.

The image map to be input shown in FIG. 4 includes a road 44, buildings 41, 42, 43, and place names 45, 4.
There are 6,47. These maps have different numbers of colors depending on the image file to be used. For example, it is assumed that the map is converted into a monochrome 256-color bmp format by using a paint included with Microsoft Windows.

First, a monochrome 256 color bm shown in FIG.
By changing the threshold value of the binarization for separating the p-type file into the road and the other data, as shown in FIG. 5, the road 51 and the buildings 52, 53, and 5 are changed.
Cut out the data of No. 4 (road data and building data) (3
1).

To extract building data, a figure having an area is subjected to labeling processing based on the binarized image of FIG. 5, and an area filter (a fixed area is applied to each of the labeled figures). A graphic element having a certain size is extracted by a filter that defines the extracted graphic and defines the extracted graphic as a graphic representing a building, and it is determined whether the building is a building based on the number of pixels occupied by the graphic element. The outlines of the buildings 71, 72, and 73 are extracted as building data.

Next, road segment data (information expressing the outer shape of the road by line segments) indicated by broken lines 62 in FIG. 6 is obtained from the cut road 51 by thinning processing (32).

Next, node data is extracted from the obtained thinned image shown in FIG. Nodes are broadly classified into intersections and end points. Intersections represent so-called road junctions (nodes other than end points such as intersections and road junctions / branches). This represents a point where a vehicle at the time of simulation (such as a boundary point with a road) appears in the area. Extracting the node data means extracting intersection data and end point data.

As shown in FIG. 8, the intersection data is extracted by first extracting the merging points of the line segments, and using the extracted n × n pixel pattern as a template for the extracted points. It is extracted by performing matching. For example, in the case of a four-way intersection, the four-way intersection is extracted with a simplified pattern as shown by a pattern 81 in FIG. 8, and in the case of a three-way intersection, the three-way intersection is simplified as in a pattern 82. Extract in the pattern that was done.

In the case of simplification, when four or more pixels are detected out of the 3 × 3 pixels, it is determined that the intersection exists (33).

Similarly to the intersection data, the end point data 7 is extracted as end point data when two simplified pixels are detected as in the pattern 83 of FIG. 8 (34).

By performing labeling processing on the intersection data and the end point data obtained by the above-described extraction, node data (node coordinates) can be obtained from the pixel position (3).
5).

The link data is extracted by defining a circle having a radius 92 centered on the intersection 91 as shown in FIG. 9 with respect to the intersection 61, for example, as shown in FIG. The radius is a distance at which each line segment intersecting at the intersection can be separated and extracted. Here, intersections of line segments intersecting with the previously defined circle of radius 92 are sequentially detected, for example, in a counterclockwise direction. Based on the intersection 93 found first, a line extending from the center 91 in the direction of the intersection 93 is drawn. An end point 94 can be detected by performing a minute search. Thereby, link data connecting the intersection 91 and the end point 94 can be extracted. By repeating the same processing in the counterclockwise direction, a link connecting the node 91 and the node 97, a link connecting the node 91 and the node 96, and a node 9
Four link data connecting the node 1 and the node 95 can be extracted (36). By the above processing, FIG.
Network data composed of node data and link data as shown in FIG.

FIG. 11 shows an example of the data structure of the node data storing the node coordinates in the network data of FIG. 10, and is composed of a node number and (x, y) coordinate values indicating the position of the node. Is done.

FIG. 12 shows an example of a data structure of link data defining the connection relation of roads in the network data of FIG. 10. The link data is composed of a link number, a start node number, and an end node number. Be composed. In addition, since it is necessary to consider the intersection on the basis of the simulation, information such as an identification number at the intersection (intersection), the number of intersections, and the presence or absence of a traffic light is also added when extracting from the image map data.

FIG. 13 shows an example of the data structure of the buildings 52, 53 and 54 extracted from the binarized image map data of FIG. 5, where the building numbers and the representative points of the rectangular coordinates are shown. , And height information at the time of stereoscopic display. Here, as for the height information, the height information of a preset standard value is held, and when the extraction is performed from the image map data, the height information is read out from the holding means and is not allowed. Thus, a three-dimensional building can be displayed when displaying a three-dimensional animation. If the actual height of the building can be read from the image map data, the setting values are individually replaced. This allows accurate building display.

FIG. 14 shows an example in which the extracted network data is superimposed on the image map data and displayed on a screen, and it is confirmed on the screen whether the network data has been correctly extracted. be able to.

In this example, it is possible to confirm that the nodes 141 and 143 and the link 142 are erroneously extracted by being displayed in a superimposed manner. In this case, the data must be corrected to correct data. However, using an operation command as shown in FIG. 15, for example, nodes 141 and 143 are deleted by a node deletion command 152. Actually, the node coordinates 111 and 112 in the node coordinate table of FIG. 11 are deleted by the command operation. Similarly, to delete the link 142, the link 121 in FIG. 12 is deleted by a command operation of the link deletion command 151.

When the editing of the network data composed of the node data and the link data is completed, it is necessary to convert the data into lane data necessary for actually driving the vehicle.

The details of the data structure of the lane data are as described in JP-A-8-83044, "Simulation apparatus". In this case, as the preprocessing, the number of lanes set in advance is determined based on the link data. Converted provisionally,
A method of setting the number of lanes for each lane based on the actual number of lanes after conversion will be described. The example of FIG.
By setting one lane in each of the up and down directions for each link data in 0, it is shown that the lane data having the one lane data configuration in FIG. 16 has been obtained.

Here, when the set value is 2, two lanes are formed on one side, and when the set value is 3, three lanes are formed on one side. For individual correction of the number of lanes, for example, in the case of lane deletion as shown in FIG. 17 for one-sided lane configuration, the lane deletion operation command 182 in FIG. 18 is selected, and the display screen in FIG. The relevant lanes 173 and 17 above
By selecting 4, the lane data can be deleted.

Similarly, in the case of adding a lane, FIG.
By selecting the lane addition command 181 and selecting the lanes 171 and 172 on the display screen in FIG. 17, lane data can be added. At this time, the number of lanes at the time of addition or deletion is determined by the number of times the corresponding lane is pressed by designating points with a mouse or the like. The display screen of the number of lanes is also updated in conjunction.

FIG. 19 is a diagram showing a detailed flow of the animation display processing.

First, the image map data before the network data is extracted is read and drawn on the screen of the display 12 (191), and then the network data considering the number of lanes is drawn while being superimposed on the image map data (192). ). When the landscape display is required, the landscape data is read and superimposed and displayed (193).

The running position of the vehicle 201 and the traffic signal 20 are obtained by executing the simulation (194).
By superimposing and displaying the second lamp color information on the image map data, a two-dimensional animation display with landscape information by an image map as shown in FIG. 20 can be realized.

Here, the travel position of the vehicle and the lamp color information required for the animation display can be determined by, for example, the above-described NETSIM or a traffic flow simulator described in JP-A-8-83044 "Simulation apparatus". It shall be calculated.

FIG. 21 shows three-dimensional data of the vehicle 212 and the traffic signal 213 based on the two-dimensional animation, and for example, the building 20 extracted first from the image map data of FIG.
By taking out the height information of FIG. 13 from FIG. 13, it is possible to make the landscape data three-dimensional. As a result, the result of the traffic flow simulation can be displayed as a three-dimensional animation, so that it is possible to easily understand a third party, such as explaining to residents and explaining to related organizations.

The three-dimensional structure can also be applied to a landscape simulation for evaluating how a display plate or a sign looks from a running vehicle.

As another aspect of the present invention, a navigation terminal can be considered.

A map based on map data and a structure for displaying the position of the navigation terminal on the map are as follows:
It is the same as the conventional navigation terminal, but the means of the traffic flow simulator described so far is mounted on the navigation device.

That is, for a conventional navigation terminal, network data for a simulator is created from map data for navigation, a simulation is performed on the basis of the network data, and the simulation result is predicted for the traffic flow and surrounding roads. Means are provided for displaying the traffic flow of vehicles passing through the vehicle using a previously created vehicle image.

By providing this configuration, it is possible to use not only past transmitted information but also future traffic flow prediction.

Although the present invention is basically described as hardware, it can be realized by installing a program recorded on a recording medium.

[0054]

According to the present invention, it is possible to automatically extract network data input to a traffic flow simulator from map data in which node data and link data are not described.

[Brief description of the drawings]

FIG. 1 is a diagram showing the overall system configuration of the present invention.

FIG. 2 is a diagram showing an overall processing flow of the present invention.

FIG. 3 is a diagram illustrating a processing flow of map image processing.

FIG. 4 is a diagram showing an example of image map data.

FIG. 5 is a diagram in which image map data is binarized.

FIG. 6 is a diagram in which binarized data is subjected to thinning processing.

FIG. 7 is a diagram in which a building is extracted from binarized data.

FIG. 8 is a diagram showing a template for searching for an intersection and an end point.

FIG. 9 is a diagram illustrating a process of extracting link data including an intersection and an end point.

FIG. 10 is a diagram showing conversion from line segment information to network data.

FIG. 11 is a diagram illustrating an example of a data structure of link data.

FIG. 12 is a diagram illustrating an example of a data structure of node data.

FIG. 13 is a diagram illustrating an example of a data structure of landscape data.

FIG. 14 is a diagram illustrating an example of correction of network data.

FIG. 15 is a diagram illustrating an example of an edit command for network data.

FIG. 16 is a diagram showing conversion from network data to lane data.

FIG. 17 is a diagram illustrating an example of correction of lane data.

FIG. 18 is a diagram illustrating an example of an edit command for lane data.

FIG. 19 is a diagram showing a flow of an animation display process.

FIG. 20 is a diagram illustrating an example of a two-dimensional animation display.

FIG. 21 is a diagram illustrating an example of a three-dimensional animation display.

[Explanation of symbols]

REFERENCE SIGNS LIST 1 image road map DB 2 handwritten map 3 image map input processing unit 4 map image processing unit 5 network conversion unit 6 network data editing unit 7 network data DB 8 traffic flow Parameter DB, 9
... Scenery data DB, 10 ... Traffic simulator, 11 ... Animation display unit, 12 ... Display.

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) G06T 7/60 150 G08G 1/0969 5H180 G08G 1/0969 G09B 29/00 Z 5L096 G09B 29/00 G06F 15 / 62 335 9A001 15/66 470J (72) Inventor Takayoshi Yokota 7-1-1, Omika-cho, Hitachi City, Ibaraki Prefecture F-term in Hitachi Research Laboratory, Hitachi, Ltd. F term (reference) 2C032 HB03 HB11 HC23 2F029 AA02 AB01 AB05 AB13 AC02 AC03 AC09 AC14 AC16 AC20 5B049 AA06 BB31 CC02 CC40 EE05 EE07 EE36 EE41 FF03 FF04 5B050 AA03 BA07 BA17 DA02 DA06 EA02 EA05 EA07 EA19 FA02 FA09 5B057 AA16 BA02 CA08 CA12 CA16 CC03 CC04 DC03 DC01 DB07 DC01 DB07 DC01 FF22 FF32 FF38 5L096 AA06 BA04 DA02 EA04 EA43 FA03 FA10 FA13 FA69 FA73 FA76 G A34 HA08 9A001 JJ77

Claims (4)

[Claims] 1. A traffic flow simulator using network data composed of node data including intersection data and end point data, and link data. The traffic flow simulator uses road data from digitized image map data, Means for separating and extracting landscape data; means for selectively extracting the link data, the intersection data and the end point data from the road data; and a traffic flow using the extracted network data. Means for performing a simulation, and display means for displaying a result of the simulation on a display device using an image representing a road created based on the network data and an image representing a vehicle. Traffic flow simulator. 2. The traffic according to claim 1, further comprising means for synthesizing a scene based on the extracted scenery data and a simulation result to be displayed on the display device and displaying the result on the display means. Flow simulator. 3. A traffic flow simulator using node data having intersection data and end point data, and network data including link data, wherein the holding means holds a pattern for specifying the shape of a road; Means for extracting network data by matching the stored pattern with the road shape of the digitized image map data; means for performing a simulation using the extracted network data; A traffic flow simulator, further comprising display means for displaying a result on a display device using an image representing a road created based on the network data and an image representing a vehicle. 4. A navigation terminal for displaying, on a display device, an image of a vehicle equipped with a navigation terminal and a map representing a position where the vehicle is located, a means for extracting network data from the data representing the map, A navigation terminal comprising: simulation means for performing a traffic flow simulation based on data; and means for displaying a simulation result on a map of the display device.