KR20090061384A - Method and apparatus for estimating traffic flow - Google Patents

Abstract

A traffic flow prediction method and an apparatus thereof are provided, which can inform a user of the traffic flow in advance by analyzing the speed change pattern of the spot causing the congestion. The stagnant section according to the time variation is searched by using the trafficking statistical information(S210). The convention stagnant section that repetitively appears among the searched stagnant sections is extracted(S220). The speed change pattern of the road affected by the extracted convention stagnant section is grasped(S230). The traffic flow is predicted based on the speed change pattern(S240).

Description

Method and apparatus for estimating traffic flow

The present invention relates to a method and apparatus for estimating traffic flow using traffic statistics information in a navigation terminal, and more specifically, to finding a specific point causing congestion by using traffic statistics information and analyzing a speed change pattern over time. It relates to a method and apparatus for predicting traffic flow through the.

In general, the navigation terminal identifies the current location of the user, displays map data corresponding to the identified current location, and guides a route based on the map data.

If a user using the navigation terminal cannot grasp the real-time road traffic situation, he / she may know that the current road is congested only when the road is actually congested. Therefore, even if the user of the navigation terminal wants to bypass the congested road, it is inevitable to avoid it unless there is a bypass road that can exit the congestion section.

When a traffic condition changes from a smooth state to a congested state, it is caused by a certain point unless it is an accident and affects another point.

Therefore, there is an urgent need for a method for predicting traffic traffic flow by finding a specific point causing traffic congestion and analyzing a speed change pattern over time.

The present invention provides a method and apparatus for predicting traffic flow by using a traffic terminal in a navigation terminal to find a specific point causing congestion and analyzing a speed change pattern over time.

The present invention also provides a traffic flow prediction method and apparatus for guiding a user to predict a congestion occurrence point or a congestion occurrence point using traffic statistics information in a navigation terminal.

The present invention also provides a traffic flow prediction method and apparatus for guiding the detour road information for the congestion section according to the traffic flow predicted using the traffic statistics information in the navigation terminal.

In accordance with another aspect of the present invention, there is provided a traffic flow prediction method comprising: searching for a congestion section based on a time change using traffic statistics information; extracting a habitual congestion section in which congestion appears repeatedly among the searched congestion sections; The method may include determining a speed change pattern of a road affected by the extracted constant traffic congestion section, and predicting a traffic flow based on the speed change pattern.

Traffic flow prediction apparatus according to an embodiment of the present invention, traffic congestion section search unit for searching the congestion section according to the time change by using the traffic statistics information, and extracts the habitual congestion section in which congestion appears repeatedly from the detected congestion section And a traffic pattern predictor configured to determine a speed change pattern of the road affected by the extracted constant traffic jam section, and a traffic flow predictor to predict traffic flow based on the speed change pattern.

According to the present invention, a user can grasp traffic flow in advance by providing a method and apparatus for predicting traffic flow by analyzing a speed change pattern over time by finding a specific point causing traffic jam by using traffic statistics information.

In addition, according to the present invention, the navigation terminal may predict the congestion occurrence point or the congestion occurrence point using the traffic statistics information and guide the prediction result to the user.

In addition, according to the present invention, according to the traffic flow predicted using the traffic statistics information in the navigation terminal by informing the user in advance of the detour road information for the congestion section before the user reaches the congestion section to proceed to the detour section to congest the congestion section Can be avoided.

1 is a view showing the configuration of a traffic flow prediction apparatus according to an embodiment of the present invention.

2 is a diagram illustrating a flow of a traffic flow prediction method according to an embodiment of the present invention.

<Explanation of symbols for the main parts of the drawings>

100: traffic flow prediction device

110: congestion section search unit 120: habitual congestion section extraction unit

130: road pattern identification unit 140: traffic flow prediction unit

150: guide

Hereinafter, a traffic flow prediction apparatus and a method thereof will be described in detail with reference to the accompanying drawings.

1 is a view showing the configuration of a traffic flow prediction apparatus according to an embodiment of the present invention.

Referring to FIG. 1, the traffic flow prediction apparatus 100 may include a congestion section search unit 110, a habitual congestion section extractor 120, a road pattern determiner 130, a traffic flow predictor 140, and a guide unit ( 150).

The congestion section search unit 110 searches for the congestion section according to the time change by using the traffic statistics information. The traffic statistics information is statistical information about traffic information for a certain period of time, and includes traffic information according to a change in time for each road. That is, the congestion section search unit 110 searches for the congestion section according to the time change with respect to the location of the current navigation terminal using the traffic statistics information.

For example, the congestion section search unit 110 may search for the point where congestion begins first by analyzing the traffic statistics information associated with the location of the navigation terminal. For example, when the user of the navigation terminal is moving in the direction of copper from Ilsan to the north side of the riverside, the congestion section search unit 100 analyzes traffic statistics information related to the copper direction in Ilsan of the riverside north road and performs the road section of the north side of the riverside. The neighborhood can be searched as a congestion section, which is the point where congestion begins first.

The stagnation stagnation section extractor 120 extracts a stagnation stagnation section in which congestion appears repeatedly among the searched stagnation sections. That is, the habitual congestion section extraction unit 120 extracts a section in which congestion occurs above the reference value among the searched congestion sections as a habitual congestion section.

For example, when the user of the navigation terminal is moving in the direction of copper from Ilsan on the riverside north road, the habitual congestion section extractor 120 repeatedly displays a bottleneck section near the operation lane among the searched congestion sections. A nearby bottleneck can be extracted as a stagnant stagnation section.

The road pattern grasping unit 130 grasps the speed change pattern of the road affected by the extracted habitual congestion section. That is, the road pattern identification unit 130 searches for sections in which the speed change occurs from the time when the congestion section is congested through the speed change of the road with respect to a road near the habitual congestion section, and thus the speed change pattern of the road according to the change in time. Can be identified.

For example, the road pattern grasping unit 130 starts near the operation street, which is the point where traffic jams start when traffic is smoothly started using traffic statistics information. After 30 minutes, the traffic jam is extended to the vicinity of Mapo Bridge. As the congestion is prolonged to the vicinity of Sogang Bridge, it is possible to grasp the speed change pattern according to the change of time.

The traffic flow prediction unit 140 predicts the traffic flow based on the speed change pattern. That is, the traffic flow prediction unit 140 predicts the traffic flow based on a result of comparing the speed change pattern with the current traffic information.

For example, the traffic flow predicting unit 140 may predict the traffic flow about how long congestion will occur or the congestion will be resolved based on the speed change pattern. For example, if the traffic flow predicting unit 140 is congested in the region where the user of the navigation terminal is currently located and the congestion has been extended to Mapo Bridge based on the speed change pattern, traffic congestion reaches Sogang Bridge after 30 minutes. The longer the traffic flow can be predicted.

The guide unit 150 guides the traffic flow prediction result to the user. That is, the guide unit 150 informs the user how long after the congestion will occur or the congestion will be resolved as a result of the traffic flow prediction.

For example, if the user 150 of the navigation terminal is currently congested in the region where the user of the navigation terminal is located and the congestion is extended to Mapo Bridge, traffic congestion prediction information that is expected to be long to Sogang Bridge after 30 minutes is provided to the user. I can guide you.

In addition, the guide unit 150 may inform the user of the detour route information for the area where congestion is expected as the traffic flow prediction result as well as the traffic flow prediction result.

As such, the traffic flow prediction apparatus 100 according to the present invention uses a traffic statistics information to find a specific point causing congestion, analyzes a speed change pattern over time, and predicts traffic flow so that a user reaches a congestion section. Before the congestion prediction result according to the traffic flow, it is possible to search for a bypass route that can avoid the congestion section in advance.

2 is a diagram illustrating a flow of a traffic flow prediction method according to an embodiment of the present invention.

Referring to FIG. 2, in step S210, the navigation terminal searches for a congestion section according to a time change by using traffic statistics information. The traffic statistics information is statistical information about traffic information for a certain period of time, and includes traffic information according to a change in time for each road. That is, in step S210, the navigation terminal analyzes the traffic statistics information and searches for the first point of the congestion section as the congestion section.

For example, when the user of the navigation terminal is moving in the direction of copper from Ilsan to the north side of the riverside, the navigation terminal analyzes traffic statistics information about the copper direction in Ilsan of the riverside north road in the road section of the riverside north road. The neighborhood can be searched as a congestion section, which is the point where congestion begins first.

For example, in step S210, the navigation terminal becomes congested and the congestion is released as time goes by. In this case, the traffic terminal can search for a situation in which the congestion is released according to the change of time using traffic statistics information.

In step S220, the navigation terminal extracts a habitual congestion section in which congestion appears repeatedly among the searched congestion sections. That is, in step S220, the navigation terminal extracts a section in which congestion occurs above a reference value among the detected congestion sections as a habitual congestion section.

For example, when a user of the navigation terminal drives a vehicle in a direction from copper to Ilsan on a riverside north road, the navigation terminal searches for a congestion section according to the traveling direction of the user in step S220. In the case of frequent congestion, the bottleneck section in the vicinity of the operation bar may be extracted as the habitual congestion section.

In step S230, the navigation terminal grasps the pattern of the speed change of the road affected by the extracted habitual congestion section. That is, in step S230, the navigation terminal searches for sections in which the speed change occurs from the time when the congestion section is congested through the speed change of the road with respect to the road near the habitual congestion section, and changes the speed of the road according to the change of time. Identify the pattern.

For example, in step S230, the navigation terminal starts in the vicinity of the operation road, which is the point where congestion starts in the first state of communication using the traffic statistics information, and after 30 minutes, the congestion lengthens to near the mapo bridge. After time, congestion is prolonged to the vicinity of Sogang Bridge, so we can grasp the pattern of speed change with the change of time.

In step S240, the navigation terminal predicts a traffic flow based on the speed change pattern. That is, in step S240, the navigation terminal predicts a traffic flow based on a result of comparing the speed change pattern with current traffic information. For example, in step S240, the navigation terminal may grasp the current traffic traffic status and compare the current traffic traffic status with a past speed change pattern to predict traffic flow within a predetermined range of the congestion section.

For example, in step S240, the navigation terminal may predict how long congestion will occur or the congestion will be resolved based on the speed change pattern.

For example, in step S240, if the navigation terminal starts congestion at the current location and the congestion is extended to Mapo Bridge, it can be predicted that the congestion will be extended to Sogang Bridge after 30 minutes.

In step S250, the navigation terminal informs the user of the traffic flow prediction result. That is, in step S250, the navigation terminal informs the user how long after the congestion will occur or the congestion will be resolved as a result of the traffic flow prediction.

For example, in step (S250), if the navigation terminal starts congestion in the area where it is currently located and the congestion is extended to Mapo Bridge, the navigation terminal may inform the user of the traffic flow prediction information that is expected to become congested to Sogang Bridge after 30 minutes. have.

In addition, in step S250, the navigation terminal may inform the user of the detour road information for the area where congestion is expected as the traffic flow prediction result as well as the traffic flow prediction result.

As such, the traffic flow prediction method according to the present invention utilizes traffic statistics information to find a specific point causing congestion using past traffic information, and analyzes a speed change pattern over time to predict traffic flow. It can help you avoid traffic jams quickly.

Meanwhile, the traffic flow prediction method according to the present invention may be implemented in the form of program instructions that can be executed by various computer means and recorded in a computer readable recording medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

As described above, the present invention has been described by way of limited embodiments and drawings, but the present invention is not limited to the above embodiments, and those skilled in the art to which the present invention pertains various modifications and variations from such descriptions. This is possible.

Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined not only by the claims below but also by the equivalents of the claims.

Claims (17)

Searching for a congestion section according to time change using the traffic statistics information; Extracting a habitual congestion section in which congestion appears repeatedly among the searched congestion sections; Identifying a speed change pattern of a road affected by the extracted habitual congestion section; And Predicting traffic flow based on the speed change pattern Traffic flow prediction method comprising a. The method of claim 1, Searching for the congestion section according to the time change using the traffic statistics information, Traffic flow prediction method for analyzing the traffic statistics information to search the first point of the congestion in the congestion section as the congestion section. The method of claim 1, Extracting a habitual congestion section in which congestion appears repeatedly among the detected congestion sections, A traffic flow prediction method of extracting a section in which congestion has occurred above a reference value among the detected congestion sections as a habitual congestion section. The method of claim 1, Identifying the pattern of speed changes of the road affected by the traffic jam section, A traffic flow prediction method for identifying a speed change pattern of a road according to a time change by searching for sections in which a speed change occurs from a time point of congestion by changing a speed of a road with respect to a road near a regular traffic congestion section. The method of claim 1, Predicting traffic flow based on the speed change pattern, And a traffic flow prediction method based on a result of comparing the speed change pattern with current traffic information. The method of claim 1, Predicting traffic flow based on the speed change pattern, A traffic flow prediction method for estimating a current traffic traffic status and predicting a traffic flow within a certain range of the congestion section by comparing the current traffic traffic status with a past speed change pattern. The method of claim 1, Predicting traffic flow based on the speed change pattern, And a method for predicting how long congestion will occur or the congestion will be resolved based on the speed change pattern. The method of claim 1, Traffic flow prediction method further comprising the step of guiding the traffic flow prediction result. A computer-readable recording medium in which a program for executing the method of any one of claims 1 to 8 is recorded. A congestion section search unit searching for a congestion section according to time change by using traffic statistics information; A stagnation stagnation section extracting unit extracting a stagnation stagnation section in which congestion appears repeatedly among the retrieved stagnation sections; A road pattern grasping unit which grasps a speed change pattern of a road affected by the extracted constant traffic congestion section; And Traffic flow prediction unit for predicting traffic flow based on the speed change pattern Traffic flow prediction device comprising a. The method of claim 10, The congestion section search unit, A traffic flow prediction apparatus for searching for the first point of congestion in the congestion section by analyzing the traffic statistics information. The method of claim 11, The habitual stagnation section extraction unit, A traffic flow prediction apparatus for extracting a section in which congestion occurs above the reference value among the detected congestion sections as a habitual congestion section. The method of claim 11, The road pattern grasping unit, A traffic flow prediction apparatus for detecting a speed change pattern of a road according to a time change by searching for sections in which a speed change occurs from a time point when the traffic jam is congested by changing the speed of the road with respect to a road adjacent to the normal traffic congestion section. The method of claim 11, The traffic flow prediction unit, And a traffic flow prediction device based on a result of comparing the speed change pattern with current traffic information. The method of claim 11, The traffic flow prediction unit, A traffic flow prediction device comprising the step of identifying the current traffic traffic status, and comparing the current traffic traffic status and the past speed change pattern to predict the traffic flow within a certain range of the congestion section. The method of claim 11, The traffic flow prediction unit, And a traffic flow prediction device predicting how long congestion will occur or the congestion will be resolved based on the speed change pattern. The method of claim 11, Traffic flow prediction device further comprising a guide for guiding the traffic flow prediction results.