KR20150072470A - System for analyzing dependence of spatiotemporal domain of traffic flow on the city and highway - Google Patents

System for analyzing dependence of spatiotemporal domain of traffic flow on the city and highway Download PDF

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KR20150072470A
KR20150072470A KR1020130158917A KR20130158917A KR20150072470A KR 20150072470 A KR20150072470 A KR 20150072470A KR 1020130158917 A KR1020130158917 A KR 1020130158917A KR 20130158917 A KR20130158917 A KR 20130158917A KR 20150072470 A KR20150072470 A KR 20150072470A
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data
heat map
graph
dependency
time
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KR1020130158917A
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김은이
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건국대학교 산학협력단
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed

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  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Abstract

The present invention relates to a system for analyzing the time and space domain dependency of the traffic flow in an urban or highway environment. The system analyzes the time and space domain dependency of the adjacent links or cone-zones in a road network in the urban or highway environment and provides the analysis of the time and space domain dependency by using quantifiable indicators. Accordingly, the present invention includes: a heat map generation unit which generates three dimensional heat maps displaying the traffic information acquired from the sensors installed on the roads on a time-space domain; a relation graph generation unit which generates the relation graph of the adjacent roads with the heat maps generated through the heat map generation unit; and a dependency estimation unit which estimates the dependency of the nodes in the graph generated by the relation graph generation unit.

Description

BACKGROUND OF THE INVENTION Field of the Invention The present invention relates to a system and method for analyzing the time and spatial domain dependency of a traffic flow in an urban area and a highway,

The present invention relates to a time and spatial domain dependency analysis system for traffic flows in urban areas and highways, and more particularly, to a system for analyzing time and spatial domain dependency of traffic flows in urban and highway roads. The present invention relates to a time and spatial domain dependency analysis system for traffic flows in urban areas and expressways, which analyze the dependency and display it as a quantified index.

Regarding the traffic flow forecasting system, there are a lot of public disclosures and registered in addition to Korean Patent Laid-Open No. 10-2009-0061384 (hereinafter referred to as "prior literature").

The above-mentioned prior art document includes a step of searching for a congestion period according to a time change using traffic statistical information; Extracting a normal-stagnant section in which a stagnation occurs repeatedly in the searched stagnant section; Determining a speed change pattern of a road affected by the extracted regular wetting period; And predicting a traffic flow based on the speed change pattern; .

However, there has been no system for analyzing the temporal and spatial domain dependency of traffic flow in urban areas and expressways, including prior art documents.

Korean Patent Publication No. 10-2009-0061384.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a system and method for analyzing dependence of adjacent roads (links, cone-zones) in a time domain and a spatial domain in a road network in a complicated urban center and a highway, The present invention has been made in view of the above problems.

In order to accomplish the above object, the present invention provides a system for analyzing time and spatial domain dependency of traffic flow in a city center and a highway, wherein a 3D heat map A heat map generating unit for generating a heat map; A relationship graph generation unit for generating a correlation graph between adjacent roads in the heat maps generated through the heat map generation unit; And a dependency estimator for estimating a dependency of the graph on the graph generated through the relational graph generator; .

The heat map generator may further include: a data collection module for collecting traffic data from a sensor on the road; A data filtering module for sampling data collected through the data collection module; And a heat map generation module for generating a three-dimensional heat map based on the data filtered through the data filtering module, using a temporal transition of a traffic flow in a congestion within a specific period; And a control unit.

The data filtering module may use the speed and logarithm data of the vehicle collected through the data collection module.

The relationship graph generation unit is characterized by expressing a correlation between adjacent roads in space-time with the connection strength of the inter-node edge.

According to the present invention as described above, the dependency in the spatiotemporal domain is numerically quantified, which can be used as an important index for matters such as traffic flow, traffic light control, and road extension.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an overall schematic diagram illustrating a system for analyzing time and spatial domain dependencies of traffic flows in a city center and a highway according to the present invention; FIG.
FIG. 2 is a diagram illustrating a result of generating a heat map using a geometric relationship according to the present invention; FIG.
FIG. 3 is an exemplary view showing a three-dimensional heat map according to the present invention. FIG.
FIG. 4 shows an example of a clique potential in the time domain according to the present invention (a) and an example (b) of a clique potential in the spatial domain.
FIG. 5 illustrates an example of a clique graph in time and space domains according to the present invention. FIG.
FIG. 6 is a diagram illustrating a connection relationship graph in a space-time domain between consonants according to the present invention; FIG.
7 is an example of the result of the session analysis according to the present invention.
FIG. 8 is a graph illustrating a graph of a connection strength estimation result between nodes according to the present invention; FIG.

Specific features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. It is to be noted that the detailed description of known functions and constructions related to the present invention is omitted when it is determined that the gist of the present invention may be unnecessarily blurred.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

The time and spatial domain dependency analysis system of the traffic flow in the urban area and the expressway according to the present invention will be described with reference to FIGS. 1 to 8. FIG.

FIG. 1 is a block diagram conceptually illustrating a time and spatial domain dependency analysis system S of a traffic flow in a city center and a highway according to the present invention. As shown in FIG. 1, the system includes a heat map generation unit 100, (200) and a dependency estimation unit (300).

The heat map generating unit 100 performs a function of generating a 3D heat map capable of efficiently representing traffic information obtained from a sensor on the road in the space-time domain. As shown in FIG. 1, Collection module 110, a data filtering module 120, and a heat map generation module 130.

Specifically, the data collection module 110 collects traffic data from the sensors on the road.

We collected traffic data from road construction to generate heat map. The collected data are divided into the Gyeongbu expressway area and the observation cone area is first divided based on this, and the geometric relation of the cone area is defined.

FIG. 2 shows an example of a result of generating a heat map using a geometric relationship. Fig. 2 (a) shows the result of specifying a list of consonants connecting major points and points on the map. The generated heat map based on this is shown in FIG. 2 (b).

In the present invention, traffic data obtained from road construction is used. The observation section of the road construction data is divided into the Gyeongbu Expressway and each section of the expressway by the Cone-zone. In the case of the congestion, several vehicle detection sensors (VDS) are attached to each lane sequentially according to the length of the section, and each VDS senses traffic flow information such as the average speed of the vehicle, the number of vehicle movements, .

The data obtained from the road construction is the data obtained from the VDS on the Gyeongbu Expressway by the time zone and the information on the conzon and VDS, and it is divided into monthly data obtained from January 2011 to June 2013. The database is designed to store the traffic flow data according to the conzon and the conzon. The tables designed for data storage are shown in [Table 1 ~ 3].

The table for storing the conzon information is designed as shown in [Table 1]. Each conzon is divided into IDs and the conzon ID is used as a primary key for distinguishing the data of the conzon data. The conzonation information includes the name of the cone and the length of the conzon (m units), the road direction information (direction toward Seoul / Busan), the start node and end node, the number of lanes, the number of lines, , A road grade code, and a device classification code.

[Table 1]

Figure pat00001

Table 2 shows the table that stores information about VDS. Each VDS is identified by ID, and VDS ID is used as a primary key for distinguishing data. The ID of the conzonation to which the VDS is attached, the branch point, the start point and end point of the VDS in the conzonation, the VDS type code, the detailed code name, the route construction order, and the length of the corresponding VDS zone.

[Table 2]

Figure pat00002

Table 3 shows traffic flow information detected from VDS. Since the VDS is attached to each lane at the same point, the basic data is divided into the ID created by combining the observation time, the VDS ID, and the VDS observation, and the combined ID is used as a basic key for distinguishing each observation data. VDS observation data consists of observation time, observation lane, traffic volume, occupancy rate and average speed.

[Table 3]

Figure pat00003

In the present invention, data obtained from road construction is parsed and stored in a DB using three tables of the designed DB. All KONBO information in the Gyeongbu Expressway was stored in the database, and the data of the VDS data attached to the KONBU Expressway was stored in 2013.

The data filtering module 120 samples the data collected through the data collection module 110.

Here, the data filtering module 120 samples and collects the collected VDS observation data on the average of one minute. At this time, 0 or negative data in observation data is regarded as VDS observation error or data error, and only observation data except this is used as valid data. Observed data are speed and number of moving vehicles. At this time, each VDS data is used by integrating the data observed in each lane.

The lane of the conzon used in the present invention integrates three VDS observations into three lanes. As a result of observations, there are empty VDS data in the same cone, and the number of moving vehicles is accumulated in three lanes and the speed is calculated by [Equation 1] and [Equation 2] below. Therefore, when there are all three lane values, the VDS value of the corresponding conzon is accumulated by accumulating the VDS data of each lane and the average value ([Equation 1]) is used. When there is no VDS observation data of some lanes, 2]). The average calculation formulas are as follows.

[Equation 1] is an equation for calculating the average by accumulating the speed of each lane and dividing by the total number of lanes. [Equation 2] means the harmonic mean of multiplying the speed of each lane by the number of vehicles and dividing the total lane by the number of moving vehicles. Where N passedcarinalllane is the number of all vehicles passing through that section, and N passedcarinlanel is the number of vehicles passing through that lane.

[Equation 1]

Figure pat00004

Where V is the velocity of the current VDS section, N lane is the number of lanes of the corresponding lane, and V l is the velocity of the lth lane.

[Equation 2]

Figure pat00005

Here, N passed car in all lane : The number of cars that passed by the lane.

The heat map generation module 130 generates a three-dimensional heat map based on the data filtered through the data filtering module 120 using the temporal transition of the traffic flow in the cornzon within a specific period.

In the given period, it shows the shape changed along the time axis (t) of the heat map. As shown in FIG. 3, such a three-dimensional heat map can represent more information than the graph of FIG.

The relationship graph generation unit 200 generates a correlation graph between adjacent roads in the heat maps generated through the heat map generation unit 100. That is, the relationship graph generating unit 200 generates a model for expressing the correlation in the space-time domain between adjacent roads.

The stochastic model is modeled as a Markov random field. In this case, A priori is defined as the dependency of the spatial domain, and the smoothness in each space and time domain is represented by the sum of the defined clique potentials.

In the present invention, the relationship with adjacent roads for the time and space domain dependency analysis is illustrated by various types of graphs.

In the graph, the consonance (or link) is represented by a single node, and current and future traffic flows can be predicted based on the traffic volume and the average speed of the adjacent consonance (or link) in the previous spatio-temporal domain. The correlation of the spatial / temporal adjacent roads in the graph is represented by the connection strength of the edges between the nodes.

Most of the cells in the heat map have similar colors (average speed) to adjacent cells, that is, geographically connected ones. This shows that when analyzing and predicting the road traffic flow, the correlation with neighboring conzones should be taken into account, not just considering a single concon. In addition, the correlation of the currently observed cells in the heat map is also observed in the temporal flow. A situation in which there are also consonants where sudden large changes occur can be interpreted as being caused by events in an accident or a nearby city. These characteristics show that traffic flow has Markovian property in spatio - temporal domain. Therefore, the heat map is represented by a heat map on a three-dimensional space that takes into consideration both temporal rate of change and spatial adjacency, rather than a one-dimensional space showing a rate of change in a simple time domain.

When there is missing data on the heat map, a process for predicting the moving number and average speed of the vehicle is performed. Most of the existing methods are predicted using the average speed in the time domain, or interpolation. These methods show good prediction results in the homogeneous domain, but show inaccurate results in areas where complex activity occurs. In order to solve the problems of the conventional method, a prediction system using a stochastic model such as an auto regressive model or a Markov random field model has been developed. These systems show good results, but require large amounts of computation time and are difficult to apply to real-time systems.

The clique that can be defined in the space-time domain is as shown in FIG.

The clique potential in the time domain is represented by (a) in FIG. There is one two-pair clique and four triple clique between t-1 and t,

Figure pat00006
and
Figure pat00007
Lt; / RTI >

Time domain

Figure pat00008
Is a four - way neighborhood. The clique potential on the spatial domain is shown in Figure 4 (b). At most times t, there were eight two-pair clique and eight triple clique. Respectively
Figure pat00009
Wow
Figure pat00010
Lt; / RTI >
Figure pat00011
Is the arm direction neighborhood.

In the cone region used in the present invention, the clique as shown in FIG. 5 was extracted. Figure 6 shows the extracted clique in graph form. Each node in the graph means the conzon, and the correlation between them is calculated through the weight. That is, the relation graph generating unit 200 analyzes the connection strength using the relation of [Equation 3].

[Equation 3]

Figure pat00012

Here, s and r are neighbor nodes in the spatial domain, and t and t-1 are neighbor nodes in the time domain.

The dependency estimation unit 300 estimates the dependency between the nodes of the graph generated through the relationship graph generation unit 200.

At this time, the dependency estimation unit 300 estimates the dependency between the nodes on the space time using the regression analysis.

Regression analysis is a technique for analyzing how one dependent variable is affected by one or more independent variables and in which relationship it is expressed. It reveals which statistical relationship the dependent variable represents by independent variables.

The present invention is applied to regression analysis to calculate the connection strength between clique-related conions. The input of the regression analysis is the input of the VDS data of the clone relation, and the reaction value for the corresponding time is input.

The correlation between input clique is first calculated through regression analysis, and the regression equation as shown in [Equation 4] is obtained.

[Equation 4]

Figure pat00013

here,

Figure pat00014
Is the weight obtained through regression analysis,
Figure pat00015
Means the neighboring conson of the present.

The results of the regression analysis using the data used in the present invention are shown in FIG. 7, and a graphical representation thereof is shown in FIG. That is, FIG. 7 is an example of the result of the session analysis, in which (a) is the number of vehicles and (b) is the speed. FIG. 8 is a graph of the inter-node connection strength estimation result, wherein (a) is the number of vehicles and (b) is the speed.

The heat map defined in the present invention represents a traffic flow diagram at a certain time. Each pixel of the heat map represents a probability of congestion in a link and a cone-zone corresponding to the corresponding region. In Equation (5), the VDS characteristic observed in the corresponding cone- Speed, occupancy rate, number of moving vehicles), and maps them to the congestion probability. In this case, t can be divided into monthly, weekly, hourly, and fractional.

[Equation 5]

Figure pat00016

The heat map generated in the present invention is used for analyzing the dependence on the traffic flow between the consonants. To this end, the heat map is generated using the traffic data for a given period from the road construction. Through statistical and probabilistic analysis of historical data, a traffic flow speculation model is defined. When a model is created, a 3D heat map is generated by estimating a traffic flow after K time based on observation data at a specific time t based on the generated model.

While the present invention has been particularly shown and described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be appreciated by those skilled in the art that numerous changes and modifications may be made without departing from the invention. Accordingly, all such appropriate modifications and changes, and equivalents thereof, should be regarded as within the scope of the present invention.

100: Heat map generator 200: Relationship graph generator
300: dependency estimation unit 110: data acquisition module
120: Data filtering module 130: Heat map generation module

Claims (4)

A heat map generating unit 100 for generating a 3D heat map capable of representing traffic information obtained from a sensor on the road in a space-time domain;
A relationship graph generation unit 200 for generating a correlation graph between adjacent roads in the heat maps generated through the heat map generation unit 100; And
A dependency estimator 300 for estimating a dependence of the graph on the graph generated through the relational graph generator 200; Time Domain and Spatial Domain Dependence Analysis of Traffic Flow in Urban and Highway.
The method according to claim 1,
The heat map generation unit 100 generates a heat map,
A data collection module 110 for collecting traffic data from sensors on the road;
A data filtering module 120 for sampling data collected through the data collection module 110; And
A heat map generation module 130 for generating a three-dimensional heat map based on the data filtered through the data filtering module 120 using the temporal transition of the traffic flow in the coronas within a specific period; Time domain and spatial domain dependency analysis of traffic flow in urban and highway.
3. The method of claim 2,
The data filtering module (120)
Wherein the speed and logarithm data of the vehicle collected through the data collection module (110) are used.
The method according to claim 1,
The relationship graph generating unit (200)
Wherein the correlation between the adjacent roads in time and space is expressed by the connection strength between the edges of the nodes.

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109064754A (en) * 2018-10-10 2018-12-21 南京宁昱通交通科技有限公司 A kind of expressway access shunts and flow Collaborative Control technology
CN111524358A (en) * 2020-05-22 2020-08-11 云南大学 Regional radiation sexual communication flow prediction method
CN113159044A (en) * 2021-04-01 2021-07-23 中国公路工程咨询集团有限公司 Deep learning-based road material identification method for convolutional neural network

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109064754A (en) * 2018-10-10 2018-12-21 南京宁昱通交通科技有限公司 A kind of expressway access shunts and flow Collaborative Control technology
CN109064754B (en) * 2018-10-10 2021-08-27 南京宁昱通交通科技有限公司 Expressway entrance diversion and flow cooperative control method
CN111524358A (en) * 2020-05-22 2020-08-11 云南大学 Regional radiation sexual communication flow prediction method
CN111524358B (en) * 2020-05-22 2022-04-15 云南大学 Regional radiation sexual communication flow prediction method
CN113159044A (en) * 2021-04-01 2021-07-23 中国公路工程咨询集团有限公司 Deep learning-based road material identification method for convolutional neural network

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