CN117037482A - Real-time road traffic running state sensing method, system, equipment and storage medium - Google Patents
Real-time road traffic running state sensing method, system, equipment and storage medium Download PDFInfo
- Publication number
- CN117037482A CN117037482A CN202310995145.9A CN202310995145A CN117037482A CN 117037482 A CN117037482 A CN 117037482A CN 202310995145 A CN202310995145 A CN 202310995145A CN 117037482 A CN117037482 A CN 117037482A
- Authority
- CN
- China
- Prior art keywords
- road
- traffic
- risk
- target
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 66
- 238000010276 construction Methods 0.000 claims abstract description 11
- 208000027744 congestion Diseases 0.000 claims description 81
- 238000012549 training Methods 0.000 claims description 28
- 238000012360 testing method Methods 0.000 claims description 24
- 230000008447 perception Effects 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000012937 correction Methods 0.000 claims description 12
- 238000012795 verification Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 5
- 230000001960 triggered effect Effects 0.000 claims description 5
- 238000005314 correlation function Methods 0.000 claims description 4
- 230000008878 coupling Effects 0.000 claims description 4
- 238000010168 coupling process Methods 0.000 claims description 4
- 238000005859 coupling reaction Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 230000005284 excitation Effects 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- 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
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a real-time sensing method, a system, equipment and a storage medium for road traffic running states, wherein the method comprises the following steps: acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map; extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model; under the condition that the influence of uncertainty factors on the traffic running state is considered, whether the road is congested or not is determined by combining the first running risk and the second running risk, and the real-time performance and the accuracy of prediction are improved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method, a system, equipment and a storage medium for sensing road traffic running states in real time.
Background
In recent years, along with the rapid development of urban economy, the automobile conservation amount is rapidly increased, the travel demands of residents are rapidly increased, the running pressure of an urban traffic system is continuously increased, the response speed in the aspects of road facilities, traffic planning, management and the like cannot be timely kept up, the phenomenon of road congestion is frequently caused, the travel time cost of the residents is increased, in addition, the density of vehicles is high during road congestion, traffic accidents are easy to be caused, and traffic delay is further aggravated.
With the continuous development of detection equipment and data transmission functions, large-scale multidimensional and real-time traffic data can be rapidly acquired. In the prior art, a time sequence characteristic of traffic data is analyzed by using an artificial intelligence method, such as a neural network, a model is built to predict a short-term traffic situation, but most models do not reflect the influence of uncertainty factors on the traffic situation, and the accuracy and the real-time update rate are required to be improved.
Therefore, there is a need to propose a real-time road traffic state sensing method, system, device and storage medium capable of ensuring high real-time and accuracy in consideration of the influence of uncertainty factors on the road operation state.
Disclosure of Invention
Aiming at the technical problems, the invention aims to solve the problems that most models in the prior art do not reflect the influence of uncertainty factors on traffic situation, and the accuracy and the real-time update rate are to be improved.
In order to achieve the above object, the present invention provides a real-time sensing method for road traffic running state, the method comprising:
acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model;
defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area, and determining a second operation risk of the grid area corresponding to the key nodes according to the traffic information;
The first operation risk and the second operation risk are acquired, the first operation risk and the second operation risk are input into a congestion degree prediction model, the potential congestion risk of the road network infrastructure is determined based on the output result of the congestion degree prediction model, and real-time perception of the road traffic operation state is achieved.
Preferably, the obtaining the road network infrastructure based on the road network database includes:
extracting a road network macroscopic plan in a target area corresponding to world coordinate points (x, y) in a road network database and a traffic signal lamp layout in the target area according to the preset world coordinate points (x, y) based on the road network database;
placing the road network macroscopic plan and the traffic signal lamp layout on a two-dimensional coordinate axis, extracting key nodes of the road network macroscopic plan and the traffic signal lamp layout according to a preset rule, and numbering the key nodes according to the sequence;
defining the sequence number as z, generating a three-dimensional coordinate system (x, y, z) based on the sequence number result and the world coordinate point, and performing unidirectional connection on the key nodes according to the sequence number z to generate the road network infrastructure.
Preferably, extracting key nodes of the road network macroscopic plan and the traffic light layout, numbering the key nodes according to the sequence includes:
generating at least one target road section based on the road network macroscopic plan and the traffic signal lamp layout;
acquiring relevant information corresponding to each target road section in a historical database, wherein the relevant information comprises at least one of the following items: traffic flow, people's flow, road length, width, visibility are normalized to the relevant information, include:
wherein X is m Normalized coefficient representing mth data attribute, Z n Custom coefficients representing the nth data, Y mn An mth data attribute representing nth data, s representing the number of data attributes;
classifying traffic signal lamps corresponding to the target road sections based on the related information after normalization processing, specifically:
defining the state set corresponding to the normalized related information as { alpha } 1 ,α 2 ...,α n Probability of congestion risk of the target road section corresponding to each state is { p } 1 ,p 2 ...,p n And (3)The grading model of the traffic signal lamp corresponding to the target road section is constructed as follows:
c i ＝f(P i ,Q i ),(i＝1,2,3,...n)
wherein E is i Represents a target risk value, r represents a scale factor, c i Represents the number of road risk states, n represents a constant, and P i Vulnerability index representing target road segment, Q i The number of external threats of the target road section is represented;
the vulnerability index acquisition method of the target road section comprises the following steps:
wherein Y is 1 Represents the traffic flow, T represents the road length,representing visibility +.>Width in state;
if the obtained target risk value E i >X 1 When X is 1 Representing a first preset value, and taking a traffic signal lamp corresponding to the target road section as the key node;
if the obtained target risk value E i ≤X 1 Screening out traffic signal lamps corresponding to the target road sections;
according to the target risk value E i And grading the key nodes according to the size of the key nodes, sorting the key nodes according to the order from big to small or from small to big based on the grading result, and numbering the key nodes according to the sorting result.
Preferably, constructing a road traffic running state perception model based on the key node, and determining the first running risk of the key node includes:
obtaining coordinate value (x) of target key node 1 ,y 1 ,z 1 ) And coordinate values (x) of adjacent key nodes corresponding to the target key node 2 ,y 2 ,z 2 ),(x 3 ,y 3 ,z 3 ),...,(x m ,y m ,z m )；
Define x of critical nodes 1 ,x 2 ,...,x m ，z 1 ,z 2 ,...,z m For (x, y) coordinates on a two-dimensional coordinate system, (x) is calculated separately 1 ,z 1 ) And (x) 2 ,z 2 )...(x m ,z m ) Edge slope value between: k= | (a) 2 -b 2 )/(a 1 -b 1 ) I, wherein (a) 2 ,b 2 ) Representing target key node coordinates, (a) 1 ,b 1 ) Adjacent key node coordinates, k representing a critical edge slope value;
if the x values of the target key node and the adjacent key nodes are the same, selecting the y value of the key node as the x value on the two-dimensional coordinate system, namely calculating (y 1 ,z 1 ) And (y) m ,z m ) A critical edge slope value k between;
critical node critical edge slope k 1 ,k 2 ,...,k m Adding to obtain the sum K of the slope of the adjacent edges;
judging whether the key node is in triggering the road traffic running state sensing model based on the sum K of the critical edge slopes of the key node, wherein the method comprises the following steps:
when the sum K of the slope of the adjacent edges>X 2 In which X is 2 A second preset value is represented, and the road traffic running state perception model is triggered;
and determining the running state of the road network infrastructure through the road traffic running state perception model so as to determine the first running risk of the key node.
Preferably, determining, by the road traffic operation state awareness model, an operation state of the road network infrastructure to determine the first operation risk of the key node includes:
collecting related data information of the road traffic running state sensing model, and dividing the related data information into a training set, a testing set and a verification set according to a preset proportion, wherein the related data information comprises the number of key nodes of a target area and the influence value of random interference factors, and the preset proportion is 7:2:1;
The calculation formula of the influence value of the random interference factor is as follows:
wherein x is s The external flow of the target road section is represented, y (n) represents an impedance coefficient, l represents a set of the flow of people or vehicles leaving the target road section, when the set is an empty set, the impedance coefficient y (n) is 0, p is 1, H(s) represents a weight coefficient, and H (p) represents a correction function;
training the initial road traffic running state sensing model by using the training set, and testing and verifying the trained initial road traffic running state sensing model by using the testing set and the verifying set;
when the accuracy of the trained initial road traffic running state sensing model is larger than a third preset value, a final road traffic running state sensing model is obtained, and a calculation formula of the final road traffic running state sensing model comprises:
wherein X represents the operation efficiency, ω ε (0, 1) represents the node coupling coefficient, f (X) represents the state function, X (t) represents the node state quantity at time t, X (t+1) represents the node state quantity at time t+1, n represents the number of key nodes in the target area, and p i Representing risk excitation probability corresponding to target state, wherein a represents probability of state transition of key node in target time period, and k n Representing a correction function, and x represents a random interference factor influence value;
determining a first operational risk of the critical node based on the operational efficiency, comprising:
when the operation efficiency X is less than or equal to X 4 In which X is 4 And representing a fourth preset value, and defining the target operation efficiency as a first operation risk value of the key node.
Preferably, acquiring the second running risk of the grid region corresponding to the key node includes:
defining the target area, wherein each 4 key nodes of the target area generate a grid area, namely connecting two adjacent key nodes into a road section, and connecting four road sections to generate a grid area;
acquiring traffic information of the grid area, wherein the traffic information of the grid area comprises traffic flow and running average speed on each road section;
weighting and assigning the traffic flow and the running average speed by using an expert weighting method to obtain a first standard value S 1 And a second standard value S 2 ；
Based on the first standard value S 1 And a second standard value S 2 Average value a= (S 1 +S 2 ) Wherein A represents an average value, i.e., a flat value when the first and second standard values are equal to each otherMean A>X 5 When X is 5 And when the fifth preset value is represented, defining the average value A as a second running risk value.
Preferably, determining the potential congestion result of the road network infrastructure based on the first and second running risks comprises:
inputting the first operation risk and the second operation risk into a congestion degree prediction model, wherein the method for acquiring the congestion degree prediction model comprises the following steps:
the method comprises the steps of collecting relevant data information of a congestion degree prediction model, and dividing the relevant data information into a training set, a testing set and a verification set according to a preset proportion, wherein the relevant data information comprises a first operation risk value, a second operation risk value, time, key nodes and the number of grid areas, and the preset proportion is 8:1:1;
training the initial congestion degree prediction model by using the training set, and testing and verifying the trained initial congestion degree prediction model by using the testing set and the verifying set;
when the accuracy of the initial congestion degree prediction model after training is larger than a third preset value, a final congestion degree prediction model is obtained, and a calculation formula of the final congestion degree prediction model comprises:
wherein H (K) 1 ,K 2 ) Representing a correlation function, K 1 Represents a first running risk value, K 2 Representing a second running risk value, ω j Representing the correlation coefficient, u j Representing a correction coefficient, V representing time assignment, n representing the sum of the number of key nodes and grid areas, and T representing an output result;
output result T of the congestion degree prediction model in response to detection>X 6 Determining potential congestion results of target key nodes, wherein X 6 Indicating a sixth preset value.
A real-time road traffic state sensing system, the system comprising:
the road network infrastructure acquisition module is used for acquiring a road network infrastructure based on a road network database, wherein the data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
the first operation risk determining module is used for extracting key nodes in the road network infrastructure, constructing a road traffic operation state sensing model based on the key nodes, and determining the first operation risk of the key nodes according to the output result of the road traffic operation state sensing model;
the second operation risk determining module is used for defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area and determining the second operation risk of the grid area corresponding to the key nodes according to the traffic information;
The potential congestion result determining module is used for acquiring the first operation risk and the second operation risk, inputting the first operation risk and the second operation risk into a congestion degree prediction model, and determining the potential congestion risk of the road network infrastructure based on the output result of the congestion degree prediction model so as to realize real-time perception of the road traffic operation state.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when the computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
According to the technical scheme, the road traffic running state real-time sensing method, the system, the equipment and the storage medium provided by the application are used for determining whether the road is congested or not by combining the first running risk and the second running risk under the condition that the influence of uncertainty factors on the traffic running state is considered, so that the real-time performance and the accuracy of prediction are improved.
Additional features and advantages of the invention will be set forth in the detailed description which follows; and none of the inventions are related to the same or are capable of being practiced in the prior art.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate the invention and together with the description serve to explain, without limitation, the invention. In the drawings:
FIG. 1 is an application environment diagram of a real-time road traffic state sensing method of the present invention;
FIG. 2 is a flow chart of the real-time sensing method of road traffic running state according to the present invention;
FIG. 3 is a block diagram of a real-time road traffic state sensing system according to the present invention;
fig. 4 is an internal structural view of the computer device of the present invention.
Description of the reference numerals
102. A terminal; 104. and a server.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
In the present invention, unless otherwise indicated, terms such as "upper, lower, inner, outer" and the like are used merely to denote orientations of the term in a normal use state or are commonly understood by those skilled in the art, and should not be construed as limitations of the term.
Referring to fig. 1-4, the present application provides a method, a system, a device and a storage medium for sensing road traffic running states in real time: the real-time road traffic running state sensing method provided by the application can be applied to an application environment shown in figure 1; the terminal 102 communicates with a data processing platform disposed on the server 104 through a network, where the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
Example 1: in one embodiment, as shown in fig. 2, a method for sensing the running state of road traffic in real time is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes the following steps:
s1: acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: original road control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution position and road network control and planning map.
It should be noted that this step specifically includes:
Extracting a road network macroscopic plan of a target area and a traffic signal lamp layout diagram in the target area, wherein data in a road network database are obtained based on a corresponding road original control plan, a construction control plan, an industry planning instruction, a traffic signal lamp distribution position and the road network control plan, and the road network macroscopic plan comprises but is not limited to an electronic document and a data book;
extracting a road network macroscopic plan in a target area corresponding to world coordinate points (x, y) in a road network database and a traffic signal lamp layout in the target area according to the preset world coordinate points (x, y) based on the road network database;
placing the road network macroscopic plan and the traffic signal lamp layout on a two-dimensional coordinate axis, extracting key nodes of the road network macroscopic plan and the traffic signal lamp layout according to a preset rule, and numbering the key nodes according to the sequence;
defining the sequence number as z, generating a three-dimensional coordinate system (x, y, z) based on the sequence number result and the world coordinate point, and performing unidirectional connection on the key nodes according to the sequence number z to generate the road network infrastructure.
The method for extracting the key nodes of the road network macroscopic plan and the traffic signal lamp layout map comprises the following steps of:
generating at least one target road section based on the road network macroscopic plan and the traffic signal lamp layout, wherein the road between two adjacent traffic signal lamps is the target road section;
acquiring relevant information corresponding to each target road section in a historical database, wherein the relevant information comprises at least one of the following items: traffic flow, people's flow, road length, width, visibility are normalized to the relevant information, include:
wherein X is m Normalized coefficient representing mth data attribute, Z n Custom coefficients representing the nth data, Y mn An mth data attribute representing nth data, s representing the number of data attributes;
classifying traffic signal lamps corresponding to the target road sections based on the related information after normalization processing, specifically:
defining the state set corresponding to the normalized related information as { alpha } 1 ,α 2 ...,α n Probability of congestion risk of the target road section corresponding to each state is { p } 1 ,p 2 ...,p n And (3)The grading model of the traffic signal lamp corresponding to the target road section is constructed as follows:
c i ＝f(P i ,Q i ),(i＝1,2,3,...n)
Wherein E is i Represents a target risk value, r represents a scale factor, c i Representing the number of road risk statesN represents a constant, P i Vulnerability index representing target road segment, Q i The number of external threats of the target road section is represented;
the vulnerability index acquisition method of the target road section comprises the following steps:
wherein Y is 1 Represents the traffic flow, T represents the road length,representing visibility +.>Width in state;
if the obtained target risk value E i >X 1 When X is 1 Representing a first preset value, and taking a traffic signal lamp corresponding to the target road section as the key node;
if the obtained target risk value E i ≤X 1 Screening out traffic signal lamps corresponding to the target road sections;
according to the target risk value E i And grading the key nodes according to the size of the key nodes, sorting the key nodes according to the order from big to small or from small to big based on the grading result, and numbering the key nodes according to the sorting result.
S2: and extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model.
It should be noted that this step specifically includes:
obtaining coordinate value (x) of target key node 1 ,y 1 ,z 1 ) And coordinate values (x) of adjacent key nodes corresponding to the target key node 2 ,y 2 ,z 2 ),(x 3 ,y 3 ,z 3 ),...,(x m ,y m ,z m )；
Define x of critical nodes 1 ,x 2 ,...,x m ，z 1 ,z 2 ,...,z m For (x, y) coordinates on a two-dimensional coordinate system, (x) is calculated separately 1 ,z 1 ) And (x) 2 ,z 2 )...(x m ,z m ) Edge slope value between: k= | (a) 2 -b 2 )/(a 1 -b 1 ) I, wherein (a) 2 ,b 2 ) Representing target key node coordinates, (a) 1 ,b 1 ) Adjacent key node coordinates, k representing a critical edge slope value;
if the x values of the target key node and the adjacent key nodes are the same, selecting the y value of the key node as the x value on the two-dimensional coordinate system, namely calculating (y 1 ,z 1 ) And (y) m ,z m ) A critical edge slope value k between;
critical node critical edge slope k 1 ,k 2 ,...,k m Adding to obtain the sum K of the slope of the adjacent edges;
judging whether the key node is in triggering the road traffic running state sensing model based on the sum K of the critical edge slopes of the key node, wherein the method comprises the following steps:
when the sum K of the slope of the adjacent edges>X 2 In which X is 2 A second preset value is represented, and the road traffic running state perception model is triggered;
determining the running state of the road network infrastructure through the road traffic running state perception model so as to determine the first running risk of the key node; obtaining coordinate value (x) of target key node 1 ,y 1 ,z 1 ) And coordinate values (x) of adjacent key nodes corresponding to the target key node 2 ,y 2 ,z 2 ),(x 3 ,y 3 ,z 3 ),...,(x m ,y m ,z m )；
Define x of critical nodes 1 ,x 2 ,...,x m ，z 1 ,z 2 ,...,z m For (x, y) coordinates on a two-dimensional coordinate system, (x) is calculated separately 1 ,z 1 ) And (3) with(x 2 ,z 2 )...(x m ,z m ) Edge slope value between: k= | (a) 2 -b 2 )/(a 1 -b 1 ) I, wherein (a) 2 ,b 2 ) Representing target key node coordinates, (a) 1 ,b 1 ) Adjacent key node coordinates, k representing a critical edge slope value;
if the x values of the target key node and the adjacent key nodes are the same, selecting the y value of the key node as the x value on the two-dimensional coordinate system, namely calculating (y 1 ,z 1 ) And (y) m ,z m ) A critical edge slope value k between;
critical node critical edge slope k 1 ,k 2 ,...,k m Adding to obtain the sum K of the slope of the adjacent edges;
judging whether the key node is in triggering the road traffic running state sensing model based on the sum K of the critical edge slopes of the key node, wherein the method comprises the following steps:
when the sum K of the slope of the adjacent edges>X 2 In which X is 2 A second preset value is represented, and the road traffic running state perception model is triggered;
and determining the running state of the road network infrastructure through the road traffic running state perception model so as to determine the first running risk of the key node.
Further, determining, by the road traffic operation state awareness model, an operation state of the road network infrastructure to determine a first operation risk of the key node includes:
Collecting related data information of the road traffic running state sensing model, and dividing the related data information into a training set, a testing set and a verification set according to a preset proportion, wherein the related data information comprises the number of key nodes of a target area and the influence value of random interference factors, and the preset proportion is 7:2:1;
the calculation formula of the influence value of the random interference factor is as follows:
wherein x is s The external flow of the target road section is represented, y (n) represents an impedance coefficient, l represents a set of the flow of people or vehicles leaving the target road section, when the set is an empty set, the impedance coefficient y (n) is 0, p is 1, H(s) represents a weight coefficient, and H (p) represents a correction function;
training the initial road traffic running state sensing model by using the training set, and testing and verifying the trained initial road traffic running state sensing model by using the testing set and the verifying set;
when the accuracy of the trained initial road traffic running state sensing model is larger than a third preset value, a final road traffic running state sensing model is obtained, and a calculation formula of the final road traffic running state sensing model comprises:
wherein X represents the operation efficiency, ω ε (0, 1) represents the node coupling coefficient, f (X) represents the state function, X (t) represents the node state quantity at time t, X (t+1) represents the node state quantity at time t+1, n represents the number of key nodes in the target area, and p i Representing risk excitation probability corresponding to target state, wherein a represents probability of state transition of key node in target time period, and k n Representing a correction function, and x represents a random interference factor influence value;
determining a first operational risk of the critical node based on the operational efficiency, comprising:
when the operation efficiency X is less than or equal to X 4 In which X is 4 And representing a fourth preset value, and defining the target operation efficiency as a first operation risk value of the key node.
S3: and defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area, and determining a second operation risk of the grid area corresponding to the key nodes according to the traffic information.
It should be noted that this step specifically includes:
defining the target area, wherein each 4 key nodes of the target area generate a grid area, namely connecting two adjacent key nodes into a road section, and connecting four road sections to generate a grid area;
acquiring traffic information of the grid area, wherein the traffic information of the grid area comprises traffic flow and running average speed on each road section;
weighting and assigning the traffic flow and the running average speed by using an expert weighting method to obtain a first standard value S 1 And a second standard value S 2 ；
Based on the first standard value S 1 And a second standard value S 2 Average value a= (S 1 +S 2 ) Wherein A represents an average value, i.e. an average value A when the first and second standard values are>X 5 When X is 5 And when the fifth preset value is represented, defining the average value A as a second running risk value.
S4: the first operation risk and the second operation risk are acquired, the first operation risk and the second operation risk are input into a congestion degree prediction model, the potential congestion risk of the road network infrastructure is determined based on the output result of the congestion degree prediction model, and real-time perception of the road traffic operation state is achieved.
It should be noted that this step specifically includes:
inputting the first operation risk and the second operation risk into a congestion degree prediction model, wherein the method for acquiring the congestion degree prediction model comprises the following steps:
the method comprises the steps of collecting relevant data information of a congestion degree prediction model, and dividing the relevant data information into a training set, a testing set and a verification set according to a preset proportion, wherein the relevant data information comprises a first operation risk value, a second operation risk value, time, key nodes and the number of grid areas, and the preset proportion is 8:1:1;
Training the initial congestion degree prediction model by using the training set, and testing and verifying the trained initial congestion degree prediction model by using the testing set and the verifying set;
when the accuracy of the initial congestion degree prediction model after training is larger than a third preset value, a final congestion degree prediction model is obtained, and a calculation formula of the final congestion degree prediction model comprises:
wherein H (K) 1 ,K 2 ) Representing a correlation function, K 1 Represents a first running risk value, K 2 Representing a second running risk value, ω j Representing the correlation coefficient, u j Representing a correction coefficient, V representing time assignment, n representing the sum of the number of key nodes and grid areas, and T representing an output result;
output result T of the congestion degree prediction model in response to detection>X 6 Determining potential congestion results of target key nodes, wherein X 6 Indicating a sixth preset value.
And sending the real-time perceived potential congestion result to the user terminal so as to remind the user of avoiding the risk road section.
The first preset value, the second preset value, the third preset value, the fourth preset value, the fifth preset value and the sixth preset value in the above embodiment may be set according to actual requirements.
In the road traffic running state real-time sensing method, the method comprises the following steps: acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map; extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model; defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area, and determining a second operation risk of the grid area corresponding to the key nodes according to the traffic information; the method comprises the steps of acquiring the first operation risk and the second operation risk, inputting the first operation risk and the second operation risk into a congestion degree prediction model, determining potential congestion risk of the road network infrastructure based on an output result of the congestion degree prediction model, and realizing real-time perception of road traffic operation states.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows; the steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders; moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Example 2: in one embodiment, as shown in fig. 3, there is provided a road traffic running state real-time sensing system, comprising: the system comprises a road network infrastructure acquisition module, a first operation risk determination module, a second operation risk determination module and a potential congestion result determination module, wherein:
the road network infrastructure acquisition module is used for acquiring a road network infrastructure based on a road network database, wherein the data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
The first operation risk determining module is used for extracting key nodes in the road network infrastructure, constructing a road traffic operation state sensing model based on the key nodes, and determining the first operation risk of the key nodes according to the output result of the road traffic operation state sensing model;
the second operation risk determining module is used for defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area and determining the second operation risk of the grid area corresponding to the key nodes according to the traffic information;
the potential congestion result determining module is used for acquiring the first operation risk and the second operation risk, inputting the first operation risk and the second operation risk into a congestion degree prediction model, and determining the potential congestion risk of the road network infrastructure based on the output result of the congestion degree prediction model so as to realize real-time perception of the road traffic operation state.
As a preferred implementation manner, in the embodiment of the present invention, the road network infrastructure acquisition module is specifically configured to:
extracting a road network macroscopic plan in a target area corresponding to world coordinate points (x, y) in a road network database and a traffic signal lamp layout in the target area according to the preset world coordinate points (x, y) based on the road network database;
Placing the road network macroscopic plan and the traffic signal lamp layout on a two-dimensional coordinate axis, extracting key nodes of the road network macroscopic plan and the traffic signal lamp layout according to a preset rule, and numbering the key nodes according to the sequence;
defining the sequence number as z, generating a three-dimensional coordinate system (x, y, z) based on the sequence number result and the world coordinate point, and performing unidirectional connection on the key nodes according to the sequence number z to generate the road network infrastructure.
As a preferred implementation manner, in the embodiment of the present invention, the road network infrastructure acquisition module is specifically further configured to:
generating at least one target road section based on the road network macroscopic plan and the traffic signal lamp layout;
acquiring relevant information corresponding to each target road section in a historical database, wherein the relevant information comprises at least one of the following items: traffic flow, people's flow, road length, width, visibility are normalized to the relevant information, include:
wherein X is m Normalized coefficient representing mth data attribute, Z n Custom coefficients representing the nth data, Y mn An mth data attribute representing nth data, s representing the number of data attributes;
Classifying traffic signal lamps corresponding to the target road sections based on the related information after normalization processing, specifically:
defining the state set corresponding to the normalized related information as { alpha } 1 ,α 2 ...,α n Probability of congestion risk of the target road section corresponding to each state is { p } 1 ,p 2 ...,p n And (3)The grading model of the traffic signal lamp corresponding to the target road section is constructed as follows:
c i ＝f(P i ,Q i ),(i＝1,2,3,...n)
wherein E is i Represents a target risk value, r represents a scale factor, c i Represents the number of road risk states, n represents a constant, and P i Vulnerability index representing target road segment, Q i The number of external threats of the target road section is represented;
the vulnerability index acquisition method of the target road section comprises the following steps:
wherein Y is 1 Represents the traffic flow, T represents the road length,representing visibility +.>Width in state;
if the obtained target risk value E i >X 1 When X is 1 Representing a first preset value, and taking a traffic signal lamp corresponding to the target road section as the key node;
if the obtained target risk value E i ≤X 1 Screening out traffic signal lamps corresponding to the target road sections;
according to the target risk value E i And grading the key nodes according to the size of the key nodes, sorting the key nodes according to the order from big to small or from small to big based on the grading result, and numbering the key nodes according to the sorting result.
As a preferred implementation manner, in the embodiment of the present invention, the first running risk determining module is specifically further configured to:
obtaining coordinate value (x) of target key node 1 ,y 1 ,z 1 ) And coordinate values (x) of adjacent key nodes corresponding to the target key node 2 ,y 2 ,z 2 ),(x 3 ,y 3 ,z 3 ),...,(x m ,y m ,z m )；
Define x of critical nodes 1 ,x 2 ,...,x m ，z 1 ,z 2 ,...,z m For (x, y) coordinates on a two-dimensional coordinate system, (x) is calculated separately 1 ,z 1 ) And (x) 2 ,z 2 )...(x m ,z m ) Edge slope value between: k= | (a) 2 -b 2 )/(a 1 -b 1 ) I, wherein (a) 2 ,b 2 ) Representing target key node coordinates, (a) 1 ,b 1 ) Adjacent key node coordinates, k representing a critical edge slope value;
if the x values of the target key node and the adjacent key nodes are the same, selecting the y value of the key node as the x value on the two-dimensional coordinate system, namely calculating (y 1 ,z 1 ) And (y) m ,z m ) A critical edge slope value k between;
critical node critical edge slope k 1 ,k 2 ,...,k m Adding to obtain the sum K of the slope of the adjacent edges;
judging whether the key node is in triggering the road traffic running state sensing model based on the sum K of the critical edge slopes of the key node, wherein the method comprises the following steps:
when the sum K of the slope of the adjacent edges>X 2 In which X is 2 A second preset value is represented, and the road traffic running state perception model is triggered;
and determining the running state of the road network infrastructure through the road traffic running state perception model so as to determine the first running risk of the key node.
As a preferred implementation manner, in the embodiment of the present invention, the first running risk determining module is specifically further configured to:
collecting related data information of the road traffic running state sensing model, and dividing the related data information into a training set, a testing set and a verification set according to a preset proportion, wherein the related data information comprises the number of key nodes of a target area and the influence value of random interference factors, and the preset proportion is 7:2:1;
the calculation formula of the influence value of the random interference factor is as follows
Wherein x is s The external flow of the target road section is represented, y (n) represents an impedance coefficient, l represents a set of the flow of people or vehicles leaving the target road section, when the set is an empty set, the impedance coefficient y (n) is 0, p is 1, H(s) represents a weight coefficient, and H (p) represents a correction function;
training the initial road traffic running state sensing model by using the training set, and testing and verifying the trained initial road traffic running state sensing model by using the testing set and the verifying set;
when the accuracy of the trained initial road traffic running state sensing model is larger than a third preset value, a final road traffic running state sensing model is obtained, and a calculation formula of the final road traffic running state sensing model comprises:
Wherein X represents the operation efficiency, ω ε (0, 1) represents the node coupling coefficient, f (X) represents the state function, X (t) represents the node state quantity at time t, X (t+1) represents the node state quantity at time t+1, n represents the number of key nodes in the target area, and p i Representing risk excitation probability corresponding to target state, wherein a represents probability of state transition of key node in target time period, and k n Representing a correction function, and x represents a random interference factor influence value;
determining a first operational risk of the critical node based on the operational efficiency, comprising:
when the operation efficiency X is less than or equal to X 4 In which X is 4 And representing a fourth preset value, and defining the target operation efficiency as a first operation risk value of the key node.
As a preferred implementation manner, in the embodiment of the present invention, the second operation risk determining module is specifically configured to:
defining the target area, wherein each 4 key nodes of the target area generate a grid area, namely connecting two adjacent key nodes into a road section, and connecting four road sections to generate a grid area;
acquiring traffic information of the grid area, wherein the traffic information of the grid area comprises traffic flow and running average speed on each road section;
Weighting and assigning the traffic flow and the running average speed by using an expert weighting method to obtainFirst standard value S 1 And a second standard value S 2 ；
Based on the first standard value S 1 And a second standard value S 2 Average value a= (S 1 +S 2 ) Wherein A represents an average value, i.e. an average value A when the first and second standard values are>X 5 When X is 5 And when the fifth preset value is represented, defining the average value A as a second running risk value.
As a preferred implementation manner, in the embodiment of the present invention, the potential congestion result determining module is specifically configured to:
inputting the first operation risk and the second operation risk into a congestion degree prediction model, wherein the method for acquiring the congestion degree prediction model comprises the following steps:
the method comprises the steps of collecting relevant data information of a congestion degree prediction model, and dividing the relevant data information into a training set, a testing set and a verification set according to a preset proportion, wherein the relevant data information comprises a first operation risk value, a second operation risk value, time, key nodes and the number of grid areas, and the preset proportion is 8:1:1;
training the initial congestion degree prediction model by using the training set, and testing and verifying the trained initial congestion degree prediction model by using the testing set and the verifying set;
When the accuracy of the initial congestion degree prediction model after training is larger than a third preset value, a final congestion degree prediction model is obtained, and a calculation formula of the final congestion degree prediction model comprises:
wherein H (K) 1 ,K 2 ) Representing a correlation function, K 1 Represents a first running risk value, K 2 Representing a second running risk value, ω j Representing the correlation coefficient, u j Representing a correction coefficient, V representing time assignment, n representing the sum of the number of key nodes and grid areas, and T representing an output result;
responsive to detectingOutput result T to the congestion degree prediction model>X 6 Determining potential congestion results of target key nodes, wherein X 6 Indicating a sixth preset value.
The specific limitation of the real-time sensing system for road traffic operation state can be referred to the limitation of the real-time sensing method for road traffic operation state hereinabove, and will not be described herein. All or part of each module in the road traffic running state real-time sensing system can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Example 3: in one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 4; the computer equipment comprises a processor, a memory, a network interface, a display screen and an input device which are connected through a system bus; wherein the processor of the computer device is configured to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium, an internal memory; the non-volatile storage medium stores an operating system and a computer program; the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media; the network interface of the computer equipment is used for communicating with an external terminal through network connection; the computer program when executed by the processor is used for realizing a real-time sensing method of the road traffic running state; the display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
s1: acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
s2: extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model;
S3: defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area, and determining a second operation risk of the grid area corresponding to the key nodes according to the traffic information;
s4: the first operation risk and the second operation risk are acquired, the first operation risk and the second operation risk are input into a congestion degree prediction model, the potential congestion risk of the road network infrastructure is determined based on the output result of the congestion degree prediction model, and real-time perception of the road traffic operation state is achieved.
In one embodiment, the processor when executing the computer program further performs the steps of:
example 4: in one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
s1: acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
S2: extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model;
s3: defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area, and determining a second operation risk of the grid area corresponding to the key nodes according to the traffic information;
s4: the first operation risk and the second operation risk are acquired, the first operation risk and the second operation risk are input into a congestion degree prediction model, the potential congestion risk of the road network infrastructure is determined based on the output result of the congestion degree prediction model, and real-time perception of the road traffic operation state is achieved.
In one embodiment, the computer program when executed by the processor further performs the steps of:
those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the simple modifications belong to the protection scope of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described further.
Moreover, any combination of the various embodiments of the invention can be made without departing from the spirit of the invention, which should also be considered as disclosed herein.
Claims (10)
1. A method for real-time sensing of road traffic running state, the method comprising:
acquiring a road network infrastructure based on a road network database, wherein data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
Extracting key nodes in the road network infrastructure, constructing a road traffic running state sensing model based on the key nodes, and determining a first running risk of the key nodes according to an output result of the road traffic running state sensing model;
defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area, and determining a second operation risk of the grid area corresponding to the key nodes according to the traffic information;
the first operation risk and the second operation risk are acquired, the first operation risk and the second operation risk are input into a congestion degree prediction model, the potential congestion risk of the road network infrastructure is determined based on the output result of the congestion degree prediction model, and real-time perception of the road traffic operation state is achieved.
2. The method for real-time sensing the running state of road traffic according to claim 1, wherein the obtaining a road network infrastructure based on the road network database comprises:
extracting a road network macroscopic plan in a target area corresponding to world coordinate points (x, y) in a road network database and a traffic signal lamp layout in the target area according to the preset world coordinate points (x, y) based on the road network database;
Placing the road network macroscopic plan and the traffic signal lamp layout on a two-dimensional coordinate axis, extracting key nodes of the road network macroscopic plan and the traffic signal lamp layout according to a preset rule, and numbering the key nodes according to the sequence;
defining the sequence number as z, generating a three-dimensional coordinate system (x, y, z) based on the sequence number result and the world coordinate point, and performing unidirectional connection on the key nodes according to the sequence number z to generate the road network infrastructure.
3. The method of claim 2, wherein extracting key nodes of the road network macroscopic plan and traffic light layout, and numbering the key nodes in sequence comprises:
generating at least one target road section based on the road network macroscopic plan and the traffic signal lamp layout;
acquiring relevant information corresponding to each target road section in a historical database, wherein the relevant information comprises at least one of the following items: traffic flow, people's flow, road length, width, visibility are normalized to the relevant information, include:
wherein X is m Normalized coefficient representing mth data attribute, Z n Custom coefficients representing the nth data, Y mn An mth data attribute representing nth data, s representing the number of data attributes;
classifying traffic signal lamps corresponding to the target road sections based on the related information after normalization processing, specifically:
defining the state set corresponding to the normalized related information as { alpha } 1 ,α 2 ...,α n Probability of congestion risk of the target road section corresponding to each state is { p } 1 ,p 2 ...,p n And (3)The grading model of the traffic signal lamp corresponding to the target road section is constructed as follows:
c i ＝f(P i ,Q i ),(i＝1,2,3,...n)
wherein E is i Represents a target risk value, r represents a scale factor, c i Represents the number of road risk states, n represents a constant, and P i Vulnerability index representing target road segment, Q i The number of external threats of the target road section is represented;
the vulnerability index acquisition method of the target road section comprises the following steps:
wherein Y is 1 Represents the traffic flow, T represents the road length,representing visibility +.>Width in state;
if the obtained target risk value E i >X 1 When X is 1 Representing a first preset value, and taking a traffic signal lamp corresponding to the target road section as the key node;
if the obtained target risk value E i ≤X 1 Screening out traffic signal lamps corresponding to the target road sections;
according to the target risk value E i And grading the key nodes according to the size of the key nodes, sorting the key nodes according to the order from big to small or from small to big based on the grading result, and numbering the key nodes according to the sorting result.
4. The method of claim 3, wherein constructing a road traffic operating state awareness model based on the key nodes, determining a first operating risk of the key nodes comprises:
obtaining coordinate value (x) of target key node 1 ,y 1 ,z 1 ) And coordinate values (x) of adjacent key nodes corresponding to the target key node 2 ,y 2 ,z 2 ),(x 3 ,y 3 ,z 3 ),...,(x m ,y m ,z m )；
Define x of critical nodes 1 ,x 2 ,...,x m ，z 1 ,z 2 ,...,z m For (x, y) coordinates on a two-dimensional coordinate system, (x) is calculated separately 1 ,z 1 ) And (x) 2 ,z 2 )...(x m ,z m ) Edge slope value between: k= | (a) 2 -b 2 )/(a 1 -b 1 ) I, wherein (a) 2 ,b 2 ) Representing target key node coordinates, (a) 1 ,b 1 ) Adjacent key node coordinates, k representing a critical edge slope value;
if the x values of the target key node and the adjacent key nodes are the same, selecting the y value of the key node as the x value on the two-dimensional coordinate system, namely calculating (y 1 ,z 1 ) And (y) m ,z m ) A critical edge slope value k between;
critical node critical edge slope k 1 ,k 2 ,...,k m Adding to obtain the sum K of the slope of the adjacent edges;
judging whether the key node is in triggering the road traffic running state sensing model based on the sum K of the critical edge slopes of the key node, wherein the method comprises the following steps:
When the sum K of the slope of the adjacent edges>X 2 In which X is 2 A second preset value is represented, and the road traffic running state perception model is triggered;
and determining the running state of the road network infrastructure through the road traffic running state perception model so as to determine the first running risk of the key node.
5. The method of claim 4, wherein determining, by the road traffic state awareness model, the operational state of the road network infrastructure to determine the first operational risk of the critical node comprises:
collecting related data information of the road traffic running state sensing model, and dividing the related data information into a training set, a testing set and a verification set according to a preset proportion, wherein the related data information comprises the number of key nodes of a target area and the influence value of random interference factors, and the preset proportion is 7:2:1;
the calculation formula of the influence value of the random interference factor is as follows:
wherein x is s Represents the extraneous flow of the target road segment, y (n) represents the impedance coefficient, l represents the set of the traffic or the flow of people leaving the target road segment, when the setWhen the set is empty, the impedance coefficient y (n) is 0, p is 1, H(s) represents a weight coefficient, and H (p) represents a correction function;
Training the initial road traffic running state sensing model by using the training set, and testing and verifying the trained initial road traffic running state sensing model by using the testing set and the verifying set;
when the accuracy of the trained initial road traffic running state sensing model is larger than a third preset value, a final road traffic running state sensing model is obtained, and a calculation formula of the final road traffic running state sensing model comprises:
wherein X represents the operation efficiency, ω ε (0, 1) represents the node coupling coefficient, f (X) represents the state function, X (t) represents the node state quantity at time t, X (t+1) represents the node state quantity at time t+1, n represents the number of key nodes in the target area, and p i Representing risk excitation probability corresponding to target state, wherein a represents probability of state transition of key node in target time period, and k n Representing a correction function, and x represents a random interference factor influence value;
determining a first operational risk of the critical node based on the operational efficiency, comprising:
when the operation efficiency X is less than or equal to X 4 In which X is 4 And representing a fourth preset value, and defining the target operation efficiency as a first operation risk value of the key node.
6. The method of claim 5, wherein obtaining the second running risk of the grid region corresponding to the key node comprises:
Defining the target area, wherein each 4 key nodes of the target area generate a grid area, namely connecting two adjacent key nodes into a road section, and connecting four road sections to generate a grid area;
acquiring traffic information of the grid area, wherein the traffic information of the grid area comprises traffic flow and running average speed on each road section;
weighting and assigning the traffic flow and the running average speed by using an expert weighting method to obtain a first standard value S 1 And a second standard value S 2 ；
Based on the first standard value S 1 And a second standard value S 2 Average value a= (S 1 +S 2 ) Wherein A represents an average value, i.e. an average value A when the first and second standard values are>X 5 When X is 5 And when the fifth preset value is represented, defining the average value A as a second running risk value.
7. The method of claim 6, wherein determining a potential congestion result of the road network infrastructure based on the first and second operational risks comprises:
inputting the first operation risk and the second operation risk into a congestion degree prediction model, wherein the method for acquiring the congestion degree prediction model comprises the following steps:
The method comprises the steps of collecting relevant data information of a congestion degree prediction model, and dividing the relevant data information into a training set, a testing set and a verification set according to a preset proportion, wherein the relevant data information comprises a first operation risk value, a second operation risk value, time, key nodes and the number of grid areas, and the preset proportion is 8:1:1;
training the initial congestion degree prediction model by using the training set, and testing and verifying the trained initial congestion degree prediction model by using the testing set and the verifying set;
when the accuracy of the initial congestion degree prediction model after training is larger than a third preset value, a final congestion degree prediction model is obtained, and a calculation formula of the final congestion degree prediction model comprises:
wherein H (K) 1 ,K 2 ) Representing a correlation function, K 1 Represents a first running risk value, K 2 Representing a second running risk value, ω j Representing the correlation coefficient, u j Representing a correction coefficient, V representing time assignment, n representing the sum of the number of key nodes and grid areas, and T representing an output result;
output result T of the congestion degree prediction model in response to detection>X 6 Determining potential congestion results of target key nodes, wherein X 6 Indicating a sixth preset value.
8. A real-time road traffic state sensing system, the system comprising:
the road network infrastructure acquisition module is used for acquiring a road network infrastructure based on a road network database, wherein the data in the road network database is acquired based on at least one of the following: the method comprises the steps of road original control and planning map, construction control and planning map, industry planning instruction book, traffic signal lamp distribution positions and road network control and planning map;
the first operation risk determining module is used for extracting key nodes in the road network infrastructure, constructing a road traffic operation state sensing model based on the key nodes, and determining the first operation risk of the key nodes according to the output result of the road traffic operation state sensing model;
the second operation risk determining module is used for defining each 4 key nodes of the target area to generate a grid area, acquiring traffic information of road sections corresponding to the grid area and determining the second operation risk of the grid area corresponding to the key nodes according to the traffic information;
the potential congestion result determining module is used for acquiring the first operation risk and the second operation risk, inputting the first operation risk and the second operation risk into a congestion degree prediction model, and determining the potential congestion risk of the road network infrastructure based on the output result of the congestion degree prediction model so as to realize real-time perception of the road traffic operation state.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310995145.9A CN117037482A (en) | 2023-08-08 | 2023-08-08 | Real-time road traffic running state sensing method, system, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310995145.9A CN117037482A (en) | 2023-08-08 | 2023-08-08 | Real-time road traffic running state sensing method, system, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117037482A true CN117037482A (en) | 2023-11-10 |
Family
ID=88622045
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310995145.9A Pending CN117037482A (en) | 2023-08-08 | 2023-08-08 | Real-time road traffic running state sensing method, system, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117037482A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118015844A (en) * | 2024-04-10 | 2024-05-10 | 成都航空职业技术学院 | Traffic dynamic control method and system based on deep learning network |
-
2023
- 2023-08-08 CN CN202310995145.9A patent/CN117037482A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118015844A (en) * | 2024-04-10 | 2024-05-10 | 成都航空职业技术学院 | Traffic dynamic control method and system based on deep learning network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI746814B (en) | Computer readable medium, car insurance risk prediction device and server | |
| US20210342722A1 (en) | Air quality prediction model training method, air quality prediction method, electronic device and storage medium | |
| Boeing | Off the grid… and back again? The recent evolution of American street network planning and design | |
| Ma et al. | A real-time crack detection algorithm for pavement based on CNN with multiple feature layers | |
| Feng et al. | Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation | |
| CN105894372A (en) | Method and device for predicting group credit | |
| CN111091196B (en) | Passenger flow data determination method and device, computer equipment and storage medium | |
| CN113570867B (en) | Urban traffic state prediction method, device, equipment and readable storage medium | |
| Bai et al. | A forecasting method of forest pests based on the rough set and PSO-BP neural network | |
| CN117037482A (en) | Real-time road traffic running state sensing method, system, equipment and storage medium | |
| Mustafa et al. | A Time Monte Carlo method for addressing uncertainty in land-use change models | |
| WO2021169174A1 (en) | Road congestion degree prediction method, apparatus, computer device, and readable storage medium | |
| CN107798636A (en) | Building information processing method, device, computer equipment and storage medium | |
| CN115455130A (en) | Fusion method of social media data and movement track data | |
| CN115952355A (en) | Next interest point recommendation system based on space-time power law attention | |
| Lagarias | Exploring land use policy scenarios with the use of a cellular automata-based model: urban sprawl containment and sustainable development in Thessaloniki | |
| Guan et al. | Evaluating the impact of water protection policy on urban growth: A case study of Jiaxing | |
| Zhang et al. | Incorporation of spatial anisotropy in urban expansion modelling with cellular automata | |
| CN115758271A (en) | Data processing method, data processing device, computer equipment and storage medium | |
| CN115455276A (en) | Method and device for recommending object, computer equipment and storage medium | |
| CN111105144A (en) | Data processing method and device and target object risk monitoring method | |
| CN116279935B (en) | Vehicle dumping early warning method and device and computer equipment | |
| Zhou et al. | A maps-to-maps approach for simulating urban land expansion based on convolutional long short-term memory neural networks | |
| CN116205392A (en) | Method, device, computer equipment and storage medium for determining vehicle route | |
| CN115546119A (en) | Crack detection method and device based on prediction frame and computer equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |