CN102708688A - Secondary fuzzy comprehensive discrimination-based urban road condition recognition method - Google Patents
Secondary fuzzy comprehensive discrimination-based urban road condition recognition method Download PDFInfo
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Abstract
The invention discloses a secondary fuzzy comprehensive discrimination-based urban road condition recognition method, and relates to intelligent traffic control. The method comprises the steps of urban road subsection division, real-time road traffic parameter reception, real-time road traffic state recognition and real-time and dynamic road traffic guiding panel display. An urban road subsection automatic division module automatically divides a road into two subsections, and the subsection division is determined by parameters such as a road length, the green time rate of traffic lights ahead, a road design saturation rate, road speed limit and a control parameter. A secondary fuzzy comprehensive urban road traffic state recognition module performs secondary fuzzy recognition on an urban road traffic state in real time on the basis of piecewise Gaussian membership function and a fuzzy mathematic theory. After secondary fuzzy comprehensive evaluation, an urban road condition recognition state is obtained and published on a road traffic guiding panel. Actual factors of an urban road are taken into full account, the load balancing of traffic is effectively realized, and the method has broad application prospect.
Description
Technical field
The invention belongs to the Computer Applied Technology field, particularly intelligent transportation control.
Background technology
Road traffic state mainly can be divided into " obstruction ", " crowding " and " unimpeded " three kinds of states.The purpose that road traffic state is passed judgment on is that the utilization certain method is analyzed real time traffic data, picks out the various traffic behaviors of road fast, for traffic control with induce foundation is provided.
The method of discrimination of road traffic state can be divided into two big types: artificial cognition method and automatic distinguishing method.The former comprises that citizen report, full-time staff's report and closed-circuit television supervision etc.The advantage of this method is convenient, direct, economical, and shortcoming is that the requirement there and then has the eyewitness, is difficult to 24 hours round-the-clock playing a role.The latter is the basis with information acquisition and treatment technology, computer technology and the communication technology, can round-the-clock watch-keeping road traffic state of living in, obtained increasing concern and tremendous development.
Domestic and international existing traffic behavior discrimination method; Major part is that the burst traffic events with highway is an object; And in urban transportation; Because receive the influence of intersection signal lamp and bicycle, its traffic stream characteristics is compared more complicated with highway, the difficulty that city traffic is differentiated automatically is bigger.The data processing method that city traffic adopted mainly comprises conventional methods such as decision tree, statistical study, smothing filtering.And the parameter index of the evolutionary process of traffic flow modes institute foundation own to change be a continuous process, the division between various states is also blured, the method for fuzzy discrimination is used for traffic flow modes differentiated and is more suitable for.Fuzzy discrimination generally adopts one-level to differentiate in the prior art, and one-level fuzzy discrimination method is to come that according to the criterion that the traffic characteristic parameter of entire road is followed maximum membership degree it is made traffic behavior to judge.Not enough below this method exists:
First; Do not consider to close in the urban road traffic lights highway section and be different to the influence of the traffic behavior of its respective stretch away from signal lamp road section traffic volume characteristic parameter; Therefore these traffic characteristic parameters comprise queue length, average velocity and lane occupancy ratio etc., and the accuracy of the traffic behavior that obtained of existing one-level fuzzy discrimination method remains further checking;
Second; During the choosing of the subordinate function in fuzzy traffic state judging; In order to simplify what mostly select for use is to fall half trapezoid formula to come linear expression; Ignore the Gaussian function and had no zero crossing and line smoothing, the clear characteristics that are more suitable for as the subordinate function of fuzzy discrimination of physical significance, rarer to traffic characteristic parameter subordinate function form and determination method for parameter in closing signal lamp highway section;
The 3rd; Owing to the reason of urban highway traffic crowding phenomenon is complicated; Every kind of traffic behavior all has certain similarity; Make traffic behavior divide to exist ambiguity, for when the unconspicuous situation of all degree of membership differences, only provide last result of determination and can not describe the objective ambiguity of traffic behavior according to following maximum membership grade principle.
The urban road state of the differentiation that for these reasons, existing traffic behavior discrimination method can not be objective and accurate.
Summary of the invention
The objective of the invention is deficiency, propose a kind of urban road traffic state discrimination method of differentiating based on the secondary fuzzy synthesis to conventional traffic state identification method.The urban road traffic state discrimination method that the present invention proposes takes into full account the traffic reality of urban road, makes up mathematical model to the automatic molecule section of urban road; Adopt no zero crossing and line smoothing and physical significance to be more suitable for Gaussian subordinate function clearly to each son section again, and then adopt the differentiation of secondary fuzzy synthesis as the characteristics of the subordinate function of fuzzy discrimination; The more objective and accurate identification of final realization urban road traffic state.
The objective of the invention is to reach like this: the urban road state is distinguished and is comprised that urban road section divides, receives in real time road traffic parameter, urban road traffic state real-time identification and road traffic automatically and induce screen Real time dynamic display several steps; Urban road section is divided in automatic the division in the module of urban road section automatically and carries out; Receive road traffic parameter in real time and in urban road section traffic parameter acquisition module, carry out, the real-time identification road traffic state carries out in secondary fuzzy synthesis urban road traffic state recognition module; Urban road section is divided module, the section traffic parameter collection of urban road and secondary fuzzy synthesis urban road traffic state recognition module automatically and is arranged in the processing server of intelligent traffic control system.
Urban road section is divided module automatically and is received from the urban road design parameter module parameter in the intelligent traffic control system; Completion is divided urban road section and results is sent to secondary fuzzy synthesis urban road traffic state recognition module; Secondary fuzzy synthesis urban road traffic state recognition module receives urban road section traffic parameter acquisition module simultaneously and carries out the urban road traffic state real-time identification through network from the video parameter and the urban road section results parameter of road collection in worksite point real-time Transmission; The real-time traffic states identification result is induced screen through the road traffic that network communication is published to intelligent traffic control system, and road traffic is induced screen Real-time and Dynamic issuing traffic state identification result.
It is that a road U is divided into two sub-section U1 and U2 automatically that said urban road section is divided automatically, and U1 and U2 divide and depend on that parameter comprises link length, front signal lamp split, highway layout saturation factor, road limits speed and controlled variable.
The secondary that said real-time identification road traffic state carries out in the theory that is based on segmentation Gauss subordinate function and fuzzy mathematics theory blurs distinguishes that step is:
(1), each road way section is set up evaluation object list set of factors
;
(2), set up evaluation collection
to each road way section;
(3), foundation, is derived single factor by the Cartesian product corresponding relation and is evaluated matrix
to the fuzzy relation mapping that son evaluation collects
from single set of factors
;
(4), first order fuzzy synthesis evaluation, select segmentation Gaussian Blur mathematical synthesis function to carry out comprehensive and it done normalization handle;
(5) secondary fuzzy synthesis evaluation;
(6), the secondary result of determination carried out fuzzy analysis judge, draw the result that the urban road state is distinguished.
A road U is divided into two sub-section U1 to the automatic division of urban road section automatically and U2 divides according to formula 1-1:
In the formula, dU1 and dU2 represent the length of way section U1 and U2 respectively, and d representes that entire road length, t represent the highway section
In the formula; DU1 and dU2 represent the length of way section U1 and U2 respectively; D representes that entire road length, t represent that highway section front signal lamp split, s represent that highway layout saturation factor, v represent road limits speed; A is the controlled variable with the total long correlation of road, and b is the controlled variable with the road limits velocity correlation.
The concrete steps of the automatic identification of said secondary fuzzy synthesis urban road traffic state are:
(1), each road way section is set up evaluation object list set of factors
; Ui=[L; V; D], wherein
representes that which highway section subclass, L represent highway section queue length ratio; V represents average speed, and D represents occupancy;
(2), set up evaluation collection
to each road way section;
; Wherein on behalf of the individual sub-highway section of road
,
belong to unimpeded state; On behalf of
individual sub-highway section,
belong to congestion state, and on behalf of
individual sub-highway section,
belong to blocked state;
(3), a fuzzy relation of setting up from single set of factors
to son evaluation collection
is shone upon; Arbitrary element
in single set of factors
the just corresponding element
in the Cartesian product
with
is unique corresponding; Derive single factor evaluation matrix
thus;
; Wherein
refers to the degree that sub-highway section
queue length is more unimpeded than being under the jurisdiction of, crowded and stop up;
refers to sub-highway section
speed and is under the jurisdiction of degree unimpeded, crowded and that stop up, and
refers to the degree that sub-highway section
occupancy is under the jurisdiction of unimpeded, crowded and obstruction;
(4), first order fuzzy synthesis evaluation; Select the segmentation Gaussian Blur mathematical synthesis function of each traffic characteristic amount to carry out comprehensively according to expertise and actual observation statistics; The weight allocation of each sub-highway section collection being represented this factor with the corresponding fuzzy set
in the set of factors
; Obtain the single combined factors evaluation collection of one-level
, and it is done normalization handle;
(5) secondary fuzzy synthesis evaluation; The previous stage evaluation is exported as evaluation matrix
; Is
with each sub-highway section to the weight fuzzy subset of whole road, then can obtain secondary fuzzy assessment output
;
(6), the secondary result of determination being carried out fuzzy analysis judges; Draw the result that urban road traffic state is distinguished: set a threshold value
; Any
all met the requirements; When a value is only arranged in
greater than
, its normalizing is arrived pairing traffic behavior; Value as
is during all greater than
, with its normalizing to " unimpeded/crowded " critical conditions; Value as
is during all greater than
, with its normalizing to " crowded/as to stop up " critical conditions.
The present invention has following advantage:
(1) taken into full account the influence of urban road practical factor, realized more objective and accurate urban road traffic state identification, dynamically controlling and induce for municipal intelligent traffic provides more effective information support.It is orderly that thereby the load balancing, the alleviation that realize urban highway traffic are blocked up, had a good transport and communication network, for the urban highway traffic harmony is laid a good foundation.
(2) the identification algorithm counting yield is high, and application prospect is extensive.
Description of drawings
Fig. 1 is at the present invention's structural representation in intelligent traffic control system.
Fig. 2 be among the embodiment the second son section to average vehicle queue length than segmentation Gauss subordinate function synoptic diagram.
Fig. 3 be among the embodiment the second son section to average velocity segmentation Gauss subordinate function synoptic diagram.
Fig. 4 be among the embodiment the second son section to lane occupancy segmentation Gauss subordinate function synoptic diagram.
Fig. 5 is the dynamic displayed map that road traffic is induced screen.Among the figure, the actual redness of inducing in the screen of solid line representative, the traffic behavior that expression is stopped up; The actual yellow of inducing in the screen of dotted line representative, expression congested traffic state; The actual green of inducing in the screen of dot-and-dash line representative is represented unimpeded traffic behavior.
Embodiment
Referring to accompanying drawing 1.It is core content of the present invention that urban road section is divided module and secondary fuzzy synthesis urban road traffic state recognition module automatically, accomplishes through a processing server.Every urban road of urban road design parameter load module foundation all to parameters such as entire road length, highway section front signal lamp split, highway layout saturation factor and road limits speed should be arranged, forms configuration file according to actual cities road traffic road network and is stored in the processing server when planning and design.The automatic module of dividing of urban road section combines automatic mathematical model of dividing that urban road is carried out the division of son section according to the urban road design parameter again.Urban road section traffic parameter is gathered according to existing ripe video acquisition analytical technology and is realized, is real-time transmitted to processing server through network from road collection in worksite point.Secondary fuzzy synthesis urban road traffic state recognition module adopts segmentation Gauss's subordinate function and fuzzy mathematics theory to realize.Road traffic induces the dynamic issuing traffic block of state of screen to accept the real-time traffic states identification result through network communication, is used for the crucial on-the-spot traffic guidance of realizing of actual cities road traffic.
In the practical implementation process, the distinguishing of entire city road condition comprises that the way section divides, receives in real time road traffic parameter, real-time identification road traffic state and road traffic automatically and induce screen Real time dynamic display several steps.
The first step: road way section is divided automatically
In the present embodiment; Set the parameter of certain urban road design: 40 kilometers/hour of maximum speed limits, traffic lights cycle be that 60 seconds and split are 1/2, the road length overall is that 1 kilometer, road saturation factor are 0.4; And controlled variable a and b among the formula of the setting 1-1 are respectively 3 and 10 kilometers/hour, according to formula 1-1
（1-1）
Calculate the way section respectively length be: dU1=0.32 kilometer dU2=0.68 kilometer.
Second step: receive road traffic parameter in real time
The road traffic parameter obtain manner is to set up video camera in road the place ahead to obtain the road real-time video, is divided in calibration position in the video according to road way section, adopts existing video analysis treatment technology to obtain sub-section real-time traffic parameter respectively.Present embodiment is got 5 minutes average vehicle queue length ratio, average speed, lane occupancies.
The 3rd step: urban road traffic state real-time identification
In the process of real-time identification road traffic state, according to following 6 the step carry out:
(1), each road way section is set up evaluation object list set of factors
; Ui=[L; V; D], wherein
representes that which highway section subclass, L represent highway section queue length ratio; V represents average speed, and D represents occupancy;
(2), set up evaluation collection
to each road way section;
; Wherein on behalf of the individual sub-highway section of road
,
belong to unimpeded state; On behalf of
individual sub-highway section,
belong to congestion state, and on behalf of
individual sub-highway section,
belong to blocked state;
(3), a fuzzy relation of setting up from single set of factors
to son evaluation collection
is shone upon; Arbitrary element
in single set of factors
the just corresponding element
in the Cartesian product
with
is unique corresponding; Derive single factor evaluation matrix
thus;
; Wherein
refers to the degree that sub-highway section
queue length is more unimpeded than being under the jurisdiction of, crowded and stop up;
refers to sub-highway section
speed and is under the jurisdiction of degree unimpeded, crowded and that stop up, and
refers to the degree that sub-highway section
occupancy is under the jurisdiction of unimpeded, crowded and obstruction;
(4), first order fuzzy synthesis evaluation; Select the segmentation Gaussian Blur mathematical synthesis function of each traffic characteristic amount to carry out comprehensively according to expertise and actual observation statistics; The weight allocation of each sub-highway section collection being represented this factor with the corresponding fuzzy set
in the set of factors
; Obtain the single combined factors evaluation collection of one-level
, and it is done normalization handle;
(5) secondary fuzzy synthesis evaluation; The previous stage evaluation is exported as evaluation matrix
; Is
with each sub-highway section to the weight fuzzy subset of whole road, then can obtain secondary fuzzy assessment output
;
(6), the secondary result of determination being carried out fuzzy analysis judges; Draw the result that urban road traffic state is distinguished: set a threshold value
; Any
all met the requirements; When a value is only arranged in
greater than
, its normalizing is arrived pairing traffic behavior; Value as
is during all greater than
, with its normalizing to " unimpeded/crowded " critical conditions; Value as
is during all greater than
, with its normalizing to " crowded/as to stop up " critical conditions.
The segmentation Gauss subordinate function of the average vehicle queue length ratio in the present embodiment second sub-highway section, average velocity, lane occupancy is like accompanying drawing 2,3, shown in 4.
The 4th step: road traffic is induced the screen Real time dynamic display
At the road traffic condition that the secondary result of determination is carried out draw after fuzzy analysis is judged, be sent to road traffic and induce screen to go up demonstration in real time, be divided into red, yellow, green three kinds of colors and show obstruction, crowded and unimpeded situation respectively.
Accompanying drawing 5 has provided the road traffic of present embodiment and has induced the screen Real time dynamic display.Actual traffic is induced the traffic behavior that red expression is stopped up in the screen, yellow expression congested traffic state, the unimpeded concrete situation of green expression.Accompanying drawing is induced the redness in the screen with the solid line that represent traffic, and the dotted line that represent traffic is induced the yellow in the screen, and the dot-and-dash line that represent traffic is induced the green in the screen.Road traffic condition comes into plain view, and the participant provides great convenience for road traffic.
Claims (3)
1. method that the urban road state of differentiating based on the secondary fuzzy synthesis is distinguished is characterized in that: the method for distinguishing comprises that urban road section divides, receives in real time road traffic parameter, urban road traffic state real-time identification and road traffic automatically and induce screen Real time dynamic display several steps; Urban road section is divided in automatic the division in the module of urban road section automatically and carries out; Receive road traffic parameter in real time and in urban road section traffic parameter acquisition module, carry out, the urban road traffic state real-time identification is carried out in secondary fuzzy synthesis urban road traffic state recognition module; Urban road section is divided module, the section traffic parameter collection of urban road and secondary fuzzy synthesis urban road traffic state recognition module automatically and is arranged in the processing server of intelligent traffic control system;
Urban road section is divided module automatically and is received the parameter from the urban road design parameter module in the intelligent traffic control system; Completion is divided urban road section and results is sent to secondary fuzzy synthesis urban road traffic state recognition module; Secondary fuzzy synthesis urban road traffic state recognition module receives urban road section traffic parameter acquisition module simultaneously and carries out the urban road traffic state real-time identification through network from the video parameter and the urban road section results parameter of road collection in worksite point real-time Transmission; The real-time traffic states identification result is induced screen through the road traffic that network communication is published to intelligent traffic control system, and road traffic is induced screen Real-time and Dynamic issuing traffic state identification result;
It is that a road U is divided into two sub-section U1 and U2 automatically that said urban road section is divided automatically, and U1 and U2 divide and depend on that parameter comprises link length, front signal lamp split, highway layout saturation factor, road limits speed and controlled variable;
Secondary that segmentation Gauss subordinate function and fuzzy mathematics theory carry out is fuzzy distinguishes that step is to said real-time identification road traffic state being based on:
(1), each road way section is set up evaluation object list set of factors
;
(2), set up evaluation collection
to each road way section;
(3), foundation, is derived single factor by the Cartesian product corresponding relation and is evaluated matrix
to the fuzzy relation mapping that son evaluation collects
from single set of factors
;
(4), first order fuzzy synthesis evaluation, select segmentation Gaussian Blur mathematical synthesis function to carry out comprehensive and it done normalization handle;
(5) secondary fuzzy synthesis evaluation;
(6), the secondary result of determination carried out fuzzy analysis judge, draw the result that the urban road state is distinguished.
2. the method that urban road state as claimed in claim 1 is distinguished is characterized in that: a road U is divided into two sub-section U1 to the automatic division of said urban road section automatically and U2 divides according to formula 1-1:
In the formula; DU1 and dU2 represent the length of way section U1 and U2 respectively; D representes that entire road length, t represent that highway section front signal lamp split, s represent that highway layout saturation factor, v represent road limits speed; A is the controlled variable with the total long correlation of road, and b is the controlled variable with the road limits velocity correlation.
3. the method that urban road state as claimed in claim 1 is distinguished is characterized in that: the concrete steps of the automatic identification of said secondary fuzzy synthesis urban road traffic state are:
(1), each road way section is set up evaluation object list set of factors
; Ui=[L; V; D], wherein
representes that which highway section subclass, L represent highway section queue length ratio; V represents average speed, and D represents occupancy;
(2), set up evaluation collection
to each road way section;
; Wherein on behalf of the individual sub-highway section of road
,
belong to unimpeded state; On behalf of
individual sub-highway section,
belong to congestion state, and on behalf of
individual sub-highway section,
belong to blocked state;
(3), a fuzzy relation of setting up from single set of factors
to son evaluation collection
is shone upon; Arbitrary element
in single set of factors
the just corresponding element
in the Cartesian product
with
is unique corresponding; Derive single factor evaluation matrix
thus;
; Wherein
refers to the degree that sub-highway section
queue length is more unimpeded than being under the jurisdiction of, crowded and stop up;
refers to sub-highway section
speed and is under the jurisdiction of degree unimpeded, crowded and that stop up, and
refers to the degree that sub-highway section
occupancy is under the jurisdiction of unimpeded, crowded and obstruction;
(4), first order fuzzy synthesis evaluation; Select the segmentation Gaussian Blur mathematical synthesis function of each traffic characteristic amount to carry out comprehensively according to expertise and actual observation statistics; The weight allocation of each sub-highway section collection being represented this factor with the corresponding fuzzy set
in the set of factors
; Obtain the single combined factors evaluation collection of one-level
, and it is done normalization handle;
(5) secondary fuzzy synthesis evaluation; The previous stage evaluation is exported as evaluation matrix
; Is
with each sub-highway section to the weight fuzzy subset of whole road, then can obtain secondary fuzzy assessment output
;
(6), the secondary result of determination being carried out fuzzy analysis judges; Draw the result that urban road traffic state is distinguished: set a threshold value
; Any
all met the requirements; When a value is only arranged in
greater than
, its normalizing is arrived pairing traffic behavior; Value as
is during all greater than
, with its normalizing to " unimpeded/crowded " critical conditions; Value as
is during all greater than
, with its normalizing to " crowded/as to stop up " critical conditions.
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| CN106790585A (en) * | 2016-12-28 | 2017-05-31 | 芜湖乐锐思信息咨询有限公司 | road traffic resource management system based on cloud computing |
| CN106530723A (en) * | 2016-12-28 | 2017-03-22 | 芜湖乐锐思信息咨询有限公司 | Traffic navigation system based on parallel data excavation |
| CN106600963A (en) * | 2016-12-28 | 2017-04-26 | 芜湖乐锐思信息咨询有限公司 | Travel path management system based on travel vehicle data mining |
| CN106781482A (en) * | 2016-12-28 | 2017-05-31 | 芜湖乐锐思信息咨询有限公司 | Novel intelligent traffic data processing system |
| CN106790141A (en) * | 2016-12-28 | 2017-05-31 | 芜湖乐锐思信息咨询有限公司 | New road traffic resource management information system based on cloud computing |
| CN106781483A (en) * | 2016-12-28 | 2017-05-31 | 芜湖乐锐思信息咨询有限公司 | Intelligent traffic management systems |
| CN106485932A (en) * | 2016-12-28 | 2017-03-08 | 芜湖乐锐思信息咨询有限公司 | Highly effective path management system |
| CN106710259A (en) * | 2017-02-18 | 2017-05-24 | 山东交通学院 | Lane occupancy rate warning method |
| CN108388540A (en) * | 2018-03-01 | 2018-08-10 | 兰州交通大学 | Road network Algorithms of Selecting based on fuzzy overall evaluation |
| CN110428628A (en) * | 2019-08-31 | 2019-11-08 | 招商局重庆交通科研设计院有限公司 | Road traffic abductive approach |
| CN114882696A (en) * | 2020-10-28 | 2022-08-09 | 华为技术有限公司 | Method and device for determining road capacity and storage medium |
| CN114882696B (en) * | 2020-10-28 | 2023-11-03 | 华为技术有限公司 | Road capacity determination method, device and storage medium |
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