CN102708688B - 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 Computer Applied Technology field, particularly intelligent transportation is controlled.
Background technology
Road traffic state mainly can be divided into " obstruction ", " crowded " and " unimpeded " three kinds of states.The object that road traffic state is passed judgment on is to use certain method to analyze real time traffic data, and Fast Identification goes out the various traffic behaviors of road, for traffic control and induction provide foundation.
The method of discrimination of road traffic state can be divided into two large classes: 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 requirement there and then has eyewitness, is difficult to 24 hours round-the-clock playing a role.It is basis that the latter be take information acquisition and treatment technology, computer technology and the communication technology, can round-the-clock watch-keeping road traffic status, obtained increasing concern and tremendous development.
Domestic and international existing traffic behavior discrimination method, major part is that to take the burst traffic events of highway be object, and in urban transportation, owing to being subject to the impact of Intersections and bicycle, its traffic stream characteristics is compared more complicated with highway, the difficulty of urban traffic status automatic discrimination is larger.The data processing method that urban traffic status adopts mainly comprises the conventional methods such as decision tree, statistical study, smothing filtering.And the variation of the parameter index of the evolutionary process of traffic flow modes institute foundation own is a continuous process, the division between various states is also fuzzy, and the method for fuzzy discrimination is used for traffic flow modes to differentiate and be more suitable for.Fuzzy discrimination generally adopts one-level to differentiate in the prior art, and one-level fuzzy discrimination method is that the criterion of following maximum membership degree according to the traffic characteristic parameter of entire road is made traffic behavior to it and judged.This method has the following disadvantages:
First, do not consider to close in urban road traffic lights section and on the impact of the traffic behavior of its respective stretch, be different away from signal lamp road section traffic volume characteristic parameter, these traffic characteristic parameters comprise queue length, average velocity and lane occupancy ratio etc., and the accuracy of the traffic behavior that therefore existing one-level fuzzy discrimination method obtains needs further to be verified;
Second, during the choosing of the subordinate function in fuzzy traffic state judging, in order to simplify what mostly select, be to fall half trapezoid formula to carry out linear expression, ignored Gauss type function and had without zero crossing and line smoothing, the clear feature being more suitable for as the subordinate function of fuzzy discrimination of physical significance, rarer for traffic characteristic parameter subordinate function form and determination method for parameter in closing signal lamp section;
The 3rd, because the reason of urban highway traffic crowding phenomenon is complicated, every kind of traffic behavior has certain similarity, traffic behavior is divided and had ambiguity, for when the unconspicuous situation of all degree of membership differences, only according to following maximum membership grade principle, provide last result of determination and can not describe the objective ambiguity of traffic behavior.
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 object of the invention is the deficiency for conventional traffic state identification method, propose a kind of urban road traffic state discrimination method of differentiating based on Two-stage Fuzzy Comprehensive.The urban road traffic state discrimination method that the present invention proposes takes into full account the traffic reality of urban road, builds mathematical model and automatically divides subsegment to urban road; For each subsegment, adopt without zero crossing and line smoothing and physical significance and be more suitable for clearly the Gaussian subordinate function as the feature of the subordinate function of fuzzy discrimination again, and then adopt Two-stage Fuzzy Comprehensive to differentiate; Finally realize the more objective and accurate identification of urban road traffic state.
The object of the invention is to reach like this: urban road state is distinguished and comprised that urban road subsegment divides, receives in real time road traffic parameter, urban road traffic state real-time identification and the several steps of road traffic induced screen Real time dynamic display automatically; Urban road subsegment is automatically divided in urban road subsegment and automatically divides in module and carry out, receive in real time road traffic parameter and carry out in urban road subsegment traffic parameter acquisition module, real-time identification road traffic state carries out in Two-stage Fuzzy Comprehensive urban road traffic state recognition module; Urban road subsegment is automatically divided module, the traffic parameter collection of urban road subsegment and Two-stage Fuzzy Comprehensive urban road traffic state recognition module and is arranged in the processing server of intelligent traffic control system.
Urban road subsegment is automatically divided module and is received from the designing of city road parameter module parameter in intelligent traffic control system, complete urban road subsegment is divided and division result is sent to Two-stage Fuzzy Comprehensive urban road traffic state recognition module, Two-stage Fuzzy Comprehensive urban road traffic state recognition module receives urban road subsegment traffic parameter acquisition module simultaneously and from video parameter and the urban road subsegment division result parameter of road collection in worksite point real-time Transmission, carries out urban road traffic state real-time identification by network, real-time traffic states identification result is published to the road traffic induced screen of intelligent traffic control system by network communication, the in real time dynamic issuing traffic state identification result of road traffic induced screen.
It is that a road U is divided into two subsegment U1 and U2 automatically that described urban road subsegment 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 controls parameter.
Described real-time identification road traffic state is being fuzzy the distinguishing of secondary that the theory based on segmentation Gauss member function and fuzzy mathematics theory is carried out, and step is:
(1), each road subsegment is set up to evaluation object list set of factors U
i;
(2), for each road subsegment, set up evaluation collection F
i;
(3), set up from single set of factors U
ito evaluation collection F
ithe mapping of fuzzy relation, by Cartesian product corresponding relation, derive single factor evaluation matrix R
i
(4), first order Fuzzy Synthetic Evaluation, select segmentation Gaussian Blur mathematical synthesis function carry out comprehensive and made normalized;
(5) Two-stage Fuzzy Comprehensive Evaluation;
(6), secondary result of determination is carried out to fuzzy analysis judgement, draw the result that urban road state is distinguished.
A road U is divided into two subsegment U1 to the automatic division of described urban road subsegment automatically and U2 divides according to formula 1-1:
In formula, d
u1and d
u2the length that represents respectively road subsegment U1 and U2,
represent entire road length, t represent section front signal lamp split, s represent highway layout saturation factor,
represent road limits speed, a is the control parameter with the total long correlation of road, and b is the control parameter with road limits velocity correlation.
The concrete steps of described Two-stage Fuzzy Comprehensive urban road traffic state automatic Identification are:
(1), each road subsegment is set up to evaluation object list set of factors U
i; U
i=[L, V, D], i=1 wherein, which section subsets 2 represent, and L represents road queue length ratio, and V represents average speed, and D represents occupancy;
(2), for each road subsegment, set up evaluation collection F
i; F
i=[f
i1, f
i2, f
i3], f wherein
i1represent that road i sub-section belongs to unimpeded state, f
i2represent that i sub-section belongs to congestion state, f
i3represent that i sub-section belongs to blocked state;
(3), set up from single set of factors U
ito evaluation collection F
ithe mapping of fuzzy relation, so the arbitrary element u in set of factors just and L, V, Cartesian product L * V * D={ (l, v, d) of D | l ∈ L, v ∈ V, the unique correspondence of corresponding element (l, v, d) in d ∈ D}, derives thus single factor and evaluates matrix R
i, R
i=[R
i1, R
i2, R
i3]
t, R wherein
i1refer to the degree that the i queue length of sub-section is more unimpeded than being under the jurisdiction of, crowded and stop up, R
i2refer to sub-section i speed and be under the jurisdiction of degree unimpeded, crowded and that stop up, R
i3refer to sub-section i occupancy and be under the jurisdiction of degree unimpeded, crowded and that stop up, wherein, l represents that queue length represents that than variable-value, v average speed variable-value, d represent occupancy variable-value;
(4), first order Fuzzy Synthetic Evaluation, select suitable segmentation Gaussian Blur mathematical synthesis function to carry out comprehensively, to each sub-section single set of factors U for collection
iinterior corresponding fuzzy set A
i=[a
i1, a
i2, a
i3] represent the weight allocation of this factor,
Wherein, the sequence number 1 or 2 in i section that desirable section is divided into, a
i1, a
i2, a
i3represent respectively the sub-section of i queue length ratio, average speed, occupancy shared proportion in fuzzy assessment, obtain one-level list combined factors evaluation collection B
i=[b
i1, b
i2, b
i3]=A
iο R
i, and made normalized, wherein b
i1, b
i2, b
i3represent that respectively the sub-section of i is under the jurisdiction of degree unimpeded, crowded, blocked state;
(5) Two-stage Fuzzy Comprehensive Evaluation, using previous stage evaluation output as evaluation matrix R
~=[B
1, B
2]
t, B wherein
1, B
2the one-level list combined factors evaluation collection that represents respectively the 1st sub-section that previous step is obtained and the 2nd sub-section is A by each sub-section to the weight fuzzy subset of whole road
~, can obtain secondary fuzzy assessment output B
~=A
~ο R
~=[b
1 ~, b
2 ~, b
3 ~], b wherein
1 ~, b
2 ~, b
3 ~after representing respectively secondary fuzzy assessment, whole road section traffic volume state is under the jurisdiction of degree unimpeded, crowded, that stop up;
(6), secondary result of determination is carried out to fuzzy analysis judgement, draw the result that urban road state is distinguished: set a threshold value λ ∈ [0,1], to any b
j ~>=λ (j=1,2,3) all meets the requirements, and wherein, value that parameter j can get 1,2,3 is unimpeded, crowded, the blocked state sign of corresponding whole road section traffic volume respectively, parameter b
j ~after representing secondary fuzzy assessment, whole road section traffic volume state is under the jurisdiction of the degree that corresponding j identifies traffic behavior.Work as b
j ~in while only having a value to be greater than λ, be normalized to corresponding traffic behavior; Work as b
1 ~, b
2 ~value while being all greater than λ, be normalized to " unimpeded/crowded " critical conditions; Work as b
2 ~, b
3 ~value while being all greater than λ, be normalized to " crowded/to stop up " critical conditions.
Tool of the present invention has the following advantages:
(1) taken into full account the impact of urban road practical factor, realized more objective and accurate urban road traffic state identification, for municipal intelligent traffic is dynamically controlled and induction provides more effective information support.Thereby the load balancing, the alleviation that realize urban highway traffic are blocked up, are had a good transport and communication network in order, for urban highway traffic harmony is laid a good foundation.
(2) identification algorithm counting yield is high, and application prospect is extensive.
Accompanying drawing explanation
Fig. 1 is at the present invention's structural representation in intelligent traffic control system.
Fig. 2 be in embodiment the second subsegment to average vehicle queue length than segmentation Gauss member function schematic diagram.
Fig. 3 be in embodiment the second subsegment to average velocity segmentation Gauss member function schematic diagram.
Fig. 4 be in embodiment the second subsegment to lane occupancy segmentation Gauss member function schematic diagram.
Fig. 5 is the dynamic demonstration figure of road traffic induced screen.In figure, solid line represents the redness in actual induced screen, represents the traffic behavior stopping up; Dotted line represents the yellow in actual induced screen, represents congested traffic state; Dot-and-dash line represents the green in actual induced screen, represents unimpeded traffic behavior.
Embodiment
Referring to accompanying drawing 1.Urban road subsegment automatically divides module and Two-stage Fuzzy Comprehensive urban road traffic state recognition module is core content of the present invention, by a processing server, completes.Every urban road of designing of city road parameter input module foundation all to there being the parameters such as entire road length, section front signal lamp split, highway layout saturation factor and road limits speed, forms configuration file according to actual cities road traffic road network and is stored in processing server when planning and design.Urban road subsegment is automatically divided module and in conjunction with the mathematical model of automatically dividing, urban road is carried out to subsegment division again according to designing of city road parameter.The video acquisition analytical technology that urban road subsegment traffic parameter gathers according to existing maturation realizes, and by network, from road collection in worksite point, is real-time transmitted to processing server.Two-stage Fuzzy Comprehensive urban road traffic state recognition module adopts segmentation Gauss member function and fuzzy mathematics theory to realize.The dynamic issuing traffic block of state of road traffic induced screen is accepted real-time traffic states identification result by network communication, for the crucial scene of actual cities road traffic, realizes traffic guidance.
In specific implementation process, the distinguishing of whole urban road state comprises that road subsegment divides, receives in real time road traffic parameter, real-time identification road traffic state and the several steps of road traffic induced screen Real time dynamic display automatically.
The first step: road subsegment is divided automatically
In the present embodiment, set the parameter of certain designing of city road: 40 kilometers/hour of maximum speed limits, traffic lights cycle be that 60 seconds and split are 1/2, road overall length is that 1 kilometer, road saturation factor are 0.4, and control parameter a and b in the formula of setting 1-1 are respectively 3 and 10 kilometers/hour, according to formula 1-1
Calculate road subsegment respectively length be: d
u1=0.32 kilometer of d
u2=0.68 kilometer.
Second step: receive in real time road traffic parameter
Road traffic parameter obtain manner is in road the place ahead, to set up video camera to obtain road real-time video, according to road subsegment, is divided in calibration position in video, adopts existing video analysis treatment technology to obtain respectively a subsegment real-time traffic parameter.The 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 steps, carry out:
(1), each road subsegment is set up to evaluation object list set of factors U
i; U
i=[L, V, D], i=1 wherein, which section subsets 2 represent, and L represents queue length ratio, and V represents average speed, and D represents occupancy.
(2), for each sub-section, set up evaluation collection F
i.F
i=[f
i1, f
i2, f
i3], f wherein
i1represent that road i sub-section belongs to unimpeded state, f
i2represent that i sub-section belongs to congestion state, f
i3represent that i sub-section belongs to blocked state.
(3), set up the evaluation of single factor, set up one from single set of factors U
ito son evaluation collection F
ithe mapping of fuzzy relation, so the arbitrary element u in set of factors with regard to and L, V, Cartesian product L * V * D={ (l of D, v, d) | l ∈ L, v ∈ V, the corresponding element (l in d ∈ D}, v, d) unique correspondence, can derive single factor evaluation matrix R thus
i, R
i=[R
i1, R
i2, R
i3]
t, R wherein
i1refer to the degree that the i queue length of sub-section is more unimpeded than being under the jurisdiction of, crowded and stop up, R
i2refer to sub-section i speed and be under the jurisdiction of degree unimpeded, crowded and that stop up, R
i3refer to sub-section i occupancy and be under the jurisdiction of degree unimpeded, crowded and that stop up.
(4), first order Fuzzy Synthetic Evaluation, select suitable segmentation Gaussian Blur mathematical synthesis function to carry out comprehensively, to each sub-section single set of factors U for collection
iinterior corresponding fuzzy set A
i=[a
i1, a
i2, a
i3] represent the weight allocation of this factor, obtain one-level list combined factors evaluation collection B
i=[b
i1, b
i2, b
i3]=A
iο R
i, and made normalized.
(5) Two-stage Fuzzy Comprehensive Evaluation, using previous stage evaluation output as evaluation matrix R
~=[B
1, B
2]
t, by each sub-section, to the weight fuzzy subset of whole road, be A
~, can obtain secondary fuzzy assessment output B
~=A
~ο R
~=[b
1 ~, b
2 ~, b
3 ~].
(6), secondary result of determination is carried out to fuzzy analysis judgement, set a threshold value λ ∈ [0,1], to any b
j ~>=λ (j=1,2,3) all meets the requirements, and works as b
j ~in while only having a value to be greater than λ, be normalized to corresponding traffic behavior; Work as b
1 ~, b
2 ~value while being all greater than λ, be normalized to " unimpeded/crowded " critical conditions; Work as b
2 ~, b
3 ~value while being all greater than λ, be normalized to " crowded/to stop up " critical conditions, such as secondary result of determination is (0.4,0.4,0.2), if get λ, be 0.3, net result judges that this road traffic state is as " unimpeded/crowded ".
The segmentation Gauss member function of the average vehicle queue length ratio in the present embodiment the second sub-section, average velocity, lane occupancy is as shown in accompanying drawing 2,3,4.
The 4th step: road traffic induced screen Real time dynamic display
At the road traffic condition that secondary result of determination is carried out drawing after fuzzy analysis judgement, be sent on road traffic induced screen and show in real time, be divided into three kinds of colors of red, yellow, and green and show respectively obstruction, crowded and unimpeded situation.
Accompanying drawing 5 has provided the road traffic induced screen Real time dynamic display of the present embodiment.The traffic behavior that in actual traffic induced screen, red expression is stopped up, the yellow congested traffic state that represents, the unimpeded concrete situation of green expression.Redness in solid line that represent traffic induced screen for accompanying drawing, the yellow in dotted line that represent traffic induced screen, the green in dot-and-dash line that represent traffic induced screen.Road traffic condition is very clear, and for road traffic, participant provides great convenience.
Claims (3)
1. the urban road state of differentiating based on Two-stage Fuzzy Comprehensive is distinguished a method for distinguishing, it is characterized in that: distinguish that method for distinguishing comprises that urban road subsegment divides, receives in real time road traffic parameter, urban road traffic state real-time identification and the several steps of road traffic induced screen Real time dynamic display automatically; Urban road subsegment is automatically divided in urban road subsegment and automatically divides in module and carry out, receive in real time road traffic parameter and carry out in urban road subsegment traffic parameter acquisition module, urban road traffic state real-time identification is carried out in Two-stage Fuzzy Comprehensive urban road traffic state recognition module; Urban road subsegment is automatically divided module, the traffic parameter collection of urban road subsegment and Two-stage Fuzzy Comprehensive urban road traffic state recognition module and is arranged in the processing server of intelligent traffic control system;
Urban road subsegment is automatically divided module and is received the parameter from the designing of city road parameter module in intelligent traffic control system, complete urban road subsegment is divided and division result is sent to Two-stage Fuzzy Comprehensive urban road traffic state recognition module, Two-stage Fuzzy Comprehensive urban road traffic state recognition module receives urban road subsegment traffic parameter acquisition module simultaneously and from video parameter and the urban road subsegment division result parameter of road collection in worksite point real-time Transmission, carries out urban road traffic state real-time identification by network, real-time traffic states identification result is published to the road traffic induced screen of intelligent traffic control system by network communication, the in real time dynamic issuing traffic state identification result of road traffic induced screen,
It is that a road U is divided into two subsegment U1 and U2 automatically that described urban road subsegment 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 controls parameter;
Described real-time identification road traffic state is being fuzzy the distinguishing of secondary of carrying out based on segmentation Gauss member function and fuzzy mathematics theory, and step is:
(1), each road subsegment is set up to evaluation object list set of factors U
i;
(2), for each road subsegment, set up evaluation collection F
i;
(3), set up from single set of factors U
ito evaluation collection F
ithe mapping of fuzzy relation, by Cartesian product corresponding relation, derive single factor evaluation matrix R
i;
(4), first order Fuzzy Synthetic Evaluation, select segmentation Gaussian Blur mathematical synthesis function carry out comprehensive and made normalized;
(5) Two-stage Fuzzy Comprehensive Evaluation;
(6), secondary result of determination is carried out to fuzzy analysis judgement, draw the result that urban road state is distinguished.
2. urban road state as claimed in claim 1 is distinguished method for distinguishing, it is characterized in that:
A road U is divided into two subsegment U1 to the automatic division of described urban road subsegment automatically and U2 divides according to formula 1-1:
In formula, d
u1and d
u2the length that represents respectively road subsegment U1 and U2,
represent entire road length, t represent section front signal lamp split, s represent highway layout saturation factor,
represent road limits speed, a is the control parameter with the total long correlation of road, and b is the control parameter with road limits velocity correlation.
3. urban road state as claimed in claim 1 is distinguished method for distinguishing, it is characterized in that: the concrete steps of described Two-stage Fuzzy Comprehensive urban road traffic state automatic Identification are:
(1), each road subsegment is set up to evaluation object list set of factors U
i; U
i=[L, V, D], i=1 wherein, which section subsets 2 represent, and L represents section queue length ratio, and V represents average speed, and D represents occupancy;
(2), for each road subsegment, set up evaluation collection F
i; F
i=[f
i1, f
i2, f
i3], f wherein
i1represent that road i sub-section belongs to unimpeded state, f
i2represent that i sub-section belongs to congestion state, f
i3represent that i sub-section belongs to blocked state;
(3), set up from single set of factors U
ito evaluation collection F
ithe mapping of fuzzy relation, single set of factors U
iin arbitrary element u just and L, V, Cartesian product L * V * D={ (l, v, d) of D | l ∈ L, v ∈ V, the unique correspondence of corresponding element (l, v, d) in d ∈ D}, derives single factor evaluation matrix R thus
i, R
i=[R
i1, R
i2, R
i3]
t, R wherein
i1refer to the degree that the i queue length of sub-section is more unimpeded than being under the jurisdiction of, crowded and stop up, R
i2refer to sub-section i speed and be under the jurisdiction of degree unimpeded, crowded and that stop up, R
i3refer to sub-section i occupancy and be under the jurisdiction of degree unimpeded, crowded and that stop up, l represents that queue length represents that than variable-value, v average speed variable-value, d represent occupancy variable-value;
(4), first order Fuzzy Synthetic Evaluation, according to expertise and actual observation statistics, select the segmentation Gaussian Blur mathematical synthesis function of each traffic characteristic amount to carry out comprehensively, to each sub-section single set of factors U for collection
iinterior corresponding fuzzy set A
i=[a
i1, a
i2, a
i3] represent the weight allocation of this factor, wherein, the sequence number 1 or 2 in i section that desirable section is divided into, a
i1, a
i2, a
i3represent respectively the sub-section of i queue length ratio, average speed, occupancy shared proportion in fuzzy assessment, obtain one-level list combined factors evaluation collection B
i=[b
i1, b
i2, b
i3]=A
iο R
i, and made normalized, wherein b
i1, b
i2, b
i3represent that respectively the sub-section of i is under the jurisdiction of degree unimpeded, crowded, blocked state;
(5) Two-stage Fuzzy Comprehensive Evaluation, using previous stage evaluation output as evaluation matrix R
~=[B
1, B
2]
t, B wherein
1, B
2the one-level list combined factors evaluation collection that represents respectively the 1st sub-section that previous step is obtained and the 2nd sub-section is A by each sub-section to the weight fuzzy subset of whole road
~, can obtain secondary fuzzy assessment output B
~=A
~ο R
~=[b
1 ~, b
2 ~, b
3 ~], b wherein
1 ~, b
2 ~, b
3 ~after representing respectively secondary fuzzy assessment, whole road section traffic volume state is under the jurisdiction of degree unimpeded, crowded, that stop up;
(6), secondary result of determination is carried out to fuzzy analysis judgement, draw the result that urban road traffic state is distinguished: set a threshold value λ ∈ [0,1], to any b
j ~>=λ (j=1,2,3) all meets the requirements, and value that parameter j can get 1,2,3 is unimpeded, crowded, the blocked state sign of corresponding whole road section traffic volume respectively, parameter b
j ~after representing secondary fuzzy assessment, whole road section traffic volume state is under the jurisdiction of the degree that corresponding j identifies traffic behavior, works as b
j ~in while only having a value to be greater than λ, be normalized to corresponding traffic behavior; Work as b
1 ~, b
2 ~value while being all greater than λ, be normalized to " unimpeded/crowded " critical conditions; Work as b
2 ~, b
3 ~value while being all greater than λ, be normalized to " crowded/to stop up " critical conditions.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5566072A (en) * | 1993-08-10 | 1996-10-15 | Mitsubishi Jidosha Kogyo Kabushiki Kaisha | Method and apparatus for estimating a road traffic condition and method and apparatus for controlling a vehicle running characteristic |
| US5696502A (en) * | 1994-03-14 | 1997-12-09 | Siemens Aktiengesellschaft | Method of sensing traffic and detecting traffic situations on roads, preferably freeways |
| JP2009145951A (en) * | 2007-12-11 | 2009-07-02 | Toyota Central R&D Labs Inc | Driver status estimation device and program |
| CN101739815A (en) * | 2009-11-06 | 2010-06-16 | 吉林大学 | On-line recognition method of road traffic congestion state |
| CN101783074A (en) * | 2010-02-10 | 2010-07-21 | 北方工业大学 | Method and system for real-time distinguishing traffic flow state of urban road |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050068938A (en) * | 2003-12-30 | 2005-07-05 | 현대자동차주식회사 | Method of a traffic conditions decision |
-
2012
- 2012-06-08 CN CN201210187773.6A patent/CN102708688B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5566072A (en) * | 1993-08-10 | 1996-10-15 | Mitsubishi Jidosha Kogyo Kabushiki Kaisha | Method and apparatus for estimating a road traffic condition and method and apparatus for controlling a vehicle running characteristic |
| US5696502A (en) * | 1994-03-14 | 1997-12-09 | Siemens Aktiengesellschaft | Method of sensing traffic and detecting traffic situations on roads, preferably freeways |
| JP2009145951A (en) * | 2007-12-11 | 2009-07-02 | Toyota Central R&D Labs Inc | Driver status estimation device and program |
| CN101739815A (en) * | 2009-11-06 | 2010-06-16 | 吉林大学 | On-line recognition method of road traffic congestion state |
| CN101783074A (en) * | 2010-02-10 | 2010-07-21 | 北方工业大学 | Method and system for real-time distinguishing traffic flow state of urban road |
Non-Patent Citations (4)
| Title |
|---|
| 刘清.城市道路交通状况的F_①-评价及其实例研究.《武汉理工大学学报(交通科学与工程版)》.2000,第24卷(第05期),第473-475页. |
| 城市道路交通状况的F_①-评价及其实例研究;刘清;《武汉理工大学学报(交通科学与工程版)》;20001025;第24卷(第05期);第473-475页 * |
| 基于模糊数学的公路交通量预测方法;窦慧丽 等;《交通科技与经济》;20100420;第28卷(第02期);第13-15页 * |
| 窦慧丽 等.基于模糊数学的公路交通量预测方法.《交通科技与经济》.2010,第28卷(第02期),第13-15页. |
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