CN102289937A - Method for automatically discriminating traffic states of city surface roads based on stop line detector - Google Patents

Method for automatically discriminating traffic states of city surface roads based on stop line detector Download PDF

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CN102289937A
CN102289937A CN2011102257736A CN201110225773A CN102289937A CN 102289937 A CN102289937 A CN 102289937A CN 2011102257736 A CN2011102257736 A CN 2011102257736A CN 201110225773 A CN201110225773 A CN 201110225773A CN 102289937 A CN102289937 A CN 102289937A
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green light
occupation rate
current period
track
saturation degree
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CN102289937B (en
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沈峰
林瑜
苏贵民
曾令榜
王佳谈
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Shanghai Seari Intelligent System Co Ltd
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Shanghai Seari Intelligent System Co Ltd
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Abstract

The invention provides a method for automatically discriminating traffic states of city surface roads based on a stop line detector. The method is characterized by comprising the following steps: periodically acquiring the flow rate and the occupation ratio of each lane at each intersection in a traffic network of city surface roads; calculating data such as the occupation ratio of a green light at an initial stage, the occupation ratio of the green light at a medium stage, the occupation ratio of the green light at a last stage and the like of each lane in the existing period; and judging whether the traffic state of the current lane is smooth, crowded or blocked in the current period. The method provided by the invention can be used for solving the problem of automatically discriminating the traffic states of entrance roads at the intersection controlled by signals in real time, improving the discrimination accuracy of the traffic states of the city surface roads, providing real-time and accurate dynamic traffic information of city surface roads for traffic administrators and travelers, and in particular providing technical guarantees for the traffic administrators to make accurate and efficient traffic management strategies and schemes.

Description

Urban ground road traffic state automatic distinguishing method based on the stop line detecting device
Technical field
The present invention relates to a kind of surface road traffic state judging method based on the stop line detecting device, relate in particular to a kind of method of utilizing the stop line detecting device to detect data and intersection signal controlling schemes differentiation urban ground road traffic state in real time, belong to the intelligent transport technology field.
Background technology
China has laid the stop line detecting device in the urban signal controlling crossing mostly at present, for providing data, supports the signal time distributing conception of formulating crossing traffic lights 3, as shown in Figure 1, stop line detecting device 1(is generally magnetic test coil) installation position be generally stop line 2 preceding 5 meters.The traffic flow parameter that stop line detecting device 1 obtains comprises flow, occupation rate and speed.Because the change of traffic flow modes can show by traffic flow parameter, therefore, many traffic engineering workers have set up multiple traffic behavior automatic distinguishing method based on this.
The at present comparatively common method based on fixed test coil differentiation traffic behavior mainly contains: pattern-recongnition method, as the California algorithm; Statistical analysis method is as standard deviation (English abbreviates SND as) algorithm; The catastrophe theory algorithm is as the McMaster algorithm; The artificial intelligence method is as single cross section neural network algorithm.
The California algorithm passes through analytically the occupation rate difference and the relative difference of downstream stop line detector acquisition, infer the traffic hazard or the congested in traffic generation moment, yet this algorithm need be demarcated for the threshold value in each place, especially it is bigger to demarcate workload in big road network, and 3 foregone conclusion spare determining steps are difficult to capture effectively all possible travel pattern in the algorithm simultaneously.The arithmetic mean of the traffic parameter data (flow or occupation rate) in m sampling period is as the predicted value of traffic parameter at moment t before standard deviation (SND) the algorithm utilization moment t, measure the change degree of traffic parameter data with the standard normal deviation again with respect to its former mean value, when it surpasses pre-set threshold, then think sporadic traffic congestion has taken place.The McMaster algorithm thinks that in three continuous sampling periods, the speed of a motor vehicle is all reduced to below the threshold value, or occupation rate surpasses threshold value, or flow and occupation rate be all outside non-congested area, and decidable has crowded existence; In continuous two sampling periods, any two threshold values that surpass separately of the speed of a motor vehicle, flow and occupation rate also can be judged as traffic congestion has taken place.
More than three kinds of typical traffic state judging algorithms all towards continuous traffic flow, because the urban ground road is subjected to the influence of traffic light signal control crossing, its traffic flow exists bigger discontinuity and periodicity, causes traditional traffic state judging algorithm to be difficult to effectively be used.Neural network method utilization a large amount of (comprising crowded and non-congestion state) traffic data in single cross section is trained algorithm, determines structure and weights that it is best.For one group of specific traffic data, by algorithm output result is compared with decision-making value, definite traffic behavior that is reflected, this algorithm need a large amount of True Datas that algorithm is trained, and the calibration process complexity, and range of application is less.
Summary of the invention
The purpose of this invention is to provide a kind of traffic state judging method based on the stop line detecting device, this method range of application is big, can be applicable to that traffic flow exists bigger discontinuity and periodic situation.
In order to achieve the above object, technical scheme of the present invention has provided a kind of urban ground road traffic state automatic distinguishing method based on the stop line detecting device, and it is characterized in that: step is:
Step 1, obtain the flow and the occupation rate in every track of each intersection in the urban ground road traffic road network by the stop line detector period, the cycle of each intersection is set at the cycle that these intersection traffic lights change, simultaneously, with
Figure 2011102257736100002DEST_PATH_IMAGE001
Divide each cycle for time granularity second, set counter a, its initial value is made as 0, and rule of thumb sets the first occupation rate threshold value Occ1, the second occupation rate threshold value Occ2, the 3rd occupation rate threshold value Occ3, the first saturation degree threshold value S1, the second saturation degree threshold value S2, the 3rd saturation degree threshold value S3 and state continuance periodicity DurN;
Step 2, by stop line detector acquisition the
Figure 883054DEST_PATH_IMAGE002
The bar track is at current period
Figure 2011102257736100002DEST_PATH_IMAGE003
In the
Figure 2011102257736100002DEST_PATH_IMAGE005
The occupation rate of individual time granularity
Figure 566714DEST_PATH_IMAGE006
Calculate green light initial stage occupation rate, green light mid-term occupation rate, green light latter stage occupation rate, red light initial stage occupancy, green light initial stage saturation degree, green light mid-term saturation degree and the green light latter stage saturation degree of every track in current period, wherein, green light initial stage and red light initial stage refer to respectively green light and red light duration before
Figure 309542DEST_PATH_IMAGE001
Second, if the green light duration can by
Figure 362949DEST_PATH_IMAGE001
Second divide exactly, then green light refers to the last of green light duration latter stage
Figure 140412DEST_PATH_IMAGE001
Second, if the green light duration can not by
Figure 558755DEST_PATH_IMAGE001
Second divide exactly, then green light refers to the last of green light duration latter stage Second, wherein,
Figure 547134DEST_PATH_IMAGE007
For with the green light duration divided by
Figure 951702DEST_PATH_IMAGE001
Second resulting remainder, green light are meant the green light duration of removing green light initial stage and green light latter stage mid-term, establish the
Figure 347786DEST_PATH_IMAGE002
The bar track is at current period
Figure 937030DEST_PATH_IMAGE008
Green light initial stage occupation rate, green light occupation rate in mid-term, green light occupation rate in latter stage, red light initial stage occupancy, green light initial stage saturation degree, green light saturation degree in mid-term and green light saturation degree in latter stage be respectively
Figure 2011102257736100002DEST_PATH_IMAGE009
,
Figure 592134DEST_PATH_IMAGE010
,
Figure 2011102257736100002DEST_PATH_IMAGE011
,
Figure 423561DEST_PATH_IMAGE012
,
Figure 2011102257736100002DEST_PATH_IMAGE013
,
Figure 113300DEST_PATH_IMAGE014
, , then have:
Figure 309664DEST_PATH_IMAGE009
,
Figure 514380DEST_PATH_IMAGE011
And Be respectively the actual measurement occupation rate at the green light initial stage, green light latter stage and the red light initial stage that obtain by the stop line detecting device,
Figure 257525DEST_PATH_IMAGE010
Arithmetic mean value for green light actual measurement occupancy of all time granularities in mid-term of obtaining by the stop line detecting device;
The
Figure 692967DEST_PATH_IMAGE002
The bar track is at current period
Figure 447296DEST_PATH_IMAGE003
In the
Figure 387570DEST_PATH_IMAGE005
The saturation degree of individual time granularity
Figure 848638DEST_PATH_IMAGE016
, wherein, For obtain by the stop line detecting device
Figure 950587DEST_PATH_IMAGE002
The bar track is at current period
Figure 628430DEST_PATH_IMAGE003
In the
Figure 106816DEST_PATH_IMAGE005
The measured discharge of individual time granularity,
Figure 422391DEST_PATH_IMAGE018
Be
Figure 2011102257736100002DEST_PATH_IMAGE019
The duration of individual time granularity,
Figure 960820DEST_PATH_IMAGE020
Be The bar track is at current period Interior saturation headway, this saturation headway obtains by historical data analysis, calculates green light initial stage saturation degree according to above-mentioned formula
Figure 312539DEST_PATH_IMAGE013
And green light saturation degree in latter stage
Figure 21869DEST_PATH_IMAGE015
, green light saturation degree in mid-term
Figure 408725DEST_PATH_IMAGE014
It then is the arithmetic mean value of green light saturation computation value of all time granularities in mid-term;
Green light initial stage occupation rate, green light occupation rate in mid-term, green light occupation rate in latter stage, red light initial stage occupancy, green light initial stage saturation degree, green light saturation degree in mid-term and the green light saturation degree in latter stage in step 3, every track of foundation judges that the traffic behavior of current track in current period is unimpeded, crowded or stops up, wherein,
Figure 228914DEST_PATH_IMAGE002
The bar track is at current period
Figure 519081DEST_PATH_IMAGE003
Interior traffic behavior
Figure 2011102257736100002DEST_PATH_IMAGE021
Determining step be:
Step 3.1, judge the
Figure 399312DEST_PATH_IMAGE002
The bar track is at current period Interior green light initial stage occupation rate
Figure 977239DEST_PATH_IMAGE009
, green light occupation rate in mid-term
Figure 121913DEST_PATH_IMAGE010
, green light occupation rate in latter stage
Figure 235362DEST_PATH_IMAGE011
, red light initial stage occupancy
Figure 98276DEST_PATH_IMAGE012
, green light initial stage saturation degree
Figure 322584DEST_PATH_IMAGE013
, green light saturation degree in mid-term And green light saturation degree in latter stage Whether satisfy condition 1 or condition 2,, then skip to step 3.7 if satisfy, otherwise, skip to step 3.2, wherein,
Condition 1 is: Occ1≤
Figure 454860DEST_PATH_IMAGE009
≤ 100% and Occ3≤
Figure 482859DEST_PATH_IMAGE010
≤ 100% and Occ3≤
Figure 602125DEST_PATH_IMAGE011
≤ 100% and Occ2≤
Figure 995060DEST_PATH_IMAGE012
≤ 100% and 0≤
Figure 629304DEST_PATH_IMAGE013
＜S1 and 0≤
Figure 133097DEST_PATH_IMAGE014
＜S1 and 0≤
Figure 434766DEST_PATH_IMAGE015
＜S1;
Condition 2 is: 0＜
Figure 497137DEST_PATH_IMAGE009
≤ 100% and Occ1≤
Figure 556360DEST_PATH_IMAGE010
＜Occ3 and Occ1≤
Figure 926162DEST_PATH_IMAGE011
＜Occ3 and Occ2≤
Figure 20019DEST_PATH_IMAGE012
≤ 100% and 0≤
Figure 817074DEST_PATH_IMAGE013
＜S2 and S2≤
Figure 363593DEST_PATH_IMAGE014
＜S3 and S2≤
Figure 271506DEST_PATH_IMAGE015
＜S3;
Step 3.2, the value of counter a is added 1, enter step 3.3;
Step 3.3, judge whether the value of counter a is not less than state continuance periodicity DurN, if then enter step 3.5, otherwise enter step 3.4;
Step 3.4, with
Figure 718406DEST_PATH_IMAGE002
The bar track is at current period
Figure 686362DEST_PATH_IMAGE003
Interior traffic behavior Be set at crowdedly, enter next step of step 3;
Step 3.5, with
Figure 431781DEST_PATH_IMAGE002
The bar track is at current period
Figure 234652DEST_PATH_IMAGE003
Interior traffic behavior
Figure 373509DEST_PATH_IMAGE021
Be set at obstruction, enter next step of step 3;
Step 3.6, the value of counter a is returned 0, enter step 3.7;
Step 3.7, judge the The bar track is at current period Interior green light initial stage occupation rate
Figure 571687DEST_PATH_IMAGE009
, green light occupation rate in mid-term
Figure 881446DEST_PATH_IMAGE010
, green light occupation rate in latter stage
Figure 952170DEST_PATH_IMAGE011
, red light initial stage occupancy
Figure 943260DEST_PATH_IMAGE012
, green light initial stage saturation degree
Figure 783040DEST_PATH_IMAGE013
, green light saturation degree in mid-term
Figure 201383DEST_PATH_IMAGE014
And green light saturation degree in latter stage Whether satisfy condition 3 or condition 4,, then skip to step 3.4 if satisfy, otherwise, skip to step 3.8, wherein,
Condition 3 is: 0＜
Figure 288605DEST_PATH_IMAGE009
≤ 100% and Occ2≤
Figure 982891DEST_PATH_IMAGE010
≤ 100% and Occ3≤
Figure 336250DEST_PATH_IMAGE011
≤ 100% and Occ1≤ ≤ Occ2 and 0≤
Figure 448879DEST_PATH_IMAGE013
＜S2 and 0≤
Figure 263252DEST_PATH_IMAGE014
＜S2 and 0≤
Figure 23397DEST_PATH_IMAGE015
＜S1;
Condition 4 is: 0＜
Figure 290431DEST_PATH_IMAGE009
≤ 100% and Occ3≤
Figure 427014DEST_PATH_IMAGE010
≤ 100% and Occ2≤
Figure 95893DEST_PATH_IMAGE011
≤ Occ3 and Occ1≤
Figure 525475DEST_PATH_IMAGE012
≤ Occ2 and 0≤
Figure 279804DEST_PATH_IMAGE013
＜S2 and 0≤
Figure 954499DEST_PATH_IMAGE014
＜S1 and 0＜
Figure 477884DEST_PATH_IMAGE015
＜S2;
Step 3.8, with
Figure 845412DEST_PATH_IMAGE002
The bar track is at current period Interior traffic behavior
Figure 299844DEST_PATH_IMAGE021
Be set at unimpededly, enter next step of step 3;
Step 4, enter next cycle, return execution in step 2 again.
Preferably, between described step 3 and described step 4, further comprise the traffic behavior of judging every road of each intersection in the urban ground road traffic road network, wherein, the
Figure 943315DEST_PATH_IMAGE022
The bar road is by equidirectional
Figure 2011102257736100002DEST_PATH_IMAGE023
The bar track is formed, and the determining step of this road traffic state is:
Steps A, setting constant
Figure 714699DEST_PATH_IMAGE024
,
Figure 2011102257736100002DEST_PATH_IMAGE025
And
Figure 381304DEST_PATH_IMAGE026
,
Figure 397802DEST_PATH_IMAGE024
,
Figure 895779DEST_PATH_IMAGE025
And
Figure 605109DEST_PATH_IMAGE026
Be used for representing unimpeded, crowded and obstruction respectively, set unimpeded lower threshold
Figure 2011102257736100002DEST_PATH_IMAGE027
, unimpeded upper limit threshold
Figure 986106DEST_PATH_IMAGE028
, crowded lower threshold
Figure 2011102257736100002DEST_PATH_IMAGE029
And crowded upper limit threshold
Figure 806295DEST_PATH_IMAGE030
, wherein,
Figure 96462DEST_PATH_IMAGE027
≤
Figure 976693DEST_PATH_IMAGE024
＜ ,
Figure 271463DEST_PATH_IMAGE029
≤
Figure 744033DEST_PATH_IMAGE025
≤
Figure 795165DEST_PATH_IMAGE030
Step B, select the
Figure 720396DEST_PATH_IMAGE022
Article one track of bar road;
Step C, the current track of judgement are at current period
Figure 882387DEST_PATH_IMAGE003
In traffic behavior whether be unimpeded, if not, enter step D, if, then with current track at current period
Figure 943884DEST_PATH_IMAGE003
Interior traffic behavior value is set at
Figure 165918DEST_PATH_IMAGE024
, judge whether to have calculated
Figure 578445DEST_PATH_IMAGE022
All tracks of bar road are if then and enter step F, select next bar track to return execution in step C if not;
Step D, the current track of judgement are at current period
Figure 42662DEST_PATH_IMAGE003
In traffic behavior whether be crowded, if not, then enter step e, if, then with current track at current period
Figure 224245DEST_PATH_IMAGE003
Interior traffic behavior value is set at
Figure 351601DEST_PATH_IMAGE025
, judge whether to have calculated All tracks of bar road are if then and enter step F, select next bar track to return execution in step C if not;
Step e, with current track at current period
Figure 755217DEST_PATH_IMAGE003
Interior traffic behavior value is set at
Figure 56885DEST_PATH_IMAGE026
, judge whether to have calculated
Figure 355143DEST_PATH_IMAGE022
All tracks of bar road are if then and enter step F, select next bar track to return execution in step C if not;
Step F, calculate the
Figure 742262DEST_PATH_IMAGE022
The bar road is at current period
Figure 548281DEST_PATH_IMAGE003
Interior road traffic state value
Figure 2011102257736100002DEST_PATH_IMAGE031
,
Figure 376560DEST_PATH_IMAGE032
, wherein,
Figure 2011102257736100002DEST_PATH_IMAGE033
Expression the
Figure 111298DEST_PATH_IMAGE022
In the bar road
Figure 2011102257736100002DEST_PATH_IMAGE035
The traffic behavior value in bar track,
Figure 657817DEST_PATH_IMAGE036
And
Figure 2011102257736100002DEST_PATH_IMAGE037
Represent respectively
Figure 273387DEST_PATH_IMAGE022
In the bar road
Figure 284068DEST_PATH_IMAGE035
The bar track is at current period
Figure 189707DEST_PATH_IMAGE003
Interior corresponding phase green light duration and corresponding phase amber light duration;
Step G, if
Figure 285839DEST_PATH_IMAGE027
≤
Figure 935127DEST_PATH_IMAGE031
＜ , then with The bar road is at current period
Figure 460283DEST_PATH_IMAGE003
Interior road traffic state
Figure 146217DEST_PATH_IMAGE038
Be set at unimpeded, otherwise, enter step H;
Step H, if
Figure 131490DEST_PATH_IMAGE029
≤
Figure 378932DEST_PATH_IMAGE031
≤
Figure 449656DEST_PATH_IMAGE030
, then with The bar road is at current period
Figure 218209DEST_PATH_IMAGE003
Interior road traffic state
Figure 698869DEST_PATH_IMAGE038
Be set at crowded, otherwise, with
Figure 427528DEST_PATH_IMAGE022
The bar road is at current period Interior road traffic state
Figure 916596DEST_PATH_IMAGE038
Be set at obstruction.
Preferably, in described step 2, if the green light duration can not by
Figure 568157DEST_PATH_IMAGE001
Second divide exactly then the
Figure 551156DEST_PATH_IMAGE002
The bar track is at current period
Figure 946366DEST_PATH_IMAGE003
Green light occupation rate in latter stage
Figure 432842DEST_PATH_IMAGE011
For with
Figure 520883DEST_PATH_IMAGE007
Second be the weighted mean value of actual measurement occupation rate of the penult time granularity of the actual measurement occupation rate of last time granularity of green light duration of duration and green light duration, the
Figure 224135DEST_PATH_IMAGE002
The bar track is at current period
Figure 423035DEST_PATH_IMAGE003
Green light saturation degree in latter stage
Figure 764018DEST_PATH_IMAGE015
For with
Figure 22961DEST_PATH_IMAGE007
It second is the weighted mean value of saturation computation value of the penult time granularity of the saturation computation value of last time granularity of green light duration of duration and green light duration.
Method provided by the invention has following characteristics: considered that one, intersection signal controls the influence of the stream of road traffic over the ground, therefore can effectively determine the urban ground road traffic state; Two, checkout equipment is unrestricted, can utilize the checkout equipments of having laid such as inductive coil, microwave radar and video; Three, considered traffic above-ground stream feature, can effectively the urban ground road traffic condition be divided into unimpeded, crowded, three kinds of traffic behaviors of obstruction by this method.
The urban ground road traffic state method of discrimination based on the stop line detecting device that the present invention proposes can solve the in real time automatic differentiation problem of signalized crossing entrance driveway traffic behavior, improved urban ground road traffic state discriminant accuracy, for traffic administration person, traffic trip person provide in real time, urban ground road dynamic information accurately, especially traffic administration strategy and scheme provide technical guarantee for traffic administration person makes accurately and efficiently.
Description of drawings
Fig. 1 is the intersection synoptic diagram;
Fig. 2 is the process flow diagram of a kind of urban ground road traffic state automatic distinguishing method based on the stop line detecting device provided by the invention;
Fig. 3 is the road traffic state decision flow chart.
Embodiment
For the present invention is become apparent, now with a preferred embodiment, and conjunction with figs. is described in detail below.
As shown in Figure 2, a kind of urban ground road traffic state automatic distinguishing method based on the stop line detecting device provided by the invention the steps include:
Step 1, obtain the flow and the occupation rate in every track of each intersection in the urban ground road traffic road network by the stop line detector period, the cycle of each intersection is set at the cycle that these intersection traffic lights change, simultaneously, with
Figure 714973DEST_PATH_IMAGE001
Second each cycle is divided for time granularity, according to the difference of needed computational accuracy, can for
Figure 451985DEST_PATH_IMAGE001
Set different numerical value second, in the present embodiment will
Figure 913053DEST_PATH_IMAGE001
Be set at 5, set counter a, its initial value is made as 0, and rule of thumb set the first occupation rate threshold value Occ1, the second occupation rate threshold value Occ2, the 3rd occupation rate threshold value Occ3, the first saturation degree threshold value S1, the second saturation degree threshold value S2, the 3rd saturation degree threshold value S3 and state continuance periodicity DurN, wherein, the first occupation rate threshold value Occ1, the second occupation rate threshold value Occ2, the 3rd occupation rate threshold value Occ3, the first saturation degree threshold value S1, the second saturation degree threshold value S2 and the 3rd saturation degree threshold value S3 can rule of thumb set different numerical value according to the difference of urban highway traffic situation, in the present embodiment, at the road traffic condition in Shanghai City, with the first occupation rate threshold value Occ1, the second occupation rate threshold value Occ2, the 3rd occupation rate threshold value Occ3, the first saturation degree threshold value S1, the second saturation degree threshold value S2, the 3rd saturation degree threshold value S3 and state continuance periodicity DurN are set at 30 respectively, 60,90,40,80 and 120.State continuance periodicity DurN reaction then be crowdedly can be converted into obstruction after lasting till certain hour, DurN can adjust according to the actual traffic situation, is made as 3 in the present embodiment.
Step 2, by stop line detector acquisition the
Figure 342898DEST_PATH_IMAGE002
The bar track is at current period
Figure 585240DEST_PATH_IMAGE003
In the
Figure 125943DEST_PATH_IMAGE005
The occupation rate of individual time granularity
Figure 707097DEST_PATH_IMAGE006
Calculate green light initial stage occupation rate, green light mid-term occupation rate, green light latter stage occupation rate, red light initial stage occupancy, green light initial stage saturation degree, green light mid-term saturation degree and the green light latter stage saturation degree of every track in current period, wherein, green light initial stage and red light initial stage refer to respectively green light and red light duration before
Figure 42264DEST_PATH_IMAGE001
Second, be preceding 5 seconds in the present embodiment, as if the green light duration can by Second divide exactly, then green light refers to the last of green light duration latter stage
Figure 787683DEST_PATH_IMAGE001
Second, if the green light duration can not by
Figure 223343DEST_PATH_IMAGE001
Second divide exactly, then green light refers to the last of green light duration latter stage
Figure 729411DEST_PATH_IMAGE007
Second, wherein,
Figure 381847DEST_PATH_IMAGE007
For with the green light duration divided by
Figure 264352DEST_PATH_IMAGE001
Second resulting remainder, green light are meant the green light duration of removing green light initial stage and green light latter stage mid-term, establish the
Figure 554519DEST_PATH_IMAGE002
The bar track is at current period Green light initial stage occupation rate, green light occupation rate in mid-term, green light occupation rate in latter stage, red light initial stage occupancy, green light initial stage saturation degree, green light saturation degree in mid-term and green light saturation degree in latter stage be respectively
Figure 872685DEST_PATH_IMAGE009
,
Figure 293302DEST_PATH_IMAGE010
,
Figure 703555DEST_PATH_IMAGE011
, ,
Figure 912874DEST_PATH_IMAGE013
,
Figure 137182DEST_PATH_IMAGE014
,
Figure 401941DEST_PATH_IMAGE015
, then have:
Figure 686292DEST_PATH_IMAGE009
And
Figure 36502DEST_PATH_IMAGE012
Be respectively the green light initial stage that obtains by the stop line detecting device and the actual measurement occupation rate at red light initial stage.
If the green light duration can by
Figure 798922DEST_PATH_IMAGE001
Divide exactly second, then
Figure 918188DEST_PATH_IMAGE011
Actual measurement occupation rate for green light latter stage of obtaining by the stop line detecting device.
If the green light duration can not by
Figure 373440DEST_PATH_IMAGE001
Divide exactly second, then
Figure 709481DEST_PATH_IMAGE011
For with
Figure 275591DEST_PATH_IMAGE007
Second be the weighted mean value of actual measurement occupation rate of the penult time granularity of the actual measurement occupation rate of last time granularity of green light duration of duration and green light duration, the duration of time granularity second from the bottom is Second, in the present embodiment
Figure 875517DEST_PATH_IMAGE001
Get 5, its concrete computing formula is:
, wherein, And The actual measurement occupation rate of representing penult time granularity and last time granularity of green light duration respectively.
Figure 242224DEST_PATH_IMAGE010
Arithmetic mean value for green light actual measurement occupancy of all time granularities in mid-term of obtaining by the stop line detecting device;
The
Figure 398399DEST_PATH_IMAGE002
The bar track is at current period
Figure 637532DEST_PATH_IMAGE003
In the
Figure 246368DEST_PATH_IMAGE005
The saturation degree of individual time granularity
Figure 357543DEST_PATH_IMAGE042
, wherein,
Figure 368224DEST_PATH_IMAGE017
For obtain by the stop line detecting device
Figure 273863DEST_PATH_IMAGE002
The bar track is at current period
Figure 369995DEST_PATH_IMAGE003
In the The measured discharge of individual time granularity,
Figure 320689DEST_PATH_IMAGE018
Be
Figure 459546DEST_PATH_IMAGE019
The duration of individual time granularity,
Figure 42974DEST_PATH_IMAGE020
Be
Figure 230373DEST_PATH_IMAGE002
The bar track is at current period Interior saturation headway, this saturation headway obtains by historical data analysis, calculates green light initial stage saturation degree according to above-mentioned formula
Figure 463088DEST_PATH_IMAGE013
, wherein,
Figure 533812DEST_PATH_IMAGE018
Be taken as
Figure 524902DEST_PATH_IMAGE001
If the green light duration can by
Figure 364682DEST_PATH_IMAGE001
Divide exactly second, then directly calculates green light saturation degree in latter stage according to above-mentioned formula
Figure 281560DEST_PATH_IMAGE015
, wherein,
Figure 574001DEST_PATH_IMAGE018
Value be
If the green light duration can not by
Figure 63069DEST_PATH_IMAGE001
Divide exactly second, then
Figure 652313DEST_PATH_IMAGE015
For with Second be the weighted mean value of saturation computation value of the penult time granularity of the saturation computation value of last time granularity of green light duration of duration and green light duration, the duration of time granularity second from the bottom is
Figure 30522DEST_PATH_IMAGE001
Second, its concrete computing formula is:
Figure 2011102257736100002DEST_PATH_IMAGE043
, wherein,
Figure 15533DEST_PATH_IMAGE044
And
Figure 2011102257736100002DEST_PATH_IMAGE045
The saturation computation value of representing penult time granularity and last time granularity of green light duration respectively.
Green light saturation degree in mid-term
Figure 41258DEST_PATH_IMAGE014
It then is the arithmetic mean value of green light saturation computation value of all time granularities in mid-term.
Green light initial stage occupation rate, green light occupation rate in mid-term, green light occupation rate in latter stage, red light initial stage occupancy, green light initial stage saturation degree, green light saturation degree in mid-term and the green light saturation degree in latter stage in step 3, every track of foundation judges that the traffic behavior of current track in current period is unimpeded, crowded or stops up, wherein,
Figure 308291DEST_PATH_IMAGE002
The bar track is at current period
Figure 179295DEST_PATH_IMAGE003
Interior traffic behavior
Figure 848174DEST_PATH_IMAGE021
Determining step be:
Step 3.1, judge the
Figure 44800DEST_PATH_IMAGE002
The bar track is at current period
Figure 533550DEST_PATH_IMAGE003
Interior green light initial stage occupation rate
Figure 966500DEST_PATH_IMAGE009
, green light occupation rate in mid-term
Figure 489885DEST_PATH_IMAGE010
, green light occupation rate in latter stage
Figure 591833DEST_PATH_IMAGE011
, red light initial stage occupancy
Figure 833459DEST_PATH_IMAGE012
, green light initial stage saturation degree
Figure 311845DEST_PATH_IMAGE013
, green light saturation degree in mid-term
Figure 689737DEST_PATH_IMAGE014
And green light saturation degree in latter stage
Figure 228165DEST_PATH_IMAGE015
Whether satisfy condition 1 or condition 2,, then skip to step 3.7 if satisfy, otherwise, skip to step 3.2, wherein,
Condition 1 is: Occ1≤ ≤ 100% and Occ3≤
Figure 472120DEST_PATH_IMAGE010
≤ 100% and Occ3≤ ≤ 100% and Occ2≤
Figure 413848DEST_PATH_IMAGE012
≤ 100% and 0≤
Figure 630066DEST_PATH_IMAGE013
＜S1 and 0≤ ＜S1 and 0≤
Figure 740421DEST_PATH_IMAGE015
＜S1;
Condition 2 is: 0＜ ≤ 100% and Occ1≤ ＜Occ3 and Occ1≤ ＜Occ3 and Occ2≤
Figure 122413DEST_PATH_IMAGE012
≤ 100% and 0≤
Figure 235862DEST_PATH_IMAGE013
＜S2 and S2≤ ＜S3 and S2≤
Figure 323084DEST_PATH_IMAGE015
＜S3;
Step 3.2, the value of counter a is added 1, enter step 3.3;
Step 3.3, judge whether the value of counter a is not less than state continuance periodicity DurN, if then enter step 3.5, otherwise enter step 3.4;
Step 3.4, with
Figure 587843DEST_PATH_IMAGE002
The bar track is at current period Interior traffic behavior
Figure 455360DEST_PATH_IMAGE021
Be set at crowdedly, enter next step of step 3;
Step 3.5, with
Figure 483359DEST_PATH_IMAGE002
The bar track is at current period
Figure 602624DEST_PATH_IMAGE003
Interior traffic behavior
Figure 57876DEST_PATH_IMAGE021
Be set at obstruction, enter next step of step 3;
Step 3.6, the value of counter a is returned 0, enter step 3.7;
Step 3.7, judge the
Figure 895382DEST_PATH_IMAGE002
The bar track is at current period
Figure 461493DEST_PATH_IMAGE003
Interior green light initial stage occupation rate
Figure 435265DEST_PATH_IMAGE009
, green light occupation rate in mid-term
Figure 61419DEST_PATH_IMAGE010
, green light occupation rate in latter stage
Figure 625036DEST_PATH_IMAGE011
, red light initial stage occupancy , green light initial stage saturation degree
Figure 88695DEST_PATH_IMAGE013
, green light saturation degree in mid-term
Figure 885750DEST_PATH_IMAGE014
And green light saturation degree in latter stage
Figure 432269DEST_PATH_IMAGE015
Whether satisfy condition 3 or condition 4,, then skip to step 3.4 if satisfy, otherwise, skip to step 3.8, wherein,
Condition 3 is: 0＜
Figure 340182DEST_PATH_IMAGE009
≤ 100% and Occ2≤
Figure 288547DEST_PATH_IMAGE010
≤ 100% and Occ3≤
Figure 256503DEST_PATH_IMAGE011
≤ 100% and Occ1≤
Figure 352635DEST_PATH_IMAGE012
≤ Occ2 and 0≤
Figure 500457DEST_PATH_IMAGE013
＜S2 and 0≤
Figure 631224DEST_PATH_IMAGE014
＜S2 and 0≤
Figure 707765DEST_PATH_IMAGE015
＜S1;
Condition 4 is: 0＜
Figure 25613DEST_PATH_IMAGE009
≤ 100% and Occ3≤
Figure 478591DEST_PATH_IMAGE010
≤ 100% and Occ2≤
Figure 463865DEST_PATH_IMAGE011
≤ Occ3 and Occ1≤
Figure 711307DEST_PATH_IMAGE012
≤ Occ2 and 0≤
Figure 516452DEST_PATH_IMAGE013
＜S2 and 0≤
Figure 6077DEST_PATH_IMAGE014
＜S1 and 0＜
Figure 845857DEST_PATH_IMAGE015
＜S2;
Step 3.8, with
Figure 264200DEST_PATH_IMAGE002
The bar track is at current period
Figure 822220DEST_PATH_IMAGE003
Interior traffic behavior
Figure 351421DEST_PATH_IMAGE021
Be set at unimpededly, enter next step of step 3;
So far, can judge the traffic behavior in every track,, then can carry out step 4 if wish to obtain the traffic behavior of every road of each intersection in the traffic network, wherein, the
Figure 311287DEST_PATH_IMAGE022
The bar road is by equidirectional
Figure 900531DEST_PATH_IMAGE023
The bar track is formed, and as shown in Figure 3, the determining step of this road traffic state is:
Step 4.1, setting constant
Figure 680269DEST_PATH_IMAGE024
,
Figure 511696DEST_PATH_IMAGE025
And
Figure 326068DEST_PATH_IMAGE026
,
Figure 86214DEST_PATH_IMAGE024
,
Figure 353247DEST_PATH_IMAGE025
And Be used for representing unimpeded, crowded and obstruction respectively, set unimpeded lower threshold , unimpeded upper limit threshold
Figure 89756DEST_PATH_IMAGE028
, crowded lower threshold
Figure 844086DEST_PATH_IMAGE029
And crowded upper limit threshold
Figure 300473DEST_PATH_IMAGE030
, wherein, ≤
Figure 925806DEST_PATH_IMAGE024
＜
Figure 167432DEST_PATH_IMAGE028
,
Figure 645818DEST_PATH_IMAGE029
≤
Figure 23709DEST_PATH_IMAGE025
≤
Figure 562138DEST_PATH_IMAGE030
At the traffic route situation in Shanghai City,
Figure 25481DEST_PATH_IMAGE024
,
Figure 806093DEST_PATH_IMAGE025
And
Figure 38491DEST_PATH_IMAGE026
Can be set at 1,3,9 respectively, unimpeded lower threshold
Figure 747821DEST_PATH_IMAGE027
, unimpeded upper limit threshold
Figure 698459DEST_PATH_IMAGE028
, crowded lower threshold
Figure 518648DEST_PATH_IMAGE029
And crowded upper limit threshold
Figure 871132DEST_PATH_IMAGE030
Can be set at 1,2,2,3 respectively;
Step 4.2, select the
Figure 751363DEST_PATH_IMAGE022
Article one track of bar road;
Step 4.3, the current track of judgement are at current period
Figure 189297DEST_PATH_IMAGE003
In traffic behavior whether be unimpeded, if not, enter step 4.4, if, then with current track at current period Interior traffic behavior value is set at
Figure 518702DEST_PATH_IMAGE024
, judge whether to have calculated
Figure 569835DEST_PATH_IMAGE022
All tracks of bar road are if then and enter step 4.6, select next bar track to return execution in step 4.3 if not;
Step 4.4, the current track of judgement are at current period
Figure 495066DEST_PATH_IMAGE003
In traffic behavior whether be crowded, if not, then enter step 4.5, if, then with current track at current period
Figure 657057DEST_PATH_IMAGE003
Interior traffic behavior value is set at
Figure 984133DEST_PATH_IMAGE025
, judge whether to have calculated
Figure 940588DEST_PATH_IMAGE022
All tracks of bar road are if then and enter step 4.6, select next bar track to return execution in step 4.3 if not;
Step 4.5, with current track at current period
Figure 353114DEST_PATH_IMAGE003
Interior traffic behavior value is set at
Figure 817332DEST_PATH_IMAGE026
, judge whether to have calculated All tracks of bar road are if then and enter step 4.6, select next bar track to return execution in step 4.3 if not;
Step 4.6, calculate the
Figure 391849DEST_PATH_IMAGE022
The bar road is at current period
Figure 963776DEST_PATH_IMAGE003
Interior road traffic state value ,
Figure 831555DEST_PATH_IMAGE032
, wherein,
Figure 395392DEST_PATH_IMAGE033
Expression the
Figure 959009DEST_PATH_IMAGE022
In the bar road
Figure 328810DEST_PATH_IMAGE035
The traffic behavior value in bar track,
Figure 484985DEST_PATH_IMAGE036
And
Figure 219723DEST_PATH_IMAGE037
Represent respectively
Figure 766242DEST_PATH_IMAGE022
In the bar road
Figure 674155DEST_PATH_IMAGE035
The bar track is at current period
Figure 622520DEST_PATH_IMAGE003
Interior corresponding phase green light duration and corresponding phase amber light duration;
Step 4.7, if
Figure 590476DEST_PATH_IMAGE027
≤
Figure 686608DEST_PATH_IMAGE031
＜
Figure 834430DEST_PATH_IMAGE028
, then with
Figure 699618DEST_PATH_IMAGE022
The bar road is at current period
Figure 776158DEST_PATH_IMAGE003
Interior road traffic state
Figure 359586DEST_PATH_IMAGE038
Be set at unimpeded, otherwise, enter step 4.8;
Step 4.8, if
Figure 812564DEST_PATH_IMAGE029
≤
Figure 532259DEST_PATH_IMAGE031
≤
Figure 779700DEST_PATH_IMAGE030
, then with The bar road is at current period
Figure 340050DEST_PATH_IMAGE003
Interior road traffic state
Figure 117513DEST_PATH_IMAGE038
Be set at crowded, otherwise, with
Figure 598173DEST_PATH_IMAGE022
The bar road is at current period
Figure 156193DEST_PATH_IMAGE003
Interior road traffic state
Figure 685394DEST_PATH_IMAGE038
Be set at obstruction.
Step 5, enter next cycle, return execution in step 2 again.

Claims (3)

1. urban ground road traffic state automatic distinguishing method based on the stop line detecting device, it is characterized in that: step is:
Step 1, obtain the flow and the occupation rate in every track of each intersection in the urban ground road traffic road network by the stop line detector period, the cycle of each intersection is set at the cycle that these intersection traffic lights change, simultaneously, with
Figure 2011102257736100001DEST_PATH_IMAGE001
Divide each cycle for time granularity second, set counter a, its initial value is made as 0, and rule of thumb sets the first occupation rate threshold value Occ1, the second occupation rate threshold value Occ2, the 3rd occupation rate threshold value Occ3, the first saturation degree threshold value S1, the second saturation degree threshold value S2, the 3rd saturation degree threshold value S3 and state continuance periodicity DurN;
Step 2, by stop line detector acquisition the
Figure 720806DEST_PATH_IMAGE002
The bar track is at current period
Figure 2011102257736100001DEST_PATH_IMAGE003
In the The occupation rate of individual time granularity
Figure 575630DEST_PATH_IMAGE006
Calculate green light initial stage occupation rate, green light mid-term occupation rate, green light latter stage occupation rate, red light initial stage occupancy, green light initial stage saturation degree, green light mid-term saturation degree and the green light latter stage saturation degree of every track in current period, wherein, green light initial stage and red light initial stage refer to respectively green light and red light duration before
Figure 293050DEST_PATH_IMAGE001
Second, if the green light duration can by
Figure 688259DEST_PATH_IMAGE001
Second divide exactly, then green light refers to the last of green light duration latter stage
Figure 938850DEST_PATH_IMAGE001
Second, if the green light duration can not by
Figure 761312DEST_PATH_IMAGE001
Second divide exactly, then green light refers to the last of green light duration latter stage
Figure 2011102257736100001DEST_PATH_IMAGE007
Second, wherein, For with the green light duration divided by
Figure 102612DEST_PATH_IMAGE001
Second resulting remainder, green light are meant the green light duration of removing green light initial stage and green light latter stage mid-term, establish the
Figure 709174DEST_PATH_IMAGE002
The bar track is at current period
Figure 702538DEST_PATH_IMAGE003
Green light initial stage occupation rate, green light occupation rate in mid-term, green light occupation rate in latter stage, red light initial stage occupancy, green light initial stage saturation degree, green light saturation degree in mid-term and green light saturation degree in latter stage be respectively
Figure 893085DEST_PATH_IMAGE008
,
Figure 2011102257736100001DEST_PATH_IMAGE009
,
Figure 567780DEST_PATH_IMAGE010
,
Figure 2011102257736100001DEST_PATH_IMAGE011
,
Figure 28849DEST_PATH_IMAGE012
, ,
Figure 396376DEST_PATH_IMAGE014
, then have:
Figure 638002DEST_PATH_IMAGE008
,
Figure 349343DEST_PATH_IMAGE010
And
Figure 930497DEST_PATH_IMAGE011
Be respectively the actual measurement occupation rate at the green light initial stage, green light latter stage and the red light initial stage that obtain by the stop line detecting device,
Figure 265664DEST_PATH_IMAGE009
Arithmetic mean value for green light actual measurement occupancy of all time granularities in mid-term of obtaining by the stop line detecting device;
The
Figure 932268DEST_PATH_IMAGE002
The bar track is at current period
Figure 11083DEST_PATH_IMAGE003
In the
Figure 446743DEST_PATH_IMAGE005
The saturation degree of individual time granularity , wherein,
Figure 648749DEST_PATH_IMAGE016
For obtain by the stop line detecting device
Figure 599388DEST_PATH_IMAGE002
The bar track is at current period
Figure 419576DEST_PATH_IMAGE003
In the
Figure 772060DEST_PATH_IMAGE005
The measured discharge of individual time granularity,
Figure 2011102257736100001DEST_PATH_IMAGE017
Be The duration of individual time granularity,
Figure 2011102257736100001DEST_PATH_IMAGE019
Be
Figure 464127DEST_PATH_IMAGE002
The bar track is at current period
Figure 822428DEST_PATH_IMAGE003
Interior saturation headway, this saturation headway obtains by historical data analysis, calculates green light initial stage saturation degree according to above-mentioned formula
Figure 232680DEST_PATH_IMAGE012
And green light saturation degree in latter stage
Figure 346130DEST_PATH_IMAGE014
, green light saturation degree in mid-term
Figure 209044DEST_PATH_IMAGE013
It then is the arithmetic mean value of green light saturation computation value of all time granularities in mid-term;
Green light initial stage occupation rate, green light occupation rate in mid-term, green light occupation rate in latter stage, red light initial stage occupancy, green light initial stage saturation degree, green light saturation degree in mid-term and the green light saturation degree in latter stage in step 3, every track of foundation judges that the traffic behavior of current track in current period is unimpeded, crowded or stops up, wherein,
Figure 433352DEST_PATH_IMAGE002
The bar track is at current period
Figure 931067DEST_PATH_IMAGE003
Interior traffic behavior
Figure 215418DEST_PATH_IMAGE020
Determining step be:
Step 3.1, judge the
Figure 565627DEST_PATH_IMAGE002
The bar track is at current period
Figure 593626DEST_PATH_IMAGE003
Interior green light initial stage occupation rate
Figure 712892DEST_PATH_IMAGE008
, green light occupation rate in mid-term
Figure 902565DEST_PATH_IMAGE009
, green light occupation rate in latter stage
Figure 802388DEST_PATH_IMAGE010
, red light initial stage occupancy
Figure 306181DEST_PATH_IMAGE011
, green light initial stage saturation degree
Figure 44068DEST_PATH_IMAGE012
, green light saturation degree in mid-term
Figure 404642DEST_PATH_IMAGE013
And green light saturation degree in latter stage
Figure 729444DEST_PATH_IMAGE014
Whether satisfy condition 1 or condition 2,, then skip to step 3.7 if satisfy, otherwise, skip to step 3.2, wherein,
Condition 1 is: Occ1≤
Figure 974612DEST_PATH_IMAGE008
≤ 100% and Occ3≤
Figure 865208DEST_PATH_IMAGE009
≤ 100% and Occ3≤
Figure 104340DEST_PATH_IMAGE010
≤ 100% and Occ2≤
Figure 713176DEST_PATH_IMAGE011
≤ 100% and 0≤
Figure 824351DEST_PATH_IMAGE012
＜S1 and 0≤
Figure 835033DEST_PATH_IMAGE013
＜S1 and 0≤
Figure 740672DEST_PATH_IMAGE014
＜S1;
Condition 2 is: 0＜
Figure 836804DEST_PATH_IMAGE008
≤ 100% and Occ1≤
Figure 486091DEST_PATH_IMAGE009
＜Occ3 and Occ1≤
Figure 351279DEST_PATH_IMAGE010
＜Occ3 and Occ2≤
Figure 926354DEST_PATH_IMAGE011
≤ 100% and 0≤
Figure 509782DEST_PATH_IMAGE012
＜S2 and S2≤
Figure 697181DEST_PATH_IMAGE013
＜S3 and S2≤
Figure 682455DEST_PATH_IMAGE014
＜S3;
Step 3.2, the value of counter a is added 1, enter step 3.3;
Step 3.3, judge counter a whether be not less than state continuance periodicity DurN, if then enter step 3.5, otherwise enter step 3.4;
Step 3.4, with
Figure 929896DEST_PATH_IMAGE002
The bar track is at current period
Figure 621DEST_PATH_IMAGE003
Interior traffic behavior
Figure 991710DEST_PATH_IMAGE020
Be set at crowdedly, enter next step of step 3;
Step 3.5, with The bar track is at current period
Figure 748369DEST_PATH_IMAGE003
Interior traffic behavior
Figure 978493DEST_PATH_IMAGE020
Be set at obstruction, enter next step of step 3;
Step 3.6, the value of counter a is returned 0, enter step 3.7;
Step 3.7, judge the
Figure 835590DEST_PATH_IMAGE002
The bar track is at current period Interior green light initial stage occupation rate
Figure 119121DEST_PATH_IMAGE008
, green light occupation rate in mid-term
Figure 102121DEST_PATH_IMAGE009
, green light occupation rate in latter stage
Figure 497330DEST_PATH_IMAGE010
, red light initial stage occupancy
Figure 482341DEST_PATH_IMAGE011
, green light initial stage saturation degree
Figure 570383DEST_PATH_IMAGE012
, green light saturation degree in mid-term
Figure 775099DEST_PATH_IMAGE013
And green light saturation degree in latter stage
Figure 974000DEST_PATH_IMAGE014
Whether satisfy condition 3 or condition 4,, then skip to step 3.4 if satisfy, otherwise, skip to step 3.8, wherein,
Condition 3 is: 0＜
Figure 314982DEST_PATH_IMAGE008
≤ 100% and Occ2≤
Figure 573925DEST_PATH_IMAGE009
≤ 100% and Occ3≤ ≤ 100% and Occ1≤
Figure 2949DEST_PATH_IMAGE011
≤ Occ2 and 0≤
Figure 980131DEST_PATH_IMAGE012
＜S2 and 0≤ ＜S2 and 0≤
Figure 323705DEST_PATH_IMAGE014
＜S1;
Condition 4 is: 0＜
Figure 864408DEST_PATH_IMAGE008
≤ 100% and Occ3≤
Figure 445562DEST_PATH_IMAGE009
≤ 100% and Occ2≤
Figure 780728DEST_PATH_IMAGE010
≤ Occ3 and Occ1≤ ≤ Occ2 and 0≤
Figure 526147DEST_PATH_IMAGE012
＜S2 and 0≤
Figure 460343DEST_PATH_IMAGE013
＜S1 and 0＜ ＜S2;
Step 3.8, with
Figure 120311DEST_PATH_IMAGE002
The bar track is at current period Interior traffic behavior
Figure 292984DEST_PATH_IMAGE020
Be set at unimpededly, enter next step of step 3;
Step 4, enter next cycle, return execution in step 2 again.
2. a kind of urban ground road traffic state automatic distinguishing method as claimed in claim 1 based on the stop line detecting device, it is characterized in that: between described step 3 and described step 4, further comprise the traffic behavior of judging every road of each intersection in the urban ground road traffic road network, wherein,
Figure 2011102257736100001DEST_PATH_IMAGE021
The bar road is by equidirectional
Figure 173215DEST_PATH_IMAGE022
The bar track is formed, and the determining step of this road traffic state is:
Steps A, setting constant
Figure 2011102257736100001DEST_PATH_IMAGE023
,
Figure 47368DEST_PATH_IMAGE024
And
Figure 2011102257736100001DEST_PATH_IMAGE025
,
Figure 405668DEST_PATH_IMAGE023
,
Figure 878238DEST_PATH_IMAGE024
And
Figure 663791DEST_PATH_IMAGE025
Be used for representing unimpeded, crowded and obstruction respectively, set unimpeded lower threshold
Figure 526705DEST_PATH_IMAGE026
, unimpeded upper limit threshold
Figure 2011102257736100001DEST_PATH_IMAGE027
, crowded lower threshold
Figure 187231DEST_PATH_IMAGE028
And crowded upper limit threshold
Figure 2011102257736100001DEST_PATH_IMAGE029
, wherein,
Figure 451990DEST_PATH_IMAGE026
≤
Figure 736341DEST_PATH_IMAGE023
＜
Figure 86551DEST_PATH_IMAGE027
,
Figure 848971DEST_PATH_IMAGE028
≤
Figure 968236DEST_PATH_IMAGE024
≤
Step B, select the Article one track of bar road;
Step C, the current track of judgement are at current period
Figure 331500DEST_PATH_IMAGE003
In traffic behavior whether be unimpeded, if not, enter step D, if, then with current track at current period Interior traffic behavior value is set at
Figure 931425DEST_PATH_IMAGE023
, judge whether to have calculated
Figure 256227DEST_PATH_IMAGE021
All tracks of bar road are if then and enter step F, select next bar track to return execution in step C if not;
Step D, the current track of judgement are at current period
Figure 298133DEST_PATH_IMAGE003
In traffic behavior whether be crowded, if not, then enter step e, if, then with current track at current period Interior traffic behavior value is set at
Figure 687580DEST_PATH_IMAGE024
, judge whether to have calculated
Figure 296416DEST_PATH_IMAGE021
All tracks of bar road are if then and enter step F, select next bar track to return execution in step C if not;
Step e, with current track at current period
Figure 407592DEST_PATH_IMAGE003
Interior traffic behavior value is set at
Figure 418273DEST_PATH_IMAGE025
, judge whether to have calculated
Figure 323912DEST_PATH_IMAGE021
All tracks of bar road are if then and enter step F, select next bar track to return execution in step C if not;
Step F, calculate the
Figure 420044DEST_PATH_IMAGE021
The bar road is at current period
Figure 803752DEST_PATH_IMAGE003
Interior road traffic state value
Figure 370737DEST_PATH_IMAGE030
,
Figure 2011102257736100001DEST_PATH_IMAGE031
, wherein,
Figure 447278DEST_PATH_IMAGE032
Expression the
Figure 968389DEST_PATH_IMAGE021
In the bar road
Figure 155788DEST_PATH_IMAGE034
The traffic behavior value in bar track,
Figure 2011102257736100001DEST_PATH_IMAGE035
And
Figure 577280DEST_PATH_IMAGE036
Represent respectively
Figure 824721DEST_PATH_IMAGE021
In the bar road
Figure 895446DEST_PATH_IMAGE034
The bar track is at current period
Figure 886535DEST_PATH_IMAGE003
Interior corresponding phase green light duration and corresponding phase amber light duration;
Step G, if
Figure 726315DEST_PATH_IMAGE026
≤
Figure 206975DEST_PATH_IMAGE030
＜ , then with The bar road is at current period
Figure 418842DEST_PATH_IMAGE003
Interior road traffic state
Figure 2011102257736100001DEST_PATH_IMAGE037
Be set at unimpeded, otherwise, enter step H;
Step H, if
Figure 8087DEST_PATH_IMAGE028
≤
Figure 53403DEST_PATH_IMAGE030
≤
Figure 323979DEST_PATH_IMAGE029
, then with
Figure 872772DEST_PATH_IMAGE021
The bar road is at current period
Figure 397032DEST_PATH_IMAGE003
Interior road traffic state
Figure 664065DEST_PATH_IMAGE037
Be set at crowded, otherwise, with
Figure 535069DEST_PATH_IMAGE021
The bar road is at current period
Figure 203948DEST_PATH_IMAGE003
Interior road traffic state
Figure 400574DEST_PATH_IMAGE037
Be set at obstruction.
3. a kind of urban ground road traffic state automatic distinguishing method based on the stop line detecting device as claimed in claim 1 or 2 is characterized in that: in described step 2, if the green light duration can not by
Figure 889324DEST_PATH_IMAGE001
Second divide exactly then the
Figure 829598DEST_PATH_IMAGE002
The bar track is at current period
Figure 352983DEST_PATH_IMAGE003
Green light occupation rate in latter stage For with Second be the weighted mean value of actual measurement occupation rate of the penult time granularity of the actual measurement occupation rate of last time granularity of green light duration of duration and green light duration, the The bar track is at current period
Figure 51370DEST_PATH_IMAGE003
Green light saturation degree in latter stage
Figure 652115DEST_PATH_IMAGE014
For with
Figure 53141DEST_PATH_IMAGE007
It second is the weighted mean value of saturation computation value of the penult time granularity of the saturation computation value of last time granularity of green light duration of duration and green light duration.
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