CN100433072C - The single-loop and single-section onsite detection method for traffic situation and traffic accident - Google Patents
The single-loop and single-section onsite detection method for traffic situation and traffic accident Download PDFInfo
- Publication number
- CN100433072C CN100433072C CNB200710037746XA CN200710037746A CN100433072C CN 100433072 C CN100433072 C CN 100433072C CN B200710037746X A CNB200710037746X A CN B200710037746XA CN 200710037746 A CN200710037746 A CN 200710037746A CN 100433072 C CN100433072 C CN 100433072C
- Authority
- CN
- China
- Prior art keywords
- traffic
- detection
- events
- called
- detection method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims description 125
- 206010039203 Road traffic accident Diseases 0.000 title 1
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000010998 test method Methods 0.000 claims abstract description 15
- 230000008859 change Effects 0.000 claims description 38
- 238000011065 in-situ storage Methods 0.000 claims description 12
- 230000010354 integration Effects 0.000 claims description 4
- 238000012360 testing method Methods 0.000 abstract description 5
- 230000004069 differentiation Effects 0.000 description 11
- 238000011144 upstream manufacturing Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 230000035945 sensitivity Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000002457 bidirectional effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000009514 concussion Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 239000007799 cork Substances 0.000 description 1
- 230000002498 deadly effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 210000005069 ears Anatomy 0.000 description 1
- 210000003128 head Anatomy 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- CCEKAJIANROZEO-UHFFFAOYSA-N sulfluramid Chemical group CCNS(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F CCEKAJIANROZEO-UHFFFAOYSA-N 0.000 description 1
Images
Landscapes
- Traffic Control Systems (AREA)
Abstract
This invention relates to one traffic situation and affairs single coil section spot test method, which uses single coil collected section car information for first spot test, wherein, it adopts traffic situation sort method, constant traffic test method and burst traffic test method and to fix these three parts into one traffic information collector; it uses traffic information collector to provide four types of information with each one driving and leaving and shape and speed.
Description
Technical field
The invention belongs to the intelligent transportation system field, only relate to the traffic of road and the detection method of traffic events, particularly a kind of single section information of vehicles that utilizes unicoil to gather detects the method for traffic and traffic events in the primary scene.
Background technology
Traffic is the traffic flow form of road, and traffic events is unusual traffic, and traffic hazard relates to the traffic events of personnel's property safety.Traffic hazard is the traffic events that intelligent transportation system is particularly paid close attention to, though directly detect also out of reachly, allows computing machine automatic transport detection incident, submits to the supvr to confirm, still can accomplish something.The basic object of traffic and the research of traffic events detection technique is the traffic flow in travels down, the fundamental purpose of research be towards all participants automatically gather with the real-time traffic situation of issue road and the unusual traffic that detects automatically with the report road towards traffic administration person be traffic events.
Referring to Fig. 1, traffic hazard has taken place when between adjacent two detection sections (square section of equidirectional traveling lane is called section), if accident impact can involve the detection section, the vehicle equipment that so generally is installed in spot upstream detection section will detect that the speed of a motor vehicle descends, density rises, and the vehicle equipment that is installed in spot detected downstream section will detect density and descend.Trace routine is according to upstream or downstream or the detected transport information of upstream and downstream vehicle equipment, when finding that certain operation result that detects section or adjacent detection section exceeds the differentiation threshold of setting, just judge between the upstream of detecting section or downstream or upstream and downstream detection section traffic events may take place.This is the basic functional principle that traffic and traffic events detect.
More famous in the world traffic and traffic events detection method have following two kinds: the one, and double break face center detection method, promptly the TSCn detection method also claims California detection method.Double break face center detection method is to wait until that could detect unusually all appears in two adjacent detection sections, though rate of false alarm is less, detection speed is slower, and rate of failing to report is bigger.Because double break face center detection method has many deficiencies, has invented a kind of the single section center detection method that needs the single section traffic data, i.e. McMaster detection method afterwards.Though the detection speed of single section center detection method increases, but its system architecture mode and source information and double break face center detection method are identical, and both performances are obviously difference not.The common feature of above-mentioned two kinds of detection methods is: trace routine is all installed concentratedly at traffic surveillance and control center, had three kinds from the source information that detects on-the-spot vehicle equipment: flow, speed and time occupation rate.For the data transmission that the alleviates magnanimity heavy burden to central computer and communication network, these source information of above-mentioned single, double section center detection method utilization all are that the cycle of uploading is generally 60s～300s through the cycle of overcompression mean value.
Generally believe in the traffic monitoring field, existing traffic and traffic events detection technique all are not so good as people's eyes and thinking, as if reasonable outwardly, just not saying the also helpless situation of those human eyes, haze sleet, pitch-dark etc. for example, can who look with fixed eyes, be devoted to one's duty night and day in the face of plodding traffic image? truth is told us, drops into the effect of the big commentaries on classics of manpower wheel for this reason and all knows fairly well, not being that the employee does not make great efforts, is unable to do what one wishes really.Though some ability at the modern intelligence machine of various fields has surpassed human, but at traffic and traffic events detection range, because the complicacy of the human traffic system that extensively gets involved, do not say and surmount manpower, so far do not come out even can partly substitute the utility system of manpower, dropping into the big commentaries on classics of manpower wheel also is a way of having no idea.Intelligent transportation system active demand advanced person's traffic and traffic events detection technique, traffic and traffic events detection technique are called progressive and revolution.
For traffic hazard, time is life, is exactly property, but among the over head and ears implacable always contradiction of existing various centers detection method: pursued detection speed and will strengthen rate of false alarm, reduced rate of false alarm and will reduce detection speed even fail to report.Detection speed and rate of false alarm have become a pair of locked together deadly enemy.In the face of traffic and traffic events detection technique consistent backward situation decades, and the lower problem of the detection sensitivity of prior art, many countries comprise that some mechanisms of China have all dropped into great amount of manpower and material resources to this Study on Technology, regrettably do not have so far to occur being put to practical achievement.
The applicant has disclosed the many disadvantages of the system architecture method of traditional vehicle detection technology and separation two places in previous patented claim 200610027885.X " traffic information single-coil field detection method ", disclose employing twin coil is changed into unicoil and moves trace routine to on-the-spot solution from the center, promoted the progress of traffic and traffic events detection technique effectively.The present invention will further disclose, and adopt which concrete detection method actually, and traffic and traffic events detection technique are made progress.
Summary of the invention
Task of the present invention provides a kind of traffic and traffic events unicoil single section in-situ check and test method, it has solved the detection speed of single, double section center detection method and the contradictory problems between the rate of false alarm, and has solved the low problem of prior art detection sensitivity.
Technical solution of the present invention is as follows:
A kind of traffic and traffic events unicoil single section in-situ check and test method, it utilizes the single section information of vehicles of unicoil collection to detect in the primary scene, take traffic sorting technique, normality traffic detection method and sudden change traffic detection method, and with this three sectoral integration be installed in one and be located in and detect in the on-the-spot traffic information collection device, utilize four kinds of source information that this traffic information collection device provides promptly: the sailing into of each tested vehicle, sail out of, vehicle and the speed of a motor vehicle;
Described traffic sorting technique is subdivided into six ranks with traffic, and is promptly idle, unimpeded, busy, crowd, block up and block, and high-sensitivity detection is carried out in the normality traffic;
Described normality traffic detection method is according to the flow of traffic flow, speed and density, set the average period in the polynary weighting running mean formula, the value of slip interval and power array, to slide and be made as 5s at interval, the span of average period is made as 60s～600s, the power array then is to calculate n weights that obtain by normal distribution law in average period, slide the finish time at interval at each, the vehicle of each car in constantly sliding each at interval, the speed of a motor vehicle, calculate present flow rate by polynary weighting running mean formula, speed, the weighting sliding average of density detects traffic and traffic events then;
Described sudden change traffic detection method according to the sailing into of vehicle, sail out of, vehicle, speed of a motor vehicle source information, the sudden change traffic object of traffic or traffic events is listed in the detection inventory, make each sudden change traffic object possess the detected parameters of traffic and traffic events and differentiate threshold, and the priority of the traffic trace routine of suddenling change is set to the superlative degree, in case detect the detection circulation that sudden change traffic object is just broken the normality traffic immediately, unconditionally change original traffic according to prediction scheme, even send event alarms.
Idle rank in the described traffic sorting technique is called for short empty, and color code is a cyan; Unimpeded rank is called for short smooth, and color code is green; Busy rank is called for short busy, and color code is yellow; Crowded rank is called for short squeezes, and color code is orange; Congestion level is called for short stifled, and color code is a pink colour; The blocking-up rank is called for short disconnected, and color code is red.
Described traffic sorting technique also comprises two inner situations of using, and does not promptly have car and stop.
No car in the described traffic sorting technique is called for short not to be had, and color code is a white; Stop to be called for short and stop, color code is brown.
The span of described average period is got 60s～300s at blocked road.
The span of described average period is got 300s～600s in controlled intersection.
Traffic of the present invention and traffic events detection method are called for short the SanQuan detection method, are made up of traffic sorting technique, normality traffic detection method and three parts of sudden change traffic detection method.These three parts are installed in the traffic information collection device integratedly, utilize the vehicle of the single section that this collector unicoil detects to sail into, sail out of, vehicle, speed information, automatic transport detection situation and traffic events at the scene.
The present invention has improved the detection sensitivity of traffic and traffic events by traffic being subdivided into sky, smooth, busy, six ranks of squeezing, block up, break; By adopting normality traffic detection method, realized tight tracking to normality traffic flow fluctuations; By adopting sudden change traffic detection method, accelerated the detection speed of traffic and traffic events.The present invention has eliminated the wrong report phenomenon by above-mentioned every technical measures, has realized the detecting automatically fast and accurately traffic and traffic events that intelligent transportation system is looked forward to always.
Description of drawings
Fig. 1 is the schematic diagram that traffic and traffic events detect automatically.
Fig. 2 is by traffic of the present invention and the assembly type traffic of traffic events unicoil single section in-situ check and test method and the synoptic diagram of traffic events testing process.
Fig. 3 is by traffic of the present invention and the dissipation type traffic of traffic events unicoil single section in-situ check and test method and the synoptic diagram of traffic events testing process.
Embodiment
Below in conjunction with Fig. 1, Fig. 2 and Fig. 3, introduce embodiments of the present invention, embodiment and technical application effect in detail.
By traffic of the present invention and traffic events unicoil single section in-situ check and test method, the single section information of vehicles that utilizes unicoil to gather, adopt following traffic sorting technique, normality traffic detection method and sudden change traffic detection method, carry out the detection of traffic and traffic events in the primary scene.
The traffic of road is the common dynamic information of paying close attention to of traffic participant and supvr, and what have thinks that it is a kind of subjective feeling amount, the outwardness amount of thinking that has, and how being defined definitely and explaining is a problem that has much dispute really.Do not seek unity of standard so far in countries in the world, this is also ripe not enough from existing traffic of another side illustration and traffic events detection technique.
By traffic sorting technique of the present invention, according to flow, speed, the density parameter of traffic flow, promptly, traffic is subdivided into six ranks according to the unobstructed and chocking-up degree of traffic, they are: idle, unimpeded, busy, crowd, block up, block.
Idle rank in the described traffic sorting technique is called for short empty (Free), and color code is a cyan; Unimpeded rank is called for short smooth (Smooth), and color code is green; Busy rank is called for short busy (Busy), and color code is yellow; Crowded rank is called for short squeezes (Crowd), and color code is orange; Congestion level is called for short stifled (jam), and color code is a pink colour; The blocking-up rank is called for short disconnected (Break), and color code is red.
Blackout conditions is most important, but very difficult to the detection of blackout conditions, does not for example have transport need and because of the people hinders the traffic cutout that thing damage etc. causes, both detected information all are to detect on the section not have vehicle '.Distinguish them for correct, the present invention has also defined one only for the inner traffic of using: no car.No car situation is called for short does not have (Nothing), and color code is a white.So-called no car is exactly to detect on the section not have flow in the time limit of setting.When finding no car, just judge Traffic interruption immediately if can determine interruption of communication, otherwise just only do not have car, in order to avoid the empty traffic hazard that happens suddenly when smooth of careless omission traffic in internal report.
In addition, the traffic hazard when occurring in the flow rareness usually sits on a volcano, isolates helpless, and finding as soon as possible and suing and labouring is the lofty mission of intelligent transportation system.In order to finish this mission, the present invention has also defined one only for the inner traffic of using: stop.The stop situation is called for short stops (Stop), and color code is brown.The so-called stop is exactly when the magnitude of traffic flow is rare, if can determine normally not sail out of the detected downstream section after a car sails out of the upstream detection section, just has vehicle to internal report and is trapped between the upstream and downstream detection section, and request is verified.
The above-mentioned sky that can externally issue automatically, smooth, busy, the six grades of traffics of squeezing, block up, break according to different change direction, can be divided into assembly type and dissipation type two classes, change to the blocking-up direction from the free time to be called assembly, otherwise claim dissipation.Blocking-up is extreme assembly type traffic, free time is extreme dissipation type traffic, all the other four kinds of traffics have bidirectional characteristic, just according to the fluctuations of traffic flow, they not only can be the assembly types but also can be the dissipation type, and traffic flow is alternate cycles between this two classes traffic again and again always.Traffic events also has the branch of assembly type and dissipation type, and above-mentioned under certain condition six kinds of traffics all may become traffic events.To the sophisticated category of traffic, achieved high-sensitivity detection to the normality traffic.
Referring to Fig. 1, the principal element that restriction traffic and traffic events detect performance has following 4 points again:
1, the inflow volume of traffic of spot,
2, the outflow volume of traffic of spot,
3, the spot is to the detection distance (its maximal value equals the stroke distances between adjacent detection section) that detects section,
4, detection sensitivity.
Fig. 2 and Fig. 3 have shown the canonical process that traffic and traffic events is detected by detection method of the present invention.As everyone knows, flow into the volume of traffic and depend primarily on transport need, the inflow volume of traffic of spot is big more usually, and detection speed is fast more.Flowing out the volume of traffic can be for current road conditions condition after depending primarily on incident and taking place, and it is more little to flow out the volume of traffic usually, and detection speed is fast more.Flowing into the volume of traffic and flowing out the volume of traffic is the objective reality that people can't control, after section spacing is determined, in order to accelerate detection speed, unique approach is to improve detection sensitivity, and the rank of traffic what, in other words the ability size of detection system perception traffic behavior variation is exactly detection sensitivity just.Number of levels is The more the better in theory, in fact can only select to be fit to the number of levels of detection method because of situation is intricate.Six grades of traffics and two traffics of only using for inside by external issue of the present invention just can realize the high-sensitivity detection to the normality traffic.
How will introduce normality traffic detection method below in detail uses six grades of traffics of the present invention and two only for the inner traffic of using the fluctuations of normality traffic flow to be carried out that highly sensitive tracking detects.
So-called normality traffic is meant the traffic behavior of the situation that do not meet accident, though when the peak because of the road equipment traffic congestion clocklike that supply falls short of demand occurs, need only the traffic hazard that does not meet accident, still belong to the normality traffic.The theoretical foundation of normality traffic detection method is that the classical formula of traffic flow three elements by famous U.S. traffic scholar Hai Tuo proposes, afterwards introduced by the famous scholar's Robert Webster of Britain magnitude of traffic flow conversion factor notion at first claims the sea to take off the Robert Webster theorem again:
K＝Q/V
K is a traffic density in the formula, is called for short density, the pcu/km of unit; Q is in the unit interval to be the standard magnitude of traffic flow after the conversion of standard vehicle according to the form below with the minibus, is called for short flow, the pcu/h of unit; V is a speed of operation, abbreviation speed, the km/h of unit.Wherein flow, speed are directly measured by vehicle equipment, and density is calculated by above-mentioned classical formula and obtained.
The normal flow conversion factor table of various vehicles:
| Vehicle | Minibus | Motor bus | Articulated coach | Buggy | Middle lorry | Truck | Heavy goods vehicle | Towed vehicle | Heavy type is pulled | Motorcycle |
| Coefficient | 1.00 | 2.22 | 3.56 | 1.11 | 1.56 | 1.78 | 2.22 | 2.89 | 3.33 | 0.44 |
The formula of the flow of normality traffic detection method of the present invention, speed, density parameter is as follows:
X in the formula
iBe the weighting running mean sample of flow/velocity/density, n=T/g, wherein T is average period, the s of unit; G is for sliding at interval the s of unit; q
jBe the power array.
The art of above-mentioned formula is called " polynary weighting running mean " formula, and key is average period, the value of interval and power array of sliding.In order as much as possible to follow the tracks of the variation of traffic behavior realistically, the present invention has adopted very little slip at interval, is fixed as 5s.Then be made as 60s～600s according to different road spans average period, and for example the span in blocked road average period is 60s～300s, and for example the span in controlled intersection average period is 300s～600s again.The power array then is to calculate n weights that obtain by normal distribution law in average period.The present invention is much better than the experience density parameter that utilizes the time occupancy data simulation because the classical traffic flow three elements formula of utilization calculates the density parameter that obtains, and has therefore abandoned the time occupancy data of always using till today in decades.
Trace routine is slided the finish time at interval at each, vehicle, the speed of a motor vehicle of each car in constantly sliding each at interval, calculate the weighting sliding average of present flow rate, speed, density as stated above, detect traffic and traffic events then as follows.
Maximum and minimum reference value according to the selected flow of different road conditions, speed, density, with behind its approximate five equilibrium as the differentiation threshold of the assembly type traffic of tool bidirectional characteristic, get velocity amplitude about 1.1 and flow and density value about 0.9 that assembly type traffic is differentiated threshold, as the differentiation threshold of peer's dissipation type traffic, about 10% Si Mite threshold is used for preventing the critical concussion that is harmful to.The differentiation threshold that idle condition and unimpeded situation are changed mutually is more special, only the experience flow of applying unit time.The differentiation threshold of blackout conditions is also more special, only uses the differentiation threshold of no car situation.In order to reduce the no car situation of inaction, the differentiation threshold of no car situation is set at times by experience.Need various unusual traffics are set the differentiation threshold that is various traffic events in addition, comprise duration etc. of the variation sum of series traffic of traffic.
By normality traffic detection method of the present invention, slide the finish time at interval at each, calculate the differentiation the threshold whether flow, speed, the density value that obtain satisfy above-mentioned various traffics more according to the method described above, comprise assembly type and dissipation type traffic, just change traffic immediately if satisfy condition, and further whether differentiation satisfies the differentiation threshold of traffic events, if satisfy condition just report immediately, otherwise just continue to keep original traffic, so move in circles.
From above introduction as can be known, normality traffic detection method of the present invention is compared with at present general in the world center detection method, because the vehicle and the speed of a motor vehicle of each car that the source information that the present invention uses was gathered at that time from the vehicle equipment at the place of living together, therefore can design so short and smallly at interval with sliding, only be 5s.And existing center detection method is made as 60s to the transmission cycle of source information (being equivalent to slip of the present invention at interval).This shows that of the present invention the slip only is 1/12nd of existing center detection method at interval, thereby the traffic that this normality traffic detection method obtains can be followed the actual traffic stream mode more in time, exactly.If detection technique stayed in the center detection method stage, just can't accomplish so in time and accurately to detect.Also have, the prerequisite that the present invention obtains this technical progress is to move the scene detecting operation to from the center.
The actual traffic stream mode is depended in other variation of traffic level, might not as shown in Fig. 2 or Fig. 3, change step by step, What is more, when running into following sudden change traffic behavior, any saltus step of changing all of a sudden all may take place, but in a single day fluctuation dissipates, and must get back in the detection circulation of normality traffic.Normality traffic detection method is the guarantee that detection system is followed the tracks of actual traffic stream, also is the basis that intelligent transportation system can be issued road real-time traffic situation in time, exactly.
So-called sudden change traffic is meant the unusual traffic behavior that is caused by unexpected traffic events.According to the peculiar character of sudden change traffic flow, in conjunction with highly believable vehicle sail into, sail out of, four kinds of source information such as vehicle and the speed of a motor vehicle, the present invention proposes and has adopted the detection method of the traffic that suddenlys change, and has opened up the new road of an acceleration detection traffic and traffic events.
On the section there be sudden change traffic object of common occurrence detecting:
1, the unexpected cataclysm of traffic density;
2, the unusual saltus step of traffic;
3, illogical vehicle is detained;
4, the travel speed that does not conform to convention;
5, the adjacent lane traffic is not normal in the section;
6, the upstream vehicle does not normally sail out of the downstream when the blocked road traffic is idle; Or the like.
Sudden change traffic detection method of the present invention is, at first above-mentioned sudden change traffic object is all listed in and detected in the inventory, make each sudden change traffic object possess the detected parameters of traffic and traffic events and differentiate threshold, and the priority of the traffic trace routine of suddenling change is set to the superlative degree, in case detect the detection circulation that sudden change traffic object is just broken the normality traffic immediately, unconditionally change original traffic according to prediction scheme, even send event alarms.
Account for 50%～70% of total amount separately by the quantity of detected traffic conversion of sudden change traffic detection method and traffic events report according to statistics, visible mutation traffic detection method is big to the contribution of traffic and the progress of traffic events detection technique.Can not accomplish this point and adopt existing center to detect rule, because countless information of vehicles fresh and alive, sudden change is abandoned by the mean value method.Perhaps be that existing domestic and international many detection methods hanker after at first rejecting the source information of sudden change, hardly realize that the sudden change information of these abnormalities has bred sudden change traffic detection method just because misdata appears in vehicle equipment inferior frequently.
The key technical indexes of estimating traffic and traffic events detection technique performance has:
1, detection speed, promptly unit detects the distance detection time, and measurement unit is s/km, is applicable to blocked road, as highway, overpass, urban express way and uncontrolled, conflict free highway section.
2, detection time, measurement unit is s, is applicable to the intersection or the highway section that are subjected to traffic signals control.
3, rate of false alarm, promptly in a period of time, for example one day 24 hours, the number percent of the number of times of error reporting and report sum.
4, rate of failing to report promptly in a period of time, is omitted the number of times of newspaper not and the number percent of the actual number of times that takes place.
More than every index should be applicable to simultaneously that from Practical significance traffic detects (externally issue with) and traffic events detects (internal report is used), yet because of traffic behavior situation complexity, still do not have unified standard and be difficult to quantize, therefore only be used for traffic events at present and detect.This in fact way also has not big harm, because the detection level of traffic events depends on the detection level of traffic after all, the big multipotency of technology that can carry out the traffic events detection with flying colors carries out traffic with flying colors and detects.In order to check the technical indicator of event detection, must be to the next exercisable definition of traffic events.The General Definition of traffic events is: traffic events is the something to write home about that influences vehicle pass-through that takes place on road.The present invention is defined as the traffic events in the technical indicator: traffic events is the traffic hazard of being assert by vehicle supervision department.
The same with the effective ways of check vehicle equipment performance, it also is that live video is verified in contrast that check traffic and traffic events detect the performance efficient ways.
Be some embodiment of the present invention below.
By classification and Detection method of the present invention, with traffic sorting technique, normality traffic detection method and this three sectoral integration of sudden change traffic detection method be installed in the SQ2000 type traffic information collection device, utilize the vehicle of the single section of this collector unicoil collection to sail into, sail out of, four kinds of source information such as vehicle and the speed of a motor vehicle, carry out traffic and traffic events in the primary scene and detect.
The applicant has opened an overpass intelligent transportation experimental system in the southwest highway section construction of Shanghai City inner circular way viaduct under the support of relevant municipal works administrative authority.This experimental system main line is about 2km, comprise 2 pairs of ring road, 18 detection sections up and down, be provided with 7 check points altogether, each point has all been installed a SQ2000 type traffic information collection device as on-the-spot checkout equipment, detect the about 330m of equispaced stroke of section, at the roof of two ends skyscraper a remote-controlled numeral that almost covers all fronts has been installed respectively and has focused Video Camera.National public's wireless data communications platform (CDMA 1x) is adopted in the data communication of this experimental system, does not lay any communications cable.The data that send have only in real time: the traffic of variation and traffic events report, therefore only need a common PC just can tackle hundreds of on-the-spot checkout equipment like a cork at the center, and carry out more extensive, deep information processing operation according to these real-time information that on-the-spot checkout equipment is uploaded.In addition, on-the-spot checkout equipment is the average overall travel speed in timed sending track also, for issuing such as travel informations such as operating range, travel speed, hourages after the central integration.On-the-spot checkout equipment just sends the average data that is spaced apart 5s at one's leisure, uses for traffic census.The broadband " cable modem " that the Video Camera end uses cable television system to provide is provided numeral.Central computer can be controlled on-the-spot checkout equipment and numeral focuses Video Camera in real time by the internet.
The applicant is under the support of relevant highway administration department, also a SQ2000 type traffic information collection device has been installed one of suburbs, Shanghai City highway busier intersection test, a remote-controlled numeral has been installed has simultaneously focused Video Camera, CDMA 1x is all adopted in data and Image Communication, because of the traffic rate of present wireless data communications platform is lower, now can only real-time grasp shoot image and passback video file.
Above-mentioned overpass experimental system is implemented test soon, be positioned at ring road mouth under the South Road, east direction Rui Jin, because the installation site of a traffic indication board and indication literal are unreasonable, five traffic hazards have taken place in more than ten days in succession, major accident together wherein, a taxicar of running at high speed has knocked this piece traffic indication board suddenly, and ratchel that is used for pushing down this traffic indication board support soars into flying up and pounds a minibus to descending to travel on the ring road, the minibus pounded of this quilt slows down detecting on the section thus, and the time only crosses the on-the-spot checkout equipment of 6s, and just the incident of blocking up has taken place in report to the center immediately.This piece traffic indication board was split afterwards just significantly descends except, the traffic hazard in this highway section.
By the logout of magnanimity and the contrast of accident video recording are verified confirmation, can significantly improve the performance that traffic and traffic events are detected by classification and Detection method of the present invention, the about at interval 330m of for example above-mentioned overpass experimental system average section, usually also be that detection speed usually less than 100s/km less than 30s detection time.In the intersection or the highway section that are subjected to traffic signals control, detection time is usually less than 3 times of traffic signals cycles, for example above-mentionedly is subjected to the signal period of the cross-road of traffic signals control to be about 100s, and detection time is less than 300s usually.The rate of false alarm of above-mentioned two embodiment is zero.Be diffused into the traffic events that detects section for failing because of the influence of the unusual traffic of various factors, it is unavoidable to fail to report phenomenon, and perhaps solution only reduces to detect the spacing of section.
Certainly, those skilled in the art will be appreciated that the foregoing description only is to be used for illustrating the present invention, and is not as limitation of the invention, as long as in connotation scope of the present invention, all will drop in the scope of claim of the present invention variation, the modification of the foregoing description.
Claims (6)
1. traffic and traffic events unicoil single section in-situ check and test method, it utilizes the single section information of vehicles of unicoil collection to detect in the primary scene, it is characterized in that, take traffic sorting technique, normality traffic detection method and sudden change traffic detection method, and with this three sectoral integration be installed in one and be located in and detect in the on-the-spot traffic information collection device, utilize four kinds of source information that this traffic information collection device provides promptly: the sailing into of each tested vehicle, sail out of, vehicle and the speed of a motor vehicle;
Described traffic sorting technique is subdivided into six ranks with traffic, and is promptly idle, unimpeded, busy, crowd, block up and block, and high-sensitivity detection is carried out in the normality traffic;
Described normality traffic detection method is according to the flow of traffic flow, speed and density, set the average period in the polynary weighting running mean formula, the value of slip interval and power array, to slide and be made as 5s at interval, the span of average period is made as 60s～600s, the power array then is to calculate n weights that obtain by normal distribution law in average period, slide the finish time at interval at each, the vehicle of each car in constantly sliding each at interval, the speed of a motor vehicle, calculate present flow rate by polynary weighting running mean formula, speed, the weighting sliding average of density detects traffic and traffic events then;
Described sudden change traffic detection method according to the sailing into of vehicle, sail out of, vehicle, speed of a motor vehicle source information, the sudden change traffic object of traffic or traffic events is listed in the detection inventory, make each sudden change traffic object possess the detected parameters of traffic and traffic events and differentiate threshold, and the priority of the traffic trace routine of suddenling change is set to the superlative degree, in case detect the detection circulation that sudden change traffic object is just broken the normality traffic immediately, unconditionally change original traffic according to prediction scheme, even send event alarms.
2. traffic according to claim 1 and traffic events unicoil single section in-situ check and test method is characterized in that, the idle rank in the described traffic sorting technique is called for short empty, and color code is a cyan; Unimpeded rank is called for short smooth, and color code is green; Busy rank is called for short busy, and color code is yellow; Crowded rank is called for short squeezes, and color code is orange; Congestion level is called for short stifled, and color code is a pink colour; The blocking-up rank is called for short disconnected, and color code is red.
3. traffic according to claim 1 and traffic events unicoil single section in-situ check and test method is characterized in that, described traffic sorting technique also comprises two inner situations of using, and does not promptly have car and stop.
4. traffic according to claim 3 and traffic events unicoil single section in-situ check and test method is characterized in that, the no car in the described traffic sorting technique is called for short not to be had, and color code is a white; Stop to be called for short and stop, color code is brown.
5. traffic according to claim 1 and traffic events unicoil single section in-situ check and test method is characterized in that the span of described average period is got 60s～300s at blocked road.
6. traffic according to claim 1 and traffic events unicoil single section in-situ check and test method is characterized in that the span of described average period is got 300s～600s in controlled intersection.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB200710037746XA CN100433072C (en) | 2007-03-01 | 2007-03-01 | The single-loop and single-section onsite detection method for traffic situation and traffic accident |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB200710037746XA CN100433072C (en) | 2007-03-01 | 2007-03-01 | The single-loop and single-section onsite detection method for traffic situation and traffic accident |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101017608A CN101017608A (en) | 2007-08-15 |
| CN100433072C true CN100433072C (en) | 2008-11-12 |
Family
ID=38726594
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB200710037746XA Active CN100433072C (en) | 2007-03-01 | 2007-03-01 | The single-loop and single-section onsite detection method for traffic situation and traffic accident |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100433072C (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104167096B (en) * | 2014-08-29 | 2016-07-06 | 南京理工大学 | A kind of urban traffic blocking method for early warning, device and early warning system |
| CN104361755B (en) * | 2014-11-30 | 2016-07-06 | 上海三全科技有限公司 | Traffic signals unicoil single section field control method |
| CN105931462B (en) * | 2016-06-27 | 2018-09-04 | 安徽四创电子股份有限公司 | Tunnel accident judgment method based on Car license recognition |
| CN108091131B (en) * | 2016-11-22 | 2021-06-04 | 中国移动通信有限公司研究院 | Traffic incident identification method and device |
| CN106781474B (en) * | 2016-12-21 | 2019-03-12 | 东南大学 | A method of traffic accident spot self-healing ability is judged based on traffic video |
| CN107038877A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | Intersection vehicle flowrate forecasting system based on vehicle electron identifying |
| CN109410578B (en) * | 2018-11-09 | 2021-01-12 | 浙江浩腾电子科技股份有限公司 | Traffic intersection section traffic flow detection method based on computer vision |
| CN111489545B (en) * | 2019-01-28 | 2023-03-31 | 阿里巴巴集团控股有限公司 | Road monitoring method, device and equipment and storage medium |
| CN110930702B (en) * | 2019-11-25 | 2022-02-15 | 沈阳世纪高通科技有限公司 | Traffic event processing method and device |
| CN115273452A (en) * | 2021-04-29 | 2022-11-01 | 阿里巴巴新加坡控股有限公司 | Road condition determination method and device and computer readable storage medium |
| CN115691095A (en) * | 2021-07-27 | 2023-02-03 | 海信集团控股股份有限公司 | Traffic incident analysis device and method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040056778A1 (en) * | 2002-09-17 | 2004-03-25 | Inductive Signature Technologies, Inc. | Vehicle speed estimation using inductive vehicle detection systems |
| CN1870070A (en) * | 2006-06-20 | 2006-11-29 | 上海三全科技有限公司 | Traffic information single-coil field detection method |
-
2007
- 2007-03-01 CN CNB200710037746XA patent/CN100433072C/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040056778A1 (en) * | 2002-09-17 | 2004-03-25 | Inductive Signature Technologies, Inc. | Vehicle speed estimation using inductive vehicle detection systems |
| CN1870070A (en) * | 2006-06-20 | 2006-11-29 | 上海三全科技有限公司 | Traffic information single-coil field detection method |
Non-Patent Citations (4)
| Title |
|---|
| 单线圈模式下交通参数估计研究. 李志恒等.交通运输系统工程与信息，第3卷第4期. 2003 |
| 单线圈模式下交通参数估计研究. 李志恒等.交通运输系统工程与信息，第3卷第4期. 2003 * |
| 突变理论在交通流分析理论中应用综述. 王英平等.交通运输系统工程与信息，第5卷第6期. 2005 |
| 突变理论在交通流分析理论中应用综述. 王英平等.交通运输系统工程与信息，第5卷第6期. 2005 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101017608A (en) | 2007-08-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100433072C (en) | The single-loop and single-section onsite detection method for traffic situation and traffic accident | |
| CN104269051B (en) | Expressway monitoring and management system | |
| CN104778834B (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| Lu et al. | Congestion evaluation from traffic flow information based on fuzzy logic | |
| CN106408956B (en) | Rapid evacuation method and control system for tunnel traffic jam | |
| CN104464295B (en) | A kind of overhead Entrance ramp intelligence restricted driving method based on video | |
| CN104751642B (en) | A kind of advanced road real-time predictor method of traffic flow operation risk | |
| CN101419750B (en) | Detecting and evaluating method for controlling traffic state at road cross based on data feature | |
| CN112305641B (en) | Expressway traffic safety meteorological internet of things monitoring and early warning system | |
| CN113643540B (en) | Rapid detection and early warning system and method for highway accidents | |
| CN110164130A (en) | Traffic incidents detection method, apparatus, equipment and storage medium | |
| CN102231235A (en) | Method for detecting and positioning traffic flow abnormal point | |
| CN115171385B (en) | Traffic incident detection system based on millimeter wave radar and video linkage | |
| CN105869398B (en) | A kind of unimpeded degree judgment method of road traffic based on K-means clusters | |
| CN113888873A (en) | Highway accident detection and early warning system and method based on short-term traffic flow | |
| CN108573607A (en) | A kind of traffic light control system and method | |
| CN109191857A (en) | A kind of wisdom traffic management system based on big data | |
| CN104851287A (en) | Method for urban road link travel time detection based on video detector | |
| CN201262784Y (en) | Detection and evaluation system for urban signal control road junction traffic status based on data characteristics | |
| CN101551940A (en) | Urban high-speed road traffic state judging and issuing system and method thereof | |
| CN107134135A (en) | A kind of method and system for alleviating highway congestion level | |
| CN105513362A (en) | Method for evaluating and verifying running state of bus in area adjacent to bus stop | |
| CN104575049B (en) | A kind of elevated ramp intellectual inducing method and device based on array radar | |
| CN105118310A (en) | Video-based single-point self-optimization signal control method and device | |
| Shladover et al. | Traffic probe data processing for full-scale deployment of vehicle-infrastructure integration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230620 Address after: Room 803, Building 2, No. 7198 Hangnan Road, Fengxian District, Shanghai, January 2014 Patentee after: Shanghai pingfeasible Intelligent Technology Co.,Ltd. Address before: 200129 room 19, No. 2511, Lane 903, Pudong Avenue, Shanghai Patentee before: SHANGHAI SANQUAN SCIENTIFIC Corp. |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: On site detection method for single line circle and single section of traffic conditions and incidents Granted publication date: 20081112 Pledgee: Shanghai Bank Co.,Ltd. Fengxian Branch Pledgor: Shanghai pingfeasible Intelligent Technology Co.,Ltd. Registration number: Y2024980001026 |