CN101540099A - Method and system for judging road traffic states - Google Patents
Method and system for judging road traffic states Download PDFInfo
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- CN101540099A CN101540099A CN200810034716A CN200810034716A CN101540099A CN 101540099 A CN101540099 A CN 101540099A CN 200810034716 A CN200810034716 A CN 200810034716A CN 200810034716 A CN200810034716 A CN 200810034716A CN 101540099 A CN101540099 A CN 101540099A
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Abstract
The invention discloses a method and a system for judging road traffic states. The method and the system use a plurality of traffic parameters as a basis for judgment, simultaneously establish a functional relation aiming at different road sections and preset the weight so as to improve the accuracy of the traffic state judge. The method comprises the following steps of: (1) selecting a plurality of traffic parameters; (2) through the sampling analysis of the traffic parameters of the road section, setting the functional relation between the plurality of the traffic parameters of the road section and crowding coefficients which correspond to the traffic parameters, and setting weighted values occupied by the plurality of the traffic parameters in the crowding degree judge of the road section; (3) in the end of each state judge period, acquiring the plurality of the traffic parameters of the road section in real time, and according to the set function, calculating the crowding coefficients which correspond to each traffic parameter; (4) carrying out a weighted average operation on the weighted values of each parameter and the crowding coefficients which correspond to the traffic parameters so as to obtain an average crowding coefficient; and (5) comparing the threshold values of the average crowding coefficient and the crowding coefficients so as to judge the road traffic state.
Description
Technical field
The present invention relates to a kind of traffic administration method and system, particularly relate to a kind of road traffic state determination methods and system.
Background technology
Along with the fast development of urban economy and the rapid increase of automobile pollution, the urban highway traffic demand increases substantially, and causes road traffic congestion and traffic jam phenomenon generally to take place.For this reason, how based on detected traffic datas of detecting device such as video, coil or microwaves, statistical study obtains traffic state information has accurately become the problem that vast traffic participant or supvr generally are concerned about.Because accurately traffic state information by modes such as Traffic Announcement or traffic guidance screen real-time offer traveler after, can induce it to select rational trip mode, trip approach etc., provide support for the communications policy analysis simultaneously, so just can improve the scientific and technological level and the operational efficiency of traffic administration, for traveler provides efficient, safe, comfortable transportation service, improve the utilization ratio of traffic resource, cut down the consumption of energy, thereby promote the faster development more quietly of urban economy.
The method of artificial judgment is adopted in the judgement of urban highway traffic running status at present more, comprise: citizen's report, full-time staff's report, civil radio, closed-circuit television supervision, aviation supervision etc., this is the main determination methods of intercity cutout road section traffic volume state.See that on the whole the major advantage of this non-automatic determination methods is convenient, direct; Shortcoming is that the requirement there and then has the eyewitness, and needs to observe continuously, needs the professional that affirmation is screened in report, and personnel's workload and intensity are all bigger.
Method relatively commonly used in addition is the method that the traffic flow parameter that utilizes the traffic detecting device to obtain is judged traffic behavior.What generally use at present is to utilize the wagon flow average velocity in a period of time of highway section to judge, if average velocity more than or equal to 40 kms/hour, then be green; If average velocity less than 20 kms/hour, be redness; Otherwise be yellow, two threshold values also can be revised sometimes.But this method select average velocity this intuitively traffic parameter judge that unreasonable part is arranged:
At first, the state of road traffic is difficult to divide with definite numeral, suppose 20 kms/hour speed as dividing unimpeded and crowded standard, and 19 kms/hour with 21 kms/hour should corresponding different traffic behavior, but in fact both of these case traffic behavior is not down significantly distinguished.
In addition, measurement period, city road situation, detecting device are layouted etc. and also state to be judged and exist influence.For example, along with the layout difference of position of traffic detecting device, can there be very big difference in judgement at same highway section, if layout in the downstream, highway section is the import of crossing, downstream, owing to be subjected to the influence of signal controlling, the speed of a motor vehicle can descend much than the detected value at upstream, highway section or middle place, be the exit of upstream detector in the upstream, highway section simultaneously, when no matter the peak still is an ebb, wagon flow all dissipates in the green light phase place and causes average speed to change not quite, these all be that urban transportation presented between the feature of cutout, it and continuous stream should be treated respectively.The result that judges of the affects traffic behavior of traffic parameter collection period for another example, measurement period is too short then to be subjected to signal controlling green light or red light phase effect big, low phenomenon when high in the time of especially can occurring during the peak; The oversize variation that then can not reflect real-time traffic behavior of measurement period, thus information delay was lost efficacy.
Each bar road section traffic volume situation of urban road has nothing in common with each other, and the detecting device in each the bar highway section position of layouting also there are differences, and the traffic state information that obtains by above method lacks accuracy.
Summary of the invention
The invention provides a kind of road traffic state determination methods, with the accuracy of raising traffic behavior judgement and the science of decision-making.
The present invention provides a kind of road traffic state to judge system in addition, with the accuracy of raising traffic behavior judgement and the science of decision-making.
For this reason, the invention provides a kind of road traffic state determination methods, its after primary-stage survey need be carried out highway section situation that state judges with traffic behavior, it is congested in traffic degree, be divided into a plurality of grades, and between each grade, set crowding coefficient threshold value, this method comprises: (1) chooses a plurality of traffic parameters; (2), set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged by sampling analysis to this road section traffic volume parameter; (3) judge the end of term in week in each state, gather above-mentioned a plurality of traffic parameters in this highway section in real time and, calculate the pairing crowding coefficient of each traffic parameter according to the function that sets; (4) weighted value of each traffic parameter crowding coefficient pairing with it done the weighted mean computing, obtain the mean crowding coefficient; (5) compare mean crowding coefficient and the crowding coefficient threshold value that sets, thereby judge road traffic state.
Further, above-mentioned a plurality of traffic parameter comprises the magnitude of traffic flow, speed and traffic occupation rate.
Further, above-mentioned steps (2) comprising: (21) choose a statistical time; (22) the traffic parameter sample value in this highway section in the statistics collection time; (23) the traffic parameter sample value collected of pre-service; (24) by the pretreated traffic parameter sample value of statistical study in conjunction with artificial judgment, set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged.
Further, above-mentioned steps (23) comprising: replenish or replace obliterated data and misdata in the traffic parameter sample value with exponential forecasting data; Gained traffic parameter sample value is carried out an exponential smoothing filtering.
Further, above-mentioned timing statistics was more than or equal to 7 days.
Further, above-mentioned timing statistics is 7 to 10 days.
Further, the weighted value sum of above-mentioned a plurality of traffic parameters is 1.
Further, above-mentioned steps (3) comprising: (31) are chosen a state and are judged the cycle; (32) judge the end of term in week in each state, gather above-mentioned a plurality of traffic parameters in this highway section in real time; (33) traffic parameter that collects of pre-service; (34) according to the function that sets, calculate the pairing crowding coefficient of each traffic parameter.
Further, above-mentioned steps (33) comprising: replenish or replace obliterated data and misdata in the traffic parameter with exponential forecasting data; The gained traffic parameter is carried out an exponential smoothing filtering.
Further, the above-mentioned state judgement cycle is 2 to 10 minutes.
Further, the above-mentioned state judgement cycle is 5 minutes.
Further, described road traffic state determination methods also comprises: the resulting road traffic state of real-time release.
The present invention provides a kind of road traffic state to judge system in addition, in order to judge the traffic behavior in a plurality of highway sections, this traffic behavior wherein, it is congested in traffic degree, be divided into a plurality of grades, and be provided with crowding coefficient threshold value between each grade, this system comprises: the parameter collection module, each state in above-mentioned each highway section is judged the end of term in week, gathers a plurality of traffic parameters in each highway section in real time; Condition judgment module, the traffic parameter that reception and processing parameter collection module are gathered, this module comprises: state judgment data storehouse stores a plurality of traffic parameters in above-mentioned each highway section and a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged in the funtcional relationship between its pairing crowding coefficient and each highway section; The threshold data storehouse stores the crowding coefficient threshold value between above-mentioned each grade; The crowding coefficients calculation block is calculated the pairing crowding coefficient of each traffic parameter in this highway section according to the funtcional relationship in certain highway section in the state judgment data storehouse; The mean crowding coefficients calculation block is done the weighted mean computing with the weighted value of corresponding road section in the crowding coefficient of each traffic parameter of crowding coefficients calculation block gained and the state judgment data storehouse, obtains the mean crowding coefficient; Compare judge module, the crowding coefficient threshold value in more above-mentioned mean crowding coefficient and the threshold data storehouse, thereby the traffic behavior of judgement corresponding road section.
Further, above-mentioned a plurality of traffic parameter comprises the magnitude of traffic flow, speed and traffic occupation rate.
Further, the above-mentioned state judgement cycle is 2 to 10 minutes.
Further, the above-mentioned state judgement cycle is 5 minutes.
Further, described traffic behavior judges that system also comprises: pretreatment module, the traffic parameter of parameter collection module collection is carried out pre-service.
Further, above-mentioned pretreatment module comprises: complete and correction module, and it replenishes or replaces obliterated data and misdata in the traffic parameter with exponential forecasting data; The noise reduction filtering module is carried out an exponential smoothing filtering to the traffic parameter of gained.
Further, described traffic behavior judges that system also comprises: traffic behavior release module, the resulting road traffic state of real-time release.
Disclosed road traffic state determination methods of the present invention and system select a plurality of traffic parameters to unite the parameter of judging as traffic behavior, for example: the magnitude of traffic flow, speed, traffic occupation rate, avoided selecting an error that traffic parameter brought, while is at the feature of a cutout, for different highway sections, set up funtcional relationship respectively, given weight has improved the accuracy of traffic behavior judgement and the science of decision-making.
Description of drawings
Fig. 1 is the schematic flow sheet of the road traffic state determination methods that one embodiment of the invention provided;
Fig. 2 be in one embodiment of the invention the funtcional relationship between traffic parameter and its pairing crowding coefficient with and in the highway section degree of crowding is judged the schematic flow sheet of the assignment procedure of shared weighted value;
Fig. 3 is the schematic flow sheet of the computation process of the pairing crowding coefficient of each traffic parameter in one embodiment of the invention;
Fig. 4 judges the calcspar of system for the road traffic state that one embodiment of the invention provided;
Fig. 5 is the implementation method process flow diagram of condition judgment module in one embodiment of the invention.
Embodiment
Judge in the prior art that in order to solve traffic behavior lacks the problem of accuracy, the present invention proposes the notion of crowding coefficient, it is to obtain by integrating a plurality of traffic parameters, and divides the traffic behavior grade according to it, specifically how to implement and will describe in detail in following examples.
Please refer to Fig. 1, it is the schematic flow sheet of the road traffic state determination methods that one embodiment of the invention provided.With traffic behavior, promptly congested in traffic degree is divided into a plurality of grades to this method behind primary-stage survey one highway section, and between each grade setting threshold, as shown in the figure, this method comprises the steps:
Step S10: choose a plurality of traffic parameters, unite the foundation of judging as traffic behavior;
Step S20:, set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged by sampling analysis to this road section traffic volume parameter;
Step S30: judge the end of term in week in each state, gather above-mentioned a plurality of traffic parameters in this highway section in real time and, calculate the pairing crowding coefficient of each traffic parameter according to the function that sets;
Step S40: the weighted value of each traffic parameter crowding coefficient pairing with it done the weighted mean computing, obtain the mean crowding coefficient;
Step S50: compare mean crowding coefficient and the threshold value that sets, thereby judge road traffic state.
Below with reference to Fig. 2 and Fig. 3, respectively step S20 and S30 are described in detail.
Wherein S20 may further comprise the steps:
S201: choose a statistical time;
S203: the traffic parameter sample value in this highway section in the statistics collection time;
S205: the traffic parameter sample value that pre-service is collected;
S207: in conjunction with artificial judgment, set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged by the pretreated traffic parameter sample value of statistical study.
The process of the traffic parameter sample value that wherein above-mentioned pre-service is collected comprises: replenish or replace obliterated data and misdata in the traffic parameter sample value with exponential forecasting data, thereby make the traffic parameter sample data more complete with accurately; Gained traffic parameter sample value is carried out an exponential smoothing filtering, to reduce the stochastic error of traffic parameter sample data.
As Fig. 3, the detailed implementation method of step S30 is as follows:
S301: the state of choosing is judged the cycle;
S303: judge the end of term in week in each state, gather a plurality of traffic parameters in this highway section in real time;
S305: the traffic parameter that pre-service collects;
S307:, calculate the pairing crowding coefficient of each traffic parameter according to the function that sets.
The process of the traffic parameter that wherein above-mentioned pre-service collects comprises: replenish or replace obliterated data and misdata in the traffic parameter with exponential forecasting data, thereby make traffic parameter more complete with accurately; The gained traffic parameter is carried out an exponential smoothing filtering, to reduce the stochastic error of traffic parameter.
For allowing above method become apparent, will be described after traffic behavior grade and the traffic parameter instantiation.In this notion that proposes the crowding coefficient, represent with M, set its span and be [0,100].Wherein 0 expression is very unimpeded, and 100 expressions are serious stops up.If traffic behavior is fallen into three classes, then setting threshold is 33 and 67, and then when 0≤M≤33, traffic behavior is unimpeded; When 33＜M≤67, traffic behavior is for crowded; When 67＜M≤100 expressions are stopped up; Classification situations such as other level Four, Pyatyi, six grades are similar, do not repeat them here.Choose magnitude of traffic flow Q simultaneously, speed V, traffic parameters such as traffic occupation rate OC carry out state and judge, then set up the crowding coefficient M funtcional relationship Mq=f (Q) different respectively with each traffic parameter at this highway section, Mv=f (V), Moc=f (OC), simultaneously according to road conditions, the detecting device given magnitude of traffic flow Q respectively such as position that layouts, speed V and traffic occupation rate OC be shared weighted value in this highway section degree of crowding is judged, be its pairing crowding coefficient Mq, Mv, the weighted value a of Moc, b, c, then with crowding coefficient Mq, Mv, Moc does the weighted mean computing, be a*Mq+b*Mv+c*Moc, obtain mean crowding coefficient M, with mean crowding coefficient M and critical value 33,67 contrasts obtain the traffic behavior in this highway section.
Wherein magnitude of traffic flow Q is the vehicle number that passes through certain highway section in the unit interval, is the integer type data, for example 2384/hour; Speed V is the average speed in this highway section, is real number type data, for example 8Km/h; And traffic occupation rate OC is the ratio that vehicle accounts for total timing statistics in a period of time through the temporal summation of traffic detecting device, be real number type data, between 0～100%, wherein 0 expression does not have vehicle to pass through, and 100% expression has the vehicle passing detection device always and do not have the space.And weighted value a, b, c are real number, itself and be 1, i.e. a+b+c=1.0.
And for different highway sections, more than funtcional relationship between three traffic parameters and its pairing crowding coefficient will look the highway section situation and different, simultaneously because the condition of road surface difference in different highway sections, the importance of each traffic parameter in each highway section degree of crowding is judged is also with different, and is different so the weighted value of above three traffic parameters also will be looked the highway section situation.It depends on the investigation of early stage to the highway section situation.
Promptly at first investigate and to carry out the condition of road surface that state is judged, the traffic flow situation, and the signal timing dial situation etc. of crossing when peak and non-peak, select suitable timing statistics as the time of collecting sample data, select the cycle of suitable time as state judgement and issue simultaneously, promptly state is judged the cycle.For example timing statistics was more than or equal to 7 days; The state judgement cycle is 2 to 10 minutes, selects for use usually 5 minutes.
Then magnitude of traffic flow Q, speed V and the traffic occupation rate OC data in the statistics collection time (for example 7 to 10 days) are carried out pre-service as sample data and to it, then binding data statistical analysis technique, artificial judgment, demarcation obtains the funtcional relationship Mq=f (Q) between the pairing crowding coefficient Mq of magnitude of traffic flow Q, speed V and traffic occupation rate OC and its, Mv and the Moc, Mv=f (V), Moc=f (OC), and demarcate weight a, b, the c of three parameters.Mq=f (Q) wherein, Mv=f (V), Moc=f (OC) is based on investigation to the original traffic data in city in conjunction with artificial judgment, expertise method, crowding coefficient that obtains by the least square method analysis and the funtcional relationship between the traffic parameter, just obtain by image data and analysis, why to set up this 3 functions, be for 3 traffic parameters unifications are arrived together, obtain final crowding coefficient, thereby avoid selecting an error that traffic parameter brought, the accuracy of judgement is provided.And a, b, c are respectively the significance levels that 3 parameters of traffic are judged for crowding, determine with expert assessment method, select driver's mass survey and expert visit, and to the table of appraising through discussion that returns, statistics obtains the weighted value of 3 parameters.
Below provide an example, so it is not in order to limiting the present invention, carries out statistical study and artificial judgment after need collecting sample data for different highway sections, since the condition of road surface difference, this funtcional relationship difference, and weighted value is also different.
Wherein Qc is a road section capacity, and the unit of Q, Qc is/hour, notices that the flow of one-period will convert; A, b, c difference 0.33,0.26,0.41.
Judge the end of term in week in each state so, gather magnitude of traffic flow Q, speed V and traffic occupation rate OC in real time and the data of collecting are carried out the data pre-service that the function that substitution then sets calculates pairing crowding coefficient Mq, Mv and Moc; Then be weighted mean a*Mq+b*Mv+c*Moc, calculate mean crowding coefficient M.
At last mean crowding coefficient M is compared with the threshold value that sets, thereby judge road traffic state.
For example, i the state in this highway section judged the Qi in the end of term in week, and it is 2384/hour that Vi, OCi detect respectively, 8 kms/hour, 45%, Qc=2500/hour; After it is carried out pre-service, the funtcional relationship Mqi=f that substitution sets (Qi), Mvi=f (Vi), Moci=f (OCi), the value of obtaining is respectively 73.13,64.49,73.83, then this highway section crowding coefficient Mi=ai*Mqi+bi*Mvi+ci*Moci is 71.17, because 71.17＞67, then the traffic behavior in this highway section is for stopping up.Certainly, can be each setting state corresponding color, for example unimpeded is green, crowded with yellow, stop up to red, thereby after obtaining traffic behavior, real-time induces screen or broadcasting station etc. to send by full-color screen, the LED literal of inducing traffic information, and the guiding driver selects suitable path.
One embodiment of the invention also provides a kind of road traffic state to judge system, in order to judge the traffic behavior in a plurality of highway sections, and traffic behavior wherein, promptly congested in traffic degree is divided into a plurality of grades, and is provided with threshold value between each grade.Please refer to Fig. 4, it is the calcspar of this system.
This system comprises parameter collection module 100 and condition judgment module 200, and wherein parameter collection module 100 is judged the end of term in week in each state in each highway section, gathers a plurality of traffic parameters in each highway section in real time; The traffic parameter that condition judgment module 200 receives and processing parameter collection module 100 is gathered.Condition judgment module 200 comprises state judgment data storehouse D1 and threshold data storehouse D2 and the crowding coefficients calculation block 210 that is attached thereto, mean crowding coefficients calculation block 220 and compares judge module 230.Wherein store above-mentioned a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged in above-mentioned a plurality of traffic parameters in each highway section and the funtcional relationship between its pairing crowding coefficient and each highway section in the D1 of state judgment data storehouse, store the crowding coefficient threshold value between each grade in each highway section in the D2 of threshold data storehouse.And crowding coefficients calculation block 210 is calculated the pairing crowding coefficient of above-mentioned a plurality of traffic parameters in certain highway section according to the funtcional relationship among the state judgment data storehouse D1; Mean crowding coefficients calculation block 220 is done the weighted mean computing with the weighted value of corresponding road section among the crowding coefficient of each traffic parameter of gained and the state judgment data storehouse D1, obtains the mean crowding coefficient; Compare the crowding coefficient threshold value in judge module 230 more above-mentioned mean crowding coefficients and the threshold data storehouse D2, thereby judge the traffic behavior of corresponding road section.
Above-mentioned traffic parameter for example is magnitude of traffic flow Q, speed V and traffic occupation rate OC, then state judgment data storehouse D1 stores the funtcional relationship Mq=f (Q) between the pairing crowding coefficient Mq of magnitude of traffic flow Q, speed V and traffic occupation rate OC and its, Mv and the Moc, Mv=f (V), weighted value a, b, the c of Moc=f (OC) and three parameters.Refer again to Fig. 5, it is the implementation method process flow diagram of condition judgment module 200 in the example for this reason.As figure, at first calculate the pairing crowding coefficient of each traffic parameter value in i highway section, promptly calculate Mqi=f (Qi), Mvi=f (Vi), Moci=f (OCi), it is corresponding to crowding coefficients calculation block 210; Then calculate the mean crowding coefficient, i.e. Mi=ai*Mqi+bi*Mvi+ci*Moci, it is corresponding to mean crowding coefficients calculation block 220; At last, relatively mean crowding coefficient Mi obtains traffic behavior with the size of crowding coefficient threshold value, and it is corresponding to comparison judge module 230.
This system also comprises pretreatment module 300 in addition, carries out pre-service with the traffic parameter that parameter collection module 100 is collected.And processing module 300 can also comprise: complete and correction process module 310, its replenish with exponential forecasting data or the replacement traffic parameter in obliterated data and misdata; And noise reduction filtering processing module 320, the traffic parameter of gained is carried out an exponential smoothing filtering.
For the resulting traffic behavior of better utilization, this system also is provided with traffic behavior release module 400 with the resulting road traffic state of real-time release.For example be each setting state corresponding color, unimpeded is green, crowded with yellow, stop up to be redness, thereby after obtaining traffic behavior, the traffic behavior in each highway section of issue on urban road GIS interface is for the driver provides road condition information intuitively; Can also induce screen or broadcasting station etc. to send by full-color screen, the LED literal of inducing, the guiding driver selects suitable path.
Traffic behavior determination methods and system are provided in the embodiment of the invention, and the data of gathering based on the traffic detecting device realize differentiating automatically to road traffic state, and issue in time, have saved a large amount of artificial judgment workloads; Select the magnitude of traffic flow, speed, traffic occupation rate to unite the parameter of judging as traffic behavior, avoided selecting an error that traffic parameter brought, while is at the feature of a cutout, for different highway sections and the detecting device position of layouting, set up funtcional relationship respectively, given weight has improved the accuracy that traffic behavior is judged, has improved the science of decision-making; The crowding coefficient can also reflect at that time the degree of crowding of traffic behavior and the situation of change in each cycle thereof, condition clock when conveniently traffic guidance being set intuitively; At arbitrary highway section of urban road, carry out threshold value respectively and demarcate, even detecting device is laid at the diverse location in highway section, also can demarcate and revise, thereby weaken owing to the engineering reason is being laid the error that detecting device brings without the position according to sample data.
, be not that protection scope of the present invention should be as the criterion with the scope that claims are contained in order to qualification the present invention below only for for example.
Claims (19)
1. road traffic state determination methods, with traffic behavior, promptly congested in traffic degree is divided into a plurality of grades behind primary-stage survey one highway section for it, and sets crowding coefficient threshold value between each grade, it is characterized in that, comprising:
(1) chooses a plurality of traffic parameters;
(2), set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged by sampling analysis to this road section traffic volume parameter;
(3) judge the end of term in week in each state, gather above-mentioned a plurality of traffic parameters in this highway section in real time and, calculate the pairing crowding coefficient of each traffic parameter according to the function that sets;
(4) weighted value of each traffic parameter crowding coefficient pairing with it done the weighted mean computing, obtain the mean crowding coefficient;
(5) compare mean crowding coefficient and the crowding coefficient threshold value that sets, thereby judge road traffic state.
2. road traffic state determination methods according to claim 1 is characterized in that, wherein above-mentioned a plurality of traffic parameters comprise the magnitude of traffic flow, speed and traffic occupation rate.
3. road traffic state determination methods according to claim 1 is characterized in that, wherein above-mentioned steps (2) comprising:
(21) choose a statistical time;
(22) the traffic parameter sample value in this highway section in the statistics collection time;
(23) the traffic parameter sample value collected of pre-service;
(24) by the pretreated traffic parameter sample value of statistical study in conjunction with artificial judgment, set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged.
4. road traffic state determination methods according to claim 3 is characterized in that, wherein above-mentioned steps (23) comprising:
Replenish or replace obliterated data and misdata in the traffic parameter sample value with exponential forecasting data;
Gained traffic parameter sample value is carried out an exponential smoothing filtering.
5. road traffic accident detection method according to claim 3 is characterized in that, wherein above-mentioned timing statistics was more than or equal to 7 days.
6. road traffic accident detection method according to claim 5 is characterized in that, wherein above-mentioned timing statistics is 7 to 10 days.
7. road traffic state determination methods according to claim 1 is characterized in that, wherein the weighted value sum of above-mentioned a plurality of traffic parameters is 1.
8. road traffic state determination methods according to claim 1 is characterized in that, wherein above-mentioned steps (3) comprising:
(31) choose a state and judge the cycle;
(32) judge the end of term in week in each state, gather above-mentioned a plurality of traffic parameters in this highway section in real time;
(33) traffic parameter that collects of pre-service;
(34) according to the function that sets, calculate the pairing crowding coefficient of each traffic parameter.
9. road traffic state determination methods according to claim 8 is characterized in that, wherein above-mentioned steps (33) comprising:
Replenish or replace obliterated data and misdata in the traffic parameter with exponential forecasting data;
The gained traffic parameter is carried out an exponential smoothing filtering.
10. road traffic state determination methods according to claim 8 is characterized in that, the wherein above-mentioned state judgement cycle is 2 to 10 minutes.
11. road traffic state determination methods according to claim 10 is characterized in that, the wherein above-mentioned state judgement cycle is 5 minutes.
12. road traffic state determination methods according to claim 1 is characterized in that, also comprises:
The resulting road traffic state of real-time release.
13. a road traffic state is judged system, in order to judge the traffic behavior in a plurality of highway sections, and this traffic behavior wherein, promptly congested in traffic degree is divided into a plurality of grades, and is provided with crowding coefficient threshold value between each grade, it is characterized in that, comprising:
The parameter collection module is judged the end of term in week in each state in above-mentioned each highway section, gathers a plurality of traffic parameters in each highway section in real time;
Condition judgment module, the traffic parameter that reception and processing parameter collection module are gathered, this module comprises:
State judgment data storehouse stores a plurality of traffic parameters in above-mentioned each highway section and a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged in the funtcional relationship between its pairing crowding coefficient and each highway section;
The threshold data storehouse stores the crowding coefficient threshold value between above-mentioned each grade;
The crowding coefficients calculation block is calculated the pairing crowding coefficient of each traffic parameter in this highway section according to the funtcional relationship in certain highway section in the state judgment data storehouse;
The mean crowding coefficients calculation block is done the weighted mean computing with the weighted value of corresponding road section in the crowding coefficient of each traffic parameter of crowding coefficients calculation block gained and the state judgment data storehouse, obtains the mean crowding coefficient;
Compare judge module, the crowding coefficient threshold value in more above-mentioned mean crowding coefficient and the threshold data storehouse, thereby the traffic behavior of judgement corresponding road section.
14. traffic behavior according to claim 13 is judged system, it is characterized in that wherein above-mentioned a plurality of traffic parameters comprise the magnitude of traffic flow, speed and traffic occupation rate.
15. traffic behavior according to claim 13 is judged system, it is characterized in that the wherein above-mentioned state judgement cycle is 2 to 10 minutes.
16. traffic behavior according to claim 15 is judged system, it is characterized in that the wherein above-mentioned state judgement cycle is 5 minutes.
17. traffic behavior according to claim 13 is judged system, it is characterized in that, also comprises:
Pretreatment module is carried out pre-service to the traffic parameter of parameter collection module collection.
18. traffic behavior according to claim 17 is judged system, it is characterized in that above-mentioned pretreatment module comprises:
Complete and correction module, it replenishes or replaces obliterated data and misdata in the traffic parameter with exponential forecasting data;
The noise reduction filtering module is carried out an exponential smoothing filtering to the traffic parameter of gained.
19. traffic behavior according to claim 13 is judged system, it is characterized in that, also comprises:
The traffic behavior release module, the resulting road traffic state of real-time release.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102176284A (en) * | 2011-01-27 | 2011-09-07 | 深圳市美赛达科技有限公司 | System and method for analyzing and determining real-time road condition information based on global positioning system (GPS) terminal |
| CN102842218A (en) * | 2011-06-23 | 2012-12-26 | 株式会社电装 | Congestion forecast device, congestion forecast data and congestion forecast method |
| CN101944291B (en) * | 2009-07-03 | 2013-01-30 | 比亚迪股份有限公司 | Method for detecting traffic flow and detection and control system |
| CN102945604A (en) * | 2012-11-07 | 2013-02-27 | 北京交通大学 | Judgment method for congestion event |
| CN103514743A (en) * | 2013-09-28 | 2014-01-15 | 上海电科智能系统股份有限公司 | Method for recognizing abnormal traffic state characteristics of real-time index data matching memory range |
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| CN104766469A (en) * | 2015-03-26 | 2015-07-08 | 中兴智能交通有限公司 | Urban traffic flow tide simulation and analysis method based on large data analysis |
| CN105023434A (en) * | 2015-07-03 | 2015-11-04 | 信融源大数据科技(北京)有限公司 | Method for obtaining congestion index of motorway |
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| CN106960571A (en) * | 2017-03-30 | 2017-07-18 | 百度在线网络技术（北京）有限公司 | Congestion in road bottleneck point determines method, device, server and storage medium |
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Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101944291B (en) * | 2009-07-03 | 2013-01-30 | 比亚迪股份有限公司 | Method for detecting traffic flow and detection and control system |
| CN102176284A (en) * | 2011-01-27 | 2011-09-07 | 深圳市美赛达科技有限公司 | System and method for analyzing and determining real-time road condition information based on global positioning system (GPS) terminal |
| CN102842218A (en) * | 2011-06-23 | 2012-12-26 | 株式会社电装 | Congestion forecast device, congestion forecast data and congestion forecast method |
| CN102842218B (en) * | 2011-06-23 | 2014-09-17 | 株式会社电装 | Congestion forecast device, congestion forecast data and congestion forecast method |
| CN102945604A (en) * | 2012-11-07 | 2013-02-27 | 北京交通大学 | Judgment method for congestion event |
| CN102945604B (en) * | 2012-11-07 | 2015-03-04 | 北京交通大学 | Judgment method for congestion event |
| CN103514743A (en) * | 2013-09-28 | 2014-01-15 | 上海电科智能系统股份有限公司 | Method for recognizing abnormal traffic state characteristics of real-time index data matching memory range |
| CN103514743B (en) * | 2013-09-28 | 2016-01-06 | 上海电科智能系统股份有限公司 | A kind of abnormal traffic state characteristic recognition method of real-time index-matched memory range |
| CN103578273B (en) * | 2013-10-17 | 2017-04-05 | 银江股份有限公司 | A kind of road traffic state estimation method based on microwave radar data |
| CN104200661A (en) * | 2014-09-05 | 2014-12-10 | 厦门大学 | Method for forecasting state changes of road traffic system |
| CN104574972A (en) * | 2015-02-13 | 2015-04-29 | 无锡物联网产业研究院 | Traffic state detection method and traffic state detection device |
| CN104574972B (en) * | 2015-02-13 | 2017-05-10 | 无锡物联网产业研究院 | Traffic state detection method and traffic state detection device |
| CN104766469B (en) * | 2015-03-26 | 2018-01-09 | 中兴智能交通股份有限公司 | Urban traffic flow tide simulating analysis based on big data analysis |
| CN104766469A (en) * | 2015-03-26 | 2015-07-08 | 中兴智能交通有限公司 | Urban traffic flow tide simulation and analysis method based on large data analysis |
| CN105023434B (en) * | 2015-07-03 | 2017-04-26 | 信融源大数据科技(北京)有限公司 | Method for obtaining congestion index of motorway |
| CN105023434A (en) * | 2015-07-03 | 2015-11-04 | 信融源大数据科技(北京)有限公司 | Method for obtaining congestion index of motorway |
| CN105355051A (en) * | 2015-12-09 | 2016-02-24 | 中兴软创科技股份有限公司 | Congestion identification method and device based on electronic police data |
| CN106652451A (en) * | 2016-12-21 | 2017-05-10 | 吴中区穹窿山倪源交通器材经营部 | Whole-course global road condition analysis system |
| CN106652419A (en) * | 2017-01-17 | 2017-05-10 | 西南科技大学 | Wireless monitoring network dynamic synchronous acquisition method based on comprehensive sensitive event driving |
| CN107146405A (en) * | 2017-01-23 | 2017-09-08 | 北京博研智通科技有限公司 | Obtain the method and system for the coefficient that is precisely obstructed |
| CN106960571A (en) * | 2017-03-30 | 2017-07-18 | 百度在线网络技术（北京）有限公司 | Congestion in road bottleneck point determines method, device, server and storage medium |
| CN106960571B (en) * | 2017-03-30 | 2020-10-16 | 百度在线网络技术（北京）有限公司 | Method and device for determining road congestion bottleneck point, server and storage medium |
| CN107146413A (en) * | 2017-06-24 | 2017-09-08 | 梧州市兴能农业科技有限公司 | A kind of smart city service platform |
| CN107590998A (en) * | 2017-08-16 | 2018-01-16 | 重庆市市政设计研究院 | A kind of road circuit node running state recognition algorithm and system based on floating car data |
| CN108847025A (en) * | 2018-08-28 | 2018-11-20 | 电子科技大学 | A kind of traffic congestion determination method |
| CN109345853A (en) * | 2018-08-30 | 2019-02-15 | 浙江工业大学 | A kind of unmanned vehicle safe driving optimization method based on GIS |
| CN110634293A (en) * | 2019-09-26 | 2019-12-31 | 同济大学 | Trunk intersection control method based on fuzzy control |
| CN110634293B (en) * | 2019-09-26 | 2021-06-04 | 同济大学 | Trunk intersection control method based on fuzzy control |
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