CN101937616A - Method for fusing traffic flow data in real time and device - Google Patents
Method for fusing traffic flow data in real time and device Download PDFInfo
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- CN101937616A CN101937616A CN 201010260603 CN201010260603A CN101937616A CN 101937616 A CN101937616 A CN 101937616A CN 201010260603 CN201010260603 CN 201010260603 CN 201010260603 A CN201010260603 A CN 201010260603A CN 101937616 A CN101937616 A CN 101937616A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/02—Detecting movement of traffic to be counted or controlled using treadles built into the road
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
Abstract
The invention discloses a method for fusing traffic flow data in real time and a device, which relates to the technical field of intelligent transportation systems, and improves the accuracy of road condition information of roads. The method comprises the following steps: determining the confidence level of the roads in various of road conditions states; when the difference of the confidence level of the road condition states with the highest confidence and the confidence level of the road condition states with the secondary high confidence level is not smaller than a preset threshold, adopting a road condition state S with the highest confidence level as the current road condition state of the road, and working out the current running speed of the road according to a running speed V corresponding to the road under the road condition state S with the highest confidence level; and otherwise, recomputating the current running speed of the road according to the running speed V corresponding to the road under each type of real-time traffic flow data, and determining the current road condition state of the road according to the current running speed. The embodiment of the invention is mainly applied to the process of fusing real-time traffic flow data.
Description
Technical field
The present invention relates to the intelligent transport technology field, relate in particular to a kind of real-time traffic flow data fusion method and device.
Background technology
Technology such as intelligent transportation system utilization computing machine, communication, artificial intelligence, sensor can provide the Real-time Traffic Information of road for the user.The user can understand the present traffic in each highway section by means such as GPS/GIS, broadcasting, information issue plates at any time in driving procedure, the traffic in each highway section can be understood at any time by equipment such as the vehicle sensors on the road, video cameras by vehicle supervision department, and at any time the traffic signals of each traffic intersection is adjusted and carried out information issue to external world.
At present, prior art is mainly passed through FCD (Floating Car Data, floating car data), various traffic flow datas such as traffic flow inductive coil data or event information data carry out the traffic conditions that independent processing obtains road respectively, choose then the traffic information that draws after wherein a kind of data are handled is filled up as the current traffic information of road, for example, choose FCD is handled the traffic information that the draws current traffic information as road.
Yet, the inventor finds that prior art is by various traffic flow datas are carried out individual processing respectively, choose traffic information that wherein a kind of traffic flow data draws current road conditions then as road, if it is unusual that selected this traffic flow data takes place, will cause accuracy reduction according to the road conditions that traffic flow data drew of the type.
Summary of the invention
Embodiments of the invention provide a kind of real-time traffic flow data fusion method and device, have improved the accuracy of the traffic information of road.
For achieving the above object, embodiments of the invention adopt following technical scheme:
A kind of real-time traffic flow data fusion method comprises:
According to each the real-time traffic flow data at least two kinds of real-time traffic flow datas, calculate the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively;
Determine the degree of belief of described each real-time traffic flow data correspondence successively;
Determine the degree of belief of described road under each road condition;
When the difference of the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief is not less than predetermined threshold value, adopt the current road condition of the highest road condition S of described degree of belief, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road;
When the difference of the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief less than predetermined threshold value, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
A kind of real-time traffic flow data fusing device comprises:
First processing unit is used for each the real-time traffic flow data according at least two kinds of real-time traffic flow datas, calculates the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively;
Second processing unit is used for the degree of belief of definite described each real-time traffic flow data correspondence successively;
Determining unit is used for determining the degree of belief of described road under each road condition;
The state integrated unit, be used for being not less than predetermined threshold value when the difference of the degree of belief of the inferior high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief, adopt the current road condition of the highest road condition S of described degree of belief, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road;
The speed integrated unit, be used for difference when the degree of belief of the inferior high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief less than predetermined threshold value, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
Real-time traffic flow data fusion method and device that the embodiment of the invention provides, by calculating the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively, and determine the degree of belief of described each real-time traffic flow data correspondence successively, can determine the degree of belief of described road under each road condition.Then, if the difference of the degree of belief of the road condition that the degree of belief of the road condition that degree of belief is the highest and degree of belief are time high is not less than predetermined threshold value, adopt the current road condition of the highest road condition S of described degree of belief so, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road; Otherwise, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
From the implementation procedure of the embodiment of the invention as can be seen, the current road condition of described road and travelling speed calculate in conjunction with at least two kinds of real-time traffic flow datas, the corresponding separately different degree of belief of described at least two kinds of traffic flow datas, choosing the traffic information that wherein a kind of traffic flow data draws with prior art compares as the current traffic information of road, the embodiment of the invention can effectively be utilized the accuracy of various real-time traffic flow datas, thereby has improved the accuracy of the traffic information of road.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
The process flow diagram of a kind of real-time traffic flow data fusion method that Fig. 1 provides for the embodiment of the invention 1;
Fig. 2 obtains the travelling speed of road and the synoptic diagram of road condition for the coil that utilizes that the embodiment of the invention 1 provides;
The process flow diagram of the another kind of real-time traffic flow data fusion method that Fig. 3 provides for the embodiment of the invention 1;
A kind of real-time traffic flow data fusing device structural drawing that Fig. 4 provides for the embodiment of the invention 2;
The another kind of real-time traffic flow data fusing device structural drawing that Fig. 5 provides for the embodiment of the invention 2.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.
Embodiment 1:
Embodiment as shown in Figure 1 provides a kind of real-time traffic flow data fusion method, specifically may further comprise the steps:
101,, calculate the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively according to each the real-time traffic flow data at least two kinds of real-time traffic flow datas.
During practical application, when obtaining described real-time traffic flow data, can in cycle regular hour, gather at least two kinds of real-time traffic flow datas that are used to merge.Because choosing of time cycle is relevant with the issue frequency of real-time traffic flow data.If it is shorter that the time cycle is chosen, can cause the limited amount of the real-time traffic flow data that obtains, the probability that extremum appears in data strengthens, if the time cycle choose oversize, then can impact the telecommunication flow information issue, telecommunication flow information for example can not upgrade in time.Thereby a kind of more excellent scheme is that the value of above-mentioned time cycle can be chosen for 5 minutes.
Need to prove that the real-time traffic flow data that the embodiment of the invention is mentioned comprises: floating car data, traffic flow inductive coil data or event information data.
Data of different types has different data characteristics, for example: FCD (Floating Car Data, floating car data) characteristics are to contain much information, wide coverage, it is bigger that accuracy rate is influenced by the Floating Car transaction module, the road information accuracy rate that grade is high more is high more, and the high more information accuracy rate of the passback frequency of FCD is high more.And for example, the data characteristics of traffic flow inductive coil data is that information is primarily aimed at expressway etc., and coverage is little, but accuracy rate is higher.Also as, event information data specifically can be divided into telecommunication flow information and event information two classes, described telecommunication flow information can be used for telecommunication flow information and merge, and event information can be used for verification.This class data major part needs manual entry, has higher accuracy, still, generally can exist the regular hour to incur loss through delay, and is special, when unexpected incidents take place, utilizes event information data to the bad judgement of the influence of traffic flow.
Thereby, according to above-mentioned dissimilar real-time traffic flow data, the road condition S of calculating road correspondence under described each real-time traffic flow data and the method for travelling speed V are also inequality, introduce road condition S and the travelling speed V that how to calculate road correspondence under FCD, traffic flow inductive coil data and event information data below respectively.
One, utilize FCD to calculate the road condition S and the travelling speed V of road correspondence.
Step 1, at first FCD is classified according to data source and vehicle ID (each Floating Car all has own unique numbering).
Step 2, then extracts GPS longitude and latitude, speed, direction, temporal information among the FCD, and the abnormal data among the FCD is filtered, and the abnormal data that needs to filter comprises:
One point data refers to that the GPS point data of same vehicle ID has only one.
Abnormal data refers to that the adjacent two GPS point mistiming of same vehicle ID be multiply by maximum speed value less than air line distance between these two GPS points.
Step 3, by map match, path culculating obtains all GPS point road corresponding of each car.Specifically can realize in the following manner: the latitude and longitude coordinates of ordering with GPS is that the road that may match is chosen at the center, generally have many, the direction of ordering by GPS filters out the road excessive with the road angle, the time order and function order of ordering by GPS and the link relation of road are determined the coupling road.
Step 4, above-mentioned three steps of process can be determined the road set that each Floating Car is passed through, and use actual motion length, working time, GPS point instantaneous velocity, obtain the travelling speed V of road, and determine its road condition S according to this travelling speed V.
Two, utilize the road condition S and the travelling speed V of traffic flow inductive coil data computation road correspondence.
Usually, the traffic flow inductive coil generally is divided into unicoil and twin coil, is that the example explanation speed of a motor vehicle is extracted below with the twin coil.
As shown in Figure 2, when vehicle during by two adjacent toroid windings, wagon detector can obtain respectively through the moment T1 of first coil with through the moment T2 of second coil, the actual range of supposing described two adjacent windings is D, and the velocity amplitude V of this vehicle can calculate according to V=D/ (T1-T2).
In the unit interval, obtain all velocity amplitudes by the vehicle of above-mentioned two adjacent windings, and calculate described all mean values by the velocity amplitude of vehicle, and utilize the travelling speed V of this mean value as road correspondence in the unit interval, draw road condition S according to travelling speed.
Three, utilize event information data to calculate the road condition S and the travelling speed V of road correspondence.
Event information data is usually by manually gathering, industry is gathered and field data acquisition in can being divided into, in industry gather mainly the transport information by listening to the FM radio station or collect data by video observation, field data acquisition is mainly by the concrete road traffic flow situation of collection personnel artificial visually examine.These two kinds of acquisition modes can directly obtain road condition S comparatively accurately, and manually estimation obtains according to road condition but travelling speed V is generally.
Need to prove that the described road condition that the embodiment of the invention is mentioned comprises: unimpeded, slowly or block up.Drawing road condition S according to travelling speed V is specially: when travelling speed V less than 20km/h, determine that road condition S is for blocking up; When travelling speed V more than or equal to 20km/h and less than 40km/h, determine that road condition S is for slowly; When travelling speed V more than or equal to 40km/h, determine that road condition S is unimpeded.
102, determine the degree of belief of described each real-time traffic flow data correspondence successively.
Concrete, this step can realize (figure does not show) by following substep:
102A, calculate the state accuracy rate of described each real-time traffic flow data in the Preset Time scope successively;
102B, determine the degree of belief of described each real-time traffic flow data correspondence according to the state accuracy rate in the Preset Time scope of conversion formula and described each real-time traffic flow data successively.Described conversion formula is: degree of belief=[state accuracy rate * 1000+0.5], wherein, [] is for rounding symbol.
103, determine the degree of belief of described road under each road condition.
Suppose in the application scenarios one at least two kinds of real-time traffic flow datas are specially the FCD1 that obtains from company 1, obtain from company 2 in the step 101 FCD2, traffic flow inductive coil data and event information data.Suppose to utilize the travelling speed of the road G correspondence that above-mentioned four kinds of real-time traffic flow datas calculate respectively and road condition shown in following table one:
| Data name | FCD1 | FCD2 | Coil | Incident |
| The state of road G | Unimpeded | Slowly | Slowly | Slowly |
| The speed of road G (km/h) | 46 | 21 | 29 | 27 |
Table one
The floating car data that " FCD1 " expression in the above-mentioned table one is obtained from company 1, the floating car data that " FCD2 " expression is obtained from company 2, " coil " expression traffic flow inductive coil data, " incident " presentation of events information data.
Suppose definite described each the self-corresponding degree of belief of four kinds of real-time traffic flow datas of step 102 as shown in Table 2:
| Data name | FCD1 | FCD2 | Coil | Incident |
| State accuracy rate (%) | 0.61 | 0.53 | 0.97 | 0.92 |
| Degree of belief | 6 | 5 | 10 | 9 |
Table two
The degree of belief of road described in this step under road condition is so: the degree of belief sum of all real-time traffic flow data correspondences that adopt when calculating described road condition.How following mask body introduction calculates the degree of belief of described road under each road condition.
For example, can learn that according to table one the real-time traffic flow data that adopts when calculating unimpeded state is " FCD1 ", can learn according to table two, should " FCD1 " corresponding degree of belief be 6, thereby can determine that the degree of belief of described road G when unimpeded state was 6;
And for example, can learn according to table one, the real-time traffic flow data that adopts when calculating slow state is " FCD2 ", " coil " and " incident ", can learn according to table two, should " FCD2 " corresponding degree of belief be 5, should " coil " corresponding degree of belief be 10, should " incident " corresponding degree of belief be 9, the degree of belief of road under slow state is so: the degree of belief sum of all real-time traffic flow data correspondences that when calculating described slow state, adopt, that is, and 5+10+9=24.
The table two that table one that draws according to step 101 and step 102 draw, this step can determine that the degree of belief of described road G under each road condition is shown in following table three:
| Unimpeded | Slowly | Block up | |
| The degree of belief of road G distributes | 6 | 24 | 0 |
Table three
104, the difference when the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief is not less than predetermined threshold value, adopt the current road condition of the highest road condition S of described degree of belief, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road.
Concrete, be example with the application scenarios one of hypothesis in the above-mentioned steps 103, described predetermined threshold value can calculate according to formula F=(M1+M2+..+Mn)/n, and wherein, n represents real-time traffic flow data number, and Mn represents the degree of belief of n kind real-time traffic flow data.Can calculate predetermined threshold value according to above-mentioned table two and be specially F=(6+5+10+9)/4=7.5.
From above-mentioned table three as can be seen, the road condition that degree of belief is the highest is slowly, and corresponding degree of belief is 24, and degree of belief time high road condition is unimpeded, and its corresponding degree of belief is 6; The difference that can draw the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief so is: 24-6=18.
At this moment, because 18＞7.5, promptly the difference of the degree of belief of the road condition that the degree of belief of the road condition that degree of belief is the highest and degree of belief are time high is not less than predetermined threshold value, can determine that the current road condition S of described road G is for slow.
The current travelling speed V of described road G specifically calculates by following process:
The value that adopts described road corresponding travelling speed V under the highest road condition of described degree of belief to be weighted on average to draw is as the current travelling speed of described road, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V.
Concrete, can learn according to table one, road G corresponding travelling speed under slow state is respectively 21km/h, 29km/h and 27km/h, the real-time traffic flow data that adopts when calculating " 21km/h " is " FCD2 ", the real-time traffic flow data that adopts when calculating " 29km/h " is " coil ", and the real-time traffic flow data that adopts when calculating " 27km/h " is " incident "; Can learn according to table two, should " FCD2 " corresponding degree of belief be 5, should " coil " corresponding degree of belief be 10, should " incident " corresponding degree of belief be 9.So, current travelling speed V=(21 * 5+29 * 10+27 * 9)/(5+10+9)=26.6km/h of described road G.
105, when the difference of the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief less than predetermined threshold value, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
For the implementation procedure of clearer description of step 105, suppose in the application scenarios two that the travelling speed of utilizing the road W correspondence that four kinds of real-time traffic flow datas calculate respectively that obtains by above-mentioned steps 101 and road condition are shown in following table four:
| Data name | FCD1 | FCD2 | Coil | Incident |
| The state of road W | Unimpeded | Slowly | Slowly | Unimpeded |
| The speed of road W (km/h) | 45 | 30 | 21 | 50 |
Table four
Wherein, the floating car data that " FCD1 " expression is obtained from company 1, the floating car data that " FCD2 " expression is obtained from company 2, " coil " expression traffic flow inductive coil data, " incident " presentation of events information data.
Suppose application scenarios two times, it is identical with above-mentioned table two to draw described each self-corresponding degree of belief of four kinds of real-time traffic flow datas by step 102, and the predetermined threshold value that can calculate two times employings of application scenarios according to above-mentioned table two is specially F=(6+5+10+9)/4=7.5.
Pass through the degree of belief of the confirmable described road W of step 103 under each road condition so shown in following table five:
| Unimpeded | Slowly | Block up | |
| The degree of belief of road W distributes | 15 | 15 | 0 |
Table five
Can learn that from above-mentioned table five road condition that degree of belief is the highest is unimpeded, its corresponding degree of belief is 15; Degree of belief time high road condition is that slowly its corresponding degree of belief also is 15.Because the difference of the degree of belief of the road condition that the degree of belief of the highest road condition of degree of belief and degree of belief are time high is zero, this difference is less than predetermined threshold value 7.5, at this moment, can recomputate the current travelling speed that draws described road at described each real-time travelling speed V that exchanges correspondence under the data according to described road, and determine the current road condition of described road according to described current travelling speed.
Concrete, can adopt described road to be weighted the current travelling speed of the described road of conduct that on average draws at described each real-time travelling speed V that exchanges correspondence under the data, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V.
The current travelling speed of calculating road W with application scenarios for two times is that example describes, can learn that according to table four and table two described road W exchanges travelling speed V corresponding under the data in real time at described four kinds and is followed successively by: 45km/h, 30km/h, 21km/h, 50km/h.The real-time traffic flow data that adopts when calculating described " 45km/h " is " FCD1 ", and its corresponding degree of belief is 6; The real-time traffic flow data that adopts when calculating described " 30km/h " is " FCD2 ", and its corresponding degree of belief is 5; The real-time traffic flow data that adopts when calculating described " 21km/h " is " coil ", and its corresponding degree of belief is 10; The real-time traffic flow data that adopts when calculating described " 50km/h " is " incident ", and its corresponding degree of belief is 9.So, the current travelling speed V=of described road W (45 * 6+30 * 5+21 * 10+50 * 9)/(6+5+10+9)=36km/h, because travelling speed is that " 36km/h " corresponding road condition is " slowly ", thereby the current road condition S that can determine described road W is " slowly ".
Embodiments of the invention are by the accuracy of state according to different real-time traffic flow datas, give their different degree of beliefs, distribute and speed is carried out the degree of belief weighted mean by analyzing the degree of belief of road under each road condition, obtain the current travelling speed and the road condition of road.Choose the traffic information that wherein a kind of traffic flow data draws with prior art and compare as the current traffic information of road, the embodiment of the invention can effectively be utilized the accuracy of various real-time traffic flow datas, thereby has improved the accuracy of the traffic information of road.
Further, as shown in Figure 3, said method can also comprise the steps 106:
106, utilize the current road condition and the current travelling speed of the described road that calculates of event information data check.
Utilizing event information data to carry out verification mainly is the verification of carrying out restricted information.For example, when occurring the restriction license event on the road, this road should not have telecommunication flow information.And for example, when occurring easily causing the accident of blocking up, the velocity amplitude on can reference road is turned down the critical value of the travelling speed of road condition correspondence, makes state be tending towards blocking up.
Embodiment 2:
As shown in Figure 4, the embodiment of the invention provides a kind of real-time traffic flow data fusing device, comprising: first processing unit 11, the second processing units 12, determining unit 13, state integrated unit 14 and the speed integrated unit 15.
Wherein, first processing unit 11 is used for each the real-time traffic flow data according at least two kinds of real-time traffic flow datas, calculates the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively;
Determining unit 13 is used for determining the degree of belief of described road under each road condition;
State integrated unit 14 is used for being not less than predetermined threshold value when the difference of the degree of belief of the inferior high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief, adopt the current road condition of the highest road condition S of described degree of belief, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road;
Speed integrated unit 15 is used for difference when the degree of belief of the inferior high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief less than predetermined threshold value, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
Further, described second processing unit can carry out function segmentation (figure does not show), specifically can comprise: computing module and modular converter.
Wherein, computing module is used for calculating successively the state accuracy rate of described each real-time traffic flow data in the Preset Time scope; Modular converter is used for determining according to the state accuracy rate in the Preset Time scope of conversion formula and described each real-time traffic flow data successively the degree of belief of described each real-time traffic flow data correspondence; Described conversion formula is: degree of belief=[state accuracy rate * 1000+0.5].
Need to prove, during concrete the application, described state integrated unit is concrete to adopt value that described road corresponding travelling speed V under the highest road condition of described degree of belief is weighted and on average the draws current travelling speed as described road, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V.
The concrete current travelling speed that adopts described road to be weighted the described road of conduct that on average draws at described each real-time travelling speed V that exchanges correspondence under the data of described speed integrated unit, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V.
Further, as shown in Figure 5, said apparatus can also comprise: verification unit 16.
Described verification unit 16 is used to utilize the current road condition and the current travelling speed of the described road that calculates of event information data check.
The real-time traffic flow data fusing device that the embodiment of the invention provides, the current road condition and the travelling speed of the described road that calculates in conjunction with at least two kinds of real-time traffic flow datas, the corresponding separately different degree of belief of described at least two kinds of traffic flow datas, choosing the traffic information that wherein a kind of traffic flow data draws with prior art compares as the current traffic information of road, the embodiment of the invention can effectively be utilized the accuracy of various real-time traffic flow datas, thereby has improved the accuracy of the traffic information of road.
The embodiment of the invention is mainly used in the process that the real-time traffic flow data is merged, and can improve the accuracy of the traffic information of road.
The above; only be the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of described claim.
Claims (10)
1. a real-time traffic flow data fusion method is characterized in that, comprising:
According to each the real-time traffic flow data at least two kinds of real-time traffic flow datas, calculate the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively;
Determine the degree of belief of described each real-time traffic flow data correspondence successively;
Determine the degree of belief of described road under each road condition;
When the difference of the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief is not less than predetermined threshold value, adopt the current road condition of the highest road condition S of described degree of belief, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road;
When the difference of the degree of belief of time high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief less than predetermined threshold value, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
2. real-time traffic flow data fusion method according to claim 1 is characterized in that, described real-time traffic flow data comprises: floating car data, traffic flow inductive coil data or event information data;
Described road condition comprises: unimpeded, slowly or block up.
3. real-time traffic flow data fusion method according to claim 1 is characterized in that, the described degree of belief of definite described each real-time traffic flow data correspondence successively comprises:
Calculate the state accuracy rate of described each real-time traffic flow data in the Preset Time scope successively;
Determine the degree of belief of described each real-time traffic flow data correspondence successively according to the state accuracy rate in the Preset Time scope of conversion formula and described each real-time traffic flow data;
Described conversion formula is: degree of belief=[state accuracy rate * 1000+0.5].
4. real-time traffic flow data fusion method according to claim 1 is characterized in that, the degree of belief of described road under road condition is: the degree of belief sum of all real-time traffic flow data correspondences that adopt when calculating described road condition.
5. real-time traffic flow data fusion method according to claim 1 is characterized in that, also comprises:
Utilize the current road condition and the current travelling speed of the described road that calculates of event information data check.
6. real-time traffic flow data fusion method according to claim 1 is characterized in that,
Described predetermined threshold value calculates according to formula F=(M1+M2+..+Mn)/n, and wherein, n represents real-time traffic flow data number, and Mn represents the degree of belief of n kind real-time traffic flow data;
The described current travelling speed that calculates described road according to described road corresponding travelling speed V under the highest road condition of described degree of belief comprises: the value that adopts described road corresponding travelling speed V under the highest road condition of described degree of belief to be weighted on average to draw is as the current travelling speed of described road, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V;
Described according to described road described each exchange in real time travelling speed V corresponding under data and recomputate the current travelling speed that draws described road and comprise: adopt described road described each exchange the current travelling speed that travelling speed V corresponding under data is weighted the described road of conduct that on average draws in real time, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V.
7. a real-time traffic flow data fusing device is characterized in that, comprising:
First processing unit is used for each the real-time traffic flow data according at least two kinds of real-time traffic flow datas, calculates the road condition S and the travelling speed V of road correspondence under described each real-time traffic flow data successively;
Second processing unit is used for the degree of belief of definite described each real-time traffic flow data correspondence successively;
Determining unit is used for determining the degree of belief of described road under each road condition;
The state integrated unit, be used for being not less than predetermined threshold value when the difference of the degree of belief of the inferior high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief, adopt the current road condition of the highest road condition S of described degree of belief, and calculate the current travelling speed of described road according to described road corresponding travelling speed V under the highest road condition S of described degree of belief as described road;
The speed integrated unit, be used for difference when the degree of belief of the inferior high road condition of the degree of belief of the highest road condition of degree of belief and degree of belief less than predetermined threshold value, recomputate the current travelling speed that draws described road according to described road at described each real-time travelling speed V that exchanges correspondence under the data, and determine the current road condition of described road according to described current travelling speed.
8. real-time traffic flow data fusing device according to claim 7 is characterized in that, described second processing unit comprises:
Computing module is used for calculating successively the state accuracy rate of described each real-time traffic flow data in the Preset Time scope;
Modular converter is used for determining according to the state accuracy rate in the Preset Time scope of conversion formula and described each real-time traffic flow data successively the degree of belief of described each real-time traffic flow data correspondence;
Described conversion formula is: degree of belief=[state accuracy rate * 1000+0.5].
9. real-time traffic flow data fusing device according to claim 7 is characterized in that, also comprises: verification unit is used to utilize event information data to check the current road condition and the current travelling speed of the described road that calculates.
10. real-time traffic flow data fusing device according to claim 7, it is characterized in that, described state integrated unit is concrete to adopt value that described road corresponding travelling speed V under the highest road condition of described degree of belief is weighted and on average the draws current travelling speed as described road, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V;
The concrete current travelling speed that adopts described road to be weighted the described road of conduct that on average draws at described each real-time travelling speed V that exchanges correspondence under the data of described speed integrated unit, wherein, weighted value is the degree of belief of the real-time traffic flow data correspondence that adopts when calculating described travelling speed V.
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| CN2010102606037A CN101937616B (en) | 2010-08-23 | 2010-08-23 | Method for fusing traffic flow data in real time and device |
| PCT/CN2011/075408 WO2012024957A1 (en) | 2010-08-23 | 2011-06-07 | Method for fusing real-time traffic stream data and device thereof |
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| CN101937616B (en) | 2012-06-27 |
| WO2012024957A1 (en) | 2012-03-01 |
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