CN101739824A - Data fusion technology-based traffic condition estimation method - Google Patents
Data fusion technology-based traffic condition estimation method Download PDFInfo
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Abstract
The invention discloses a data fusion technology-based traffic condition estimation method. The method comprises the following steps: acquiring current traffic condition information by using a GPS, an annular vehicle detector and other sensors based on the principle that much information is helpful for estimating the traffic condition more accurately, then preprocessing the traffic condition information, and performing fusion processing on the traffic condition information and sensor data of weather, time period and different moments together by adopting a Bayesian network to acquire accurate information of the current traffic condition. Besides the current sensor date, the method takes multiple other factors influencing the traffic condition into consideration, and performs fusion processing so as to avoid inaccurate judgment problem brought by single sensor information. The traffic condition estimation method is particularly suitable for traffic environments of large cities.
Description
Technical field
The present invention relates to a kind of traffic condition estimation method, relate to particularly that to overcome the state estimation that causes in the individual data source not accurate enough, a kind of traffic condition estimation method of big these shortcomings of error based on the data fusion technology.
Background technology
The development of world car industry and family-sized car universal day by day, the problem of blocking up of road traffic becomes the major issue of urban transportation; The progress at full speed of infotech is that comprehensive transport solution problem has been brought opportunity on the other hand.Be exactly under this background, advanced traffic information management system has been subjected to paying close attention to widely prior to the other system of intelligent transportation system, all obtained development at full speed in countries in the world, be applied to dynamic route planning, dynamic navigation, road network and coordinate various aspects such as traffic signal system, dynamic traffic scheduling.Wherein, the estimation of road net traffic state is the key components of traffic information management system in real time.
Road net traffic state is estimated in real time relevant with the transport information that is adopted, different transport information has determined estimation approach and precision.At present, many correlative studys have been arranged in the world, and wherein, that representative is Martin L.Hazclton (" Estimating Vehicle Speed from Count andOccupancy data; Journal of Data Science 2 (2004), 231-244 ").MartinL.Hazclton considers effectively and modeling to have handled Road Detection loop data error rate big, the problem that reliability is low, and the result is gratifying, but he is the research of carrying out on the expressway, only be applicable to that traffic flow is the situation of continuous stream, and the traffic flow in city stops between being, the traffic flow modes that is not suitable for city road network is estimated.Utilize the detection ring data that city road network is carried out traffic flow and estimate urban infrastructure is had relatively high expectations, often get less than enough desired datas in a lot of cities, and the high problem of error rate can not get solving effectively.
In patent documentation " method for estimating city road network traffic flow state, application number: 200510026214.7 ", the applicant has proposed a kind of method for estimating state of the traffic control information based on gps data and self-adaptation traffic control system (SCATS).But use gps data merely, have a lot of error situations, cause erroneous judgement easily.Such as, gps data shows that Vehicle Speed is 0, system generally is judged as traffic congestion, but current vehicle also may be because on-board and off-board, vehicle GPS data that perhaps cause and show that Vehicle Speed is 0 because of red light, be not real traffic congestion, the misjudgment of the easy like this traffic behavior that causes.And the information of traffic control system and be inconvenient to obtain.
Summary of the invention
At the problems referred to above, fundamental purpose of the present invention is at the deficiencies in the prior art, propose a kind of new traffic flow modes method of estimation that is used for city road network based on data fusion, it is not accurate enough to overcome the state estimation that causes in the individual data source, the shortcoming that error is bigger.
The present invention solves above-mentioned technical matters by following technical proposals: in order to realize such purpose, the data source of technical scheme of the present invention comes from, one class from sensing data, comprise the GPS receiver that loads on annular wagon detector and the vehicle.The annular wagon detector can obtain the mean velocity information that current road vehicle travels; The GPS receiver that loads on the vehicle also can obtain the car speed information of current travels down, if receive the GPS information that many cars on the same highway section send over simultaneously, just can obtain the mean velocity information of vehicle on this highway section.
Another kind of information is and the closely-related non-sensor information of traffic, comprises the current weather situation, current information of time.These information are also to the very important influence of whether having blocked up of a road.
Last category information is the sensor historical information, comprises the vehicle mean velocity information of the past GPS receiver acquisition in the short time now and the road vehicle mean velocity information that annular wagon detector obtains.
This method mainly comprises following step:
(1). by the traffic related information on sensor acquisition road surface;
(2). the sensing data to sensor acquisition in the step (1) carries out pre-service;
(3). sensing data and current weather, the time of gathering are adopted Bayesian network method, data are carried out fusion treatment;
(4). by the data estimation traffic that obtains after the fusion treatment.
Be described respectively below:
1. gps data is carried out pre-service:
The current real-time GPS data that can provide the vehicle GPS receiver that comprises distance, time, velocity information to pass over is considered as the sampled point of wagon flow on each oriented highway section.The pre-service of gps data mainly comprises at first and to carry out road matching according to the latitude and longitude information in the gps data, determines that gps data comes from the vehicle on that highway section; To try to achieve the average velocity of this highway section vehicle according to the car speed information in all gps datas on this highway section then.
2. annular wagon detector data are carried out pre-service:
Because annular wagon detector can directly provide the average velocity of the vehicle ' on the highway section, no longer need other processing.
3. delimit the congestion in road state by average velocity:
With t
0The average velocity of each oriented highway section road direction is the delimitation that index is carried out the congestion in road state in the city road network constantly.The average velocity in highway section in the city road network is divided into five speed class, corresponding respectively unimpeded, more unimpeded, obstructed, five kinds of congestion in road states block up, seriously block up.Judge the congestion status in each oriented highway section according to the residing speed class of average velocity of each oriented highway section correspondence.
4. the Bayesian network that is used for data fusion makes up:
According to the cause-effect relationship between the data, make up Bayesian network.Each node in the Bayesian network is represented a stochastic variable, and arrow points to the result by reason.The probability distribution table of the stochastic variable of each node representative then obtains through observing statistics and experiment.
5. based on the data fusion of variable method of elimination
It is a kind of Bayesian network inference method commonly used that variable disappears that unit sends out.Progressively cancellation does not belong to query interface, and just its dependent variable of traffic variable finally obtains on the basis of known evidence variable, and query interface is the probability distribution situation of traffic just.Traffic after estimating to obtain merging.
Positive progressive effect of the present invention is: the traffic condition estimation method based on the data fusion technology provided by the invention has following advantage:
1. this method has also been considered a plurality of other factors that influence traffic, and has been carried out fusion treatment except current sensor data, and it is not accurate enough to have overcome the state estimation that causes in the individual data source, the shortcoming that error is bigger.
Description of drawings
Fig. 1 is the FB(flow block) of urban road network traffic situation method of estimation;
Fig. 2 is the Bayesian network synoptic diagram that is used for data fusion.
Embodiment
Provide preferred embodiment of the present invention below in conjunction with accompanying drawing, to describe technical scheme of the present invention in detail.The FB(flow block) of Fig. 1 urban road network traffic situation method of estimation, as shown in Figure 1, the traffic condition estimation method based on the data fusion technology provided by the invention comprises following a few step:
(1). by the traffic related information on sensor acquisition road surface;
(2). the sensing data to sensor acquisition in the step (1) carries out pre-service;
(3). sensing data and current weather, the time of gathering are adopted Bayesian network method, data are carried out fusion treatment;
(4). by the data estimation traffic that obtains after the fusion treatment.
Be described respectively below:
1. data pre-service
Input data of the presently claimed invention as shown in Figure 2, comprise that GPS measures average velocity, annular wagon detector measuring vehicle average velocity and a last moment annular wagon detector measuring vehicle average velocity of current average velocity, a last GPS measurement constantly, and weather and time period data.Average velocity v can be divided into 5 speed class according to the actual traffic state, traffic behavior seriously blocks up corresponding to v≤10 kilometer/hour, blocks up corresponding to 10＜v≤20, and is not smooth corresponding to 20＜v≤30, more unimpeded corresponding to 30＜v≤50, unobstructed corresponding to v＞50.Weather data is divided into abominable, relatively poor, general and better according to the influence degree to traffic.Time period is divided into peak period, crowded period, general period and unimpeded period according to the traffic impact degree.
2. Bayesian network makes up
The bayesian network structure that makes up as shown in Figure 2, arrow is represented cause-effect relationship to be arranged between the two if improve the data fusion precision, can increase more data, such as increasing more historical sensing data, perhaps increase more sensing data type, for example, video data or the like.For determining of the probability distribution of each node in the Bayesian network, adopt the method for actual measurement and expert authorization.By the observation long-term to a certain highway section, can determine, under different weather conditions, the situation of traffic, thus can determine conditional probability distribution from weather to the traffic.
3. based on the data fusion of variable method of elimination
The input fusion process is as follows:
1. initialization
Construct an initial intermediate function formation, the corresponding function of all nodes in the Bayesian network is put into wherein; Variablees such as the sensing data that initial function formation basis is obtained, weather are set its initial value then; Construct the metavariable sequence of waiting to disappear then, this sequence does not comprise known preset value variable and traffic variable to be asked.
The metavariable that is still waiting to disappear in the metavariable sequence if 2. wait to disappear then repeat this step
Construct a new function formation, the function relevant with the metavariable of waiting to disappear in original function formation all moved into new function formation; Construct a new function, this function is the result of calculation of new function formation; To all involved in new function formation variablees assignment successively, promptly from (0,0,0) always assignment to (m, n ... z) (m wherein, n ...., z etc. are the maximum occurrences classification numbers of each variable), each assignment is calculated company's value of taking advantage of of new function formation, and adding up afterwards by the value of the metavariable of waiting to disappear this companys value of taking advantage of, the value of gained is exactly the functional value of new function under current assignment condition; The new function that calculating is finished joins in original function formation;
3. all functions in the present function formation are merged into a function and carried out normalized, last resulting function is exactly a probability distribution of waiting to ask traffic under current known conditions, and the traffic that promptly obtains current maximum probability is estimated.
More than show and described ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; that describes in the foregoing description and the instructions just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications; these changes and improvements all fall in the claimed scope of the invention, and the claimed scope of the present invention is defined by appending claims and equivalent thereof.
Claims (4)
1. traffic condition estimation method based on the data fusion technology, it is characterized in that: described method comprises the steps:
(1). by the traffic related information on sensor acquisition road surface;
(2). the sensing data to sensor acquisition in the step (1) carries out pre-service;
(3). sensing data and current weather, the time of gathering are adopted Bayesian network method, data are carried out fusion treatment;
(4). by the data estimation traffic that obtains after the fusion treatment.
2. the traffic condition estimation method based on the data fusion technology according to claim 1, it is characterized in that: described sensor comprises two classes: GPS (GPS) receiver that a class is on the automobile to be installed, another kind of is to be embedded in the underground annular wagon detector of road.
3. the traffic condition estimation method based on the data fusion technology according to claim 1 is characterized in that: described sensing data had both comprised the sensing data of current time also comprising the sensor historical data.
4. the traffic condition estimation method based on the data fusion technology according to claim 2, it is characterized in that: described data of carrying out fusion treatment comprise that GPS (GPS) measures current average velocity, last average velocity, annular wagon detector measuring vehicle average velocity and the last moment annular wagon detector measuring vehicle average velocity measured of GPS (GPS) constantly, and weather and time period data.
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| CN102289932A (en) * | 2011-06-17 | 2011-12-21 | 同济大学 | Dynamic OD (Origin Destination) matrix estimating method based on AVI (Automatic Vehicle Identification) device |
| CN102436740A (en) * | 2011-09-29 | 2012-05-02 | 东南大学 | Automatic detection method of traffic incident on highway |
| CN102646332A (en) * | 2011-02-21 | 2012-08-22 | 日电（中国）有限公司 | Traffic state estimation device and method based on data fusion |
| CN102932812A (en) * | 2012-11-06 | 2013-02-13 | 武汉大学 | Vehicle sensor concurrent monitoring method facing road conditions |
| CN103839412A (en) * | 2014-03-27 | 2014-06-04 | 北京建筑大学 | Combined estimation method for road junction dynamic steering proportion based on Bayes weighting |
| CN103903436A (en) * | 2012-12-28 | 2014-07-02 | 上海优途信息科技有限公司 | Expressway traffic jam detecting method and system based on floating car |
| CN104021672A (en) * | 2014-06-09 | 2014-09-03 | 北京世纪高通科技有限公司 | Method and device for obtaining traffic jam index |
| CN104933856A (en) * | 2014-03-17 | 2015-09-23 | 径卫视觉科技（上海）有限公司 | Real-time evaluation system and method for road conditions |
| CN105096590A (en) * | 2014-04-23 | 2015-11-25 | 株式会社日立制作所 | Traffic information generation method and device |
| CN105808918A (en) * | 2015-08-16 | 2016-07-27 | 姜涵 | Road full flow back-stepping approach based on data fusion |
| CN106133751A (en) * | 2014-03-20 | 2016-11-16 | 罗伯特·博世有限公司 | For the method and apparatus running vehicle |
| CN106251630A (en) * | 2016-10-13 | 2016-12-21 | 东南大学 | A kind of laddering EKF traffic status of express way method of estimation based on multi-source data |
| CN108109382A (en) * | 2018-02-05 | 2018-06-01 | 青岛大学 | A kind of congestion points based on composite network, congestion line, the discovery method of congestion regions |
| CN108275158A (en) * | 2017-01-05 | 2018-07-13 | 大唐高鸿信息通信研究院（义乌）有限公司 | The driving behavior evaluating method of vehicle-mounted short haul connection net |
| CN108734955A (en) * | 2017-04-14 | 2018-11-02 | 腾讯科技（深圳）有限公司 | The method and device of predicting road conditions state |
| CN111326002A (en) * | 2020-02-26 | 2020-06-23 | 公安部交通管理科学研究所 | Prediction method, device and system for environment perception of automatic driving automobile |
| CN111429728A (en) * | 2020-05-06 | 2020-07-17 | 辽宁百思特达半导体科技有限公司 | Intelligent traffic system based on intelligent lamp pole and method thereof |
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- 2009-11-12 CN CN2009101986924A patent/CN101739824B/en not_active Expired - Fee Related
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| CN102646332A (en) * | 2011-02-21 | 2012-08-22 | 日电（中国）有限公司 | Traffic state estimation device and method based on data fusion |
| CN102646332B (en) * | 2011-02-21 | 2014-03-12 | 日电（中国）有限公司 | Traffic state estimation device and method based on data fusion |
| CN102289932B (en) * | 2011-06-17 | 2013-10-23 | 同济大学 | Dynamic OD (Origin Destination) matrix estimating method based on AVI (Automatic Vehicle Identification) device |
| CN102289932A (en) * | 2011-06-17 | 2011-12-21 | 同济大学 | Dynamic OD (Origin Destination) matrix estimating method based on AVI (Automatic Vehicle Identification) device |
| CN102436740A (en) * | 2011-09-29 | 2012-05-02 | 东南大学 | Automatic detection method of traffic incident on highway |
| CN102932812B (en) * | 2012-11-06 | 2014-11-19 | 武汉大学 | Vehicle sensor concurrent monitoring method facing road conditions |
| CN102932812A (en) * | 2012-11-06 | 2013-02-13 | 武汉大学 | Vehicle sensor concurrent monitoring method facing road conditions |
| CN103903436A (en) * | 2012-12-28 | 2014-07-02 | 上海优途信息科技有限公司 | Expressway traffic jam detecting method and system based on floating car |
| CN104933856A (en) * | 2014-03-17 | 2015-09-23 | 径卫视觉科技（上海）有限公司 | Real-time evaluation system and method for road conditions |
| CN106133751A (en) * | 2014-03-20 | 2016-11-16 | 罗伯特·博世有限公司 | For the method and apparatus running vehicle |
| CN103839412B (en) * | 2014-03-27 | 2015-12-02 | 北京建筑大学 | A kind of crossing dynamic steering ratio combination method of estimation based on Bayes's weighting |
| CN103839412A (en) * | 2014-03-27 | 2014-06-04 | 北京建筑大学 | Combined estimation method for road junction dynamic steering proportion based on Bayes weighting |
| CN105096590A (en) * | 2014-04-23 | 2015-11-25 | 株式会社日立制作所 | Traffic information generation method and device |
| CN105096590B (en) * | 2014-04-23 | 2019-07-26 | 株式会社日立制作所 | Traffic information creating method and traffic information generating device |
| CN104021672A (en) * | 2014-06-09 | 2014-09-03 | 北京世纪高通科技有限公司 | Method and device for obtaining traffic jam index |
| CN105808918A (en) * | 2015-08-16 | 2016-07-27 | 姜涵 | Road full flow back-stepping approach based on data fusion |
| CN105808918B (en) * | 2015-08-16 | 2019-04-05 | 北京数行健科技有限公司 | A kind of road full flow Backstipping design based on data fusion |
| CN106251630B (en) * | 2016-10-13 | 2018-09-07 | 东南大学 | A kind of progressive Extended Kalman filter traffic status of express way method of estimation based on multi-source data |
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| CN108275158A (en) * | 2017-01-05 | 2018-07-13 | 大唐高鸿信息通信研究院（义乌）有限公司 | The driving behavior evaluating method of vehicle-mounted short haul connection net |
| CN108275158B (en) * | 2017-01-05 | 2020-09-15 | 大唐高鸿信息通信(义乌)有限公司 | Driving behavior evaluation method of vehicle-mounted short-distance communication network |
| CN108734955A (en) * | 2017-04-14 | 2018-11-02 | 腾讯科技（深圳）有限公司 | The method and device of predicting road conditions state |
| CN108734955B (en) * | 2017-04-14 | 2021-06-11 | 腾讯科技（深圳）有限公司 | Method and device for predicting road condition state |
| US11305780B2 (en) | 2017-04-14 | 2022-04-19 | Tencent Technology (Shenzhen) Company Ltd | Road condition status prediction method, device, and server, and storage medium |
| CN108109382A (en) * | 2018-02-05 | 2018-06-01 | 青岛大学 | A kind of congestion points based on composite network, congestion line, the discovery method of congestion regions |
| CN108109382B (en) * | 2018-02-05 | 2020-08-04 | 青岛大学 | Congestion point, congestion line and congestion area discovery method based on composite network |
| CN111326002A (en) * | 2020-02-26 | 2020-06-23 | 公安部交通管理科学研究所 | Prediction method, device and system for environment perception of automatic driving automobile |
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