CN102708677A - Traffic state evaluation rule extracting method based on rough set theory - Google Patents
Traffic state evaluation rule extracting method based on rough set theory Download PDFInfo
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Abstract
The invention provides a traffic state evaluation rule extracting method based on a rough set theory, comprising the following steps of: (1) data preparation: experimental data is from traffic information data collected by an SCATS (Sydney Coordinated Adaptive Traffic System) coil and is pre-processed to maintain data collecting time, a road position, a road coil saturation level and a road vehicle speed; (2) data sampling; (3) data discretization; (4) rule set extraction: a value reduction method of a rough set is utilized to carry out reduction on a discretized decision to obtain a rule set; and (5) a secondary extraction method of the rule set: the secondary extraction method takes a rule matching accuracy as the standard. A traffic state evaluation rule extracted by the method disclosed by the invention has the advantages of high matching accuracy, high matching effective rate, strong adaptability and the like; and the traffic state evaluation rule extracting method be used for more comprehensively analyzing and evaluating a real-time traffic condition and realizing induction and management on traffic.
Description
Technical field
The invention belongs to the intelligent transport technology field, especially a kind of traffic behavior Rules of Assessment method for distilling.
Background technology
In today of urbanization high-speed cruising, urban road is crowded, and vehicle blocks up and can't bear during the peak, has had a strong impact on daily life, and has brought harmful effects such as environmental pollution, the wasting of resources.Through analyzing traffic data, assessment traffic behavior Changing Pattern is realized effectively inducing, and is the main means of alleviating traffic.Wherein utilize rough set theory that traffic data is handled, become one of effective treating method that carries out traffic behavior assessment and prediction.Retrieval through to the prior art document is found; Zhang Yuanliang equals in " based on the capital city traffic congestion warning algorithm analysis of rough set " on being published in " transport science and techonologies are with economic " in 2009; With the rough set is foundation, and the generation rule of extracting traffic congestion is analyzed and judged the real road situation; Liu Hao equals on " highway communication science and technology ", to deliver in 2008 " extracting the urban road journey time prediction of calculating based on the rough set transport information ".Though the method based on rough set has obtained very big achievement, when Rule Extraction, has removed the inconsistent rule of part, thereby has caused problems such as the rule match rate is low.How to improve traffic behavior Rules of Assessment matching rate and applicability and become the intelligent transportation field problem demanding prompt solution.
Summary of the invention
In order to overcome the deficiency that the rule match rate is lower, applicability is relatively poor of existing traffic behavior Rules of Assessment method for distilling, the present invention provides a kind of rule match rate, good traffic behavior Rules of Assessment method for distilling based on rough set theory of applicability of improving.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of traffic behavior Rules of Assessment method for distilling based on rough set theory, said method for distilling may further comprise the steps:
(1) data are prepared: experimental data derives from the SCATS coil and collects traffic information data, and it is done retention data acquisition time after the pre-service, site of road, road map saturation degree and road vehicle speed;
(2) data sampling: adopt ebb according to the actual traffic peak phase, middle peak, the transport information of each one group of period of peak, wherein, time, site of road, road map saturation degree, road vehicle speed was as the decision attribute of decision table as the conditional attribute of decision table;
(3) data discreteization;
(4) rule set extracts: the decision table of the value reduction method that utilizes rough set after to discretize carries out yojan and obtains rule set, the concrete steps of value yojan:
4.1) go redundancyization to decision table;
4.2) each object carried out the deletion of redundancy of attribute value;
4.3) obtain minimum yojan, obtain rule set;
(5) rule set second extraction method: the second extraction method is a standard with the rule match accuracy rate, and concrete extraction step is:
5.1) object set is divided into the conflict and two rule sets that do not conflict;
5.2) rule of not conflicting directly is included into the secondary rule set after the second extraction;
5.3) rule of conflict calculates the matching accuracy rate that it uses the new data source, gets the highest secondary rule set that is included into;
5.4) merge and once finally to be collected with the secondary rule set, be i.e. final=rule set+secondary rule set of collection.
Beneficial effect of the present invention mainly shows: the second extraction method is applied to the extraction of traffic behavior Rules of Assessment, compares the traffic behavior Rules of Assessment that additive method can obtain having higher rule match rate and applicability.Owing to overcome these prior aries insoluble difficulty aspect practical engineering application, therefore really realized traffic behavior is monitored and assessed.
The present invention is directed to the needs that traffic is monitored in real time in the intelligent transportation system; Utilize rough set theory; Introduce the notion of coverage, degree of confidence, rule match accuracy rate; Propose the second extraction method of traffic behavior Rules of Assessment, solved the problem of existing method loss of accuracy when Rule Extraction, also eliminated the major obstacle that realizes real practical applications.The present invention uses the second extraction method, obtains matching accuracy rate, efficient height, and the traffic behavior Rules of Assessment that applicability is strong finally provides foundation for analysis and evaluation real-time traffic situation.
Description of drawings
Fig. 1 is the inventive method process flow diagram.
Embodiment
Below in conjunction with accompanying drawing the present invention is further described.
With reference to Fig. 1, a kind of traffic behavior Rules of Assessment method for distilling based on rough set theory, said method for distilling may further comprise the steps:
(1) data are prepared:
Experimental data derives from the traffic information data that the SCATS coil collects, and it is done retention data acquisition time after the pre-service, Data Source coil numbering (being site of road), road map saturation degree, road vehicle speed contents such as (being the traffic congestion situation).
(2) data sampling:
The SDI amount is huge, the sampling of experimental basis actual traffic peak phase, and the transport information of one group of data period is respectively got on ebb, Zhong Feng, peak.Wherein road vehicle speed is as the decision attribute of decision table as the conditional attribute of decision table time, site of road, road map saturation degree.
(3) data discreteization:
Employing experience split plot design and equidistant partitioning integrated data processing, with data acquisition time, site of road, road map saturation degree and road vehicle speed are done discretize respectively and are handled.
(4) rule set extracts:
The decision table of the value reduction method that utilizes rough set after to discretize carries out yojan and obtains rule set.The concrete steps of value yojan:
4.1) go redundancyization to decision table.Object deletion with wherein identical only keeps different objects, obtains new decision table after compression;
4.2) each object carried out the deletion of redundancy of attribute value;
4.3) adopt heuristic value Algorithm for Reduction, obtain minimum yojan;
4.4) according to minimum yojan, obtain rule set.
(5) rule set second extraction method:
At first carry out the first time of rule set and extract, introduce degree of confidence and coverage, from rule set, extract higher " credible " rule of degree of confidence and be included into rule set one time as the standard of once extracting.From rule set, extract " credible " rule through setting higher degree of confidence and coverage.
The object of clear and definite second extraction:
The rule of the redundancy rule collection after the second extraction object set=first time extracts-conflict with rule set.
The second extraction method is a parameter with the rule match accuracy rate, and concrete extraction step is:
5.1) object set is divided into the conflict and two rule sets that do not conflict;
5.2) rule of not conflicting directly is included into the secondary rule set after the second extraction;
5.3) rule of conflict calculates the matching accuracy rate that it uses the new data source, get the highest one and be included into the secondary rule set;
5.4) merge and once finally to be collected (=rule set+secondary rule set of final collection) with the secondary rule set.
Instance: choose three days the SCATS coil traffic data in zone, Shanghai.
Shown in Fig. 1 the inventive method process flow diagram, present embodiment practical implementation step (is used the programming of Visual C Plus Plus) as follows:
(1) data are prepared:
The original traffic information data (like table 1) that to take from relevant departments are carried out pre-service, remove irrelevant attribute, retention data acquisition time, coil numbering (site of road), road map saturation degree, contents such as road vehicle speed.
Table 1 source data form shfft
(2) data sampling:
The sampling of traffic peak phase, the transport information of one group of data period is respectively got on ebb, Zhong Feng, peak.Wherein road vehicle speed is as the decision attribute of decision table as the conditional attribute of decision table time, site of road, road map saturation degree.
(3) data discreteization:
Employing experience split plot design and equidistant partitioning integrated data processing divide the time (C1) into basic, normal, high three peaks, count 1,2,3 respectively; Site of road (C2) is divided into high, medium and low three heavy duty zones, counts 1,2,3 respectively; With road map saturation degree (C3) and road vehicle speed (D1) get maximum, minimum value is done equidistance and is not counted 0,1,2,3,4,5 discrete dividing, and generates decision table (like table 2) according to the traffic behavior relation.
Table 2 decision table
(4) rule set extracts:
The decision table of the value reduction method that utilizes rough set after to discretize carries out yojan and obtains rule set.The concrete steps of value yojan:
1. decision table is gone redundancyization.Data object deletion identical in the decision table only keeps different data objects, obtains new decision table after the compression;
2. each data object is carried out the deletion of redundancy of attribute value;
3. adopt heuristic value Algorithm for Reduction, obtain minimum yojan, obtain 33 rules.
(5) rule set second extraction method:
At first calculate the degree of confidence and the coverage of every rule in the rule set, set 0.7 degree of confidence and 0.05 coverage is a threshold value, rule set is extracted, obtain credible rule set (like table 3).
The credible rule set of table 3
Redundancy rule after extracting is for the first time concentrated and is removed the rule of conflicting with rule set, is second extraction object set (be second extraction object set=rule set-credible rule set-with the rule of credible rule conflict).The second extraction method is a standard with the rule match accuracy rate, and concrete extraction step is:
1. object set is divided into conflict and non-two rule sets that conflict;
2. non-conflict rule directly is included into the secondary rule set after the second extraction;
3. for conflict rule (like table 4),
Conflict rule collection in table 4 object set
Calculate the matching accuracy rate that it is applied to the new data source, get the highest one and be included into the secondary rule set;
The calculating of rule match accuracy rate is contrast with second day traffic data all.Second extraction is a main standard with the rule match accuracy rate, and degree of confidence and coverage are respectively second, third standard collisions rule set and carry out Rule Extraction.In table 4, corresponding second extraction method is: in conflict rule 1 ~ 4, with selection rule 2; Selection rule 6 in rule 5 ~ 8; Selection rule 9 in rule 9 ~ 12 is included into the second extraction rule set with rule 2,6,9 at last.
4. merge credible rule set and secondary rule set and obtain final rule set (like table 5)
The final rule set of table 5
Traffic behavior Rules of Assessment and True Data matching accuracy rate that the method is extracted reach more than 90%, mate efficient reaching more than 99%, compare with at present existing method to have stronger applicability.
Claims (1)
1. traffic behavior Rules of Assessment method for distilling based on rough set theory, it is characterized in that: said method for distilling may further comprise the steps:
(1) data are prepared: experimental data derives from the traffic information data that the SCATS coil collects, and it is done retention data acquisition time after the pre-service, site of road, road map saturation degree and road vehicle speed;
(2) data sampling: adopt ebb according to the actual traffic peak phase, middle peak, the transport information of each one group of period of peak, wherein, time, site of road, road map saturation degree, road vehicle speed was as the decision attribute of decision table as the conditional attribute of decision table;
(3) data discreteization;
(4) rule set extracts: the decision table of the value reduction method that utilizes rough set after to discretize carries out yojan and obtains rule set, the concrete steps of value yojan:
4.1) go redundancyization to decision table;
4.2) each object carried out the deletion of redundancy of attribute value;
4.3) obtain minimum yojan, obtain rule set;
(5) rule set second extraction method: the second extraction method is a standard with the rule match accuracy rate, and concrete extraction step is:
5.1) object set is divided into the conflict and two rule sets that do not conflict;
5.2) rule of not conflicting directly is included into the secondary rule set after the second extraction;
5.3) rule of conflict calculates the matching accuracy rate that it uses the new data source, gets the highest secondary rule set that is included into;
5.4) merge and once finally to be collected with the secondary rule set, be i.e. final=rule set+secondary rule set of collection.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109273058A (en) * | 2018-09-20 | 2019-01-25 | 中轻国环（北京）环保科技有限公司 | A kind of composite algorism for the exceeded early warning of anaerobic processes volatile fatty acid |
| CN109376473A (en) * | 2018-11-23 | 2019-02-22 | 北汽福田汽车股份有限公司 | The calculation method and device of vehicle control device, vehicle and its course continuation mileage |
| CN109543706A (en) * | 2018-09-19 | 2019-03-29 | 南昌工程学院 | A kind of conflict analysis System and method for based on rough set theory of big data driving |
| CN111862600A (en) * | 2020-06-22 | 2020-10-30 | 中汽研智能网联技术(天津)有限公司 | Traffic efficiency assessment method based on vehicle-road cooperation |
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- 2012-05-30 CN CN2012101797500A patent/CN102708677A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109543706A (en) * | 2018-09-19 | 2019-03-29 | 南昌工程学院 | A kind of conflict analysis System and method for based on rough set theory of big data driving |
| CN109273058A (en) * | 2018-09-20 | 2019-01-25 | 中轻国环（北京）环保科技有限公司 | A kind of composite algorism for the exceeded early warning of anaerobic processes volatile fatty acid |
| CN109376473A (en) * | 2018-11-23 | 2019-02-22 | 北汽福田汽车股份有限公司 | The calculation method and device of vehicle control device, vehicle and its course continuation mileage |
| CN109376473B (en) * | 2018-11-23 | 2021-06-18 | 北汽福田汽车股份有限公司 | Vehicle controller, vehicle and method and device for calculating driving mileage of vehicle |
| CN111862600A (en) * | 2020-06-22 | 2020-10-30 | 中汽研智能网联技术(天津)有限公司 | Traffic efficiency assessment method based on vehicle-road cooperation |
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