CN113255723B - Black car identification method based on track characteristics and pedestrian and vehicle association - Google Patents
Black car identification method based on track characteristics and pedestrian and vehicle association Download PDFInfo
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- 230000002159 abnormal effect Effects 0.000 claims description 3
- 239000006185 dispersion Substances 0.000 claims description 3
- 230000002123 temporal effect Effects 0.000 claims description 3
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Abstract
The invention discloses a black car identification method based on track characteristics and pedestrian and vehicle association, and particularly relates to the technical field of intelligent transportation, and the method comprises the following specific steps: the method comprises the following steps: confirming whether all registered vehicles have operation qualification; step two: determining an alternative feature list; step three: calculating the man-vehicle related information; step four: screening a significant feature list of commercial vehicles; step five: and determining a black car target. The invention searches vehicles with similar characteristics with the characteristics of the vehicles in legal operation by setting the steps from the first step to the second step and by means of the characteristics of the vehicles in legal operation and the information associated with people and vehicles to search vehicles which have no operation quality but are likely to operate, screens the characteristics of the black vehicles by utilizing calculation through setting the steps from the third step to the fifth step, has clear characteristics description of the black vehicles, accurate definition and high identification precision rate, is favorable for maintaining normal transportation market order, reduces the occurrence of traffic accidents, protects the legal rights and interests of passengers and the travel safety, and is favorable for social stability.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a black car identification method based on track characteristics and human-vehicle association.
Background
The black car refers to a vehicle which does not transact any related procedures in the transportation management department, receives the operation license and implements illegal operation by paid services. In recent years, the number of black cars is increased year by year, the normal transportation market order is seriously disturbed, the occurrence of traffic accidents is increased, the legal rights and interests of passengers are damaged, the traveling safety of the passengers is seriously influenced, and unstable factors are brought to the society.
Therefore, government departments also pay more attention to the work of hitting black cars, and develop a series of researches on black car identification for the work, the existing black car identification is mostly distinguished by methods such as authority authentication, machine learning algorithm, k-means algorithm hard clustering and manual work, but the machine learning classification algorithm is obviously not suitable by utilizing the existing black and white car samples; in addition, the hard clustering is carried out by using the k-means algorithm, the problem that the clustering result is uncontrollable also exists, because the number of the black cars is small, the clustering result is likely to dilute the black car samples in different classes, so that the problem that the modeling needs to be carried out again is solved, even if the existing characteristic system is adjusted, an expected result is not necessarily obtained, and meanwhile, because the interpretation capability of the clustering result on the business is poor, the final model has the condition of lacking persuasion; in addition, the methods cannot monitor increase and decrease of the black car and a walking route in real time, cannot update information in time, and are not beneficial to tracking the black car, so that the methods need to be put into work in feature screening, but the traditional method for paying attention to feature engineering often does not enough in practice, definition of the black car is too loose, feature depiction is not clear, and data analysis and data mining are not accurate.
Disclosure of Invention
Therefore, the invention provides a black car identification method based on track characteristics and association of people and cars, through the arrangement from step one to step two, by means of the characteristics of legal operation vehicles and the information associated with people and cars, vehicles with similar characteristics are searched for vehicles which have no operation qualification but can be operated, the information updating period is short, the efficiency is high, the vitality is strong, the black car tracking is facilitated, through the arrangement from step three to step five, the characteristics of the black car are screened by calculation, the characteristics of the black car are clearly depicted, the definition is accurate, the identification accuracy rate is high, the black car can be effectively tracked and eliminated, the normal transportation market order is facilitated to be maintained, the occurrence of traffic accidents is reduced, the legal rights and the traveling safety of passengers are protected, the social stability is facilitated, so that the problem that the black car loading on the market is not reduced due to the poor tracking accuracy of various black car identification methods in the prior art is solved, the legal rights and the safety of the passengers are damaged.
In order to achieve the above purpose, the invention provides the following technical scheme: a black car identification method based on track characteristics and pedestrian and car association comprises the following specific steps:
further, the step of confirming whether all the registered vehicles have operation qualification in the step one comprises the following steps:
the first step is as follows: the operation card information table is associated with the motor vehicle information table through the identity card to confirm whether the vehicle has effective operation qualification;
the second step is that: and (5) determining whether the vehicle owner has the operation qualification or not, searching the type of the vehicle owner, and if the type of the vehicle owner is individual and the operation qualification is not available, performing the step two.
Further, in the step two, determining an alternative feature list, selecting the alternative feature list from two angles of the self track feature and the human-vehicle interaction track feature:
each angle determines a list of candidate features from a temporal, spatial, spatiotemporal interaction perspective, including: the number of times of occurrence of the vehicle, the number of days of occurrence of the vehicle, the number of times of occurrence of the vehicle at a key site, the number of days of occurrence of the vehicle at the key site, the maximum stay time of the vehicle at the key site, the number of times of association between a person and a vehicle, the number of days of association between a person and a vehicle, the number of times of association between a person and other vehicles, and the number of times of association between a person and other vehicles are associated;
the method comprises the following steps of associating, namely simultaneously appearing at certain sites within two minutes, wherein information acquisition devices are arranged at the sites and can capture passing license plates, passing faces and mobile phone cards;
the important sites, that is, the black car high frequency appearance sites which are already grasped, include not only the black car high frequency appearance sites confirmed by the case, but also the black car high frequency appearance sites determined by the significance test based on the last month trajectories of all the black cars found in the near future.
Further, the calculation of the human-vehicle association condition in the step three is to calculate the corresponding human-vehicle association condition according to the candidate feature list in the step two.
Further, the salient features of the commercial vehicle are screened in the fourth step, whether a set of values corresponding to the candidate features of the commercial vehicle and the non-commercial vehicle has a salient difference or not is compared by means of a salient test on the basis of the assumption that the black vehicles are only a few, if the set has the salient difference, the feature is considered to be salient, and the salient feature list C is screened sequentially according to the mode.
Further, the determination of the black car in the step fiveThe object is to calculate a set formed by characteristic values corresponding to each remarkable characteristic of the commercial vehicle by means of the result of the step four characteristic significance check, the set forms a distribution, and the ith remarkable characteristic of the vehicle A is recorded as
，
The corresponding set of eigenvalues is denoted
The probability that vehicle A with i non-operating vehicle characteristics is a non-operating vehicle is recorded
Then, the vehicle characteristic of the A vehicle and the non-operating vehicle with the probability greater than P is considered
The method has consistency, sets corresponding to different characteristics,
with different degrees of dispersion, for example: two notable features
And
the corresponding feature value sets are respectively marked as
And
，
and
respectively, are recorded as
And
if, if
Is greater than
Then, it is reasonable to consider the feature
Ratio of
More significantly, assuming that the non-commercial B cars have consistency in m characteristics, the possibility of being a black car is noted as
Where M is a set of the M features,
a characteristic number having an abnormal operation characteristic for a normally operated vehicle,
for the probability that a normally operating vehicle is a black vehicle, if m exceeds a specified threshold
P exceeds a specified threshold
And then the vehicle B is considered as a black vehicle.
The invention has the following advantages:
1. compared with the prior art, the method has the advantages that through the arrangement of the first step to the second step, the vehicles with similar characteristics are searched for by means of the characteristics of legal operation vehicles and the information associated with the people and the vehicles, so that the vehicles which have no operation qualification but can be operated are searched for, the information updating period is short, the efficiency is high, the vitality is strong, and the method is favorable for tracking the black vehicles;
2. through the arrangement of the third step to the fifth step, compared with the prior art, the method has the advantages that the characteristics of the black car are screened by utilizing calculation, the characteristics of the black car are clearly depicted, the definition is accurate, the identification accuracy is high, the black car can be effectively tracked and eliminated, the normal transportation market order is favorably maintained, the occurrence of traffic accidents is reduced, the legal rights and the travel safety of passengers are protected, and the social stability is favorably realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a black car identification method based on track characteristics and association of people and cars,
the method comprises the following specific steps:
the method comprises the following steps: confirming whether all registered vehicles have operation qualification: the operating card information table is associated with the motor vehicle information table through the identity card to determine whether the vehicle has effective operating qualification, wherein whether one identity card corresponds to a plurality of vehicles or a plurality of identity cards correspond to the same vehicle, the calculation of the following steps is carried out in sequence;
step two: determining a list of alternative features: and (3) selecting an alternative feature list from two angles of self-track features and human-vehicle interaction track features by sampling according to experience:
each angle determines a list of candidate features from a temporal, spatial, spatiotemporal interaction perspective, including: the number of times of occurrence of the vehicle, the number of days of occurrence of the vehicle, the number of times of occurrence of the vehicle at a key site, the number of days of occurrence of the vehicle at the key site, the maximum stay time of the vehicle at the key site, the number of times of association between a person and a vehicle, the number of days of association between a person and a vehicle, the number of times of association between a person and other vehicles, and the number of times of association between a person and other vehicles are associated;
the method comprises the following steps of associating, namely simultaneously appearing at certain sites within two minutes, wherein information acquisition devices are arranged at the sites and can capture passing license plates, passing faces and mobile phone cards;
the important sites, namely the black car high-frequency occurrence sites which are mastered, comprise not only the black car high-frequency occurrence sites confirmed by cases, but also the black car high-frequency occurrence sites determined by the significance test according to the recently discovered one-month trajectories of all black cars;
step three: calculating the human-vehicle related information: calculating corresponding human-vehicle association conditions according to the alternative feature list in the step two, wherein only the human-vehicle association conditions of the last 30 days are calculated because the longer the data is, the smaller the influence on the current data is;
step four: screening a significance feature list of commercial vehicles: on the basis of the assumption that the number of black cars is only a few, by means of significance test, comparing whether a set formed by values corresponding to all candidate features of a commercial vehicle and a non-commercial vehicle has significance differences, if the significance differences exist, considering the features to be significant, and sequentially screening out a significant feature list C according to the mode;
step five: determining a black car target: calculating a set of characteristic values corresponding to each remarkable characteristic of the commercial vehicle by using the result of the step four characteristic significance check, wherein the set forms a distribution, and recording the ith remarkable characteristic of the vehicle A as
，
The corresponding set of eigenvalues is denoted
The probability that vehicle A with i non-operating vehicle characteristics is a non-operating vehicle is recorded
Then, the vehicle characteristic of the A vehicle and the non-operating vehicle with the probability greater than P is considered
The method has consistency, sets corresponding to different characteristics,
with different degrees of dispersion, for example: two notable features
And
the corresponding feature value sets are respectively marked as
And
，
and
respectively, are recorded as
And
if, if
Is greater than
Then, it is reasonable to consider the feature
Ratio of
More significantly, assuming that the non-commercial B cars have consistency in m characteristics, the possibility of being a black car is noted as
Where M is a set of the M features,
a characteristic number having an abnormal operation characteristic for a normally operated vehicle,
for the probability that a normally operating vehicle is a black vehicle, if m exceeds a specified threshold
P exceeds a specified threshold
And then the vehicle B is considered as a black vehicle.
The method comprises the following steps of respectively adopting a permission authentication method, a machine learning algorithm, a k-means algorithm hard clustering method, an artificial identification method and the embodiment to track and identify 100 black vehicles in east-light county, poachy, cang county, haixing county and Yanshan county in Hebei Cangzhou within 100 days to obtain the following data:
as can be seen from the above table, the black car can be tracked by the authority authentication method, the machine learning algorithm, the k-means algorithm hard aggregation method and the manual identification method in the embodiment, but the recognition accuracy of the black car recognition method based on the track characteristics and the association between the human and the car is the highest, through the setting from the step one to the step two, the vehicle with similar characteristics to the human and the car is searched by means of the characteristics of the legal operation vehicle and the information associated with the human and the car, so as to search the vehicle which has no operation qualification but is likely to operate, the information updating period is short, the efficiency is high, the vitality is strong, the black car can be tracked favorably, through the setting from the step three to the step five, the characteristics of the black car are screened by utilizing the calculation, the characteristics of the black car are depicted clearly, the definition is accurate, the recognition accuracy is high, the black car can be tracked and eliminated effectively, the normal transportation market order is maintained, and the occurrence of traffic accidents is reduced, the legal rights and interests of passengers and the safety of travel are protected, and the social stability is facilitated.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (4)
1. A black car identification method based on track characteristics and pedestrian and car association is characterized in that: the method comprises the following specific steps:
the method comprises the following steps: confirming whether all registered vehicles have operation qualification;
step two: determining an alternative feature list;
step three: calculating the man-vehicle related information;
step four: screening a significance feature list of commercial vehicles: on the basis of the assumption that the number of black cars is only a few, by means of significance test, comparing whether a set formed by values corresponding to all candidate features of a commercial vehicle and a non-commercial vehicle has significance differences, if the significance differences exist, considering the features to be significant, and sequentially screening out a significant feature list C according to the mode;
step five: determining a black car target; calculating a set of characteristic values corresponding to each remarkable characteristic of the commercial vehicle by using the result of the step four characteristic significance check, wherein the set forms a distribution, and the ith remarkable characteristic of the vehicle A is marked as ci，ciThe corresponding set of eigenvalues is denoted as DiThe probability that the vehicle A with the characteristics of i non-operating vehicles is a non-operating vehicle is recorded as piThen, the vehicle with the probability of the A vehicle and the non-operating vehicle larger than the P is considered to be in the characteristic ciThe sets D have consistency and correspond to different featuresiWith different degrees of dispersion, two prominent features cjAnd ckThe corresponding feature value sets are denoted as DjAnd Dk，DjAnd DkRespectively, the standard deviation of (A) is expressed as sigmajAnd σkIf σ isjGreater than sigmakThen, consider feature cjIs significantly greater than feature ckIf the non-commercial vehicle B has consistency in m characteristics, the possibility of being a black vehicle is recorded as
Where M is the set of M features, TmNumber of features, T, which are characteristic of abnormal operation for normally operating vehiclespFor the probability that a normally operating vehicle is a black vehicle, if m exceeds a specified threshold TmP exceeds a specified threshold TpAnd then the vehicle B is considered as a black vehicle.
2. The method for identifying the black car based on the track characteristics and the association of the people and the cars as claimed in claim 1, wherein: the step one of confirming whether all the registered vehicles have the operation qualification comprises the following steps:
the first step is as follows: the operation card information table is associated with the motor vehicle information table through the identity card to confirm whether the vehicle has effective operation qualification;
the second step is that: and (5) determining whether the vehicle owner has the operation qualification or not, searching the type of the vehicle owner, and if the type of the vehicle owner is individual and the operation qualification is not available, performing the step two.
3. The method for identifying the black car based on the track characteristics and the association of the people and the cars as claimed in claim 1, wherein: determining an alternative feature list in the step two, and selecting the alternative feature list from two angles of the self track feature and the human-vehicle interaction track feature:
each angle determines a list of candidate features from a temporal, spatial, spatiotemporal interaction perspective, including: the number of times of occurrence of the vehicle, the number of days of occurrence of the vehicle, the number of times of occurrence of the vehicle at a key site, the number of days of occurrence of the vehicle at the key site, the maximum stay time of the vehicle at the key site, the number of times of association between a person and a vehicle, the number of days of association between a person and a vehicle, the number of times of association between a person and other vehicles, and the number of times of association between a person and other vehicles are associated;
the method comprises the following steps of associating, namely simultaneously appearing at certain sites within two minutes, wherein information acquisition devices are arranged at the sites and can capture passing license plates, passing faces and mobile phone cards;
the important sites, that is, the black car high frequency appearance sites which are already grasped, include not only the black car high frequency appearance sites confirmed by the case, but also the black car high frequency appearance sites determined by the significance test based on the last month trajectories of all the black cars found in the near future.
4. The method for identifying the black car based on the track characteristics and the association of the people and the cars as claimed in claim 1, wherein: and calculating the human-vehicle association condition in the third step, namely calculating the corresponding human-vehicle association condition according to the candidate feature list in the second step.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104268599A (en) * | 2014-09-29 | 2015-01-07 | 中国科学院软件研究所 | Intelligent unlicensed vehicle finding method based on vehicle track temporal-spatial characteristic analysis |
| CN109634946A (en) * | 2018-12-06 | 2019-04-16 | 南京森根科技发展有限公司 | A kind of track intelligent Matching association analysis algorithm model excavated based on big data |
| CN111930791A (en) * | 2020-05-28 | 2020-11-13 | 中南大学 | Similarity calculation method and system for vehicle track and storage medium |
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| US9286029B2 (en) * | 2013-06-06 | 2016-03-15 | Honda Motor Co., Ltd. | System and method for multimodal human-vehicle interaction and belief tracking |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268599A (en) * | 2014-09-29 | 2015-01-07 | 中国科学院软件研究所 | Intelligent unlicensed vehicle finding method based on vehicle track temporal-spatial characteristic analysis |
| CN109634946A (en) * | 2018-12-06 | 2019-04-16 | 南京森根科技发展有限公司 | A kind of track intelligent Matching association analysis algorithm model excavated based on big data |
| CN111930791A (en) * | 2020-05-28 | 2020-11-13 | 中南大学 | Similarity calculation method and system for vehicle track and storage medium |
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Denomination of invention: A Black Car Recognition Method Based on Trajectory Features and Human Vehicle Association Granted publication date: 20211026 Pledgee: Zijin Branch of Nanjing Bank Co.,Ltd. Pledgor: Nanjing sengen Safety Technology Co.,Ltd. Registration number: Y2024980005495 |