CN114495497B - Method and system for judging and interpolating traffic abnormal data - Google Patents
Method and system for judging and interpolating traffic abnormal data Download PDFInfo
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Abstract
The invention belongs to the field of abnormal data processing, and provides a method and a system for judging and interpolating traffic abnormal data. The method comprises the steps of obtaining traffic abnormal data in a certain road section; determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data; the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data; filling the abnormal data to obtain the traffic data in the road section after correction.
Description
Technical Field
The invention belongs to the field of abnormal data processing, and particularly relates to a method and a system for judging and interpolating traffic abnormal data.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
At present, most of the erroneous traffic data judging methods are threshold value interval judging methods, and the threshold value interval is mainly based on the relevant information of the road section, wherein threshold value parameters are effective value ranges of speed, flow and occupancy rate respectively. By comparing the traffic data with the threshold interval, whether the data is abnormal or not is judged, and the judgment method is not comprehensive enough for judging the error data and possibly has the condition of missed judgment. The filling method is to obtain the weighted average value of the adjacent data before and after the abnormal data. The traffic abnormal data judging method is simple, the filling accuracy is not high, the data at the next moment of abnormal data is needed, and the data cannot be filled in time.
Therefore, the prior method has the possibility of missed judgment of traffic data. After abnormal traffic data is found, the problems of low filling accuracy rate, untimely filling and the like of the traffic abnormal data exist. Therefore, the existing traffic abnormal data judging and filling method has certain limitations and needs to be optimized and improved.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a method and a system for judging and interpolating traffic abnormal data, which judge whether the traffic data is missing or not by judging the blank state of corresponding data fields, judge whether the traffic data is wrong or not by a threshold interval and a traffic data logic relation, and online fill the traffic abnormal data by a Newton interpolation method based on a time space sequence; the method has the advantages of high identification precision, high filling accuracy and timely filling.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a first aspect of the present invention provides a method of discriminating and interpolating traffic abnormality data.
A method for judging and interpolating traffic abnormal data comprises the following steps:
acquiring traffic abnormal data in a certain road section;
determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data; the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data;
filling the abnormal data to obtain the traffic data in the road section after correction.
A second aspect of the present invention provides a discriminating and interpolating system of traffic abnormality data.
A discriminating and interpolating system of traffic abnormality data includes:
a data acquisition module configured to: acquiring traffic abnormal data in a certain road section;
an anomaly data determination module configured to: determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data; the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data;
a padding data module configured to: filling the abnormal data to obtain the traffic data in the road section after correction.
A third aspect of the present invention provides a computer-readable storage medium.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps in the traffic abnormality data discrimination and interpolation method as described in the first aspect above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the method of discriminating and interpolating traffic anomaly data as described in the first aspect above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a new method for judging error data according to the speed, flow and occupancy threshold interval in traffic data and the logic relation among the speed, flow and occupancy threshold interval.
The invention provides a Newton interpolation method based on a time space sequence, which is characterized in that a predicted value can be calculated by calculating the Newton interpolation of a time sequence and a space sequence of a plurality of points near normal data, comparing the average absolute error of the Newton interpolation of the two sequences, selecting a sequence value with smaller error to reconstruct a Newton interpolation polynomial, and bringing traffic abnormal data into the interpolation polynomial at the moment. Compared with a method for averaging historical data, the method improves the accuracy of filling the abnormal traffic data.
The traffic abnormal data detection and filling method can detect abnormal traffic data in real time, does not need the next time data of the abnormal data, and can interpolate the abnormal data on line in time.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a method of discriminating and interpolating traffic anomaly data shown in the present invention;
FIG. 2 is a missing data discrimination flow chart illustrating the present invention;
FIG. 3 is a flow chart of error data discrimination shown in the present invention;
FIG. 4 is a flowchart of the interpolation of outlier data shown in the present invention;
FIG. 5 is a traffic data loss identification map of the present invention;
FIG. 6 is an identification chart of the traffic logic determination first broad class error condition of the present invention;
FIG. 7 is an identification chart of the traffic logic of the present invention to determine that the third largest class of conditions is further determined to be erroneous data;
FIG. 8 is a padding diagram of missing data in accordance with the present invention;
fig. 9 is a padding diagram of error data according to the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
Example 1
As shown in fig. 1, this embodiment provides a method for discriminating and interpolating traffic anomaly data, and this embodiment is illustrated by applying the method to a server, and it can be understood that the method may also be applied to a terminal, and may also be applied to a system and a terminal, and implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, which is not limited herein. In this embodiment, the method includes the steps of:
acquiring traffic abnormal data in a certain road section;
determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data; the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data;
filling the abnormal data to obtain the traffic data in the road section after correction.
The method provided by the embodiment comprises two steps of identifying abnormal traffic data and filling the abnormal data.
Wherein, the discrimination of the abnormal traffic data comprises the identification of missing data and the identification of error data.
1. Identification of missing data:
the missing data mainly comprises three types, namely, single data missing, including speed missing, flow missing and occupancy missing. Two data loss, including speed and flow loss, speed and occupancy loss, flow and occupancy loss. Three data were missing.
The missing data identification principle is that the missing condition of data is judged by carrying out piece-by-piece access on data of three fields of speed, flow and occupancy. If the speed field of a certain piece of data is missed and other field data corresponding to the piece of data is not missed, the speed in the single data is missed, and the recognition principle of the missing of other single data is the same. If two fields of speed and flow of accessing a certain piece of data are missing, the occupancy field data corresponding to the piece of data are not missing, and the other two data are missing, so that the recognition principle of the other two data is the same. If three field data of speed, flow and occupancy of a certain piece of data are simultaneously deleted, three data are deleted. After the missing data is identified, the data missing type and the missing number are displayed, and the distinguishing flow is shown in fig. 2.
2. Identification of erroneous data:
the error data is mainly judged by comparing the traffic data with a threshold value interval and a traffic data logic judging method. When the traffic data is identified and judged, the threshold ranges set by different road grades and control types are different.
(1) Threshold interval method
Traffic flow: the traffic detector completes the collection of traffic flow data in a relatively short time, and no vehicles may pass in a relatively short time, so the minimum value of the alternating current flow can be zero, and the maximum value is related to the traffic capacity and the sampling interval of the traffic road. The traffic capacity of each road is different, and the sampling interval of the acquisition equipment is also possibly different, so that the maximum traffic flow of each road is assigned according to the historical traffic capacity of the road and the acquisition interval combined with the actual situation, and can be slightly corrected according to the experience value.
Speed of: the speed detection of the vehicle is done in a rather short time interval. In combination with the actual situation, each road can limit speed, the speed limits are different, overspeed of the road vehicles can occur, and random errors can exist, so that the minimum speed value can be zero, and the maximum speed value can be obtained through road speed limit and empirical value correction.
Occupancy rate: the occupancy rate referred to herein means a time occupancy rate which is a ratio of a time when a road is detected as having a vehicle to a total time detected by a traffic detector, and in a traffic detector operating time, an extreme situation may occur, and the road is always present or the road is not always present, so that the minimum value of the time occupancy rate is defined as 0 and the maximum value is 100%.
(2) Traffic logic judgment
The collected data can be classified according to the logic relationship among the speed, the flow and the occupancy. The logical relationships between the three can be divided into three major categories, the first major category: one of the three is not zero, and the other is zero; second general class: the speed is zero or the flow is zero, and the other two are not zero; third general class: the three are all zero, the three are not zero, the occupancy is zero, and the speed and the flow are not zero. The first and second major classes are not logically coincident and are directly considered error data. The third category requires further discrimination.
Third general class:
1. there are two possibilities that the traffic data are zero, one is that there is less traffic during the operation time of the traffic detector, no vehicles pass during this detection time, and the traffic data are considered to be correct data, and this is generally characterized by zero or less front-to-back proximity data. Another is the loss of data due to failure in uploading data for various reasons. The distinguishing method comprises the following steps: based on traffic flow knowledge, at low flows, the arrival of vehicles is random, subject to a specific probability distribution. Through probability estimation, a minimum number of vehicles passing within a sampling interval can be determined. And calculating the average value of the flow rate of the adjacent sampling interval, and if the average value of the adjacent sampling interval is larger than the minimum vehicle number, the probability of zero flow rate in the sampling interval is close to zero, and the data in the case are regarded as error data.
2. The occupancy is zero but the flow and the vehicle speed are not zero, and the two cases are divided. The first case is when the flow rate is small, the occupancy is less than one percent, and at this time, the occupancy is zero and the flow rate and the vehicle speed are not zero due to the format display reason, and the data is regarded as correct data; the second case is a data error due to some reason. And (3) further judging: a minimum flow limit is set and the occupancy can only be shown to zero if the flow is less than the limit. The minimum flow limit can be calculated from the relationship of road traffic flow, vehicle speed and occupancy, and if the actual flow is greater than that, the data is considered erroneous. In order to ensure correct data retention and error data rejection, the limit value of the minimum flow needs to be set larger. In order to meet the conditions, the speed of the vehicle in the acquisition section can be limited by the speed limit of the reference road section; the average effective body length may take a minimum effective body length value; the occupancy takes the value as small as possible.
3. All three are not zero and are divided into two cases. When all three are not zero, the actual average effective vehicle length is calculated through the history data of the adjacent lanes or the history data of the own lanes, and the average effective vehicle length can be obtained through the relationship among the road traffic flow, the vehicle speed and the occupancy. The first case is that the value calculated by the three relationships is within the actual average effective body length section, and the data is considered to be correct, and the second case is that the value calculated by the three relationships is not within the actual average effective body length section, and is considered to be error data.
The traffic anomaly data discrimination flow is shown in fig. 3.
2. Filling in abnormal data
(1) For the loss of single data, the loss value can be calculated by utilizing a logic formula among the speed, the flow and the occupancy, wherein the effective average vehicle length can be obtained according to the last time of single loss data or the data of adjacent lanes, and the obtained data can be filled.
(2) The processing of two or three data deletions and erroneous data can be padded by newton interpolation based on time-space sequence data. The filling steps are as follows:
1) And selecting a plurality of normal data nodes near the abnormal traffic data, constructing an interpolation function N (x) by using N data points of the time sequence and the space sequence, and requiring the constructed interpolation function to pass through the selected points.
2) And selecting normal data nodes to be assumed to be abnormal traffic data, respectively calculating average absolute percentage errors of predicted values calculated by Newton based on time sequences and Newton interpolation based on space sequences and the normal data, and then comparing the two average absolute percentage errors. And if the comparison result is smaller and the interpolation effect is better, the Newton interpolation method based on the sequence is selected to fill the abnormal traffic data. The average absolute percentage error is defined as the absolute value of the real value minus the predicted value divided by the real value multiplied by one hundred percent.
3) If the Newton interpolation method based on the time sequence is selected, reconstructing a pull Newton interpolation polynomial by using the first n data of the abnormal traffic data moment, and bringing the moment of the abnormal traffic data into the interpolation polynomial to calculate a predicted value; if the Newton interpolation method based on the space sequence is selected, reconstructing Newton interpolation polynomials by using n data before adjacent lanes at the same moment of abnormal traffic data, and bringing the moment of the abnormal traffic data into the interpolation polynomials to calculate the predicted value.
The abnormal data interpolation flow is shown in fig. 4.
In order to verify the accuracy of the proposed method of the present embodiment, the following description is made:
fig. 5 shows that missing data is detected by using an abnormal traffic data discrimination method, the type of data missing is determined by accessing fields of speed, flow rate and occupancy rate one by one, the number of missing data is counted by the missing condition of the three fields, and the data quality is fed back to the data quality detection, so that the quality of the data is determined, and the missing traffic data is not drawn in the graph.
Fig. 6 and 7 are diagrams showing the abnormal traffic data discriminating method for detecting traffic error data, wherein the detected error data is drawn by colors different from normal data points in the diagrams, and the number of error data is accumulated and fed back to the data quality detection, so as to judge the quality of the data. The data drawn by the first and third major categories of traffic logic are framed in the diagram by square frames, and the two error conditions are judged according to a threshold interval method, so that opposite results are obtained. The first and third major types of traffic logic data may be in the threshold value interval, but the three have wrong logic relationship, so the method judges traffic abnormal data more comprehensively and is not easy to miss judgment.
Fig. 8 and 9 are real-time interpolation of missing data and erroneous data, respectively, and the predicted value is calculated by the above-mentioned newton interpolation method based on the time-space sequence. At this time, predicted values of missing and erroneous data are plotted in a graph, the color of the erroneous data point in the graph is changed to the color of the normal data point, and the missing data and the number of erroneous data pieces are cleared.
Example two
The embodiment provides a system for judging and interpolating traffic abnormal data.
A discriminating and interpolating system of traffic abnormality data includes:
a data acquisition module configured to: acquiring traffic abnormal data in a certain road section;
an anomaly data determination module configured to: determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data; the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data;
a padding data module configured to: filling the abnormal data to obtain the traffic data in the road section after correction.
It should be noted that, the data acquisition module, the abnormal data determination module, and the padding data module are the same as the examples and application scenarios implemented by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the traffic abnormality data discrimination and interpolation method as described in the above embodiment.
Example IV
The present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps in the method for determining and interpolating traffic anomaly data according to the first embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A method for discriminating and interpolating traffic anomaly data, comprising:
acquiring traffic abnormal data in a certain road section;
determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data;
the missing data comprises three types, namely, single data missing, including speed missing, flow missing and occupancy missing; two data deletions, including speed and flow loss, speed and occupancy loss, flow and occupancy loss; three data were missing; the data of the three fields of speed, flow and occupancy are accessed one by one to judge the data missing condition;
the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data;
the discriminating process of the error data further includes:
if the speed, the flow and the occupancy are all zero, calculating the average value of the flow adjacent to the sampling interval before the moment; under low flow, the vehicles pass through the road section and obey specific probability distribution, the minimum vehicle occurrence number is calculated in an allowable error range, the minimum vehicle occurrence number is compared with the calculated flow average value, and if the average value close to a set sampling interval is larger than the minimum vehicle occurrence number, the data is error data;
the flow is not zero, the occupancy is zero, and the speed is not zero; setting a minimum flow limit, wherein the vehicle speed in the section can be limited by the road section, the average effective vehicle body length is a minimum effective vehicle body length value, and the occupancy rate is a minimum value according to the strict degree of the error; calculating a flow value through a traffic flow theory, and if the calculated flow is larger than the minimum flow limit value, obtaining the data as error data;
if the speed, the flow and the occupancy are all not zero, obtaining the average effective vehicle body length according to the fact that the average effective vehicle body length is equal to the interval vehicle speed multiplied by the interval occupancy divided by the vehicle flow, and if the average effective vehicle body length is not within the actual average effective vehicle body length interval, the data are error data;
filling the abnormal data to obtain traffic data in the road section after correction;
calculating a missing value of the single data by utilizing a logic formula among speed, flow and occupancy, wherein the effective average vehicle length is calculated according to the last time of the single missing data or the data of adjacent lanes, and filling the calculated data;
the filling of the abnormal data comprises the following steps: filling abnormal data by adopting a Newton interpolation method based on time-space sequence data, which comprises the following specific steps:
constructing Newton interpolation polynomials by utilizing a plurality of points of the time sequence and the space sequence data, selecting a plurality of normal data before abnormal data for testing, and comparing the average absolute errors of the Newton interpolation of the time sequence and the space sequence;
and (3) selecting sequence data with small average absolute error, reconstructing Newton interpolation polynomials by using selected sequence data points of 1-n moments before abnormal data, bringing time independent variables of the abnormal data into the interpolation polynomials, and calculating a predicted value.
2. The method for determining and interpolating traffic abnormality data according to claim 1, wherein said missing data includes at least one missing data.
3. The method for determining and interpolating traffic abnormality data according to claim 1, wherein the determining that the relationship among speed, flow rate and occupancy does not correspond to a logical condition includes:
one of the speed, the flow and the occupancy is not zero, and the other is zero;
the speed, the flow and the occupancy rate are zero or the flow is zero, and the other two are not zero.
4. A system for discriminating and interpolating traffic abnormality data, comprising:
a data acquisition module configured to: acquiring traffic abnormal data in a certain road section;
an anomaly data determination module configured to: determining the type of the traffic anomaly data; the types of the traffic abnormal data comprise missing data and error data;
the missing data comprises three types, namely, single data missing, including speed missing, flow missing and occupancy missing; two data deletions, including speed and flow loss, speed and occupancy loss, flow and occupancy loss; three data were missing; the data of the three fields of speed, flow and occupancy are accessed one by one to judge the data missing condition;
the discriminating process of the error data comprises the following steps: judging whether the speed, the flow and the occupancy of the road section are in a set threshold value interval, if not, determining that the road section is error data; judging whether the relation among the speed, the flow and the occupancy rate accords with logic or not, and if not, judging the relation to be error data;
the discriminating process of the error data further includes:
if the speed, the flow and the occupancy are all zero, calculating the average value of the flow adjacent to the sampling interval before the moment; under low flow, the vehicles pass through the road section and obey specific probability distribution, the minimum vehicle occurrence number is calculated in an allowable error range, the minimum vehicle occurrence number is compared with the calculated flow average value, and if the average value close to a set sampling interval is larger than the minimum vehicle occurrence number, the data is error data;
the flow is not zero, the occupancy is zero, and the speed is not zero; setting a minimum flow limit, wherein the vehicle speed in the section can be limited by the road section, the average effective vehicle body length is a minimum effective vehicle body length value, and the occupancy rate is a minimum value according to the strict degree of the error; calculating a flow value through a traffic flow theory, and if the calculated flow is larger than the minimum flow limit value, obtaining the data as error data;
if the speed, the flow and the occupancy are all not zero, obtaining the average effective vehicle body length according to the fact that the average effective vehicle body length is equal to the interval vehicle speed multiplied by the interval occupancy divided by the vehicle flow, and if the average effective vehicle body length is not within the actual average effective vehicle body length interval, the data are error data;
a padding data module configured to: filling the abnormal data to obtain traffic data in the road section after correction;
calculating a missing value of the single data by utilizing a logic formula among speed, flow and occupancy, wherein the effective average vehicle length is calculated according to the last time of the single missing data or the data of adjacent lanes, and filling the calculated data;
the filling of the abnormal data comprises the following steps: filling abnormal data by adopting a Newton interpolation method based on time-space sequence data, which comprises the following specific steps:
constructing Newton interpolation polynomials by utilizing a plurality of points of the time sequence and the space sequence data, selecting a plurality of normal data before abnormal data for testing, and comparing the average absolute errors of the Newton interpolation of the time sequence and the space sequence;
and (3) selecting sequence data with small average absolute error, reconstructing Newton interpolation polynomials by using selected sequence data points of 1-n moments before abnormal data, bringing time independent variables of the abnormal data into the interpolation polynomials, and calculating a predicted value.
5. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the steps in the discriminating and interpolating method of traffic abnormality data according to any one of claims 1-3.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of discriminating and interpolating traffic anomaly data according to any one of claims 1-3 when the program is executed.
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