CN111179591B - Road network traffic time sequence characteristic data quality diagnosis and restoration method - Google Patents
Road network traffic time sequence characteristic data quality diagnosis and restoration method Download PDFInfo
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Abstract
A road network traffic time sequence characteristic data quality diagnosis and restoration method comprises the following steps: s1, obtaining a regional road network topological structure, and constructing a traffic data prediction model; s2, acquiring historical traffic data, training a model and checking the prediction precision of the model; s3, fusing the traffic data acquired by the traffic detector with the traffic data generated, and detecting and repairing the abnormal data in real time; and S4, performing incremental training on the prediction model to ensure the prediction precision of the model. The invention has the beneficial effects that: and predicting a mode of the data on the day and the comprehensive production data of the combined road network data based on historical data and comprehensively judging the quality of the data, and finally realizing high-quality data with reasonable continuous output and strong relevance.
Description
Technical Field
The invention belongs to the field of traffic information processing, and relates to a road network traffic time sequence characteristic data quality diagnosis and restoration method based on a deep learning method of a graph neural network.
Background
With the development of traffic technology, the use of various algorithms in traffic control becomes more extensive, and the traffic control becomes intelligent and automatic to a great extent.
However, many algorithms can only be applied in a laboratory or in some test point areas, and cannot be fully automatically operated in a large area, which is the fundamental reason that many algorithms such as KNN, CNN, and LSTN have high requirements on data, and unqualified input data can directly cause the unqualified output result of the algorithm. In practical situations, the application of many traffic algorithms is often limited because detectors cannot be guaranteed or the produced data do not meet the actual conditions.
In the existing research, people focus on the quality of data, 1. focus mainly on missing data parts and neglect to check the quality of produced data; 2. for missing portions of data, researchers typically fill in the data using default values for the patching convention or by linear fitting the data over a period of time; 3. for the data of the whole road network, researchers usually only pay attention to the data quality feedback of a certain intersection/road segment/lane, and lack comprehensive quality comparison of adjacent or similar intersection/road segment/lane data. Therefore, it is urgent to construct an effective and robust data production method and a data production link for strictly detecting the data quality.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a road network traffic time sequence characteristic data quality diagnosis and restoration method based on a deep learning method of a neural network, which is a method for predicting the mode of the data of the same day and the comprehensive production data of the combined road network data and comprehensively judging the quality of the data based on historical data, and finally realizes high-quality data with reasonable continuous output and strong relevance.
The technical scheme adopted by the invention is as follows:
a road network traffic time sequence characteristic data quality diagnosis and restoration method comprises the following steps:
s1, obtaining a regional road network topological structure, and constructing a traffic data prediction model;
s2, acquiring historical traffic data, training a model and checking the prediction precision of the model;
s3, fusing the traffic data acquired by the traffic detector with the traffic data generated, and detecting and repairing the abnormal data in real time;
and S4, performing incremental training on the prediction model to ensure the prediction precision of the model.
Further, the traffic data prediction model in step S1 is a convolutional neural networkA network prediction model and preset TkAnd constructing a fusion model of the multi-scale prediction result at different time intervals.
Further, an input matrix of the graph convolution neural network prediction model is constructed as follows:
the determined road network system comprises the following steps: g ═ V, E, a, where V denotes the set of all ports in the network G; e represents the whole edge set in the road network G; a represents all intersection adjacency matrixes of the road network G; on the basis of this, so as to
The c-th vector on the time slice of the intersection i at the time t is expressed by
Representing the set of all vector factors of the intersection i at the moment t; by using
N belongs to V to represent the set of all vector factors of all intersections at the moment t; by using
To represent all historical data of all vector factors for all intersections in the past tau time slices.
Further, the training in step S2 adopts a space-time attention mechanism to learn the temporal relationship between the intersection and the intersection itself, and the specific steps are as follows:
(1) performing double learning on road network data from a space level and a time level by constructing a space-time learning network, and constructing an association relation between road network intersection objects, adjacent time slices and data association between adjacent periodic slices;
(2) performing spatial convolution calculation on each sampling time slice in spatial dimension, and obtaining an incidence relation between intersections by adjusting intersection incidence matrixes so as to determine intersection spatial incidence information; performing convolution calculation on each sampling time slice of each intersection object in a time dimension, and predicting data on a continuous time axis by training a prediction model in a single intersection time dimension;
(3) for three input data vectors with different time granularities, fusion output is carried out on the trained data with different time granularities by giving different weights to the three input data vectors;
(4) and selecting a traditional Mean Square Error (MSE) as a loss function, training the model until the MSE is the minimum, realizing error back propagation by adopting a random gradient descent (SGD) method, and training the model.
Further, the data abnormality in step S3 includes: data missing, suspected noise in the data, and poor data correlation.
Further, for the case of data missing, the specific process of performing the patching processing on the data is as follows:
for data loss of individual intersections, data required by the current time of the intersection object can be predicted through a data model, the intersection data is directly repaired, and the current data of adjacent intersections can be recorded for retraining the next prediction model;
for the condition of data loss of the large-scale intersection, not only data prediction needs to be carried out on the intersection object with the corresponding data loss, but also the current finally repaired data is recorded and is used for retraining a prediction model next time;
if the large-scale data repairing operation is frequently carried out within a certain time, an alarm prompt needs to be carried out on a worker for checking the faults of hardware measures or data input steps.
Further, for the case of data suspected to be noisy, the specific steps required to be recorded are as follows:
1) if only noise data are generated at individual intersections or individual data, the noise data can be directly deleted and repaired by using the data prediction model;
2) if data of suspected noise points frequently appear in a plurality of data production intervals, corresponding records need to be carried out on intersection objects and time produced by the data,
2.1) if the noise data has the characteristics of much noise and no distribution rule, directly identifying the noise data as an abnormal data value, and directly predicting the required time point of the current intersection object and directly restoring the data by using a prediction model;
2.2) if the noisy point data presents the characteristic of a large and regular data distribution, corresponding data comparison needs to be carried out on the change trend of the data attribute of the intersection on the continuous time axis and the topological data association attribute between the intersection object and the peripheral adjacent object; if the adjacent intersections all present the same or similar noise data and the topological correlation attributes exist in the intersection data, manual investigation is carried out on the data source and the data aggregation production process; if for data with the same time granularity, adjacent intersections with similar intersection states have larger difference on noise point data, but for the condition that the data distribution is continuous and data mutation does not exist on the time axis of the corresponding intersection, the data aggregation process and the data source of the detector need to be examined, and the road-gateway connection data are examined to find the problem of poor topological relevance of the intersection data.
Further, for the case of poor data relevance, if the data distribution characteristics of the adjacent intersections have large differences, the data level is used for analyzing the traffic data change trend of the adjacent intersections and the relevance of the traffic data on the time axis, judging whether the data change of the adjacent intersections is normal conduction of the traffic data on the time axis, and if not, inputting by using the latest data and retraining the prediction model to obtain the prediction model based on the current latest data.
Further, whether the data change of the adjacent intersection is normal transmission of the traffic data on the time axis can be judged by the congestion alarm amount, which is specifically as follows:
selecting M adjacent intersections around the single intersection, and determining the actual time delay delta T of the two objects by comparing the time difference delta T when the continuous alarm times between the single intersection and the adjacent intersections reach the preset threshold value with the maximum continuous alarm time*Comparing the data with the time difference Delta T' of the data change trend between two intersections predicted by the prediction model,
ΔT*＝ΔT*S
Wherein the content of the first and second substances,
the alarm starting time for the intersection a is set,
alarming starting time for the intersection b, and selecting the number of time intervals for congestion alarming data by n; t isa,iFor crossing a alarm duration, Tb,iThe alarm duration time of the intersection b is set,
the maximum continuous alarm frequency of the intersection a,
the maximum continuous alarm frequency of the intersection b is obtained;
represents the corresponding data of the b-th intersection in the T + f-th group prediction data on the time axis,
for predicting data corresponding to the intersection a in the T-th group on the time axis, TτFor data update time granularity, Threshold is a set Threshold for predicting data difference,
if Δ T*Differs in value from Δ T' by less than phaseAnd if the data is the same as the intersection data, determining that the data characteristic between intersections is changed into normal conduction of the data on the time axis.
Further, whether the data change of the adjacent intersection is normal transmission of the traffic data on the time axis can be judged by the speed of the adjacent intersection, which is specifically as follows:
the intersection with more advanced data change characteristics is used as a current data template, a time period with larger speed change trend every day is set as an experimental time interval, corresponding time speed values of the intersection with slower data change characteristics are searched for by accumulating corresponding delta T 'on a data time axis of the intersection with more backward data change characteristics, the delta T' is a data change trend time difference between two intersections predicted by a prediction model, and if the frequency that the difference value of the speed values of the two intersections in the selected experimental time interval exceeds a speed fluctuation interval (namely the threshold range of positive and negative change of data) is smaller than a set threshold, the change of the data characteristics between the intersections can be determined as the normal conduction influence time of the data on the time axis.
Further, step S3 includes, after the data is repaired by using the prediction model, checking the quality of the data produced on the day, passing the qualified data, and feeding back the unqualified data by using a method of reproducing or retraining the data prediction model and checking again, wherein the data quality checking method includes:
xi is the sampling frequency of certain traffic data at the intersection every day;
predicting the predicted value of the data for the data prediction model at the ith sampling;
producing a real-time value of the data generated for the data at the ith sampling; threshold is the Threshold range within which the two differ by an acceptable amount; m is the deviation between the data produced under the intersection data sampling frequency and each sampling frequency and the data prediction model prediction data, and the smaller the numerical value is, the better the data quality is considered; otherwise, the data quality is considered to be poor.
Further, intersection data with obviously poor data quality is further analyzed, if the periodic poor data quality condition occurs in a certain intersection or a certain sub-area, the actual condition of the intersection or the area is firstly analyzed, if the intersection or the area has the same periodic occurrence activity in a real road network, a new data prediction model is constructed by using the latest produced real-time data, and the data of the day is predicted, repaired and detected by using different data prediction models according to the actual condition of the day;
if the intersection is in a special condition after the actual road network condition is observed, the deviation between the data and the prediction model is large in the production time and on the continuous time axis, and similar conditions occur in a certain number of intersections in a corresponding area by integrating the data change conditions of the intersection and the surrounding intersections, a method for retraining the data prediction model and monitoring the quality of the intersection with the real-time production data of the day again need to be considered, and an actual and objective data quality detection result is generated.
The invention has the beneficial effects that: and predicting a mode of the data on the day and the comprehensive production data of the combined road network data based on historical data and comprehensively judging the quality of the data, and finally realizing high-quality data with reasonable continuous output and strong relevance.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of different vector factors at an intersection of the present invention.
FIG. 3 is a schematic structural diagram of a graph convolution neural network training process of the present invention.
FIG. 4 is a schematic structural diagram of a neural network training model of the present invention.
FIG. 5 is a schematic diagram of the irregular noise distribution of the present invention.
FIG. 6 is a schematic diagram of the regular noise distribution of the present invention.
FIG. 7 is a comparison diagram of the speed relevance of the adjacent crossing of the present invention.
FIG. 8 is a schematic diagram of real-time data and data model trend in accordance with the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are not intended to limit the invention to these embodiments. It will be appreciated by those skilled in the art that the present invention encompasses all alternatives, modifications and equivalents as may be included within the scope of the claims.
Referring to fig. 1, the invention provides a road network traffic time series characteristic data quality diagnosis and repair method, which comprises the following steps:
s1, obtaining a regional road network topological structure, and constructing a traffic data prediction model;
s2, acquiring historical traffic data, training a model and checking the prediction precision of the model;
s3, fusing the traffic data acquired by the traffic detector with the traffic data generated, and detecting and repairing the abnormal data in real time;
and S4, performing incremental training on the prediction model to ensure the prediction precision of the model.
The specific process is as follows:
in an actual road network system, under the condition that no significant or unpredictable things happen, 1. dynamic data of each intersection has continuous and predictable attributes on a continuous time axis based on the dynamic data; 2. the dynamic data of each intersection has similar and predictable attributes at corresponding time points based on the continuous period of the dynamic data; 3. the dynamic data of each intersection object of the whole road network has road network topological relevance and predictable attributes based on other objects. Therefore, for a single intersection data, not only the correlation of the intersection data on the time axis is needed to be related, but also the correlation relationship between intersections as a part of the whole road network is considered, so that the current intersection data is correctly and reasonably predicted by integrating time and geographic information.
For each intersection, in unit time, several required indicators, such as speed, flow, saturation, etc., need to be detected as measurement factors. In an actual road network, since the measurement factor of a certain intersection also changes continuously on a continuous time axis, as shown in fig. 2, a continuously time-varying vector can be constructed for each intersection by recording the plurality of measurement factors.
For a certain road network system G ═ (V, E, a), where V denotes the set of all the road ports in the road network G; e represents the whole edge set in the road network G; a denotes the entire intersection adjacency matrix of the road network G. On the basis of this, so as to
The c-th vector on the time slice of the intersection i at the time t can be further used
Representing the set of all vector factors of the intersection i at the moment t; further, it can be used
N belongs to V to represent the set of all vector factors of all intersections at the moment t; further, it can be used
To represent all historical data of all vector factors for all intersections in the past tau time slices.
The traffic data are sampled at different time intervals, traffic laws of different scales can be observed, and in order to improve the accuracy of a prediction result, the invention provides a multi-scale prediction result fusion model.
Sampling the original frequency data by TkThe scale of the interval is used for resampling, and canObtaining a training data set at the scale
For example:
I. sampling at 10 min intervals
The data of the next time slice can be predicted;
II. Sampling at 1 hour intervals
The data at this time point for the next hour can be predicted;
III, sampling at 1 day intervals
The data at this time point in the tomorrow may be predicted.
However, for the road network data, not only the intersection data is predicted in time, but also the data relation caused by the topological relation between intersections occupies a more important component on a single time slice, and the interaction between intersections plays a very important role in the interaction of dynamic data in the spatial dimension. The invention adopts a space-time attention mechanism to learn the time relation between intersections and the intersections, and the intersections per se, and the method comprises the following specific steps:
(1) the spatial attention mechanism is as follows:
wherein
Represents TkInputting data of an r sampling period under the sampling frequency; cr-1An r-th data channel of the data channels representing the input data; vs，bs∈RN×N,
Representing learning parameters in a spatial attention mechanism; sigma is an activation parameter; si,j,S'i,jAnd respectively representing the incidence relation between the intersection i and the intersection j in the road network matrix before and after the convolutional network learning.
(2) Time attention mechanism:
similar to the spatial attention mechanism described above, Ve,
Representing a learning parameter in a temporal attention mechanism; sigma is an activation parameter; ei,j,E'i,jAnd respectively representing the association relationship between the time i and the time j before and after the convolutional network learning.
Therefore, as shown in fig. 2, the spatial-temporal learning network is constructed to perform dual learning on the road network data from the spatial level and the temporal level, so as to construct the association relationship between road network intersection objects and the data association between adjacent time slices and adjacent period slices.
(3) Convolution of spatial dimensions
Based on the idea of atlas theory, every point on the map can be regarded as an input of a signal. Therefore, in each sampling time slice, the data attributes on the graph need to be converted into an algebraic form, and then the data attributes of the graph need to be analyzed and judged. Inspired by the theory of maps, the attributes of each map can be represented by a laplacian matrix and its eigenvalues, as shown in equations (5) and (6):
L＝D-A (5)
L＝IN-D-1/2AD-1/2 (6)
wherein L is a Laplace matrix of a graph corresponding to the road network; d is a degree matrix of the road network diagram; a is an adjacent matrix of the road network graph; i isNIs an identity matrix.
With knowledge of linear algebra, we can decompose the laplacian matrix into the following form:
L＝UΛUT (7)
wherein U is a Fourier transform matrix, and Λ is a characteristic value of an L matrix;
and all intersection data input x on the graph at a certain time ttThe fourier transform of (d) can be expressed as:
thus, x on the graph GtThe data input of (a) can be filtered by the convolution operator as:
gθ*Gxt＝gθ(L)xt＝gθ(UΛUT)xt＝Ugθ(Λ)UTxt (10)
(4) Convolution of time dimension
Similar to the above-described spatial dimension graph convolution, the graph convolution of the temporal dimension can also be understood similarly:
the data at the time t on the graph is updated from the data at the previous time t-1, such as G in formula (11) representing convolution operation, phi is a time convolution kernel parameter, GθFor convolution kernel, ReLU is a rectifying linear unit to activate the module;
and finally, carrying out final integrated output. For data inputs of different time dimensions (e.g., weekly period, daily period, adjacent period), the weight of each dimension data is completely different.
As shown in the above-mentioned (12),
learning parameters (weights) representing the three different dimensional time data inputs,
representing data input in three different dimensions, Θ is the hadamard product.
As shown in fig. 3, in the training process, in the spatial dimension, spatial convolution calculation is performed on each sampling time slice, and the incidence relation between intersections is obtained by adjusting the intersection incidence matrix, so as to determine intersection spatial incidence information; and in the time dimension, performing convolution calculation on each sampling time slice of each intersection object, and predicting data on a continuous time axis by training a prediction model in the time dimension of a single intersection.
(5) Data fusion layer
And for three input data vectors with different time granularities, performing fusion output on the trained data with different time granularities by giving different weights to the three input data vectors.
(6) Computation of loss function
The loss function selects the traditional Mean Square Error (MSE), and the training goal of the model is to minimize the MSE:
where N is the sample size of the training,
is the ith group of data in the prediction result,
is the ith set of data in the actual result. And a random gradient descent (SGD) method is adopted to realize error back propagation, and the model is trained. Compared with the traditional batch gradient descent method, the random gradient descent method has a faster updating speed.
The graph convolution neural network training model of the present invention is shown in FIG. 4.
After the neural network prediction model is completed, the method can be applied to the production process and the self-checking process of data. For real-time data which is produced on line and is based on a road network detector, each new data is not only an expression form of data continuity existing between the data with the previous time granularity and the same intersection object in theory, but also a topological data relevance existing in spatial relevance with surrounding intersection objects.
In a real-time data production environment, the following problems often exist: 1. individual intersection object data is missing; 2. large-scale intersection object data is missing; 3. data suspected noise points; 4. poor data relevance and the like.
First, in the case of data loss, a repair process is required for the data.
For data loss of individual intersections, data required by the current time of the intersection object can be predicted through a data model, the intersection data is directly repaired, and the current data of adjacent intersections can be recorded for retraining the next prediction model;
for the condition of data loss of the large-scale intersection, not only data prediction needs to be carried out on the intersection object with the corresponding data loss, but also the current finally repaired data is recorded and is used for retraining a prediction model next time; if the large-scale data repairing operation is frequently carried out within a certain time, an alarm prompt needs to be carried out on a worker for checking the faults of hardware measures or data input steps.
For the case of data suspected to be noisy, it needs to be recorded.
1) If only noise data are generated at individual intersections or individual data, the noise data can be directly deleted and repaired by using the data prediction model;
2) if data of suspected noise points frequently appear in a plurality of next data production intervals, the corresponding record needs to be carried out on the intersection objects and the time of the data production,
2.1) if the noisy point data has the characteristics of much and no distribution rule, as shown in fig. 5, the noisy point data can be directly identified as an abnormal data value, and the data prediction model directly performs data prediction on the demand time point of the current intersection object and directly performs data restoration.
2.2) if the noisy point data presents the characteristic of a large and regular distribution of data quantity, as shown in fig. 6, it is necessary to perform corresponding data comparison on the change trend of the data attribute on the time axis of the intersection itself and the topological data association attribute between the intersection object and the surrounding neighboring objects,
if the adjacent intersections present the same or similar noise data and the topological correlation attributes exist based on the intersection data, the data sources and the data aggregation production process are manually checked according to the existing related steps; if for data with the same time granularity, adjacent intersections with similar intersection states have larger difference on noise point data, but for the condition that the data distribution is continuous and data mutation does not exist on the time axis of the corresponding intersection, the data aggregation process and the data source of the detector need to be examined, and the road-gateway connection data are examined to find the problem of poor topological relevance of the intersection data.
For the problem of poor data correlation, if there is a large difference in data distribution characteristics of adjacent intersections, as shown in fig. 7. In general, the same or similar intersection attribute factor change trend should exist between adjacent intersections. If the intersection data change trends are greatly different, the intersection change trends are analyzed from the data layer, and if the data change trends among intersections meet the requirement that the change trends have a difference delta T on the time axis within a certain time interval, the association of each piece of traffic data on the time axis needs to be calculated.
The embodiment takes speed and congestion warning amount as examples for calculation and explanation:
selecting M adjacent intersections around the single intersection, and determining the actual time delay delta T of the two objects by comparing the time difference delta T when the continuous alarm times between the single intersection and the adjacent intersections reach the preset threshold value with the maximum continuous alarm time*The time value is used as a delay factor.
Alarm time difference, alarm similarity calculation and time delay are shown in (14), (15) and (16):
ΔT*＝ΔT*S (16)
wherein in (14)
Is a road junctiona, the starting time of the alarm,
the method comprises the steps that (1) alarm starting time is set for an intersection b, n is a time period selected for congestion alarm data, 7 practical sections with high alarm frequency and large alarm frequency change amplitude are used as alarm data extraction time intervals for 7 hours, 8 hours, 9 hours, 16 hours, 17 hours, 18 hours and 19 hours in the text respectively, and n is 7; (15) middle Ta,iFor crossing a alarm duration, Tb,iThe alarm duration time of the intersection b is set,
the maximum continuous alarm frequency of the intersection a,
setting the threshold value of the continuous alarming times of the text as 3; the delta T' is the time difference of data change trend between two intersections predicted by the prediction model,
represents the corresponding data of the b-th intersection in the T + f-th group prediction data on the time axis,
for predicting data corresponding to the intersection a in the T-th group on the time axis, TτThreshold is a set Threshold for the predicted data difference for the data update time granularity.
If Δ T*If the numerical difference from the value of delta T' is smaller than the corresponding threshold value, the data characteristic change between the intersections can be determined as the normal conduction time of the data on the time axis; if Δ T*If the difference between the data prediction model and the value of delta T' is larger, the latest data is adopted for inputting the data prediction model, and the prediction model is retrained, so that the data prediction model based on the current latest data is obtained.
If the speed is taken as data input, judging whether the data change between adjacent intersections generates data mutation or the data change difference between adjacent intersections is overlarge, taking the intersection with advanced data change characteristics as the current data template, setting the time period with large speed change trend every day as the experimental time interval (for example, the time interval from 6 hours to 8 hours every day, 16 hours to 17 hours, generally the time intersection point from flat peak to peak), corresponding time speed values of intersections with slower data change characteristics are searched by accumulating corresponding delta T' on an intersection data time axis with laggard data change characteristics, if the difference value of the speed values of the two exceeds a speed fluctuation interval (namely a threshold range in which data can be changed positively and negatively) within a selected experimental time interval is less than a set threshold, the data characteristic change between the intersections can be considered as the normal conduction influence time of the data on the time axis; and on the contrary, a new data prediction model is trained to obtain a new data prediction model by adopting a new data prediction model retraining mode corresponding to the corresponding data patch model.
And obtaining a new data prediction model after data repairing, checking the quality of data produced on the same day, passing qualified data, and feeding back unqualified data by adopting a mode of reproducing or retraining the data prediction model and checking again.
After the intersections affected by unpredictable events (including emergency events such as road section maintenance, traffic accidents, special case tasks and the like) are eliminated, the data of the intersections which normally run for one day are analyzed. In general, the data produced and the data template should be continuous lines which normally fluctuate mutually in the same time period, and the values of the two are basically the same or the difference value is a certain threshold value, and the data change is shown in fig. 8.
The intersection data characteristic change trend takes the change trend of the data predicted by the data prediction model as a template, and fluctuation change is carried out around the template, so that the data quality of the production data can be correspondingly judged by using the following method:
xi is the sampling frequency of certain traffic data at the intersection every day;
predicting the predicted value of the data for the data prediction model at the ith sampling;
producing a real-time value of the data generated for the data at the ith sampling; threshold is the Threshold range within which the two differ by an acceptable amount; m is the deviation between the data produced under the intersection data sampling frequency and each sampling frequency and the data prediction model prediction data, and the smaller the numerical value is, the better the data quality is considered; otherwise, the data quality is considered to be poor. The quantization processing based on the sampling frequency is performed on the quality of the data by the formulas (18) and (19), and thus, the quality of the data is not only judged in the past with a fuzzy word eye, such as good quality, generally poor quality, and the like.
Further analyzing intersection data with obviously poor data quality, if the periodic poor data quality condition occurs in a certain intersection or a certain sub-area, firstly analyzing the actual condition of the intersection or the area, if the intersection or the area has the same periodic occurrence activities (such as market opening, road maintenance, entertainment activities and the like) in a real road network, constructing a new data prediction model by using the latest produced real-time data, and predicting, repairing and detecting the data of the same day by using different data prediction models according to the actual condition of the same day;
if special conditions, such as traffic accidents and special guarantees, occur at intersections after the actual road network condition is observed, the deviation between data and the prediction model is large in the production time and on the continuous time axis, and similar conditions occur at a certain number of intersections in a corresponding area by integrating the data change conditions of the intersections and the surrounding intersections, a method for retraining the data prediction model and monitoring the quality of the data with the real-time production data of the day again need to be considered, and an actual and objective data quality detection result is generated.
According to the method, the mode of the data on the day and the comprehensive production data of the combined road network data is predicted based on the historical data, the quality of the data is comprehensively judged, and finally high-quality data with reasonable continuous output and strong relevance is realized.
Claims (8)
1. A road network traffic time sequence characteristic data quality diagnosis and restoration method comprises the following steps:
s1, obtaining a regional road network topological structure, and constructing a traffic data prediction model;
s2, acquiring historical traffic data, training a model and checking the prediction precision of the model;
s3, fusing the traffic data acquired by the traffic detector with the traffic data generated, and detecting and repairing the abnormal data in real time; the data abnormal condition comprises the following steps: data loss, suspected data noise and poor data correlation; for the condition of poor data relevance, if the data distribution characteristics of the adjacent intersections have large differences, analyzing the change trend of the traffic data of the adjacent intersections and the relevance of the traffic data on a time axis from a data level, judging whether the data change of the adjacent intersections is normal conduction of the traffic data on the time axis, and if not, inputting by adopting the latest data and retraining the prediction model again to obtain the prediction model based on the current latest data; whether the data change of the adjacent intersection is normal transmission of traffic data on a time axis can be judged through the congestion alarm amount, and the method specifically comprises the following steps:
selecting M adjacent intersections around the single intersection, and determining the actual time delay delta T of the two objects by the time difference delta T and the alarm similarity S when the continuous alarm times between the single intersection and the adjacent intersections reach the preset threshold value*Comparing the time difference with the data change trend time difference delta T' between two intersections predicted by the prediction model,
ΔT*＝ΔT*S
wherein the content of the first and second substances,
the alarm starting time for the intersection a is set,
alarming starting time for the intersection b, and selecting the number of time intervals for congestion alarming data by n; t isa,iFor crossing a alarm duration, Tb,iThe alarm duration time of the intersection b is set,
the maximum continuous alarm frequency of the intersection a,
the maximum continuous alarm frequency of the intersection b is obtained;
represents the corresponding data of the b-th intersection in the T + f-th group prediction data on the time axis,
for predicting data corresponding to the intersection a in the T-th group on the time axis, TτUpdating time granularity for data, wherein Threshold is a set Threshold value of a predicted data difference value;
if Δ T*If the numerical difference from the delta T' is smaller than the corresponding threshold value, the data characteristic change between the intersections can be determined to be the normal conduction of the data on the time axis;
after the prediction model is adopted for data repair, the quality of data produced on the day is checked, qualified data is passed, and unqualified data is fed back in a mode of reproducing or retraining the data prediction model and checking again, wherein the data quality checking method comprises the following steps:
xi is the sampling frequency of certain traffic data at the intersection every day;
predicting the predicted value of the data for the data prediction model at the ith sampling;
producing a real-time value of the data generated for the data at the ith sampling; threshold is the Threshold range within which the two differ by an acceptable amount; m is the deviation between the data produced under the intersection data sampling frequency and each sampling frequency and the data prediction model prediction data, and the smaller the numerical value is, the better the data quality is considered; otherwise, the data quality is considered to be poor;
and S4, performing incremental training on the prediction model to ensure the prediction precision of the model.
2. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 1, characterized in that: the traffic data prediction model in step S1 is a convolutional neural network prediction model, and T is presetkAnd constructing a fusion model of the multi-scale prediction result at different time intervals.
3. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 2, characterized in that: the input matrix of the graph convolution neural network prediction model is constructed as follows:
the determined road network system comprises the following steps: g ═ V, E, a, where V denotes the set of all ports in the network G; e represents the whole edge set in the road network G; a represents all intersection adjacency matrixes of the road network G; on the basis of this, so as to
The c-th vector on the time slice of the intersection i at the time t is expressed by
Representing the set of all vector factors of the intersection i at the moment t; by using
To represent the set of all vector factors of all intersections at the time t; by using
To represent all historical data of all vector factors for all intersections in the past tau time slices.
4. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 2, characterized in that: the training in step S2 adopts a space-time attention mechanism to learn the temporal relationship between intersections and the intersection itself, and the specific steps are as follows:
(1) performing double learning on road network data from a space level and a time level by constructing a space-time learning network, and constructing an association relation between road network intersection objects, adjacent time slices and data association between adjacent periodic slices;
(2) performing spatial convolution calculation on each sampling time slice in spatial dimension, and obtaining an incidence relation between intersections by adjusting intersection incidence matrixes so as to determine intersection spatial incidence information; performing convolution calculation on each sampling time slice of each intersection object in a time dimension, and predicting data on a continuous time axis by training a prediction model in a single intersection time dimension;
(3) for input data vectors of different time intervals, performing fusion output on the trained data of different time granularities by giving different weights to the input data vectors;
(4) and selecting a mean square error by the loss function, training the model until the mean square error MSE is minimum, realizing error back propagation by adopting a random gradient descent method, and training the model.
5. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 1, characterized in that: for the case of data missing, the specific process of performing the patching processing on the data is as follows:
for data loss of individual intersections, the data required by the current time of the intersection object can be predicted through the prediction model, the intersection data is directly repaired, and the current data of the adjacent intersections can be recorded for retraining the prediction model at the next time;
for the condition of data loss of the large-scale intersection, not only data prediction needs to be carried out on the intersection object with the corresponding data loss, but also the current finally repaired data is recorded and is used for retraining a prediction model next time; if the large-scale data repairing operation is frequently carried out within a certain time, an alarm prompt needs to be carried out on a worker for checking the faults of hardware measures or data input steps.
6. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 1, characterized in that: for the suspected noise of the data, the specific steps required to be recorded are as follows:
1) if only noise data are generated at individual intersections or individual data, the noise data can be directly deleted and repaired by using the data prediction model;
2) if data of suspected noise points frequently appear in a plurality of data production intervals, corresponding records need to be carried out on intersection objects and time produced by the data,
2.1) if the noise data has the characteristics of much noise and no distribution rule, directly identifying the noise data as an abnormal data value, and directly predicting the required time point of the current intersection object and directly restoring the data by using a prediction model;
2.2) if the noisy point data presents the characteristic of a large and regular data distribution, corresponding data comparison needs to be carried out on the change trend of the data attribute of the intersection on the continuous time axis and the topological data association attribute between the intersection object and the peripheral adjacent object; if the adjacent intersections all present the same or similar noise data and the topological correlation attributes exist in the intersection data, manual investigation is carried out on the data source and the data aggregation production process; if for data with the same time granularity, adjacent intersections with similar intersection states have larger difference on noise point data, but for the condition that the data distribution is continuous and data mutation does not exist on the time axis of the corresponding intersection, the data aggregation process and the data source of the detector need to be examined, and the road-gateway connection data are examined to find the problem of poor topological relevance of the intersection data.
7. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 1, characterized in that: whether the data change of the adjacent intersection is normal conduction of traffic data on a time axis can be judged by the speed of the adjacent intersection, and the specific steps are as follows:
the intersection with more advanced data change characteristics is used as a current data template, a time period with larger speed change trend every day is set as an experimental time interval, corresponding time speed values of the intersection with slower data change characteristics are searched for by accumulating corresponding delta T 'on a data time axis of the intersection with more backward data change characteristics, the delta T' is a time difference of data change trends between two intersections predicted by a prediction model, and if the number of times that the difference value of the speed values of the two intersections exceeds a speed fluctuation interval in the selected experimental time interval is smaller than a set threshold value, the data characteristic change between the intersections can be determined as the normal conduction influence time of the data on the time axis.
8. The road network traffic time series characteristic data quality diagnosis and restoration method according to claim 1, characterized in that: further analyzing intersection data with obviously poor data quality, if the periodic poor data quality condition occurs in a certain intersection or a certain sub-area, firstly analyzing the actual condition of the intersection or the area, if the intersection or the area has the same periodic occurrence in a real road network, constructing a new data prediction model by using the latest produced real-time data, and predicting, repairing and detecting the data of the day by using different data prediction models according to the actual condition of the day;
if the intersection is in a special condition after the actual road network condition is observed, the deviation between the data and the prediction model is large in the production time and on the continuous time axis, and similar conditions occur in a certain number of intersections in a corresponding area by integrating the data change conditions of the intersection and the surrounding intersections, a method for retraining the data prediction model and monitoring the quality of the intersection with the real-time production data of the day again need to be considered, and an actual and objective data quality detection result is generated.
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