CN108090635A - A kind of pavement performance Forecasting Methodology based on Cluster Classification - Google Patents
A kind of pavement performance Forecasting Methodology based on Cluster Classification Download PDFInfo
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Abstract
The invention discloses a kind of pavement performance Forecasting Methodology based on Cluster Classification, this method includes：(1) Road Detection data are collected, are recorded as the first historical time sequence；(2) the first historical time sequence is cleaned, rejects the data for having conserved section, be then recorded as the second historical time sequence；(3) according to road surface index measuring similarity, according to Clustering Model, the second historical time sequence is subjected to Cluster Classification；(4) weight of each classification time series in the second historical time sequence and the time sequence function of each index in each classification time series are calculated respectively；(5) according to the weight and time sequence function of each classification time series, all time sequence functions for clustering each indexs of classification are calculated；(6) pavement performance integrated forecasting function is determined.The present invention improves the accuracy of pavement performance prediction.
Description
Technical Field
The invention relates to a road performance prediction method, in particular to a road performance prediction method based on cluster classification.
Background
And the road maintenance scheme is made according to the maintenance decision result, and the final scheme of the maintenance decision is determined by the road performance prediction result. Therefore, the accurate road performance prediction model can provide an effective and scientific maintenance decision, so that the road maintenance is more targeted and scientific.
At the present stage, the prediction method of the road surface performance is to firstly investigate the road condition and then predict the road surface performance according to the investigation data by using a road surface performance prediction model. However, in a road, some road sections are not usually maintained, some road sections are maintained, and the service performance of different road sections in the same road may be greatly different. When road surface performance prediction is carried out, if data of a section which is maintained is not removed, noise of detected data is increased, and the error of a prediction result is large. If the road sections with different road performances are studied together and processed uniformly, the information is not concentrated enough, and a lot of key information is lost. The loss of the key information can reduce the accuracy of performance prediction, so that the decision of maintenance measures is influenced, and finally, the pavement diseases cannot be treated in a targeted manner.
Disclosure of Invention
The invention aims to: in order to overcome the defects of the prior art, the invention provides a road performance prediction method based on cluster classification, and the method solves the problem of low accuracy of road performance prediction.
The technical scheme is as follows: the invention relates to a road performance prediction method based on cluster classification, which comprises the following steps:
(1) Collecting road detection data, and recording the road detection data as a first historical time sequence;
(2) Cleaning the first historical time sequence, removing data of the maintained road section, and recording the data as a second historical time sequence;
(3) According to the road index similarity measurement, according to a clustering model, clustering and classifying the second historical time sequence;
(4) Respectively calculating the weight of each category time sequence in the second historical time sequence and the time sequence function of each index in each category time sequence;
(5) Calculating the time sequence function of each index of all cluster categories according to the weight of each category time sequence and the time sequence function;
(6) And determining a road performance comprehensive prediction function.
Preferably, in the step (3), the road surface indexes include a road surface running quality index RQI, a road surface rutting condition index RDI, and a road surface damage condition index PCI.
Preferably, in step (3), the clustering model is based on euler distance:
wherein x is ik Is the k-th component, x, of the feature vector i jk Is the kth component of the feature vector j and m is the total number of components.
Preferably, in step (5), the weight calculation formula is:
α i ＝x i /X
wherein x is i And X is the number of all the unserviced sections in the second time sequence.
Preferably, in step (5), the RQI time series function of each type of time series is calculated as:
wherein i is the ith class for classifying the second historical time sequence, t is the number of years and t is not less than 1,Q i And q is i Are respectively RQI i (t) coefficients and indices;
the RDI time series function calculation formula of each type of time series is as follows:
wherein i is the ith class for classifying the second historical time sequence, t is the number of years and t is not less than 1,D i And d i Are respectively RDI i (t) coefficients and indices;
the PCI time sequence function calculation formula of each type of time sequence is as follows:
wherein i is the ith class for classifying the second historical time sequence, t is the number of years and t is not less than 1,C i And c i Are respectively PCI i The coefficient and the index of (t).
Preferably, in step (5), the RQI time-series functions of all cluster categories are:
wherein the content of the first and second substances,n is the total number of cluster classifications;
the RDI time series function for all cluster categories is:
wherein the content of the first and second substances,n is the total number of cluster classifications;
the PCI time series function for all cluster categories is:
wherein the content of the first and second substances,n is the total number of cluster classifications.
Has the advantages that: the method eliminates the data of the maintained road sections, divides the road sections into different categories by using cluster analysis, captures the differences of samples of different categories, respectively studies the road performance of different road sections, and improves the accuracy of road performance prediction.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of experimental data comparing the method of use of the present invention with the conventional method.
Detailed Description
Example 1
In order to accurately predict the road performance, the invention needs to eliminate the data of the maintained road sections and classify the non-maintained road sections, as shown in fig. 1.
Step 1, collecting road detection data, and recording the data as a first historical time sequence.
And 2, cleaning the first historical time sequence, checking data consistency, processing invalid values and missing values, eliminating data of the maintained road section, and recording the processed first historical time sequence as a second historical time sequence.
And 3, dividing the second historical time sequence into a first category time sequence, a … … and an Nth category time sequence according to the road running quality index RQI, the road rut condition index RDI and the road damage condition index PCI similarity measurement and the clustering model, wherein N is more than 1.
Different clustering methods have different classification results, and the Euclidean distance is the most common method and is suitable for the condition that the standards of all vectors are unified, so that the similarity is reflected by selecting the method. The clustering model is based on Euler distance:wherein x is ik Is the k-th component, x, of the feature vector i jk Is the kth component of the feature vector j and m is the total number of components.
Step 4, respectively calculating the weight alpha of each category time sequence i 。
The weights are calculated as follows: alpha is alpha i ＝x i and/X. Wherein x is i All the number of the segments in the ith category time sequence, and X is the number of all the unserved segments in the second category time sequence.
Step 5, respectively calculating RQI, RDI and PCI time sequence function RQI in each category time sequence i (t)、RDI i (t)、PCI i (t) of (d). According to the weight alpha of each class i And RQI i And (t) calculating RQI time series functions RQI (t) of all cluster types through weighted average. And similarly, respectively obtaining the RDI and the PCI time series functions RDI (t) and PCI (t) of all the cluster types.
The time sequence function of the RQI in the ith category and the tth year (t is more than or equal to 1) is as follows:
the class i year-t (t ≧ 1) RDI time series function is:
the ith type and t (t is more than or equal to 1) year PCI time sequence function is as follows:
wherein Q is i And q is i Are respectively RQI i (t) coefficients and indices; d i And d i Are respectively RDI i (t) coefficients and indices; c i And c i Are respectively PCI i The coefficient and the exponent of (t).
The RQI time series function for all the uncured sections is:
the RDI time series function for all the sections not maintained is:
the PCI time series function for all the sections that are not maintained is:
wherein the content of the first and second substances,
and 6, determining a road performance comprehensive prediction function PQI (t) by using the RQI (t), the RDI (t) and the PCI (t).
The road performance prediction model is as follows:
PQI(t)＝[0.4×RQI(t)+0.15×RDI(t)+0.35×PCI(t)]/0.9。
of these, 0.4,0.15 and 0.35 are the values given by the specification.
Example 2
The steps of the invention when used on a specific road include:
first, since a road is divided into a plurality of sections, detection data of the plurality of sections in the road is collected and recorded as a first historical time series,
and performing data cleaning on the first historical time sequence, checking data consistency, and processing invalid values and missing values. Then, the sections that have been maintained are removed according to the maintenance data of the past year, as shown in table 1.
Section of cured road needing to be removed
The remaining detection data is recorded as a second historical time series.
And classifying the second historical time sequence by utilizing a clustering model according to the second historical time sequence and according to the similarity measurement of the road surface damage condition index PCI, the road surface rutting condition index RDI and the road surface running quality index RQI. The three segments with similar indexes are classified into one category. All the sections of the uncured section were classified into 9 categories as shown in table 2.
Calculating the weight alpha of each category according to a weight formula i As shown in table 3.
| Clustering categories | Weight α i |
| First kind | 0.11 |
| Second class | 0.2 |
| Class III | 0.05 |
| Class IV | 0.15 |
| Fifth class | 0.01 |
| Class six | 0.25 |
| Class seven | 0.01 |
| Class VIII | 0.2 |
| Ninth class | 0.02 |
The time series function of RQI, RDI and PCI in each category is calculated as follows.
Wherein, RQI i (t)、RDI i (t) and PCI i And (t) is respectively RQI, RDI and PCI corresponding to all road sections of the ith class in the t (t is more than or equal to 1) th year. Q i And q is i Are respectively RQI i (t) coefficients and indices; d i And d i Are respectively RDI i (t) coefficients and indices; c i And c i Are respectively PCI i The coefficient and the index of (t). These parameters were determined by regression based on the road survey data over the years, as shown in table 4.
Parameters of time series function of performance index for each road
| Cluster classification | Q i | q i | D i | d i | C i | C i |
| First kind | 95.331 | 0.001 | 91.7142 | 0.015 | 97.9669 | 0.00227 |
| Second class | 95.1956 | 0.000671 | 96.9709 | 0.0387 | 100.3 | 0.00726 |
| Class III | 95.3232 | 0.00139 | 96.5424 | 0.0333 | 100.3 | 0.00548 |
| Class IV | 95.4014 | 0.00187 | 99.3507 | 0.0564 | 99.5128 | 0.00495 |
| Fifth class | 96.4148 | 0.00738 | 104.1 | 0.0876 | 101.7 | 0.0116 |
| Class six | 95.227 | 0.0009 | 95.163 | 0.0307 | 99.0654 | 0.00343 |
| Class seven | 105 | 0.053 | 94.8778 | 0.0132 | 100.8 | 0.0038 |
| Class VIII | 95.5087 | 0.00165 | 96.922 | 0.065 | 100.2 | 0.00662 |
| Ninth class | 95.9858 | 0.00336 | 97.0159 | 0.0368 | 104.2 | 0.0327 |
According to the weight alpha of each cluster category i And Q i Weighted average calculation
According to the weight alpha of each cluster category i And q is i Weighted average calculation
Determine RQI time series functions for all cluster classes:
similarly, determining the RDI time sequence functions and the PCI time sequence functions of all the sections which are not maintained:
RDI(t)＝96.32×e -0.042t 、PCI(t)＝99.7×e -0.0057t 。
and substituting the time-series functions into the comprehensive road performance prediction model according to the calculated time-series functions of RQI, RDI and PCI to finally determine the comprehensive road performance prediction function of the damaged road:
PQI(t)＝42.42e -0.0018t +16.05e -0.042t +38.77e -0.0057t 。
at present, according to the existing method, when a road performance comprehensive prediction function is calculated, a maintained road section is not removed, so that PCI, RDI and RQI of a perfect road section are all calculated, the PQI index is higher, and the actual situation cannot be truly reflected. In addition, in the existing method, the road sections are not classified, and the arithmetic mean value of PCI, RDI and RQI is directly adopted, so that a lot of effective information is hidden, and the difference between the prediction result and the actual situation is large.
The PQI (t) calculated by the prior art method and the method of the present invention are compared as shown in fig. 2. As a result, the PQI index predicted by the existing method is almost unchanged, and is still about 95 in the 25 th year, which is seriously inconsistent with the actual situation. Therefore, compared with the existing method, the method can accurately and effectively predict the road performance comprehensive function PQI (t) and improve the prediction accuracy.
Claims (6)
1. A road performance prediction method based on cluster classification is characterized by comprising the following steps:
(1) Collecting road detection data, and recording the road detection data as a first historical time sequence;
(2) Cleaning the first historical time sequence, removing data of the maintained road section, and recording the data as a second historical time sequence;
(3) According to the similarity measurement of the road indexes, carrying out cluster classification on the second historical time sequence according to a cluster model;
(4) Respectively calculating the weight of each category time sequence in the second historical time sequence and the time sequence function of each index in each category time sequence;
(5) Calculating the time sequence function of each index of all clustering categories according to the weight of each category time sequence and the time sequence function;
(6) And determining a road performance comprehensive prediction function.
2. The method for predicting road performance based on cluster classification as claimed in claim 1, wherein in step (3), the road indexes include a road running quality index RQI, a road rutting condition index RDI, and a road damage condition index PCI.
3. The road use performance prediction method based on cluster classification as claimed in claim 1, wherein in step (3), the cluster model is a euler distance-based cluster model:
wherein，x ik Is the k-th component, x, of the feature vector i jk Is the kth component of the feature vector j and m is the total number of components.
4. The cluster classification-based road use performance prediction method according to claim 1, wherein in the step (5), the weight calculation formula is as follows:
α i ＝x i /X
wherein x is i And X is the number of all the unserviced sections in the second time sequence.
5. The cluster classification-based road use performance prediction method according to claim 1, wherein in the step (5), the RQI time series function calculation formula of each type of time series is as follows:
wherein i is the ith class for classifying the second historical time sequence, t is the number of years and t is not less than 1,Q i And q is i Are respectively RQI i (t) coefficients and indices;
the RDI time series function calculation formula of each type of time series is as follows:
wherein i is the ith class for classifying the second historical time sequence, t is the number of years and t is not less than 1,D i And d i Are respectively RDI i (t) coefficients and indices;
the PCI time sequence function calculation formula of each type of time sequence is as follows:
wherein i is the ith class for classifying the second historical time sequence, t is the number of years and t is not less than 1,C i And c i Are respectively PCI i The coefficient and the index of (t).
6. The cluster classification-based road use performance prediction method according to claim 1, wherein in step (5), the RQI time-series functions of all cluster classes are:
wherein the content of the first and second substances,n is the total number of cluster classifications;
the RDI time series function for all cluster categories is:
wherein, the first and the second end of the pipe are connected with each other,n is the total number of cluster classifications;
the PCI time series function for all cluster categories is:
wherein the content of the first and second substances,n is the total number of cluster classifications.
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