CN106652443B - Method for predicting short-time traffic volume of expressway with similar longitudinal and transverse dimensions - Google Patents

Method for predicting short-time traffic volume of expressway with similar longitudinal and transverse dimensions Download PDF

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CN106652443B
CN106652443B CN201610916541.8A CN201610916541A CN106652443B CN 106652443 B CN106652443 B CN 106652443B CN 201610916541 A CN201610916541 A CN 201610916541A CN 106652443 B CN106652443 B CN 106652443B
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vector
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CN106652443A (en
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李松江
杨迪
邱宁佳
袁强
王鹏
李岩芳
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Changchun University of Science and Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q50/40—
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

Abstract

The invention relates to a method for predicting short-time traffic volume of a highway with similar longitudinal and transverse dimensions, which is characterized in that adopted equipment comprises a server A, a server B, a server C and a computer; wherein, the server A is the existing system server, the server B is the data ETL server, and the server C is the data warehouse server; the original data in the server A is cleaned and converted according to corresponding rules through the server B and finally loaded into the server C, and the computer acquires the data through the report display system to display and analyze the data; the method improves seasonal anti-interference capability in traffic volume prediction, and enlarges the application range of the short-time traffic volume prediction method.

Description

Method for predicting short-time traffic volume of expressway with similar longitudinal and transverse dimensions
Technical Field
The invention relates to a method for predicting short-time traffic volume of highways with similar longitudinal and transverse dimensions, and belongs to the technical field of computer data mining.
Background
At present, traffic prediction has been considered as a problem from different angles: such as time series problems, regression and function approximation problems, clustering or pattern recognition problems, and even all of the above. Cheng et al uses the spatio-temporal autocorrelation structure of the road network to build a spatio-temporal prediction model. Thomas et al performed short-term and long-term predictions using a single time series after extensive learning of 20 intersections in almiro, the netherlands. Qi et al propose an adaptive single exponential smoothing method to predict short-term traffic flow by optimizing an exponential smoothing coefficient through approximate dynamic programming. Jiang et al introduced a multiple linear regression minimum absolute shrinkage and selection operator method (Lasso) in combination with the nonlinear characteristics of Neural Networks (NN), and proposed a Lasso-NN combinatorial model. Xu et al propose a short-time traffic prediction Cluster-NN model of spatial clustering. Wang et al propose prediction methods based on k-nearest neighbor non-parametric regression. Xu et al propose an adaptive weight particle swarm neural network traffic flow prediction (PSOA-NN) model. Li et al propose a mode to predict short term traffic flow using a Support Vector Machine (SVM) with a time dependent structure. Castillo et al propose a traffic model adapted to random dynamic demands using a generalized Beta-Gaussian Bayesian network. Vlahogianni et al tested short term traffic using an autoregressive time series model in conjunction with a neural network.
In summary, in the conventional short-time traffic volume prediction, an operator is generally required to have a deeper understanding and mastering on the related knowledge, which is not beneficial to large-scale popularization and application.
Disclosure of Invention
The invention aims to provide a method for predicting short-time traffic volume of a highway with longitudinal and transverse similarity, which is used for predicting according to the similarity of traffic volume sequences and the recent similarity combined with a historical distribution rule. The traffic data is first calibrated for the number of weeks and days per week according to the rules of the week and day of the year, and the data is classified according to the calibrated days. And constructing traffic data of the same week number and the same day in different years as a longitudinal data sequence matrix, constructing traffic data of a week before a predicted day in the same year as a transverse data sequence matrix, respectively calculating average aggregate vectors of the longitudinal sequence and the transverse sequence, and obtaining a longitudinal sequence aggregate vector and a transverse sequence aggregate vector through weighted summation of the longitudinal sequence average aggregate vector sequence and the transverse sequence average aggregate vector sequence. Then, the total traffic volume of a single day is predicted, a distribution rule data sequence vector is obtained by combining the traffic volume distribution rule of the single day, and a final traffic volume prediction data sequence vector is determined according to the sum and average of the vertical sequence aggregate vector and the horizontal sequence aggregate vector and the distribution rule data sequence vector, so that the short-time traffic volume prediction process is completed; the method improves seasonal anti-interference capability in traffic volume prediction, and enlarges the application range of the short-time traffic volume prediction method.
The technical scheme of the invention is realized as follows: the method for predicting the short-time traffic volume of the expressway with the similar vertical and horizontal directions is characterized in that adopted equipment comprises a server 1, a server 2, a server 3 and a computer 1; wherein, the server 1 is the existing system server, the server 2 is the data ETL server, and the server 3 is the data warehouse server; the original data in the server 1 is cleaned and converted according to corresponding rules through the server 2 and finally loaded into the server 3, and the computer 1 acquires the data through a report display system for displaying and analyzing; the method comprises the following specific steps:
step 1, collecting and counting half-hour traffic volume and single-day traffic volume data of highway charging data according to half-hour segmentation and date;
step 2, constructing a longitudinal data sequence matrix as f according to half-hour traffic data1,b,c,k,f2,b,c,k,…,fa-1,b,c,k]TWherein f isa,b,c,kRepresenting a traffic sequence vector, a being 1, 2, …, n representing the first year from the earliest year, b being 1, 2, …, n representing the week of the year a, c being 1, 2, 3, 4, 5, 6, 7 representing the day of the week b of the year a, k being 1, 2, …, 48 representing the dimension, i.e. the data of the number of days in the sequence vector of day c of the week b of the year a;
step 3, dividing the longitudinal data sequence matrix in the step 2 into k sequences according to different k values, planning and solving each sequence by using a least square method in linear regression respectively to obtain a next solving result, and combining the k results into a longitudinal sequence average resultant vector from small to large according to the k values
Figure GDA0002080454880000021
Step 4, constructing a transverse data sequence matrix [ f ]a,b,c-7,k,fa,b,c-6,k,…,fa,b,c-1,k]TSelecting data of a week before a predicted day when a transverse data sequence matrix is constructed;
step 5, calculating the average resultant vector of the transverse sequence
Figure GDA0002080454880000022
Obtained by weighted summation of the data of 7 days in the transverse data sequence matrix, wherein omega1+…+ω7Since the data on day 1 is similar to the data on the predicted day, a large weight ω is given1Equal to 0.7, the rest are equally distributed;
step 6, determining a longitudinal average resultant vector
Figure GDA0002080454880000023
Sum of transverse average resultant vector
Figure GDA0002080454880000024
The weight distribution of (2) determining the weighting coefficient by calculating the vector similarity coefficient, and setting the weighting coefficient of the longitudinal average resultant vector as c (0)<c<1) If the sum of the weighting coefficients is 1, the weighting coefficient of the horizontal average resultant vector is 1-c, using the following formula
Figure GDA0002080454880000031
Solving a value c;
step 7, calculating a longitudinal average resultant vector according to the weight calculated in the step 6
Figure GDA0002080454880000032
Sum of transverse average resultant vector
Figure GDA0002080454880000033
The vertical and horizontal sequence resultant vector obtained by weighted summation
Figure GDA0002080454880000034
Step 8, constructing a single-day total traffic volume sequence vector [ V ] by using the single-day traffic volume data summarized in the step 11,b,c,V2,b,c,...,Va-1,b,c]In which V isa,b,cRepresents an overall traffic volume on day c in week b of year a, where a is 1, 2, …, n, representing the first year from the earliest year, and b is 1, 2, …, n, representing the week a; c-1, 2, 3, 4, 5, 6, 7 denotes the day after week b of a year;
and 9, planning and solving the vector in the step 8 by using a least square method in linear regression to obtain a next solving result Va,b,c；
Step 10, analyzing a plurality of groups of half-hour traffic data to obtain that the traffic distribution of each day is basically consistent, and obtaining a traffic distribution law according to the proportion of the traffic of each time period in the whole;
step 11, solving result V obtained in step 9a,b,cCalculating a distribution rule data sequence vector according to the distribution law obtained in the step 10
Figure GDA0002080454880000035
Step 12, integrating the vertical and horizontal sequences obtained in the step 7
Figure GDA0002080454880000036
And the distribution rule data sequence vector obtained in step 11
Figure GDA0002080454880000037
Superposing according to the sum and average to obtain the final traffic prediction data sequence vector fa,b,c,k；
By utilizing the steps, the prediction data of the short-time traffic volume of the expressway can be obtained.
The invention has the advantages that the prediction is carried out according to the synchronous similarity and the recent similarity of the traffic volume sequence and the historical distribution rule, so that the seasonal interference in the traffic volume prediction is greatly reduced; the advantages are that:
1. the method for predicting the short-time traffic volume of the expressway utilizes historical synchronization data to predict, and can greatly reduce the influence of seasonal factors of the traffic volume.
2. The prediction method of the short-time traffic volume of the expressway can better enable the prediction result to accord with the actual situation by combining the half-hour segmented distribution rule.
3. The prediction method for the short-time traffic volume of the expressway analyzes and researches the synchronous similarity and the recent similarity of the traffic volume sequence, and has application continuity.
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FIG. 1 is a diagram of the structure of equipment required for a method for predicting short-term traffic volume on a highway with similar vertical and horizontal directions
Detailed Description
The invention is further described with reference to the accompanying drawings in which: as shown in fig. 1, the method for predicting short-term traffic volume of highways with similar vertical and horizontal directions adopts equipment comprising a server 1, a server 2, a server 3 and a computer 1; the server 1 is an existing system server, the server 2 is a data ETL server, and the server 3 is a data warehouse server; the original data in the server 1 is cleaned and converted according to corresponding rules through the server 2 and finally loaded into the server 3, and the computer 1 acquires the data through a report display system for displaying and analyzing; the method comprises the following specific steps:
step 1, collecting and counting half-hour traffic volume and single-day traffic volume data of highway charging data according to half-hour segmentation and date;
step 2, constructing a longitudinal data sequence matrix as f according to half-hour traffic data1,b,c,k,f2,b,c,k,…,fa-1,b,c,k]TWherein f isa,b,c,kRepresenting a traffic sequence vector, a being 1, 2, …, n representing the first year from the earliest year, b being 1, 2, …, n representing the week of the year a, c being 1, 2, 3, 4, 5, 6, 7 representing the day of the week b of the year a, k being 1, 2, …, 48 representing the dimension, i.e. the data of the number of days in the sequence vector of day c of the week b of the year a;
step 3, dividing the longitudinal data sequence matrix in the step 2 into k sequences according to different k values, planning and solving each sequence by using a least square method in linear regression respectively to obtain a next solving result, and combining the k results into a longitudinal sequence average resultant vector from small to large according to the k values
Figure GDA0002080454880000041
Step 4, constructing a transverse data sequence matrix [ f ]a,b,c-7,k,fa,b,c-6,k,…,fa,b,c-1,k]TSelecting data of a week before a predicted day when a transverse data sequence matrix is constructed;
step 5, calculating the average resultant vector of the transverse sequence
Figure GDA0002080454880000042
Obtained by weighted summation of the data of 7 days in the transverse data sequence matrix, wherein omega1+…+ω7Since the data on day 1 is similar to the data on the predicted day, a large weight ω is given1Equal to 0.7, the rest are equally distributed;
step 6, determining a longitudinal average resultant vector
Figure GDA0002080454880000043
Sum of transverse average resultant vector
Figure GDA0002080454880000044
The weight distribution of (2) determining the weighting coefficient by calculating the vector similarity coefficient, and setting the weighting coefficient of the longitudinal average resultant vector as c (0)<c<1) If the sum of the weighting coefficients is 1, the weighting coefficient of the horizontal average resultant vector is 1-c, using the following formula
Figure GDA0002080454880000045
Solving a value c;
step 7, calculating a longitudinal average resultant vector according to the weight calculated in the step 6
Figure GDA0002080454880000046
Sum of transverse average resultant vector
Figure GDA0002080454880000047
The vertical and horizontal sequence resultant vector obtained by weighted summation
Figure GDA0002080454880000048
Step 8, constructing a single-day total traffic volume sequence vector [ V ] by using the single-day traffic volume data summarized in the step 11,b,c,V2,b,c,...,Va-1,b,c]In which V isa,b,cRepresents an overall traffic volume on day c in week b of year a, where a is 1, 2, …, n, representing the first year from the earliest year, and b is 1, 2, …, n, representing the week a; c-1, 2, 3, 4, 5, 6, 7 denotes the day after week b of a year;
and 9, planning and solving the vector in the step 8 by using a least square method in linear regression to obtain a next solving result Va,b,c；
Step 10, analyzing a plurality of groups of half-hour traffic data to obtain that the traffic distribution of each day is basically consistent, and obtaining a traffic distribution law according to the proportion of the traffic of each time period in the whole;
step 11, solving result V obtained in step 9a,b,cCalculating a distribution rule data sequence vector according to the distribution law obtained in the step 10
Figure GDA0002080454880000051
Step 12, integrating the vertical and horizontal sequences obtained in the step 7
Figure GDA0002080454880000052
And the distribution rule data sequence vector obtained in step 11
Figure GDA0002080454880000053
Superposing according to the sum and average to obtain the final traffic prediction data sequence vector fa,b,c,k；
By utilizing the steps, the prediction data of the short-time traffic volume of the expressway can be obtained.

Claims (1)

1. The method for predicting the short-time traffic volume of the expressway with the similar vertical and horizontal directions is characterized in that adopted equipment comprises a server 1, a server 2, a server 3 and a computer 1; wherein, the server 1 is the existing system server, the server 2 is the data ETL server, and the server 3 is the data warehouse server; the original data in the server 1 is cleaned and converted according to corresponding rules through the server 2 and finally loaded into the server 3, and the computer 1 acquires the data through a report display system for displaying and analyzing; the method comprises the following specific steps:
step 1, collecting and counting half-hour traffic volume and single-day traffic volume data of highway charging data according to half-hour segmentation and date;
step 2, according to half-smallThe time traffic data constructs a longitudinal data sequence matrix as f1,b,c,k,f2,b,c,k, …,fa-1,b,c,k]T
Figure DEST_PATH_IMAGE001
Wherein f isa,b,c,kRepresenting a traffic sequence vector, a =1, 2, …, n representing the first year from the first year, b =1, 2, …, m representing the week of the year a, c =1, 2, 3, 4, 5, 6, 7 representing the day of the week b of the year a, k =1, 2, …, 48 representing the dimension, i.e. the data, of the sequence vector at day c of the week b of the year a;
step 3, dividing the longitudinal data sequence matrix in the step 2 into k sequences according to different k values, planning and solving each sequence by using a least square method in linear regression respectively to obtain a next solving result, and combining the k results into a longitudinal sequence average resultant vector from small to large according to the k values
Figure 914072DEST_PATH_GDA0002080454880000043
Wherein 1_ a-1 denotes the 1 st to a-1 st years from the first year;
step 4, constructing a transverse data sequence matrix [ f ]a,b,c-7,k,fa,b,c-6,k, … ,fa,b,c-1,k]TSelecting data of a week before a predicted day when a transverse data sequence matrix is constructed;
step 5, calculating the average resultant vector of the transverse sequence
Figure 324325DEST_PATH_GDA0002080454880000042
And c-7_ c-1 represents the sum of the data weighted by the number of 7 days in the transverse data sequence matrix from the c-7 th week to the c-1 th week, wherein ω is1+ … + ω7Since the data on day 1 is similar to the data on the predicted day, a large weight ω is given1Equal to 0.7, the rest are equally distributed;
step 6, determining a longitudinal average resultant vector
Figure 437774DEST_PATH_GDA0002080454880000043
Sum of transverse average resultant vector
Figure 363005DEST_PATH_GDA0002080454880000044
The weight distribution of the vector is determined by calculating the vector similarity coefficient, the total weight of the influence of the vertical and horizontal sequence synthetic vectors on the prediction is 1, the influence weight of the vertical average synthetic vector is c, the influence weight of the horizontal average synthetic vector is 1-c, and the following formula is utilized
Figure 587313DEST_PATH_GDA0002080454880000045
Solving a value c;
step 7, calculating a longitudinal average resultant vector according to the weight calculated in the step 6
Figure 648810DEST_PATH_GDA0002080454880000046
Sum of transverse average resultant vector
Figure 933160DEST_PATH_GDA0002080454880000047
The vertical and horizontal sequence resultant vector obtained by weighted summation
Figure DEST_PATH_GDA0002080454880000052
The sum vector is obtained by fusing a longitudinal traffic flow average sum vector calculated based on the c week of the b month from the first 1 st year to the a-1 st year with a transverse traffic flow average sum vector calculated based on the c-7 week to the c-1 week of the b month of the a year;
step 8, constructing a single-day total traffic volume sequence vector using the single-day traffic volume data summarized in step 1V 1,b,c ,V 2,b,c , ... ,V a-1,b,c ]WhereinV a,b,c Represents a total traffic volume representing day c in week b of a year, where a =1, 2, …, n, represents the year from the first year, b =1, 2, …, m, represents the week of a year; c =1, 2, 3, 4, 5, 6, 7 denotes the day after b weeks of a year;
and 9, planning and solving the vector in the step 8 by using a least square method in linear regression to obtain the next solving resultV a,b,c ；
Step 10, analyzing a plurality of groups of half-hour traffic data to obtain that the traffic distribution of each day is basically consistent, and obtaining a traffic distribution law according to the proportion of the traffic of each time period in the whole;
step 11, solving results obtained in step 9V a,b,c Calculating a distribution rule data sequence vector according to the distribution law obtained in the step 10
Figure DEST_PATH_GDA0002080454880000051
；
Step 12, integrating the vertical and horizontal sequences obtained in the step 7
Figure 781905DEST_PATH_GDA0002080454880000052
And the distribution rule data sequence vector obtained in step 11
Figure DEST_PATH_GDA0002080454880000053
Superposing according to the addition and the average to obtain the final traffic prediction data sequence vectorf a,b,c,k ；
By utilizing the steps, the prediction data of the short-time traffic volume of the expressway can be obtained.
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