CN105513357A - Method for calibrating traffic flow basic map parameter on the basis of microwave data - Google Patents

Method for calibrating traffic flow basic map parameter on the basis of microwave data Download PDF

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Publication number
CN105513357A
CN105513357A CN201511017432.4A CN201511017432A CN105513357A CN 105513357 A CN105513357 A CN 105513357A CN 201511017432 A CN201511017432 A CN 201511017432A CN 105513357 A CN105513357 A CN 105513357A
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density
intensity value
microwave data
traffic flow
equivalent vehicle
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CN105513357B (en
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柳展
陈喜群
樊锦祥
陈才君
张帅超
张书浆
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Yinjiang Technology Co.,Ltd.
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Enjoyor Co Ltd
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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

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention relates to a method for calibrating a traffic flow basic map parameter on the basis of microwave data. The method comprises steps of: reading original data, setting an equivalent vehicle length, and calculating a density value; second, setting the adjusting ratio d1 of the equivalent vehicle length, eliminating a ratio dp of noise points, the bandwidth dm of the noise points, limit of error threshold critical density e<Kc>, a traffic capacity eC, and a block density e<Km>; third, equally proportionally eliminating noise points on the left side and the right side of the critical density in order to guarantee that the eliminated noise point range does not exceed a maximum bandwidth; fourth, linearly fitting a front segment and a rear segment, wherein a fitted straight line in the front segment is required to pass an original point, and a fitted straight line in the rear segment is required to pass a block density value point; and determining whether fitted parameters satisfy a condition of convergence, ending calibration if yes, or adjusting the equivalent vehicle length and recalculating the density if not, and going to the third step. The method is simple, effective, and convenient to operate, and solve the limitation of a conventional traffic parameter calibrating method.

Description

A kind of traffic flow parent map parameter calibration method based on microwave data
Technical field
The present invention relates to Traffic Information Engineering & Control field, particularly relate to a kind of traffic flow parent map parameter calibration method based on microwave data.
Background technology
In recent years, the disaster caused because meteorological disaster is occurred frequently gets more and more.On present society, industry-by-industry is paid much attention to for the construction of emergency communication system, and existing emergency communication system meets business demand when reply disaster and emergent accident.But also there is some business challenge: (1) video moves the comprehensive access aspect of air-ground one still existing defects, need to build a set of removable and can incorporate the unmanned plane remote video capturing system of existing emergency commading system, the one as conventional video signals acquisition mode extends; (2) simultaneously, because the eccentricity of disaster spot is with disguised, the transmission of wireless signals of emergency communication system often receives mountain, buildings etc. and blocks, and affects transmission quality and the service flexibility of radio channel.Existing emergency communication system can reach the quorum sensing inhibitor of 5-10km, but China is vast in territory, and physical features is complicated, and power tower line is often distributed in meagrely-populated gully, stops seriously and not easily arrives; Be badly in need of adopting the mode of aerial signal relaying to erect inside and outside radio communication bridge.
Summary of the invention
The present invention overcomes above-mentioned weak point, and object is to provide a kind of traffic flow parent map parameter calibration method based on microwave data, and the present invention by obtaining microwave data, and carries out the demarcation of data analysis and parameter on this basis; The inventive method is simple and effective, convenient operation, overcomes the deficiency that traditional parameter calibration method can not be applied to the city expressway parameter calibration of complex condition, solves the confinement problems that existing traffic parameter scaling method is suitable for.
The present invention achieves the above object by the following technical programs: a kind of traffic flow parent map parameter calibration method based on microwave data, comprises step as follows:
(1) read raw microwave data, set equivalent vehicle commander's initial value and scope;
(2) utilize raw microwave data and equivalent vehicle commander's bulk density value K, make the close parent map of stream;
(3) the ratio d of equivalent vehicle commander adjustment is set l, reject the ratio d shared by noise p, reject the bandwidth d of noise m, limits of error threshold value critical density traffic capacity e c, jam density
(4) using the flow maximum in raw microwave data as the initial traffic capacity, the density value in raw microwave data corresponding to flow maximum is as critical intensity value;
(5) equal proportion rejects the noise of the critical intensity value left and right sides, and calculates the critical intensity value left and right sides boundary value after rejecting noise;
(6) respectively linear fit is carried out to critical intensity value left data and right side data, and judge whether each parameter value after matching meets the condition of convergence;
(7) if meet the condition of convergence, then obtain parameter calibration result, demarcate and terminate; If do not meet the condition of convergence, then judging that whether two fitting a straight lines are intersecting at critical intensity value place, adjust equivalent vehicle commander according to judged result, recalculating density value Posterior circle and performing step (5) to step (7).
As preferably, described raw microwave data comprise flow q, time occupancy Occu, speed v.
As preferably, described equivalent vehicle commander L initial value is 8m, and scope is 8m≤L≤10m.
As preferably, the computing formula of described step (2) density value K is as follows:
K = O c c u &times; 1000 L
Wherein, K is density, and Occu is occupation rate, and L is equivalent vehicle commander.
As preferably, described step (5) adopts equal proportion elimination method to reject the noise of the critical intensity value left and right sides, and method is as follows:
A () calculates the length shared by ratio rejected a little on the left of critical intensity value is d pk ci (), the length rejected on the right side of critical intensity value shared by ratio is a little d p(K m(i)-K c(i));
B (), for ensureing that the length rejected shared by point is no more than the maximum bandwidth rejecting noise, getting and rejecting noise bandwidth is min (d m, d pk c(i)), min (d m, d p(K m(i)-K c(i))); Wherein, i=1,2,3...... are iterations.
As preferably, the method that described step (6) carries out linear fit to critical intensity value left data and right side data be respectively fitting a straight line that the matching of critical intensity value left data is obtained by initial point, the fitting a straight line obtained data fitting on the right side of critical intensity value is by jam density value point.
As preferably, the described condition of convergence is as follows:
( i ) - - - | K c ( i + 1 ) - K c ( i ) | &le; e K c ;
(ii)|C(i+1)-C(i)|≤e C；
( i i i ) - - - | K m ( i + 1 ) - 1000 / L ( i ) | &le; e K m .
As preferably, the parameter calibration result that described step (7) obtains comprises: free stream velocity V f, road section capacity C, dissipation velocity of wave W, jam density K m.
As preferably, described step (7) adjusts equivalent vehicle commander according to judged result, as follows:
1) if then L (i+1)=L (i) (1+d l);
2) if then L (i+1)=L (i) (1-d l);
Wherein, also need to carry out threshold range judgement to the equivalent vehicle commander after adjustment, if L (i+1) >=10, then fixing equivalent vehicle commander is 10; If L (i+1)≤8, then fixing equivalent vehicle commander is 8.
As preferably, the described slope to the fitting a straight line that the matching of critical intensity value left data obtains is free stream velocity V f; Be dissipation velocity of wave W to the slope of the fitting a straight line that data fitting on the right side of critical intensity value obtains, the intersection point of itself and X-axis is jam density K m; Article two, the intersection point of fitting a straight line is road section capacity C.
Beneficial effect of the present invention is: (1) this method proposes the concept of equivalent vehicle commander, and utilize time occupancy and equivalent vehicle commander's bulk density, demarcate the correlation parameter of parent map afterwards, method is simple and effective, convenient operation; (2) this method overcomes the deficiency that traditional parameter calibration method based on highway data can not be applied to the city expressway parameter calibration of complex condition, solves the confinement problems that existing traffic parameter scaling method is suitable for.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the inventive method;
Fig. 2 is the stream close parent map of the embodiment of the present invention based on microwave data;
Fig. 3 is the elimination method schematic diagram of the adjustment of embodiment of the present invention critical density and noise;
Fig. 4 is the calibration result schematic diagram of the embodiment of the present invention.
Embodiment
Below in conjunction with specific embodiment, the present invention is described further, but protection scope of the present invention is not limited in this:
Embodiment: the embodiment of the present invention detects data based on the microwave obtaining city expressway to implement, and with the data instance used in this method, acquisition time is one day 24 hours, every the microwave data of 5 minutes, mainly comprise flow, speed, the fields such as time occupancy.
As shown in Figure 1, a kind of traffic flow parent map parameter calibration method based on microwave data, comprises step as follows
(1) read in raw data and comprise flow q, time occupancy Occu, and speed v;
(2) set initial equivalent vehicle commander L=8m, utilize equivalent vehicle commander's bulk density value;
Concrete derivation is as follows:
For single unit vehicle, the time spent on the detector is by the speed v of single unit vehicle i, vehicle commander l idetermine with the length d of detecting device itself, i=1,2,3 ... n is i-th car:
O c c u = &Sigma; i ( l i + d ) / v i T = 1 T &Sigma; i l i v i + d T &Sigma; i 1 v i
The molecule denominator of above formula Section 2 is multiplied by N simultaneously, then by flow definition and section mean speed expression formula substitutes into and can obtain:
O c c u = 1 T &Sigma; i l i v i + d &CenterDot; N T &CenterDot; 1 N &CenterDot; &Sigma; i 1 v i = 1 T &Sigma; i l i v i + d &CenterDot; q v &OverBar; s
By fundamental formular substitute into:
O c c u = 1 T &Sigma; i l i v i + d &CenterDot; K
Wherein T is the summation of time headway, and K is density.The molecule denominator of above formula is obtained divided by N simultaneously:
O c c u = &Sigma; i l i v i T + d &CenterDot; K = 1 N &Sigma; i l i v i 1 N &Sigma; i h i + d &CenterDot; K = 1 N &Sigma; i l i v i h &OverBar; + d &CenterDot; K
Assumed vehicle body length l gets definite value, and so above formula can abbreviation be:
O c c u = 1 N &Sigma; i l i v i h &OverBar; + d &CenterDot; K = 1 h &OverBar; &CenterDot; l &CenterDot; 1 N &Sigma; i l i v i + d &CenterDot; K = l &CenterDot; q v &OverBar; s + d &CenterDot; K = ( l + d ) &CenterDot; K = L &CenterDot; K
In formula: L-length of wagon and detecting device length sum, i.e. equivalent vehicle commander.
Can obtain thus:
K = O c c u &times; 1000 L
Wherein: K is density, Occu is occupation rate, and L is equivalent vehicle commander.
(3) the close parent map of stream based on microwave data is made, as shown in Figure 2.
(4) the ratio d of equivalent vehicle commander adjustment is set l, reject the ratio d shared by noise pand reject the bandwidth d of noise m, critical density traffic capacity e c, jam density limits of error threshold value, the span 8m≤L≤10m of equivalent vehicle commander;
(5) density value corresponding time maximum using raw data flow as initial criticality density value, K c(1)=K (max (q)); Using the maximal value of raw data flow as the initial traffic capacity, C (1)=max (q), wherein (1) represents first time iteration, i.e. initial value.
(6) calculate the critical density left and right sides and reject the boundary value after noise, and arrange in the process of iterative computation, the ratio rejecting noise does not change, reject noise maximum magnitude be no more than bandwidth.
Because the point near critical intensity value can produce larger error to fitting result, therefore the elimination method of equal proportion will be adopted in the process of matching, ensure that the ratio shared by noise that the critical intensity value left and right sides is rejected does not change, and maximum rejecting noise bandwidth is set.Detailed process is as follows, calculates on the left of critical intensity value, rejects the length d shared by ratio of point pk ci (), calculates on the right side of critical intensity value, reject the length d shared by ratio of point p(K m(i)-K c(i)).For ensureing that the length rejected shared by point is no more than maximum bandwidth, therefore getting and rejecting noise bandwidth is min (d m, d pk c(i)), min (d m, d p(K m(i)-K c(i))).The elimination method of critical density adjustment and noise as shown in Figure 3.I=1,2,3...... are iterations.
(7) force first paragraph fitting a straight line on the left of critical intensity value to pass through initial point, on the right side of forcing critical intensity value, second segment fitting a straight line is by jam density value point.Thus obtain slope and the free stream velocity V of first paragraph fitting a straight line f, the slope of second segment fitting a straight line and dissipation velocity of wave W, the intersection point road section capacity C of two straight lines, the intersection point jam density K of second segment fitting a straight line and X-axis m.Wherein theoretical jam density value is 1000/L (i).Jam density value after adjustment is judged, judges whether to meet threshold range.I=1,2,3...... are iterations.
Citing: K mspan be [100,125]
Force second segment fitting a straight line to cross jam density value point, following three kinds of situation discussion can be divided into.
Situation one: if fitting a straight line and X-axis intersection point >125, then force fitting a straight line by point (125,0).
Situation two: if fitting a straight line and X-axis intersection point <100, then force fitting a straight line by point (100,0).
Situation three: if fitting a straight line and X-axis intersection point >=100 and≤125, then normal matching.
(8) judge whether each parameter value after matching meets the condition of convergence, and the described condition of convergence is as follows:
( i ) - - - | K c ( i + 1 ) - K c ( i ) | &le; e K c ;
(ii)|C(i+1)-C(i)|≤e C；
( i i i ) - - - | K m ( i + 1 ) - 1000 / L ( i ) | &le; e K m .
If meet the condition of convergence, then obtain parameter calibration result as shown in Figure 4.If do not meet the condition of convergence, then judge whether two straight lines intersect at critical density place, then carry out the adjustment of equivalent vehicle commander.I=1,2,3...... are iterations.
Following two kinds of situations can be divided into discuss.
Situation one: if then L (i+1)=L (i) (1+d l).
Situation two: if then L (i+1)=L (i) (1-d l).
Equivalent vehicle commander after adjustment is judged, judges whether to meet threshold range.
Citing:
If L (i+1) >=10, then fixing equivalent vehicle commander is 10; If L (i+1)≤8, then fixing equivalent vehicle commander is 8.
(9) recalculate density K=Occu × 1000/L (i+1), be transferred to (6) step.I=1,2,3...... are iterations.
The present invention is by proposing the concept of equivalent vehicle commander, and utilize time occupancy and equivalent vehicle commander's bulk density, then demarcate the correlation parameter of parent map, method is simple and effective, convenient operation.The parameter calibrated comprises: free stream velocity V f, road section capacity C, dissipation velocity of wave W, jam density K m.
Why the present invention selects sectional linear fitting instead of other nonlinear fittings (such as: para-curve etc.), because 5 of sectional linear fitting parameters all have clear and definite physical significance, namely the slope of first paragraph fitting a straight line is free stream velocity V f, the slope of second segment fitting a straight line is dissipation velocity of wave W, and the intersection point of two straight lines is road section capacity C, and the density corresponding to the intersection point of two straight lines is critical density K c, the intersection point jam density K of second segment fitting a straight line and X-axis m.
The know-why being specific embodiments of the invention and using described in above, if the change done according to conception of the present invention, its function produced do not exceed that instructions and accompanying drawing contain yet spiritual time, must protection scope of the present invention be belonged to.

Claims (10)

1., based on a traffic flow parent map parameter calibration method for microwave data, it is characterized in that comprising step as follows:
(1) read raw microwave data, set equivalent vehicle commander's initial value and scope;
(2) utilize raw microwave data and equivalent vehicle commander's bulk density value K, make the close parent map of stream;
(3) the ratio d of equivalent vehicle commander adjustment is set l, reject the ratio d shared by noise p, reject the bandwidth d of noise m, limits of error threshold value critical density traffic capacity e c, jam density
(4) using the flow maximum in raw microwave data as the initial traffic capacity, the density value in raw microwave data corresponding to flow maximum is as critical intensity value;
(5) equal proportion rejects the noise of the critical intensity value left and right sides, and calculates the critical intensity value left and right sides boundary value after rejecting noise;
(6) respectively linear fit is carried out to critical intensity value left data and right side data, and judge whether each parameter value after matching meets the condition of convergence;
(7) if meet the condition of convergence, then obtain parameter calibration result, demarcate and terminate; If do not meet the condition of convergence, then judging that whether two fitting a straight lines are intersecting at critical intensity value place, adjust equivalent vehicle commander according to judged result, recalculating density value Posterior circle and performing step (5) to step (7).
2. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: described raw microwave data comprise flow q, time occupancy Occu, speed v.
3. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: described equivalent vehicle commander L initial value is 8m, and scope is 8m≤L≤10m.
4. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: the computing formula of described step (2) density value K is as follows:
K = O c c u &times; 1000 L
Wherein, K is density, and Occu is occupation rate, and L is equivalent vehicle commander.
5. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: described step (5) adopts equal proportion elimination method to reject the noise of the critical intensity value left and right sides, and method is as follows:
A () calculates the length shared by ratio rejected a little on the left of critical intensity value is d pk ci (), the length rejected on the right side of critical intensity value shared by ratio is a little d p(K m(i)-K c(i));
B (), for ensureing that the length rejected shared by point is no more than the maximum bandwidth rejecting noise, getting and rejecting noise bandwidth is min (d m, d pk c(i)), min (d m, d p(K m(i)-K c(i))); Wherein, i=1,2,3.. are iterations.
6. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, it is characterized in that: the method that described step (6) carries out linear fit to critical intensity value left data and right side data be respectively fitting a straight line that the matching of critical intensity value left data is obtained by initial point, the fitting a straight line obtained data fitting on the right side of critical intensity value is by jam density value point.
7. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: the described condition of convergence is as follows:
( i ) - - - | K c ( i + 1 ) - K c ( i ) | &le; e K c ;
(ii)|C(i+1)-C(i)|≤e C；
( i i i ) - - - | K m ( i + 1 ) - 1000 / L ( i ) | &le; e K m .
8. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: the parameter calibration result that described step (7) obtains comprises: free stream velocity V f, road section capacity C, dissipation velocity of wave W, jam density K m.
9. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 1, is characterized in that: described step (7) adjusts equivalent vehicle commander according to judged result, as follows:
1) if then L (i+1)=L (i) (1+d l);
2) if then L (i+1)=L (i) (1-d l);
Wherein, also need to carry out threshold range judgement to the equivalent vehicle commander after adjustment, if L (i+1) >=10, then fixing equivalent vehicle commander is 10; If L (i+1)≤8, then fixing equivalent vehicle commander is 8.
10. a kind of traffic flow parent map parameter calibration method based on microwave data according to claim 6, is characterized in that: the described slope to the fitting a straight line that the matching of critical intensity value left data obtains is free stream velocity V f; Be dissipation velocity of wave W to the slope of the fitting a straight line that data fitting on the right side of critical intensity value obtains, the intersection point of itself and X-axis is jam density K m; Article two, the intersection point of fitting a straight line is road section capacity C.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107356837A (en) * 2017-07-11 2017-11-17 国网福建省电力有限公司 A kind of improved reactor interturn short circuit fault detection recognition method
CN110956800A (en) * 2018-09-27 2020-04-03 杭州海康威视***技术有限公司 Road section traffic data preprocessing method and device and electronic equipment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102521438A (en) * 2011-12-02 2012-06-27 东南大学 Traffic conflict simulation two-stage parameter calibrating method
CN103077275A (en) * 2013-01-06 2013-05-01 东南大学 Parameter calibration method of highway ramp simulation model
JP2013210941A (en) * 2012-03-30 2013-10-10 I-Transport Lab Co Ltd Traffic flow prediction device, traffic flow prediction method, and traffic flow prediction program
CN103761138A (en) * 2014-01-16 2014-04-30 昆明理工大学 Parameter correction method for traffic simulation software
CN103996289A (en) * 2014-06-06 2014-08-20 北方工业大学 Flow-speed matching model and travel time forecasting method and system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102521438A (en) * 2011-12-02 2012-06-27 东南大学 Traffic conflict simulation two-stage parameter calibrating method
JP2013210941A (en) * 2012-03-30 2013-10-10 I-Transport Lab Co Ltd Traffic flow prediction device, traffic flow prediction method, and traffic flow prediction program
CN103077275A (en) * 2013-01-06 2013-05-01 东南大学 Parameter calibration method of highway ramp simulation model
CN103761138A (en) * 2014-01-16 2014-04-30 昆明理工大学 Parameter correction method for traffic simulation software
CN103996289A (en) * 2014-06-06 2014-08-20 北方工业大学 Flow-speed matching model and travel time forecasting method and system

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ZHONG RENXIN等: "Towards Automatic Model Calibration of First-order Traffic Flow Model", 《CONTROL CONFERENCE IEEE》 *
ZHU JIANG等: "calibration of traffic dynamics models with data mining", 《IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS》 *
徐程等: "交通流速度密度模型特性分析", 《公路交通科技》 *
江竹等: "基于数据挖掘的交通仿真模型参数标定", 《交通运输***工程与信息》 *
江竹等: "基于机器学习的中观交通仿真器模型参数标定", 《***仿真学报》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107356837A (en) * 2017-07-11 2017-11-17 国网福建省电力有限公司 A kind of improved reactor interturn short circuit fault detection recognition method
CN107356837B (en) * 2017-07-11 2019-11-26 国网福建省电力有限公司莆田供电公司 A kind of improved reactor interturn short circuit fault detection recognition method
CN110956800A (en) * 2018-09-27 2020-04-03 杭州海康威视***技术有限公司 Road section traffic data preprocessing method and device and electronic equipment
CN110956800B (en) * 2018-09-27 2021-07-23 杭州海康威视***技术有限公司 Road section traffic data preprocessing method and device and electronic equipment

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