CN103035124B - Based on the traffic congestion monitoring forecasting procedure of band dissipative term macroscopic traffic flow - Google Patents
Based on the traffic congestion monitoring forecasting procedure of band dissipative term macroscopic traffic flow Download PDFInfo
- Publication number
- CN103035124B CN103035124B CN201210593370.1A CN201210593370A CN103035124B CN 103035124 B CN103035124 B CN 103035124B CN 201210593370 A CN201210593370 A CN 201210593370A CN 103035124 B CN103035124 B CN 103035124B
- Authority
- CN
- China
- Prior art keywords
- traffic
- image
- error
- traffic congestion
- partiald
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
Be difficult to directly to the technological deficiency of traffic congestion monitoring forecast to overcome existing traffic flow model, the invention provides a kind of traffic congestion monitoring forecasting procedure based on band dissipative term macroscopic traffic flow, the method obtains car speed, density and flow information by the video image of CCTV camera, forecast by the traffic congestion of generation according to newly-established traffic congestion model, solve the technical matters that traffic congestion can not be forecast in time.
Description
Technical field
The present invention relates to a kind of modeling method, particularly a kind of traffic congestion monitoring forecasting procedure based on band dissipative term macroscopic traffic flow.
Background technology
Communications and transportation is closely-related significant problem with national economy.The transportation network setting up unimpeded prosperity is the set objective of national development, the up-to-dateness of traffic and transportation system and the advanced degree of traffic administration, is the important symbol of a measurement modernization of the country development; Therefore, communications and transportation cause, particularly highway transportation, be subject to the great attention of every country government, obtains in recent years and develop rapidly; Communications and transportation whether unimpeded, to the development of urban economy, the quality of life of people, the international fame of regional and even whole country has very important impact, and in order to alleviate traffic jam issue, domestic and international academia all drops into larger energy research model problem.Traffic flow model research is the basis of traffic system research, simultaneously as one of the fundamental research content of intelligent transportation system (abbreviation ITS), also significant to the development of ITS.
The research of traffic flow theory starts from nineteen thirties, and what traffic flow theory the earliest adopted substantially is probability theory method.After the fifties, along with developing rapidly of auto industry, road traffic flow increases severely, in traffic flow, vehicle independence is more and more less, probability theory method is no longer applicable, so people are from different conceptual frameworks, proposes various new traffic flow theory model.
Existing traffic flow theory model is divided three classes substantially: microvisual model, mesoscopic model and macromodel.Microvisual model is that traffic flow is considered as far from equilibrium state, a large amount of dispersion, interactional self-driven particle.Mesoscopic model is then the gas movable model based on probability description, also has scholar to incorporate this class model into microscopic approach.Macromodel is then compressible continuous fluid medium traffic flow being considered as being made up of a large amount of vehicle, the average behavior of research vehicle body, the not explicit appearance of individual character of single unit vehicle.In macroscopic traffic flow, traffic flow is regarded as the compressible continuous fluid medium be made up of a large amount of vehicle, and the average behavior of research vehicle collective, the individual character of single unit vehicle does not highlight.Macroscopic traffic flow portrays traffic flow with the average density ρ of vehicle, average velocity v and flow q, study they the equation that meets.
Compared with microvisual model, macromodel can portray the collective behavior of traffic flow better, thus for designing effective traffic control strategy, simulation and estimating that the traffic engineering problem such as effect of road geometry modification provides foundation.Numerical evaluation aspect, simulation Macro-traffic Flow required time is studied number of vehicles in traffic system with institute and is had nothing to do, with studied road, numerical method choose and middle space x, time t discrete steps Δ x relevant with Δ t.So macroscopic traffic flow is comparatively suitable for the traffic flow problem of the traffic system processing a large amount of vehicle composition.In macro traffic model, the continuous traffic flow model that Lighthill and Whitham proposes has illustrative, Richards also independently proposes similar model simultaneously, these two models are collectively referred to as LWR model, LWR model is with ρ (x, t) and v(x, t) represent that t is positioned at average density and the average velocity of the traffic flow of x place, they meet following continuity equation
Above formula represents number of vehicles conservation.For making equation close, assume that the speed-density relation of balance simultaneously
v(x,t)=V
e[ρ(x,t)]
Wherein, V
e[ρ (x, t)] is equilibrium rate.Following equation can be obtained like this
As time goes on, the nonlinear motion ripple described by above formula is evolved into sharp wave structure gradually, and wavefront is more and more precipitous, until vertically, which results in the discontinuous of traffic flow density; But the traffic density distribution in reality is so not extreme, in order to make shock wave structure smoothing to a certain extent, adds dissipative term in above formula
object is the diffusional effect of reflection vehicle, makes nonlinear motion ripple continuous distribution, so model conversation is
Wherein D is dissipation factor.
Suppose there is the equilibrium rate-density relationship as lower linear
Model is just converted into famous Burgers equation (see document J.M.Burgers.Amathematicalmodelillustratingthetheoryoftur bulence [J] .AdvancesinAppliedMechanics1948,2:171-199)
Function in formula
But, Burgers equation can not directly provide traffic congestion condition, particularly directly provide the whole description of traffic jam issue when various traffic parameter change, make traffic system research worker be not easy to direct use, there is the technical matters being difficult to forecast traffic congestion.
Summary of the invention
Be difficult to directly to the technological deficiency of traffic congestion monitoring forecast to overcome Burgers traffic flow model, the invention provides a kind of traffic congestion monitoring forecasting procedure based on band dissipative term macroscopic traffic flow, the method obtains car speed, density and flow information by the video image of CCTV camera, forecast by the traffic congestion of generation according to newly-established traffic congestion model, solve the technical matters that traffic congestion can not be forecast in time.
The technical solution adopted for the present invention to solve the technical problems is: a kind of traffic congestion monitoring forecasting procedure based on band dissipative term macroscopic traffic flow, is characterized in adopting following steps:
When 1, obtaining car speed, density and flow information by the video image of CCTV camera, consider that actual monitored video camera works at crossing throughout the year, the impossible artificial frequent correction image Processing Algorithm of mode, select image processing algorithm according to the composition error performance index of the overall process of following image procossing:
min(e
z)=min{e
para{e
seg[e
pre(e
samp)]}}
In formula, e
zfor the global error of image zooming-out traffic parameter, min (e
z) be the e by selecting the image processing method of various combination to obtain
zminimum value, e
sampfor image sampling error, e
prefor image and process errors, e
segfor vehicles segmentation error in image, e
parafor the error according to segmentation image zooming-out traffic parameter;
2, the macroscopic traffic flow in given section is set up
In formula,
for state variable, x is position, and t is the time, and D is dissipation factor, and ρ is the average density of vehicle, v is that vehicle is on average fast, degree v
mfor maximal rate, ρ
mfor saturation traffic density during traffic congestion;
3, when state variable η is tending towards infinite in time, this section will be tending towards obstruction density and produce traffic congestion, send traffic more to block up forecast to this section;
4, the restriction of discontinuity Induction Control is adopted to sail sending a car of this section into.
The invention has the beneficial effects as follows: by all image method integrally, image processing algorithm is selected according to the composition error performance index of the overall process of following image procossing, and forecast by the traffic congestion of generation according to newly-established traffic congestion model, solve the technical matters that traffic congestion can not be forecast in time.
Below in conjunction with embodiment, the present invention is elaborated.
Embodiment
When 1, obtaining car speed, density and flow information by the video image of CCTV camera, consider that actual monitored video camera works at crossing throughout the year, the impossible artificial frequent correction image Processing Algorithm of mode, select image processing algorithm according to the composition error performance index of the overall process of following image procossing:
min(e
z)=min{e
para{e
seg[e
pre(e
samp)]}}
=k
sampk
prek
sege
para
In formula, e
zfor the global error of image zooming-out traffic parameter, min (e
z) be the e by selecting the image processing method of various combination to obtain
zminimum value, e
sampfor image sampling error, e
prefor image and process errors, e
segfor vehicles segmentation error in image, e
parafor the error according to segmentation image zooming-out traffic parameter, k
samp>1 is image sampling error coefficient, k
pre>1 is image and process errors coefficient, k
seg>=1 is the error coefficient of vehicles segmentation error in image;
2, the macroscopic traffic flow in given section is set up
In formula,
for state variable, x is position, and t is the time, and D is dissipation factor, and ρ is the average density of vehicle, v is that vehicle is on average fast, v
mfor maximal rate, ρ
mfor saturation traffic density during traffic congestion;
3, when state variable η is tending towards infinite in time, this section will be tending towards obstruction density and produce traffic congestion, send traffic more to block up forecast to this section;
4, the restriction of discontinuity Induction Control is adopted to sail sending a car of this section into.
Claims (1)
1., based on a traffic congestion monitoring forecasting procedure for band dissipative term macroscopic traffic flow, be characterized in adopting following steps:
(1) when obtaining car speed, density and flow information by the video image of CCTV camera, consider that actual monitored video camera works at crossing throughout the year, the impossible artificial frequent correction image Processing Algorithm of mode, select image processing algorithm according to the composition error performance index of the overall process of following image procossing:
In formula,
for the global error of image zooming-out traffic parameter,
for what obtained by the image processing method of selection various combination
minimum value,
for image sampling error,
for Image semantic classification error,
for vehicles segmentation error in image,
for the error according to segmentation image zooming-out traffic parameter,
for image sampling error coefficient,
for Image semantic classification error coefficient,
for the error coefficient of vehicles segmentation error in image;
(2) macroscopic traffic flow in given section is set up
In formula,
for state variable,
for position,
for the time,
for dissipation factor,
for maximal rate,
for average density,
for saturation traffic density during traffic congestion;
(3) state variable is worked as
when being tending towards infinite in time, this section will be tending towards obstruction density and produce traffic congestion, send traffic more to block up forecast to this section;
(4) restriction of discontinuity Induction Control is adopted to sail sending a car of this section into.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201210593370.1A CN103035124B (en) | 2012-12-30 | 2012-12-30 | Based on the traffic congestion monitoring forecasting procedure of band dissipative term macroscopic traffic flow |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201210593370.1A CN103035124B (en) | 2012-12-30 | 2012-12-30 | Based on the traffic congestion monitoring forecasting procedure of band dissipative term macroscopic traffic flow |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103035124A CN103035124A (en) | 2013-04-10 |
CN103035124B true CN103035124B (en) | 2015-12-02 |
Family
ID=48021984
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201210593370.1A Expired - Fee Related CN103035124B (en) | 2012-12-30 | 2012-12-30 | Based on the traffic congestion monitoring forecasting procedure of band dissipative term macroscopic traffic flow |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103035124B (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104952123A (en) * | 2015-05-27 | 2015-09-30 | 关晓芙 | Vehicle-mounted equipment installed on vehicle as well as related equipment and method |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101807345A (en) * | 2010-03-26 | 2010-08-18 | 重庆大学 | Traffic jam judging method based on video detection technology |
CN102147971A (en) * | 2011-01-14 | 2011-08-10 | 赵秀江 | Traffic information acquisition system based on video image processing technology |
CN102254424A (en) * | 2011-06-02 | 2011-11-23 | 西北工业大学 | Stable modeling method for macroscopic traffic flow |
CN102542828A (en) * | 2011-11-18 | 2012-07-04 | 厦门市鼎朔信息技术有限公司 | System and method for solving traffic jam |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101739820B (en) * | 2009-11-19 | 2012-09-26 | 北京世纪高通科技有限公司 | Road condition predicting method and device |
WO2011126215A2 (en) * | 2010-04-09 | 2011-10-13 | 고려대학교 산학협력단 | Traffic flow control and dynamic path providing system linked with real-time traffic network structure control based on bidirectional communication function-combined vehicle navigation, and method thereof |
-
2012
- 2012-12-30 CN CN201210593370.1A patent/CN103035124B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101807345A (en) * | 2010-03-26 | 2010-08-18 | 重庆大学 | Traffic jam judging method based on video detection technology |
CN102147971A (en) * | 2011-01-14 | 2011-08-10 | 赵秀江 | Traffic information acquisition system based on video image processing technology |
CN102254424A (en) * | 2011-06-02 | 2011-11-23 | 西北工业大学 | Stable modeling method for macroscopic traffic flow |
CN102542828A (en) * | 2011-11-18 | 2012-07-04 | 厦门市鼎朔信息技术有限公司 | System and method for solving traffic jam |
Also Published As
Publication number | Publication date |
---|---|
CN103035124A (en) | 2013-04-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shu et al. | Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China | |
Thaker et al. | The impact of traffic-flow patterns on air quality in urban street canyons | |
CN102592447B (en) | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) | |
CN105513359B (en) | A kind of urban expressway traffic method for estimating state based on smart mobile phone movement detection | |
Zhai et al. | Self-delayed feedback car-following control with the velocity uncertainty of preceding vehicles on gradient roads | |
Al-Arkawazi | Measuring the influences and impacts of signalized intersection delay reduction on the fuel consumption, operation cost and exhaust emissions | |
CN102610092A (en) | Urban road speed predication method based on RBF (radial basis function) neural network | |
CN102254422A (en) | Stable modeling method for Payne-Whitham (PW) macroscopic traffic flow model | |
Yang et al. | Optimization of variable speed limits for efficient, safe, and sustainable mobility | |
CN106370198A (en) | Route selection method taking outgoing delayed reaction into account | |
CN102254425A (en) | Speed-correcting stable modeling method for discrete model of macroscopic traffic flow | |
CN102254423A (en) | Modeling method for stability of discrete model of macroscopic traffic flow | |
Park et al. | Calibration and validation of TRANSIMS microsimulator for an urban arterial network | |
CN102800195A (en) | Macroscopic traffic flow model modeling method based on microcosmic OVDM (Optimal velocity difference model) car-following model | |
CN103021180B (en) | Correction speed based traffic jam monitoring and forecasting method for macroscopic traffic flow discrete model | |
Zhai et al. | A new continuum model with driver’s continuous sensory memory and preceding vehicle’s taillight | |
CN103035124B (en) | Based on the traffic congestion monitoring forecasting procedure of band dissipative term macroscopic traffic flow | |
CN103021181B (en) | Traffic congestion monitoring and predicting method based on macro discrete traffic flow model | |
CN102254424A (en) | Stable modeling method for macroscopic traffic flow | |
Liu | Wind forecasting in railway engineering | |
CN106447077A (en) | Resource evaluation method and resource evaluation system | |
Wolny et al. | Challenges, opportunities and barriers to sustainable transport development in functional urban areas | |
CN103035128B (en) | Traffic flow simulation system based on FPGA (Field Programmable Gate Array) array unified intelligent structure | |
Chen et al. | Special issue: smart transportation: theory and practice | |
CN103093616B (en) | Based on the traffic congestion monitoring forecasting procedure of macroscopic traffic stream sticky model |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20151202 Termination date: 20211230 |