CN103093616B - Based on the traffic congestion monitoring forecasting procedure of macroscopic traffic stream sticky model - Google Patents
Based on the traffic congestion monitoring forecasting procedure of macroscopic traffic stream sticky model Download PDFInfo
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Abstract
Be difficult to directly to the technological deficiency of traffic congestion monitoring forecast to overcome traffic flow model, the invention provides a kind of traffic congestion based on macroscopic traffic stream sticky model monitoring forecasting procedure, 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 monitoring of the traffic congestion based on macroscopic traffic stream sticky model forecasting procedure.
Background technology
In recent years, along with the quantity of the various vehicles increases greatly, facility, road, the traffic control system this speed of development of very difficult adaptation of a lot of country in the world, particularly inharmonious, the traffic dispersion system of big or middle urban transportation infrastructure insufficiency, traffic signalization lacks, vehicle scheduling and all many-sided reasons such as the confusion of management, the friendship rule consciousness of traffic participant result in urban transportation comparatively crowding phenomenon, has caused again a series of socioeconomic problem such as traffic safety, environmental pollution thus;
Because traffic problems are complicated Iarge-scale system problems, it has related to the Comprehensive Control of urban traffic network, the synthetical collection of transport information and network transmission technology, traffic intelligent information fusion preconditioning technique, Traffic flow guidance technology, and vehicle transport intelligent dispatching method, municipal intelligent traffic planing method, traffic safety detects, many-sided contents such as traffic environment overall evaluation system, and influence each other between each factor above-mentioned, mutual restriction, the extremely strong synthesis of a correlativity, be difficult to adopt unified description form to portray this challenge, therefore, the description for traffic system is also of all kinds, and the both macro and micro model analysis traffic characteristics person wherein adopting hydromechanical viewpoint to set up is in the majority,
In macroscopic traffic flow, traffic flow is regarded as the compressible continuous fluid medium be made up of a large amount of vehicle, the average behavior of research vehicle collective, and the individual character of single unit vehicle does not highlight; Macroscopic traffic flow is with the average density of vehicle
, average velocity
and flow
portray traffic flow, 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, the choosing and middle space of numerical method
, the time
discrete steps
with
relevant; So macroscopic traffic flow is comparatively suitable for the traffic flow problem of the traffic system processing a large amount of vehicle composition; The more a kind of model of current research be K uhne proposed to be similar to as follows in 1984 Navier-Stockes equation the non-equilibrium Payne model of viscous (see document R.D. K ü hne. Non-linearity stochastics of unstable traffic flow [C]. In C.F.Daganzo (Ed), Transportation and Traffic Flow Theory. Elsevier Science Publishers 1994,367-386.)
In formula,
for average density,
for average velocity,
equilibrium rate,
for the time interval,
constant,
the coefficient of viscosity: when
time, this model is Payne model; Compared with Payne model, model adds the Derivative Terms of a shape as viscous fluid, namely with the coefficient of viscosity at kinetics equation right-hand member
viscous item
, its role is to the impact eliminating Payne model discontinuous solution, the uncontinuity that smooth Payne model comprises, makes model to describe continuum traffic flow.
But, above-mentioned model 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 traffic flow model, the invention provides a kind of traffic congestion based on macroscopic traffic stream sticky model monitoring forecasting procedure, 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 monitoring of the traffic congestion based on macroscopic traffic stream sticky model forecasting procedure, 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:
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;
2, the macroscopic traffic flow in given section is set up
In formula,
for state variable,
for state variable,
for average density,
for average velocity,
for position,
for the time,
for the time interval,
constant,
the coefficient of viscosity,
for equivalent speed,
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, 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:
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, the macroscopic traffic flow in given section is set up
In formula,
for state variable,
for state variable,
for average density,
for average velocity,
for position,
for the time,
for the time interval,
constant,
the coefficient of viscosity,
for equivalent speed,
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, 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 macroscopic traffic stream sticky model, 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;
2) macroscopic traffic flow in given section is set up
In formula,
for state variable,
for state variable,
for average density,
for average velocity,
for position,
for the time,
for the time interval,
constant,
the coefficient of viscosity,
for equivalent speed,
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.
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Citations (7)
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| EP0763712A1 (en) * | 1995-09-18 | 1997-03-19 | UNION SWITCH & SIGNAL Inc. | Vehicle navigator system |
| EP1507360A1 (en) * | 2003-08-14 | 2005-02-16 | AT&T Corp. | Method and apparatus for sketch-based detection of changes in network traffic |
| CN101639871A (en) * | 2009-07-23 | 2010-02-03 | 上海理工大学 | Vehicle-borne dynamic traffic information induction system analog design method facing behavior research |
| CN102289930A (en) * | 2011-06-02 | 2011-12-21 | 西北工业大学 | Method for stably building macroscopic traffic flow velocity gradient-viscosity model |
| CN102289931A (en) * | 2011-06-02 | 2011-12-21 | 西北工业大学 | Method for stably building macroscopic traffic flow viscosity model |
| CN102436751A (en) * | 2011-09-30 | 2012-05-02 | 上海交通大学 | Short-term forecasting method of traffic flow based on urban macroscopic road network model |
| CN102610114A (en) * | 2012-03-09 | 2012-07-25 | 西安费斯达自动化工程有限公司 | Method for setting road information display board based on discrete model |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8573731B2 (en) * | 2009-04-30 | 2013-11-05 | Hewlett-Packard Development Company, L.P. | Density error correction |
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0763712A1 (en) * | 1995-09-18 | 1997-03-19 | UNION SWITCH & SIGNAL Inc. | Vehicle navigator system |
| EP1507360A1 (en) * | 2003-08-14 | 2005-02-16 | AT&T Corp. | Method and apparatus for sketch-based detection of changes in network traffic |
| CN101639871A (en) * | 2009-07-23 | 2010-02-03 | 上海理工大学 | Vehicle-borne dynamic traffic information induction system analog design method facing behavior research |
| CN102289930A (en) * | 2011-06-02 | 2011-12-21 | 西北工业大学 | Method for stably building macroscopic traffic flow velocity gradient-viscosity model |
| CN102289931A (en) * | 2011-06-02 | 2011-12-21 | 西北工业大学 | Method for stably building macroscopic traffic flow viscosity model |
| CN102436751A (en) * | 2011-09-30 | 2012-05-02 | 上海交通大学 | Short-term forecasting method of traffic flow based on urban macroscopic road network model |
| CN102610114A (en) * | 2012-03-09 | 2012-07-25 | 西安费斯达自动化工程有限公司 | Method for setting road information display board based on discrete model |
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