CN103035124A - Traffic jam monitoring forecast method based on macroscopic traffic flow model with dissipation item - Google Patents

Abstract

The invention provides a traffic jam monitoring forecast method based on a macroscopic traffic flow model with a dissipation item, and aims to overcome the technical defect that the existing traffic flow model is difficult to directly monitor and forecast traffic jams. The method obtains vehicle speed, density and flow information through video images of a monitoring camera, and forecasts traffic jams which will occur according to the newly established traffic jam model to solve the technical problem that the traffic jams cannot be forecasted in time.

Description

Based on the traffic congestion monitoring forecasting procedure with the dissipative term macroscopic traffic flow

Technical field

The present invention relates to a kind of modeling method, particularly a kind of based on the traffic congestion monitoring forecasting procedure with the dissipative term macroscopic traffic flow.

Background technology

Communications and transportation is and the closely-related significant problem of national economy.The transportation network of setting up unimpeded prosperity is the set objective of national development, and the advanced degree of the up-to-dateness of traffic and transportation system and traffic administration is the important symbol of weighing a modernization of the country development; Therefore, communications and transportation cause, particularly highway transportation are subject to the great attention of every country government, have obtained in recent years developing rapidly; Communications and transportation whether unimpeded, to the development of urban economy, people's quality of life, regional and even international fame whole country has very important impact, and in order to alleviate traffic jam issue, academia all drops into larger energy research model problem both at home and abroad.Traffic flow model research is the basis of traffic system research, and is as one of fundamental research content of intelligent transportation system (abbreviation ITS), also significant to the development of ITS simultaneously.

The research of traffic flow theory starts from nineteen thirties, and what traffic flow theory the earliest adopted basically is probability theory method.Behind the fifties, along with developing rapidly of auto industry, the road traffic flow increases severely, vehicle independence is more and more less in the traffic flow, probability theory method is no longer applicable, so people, have proposed various new traffic flow theory models from different conceptual frameworks.

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 the far from equilibrium attitude, a large amount of disperse, interactional self-driven particles.Mesoscopic model then is based on the gas movable model of probability description, also has the scholar to incorporate this class model into microscopic approach.Macromodel then is that traffic flow is considered as the compressible continuous fluid medium that is comprised of a large amount of vehicles, the average behavior of research vehicle body, the not explicit appearance of the individual character of single unit vehicle.In the macroscopic traffic flow, traffic flow is regarded as the compressible continuous fluid medium that is comprised of a large amount of vehicles, the average behavior of research vehicle collective, and the individual character of single unit vehicle does not highlight.Macroscopic traffic flow is studied the equation that they satisfy with average density ρ, average velocity v and the flow q portrayal traffic flow of vehicle.

Compare with microvisual model, macromodel can be portrayed the collective behavior of traffic flow better, thereby for designing effective traffic control strategy, simulation and estimating that the traffic engineering problems such as effect of road geometry modification provide foundation.Numerical evaluation aspect, simulation Macro-traffic Flow required time studys with institute that number of vehicles has nothing to do in the traffic system, with research road, numerical method choose and the discrete steps Δ x of middle space x, time t relevant with Δ t.So macroscopic traffic flow is suitable for processing the traffic flow problem of the traffic system that a large amount of vehicles form.In macro traffic model, the continuous traffic flow model that Lighthill and Whitham propose has illustrative, Richards has also independently proposed similar model simultaneously, these two models are collectively referred to as the LWR model, the LWR model is with ρ (x, t) and v(x, t) expression t is positioned at average density and the average velocity of the traffic flow of x place, their satisfied following continuity equations constantly

$\frac{\∂\mathrm{\ρ}}{\∂t}+\frac{\∂\left(\mathrm{\ρv}\right)}{\∂x}=0$

Following formula represents the number of vehicles conservation.For making the equation sealing, supposed simultaneously the speed-density relation of balance

v(x,t)=V _e[ρ(x,t)]

Wherein, V _e[ρ (x, t)] is equilibrium rate.Can obtain following equation like this

$\frac{\∂\mathrm{\ρ}}{\∂t}+\left\{V_e[\mathrm{\ρ}(x,t)\right]+\mathrm{\ρ}\frac{\∂}{\∂\mathrm{\ρ}}{V}_e\left[\mathrm{\ρ}(x,t)\right]\}\frac{\∂\mathrm{\ρ}}{\∂x}=0$

As time goes on, it is the structure of shock wave that the nonlinear motion ripple of being described by following formula develops gradually, and wavefront is more and more precipitous, until vertical, this has just caused the discontinuous of traffic flow density; But the traffic density in the reality distributes so not extreme, in order to make to a certain extent the shock wave structure smoothing, adds dissipative term in following formula Purpose is the diffusional effect of reflection vehicle, makes nonlinear motion ripple continuous distribution, so model conversation is

$\frac{\∂\mathrm{\ρ}}{\∂t}+\left\{V_e\right[\mathrm{\ρ}(x,t)]+\mathrm{\ρ}\frac{\∂}{\∂\mathrm{\ρ}}{V}_e[\mathrm{\ρ}(x,t)\left]\right\}\frac{\∂\mathrm{\ρ}}{\∂x}=D\frac{{\∂}^2\mathrm{\ρ}}{\∂x^2}$

Wherein D is dissipation factor.

Suppose the equilibrium rate-density relationship just like lower linear

Model just be converted into famous Burgers equation (see document J.M.Burgers.A mathematical modelillustrating the theory of turbulence[J] .Advances inApplied Mechanics 1948,2:171-199)

$\frac{\∂R}{\∂t}+R\frac{\∂R}{\∂x}=D\frac{{\∂}^{2}R}{{\∂x}^2}$

Function in the formula

$R=v_m(1-\frac{2\mathrm{\ρ}}{{\mathrm{\ρ}}_m})$

Yet, the Burgers equation can not directly provide the traffic congestion condition, the integral body that particularly directly provides traffic jam issue when various traffic parameters change is described, so that the traffic system research worker is not easy to direct use, has the technical matters that is difficult to forecast traffic congestion.

Summary of the invention

Be difficult to the technological deficiency that directly monitoring is forecast to traffic congestion in order to overcome the Burgers traffic flow model, the invention provides a kind of based on the traffic congestion monitoring forecasting procedure with the dissipative term macroscopic traffic flow, the method obtains car speed, density and flow information by the video image of rig camera, to the traffic congestion that occurs is forecast, solved the technical matters that traffic congestion can not in time be forecast according to newly-established traffic congestion model.

The technical solution adopted for the present invention to solve the technical problems is: a kind of based on the traffic congestion monitoring forecasting procedure with the dissipative term macroscopic traffic flow, be characterized in adopting following steps:

When 1, passing through video image acquisition car speed, density and the flow information of rig camera, consider that the actual monitored video camera works at the 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 processing with hypograph:

min(e _z)=min{e _para{e _seg[e _pre(e _samp)]}}

In the formula, e _zBe the global error of image extraction traffic parameter, min (e _z) be the e that obtains by the image processing method of selecting various combination _zMinimum value, e _SampBe image sampling error, e _PreBe image and process errors, e _SegBe vehicles segmentation error in the image, e _ParaFor extract the error of traffic parameter according to split image;

2, set up the macroscopic traffic flow in given highway section

$\frac{\∂\mathrm{\η}}{\∂t}+v_m(\frac{2}{{\mathrm{\ρ}}_m\mathrm{\η}}-1)\frac{\∂\mathrm{\η}}{\∂x}=D[\frac{{\∂}^2\mathrm{\η}}{{\∂x}^2}-\frac{2}{\mathrm{\η}}{\left(\frac{\∂\mathrm{\η}}{\∂x}\right)}^2]$

In the formula, Be state variable, x is the position, and t is the time, and D is dissipation factor, and ρ is that average density, the v of vehicle is on average speed of vehicle, degree v _mBe maximal rate, ρ _mSaturated traffic density during for traffic congestion;

3, when state variable η is tending towards infinite in time, this highway section will be tending towards obstruction density and produce traffic congestion, and the traffic forecast of more blocking up is sent in this highway section;

4, adopt the restriction of discontinuity Induction Control to sail sending a car of this highway section into.

The invention has the beneficial effects as follows: do all image method as a whole, composition error performance index according to the overall process of processing with hypograph are selected image processing algorithm, and to the traffic congestion that occurs is forecast, solved the technical matters that traffic congestion can not in time be forecast according to newly-established traffic congestion model.

Below in conjunction with embodiment the present invention is elaborated.

Embodiment

When 1, passing through video image acquisition car speed, density and the flow information of rig camera, consider that the actual monitored video camera works at the 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 processing with hypograph:

min(e _z)=min{e _para{e _seg[e _pre(e _samp)]}}

=k _sampk _prek _sege _para

In the formula, e _zBe the global error of image extraction traffic parameter, min (e _z) be the e that obtains by the image processing method of selecting various combination _zMinimum value, e _SampBe image sampling error, e _PreBe image and process errors, e _SegBe vehicles segmentation error in the image, e _ParaFor extract the error of traffic parameter, k according to split image _Samp1 be the image sampling error coefficient, k _Pre1 be image and process errors coefficient, k _Seg〉=1 is the error coefficient of vehicles segmentation error in the image;

2, set up the macroscopic traffic flow in given highway section

$\frac{\∂\mathrm{\η}}{\∂t}+v_m(\frac{2}{{\mathrm{\ρ}}_m\mathrm{\η}}-1)\frac{\∂\mathrm{\η}}{\∂x}=D[\frac{{\∂}^2\mathrm{\η}}{{\∂x}^2}-\frac{2}{\mathrm{\η}}{\left(\frac{\∂\mathrm{\η}}{\∂x}\right)}^2]$

In the formula,

Be state variable, x is the position, and t is the time, and D is dissipation factor, and ρ is that average density, the v of vehicle is on average speed of vehicle, v _mBe maximal rate, ρ _mSaturated traffic density during for traffic congestion;

3, when state variable η is tending towards infinite in time, this highway section will be tending towards obstruction density and produce traffic congestion, and the traffic forecast of more blocking up is sent in this highway section;

4, adopt the restriction of discontinuity Induction Control to sail sending a car of this highway section into.

Claims (1)

1. one kind based on the traffic congestion of dissipative term macroscopic traffic flow monitoring forecasting procedure, is characterized in adopting following steps:

When (1) passing through video image acquisition car speed, density and the flow information of rig camera, consider that the actual monitored video camera works at the 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 processing with hypograph:

min(e _z)=min{e _para{e _seg[e _pre(e _samp)]}}

In the formula, e _zBe the global error of image extraction traffic parameter, min (e _z) be the e that obtains by the image processing method of selecting various combination _zMinimum value, e _SampBe image sampling error, e _PreBe image and process errors, e _SegBe vehicles segmentation error in the image, e _ParaFor extract the error of traffic parameter according to split image;

(2) set up the macroscopic traffic flow in given highway section

$\frac{\∂\mathrm{\η}}{\∂t}+v_m(\frac{2}{{\mathrm{\ρ}}_m\mathrm{\η}}-1)\frac{\∂\mathrm{\η}}{\∂x}=D[\frac{{\∂}^2\mathrm{\η}}{{\∂x}^2}-\frac{2}{\mathrm{\η}}{\left(\frac{\∂\mathrm{\η}}{\∂x}\right)}^2]$

In the formula,

Be state variable, x is the position, and t is the time, and D is dissipation factor, and ρ is that average density, the v of vehicle is on average speed of vehicle, degree v _mBe maximal rate, ρ _mSaturated traffic density during for traffic congestion;

(3) when state variable η is tending towards infinite in time, this highway section will be tending towards obstruction density and produce traffic congestion, and the traffic forecast of more blocking up is sent in this highway section;

(4) adopt the restriction of discontinuity Induction Control to sail sending a car of this highway section into.