CN105448080A - Modeling method considering influence of sub-adjacent vehicles to traffic flow time lag car-following model stability - Google Patents

Abstract

The invention discloses a modeling method considering the influence of sub-adjacent vehicles to traffic flow time lag car-following model stability. The modeling method includes that a microscopic traffic flow model DOVM (as shown in the description) including reaction time lag is established; the influence of sub-adjacent vehicles to traffic flow is considered, and an optimal speed function V(Deltax<n>, Deltax<n>+1)=(1-p) U (Deltax<n>) + pU (Deltax<n>+1) is selected, the optimal speed function is obtained through measured data fitting, wherein 0<=p<1/2, and an influencing factor representing the sub-adjacent vehicles refers to U(Deltax<n>)=16.8[tanh0.0860(Deltax<n>-25)+0.913]; a new traffic flow model GDOVM is established and stability analysis is carried out, the optimal speed function is put into the microscopic traffic flow model DOVM including reaction time lag and the new traffic flow model GDOVM is obtained, and according to the critical stability conditions, the influence of the reaction time lag and the sub-adjacent vehicle influencing factors to the system stability area can be obtained.

Description

Consider that time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method

Technical field

The application relates to traffic flow control method technical field, and specifically, relating to a kind of consideration time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method.

Background technology

Traffic is the advance of the modernization of the lifeblood of national economy, traffic and transportation system, traffic administration, is one of important symbol of a measurement modernization of the country.Along with expanding economy and scientific and technological progress, the transportation of high speed development can not only promote the interflow of commodities and people's contact, shortens the travel time, increases work efficiency, also make significant contribution to socio-economic development simultaneously.Such as, but the high speed development of traffic also brings many problems: traffic congestion, traffic hazard, environmental pollution etc., to work and the puzzlement all caused in various degree of living of people.Therefore, the hot issue that rational solution transport solution problem becomes people's extensive concern is proposed.

Traffic flow theory be research traffic flow in time with model and the method for Spatial Variation.Its goal in research sets up the mathematical model that correctly can describe actual traffic general characteristic, and through parameter identification and Computer Numerical Simulation, disclose the feature essence of various traffic behavior, seek the basic law controlling traffic flow, and provide reliable theoretical foundation for the planning of traffic engineering department and design, finally reach and the object controlled in real time is carried out to traffic system.Traffic flow model is roughly divided into three classes: microvisual model, mesoscopic model and macromodel.Dissimilar traffic flow model respectively has superiority when describing actual traffic, there is again certain contact.Therefore, in Traffic Flow Modeling, also there is not the traffic system of pervasive model to complexity so far and describe uniformly.

Traffic flow following-speed model is the microcosmic traffic flow model of a quasi-representative, and it is described on the single track that cannot overtake other vehicles, and when vehicle queue travels, rear car follows the transport condition of front truck, and is illustrated with mathematical model analyze.Vehicle following-model understands the characteristic of Single-Lane Traffic one by one with the mode of speeding by observing each vehicle.The research of this characteristic can be used for describing the stability of traffic flow; Detect highway to get on the car the characteristic of fleet; Inspection management technology and the communication technology, make rear-end collision reduce to bottom line; In addition, can also be used for analyzing, calculating road passage capability.In following-speed model, Following Car drivers ' behavior is subject to several factors impact, mainly comes from the factor such as personal feature of the actual design of road, the mechanical property of vehicle and driver self.Wherein the personal feature of driver uses reaction time lag parameter to represent in mathematical model.In time lag vehicle following-model, document " Analysisofoptimalvelocitymodelwithexplicitdelay, Phys.Rev.E., 1998,58,5429-5435. " discloses a kind of time lag optimal speed model (hereinafter referred to as DOVM)

${\stackrel{\&CenterDot;\&CenterDot;}{x}}_n(t+\mathrm{\&tau;})=a\{U\left({\mathrm{\&Delta;x}}_n\right(t\left)\right)-{\stackrel{\&CenterDot;}{x}}_n\left(t\right)\}$

In formula, x _n(t) be n-th car in the position of moment t, be the speed of n-th car at moment t, be the acceleration of n-th car at moment t, Δ x _n(t)=x _n+1(t)-x _nt () represents the space headway between continuous print two cars, a is sensitivity coefficient, and τ is that reaction time lag comprises the reaction time lag of driver and mechanical time lag, U (Δ x _n) meet following two basic characteristics for optimal speed function: (1) U (Δ x _n) be monotonically increasing function, i.e. U'(Δ x _n)>=0; (2) | U (Δ x _n) | there is the upper bound.This model solves unlimited acceleration problem, can simulate many qualitative features of actual traffic stream, as traffic unstability, obstruction develop, loiter.And give the critical time lag that is brought out traffic congestion, point out when reacted stagnant when being greater than critical value traffic congestion occur.

Although above-mentioned model indicates that reaction time lag has important impact to vehicle follow gallop behavior, as: reaction time lag may cause system unstability and produce bifurcation, that is, above-mentioned prior art provide only a kind of model, how resolution system unstability can not be known by above-mentioned technology and produce the problem of bifurcation, therefore, above-mentioned technology does not provide suitable scheme and revises the harmful effect that reaction time lag causes, and thus makes model have certain limitation in actual applications.In addition, even if the prior art mentioned in background technology is combined with other prior aries, also do not go out corresponding scheme and solve system unstability and produce the problem of bifurcation.

Summary of the invention

In view of this, technical problems to be solved in this application there is provided a kind of consideration time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method, the harmful effect that in time lag optimal speed model, reaction time lag causes can be made up, thus can describe better and traffic conditions that is virtually reality like reality.

In order to solve the problems of the technologies described above, the application has following technical scheme:

A kind of consideration time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method, it is characterized in that, comprising:

Set up the microcosmic traffic flow model DOVM containing reaction time lag:

${\stackrel{\&CenterDot;\&CenterDot;}{x}}_n(t+\mathrm{\&tau;})=a\left(V\right({\mathrm{\&Delta;x}}_n\left(t\right),{\mathrm{\&Delta;x}}_{n+1}\left(t\right))-{\stackrel{\&CenterDot;}{x}}_n(t\left)\right),$

Wherein, x _n(t) be n-th car in the position of moment t, be the speed of n-th car at moment t, be the acceleration of n-th car at moment t, Δ x _n(t)=x _n+1(t)-x _nt () represents the space headway between continuous print two cars, a is sensitivity coefficient, and τ is reaction time lag, comprises the reaction time lag of driver and mechanical time lag, V (Δ x _n(t), Δ x _n+1(t)) be depend on adjacent vehicle space headway Δ x _n(t) and time adjacent vehicle space headway Δ x _n+1the optimal speed function of (t);

Consider that time adjacent vehicle is on the impact of traffic flow, chooses optimal speed function:

V(Δx _n,Δx _n+1)＝(1-p)U(Δx _n)+pU(Δx _n+1)

Described optimal speed function is obtained by measured data matching, and wherein, 0≤p<1/2, represents the factor of influence of time adjacent vehicle, U (Δ x _n)=16.8 [tanh0.0860 (Δ x _n-25)+0.913];

Set up new traffic flow model GDOVM, line stabilization analysis of going forward side by side:

Optimal speed function is substituted into the microcosmic traffic flow model DOVM containing reaction time lag, obtains new traffic flow model GDOVM:

$\begin{array}{c}{\stackrel{\&CenterDot;\&CenterDot;}{x}}_n(t+\mathrm{\&tau;})=a\{(1-p)\&CenterDot;(16.8\&lsqb;\tanh 0.0860({\mathrm{\&Delta;x}}_n-25)+0.913\&rsqb;)\\ +p\&CenterDot;(16.8\&lsqb;\tanh 0.0860({\mathrm{\&Delta;x}}_n-25)+0.913\&rsqb;)-{\stackrel{\&CenterDot;}{x}}_n\left(t\right)\}\end{array}$

The lienarized equation that described new traffic flow model GDOVM is corresponding is:

${\stackrel{\&CenterDot;\&CenterDot;}{y}}_n(t+\mathrm{\&tau;})=a\&lsqb;(1-p){\mathrm{f\&Delta;y}}_n\left(t\right)+{\mathrm{pf\&Delta;y}}_{n+1}\left(t\right)-{\stackrel{\&CenterDot;}{y}}_n\left(t\right)\&rsqb;$

Wherein, y _nt () is the disturbance that n-th car is subject to, f=U ' (b), Δ y _n(t)=y _n+1(t)-y _nt (), if the solution of lienarized equation is y _n,j(t)=exp (i α _jn+i ω _jt), α _j=2 π j/N (j=1,2,3 ..., N), ω _jmeet following condition:

$-{\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)}^2\exp \&lsqb;i\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)a\mathrm{\&tau;}\&rsqb;=\frac{f}{a}\left(\exp \right({\mathrm{i\&alpha;}}_j)-1)\&lsqb;1+p\left(\exp \right({\mathrm{i\&alpha;}}_j)-1)\&rsqb;-i\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)$

Reaction time lag is obtained with time adjacent vehicle factor of influence to the impact in system stability region according to marginal stability condition.

Preferably, wherein, describedly obtain reaction time lag and time adjacent vehicle factor of influence to the impact in system stability region according to marginal stability condition, be further:

Make a=1.0, Im ω=0, obtain the critical curve of linear stable at (f/a, α) polar coordinate plane, when parameter drop on annular region that critical curve surrounds inner time, system linear is stablized, otherwise system is unstable;

Along with the increase of reaction time lag, described annular region internal area reduces, and system linearity stability region reduces;

Along with the increase of secondary adjacent vehicle factor of influence, described annular region internal area increases, and system linearity stability region increases.

Preferably, wherein, according to the new traffic flow model GDOVM set up, selecting system parameter is:

Circumferential highway length L=2500 rice, vehicle number N=100, initial disturbance is the even stochastic distribution on [-2,2];

The situation that the space headway of traffic flow model GDOVM new under verifying initial action of small disturbance and velocity distribution change with sensitivity coefficient and time adjacent vehicle factor of influence.

Preferably, wherein, comprise further: under verifying initial action of small disturbance, space headway, the velocity distribution of new traffic flow model GDOVM and microcosmic time lag traffic flow model DOVM.

Compared with prior art, the method described in the application, reaches following effect:

First, according to the microcosmic traffic flow model GDOVM of the modeling method foundation that the present invention proposes, consider that time adjacent vehicle is on the impact of traffic flow, time adjacent vehicle factor of influence is introduced when choosing optimal speed function, and analyze reaction time lag with time adjacent vehicle impact therefore on the impact of system stability according to marginal stability conditions correlation, result shows in modeling process, to introduce time adjacent vehicle factor of influence to optimal speed function, effectively can make up the harmful effect that the reaction time lag due to driver causes system, the stability of system is strengthened.

Second, according to the microcosmic traffic flow model GDOVM of the modeling method foundation that the present invention proposes, when there is labile factor, the significantly vibration of vehicle headstock distance and speed can not be caused, vehicle stream is even wagon flow, the evolutionary process of vehicle-state there will not be and stops and goes, produce irrational phenomenons such as fork, compare conventional traffic flow model DOVM, the new microcosmic traffic flow model GDOVM that the present invention sets up has larger improvement in stability, there is good stability, thus can traffic conditions that is virtually reality like reality better, it can be traffic control, decision-making provides theoretical foundation.

Accompanying drawing explanation

Accompanying drawing described herein is used to provide further understanding of the present application, and form a application's part, the schematic description and description of the application, for explaining the application, does not form the improper restriction to the application.In the accompanying drawings:

Fig. 1 considers in the present invention that time adjacent vehicle affects the process flow diagram of traffic flow time lag following-speed model Stability Modeling method;

Fig. 2 is the figure that in the present invention, system linearity stabilized zone changes with reaction time lag;

Fig. 3 is the figure that in the present invention, system linearity stabilized zone changes with secondary adjacent vehicle factor of influence;

Fig. 4 is time adjacent vehicle factor of influence p=0.1 in the present invention, during reaction time lag τ=0.28, and the figure that headstock distance-speed phasor changes with sensitivity coefficient;

Fig. 5 is reaction time lag τ=0.28 in the present invention, during sensitivity coefficient a=2.0, and the figure that headstock distance-speed phasor changes with secondary adjacent vehicle factor of influence;

Fig. 6 is time adjacent vehicle factor of influence p=0.1 in the present invention, during reaction time lag τ=0.28, and the figure that the velocity distribution of new traffic flow model GDOVM changes with sensitivity coefficient;

Fig. 7 is sensitivity coefficient a=2.0 in the present invention, during reaction time lag τ=0.28, and the figure that the velocity distribution of new traffic flow model GDOVM changes with secondary adjacent vehicle factor of influence;

Fig. 8 is sensitivity coefficient a=2.0 in the present invention, and during reaction time lag τ=0.28, the headstock of new traffic flow model GDOVM and microcosmic time lag traffic flow model DOVM is apart from the comparison diagram distributed;

Fig. 9 is sensitivity coefficient a=2.0 in the present invention, during reaction time lag τ=0.28, and the comparison diagram of the velocity distribution of new traffic flow model GDOVM and microcosmic time lag traffic flow model DOVM.

Embodiment

As employed some vocabulary to censure specific components in the middle of instructions and claim.Those skilled in the art should understand, and hardware manufacturer may call same assembly with different noun.This specification and claims are not used as with the difference of title the mode distinguishing assembly, but are used as the criterion of differentiation with assembly difference functionally." comprising " as mentioned in the middle of instructions and claim is in the whole text an open language, therefore should be construed to " comprise but be not limited to "." roughly " refer to that in receivable error range, those skilled in the art can solve the technical problem within the scope of certain error, reach described technique effect substantially.In addition, " couple " word and comprise directly any and indirectly electric property coupling means at this.Therefore, if describe a first device in literary composition to be coupled to one second device, then represent described first device and directly can be electrically coupled to described second device, or be indirectly electrically coupled to described second device by other devices or the means that couple.Instructions subsequent descriptions is implement the better embodiment of the application, and right described description is for the purpose of the rule that the application is described, and is not used to the scope limiting the application.The protection domain of the application is when being as the criterion depending on the claims person of defining.

Embodiment 1

Shown in Figure 1ly consider for a kind of described in the application the specific embodiment that time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method, described in the present embodiment, method comprises the following steps:

Step 101, foundation contain the microcosmic traffic flow model DOVM of reaction time lag:

${\stackrel{\&CenterDot;\&CenterDot;}{x}}_n(t+\mathrm{\&tau;})=a\left(V\right({\mathrm{\&Delta;x}}_n\left(t\right),{\mathrm{\&Delta;x}}_{n+1}\left(t\right))-{\stackrel{\&CenterDot;}{x}}_n(t\left)\right),$

Wherein, x _n(t) be n-th car in the position of moment t, be the speed of n-th car at moment t, be the acceleration of n-th car at moment t, Δ x _n(t)=x _n+1(t)-x _nt () represents the space headway between continuous print two cars, a is sensitivity coefficient, and τ is reaction time lag, comprises the reaction time lag of driver and mechanical time lag, V (Δ x _n(t), Δ x _n+1(t)) be depend on adjacent vehicle space headway Δ x _n(t) and time adjacent vehicle space headway Δ x _n+1the optimal speed function of (t);

Step 102, consideration time adjacent vehicle, on the impact of traffic flow, choose optimal speed function:

V(Δx _n,Δx _n+1)＝(1-p)U(Δx _n)+pU(Δx _n+1)

Described optimal speed function is obtained by measured data matching, and wherein, 0≤p<1/2, represents the factor of influence of time adjacent vehicle, U (Δ x _n)=16.8 [tanh0.0860 (Δ x _n-25)+0.913];

Step 103, set up new traffic flow model GDOVM, line stabilization analysis of going forward side by side:

Optimal speed function is substituted into the microcosmic traffic flow model DOVM containing reaction time lag, obtains new traffic flow model GDOVM:

$\begin{array}{c}{\stackrel{\&CenterDot;\&CenterDot;}{x}}_n(t+\mathrm{\&tau;})=a\{(1-p)\&CenterDot;(16.8\&lsqb;\tanh 0.0860({\mathrm{\&Delta;x}}_n-25)+0.913\&rsqb;)\\ +p\&CenterDot;(16.8\&lsqb;\tanh 0.0860({\mathrm{\&Delta;x}}_n-25)+0.913\&rsqb;)-{\stackrel{\&CenterDot;}{x}}_n\left(t\right)\}\end{array}$

The lienarized equation that described new traffic flow model GDOVM is corresponding is:

${\stackrel{\&CenterDot;\&CenterDot;}{y}}_n(t+\mathrm{\&tau;})=a\&lsqb;(1-p){\mathrm{f\&Delta;y}}_n\left(t\right)+{\mathrm{pf\&Delta;y}}_{n+1}\left(t\right)-{\stackrel{\&CenterDot;}{y}}_n\left(t\right)\&rsqb;$

Wherein, y _nt () is the disturbance that n-th car is subject to, f=U ' (b), Δ y _n(t)=y _n+1(t)-y _nt (), if the solution of lienarized equation is y _n,j(t)=exp (i α _jn+i ω _jt), α _j=2 π j/N (j=1,2,3 ..., N), ω _jmeet following condition:

$-{\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)}^2\exp \&lsqb;i\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)a\mathrm{\&tau;}\&rsqb;=\frac{f}{a}\left(\exp \right({\mathrm{i\&alpha;}}_j)-1)\&lsqb;1+p\left(\exp \right({\mathrm{i\&alpha;}}_j)-1)\&rsqb;-i\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)$

Reaction time lag is obtained with time adjacent vehicle factor of influence to the impact in system stability region according to marginal stability condition.

The stability dependency of time lag traffic flow model is in the size of reaction time lag, and reaction time lag may cause system unstability and produce bifurcation.Therefore, the present invention is by choosing suitable optimal speed function and considering the harmful effect that the impact of time adjacent vehicle makes up reaction time lag and brings.

In above-mentioned steps 103, describedly obtain reaction time lag and time adjacent vehicle factor of influence to the impact in system stability region according to marginal stability condition, be further: make a=1.0, Im ω=0, at (f/a, α) polar coordinate plane obtains the critical curve of linear stable, if for all mould α _jthere is Im ω _j>0, then system is linear stable.When parameter drop on annular region that critical curve surrounds inner time, system linear is stablized, otherwise system is unstable; As seen from Figure 2, along with the increase (reaction time lag from 0.0 to 0.2 again to 0.4) of reaction time lag, described annular region internal area reduces gradually, thus system linearity stability region reduces, that is, along with the increase of reaction time lag, the stability of system weakens, the stability of reaction time lag size influential system is described, traffic flow under initial disturbance can be impelled to stop and go the formation of phenomenon.As seen from Figure 3, along with the increase (secondary adjacent vehicle factor of influence from 0 to 0.1 again to 0.2) of secondary adjacent vehicle factor of influence, described annular region internal area increases gradually, system linearity stability region increases, that is, along with the increase of secondary adjacent vehicle factor of influence, the stability of system strengthens, illustrate and consider that time probability of adjacent vehicle impact is larger, the possibility that traffic flow gets congestion is less.Therefore, by introducing time adjacent vehicle factor of influence to optimal speed function in modeling process, the harmful effect that reaction time lag causes system can be made up.

According to the new traffic flow model GDOVM set up, selecting system parameter is: circumferential highway length L=2500 rice, vehicle number N=100, and initial disturbance is the even stochastic distribution on [-2,2]; The situation that the velocity distribution of traffic flow model GDOVM new under verifying initial action of small disturbance changes with sensitivity coefficient.Fig. 4 is τ=0.28, during p=0.1, the situation that traffic flow model GDOVM velocity distribution new under initial action of small disturbance changes with sensitivity coefficient, as seen from Figure 4, when sensitivity coefficient is 2.2, the interference of initial disturbance to system be less than initial disturbance when sensitivity coefficient is 2.0 to the interference of system (, slow ring corresponding when slow ring corresponding during a=2.2 is less than a=2.0), and sensitivity coefficient is when being 2.4, initial disturbance to be less than again when sensitivity coefficient is 2.2 initial disturbance to the interference of system to the interference of system, when sensitivity coefficient is 2.6, initial disturbance to be less than again when sensitivity coefficient is 2.4 initial disturbance to the interference of system to the interference of system, therefore as shown in Figure 4, along with the increase of sensitivity coefficient, the interference of initial disturbance to system is less, the stability of system is stronger, this illustrates that sensitivity coefficient is just in time contrary on the impact of model GDOVM with reaction time lag.

Fig. 5 is τ=0.28, during a=2.0, the situation that traffic flow model GDOVM velocity distribution new under initial action of small disturbance changes with secondary adjacent vehicle factor of influence, as seen from Figure 5, when secondary adjacent vehicle factor of influence is 0.1, the interference of initial disturbance to system is less than initial disturbance when time adjacent vehicle factor of influence is 0.0 to the interference of system (that is, slow ring corresponding when corresponding during p=0.1 slow ring is less than p=0.0), and during p=0.0, model GDOVM deteriorates to model DOVM; And secondary adjacent vehicle factor of influence is when being 0.2, initial disturbance to be less than again when time adjacent vehicle factor of influence is 0.1 initial disturbance to the interference of system to the interference of system, therefore as shown in Figure 5, along with the increase of secondary adjacent vehicle factor of influence, the interference of initial disturbance to system is less, the stability of system is stronger, and this illustrates that new model GDOVM is more stable than model DOVM.

The present invention further comprises: under verifying initial action of small disturbance, and new traffic flow model GDOVM and the headstock distance of microcosmic traffic flow model DOVM and velocity distribution are with the situation of sensitivity coefficient and time adjacent vehicle factor of influence change.Fig. 6 is τ=0.28, p=0.1, a=2.0,2.2, and when 2.4, the velocity distribution situation of traffic flow model GDOVM new under initial action of small disturbance.As shown in Figure 6, under this group parameter choose, work as a=2.0, when 2.2, initial microvariations result in the generation of the phenomenon that stops and goes; And as a=2.4, the phenomenon that stops and goes is eased, vehicle stream is even wagon flow.This illustrates increases the stability that sensitivity coefficient can improve model GDOVM.Fig. 7 is τ=0.28, a=2.0, p=0.0,0.1, and when 0.3, the velocity distribution situation of traffic flow model GDOVM new under initial action of small disturbance.As shown in Figure 7, under this group parameter choose, as p=0.0, new traffic flow model GDOVM deteriorates to conventional traffic flow model DOVM, and now initial microvariations result in the generation of the phenomenon that stops and goes, and blocking up appears in traffic flow; As p=0.1, initial microvariations cause the vibration of speed to reduce, and the phenomenon that stops and goes is alleviated to some extent; As p=0.3, the phenomenon that stops and goes is inhibited, and traffic flow is even wagon flow.This illustrates considers that time adjacent vehicle impact can improve the stability of traffic flow model, suppresses traffic congestion, also illustrates that new traffic flow model GDOVM is better than the stability of conventional traffic flow model DOVM, can portray traffic characteristics better.

Fig. 8 and Fig. 9 is respectively τ=0.28, when p=0.2, a=2.0, and the headstock distance of traffic flow model GDOVM new under initial action of small disturbance and microcosmic traffic flow model DOVM (conventional traffic flow model) and velocity distribution situation.From Fig. 8 and Fig. 9, in conventional traffic flow model DOVM, initial microvariations cause the significantly vibration of vehicle headstock distance and speed, create the phenomenon that stops and goes; And in new traffic flow model GDOVM, initial microvariations can not cause the significantly vibration of vehicle headstock distance and speed, vehicle stream is even wagon flow, i.e. when there is labile factor (as: opening and closing etc. of traffic hazard, traffic lights, traffic circle mouth), in new traffic flow model GDOVM there is not irrational result in the evolutionary process of vehicle-state.This illustrates that new traffic flow model GDOVM compares conventional traffic flow model DOVM and is improved in stability, can the real traffic conditions of simulate.

Consideration provided by the invention time adjacent vehicle affects in traffic flow time lag following-speed model Stability Modeling method, the foundation of function, choosing of parameters numerical value, the method of sampling of numerical value, the setting model of numerical value and the conclusion finally drawn are all summed up by unlimited test and test to draw, the present invention is by limiting the numerical value in model, introduce suitable secondary adjacent vehicle factor of influence, the final harmful effect that effectively compensate for reaction time lag and system is caused, solve the problem that the harmful effect that cannot cause reaction time lag in prior art is revised.Even if disclose similar model in prior art, because the sampling condition of its numerical value and setting model all do not set, the content that those skilled in the art use for reference prior art can not solve technical matters solved by the invention by the test of limited number of time, i.e. how efficiently and effectively resolution system unstability and produce the problem of bifurcation.

Known by above each embodiment, the beneficial effect that the application exists is:

First, according to the microcosmic traffic flow model GDOVM of the modeling method foundation that the present invention proposes, consider that time adjacent vehicle is on the impact of traffic flow, time adjacent vehicle factor of influence is introduced when choosing optimal speed function, and analyze reaction time lag with time adjacent vehicle factor of influence to the impact of system stability according to marginal stability conditions correlation, result shows, time adjacent vehicle factor of influence is introduced to optimal speed function in modeling process, effectively can make up the harmful effect that the reaction time lag due to driver causes system, the stability of system is strengthened.

Second, according to the microcosmic traffic flow model GDOVM of the modeling method foundation that the present invention proposes, when there is labile factor, the significantly vibration of vehicle headstock distance and speed can not be caused, vehicle stream is even wagon flow, the evolutionary process of vehicle-state there will not be and stops and goes, produce irrational phenomenons such as fork, compare conventional traffic flow model DOVM, the new microcosmic traffic flow model GDOVM that the present invention sets up has larger improvement in stability, there is good stability, thus can traffic conditions that is virtually reality like reality better, it can be traffic control, decision-making provides theoretical foundation.

Those skilled in the art should understand, the embodiment of the application can be provided as method, device or computer program.Therefore, the application can adopt the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the application can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to magnetic disk memory, CD-ROM, optical memory etc.) of computer usable program code.

Above-mentioned explanation illustrate and describes some preferred embodiments of the application, but as previously mentioned, be to be understood that the application is not limited to the form disclosed by this paper, should not regard the eliminating to other embodiments as, and can be used for other combinations various, amendment and environment, and can in invention contemplated scope described herein, changed by the technology of above-mentioned instruction or association area or knowledge.And the change that those skilled in the art carry out and change do not depart from the spirit and scope of the application, then all should in the protection domain of the application's claims.

Claims (4)

1. consider that time adjacent vehicle affects a traffic flow time lag following-speed model Stability Modeling method, is characterized in that, comprising:

Set up the microcosmic traffic flow model DOVM containing reaction time lag:

${\stackrel{\&CenterDot;\&CenterDot;}{x}}_n(t+\mathrm{\&tau;})=a\left(V\right({\mathrm{\&Delta;x}}_n\left(t\right),{\mathrm{\&Delta;x}}_{n+1}\left(t\right))-{\stackrel{\&CenterDot;}{x}}_n(t\left)\right),$

Wherein, x _n(t) be n-th car in the position of moment t, be the speed of n-th car at moment t, be the acceleration of n-th car at moment t, Δ x _n(t)=x _n+1(t)-x _nt () represents the space headway between continuous print two cars, a is sensitivity coefficient, and τ is reaction time lag, comprises the reaction time lag of driver and mechanical time lag, V (Δ x _n(t), Δ x _n+1(t)) be depend on adjacent vehicle space headway Δ x _n(t) and time adjacent vehicle space headway Δ x _n+1the optimal speed function of (t);

Consider that time adjacent vehicle is on the impact of traffic flow, chooses optimal speed function:

V(Δx _n,Δx _n+1)＝(1-p)U(Δx _n)+pU(Δx _n+1)

Described optimal speed function is obtained by measured data matching, and wherein, 0≤p<1/2, represents the factor of influence of time adjacent vehicle, U (Δ x _n)=16.8 [tanh0.0860 (Δ x _n-25)+0.913];

Set up new traffic flow model GDOVM, line stabilization analysis of going forward side by side:

Optimal speed function is substituted into the microcosmic traffic flow model DOVM containing reaction time lag, obtains new traffic flow model GDOVM:

$\begin{array}{c}{\stackrel{\&CenterDot;\&CenterDot;}{x}}_n\left(t+\mathrm{\&tau;}\right)=a\{\left(1-p\right)\&CenterDot;\left(16.8\&lsqb;\tanh 0.0860\left({\mathrm{\&Delta;x}}_n-25\right)+0.913\&rsqb;\right)\\ +p\&CenterDot;\left(16.8\&lsqb;\tanh 0.0860\left({\mathrm{\&Delta;x}}_{n+1}-25\right)+0.913\&rsqb;\right)-{\stackrel{\&CenterDot;}{x}}_n\left(t\right)\}\end{array}$

The lienarized equation that described new traffic flow model GDOVM is corresponding is:

${\stackrel{\&CenterDot;\&CenterDot;}{y}}_n(t+\mathrm{\&tau;})=a\&lsqb;(1-p){\mathrm{f\&Delta;y}}_n\left(t\right)+{\mathrm{pf\&Delta;y}}_{n+1}\left(t\right)-{\stackrel{\&CenterDot;}{y}}_n\left(t\right)\&rsqb;$

Wherein, y _nt () is the disturbance that n-th car is subject to, f=U ' (b), Δ y _n(t)=y _n+1(t)-y _nt (), if the solution of lienarized equation is y _n,j(t)=exp (i α _jn+i ω _jt), α _j=2 π j/N (j=1,2,3 ..., N), ω _jmeet following condition:

$-{\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)}^2\exp \&lsqb;i\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)a\mathrm{\&tau;}\&rsqb;=\frac{f}{a}\left(\exp \right({\mathrm{i\&alpha;}}_j)-1)\&lsqb;1+p\left(\exp \right({\mathrm{i\&alpha;}}_j)-1)\&rsqb;-i\left(\frac{{\mathrm{\&omega;}}_j}{a}\right)$

Reaction time lag is obtained with time adjacent vehicle factor of influence to the impact in system stability region according to marginal stability condition.

2. consider that time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method, is characterized in that according to claim 1,

Describedly obtain reaction time lag and time adjacent vehicle factor of influence to the impact in system stability region according to marginal stability condition, be further:

Make a=1.0, Im ω=0, obtain the critical curve of linear stable at (f/a, α) polar coordinate plane, when parameter drop on annular region that critical curve surrounds inner time, system linear is stablized, otherwise system is unstable;

Along with the increase of reaction time lag, described annular region internal area reduces, and system linearity stability region reduces;

Along with the increase of secondary adjacent vehicle factor of influence, described annular region internal area increases, and system linearity stability region increases.

3. consider that time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method, is characterized in that, comprises further according to claim 1:

According to the new traffic flow model GDOVM set up, selecting system parameter is:

Circumferential highway length L=2500 rice, vehicle number N=100, initial disturbance is the even stochastic distribution on [-2,2];

The situation that the space headway of traffic flow model GDOVM new under verifying initial action of small disturbance and velocity distribution change with sensitivity coefficient and time adjacent vehicle factor of influence.

4. consider that time adjacent vehicle affects traffic flow time lag following-speed model Stability Modeling method, is characterized in that, comprises further according to claim 3:

Under verifying initial action of small disturbance, space headway, the velocity distribution of new traffic flow model GDOVM and microcosmic time lag traffic flow model DOVM.