CN105243431A - Traffic flow evacuation time estimation method - Google Patents

Abstract

The invention discloses a traffic flow evacuation time estimation method, which mainly solves problems that discreteness of an evacuation vehicle scale and randomness of road section non-evacuation flow are not fully considered when the evacuation time is estimated in the prior art. The method of the technical scheme comprises steps: firstly, a current road condition is judged through congestion detection algorithm, when the road is judged to be in a congestion state, an evacuation strategy selection model is built according to the current road traffic flow, and the objective function of the model uses the shortest evacuation time as a principle and uses basic requirements that the traffic flow in each branch in a road network needs to be balanced as constraint conditions; and then, ant colony algorithm is used for solving the model, and the shortest evacuation time and the longest evacuation time for the current congestion state are obtained. The method of the invention can accurately estimate the needed time for evacuating vehicles when traffic congestion happens, effective information is provided for vehicles yet to come, a theoretical basis is provided for correct traffic commanding by a traffic management department, harms caused by emergent events are reduced to the minimal as much as possible, and the method can be used for traffic management for the urban road network.

Description

Traffic flow evacuation time method of estimation

Technical field

The invention belongs to technical field of transportation, particularly a kind of traffic flow evacuation time method of estimation, can be used in city road network.

Background technology

Along with city road network scale constantly increase, traffic trip and the odjective cause such as logistics demand constantly increases, disaster event takes place frequently, quantity, the occurrence frequency of road traffic accident, affect range and the extent of injury all in obvious ascendant trend.In urban transportation, once often cause the vehicle queue in this section after having an accident, occur traffic jam, add the difficulty that transportation industry is implemented social management and safeguarded stability, the maximization hindering road traffic Social benefit and economic benefit plays.Therefore, when road generation traffic jam, correct estimating vehicle evacuates required time, forewarning function can not only be provided to the vehicle do not arrived, so that whether it selects to continue to advance to this congested road by respective different demand, and correctly command driving to offer theoretical foundation for vehicle supervision department, harm accident brought drops to minimum as far as possible.

At present, about the prior art estimating the relief of traffic stream time, mainly contain following several:

The people such as Kong Huihui in " estimation of the queue length that traffic hazard causes and resolution time " (railway transportation with economic) after proposition traffic flow wave theory analysis generation traffic hazard section to get on the bus the formation of queuing up and an evanishment, derive the formula of queue length and resolution time, and illustrated by example and shunt the traffic capacity that can improve road in advance according to the estimated value of evacuation time.Although this method uses extensively in estimation evacuation time, because queue length is not unalterable, can constantly have subsequent vehicle to arrive, not consider in literary composition, therefore institute's evaluation time lacks accuracy.

The people such as Yuan Yuan consider evacuation time and route complexity factor in " considering the emergency evacuation Bi-objective path Choice Model of route complexity " (Operations research and mamagement science), establish the double-goal optimal model of emergency evacuation routing.Model, using the shortest for total evacuation time and route complexity is minimum as optimization aim, considers that disaster diffusion couple evacuates the Real Time Effect of network traffic situation, is expressed as continuous decreasing function in time by the passage rate in each segmental arc simultaneously.Devise the ant colony optimization algorithm of solving model, simulation result indicates validity and the feasibility of model and algorithm.But this method does not take into full account evacuates the discreteness of Fleet Size and the randomness of follow-up arrival vehicle, and the unavoidable deviation of estimation result is larger.

Summary of the invention

The object of the invention is to, for above-mentioned existing methodical deficiency, propose a kind of traffic flow evacuation time method of estimation, taken into full account the randomness of evacuating the non-discharge value of discreteness and section of Fleet Size, thus evacuation time is more accurately provided.

Technical thought of the present invention is: judge present road situation by crowded detection algorithm, when road be judged as block up time, build Evacuation Strategies preference pattern according to the present road volume of traffic and use ant group algorithm to solve this model, drawing the shortest and the longest evacuation time of current congestion status.

According to above-mentioned thinking, performing step of the present invention comprises as follows:

(1) judge present road situation by crowded detection algorithm, for the section being judged to be traffic congestion state, the traffic flow that will evacuate is divided into exterior traffic stream and follow-up random exterior traffic stream;

(2) adopt discrete segment that exterior traffic stream is expressed as q=[q1, q2], wherein, q1 is interval limit value; Q2 is interval higher limit;

(3) meet the characteristic of Poisson distribution according to the traffic flow distribution under low-density state, the probability density function building the follow-up exterior traffic stream in section is:

$P(X=k)={\frac{\left(\mathrm{\λ}t\right)}{k!}}^k.\exp (-\mathrm{\λ}t)$

In formula: t is the duration in counting interval, k is the vehicle number arrived in the t of interval, and P (X=k) is for arriving the probability of k car in counting interval t, λ is vehicle mean arrival rate;

(4) above-mentioned traffic flow Evacuation Strategies preference pattern is built:

(4a) basis exterior traffic stream q and follow-up random exterior traffic stream q _i' _j, design object function makes evacuation time the shortest:

${\mathrm{minT}}^{\&PlusMinus;}=\underset{i,j}{\overset{N}{\&Sigma;}}{\&Integral;}_0^{q_{ij}^{\&PlusMinus;}+q_{ij}^{\&prime;}}{t}_{ij}\left(x\right)dx\mathrm{...}<1>$

Wherein :+and-being respectively the upper and lower bound of variate-value, i, j are for evacuating node, and N is node total number, and T is the T.T. of whole evacuation process, q _ijbe i-th section discharge value evacuating that node evacuates node to jth, q _i' _jfor follow-up random exterior traffic flow, t _ijbe i-th and evacuate node to a jth evacuation node road traffic delay required time;

(4b) the following constraint condition of the requirement design object function of balance is needed according to branch road traffic flow each in road network:

$\underset{i=1}{\overset{N}{\&Sigma;}}{q}_{ij}^{\&PlusMinus;}-\underset{i=1}{\overset{N}{\&Sigma;}}{q}_{ji}^{\&PlusMinus;}=0,\&ForAll;i,j\&Element;V_2\mathrm{...}\left(2\right)$

$\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{sj}^{\&PlusMinus;}-\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{jt}^{\&PlusMinus;}=0,\&ForAll;j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{......}<3>$

$P(0\&le;(q_{ij}^{\&PlusMinus;}+q_{ij}^{\&prime;})/c_{ij}\&le;\mathrm{\&beta;})\&GreaterEqual;\&part;,\&ForAll;i,j\&Element;V_2\mathrm{......}<4>$

$\underset{k=1}{\overset{K_{st}}{\&Sigma;}}{h}_k^{\&PlusMinus;st}=\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{jt}^{\&PlusMinus;},\&ForAll;j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{...}<5>$

$\underset{k\&Element;K_{st}}{\&Sigma;}\underset{st\&Element;W}{\&Sigma;}{h}^{\&PlusMinus;}{k}^{st}{\mathrm{\&delta;}}_{i,k}^{st,k}=q_{i,j}^{\&PlusMinus;},\&ForAll;i,j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{...}<6>$

$0\&le;q_{ij}^{\&PlusMinus;}<C_{ij},\&ForAll;i,j\&Element;V\mathrm{...}<7>$

$h_k^{\&PlusMinus;}\&GreaterEqual;0,\&ForAll;k\&Element;K_{st}\mathrm{...}<8>$

In its Chinese style <2> for the arbitrary meaning, ∈ is the meaning belonged to, V ₂for intermediate node set, q _ijfor node i flows into the flow of node j, q _jtfor node j flows out to the flow of node i, this formula represents that the influx of intermediate node j in road network equals discharge.

V in formula <3> ₁for start node set, V ₃for destination set, q _sjfor the section relief of traffic flow of starting point s to node j, q _jtfor the section relief of traffic flow of node j t to terminal, this formula represents that in road network, the discharge of starting point equals terminal discharge.

In formula <4>, β is the upper limit of road section saturation, and α is confidence level, c _ijfor the road link speed in i to j section, this formula represents that the probability that road section saturation is in [0, β] is not less than confidence level α;

In formula <5>, Kst is the active path set of starting point s to terminal between t, h ^st _kfor the relief of traffic flow of starting point s to terminal between t on the k of path, this formula represents that the flow summation of starting point s to terminal on each path of t equals the flow that each intermediate node flows into terminal t;

In formula <6>, W is the set of paths of connection source s to terminal between t, and h is the magnitude of traffic flow on the h of path, k ^stfor from starting point s to the magnitude of traffic flow of terminal t, if represent section a _ijon the kth paths of connection source s to terminal between t, its value is 1, otherwise is 0; This formula represents that node i to the link flow of node j equals each path flow summation through this section;

Formula <7> represents the traffic capacity C evacuating node i and be not more than this section to the link flow evacuating node j _ij;

Formula <8> represents the flow h on the k of section _kfor just;

(5) adopt ant group algorithm ACO to solve above-mentioned Evacuation Strategies preference pattern, solve the shortest time needed for the traffic flow of evacuation present road and maximum duration.

Compared with prior art, the present invention has the following advantages:

First, the relief of traffic stream that the present invention adopts discrete segment to represent to have arrived uncertain, the relief of traffic stream of follow-up arrival is similar to Poisson distribution, take into full account the randomness of evacuating the discreteness of Fleet Size and the discharge value of follow-up arrival, overcome and use classical traffic flow wave theory to consider incomplete shortcoming, improve the accuracy predicted the outcome.

The second, the present invention uses ant group algorithm ACO to solve evacuation preference pattern, makes the solving result of model more accurate, because ant group algorithm has Distributed Calculation and the stronger ability solving challenge, and hunting zone is wide, fast convergence rate.

Accompanying drawing explanation

Fig. 1 of the present inventionly realizes general flow chart;

Fig. 2 judges the sub-process figure that present road is crowded in the present invention.

Embodiment

Specific embodiment of the invention environment carries out in city road network.Because city road network circumstance complication is changeable, so the present invention implements under prerequisite as described below:

1, the impact of traffic lights in city road network is not considered in the estimation of evacuation time;

2, the traffic congestion caused by weather reason and traffic hazard is not distinguished, take same evacuation strategy;

3, suppose to the evacuation strategy of vehicle it is enforce, such as traffic police commander, does not consider the behavior taking other unfavorable evacuations because of individual interest.

Below in conjunction with accompanying drawing 1, the invention will be further described.

Step 1, judges present road state,

Present road state is divided into traffic congestion and unimpeded two states usually.The detection of present road state is judged by existing crowded detection algorithm.

With reference to Fig. 2, being implemented as follows of this step:

(1a) traffic congestion judges in advance:

Upstream and downstream in section sets up detecting device A and lower detecting device B respectively, for detecting every real-time parameter in section.

By the flow value q detected in upper detecting device A place jth-1 and the jth cycle _aand q (j-1) _a(j), and upper detecting device A place jth-1 and the occupation rate C that detects in the jth cycle _aand C (j-1) _aj (), draws the relative increment Δ q of flow in the detecting device A place jth cycle _aoccupation rate relative increment Δ C in (j) and the upper detecting device A place jth cycle _a(j):

Δq _A(j)＝[q _A(j)-q _A(j-1)]/q _A(j-1)，

ΔC _A(j)＝[C _A(j)-C _A(j-1)]/C _A(j-1)；

By the relative increment Δ q of flow in the jth cycle at upper detecting device A place _a(j) and occupation rate relative increment Δ C _aj () compares: if Δ q _a(j)>=Δ C _aj (), then judge that possibility will not get congestion within several cycle in future, otherwise be judged to likely to get congestion, perform step (1b);

(1b) traffic congestion judges:

By the occupation rate C detected in the upper detecting device A jth cycle in section _athe occupation rate C detected in (j) and the lower detecting device B place jth cycle _bj (), obtains the average occupancy absolute difference Δ C of upper and lower detecting device A, B _aB(j) and average occupancy relative mistake Δ C ' _aB(j):

ΔC _AB(j)＝C _A(j)-C _B(j)，

ΔC′ _AB(j)＝[C _A(j)-C _B(j)]/C _B(j)；

The average occupancy absolute difference Δ C of upper and lower detecting device A, B is selected according to specific road traffic condition _aBthe relative mistake Δ C ' of the threshold parameter α of (j) and the average occupancy of upstream and downstream detecting device _aBj the value of the threshold parameter β of (), α and β changes with the difference in place in time, in practical engineering application, the setting of these two values must be based upon on the basis of a large amount of analysis of statistical data;

According to the average occupancy absolute difference Δ C of upper and lower detecting device A, B _aBthe relative mistake Δ C ' of the average occupancy of (j) and threshold alpha thereof and upper and lower detecting device A, B _aBj () and threshold value beta thereof carry out judgement traffic:

If Δ C _aB(j) > α, and Δ C ' _aBj () > β, be then judged to be that traffic congestion has occurred, perform step (1c), otherwise being judged to not block up occurs not process.

(1c) for being judged to be that the section of traffic congestion state is evacuated, the traffic flow that will evacuate is divided into exterior traffic stream and follow-up random exterior traffic stream.

Step 2, sets the expression of exterior traffic stream.

Because the traffic flow arrived has uncertainty, therefore adopt discrete segment that exterior traffic stream is expressed as q=[q1, q2] wherein, q1 is interval limit value; Q2 is interval higher limit.

Step 3, sets the expression of follow-up exterior traffic stream.

Owing to predicting that follow-up exterior traffic stream and car speed difficulty are comparatively large and precision of prediction is not high at short notice, therefore follow-up exterior traffic stream can be regarded as and there is randomness.Probability density function is adopted to describe the randomness of variable; Usually be in low-density state according to follow-up exterior traffic stream, the traffic flow under low-density state meets the spy of Poisson distribution usually, and the probability density function building the follow-up exterior traffic stream in section is:

$P(X=k)={\frac{\left(\mathrm{\&lambda;}t\right)}{k!}}^k.\exp (-\mathrm{\&lambda;}t)$

In formula: t is the duration in counting interval, k is the vehicle number arrived in the t of interval, and P (X=k) is for arriving the probability of k car in counting interval t, λ is vehicle mean arrival rate.

Step 4, builds the Evacuation Strategies preference pattern of above-mentioned traffic flow.

Evacuate in modeling in traffic flow, the dynamic assignment of network traffic flow in evacuation process is mainly simulated in existing research, comprises least cost flow model, path max-flow model, dynamically road grid traffic flow model, network flow model etc. based on track.Above-mentioned model is all evacuated for the traffic flow under determinacy condition, does not take into full account the discreteness of relief of traffic stream scale and the randomness of follow-up exterior traffic stream.Given this, the present invention adopts Chance-constrained Model to carry out Evacuation Strategies selection, Chance-constrained Model requires that the chance that constraint condition is set up is in a certain given confidence level, is to be issued to optimum theory at certain probability meaning, can effectively solve with probabilistic decision problem.

The Chance-constrained Model that this Evacuation Strategies is selected comprises the related constraint condition of objective function and objective function.Its objective function is with the shortest principle construction of evacuation time, and its constraint condition needs the requirement of balance to build with branch road traffic flow each in road network.

(4a) basis exterior traffic stream q and follow-up random exterior traffic stream q ' _ij, design object function makes evacuation time the shortest:

${\mathrm{minT}}^{\&PlusMinus;}=\underset{i,j}{\overset{N}{\&Sigma;}}{\&Integral;}_0^{q_{ij}^{\&PlusMinus;}+q_{ij}^{\&prime;}}{t}_{ij}\left(x\right)dx\mathrm{...}<1>$

Wherein :+and-being respectively the upper and lower bound of variate-value, i, j are for evacuating node, and N is node total number, and T is the T.T. of whole evacuation process, q _ijbe i-th section discharge value evacuating that node evacuates node to jth, q ' _ijfor follow-up random exterior traffic flow, t _ijbe i-th and evacuate node to a jth evacuation node road traffic delay required time;

(4b) the following constraint condition of the requirement design object function of balance is needed according to branch road traffic flow each in road network:

(4b1) equal the requirement of discharge according to the influx of a jth node middle in road network, set the first constraint condition;

$\underset{i=1}{\overset{N}{\&Sigma;}}{q}_{ij}^{\&PlusMinus;}-\underset{i=1}{\overset{N}{\&Sigma;}}{q}_{ji}^{\&PlusMinus;}=0,\&ForAll;i,j\&Element;V_2\mathrm{...}\left(2\right)$

In formula <2> represent that ∈ represents and belongs to, V arbitrarily ₂for intermediate node set, q _ijbe the flow that i-th node flows into a jth node, q _jtfor a jth node flows out to the flow of i-th node;

(4b2) equal the requirement of terminal discharge according to the discharge of starting point in road network, set the second constraint condition;

$\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{sj}^{\&PlusMinus;}-\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{jt}^{\&PlusMinus;}=0,\&ForAll;j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{......}<3>$

In formula <3>, V1 is start node set, and V3 is destination set, and qsj is the section relief of traffic flow of starting point s to a jth node, q _jtfor the section relief of traffic flow of jth node t to terminal, this formula represents that in road network, the discharge of starting point equals terminal discharge;

(4b3) probability being in [0, β] according to road section saturation is not less than the requirement of confidence level α, setting the 3rd constraint condition;

$P(0\&le;(q_{ij}^{\&PlusMinus;}+q_{ij}^{\&prime;})/c_{ij}\&le;\mathrm{\&beta;})\&GreaterEqual;\&part;,\&ForAll;i,j\&Element;V_2\mathrm{......}<4>$

In formula <4>, β is the upper limit of road section saturation, and α is confidence level, c _ijfor the road link speed in i to j section;

(4b4) equal according to the flow summation on starting point the s to terminal each path of t the requirement that each intermediate node flows into the flow of terminal t, setting the 4th constraint condition;

$\underset{k=1}{\overset{K_{st}}{\&Sigma;}}{h}_k^{\&PlusMinus;st}=\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{jt}^{\&PlusMinus;},\&ForAll;j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{...}<5>$

In formula <5>, Kst is the active path set of starting point s to terminal between t, h ^st _kfor the relief of traffic flow of starting point s to terminal between t on the k of path;

(4b5) requirement of each path flow summation through this section is equaled according to i-th node to the link flow of a jth node, setting the 5th constraint condition;

$\underset{k\&Element;K_{st}}{\&Sigma;}\underset{st\&Element;W}{\&Sigma;}{h}^{\&PlusMinus;}{k}^{st}{\mathrm{\&delta;}}_{i,k}^{st,k}=q_{i,j}^{\&PlusMinus;},\&ForAll;i,j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{...}<6>$

In formula <6>, W is the set of paths of connection source s to terminal between t, and h is the magnitude of traffic flow on the h of path, k ^stfor from starting point s to the magnitude of traffic flow of terminal t, if represent section a _ijon the kth paths of connection source s to terminal between t, its value is 1, otherwise is 0;

(4b6) be not more than the traffic capacity C in this section to the link flow evacuating a jth node according to evacuation i-th node _ijrequirement, setting the 6th constraint condition;

$0\&le;q_{ij}^{\&PlusMinus;}<C_{ij},\&ForAll;i,j\&Element;V\mathrm{...}<7>$

(4b7) according to the flow h on the k of section _kfor positive requirement, setting the 6th constraint condition;

$h_k^{\&PlusMinus;}\&GreaterEqual;0,\&ForAll;k\&Element;K_{st}\mathrm{...}<8>$

Step 5 adopts ant group algorithm ACO to solve above-mentioned Evacuation Strategies preference pattern.

The present invention is used ant group algorithm ACO and is solved Evacuation Strategies preference pattern by Matlab software programming, because ant group algorithm has Distributed Calculation and the stronger ability solving challenge, and hunting zone is wide, fast convergence rate, and the solving result of model can be made more accurate.

Specific algorithm step is as follows:

(5a) value and the road section traffic volume flow of decision variable is got solve the optimum solution lower limit of objective function

(5a1) initiation parameter, determines that ant is at the starting point A evacuated in road network and terminal B, and setting each path initial information element intensity is τ ⁰ _ij, iterations K=0, and convert evacuation road net data to adjacency matrix;

(5a2) m ant is put on starting point A, determine according to adjacency matrix the section node that ant goes to, and deposit in taboo list;

(5a3) iterations K=K+1 is set;

(5a4) transition rule according to the following formula, makes ant be transferred to next node, and records the transfer path of every ant.

η _ij＝1/t _ij............................................................................................<10>

In formula: τ _ijfor pheromones intensity, represent pheromones intensity τ _ijto the relative importance evacuating road network,

η _ijfor heuristic information, γ represents heuristic information η _ijto the relative importance evacuating road network, γ > 0; Q is the random number that obedience is uniformly distributed between [0,1]; q ₀for control variable, 0≤q ₀≤ 1;

(5a5) according to the transfer path of m ant, draw this circulation optimum solution: if m ant can find respective one from starting point A to the transfer path of terminal B, then to these ants process path on pheromones intensity upgrade according to the following formula and change taboo list, draw the optimal path of this time circulation:

${\mathrm{\&tau;}}_{ij}=(1-\mathrm{\&rho;}).{\mathrm{\&tau;}}_{ij}+\mathrm{\&rho;}.\underset{h=1}{\overset{m}{\&Pi;}}{\mathrm{\&Delta;\&tau;}}_{h,i,j}\mathrm{...}<11>$

In formula: ρ is pheromones evaporation coefficient; Δ τ _{h, i, j}it is the pheromones variable quantity on the section of h ant in this circulation from i to j;

The shortest time T needed for this time evacuating is drawn again by the objective function of model _k, i.e. the optimum solution of this time circulation;

Otherwise, return (5a3);

(5a6) output model optimum solution:

Determine whether K iteration, if completed K iteration, then the optimum solution lower limit of output model if also do not complete K iteration, then return step (5a2).

(5b) value and the road section traffic volume flow of decision variable is got perform above-mentioned steps respectively and obtain objective function optimum solution higher limit

(5c) combining step (5a) and (5b) obtain objective function optimum solution namely the upper and lower bound value of evacuating current road segment traffic flow required time is solved.

More than describing is only example of the present invention, does not form the bright any restriction of we.Obviously for those skilled in the art; after having understood content of the present invention and principle; all may when not deviating from the principle of the invention, structure; carry out the various correction in form and details and change, but the correction of these basic inventive ideas and change are still within claims of the present invention.

Claims (2)

1. traffic flow evacuation time method of estimation, comprises the steps:

(1) judge present road situation by crowded detection algorithm, for the section being judged to be traffic congestion state, the traffic flow that will evacuate is divided into exterior traffic stream and follow-up random exterior traffic stream;

(2) adopt discrete segment that exterior traffic stream is expressed as q=[q1, q2], wherein, q1 is interval limit value; Q2 is interval higher limit;

(3) meet the characteristic of Poisson distribution according to the traffic flow distribution under low-density state, the probability density function building the follow-up exterior traffic stream in section is:

$P(X=k)={\frac{\left(\mathrm{\&lambda;}t\right)}{k!}}^k.\exp (-\mathrm{\&lambda;}t)$

In formula: t is the duration in counting interval, k is the vehicle number arrived in the t of interval, and P (X=k) is for arriving the probability of k car in counting interval t, λ is vehicle mean arrival rate;

(4) above-mentioned traffic flow Evacuation Strategies preference pattern is built:

(4a) basis exterior traffic stream q and follow-up random exterior traffic stream q ' _ij, design object function makes evacuation time the shortest:

${\mathrm{minT}}^{\&PlusMinus;}=\underset{i,j}{\overset{N}{\&Sigma;}}{\&Integral;}_0^{q_{ij}^{\&PlusMinus;}+q_{ij}^{\&prime;}}{t}_{ij}\left(x\right)dx\mathrm{...}<1>$

Wherein :+and-being respectively the upper and lower bound of variate-value, i, j are for evacuating node, and N is node total number, and T is the T.T. of whole evacuation process, q _ijbe i-th section discharge value evacuating that node evacuates node to jth, q ' _ijfor follow-up random exterior traffic flow, t _ijbe i-th and evacuate node to a jth evacuation node road traffic delay required time;

(4b) the following constraint condition of the requirement design object function of balance is needed according to branch road traffic flow each in road network:

$\underset{i=1}{\overset{N}{\&Sigma;}}{q}_{ij}^{\&PlusMinus;}-\underset{i=1}{\overset{N}{\&Sigma;}}{q}_{ji}^{\&PlusMinus;}=0,\&ForAll;i,j\&Element;V_2\mathrm{...}\left(2\right)$

$\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{sj}^{\&PlusMinus;}-\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{jt}^{\&PlusMinus;}=0,\&ForAll;j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{......}<3>$

$P(0\&le;(q_{ij}^{\&PlusMinus;}+q_{ij}^{\&prime;})/c_{ij}\&le;\mathrm{\&beta;})\&GreaterEqual;\&part;,\&ForAll;i,j\&Element;V_2\mathrm{......}<4>$

$\underset{k=1}{\overset{K_{st}}{\&Sigma;}}{h}_k^{\&PlusMinus;st}=\underset{j=1}{\overset{N}{\&Sigma;}}{q}_{jt}^{\&PlusMinus;},\&ForAll;j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{...}<5>$

$\underset{k\&Element;K_{st}}{\&Sigma;}\underset{st\&Element;W}{\&Sigma;}{h}^{\&PlusMinus;}{k}^{st}{\mathrm{\&delta;}}_{i,k}^{st,k}=q_{i,j}^{\&PlusMinus;},\&ForAll;i,j\&Element;V_2,\&ForAll;S\&Element;V_1,\&ForAll;t\&Element;V_3\mathrm{...}<6>$

$0\&le;q_{ij}^{\&PlusMinus;}<C_{ij},\&ForAll;i,j\&Element;V\mathrm{...}<7>$

$h_k^{\&PlusMinus;}\&GreaterEqual;0,\&ForAll;k\&Element;K_{st}\mathrm{...}<8>$

In its Chinese style <2> for the arbitrary meaning, ∈ is the meaning belonged to, V ₂for intermediate node set, q _ijfor node i flows into the flow of node j, q _jtfor node j flows out to the flow of node i, this formula represents that the influx of intermediate node j in road network equals discharge.

V in formula <3> ₁for start node set, V ₃for destination set, q _sjfor the section relief of traffic flow of starting point s to node j, q _jtfor the section relief of traffic flow of node j t to terminal, this formula represents that in road network, the discharge of starting point equals terminal discharge.

In formula <4>, β is the upper limit of road section saturation, and α is confidence level, c _ijfor the road link speed in i to j section, this formula represents that the probability that road section saturation is in [0, β] is not less than confidence level α;

In formula <5>, Kst is the active path set of starting point s to terminal between t, h ^st _kfor the relief of traffic flow of starting point s to terminal between t on the k of path, this formula represents that the flow summation of starting point s to terminal on each path of t equals the flow that each intermediate node flows into terminal t;

In formula <6>, W is the set of paths of connection source s to terminal between t, and h is the magnitude of traffic flow on the h of path, k ^stfor from starting point s to the magnitude of traffic flow of terminal t, if represent section a _ijon the kth paths of connection source s to terminal between t, its value is 1, otherwise is 0; This formula represents that node i to the link flow of node j equals each path flow summation through this section;

Formula <7> represents the traffic capacity C evacuating node i and be not more than this section to the link flow evacuating node j _ij;

Formula <8> represents the flow h on the k of section _kfor just;

(5) adopt ant group algorithm ACO to solve above-mentioned Evacuation Strategies preference pattern, solve the shortest time needed for the traffic flow of evacuation present road and maximum duration.

2. traffic flow evacuation time method of estimation according to claim 1, the employing ant group algorithm ACO wherein described in step (5) solves above-mentioned Evacuation Strategies preference pattern, carries out as follows:

(5a) value and the road section traffic volume flow of decision variable is got solve the optimum solution lower limit of objective function

(5a1) initiation parameter, determines that ant is at the starting point A evacuated in road network and terminal B, and setting each path initial information element intensity is τ ⁰ _ij, iterations K=0, and convert evacuation road net data to adjacency matrix;

(5a2) m ant is put on starting point A, determine according to adjacency matrix the section node that ant goes to, and deposit in taboo list;

(5a3) iterations K=K+1 is set;

(5a4) transition rule according to the following formula, makes ant be transferred to next node, and records the transfer path of every ant.

η _ij＝1/t _ij............................................................................................<10>

In formula: τ _ijfor pheromones intensity, represent pheromones intensity τ _ijto the relative importance evacuating road network, η _ijfor heuristic information, γ represents heuristic information η _ijto the relative importance evacuating road network, γ > 0; Q is the random number that obedience is uniformly distributed between [0,1]; q ₀for control variable, 0≤q ₀≤ 1;

(5a5) according to the transfer path of m ant, draw this circulation optimum solution: if m ant can find respective one from starting point A to the transfer path of terminal B, then to these ants process path on pheromones intensity upgrade according to the following formula and change taboo list, draw the optimal path of this time circulation:

${\mathrm{\&tau;}}_{ij}=(1-\mathrm{\&rho;}).{\mathrm{\&tau;}}_{ij}+\mathrm{\&rho;}.\underset{h=1}{\overset{m}{\&Pi;}}{\mathrm{\&Delta;\&tau;}}_{h,i,j}\mathrm{...}<11>$

In formula: ρ is pheromones evaporation coefficient; Δ τ _{h, i, j}it is the pheromones variable quantity on the section of h ant in this circulation from i to j;

The shortest time T needed for this time evacuating is drawn again by the objective function of model _k, i.e. the optimum solution of this time circulation;

Otherwise, return (5a3);

(5a6) output model optimum solution:

Determine whether K iteration, if completed K iteration, then the optimum solution lower limit of output model if also do not complete K iteration, then return step (5a2).

(5b) value and the road section traffic volume flow of decision variable is got perform above-mentioned steps respectively and obtain objective function optimum solution higher limit

(5c) combining step (5a) and (5b) obtain objective function optimum solution namely the upper and lower bound value of evacuating current road segment traffic flow required time is solved.