CN106910350A - A kind of method for finding signalized crossing group's critical path - Google Patents

Abstract

The invention discloses a kind of method for finding signalized crossing group's critical path, comprise the following steps：(1) every data message in all sections of intersection group and intersection is obtained；(2) the discreteness coupling index I of each path origin and destination of intersection group is calculated according to these data₁；(3) the retardancy coupling index I in each section of intersection group is calculated₂；(4) makeAs the total correlation index of a paths,It is all section I in the path₂Average value, with the path of population algorithm search intersection group, draw the paths of I highests one as critical path.The present invention is finely adjusted to path related degree model, search degree of association highest path, all paths need not be traveled through, greatly reduce operand, so as to reduce the operation time that whole signalized crossing group time-space distribution coordinates and optimizes system, the real-time and validity of control are coordinated in enhancing, increase its manageable intersection group scale.

Description

A kind of method for finding signalized crossing group's critical path

Technical field

The present invention relates to traffic signalization field, especially a kind of side for finding signalized crossing group's critical path Method.

Background technology

Intersection of the China on urban road network arterial highway is generally signalized crossing, and single point signals control is carried out respectively System.Carry out vehicle when single point signals are controlled frequently to stop at the intersection, therefore cause that road network operational efficiency is low, trip delay The traffic problems such as increase.To reduce down time of the vehicle on each intersection, intersection group time-space distribution optimization system is led to The real-time monitoring volume of traffic is crossed, control is coordinated using multiple intersections as an entirety, to reduce the congestion of intersection group.

Existing intersection group coordinated control system is divided into five parts, comprising the identification of target intersection, intersection group model Enclose delimitation, critical path retrieval, five steps of time-space distribution optimization and online real-time adjustment.Wherein, critical path search module In the model of use, the calculating of the path degree of association has one to go dimension step so that the calculation of relationship degree in each path is depended on All path retardancy degrees of association and the extreme value of the discrete type degree of association, travel through all paths in intersection unavoidably, cause the mould Block is calculated and taken considerable time, usually reach several minutes, the magnitude of traffic flow has occurred that change in this period, is weakened whole The real-time of coordinated control system, influences Adjustment effect.

The content of the invention

The technical problems to be solved by the invention are, there is provided a kind of side for finding signalized crossing group's critical path Method, can reduce operand, increase manageable intersection group scale.

In order to solve the above technical problems, the present invention provides a kind of method for finding signalized crossing group's critical path, Comprise the following steps：

(1) obtain each item data of intersection group, including intersection group scope X, intersection quantity N, crucial intersection The number of track-lines of the connected mode in K, intersection and section and each each entrance driveway of intersection, canalization scheme, queue length, car The volume of traffic of speed, each flow direction；

(2) data of all edge intersections are traveled through, the discreteness degree of association in all paths in intersection group is calculated I₁, computing formula is as follows：

I₁=n_d/n₀

n_d--- the vehicle number (veh) that fleet passes through in the starting point green time of path

n_o--- the vehicle number (veh) that fleet passes through in path termination green time；

(3) all sections in traversal intersection group, calculate the retardancy degree of association I of its two ends intersection₂, unidirectional N The retardancy degree of association of lane segment mComputing formula is：

L --- road section length (m)

--- function section length (m) in n-th of section track

--- the vehicle queue length (m) in section m nth bars track

--- deceleration distance (m)

--- perception-reaction distance (m)；

(4) path of population interconnection degree I maximums in road network and then by genetic algorithm is drawn, there is the path in M bars section, its Population interconnection degree is as follows：

Preferably, in step (4), genetic algorithm is concretely comprised the following steps：

(41) for each intersection is numbered, i.e. X={ 0,1 ..., N } represents a paths, i.e. R={ x with numbered sequence_n, R It is a paths, x_nRepresent n-th of path intersection；The outside random search since crucial intersection, generates a two ends Path R in intersection group border, repeatedly generates multiple R, constitutes initial population

(42) existTwo intersecting paths of middle random selection：

Rand (set) --- an element is randomly choosed in set

Make X₁₂=R₁∩R₂If, | X₁₂| >=2, two intersection points of this two paths are taken at random：

x₁=rand (X₁₂),x₂=rand (X₁₂)

By R₁、R₂In x₁、x₂Between part exchange, generation filial generation path R_F1、R_F2；If | X₁₂|=1, then by intersection point x ∈ X₁₂Part afterwards exchanges, and generates R_F1、R_F2；The process is repeated several times, all filial generation paths that will be generated constitute progeny populationSimulate mating, the reproductive process of nature；

(43) calculateThe degree of association I in all paths, andRandomly select multiple path R：

Adaptive probability is taken, the new probability formula being selected of each path R is as follows：

p_min、p_maxIt is customized parameter

f_max——In all paths f_RnMaximum

f_min——Middle f_RnMinimum value

i_R--- the degree of association I of path R_R Descending ranking in all paths

An intersection x is randomly choosed in selected R_iIf i ≠ 0 ∧ i ≠ | R | chooses x_i-1Another phase Adjacent intersection x '_i：

x_i=rand (R)

x′_i=rand (x | x ∈ X ∧ join (x, x_i-1)})

Otherwise choose other edge intersection x '_i；

If x '_iWith follow-up intersection x_i+iIt is joined directly together, then by the x in R_iReplace with x '_i；Otherwise search x_i+iIt is most short Path r '={ x '_i,x′_i+1,…,x′_i+i′, replace the x in R with r '_i；The step simulates biological gene mutation process.

(44) recalculateThe degree of association I in all paths, screens to it, only leaves identical with parental generation population quantity A part of path, constituteR removed probability in path is：

(45) if the degree of association in the path of population is not up to the condition of convergence, also not up to predetermined maximum iteration, makesAnd return to step (42)；Otherwise stop iteration and by current degree of association highest path R_maxAs critical path.

Beneficial effects of the present invention are：The present invention path related degree model is finely adjusted, per paths the degree of association with Other paths of intersection group decouple, and can individually calculate, and such that it is able to ask for approximate solution using heuritic approach, search for the degree of association Highest path, it is not necessary to travel through all paths, greatly reduce operand, so as to reduce during whole signalized crossing group Null resource coordinates and optimizes the operation time of system, and enhancing coordinates the real-time and validity of control, increases its manageable friendship Prong group's scale.

Brief description of the drawings

Fig. 1 is method of the present invention schematic flow sheet.

Specific embodiment

As shown in figure 1, a kind of method for finding signalized crossing group's critical path, comprises the following steps：

(1) investigation obtains each item data of intersection group, including the scope X of intersection group, intersection quantity N, crucial hands over The connected mode in prong K, intersection and section, the number of track-lines M of each entrance driveway, canalization scheme etc., and obtained by electronic equipment Take the real-time traffic stream informations such as queue length, speed, the volume of traffic of each flow direction and the speed of each entrance driveway.

(2) data of all edge intersections are traveled through, the discreteness degree of association in all paths in intersection group is calculated I₁, computing formula is as follows：

I₁=n_d/n₀

n_d--- the vehicle number (veh) that fleet passes through in the starting point green time of path

n_o--- the vehicle number (veh) that fleet passes through in path termination green time

(3) all sections in traversal intersection group, calculate the retardancy degree of association I of its two ends intersection₂.Unidirectional N The retardancy degree of association of lane segment mComputing formula is：

L --- road section length (m)

--- function section length (m) in n-th of section track

--- the vehicle queue length (m) in section m nth bars track

--- deceleration distance (m)

--- perception-reaction distance (m)

(4) path of population interconnection degree I maximums in road network and then by genetic algorithm is drawn.There is the path in M bars section, its Population interconnection degree is as follows：

Genetic algorithm is concretely comprised the following steps：

(40) condition that iteration terminates is set：

And maximum iteration T is set；

(41) all intersections are made for set X, is each intersection numbering, be i.e. X={ 0,1 ..., N } is represented with numbered sequence One paths.That is R={ x_n, R is a paths, x_nRepresent n-th of path intersection.Since crucial intersection outwards with Machine is searched for, and generation one is reached the path on intersection group border by crucial intersection：

R₁={ x_n|x_n=rand (X) ∧ (n=0 ∨ join (x_n,x_n-1)}

R₂={ x_n|x_n=rand (X) ∧ join (x_n-1,x_n)}

Rand (set) --- an element is randomly choosed in set

Multiple R are repeatedly generated, initial population is constituted

(42) existTwo intersecting paths of middle random selection：

Make X₁₂=R₁∩R₂If, | X₁₂| >=2, two intersection points of this two paths are taken at random：

x₁=rand (X₁₂),x₂=rand (X₁₂)

By R₁、R₂In x₁、x₂Between part exchange, generation filial generation path R_F1、R_F2.If | X₁₂|=1, then by intersection point x ∈ X₁₂Part afterwards exchanges, and generates R_F1、R_F2.The process is repeated several times, all filial generation paths that will be generated constitute progeny population

(43) calculateThe degree of association I in all paths, andRandomly select multiple path R：

Take adaptive probability, the selected new probability formula of path R is as follows：

p_min、p_maxIt is customized parameter

f_max——In all paths f_RnMaximum

f_min——Middle f_RnMinimum value

i_R--- the degree of association I of path R_R In descending ranking in all path degrees of association

An intersection x is randomly choosed in R_iIf i ≠ 0 ∧ i ≠ | R | chooses x_i-1Another Adjacent Intersections x′_i：

x_i=rand (R)

x′_i=rand (x | x ∈ X ∧ join (x, x_i-1)})

Otherwise choose other edge intersection x '_i。

If x '_iWith follow-up intersection x_i+iIt is joined directly together, then by the x in R_iReplace with x '_i；Otherwise search x_i+iIt is most short Path r '={ x '_i,x′_i+1,…,x′_i+i′, replace the x in R with r '_i。

(44) recalculateThe degree of association I in all paths, screens to it, only leaves identical with parental generation population quantity A part of path, constituteR removed probability in path is：

(45) if the degree of association in the path of population is not up to condition of convergence P, iterations is also not up to predetermined maximum and changes Generation number T, then makeAnd return to step 42)；Otherwise stop iteration and by current degree of association highest path R_maxAs Critical path.

This method is intended to retrieve mould to the critical path that urban road signalized crossing group's time-space distribution coordinates system Block is targetedly improved, and improves solving speed.

Although the present invention is illustrated and has been described with regard to preferred embodiment, it is understood by those skilled in the art that Without departing from scope defined by the claims of the present invention, variations and modifications can be carried out to the present invention.

Claims (2)

1. it is a kind of find signalized crossing group critical path method, it is characterised in that comprise the following steps：

(1) obtain each item data of intersection group, including intersection group scope X, intersection quantity N, key intersection K, friendship It is the connected mode in prong and section and the number of track-lines of each each entrance driveway of intersection, canalization scheme, queue length, speed, each The volume of traffic of flow direction；

(2) data of all edge intersections are traveled through, the discreteness degree of association I in all paths in intersection group is calculated₁, meter Calculate formula as follows：

I₁=n_d/n₀

n_d--- the vehicle number (veh) that fleet passes through in the starting point green time of path

n_o--- the vehicle number (veh) that fleet passes through in path termination green time；

(3) all sections in traversal intersection group, calculate the retardancy degree of association I of its two ends intersection₂, unidirectional N tracks road The retardancy degree of association of section mComputing formula is：

$I_2^m=\frac{max(D_1^m,D_2^m,...,D_n^m,...,D_N^m)}{L}$

$D_n^m=d_{1n}^m+d_{2n}^m+d_{3n}^m$

L --- road section length (m)

--- function section length (m) in n-th of section track

--- the vehicle queue length (m) in section m nth bars track

--- deceleration distance (m)

--- perception-reaction distance (m)；

(4) path of population interconnection degree I maximums in road network and then by genetic algorithm is drawn, has the path in M bars section, its totality The degree of association is as follows：

$I=I_1+\stackrel{\‾}{I_2^m}$

$\stackrel{\‾}{I_2^m}={\mathrm{\Σ}}_{m=1}^M\frac{I_2^m}{M}.$

2. the method for finding signalized crossing group's critical path as claimed in claim 1, it is characterised in that step (4) In, genetic algorithm is concretely comprised the following steps：

(41) for each intersection is numbered, i.e. X={ 0,1 ..., N } represents a paths, i.e. R={ x with numbered sequence_n, R is one Paths, x_nRepresent n-th of path intersection；The outside random search since crucial intersection, one two ends of generation are in The path R on intersection group border, repeatedly generates multiple R, constitutes initial population

(42) existTwo intersecting paths of middle random selection：

Rand (set) --- an element is randomly choosed in set

Make X₁₂=R₁∩R₂If, | X₁₂| >=2, two intersection points of this two paths are taken at random：

x₁=rand (X₁₂),x₂=rand (X₁₂)

By R₁、R₂In x₁、x₂Between part exchange, generation filial generation path R_F1、R_F2；If | X₁₂|=1, then by intersection point x ∈ X₁₂Afterwards Part exchange, generate R_F1、R_F2；The process is repeated several times, all filial generation paths that will be generated constitute progeny populationSimulate mating, the reproductive process of nature；

(43) calculateThe degree of association I in all paths, andRandomly select multiple path R：

Adaptive probability is taken, the new probability formula being selected of each path R is as follows：

$p\left(R\right)=\left\{\begin{array}{cc}{p}_{\min },& I_x\&GreaterEqual;\stackrel{\&OverBar;}{I}\\ p_{\max }-\frac{(p_{max}-p_{\min })(f_R-f_{min})}{f_{max}-f_{min}},& I_x<\stackrel{\&OverBar;}{I}\end{array}\right.$

p_min、p_maxIt is customized parameter

f_max——In all paths f_RnMaximum

f_min——Middle f_RnMinimum value

i_R--- the degree of association I of path R_R Descending ranking in all paths

An intersection x is randomly choosed in selected R_iIf i ≠ 0 ∧ i ≠ | R | chooses x_i-1Another adjacent intersection Mouth x '_i：

x_i=rand (R)

x′_i=rand (x | x ∈ X ∧ join (x, x_i-1)})

Otherwise choose other edge intersection x '_i；

If x '_iWith follow-up intersection x_i+iIt is joined directly together, then by the x in R_iReplace with x '_i；Otherwise search x_i+iMost short path R '={ x '_i,x′_i+1,…,x′_i+i′, replace the x in R with r '_i；The step simulates biological gene mutation process.

(44) recalculateThe degree of association I in all paths, screens to it, only leaves and parental generation population quantity identical one Part path, is constitutedR removed probability in path is：

(45) if the degree of association in the path of population is not up to the condition of convergence, also not up to predetermined maximum iteration, makesAnd return to step (42)；Otherwise stop iteration and by current degree of association highest path R_maxAs critical path.