CN103481918A - Operation regulating method of high-speed railway train based on feedback regulation - Google Patents

Abstract

The invention discloses an operation regulating method of a high-speed railway train based on feedback regulation, which is used for solving a defect that conflict resolution is not sequentially carried out after conflict severity sequencing in the prior art. The operation regulating method comprises the following steps: firstly, obtaining operation state, passed stations and interval state of the train at the current moment; then, detecting whether a conflict happens or not or whether a potential conflict exists in a future moment or not, if no, updating data information when the future moment comes, and repeating the step, otherwise, entering in the next step; and finally, carrying out train operation conflict resolution with the minimal conflict resolution cost according to a train operation regulating rule, regulating a train operation scheme until the train operation is finished according to the resolution result, otherwise, returning to the last step. The operation regulating method disclosed by the invention is used for carrying out conflict resolution according to the train operation regulating rule by sequencing and then carrying out resolution, so that feedback regulation can be realized, and therefore, the operation regulating method has advantages of high real-time performance and high integrity.

Description

A kind of high speed rail train operation method of adjustment based on feedback regulation

Technical field

The present invention relates to a kind of high speed rail train operation method of adjustment, specifically, refer to and a kind ofly take the dynamic property of implementing high speed train operation after certain train operation adjustment scheme, high speed railway operation conflict high speed train influence on system operation degree is carried out to the Method for Train Operation Adjustment that scheme optimization is adjusted in train operation as feedback information.

Background technology

Train operation adjustment problem is a typical resource scheduling, and expert and scholar are devoted to the research history of existing nearly 50 years of this problem both at home and abroad.Expert and scholar have successively set up the mathematical programming model of operation adjustment problem and the dynamic approach based on discrete event solves the train adjustment model and utilize the methods such as genetic algorithm, coordination optimization algorithm, branch boundary to be solved model, build the expert system of plan of adjusting train operation, improved the intelligent of Operation and dispatching system.The propositions such as the Shi Feng mode of priority of conflicting is the earliest cleared up conflict in train diagram to realize the train operation adjustment, the problem of combination although this method can avoid conflict, but the conflict of different classes of and the order of severity is not treated with a certain discrimination, may cause serious conflict to clear up condition and can not get well clearing up due to shortage.

Summary of the invention

The invention provides a kind of high speed rail train operation method of adjustment based on feedback regulation, purpose is the seriousness of conflict is sorted from high to low, and according to the initiation reason of this order conflict removal, in order to avoid the conflict that has cleared up in place produces conflict with other form at another place by certain transfer and conversion.

To achieve these goals, the technical solution used in the present invention is as follows:

A kind of high speed rail train operation method of adjustment based on feedback regulation comprises the following steps:

(1) obtain the current time train running state, pass through a station and state of section;

(2) detect and whether have conflict occur or in future time, whether potential conflict arranged, if not, when future time arrives, upgrade data message, and repeat this step, otherwise, next step entered;

(3) carry out the train operation conflict resolution according to the train operation regulation rule, and result is adjusted the train operation scheme until train operation finishes by clearing up, otherwise, previous step returned to.

For further illustrating the foundation of conflict resolution, in described step (3), the method for conflict resolution is as follows:

(3a) carry out train operation conflict tolerance;

(3b) determine train operation conflict resolution order;

(3c), according to the Removing Tactics of single conflict, calculate one of them conflict resolution cost;

(3d) formulate the scheme of clearing up of this conflict;

(3e) judge whether all conflicts to clear up scheme definite, if determined the scheme of clearing up of all conflicts, this conflict resolution process finishes, if do not determined the scheme of clearing up of all conflicts, repeating step (3c)～(3e).

Further, described definite train operation conflict resolution order is to sort from high to low according to the severity of conflict.

As multiple preferably, the Removing Tactics of single conflict is translation operation line, exchange travel line, change is stopped and overtaking at least one.

When upgrading the train operation scheme, need to meet the constraint that the middle adjustment of described step (3) train operation adjustment scheme is subject to following 11 aspects simultaneously:

(I) train interval time of run constraint;

(II) train dwelling time-constrain;

(III) set out tracking interval constraint of train;

(IV) train arrives the tracking interval constraint;

(V) need handle the constraint at the time of departure of passenger's business station;

(VI) station is to the constraint of hair line number;

(VII) motor train unit connecting time constraint;

(VIII) passenger transference time-constrain;

(IX) overline train cross-line time-constrain;

(X) Window time constraint;

(XI) logical constraint.

Compared with prior art, the present invention has following beneficial effect:

The present invention is after detecting conflict and prediction and may clashing in the future, the sequence of the seriousness of at first being conflicted, make comparatively seriously may to make conflict that life and property loss is larger at first to make the action of conflict resolution, and feed back to present stage, clear up cost with Min-conflicts and clear up in advance conflict so that in the future Analysis of Train Operation Order disorder greatly appears in high speed rail train operation, improve the train operation quality.

The integrated use that the present invention simultaneously judges, measures, predicts, clears up technology by conflict solves train operation adjustment problem, reduce by the appropriate design Strategy of Conflict Resolution computation complexity that problem is adjusted in train operation, improve train operation and adjust optimization efficiency, there is high real-time and high globality.

The accompanying drawing explanation

Fig. 1 is diagram of circuit of the present invention.

The diagram of circuit that Fig. 2 is conflict resolution method in the present invention.

Fig. 3 is example periodic plan running chart before simulation run.

Fig. 4 is the late 600s plan for adjustment of train 1 figure in emulation sight one.

Fig. 5 is the late 600s operation of train 1 conflict resolution cost diagram of curves in emulation sight one.

Fig. 6 is the late 1800s operation of train 1 conflict resolution cost diagram of curves in emulation sight two.

The specific embodiment

Below in conjunction with accompanying drawing and embodiment, the invention will be further described, and embodiments of the present invention include but not limited to the following example.

Embodiment

The train operation conflict resolution is an order to conflict train occupation transport resource and the problem that redefines of time.Different train operation conflict resolution schemes, by bringing different train operations to adjust effect, affect the train traffic control quality.Therefore, the quality of train operation conflict resolution scheme directly affects the effect of train traffic control.Clearing up of train operation conflict is eliminate between train the overlapping of transport resource holding time or make the utilization of technical equipment meet the technical operation requirement of train by change train operation order with to time.

As depicted in figs. 1 and 2, implementation procedure of the present invention is as follows:

(1) at first, obtain the current time train running state, pass through a station and state of section, to facilitate subsequent calculations.

(2) detect and whether have conflict occur or in future time, whether potential conflict arranged, if not, when future time arrives, upgrade data message, and repeat this step, otherwise, next step entered;

Therefore for detection, whether conflict or potential conflict being arranged is that prior art seldom explains.

(3) carry out the train operation conflict resolution according to the train operation regulation rule, and result is adjusted the train operation scheme until train operation finishes by clearing up, otherwise, previous step returned to.

Step (3) is that the present embodiment is by the step described in detail.

In all kinds of train operation conflicts of high speed railway, interval may be occurred in, also station may be occurred in.The time interval between two adjacent trains at station conflict, to use conflict, the conflict of motor train unit connecting time, train operation to hair line be to occur in conflicting of station with passenger transference time conflict, overline train cross-line time conflict, and interval conflict, vehicling operation conflicts with maintenance activity is to occur in interval conflict.Headway conflict in the time interval between two adjacent trains at station conflict and the current station occurred in conflict of clearing up of conflicting to the hair line utilization complete, and interval clearing up of conflicting need to complete at the station of conflicting before occurring.The conflict of motor train unit connecting time, train operation are conflicting in section destination stop or specific several station generations with passenger transference time conflict, overline train cross-line time conflict, need to be in its place ahead each station constantly carry out project setting and correction according to the operation possibility in train future and cleared up, the evaluation foundation of the present embodiment using these a few class conflicts as aforementioned all kinds of conflict resolution scheme feasibilities, when the train operation conflict prediction, emphasis is considered possibility the feedback information using it as the conflict resolution scheme that these a few class conflicts occur.But no matter the high speed rail train operation conflict occurs in station or interval, and clearing up all of train operation conflict completes AT STATION.

Therefore:

A. at first carry out train operation conflict tolerance, judge the order of severity of all conflicts.

B. the tolerance that conflict is conflicted grade to difference, sort according to the order of severity of each conflict.In order to reduce the complexity of conflict resolution, the most serious conflict of each selection is preferentially cleared up, and preferential the elimination has the greatest impact, endangers the most serious train operation conflict to train travelling process.

C. next solve successively each conflict, each conflict is formulated to concrete Removing Tactics, and different Removing Tactics will cause different integral train running statees, and follow-up train operation is produced to different impacts.Should be noted, the conflict tactful formulation that disappears mainly solves two problems: the time-shifting amount of the operation order of conflict train and conflict train.

Choose 3 indices of train running and assess the running state of train.

First index: the conflict time is cleared up cost

If respectively as the unit time cost of conflict train time-shifting, making the product of the Train Schedule translational movement of gained scheme under itself and corresponding Removing Tactics is conflict resolution time cost P using two train operation eigenwerts _cT.If two row trains are respectively i and j, the translational movement at the k station is respectively Δ T _ikwith Δ T _jk, two row trains are respectively T in the train operation eigenwert at j station _ikand T _jk, the conflict resolution time cost of train i and j is:

$P_{\mathrm{CT}}^{\mathrm{ik}}=T_{\mathrm{ik}}\×\mathrm{\Δ}{T}_{\mathrm{ik}}$

$P_{\mathrm{CT}}^{\mathrm{jk}}=T_{\mathrm{jk}}\×\mathrm{\Δ}{T}_{\mathrm{jk}}$

This conflict resolution scheme is R _k, R _kthe conflict resolution time cost be P _cT(R _k, s (k), i, j), have:

P _CT(R _k,s(k),i,j)=T _ik×ΔT _ik+T _jk×ΔT _jk

The P of corresponding conflict scheme in Strategy of Conflict Resolution _cTless, the effect of its conflict resolution is more excellent, when conflict resolution as the conflict resolution scheme definite foundation.

Second index: aftereffect conflict

Clear up the conflict of train i at the k station after, the summation of following all kinds of train operation conflict of train possibility is called the aftereffect conflict of conflict resolution scheme, is designated as

and have:

$V_{\mathrm{CT}}^{\mathrm{ik}}=\underset{j}{\overset{4}{\mathrm{\Σ}}}{P}_1\left(C_{\mathrm{Pj}}^i\right)+\underset{k+1}{\overset{n}{\mathrm{\Σ}}}\underset{r}{\overset{2}{\mathrm{\Σ}}}{P}_2\left(C_{\mathrm{Pr}}^{\mathrm{ik}}\right)$

Equal sign the right plus sige first half in formula

for the conflict prediction value of order run-mode drift net, for all kinds of conflict possibility of train after conflict resolution value, comprise 4 class conflicts; For selecting the conflict prediction value of run-mode drift net,

prop up for two workflows of each transport resource after conflict resolution the possibility value that net conflicts, comprise station tracking interval conflict and use conflict two classes to hair line, wherein n is section terminal station sequence number.

The 3rd index: train operation conflict resolution cost

The conflict resolution cost of train i and j is:

${\hat{P}}_{\mathrm{CT}}^{\mathrm{ik}}=T_{\mathrm{ik}}\×\mathrm{\Δ}{T}_{\mathrm{ik}}\×V_{\mathrm{CT}}^{\mathrm{ik}}$

${\hat{P}}_{\mathrm{CT}}^{\mathrm{jk}}=T_{\mathrm{jk}}\×\mathrm{\Δ}{T}_{\mathrm{jk}}\×V_{\mathrm{CT}}^{\mathrm{jk}}$

R _jthe conflict resolution cost for:

$\begin{array}{c}{\hat{P}}_{\mathrm{CT}}(R_k,s\left(k\right),i,j)={\hat{P}}_{\mathrm{CT}}^{\mathrm{ik}}+{\hat{P}}_{\mathrm{CT}}^{\mathrm{jk}}\\ =T_{\mathrm{ik}}\×\mathrm{\Δ}{T}_{\mathrm{ik}}\left(R_k\right)\×V_{\mathrm{CT}}^{\mathrm{ik}}\left(R_k\right)+T_{\mathrm{jk}}\×\mathrm{\Δ}{T}_{\mathrm{jk}}\left(R_k\right)\×V_{\mathrm{CT}}^{\mathrm{jk}}\left(R_k\right)\end{array}$

In order to realize the optimum of train operation effect, when carrying out conflict resolution, always preferentially select the scheme of conflict resolution Least-cost.Wish is pursued the scheme of all train conflict resolution Least-costs,, train that aftereffect conflict large large for certain train operation eigenwert, need to formulate in detail its train operation plan, reduce itself and the conflicting of other trains, otherwise will the operation of other trains be had an immense impact on, in order to realize this target, need to make the translational movement of this train to be tending towards minimum, carry out the train operation of emphasis train priority support and adjust principle.

Therefore after definite conflict resolution order, train translational movement to each conflict under certain Removing Tactics, calculate the rear skirmish under train operation characteristic value and this train translational movement, and determine the operation order of conflict train and the actual time of origin of each train activity according to the conflict resolution cost.

Be subject to the impact of randol noise delay time in the present embodiment, therefore propose to reduce possibility that conflict occurs, stop and overtaking and four kinds of strategies of mathematical programming to clearing up the translation operation line, exchange travel line, the change that play a decisive role:

Strategy I: the translation operation line refers to be less than relevant operation or work standard and when the train operation conflict occurs when the technical operations of train interval time, by the reasonable translation conflict point travel line of being correlated with, make technical operations of train interval greater than etc. relevant production time standard.

Strategy II: the exchange travel line refer to when occurring or be about to the train operation conflict, by the exchange correlation train path at the starting order at station to eliminate corresponding conflict.

Strategy III: change is stopped and the overtaking scheme refers to that this strategy is the bus stop, number of stops of the train by changing and to be avoided and overtaking point and then reaches and eliminate the target that train operation conflicts, and it is the expansion of tactful II.

Strategy IV: mathematic programming methods refers to according to certain conflict resolution and train operation adjustment aim, utilize mathematic programming methods to seek the Combinatorial Optimization of train operation conflict, be about to aforementioned three kinds of strategies and be optimized permutation and combination, formulate the conflict resolution scheme of minimum cost from overall angle.

Wherein single conflict resolution relates to again multiple conflict, as conflicted, to hair line, use conflict and other conflicts in time interval between two adjacent trains at station conflict, interval.

If we illustrate that every kind of conflict is by different strategy calculating conflict resolution costs successively.

The time interval between two adjacent trains at station conflict is divided into again the interarrival time conflict and the interval time conflict of setting out.

One, conflict resolution interarrival time conflicted

Under strategy I, interarrival time conflict resolution cost is:

${\hat{P}}_{\mathrm{CT}}^1=T_{\mathrm{ik}}\×\mathrm{\Δ}{T}_i^1\×V_{\mathrm{CT}}^{\mathrm{ik}}\left(1\right)+T_{\mathrm{jk}}\×\mathrm{\Δ}{T}_j^1\×V_{\mathrm{CT}}^{\mathrm{jk}}\left(1\right)$

Under strategy II, interarrival time conflict resolution cost is:

${\hat{P}}_{\mathrm{<figure-callout\; id="2"\; label="CT"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">CT</figure-callout>}}^2=T_{\mathrm{ik}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; i"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_i^{}\&times;V_{\mathrm{CT}}^{\mathrm{ik}}\left(2\right)+T_{\mathrm{jk}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; j"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_j^{}\&times;V_{\mathrm{CT}}^{\mathrm{jk}}\left(2\right)=T_{\mathrm{jk}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; i"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_i^{}\&times;V_{\mathrm{CT}}^{\mathrm{ik}}\left(2\right)$

Under strategy III, interarrival time conflict resolution cost is:

${\hat{P}}_{\mathrm{<figure-callout\; id="3"\; label="CT"\; filenames="131014134935.png"\; state="\{\{state\}\}">CT</figure-callout>}}^3=T_{\mathrm{ik}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="3"\; label="T\; i"\; filenames="131014134935.png"\; state="\{\{state\}\}">T</figure-callout>}}_i^{}\&times;V_{\mathrm{CT}}^{\mathrm{ik}}\left(3\right)+T_{\mathrm{jk}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; j"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_j^{}\&times;V_{\mathrm{CT}}^{\mathrm{jk}}\left(3\right)=T_{\mathrm{ik}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; i"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_i^{}\&times;V_{\mathrm{CT}}^{\mathrm{ik}}\left(1\right)$

Therefore final Removing Tactics reaches determines that the foundation of clearing up scheme is for making the conflict resolution Least-cost:

${\hat{P}}_{\mathrm{CT}}^{*}=\min \{{\hat{P}}_{\mathrm{CT}}^1,{\hat{P}}_{\mathrm{<figure-callout\; id="2"\; label="CT"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">CT</figure-callout>}}^2,{\hat{P}}_{\mathrm{CT}}^3\}$

In formula, i is first train, and j is and first second train that train is adjacent, and k means k platform, and T means the time, and Δ T means translational movement, and V means the aftereffect conflict.

Two, the conflict resolution interval time that sets out conflicted

Under strategy I, interarrival time conflict resolution cost is:

${\hat{P}}_{\mathrm{CT}}^1=T_{\mathrm{ik}}\&times;\mathrm{\&Delta;}{T}_j^1\&times;V_{\mathrm{CT}}^{\mathrm{jk}}\left(1\right)=T_{\mathrm{jk}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; j"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_j^{}\&times;V_{\mathrm{CT}}^{\mathrm{jk}}\left(1\right)\&times;(I_f-T_z)$

Under strategy II, interarrival time conflict resolution cost is:

${\hat{P}}_{\mathrm{<figure-callout\; id="2"\; label="CT"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">CT</figure-callout>}}^2=T_{\mathrm{ik}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; i"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_i^{}\&times;V_{\mathrm{CT}}^{\mathrm{ik}}\left(2\right)+T_{\mathrm{jk}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; j"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_j^{}\&times;V_{\mathrm{CT}}^{\mathrm{jk}}\left(2\right)=T_{\mathrm{ik}}\&times;\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="2"\; label="T\; i"\; filenames="131014134939.png,131014134947.png"\; state="\{\{state\}\}">T</figure-callout>}}_i^{}\&times;(I_f+T_z)$

Therefore final Removing Tactics reaches determines that the foundation of clearing up scheme is for making the conflict resolution Least-cost:

${\hat{P}}_{\mathrm{CT}}^{*}=\min \{{\hat{P}}_{\mathrm{CT}}^1,{\hat{P}}_{\mathrm{CT}}^2\}$

In like manner can obtain other conflict resolution costs.

(4) formulate the scheme of clearing up of conflict according to the Removing Tactics of conflict, and adjust the math modeling of train operation, be:

$\underset{i=1}{\overset{N_D}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{K_D}{\mathrm{\&Sigma;}}}{T}_{i,k}\&times;\mathrm{\&Delta;}{T}_{i,k}\&times;V_{\mathrm{CT}}^{i.k}+\underset{i=1}{\overset{N_D}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{K_D}{\mathrm{\&Sigma;}}}{T}_{j,k}\&times;\mathrm{\&Delta;}{T}_{j,k}\&times;V_{\mathrm{CT}}^{j.k}$

The urine of above numerical value is definite conflict resolution scheme.

Wherein, T _i,kand T _j,kbe respectively uplink and downlink train i and j and carry out the train operation eigenwert in train operation conflict metrics process at the k station respectively.

Δ T _i,kwith Δ T _j,kbe respectively the uplink and downlink train operation and depart from the operational plan time, comprising that arrival time departed from the time leaving from station departs from.

with

be respectively the aftereffect conflict of descending after conflict resolution and the up train, the function of they are that train is actual arrive at a station/time-division leaving from station, be to determine all kinds of conflict possibility sum in future of a train arrival/obtain by the train operation conflict prediction after the time-division leaving from station.

The math modeling of this train operation conflict resolution scheme is subject to multiple constraint, is the non-linear mixed-integer programming model of single goal.From model, can find out, the quantity of its independent variable and constraint condition is very large, therefore this math modeling must meet all constraint condition simultaneously and could really complete conflict resolution, the present embodiment will illustrate the particular content of constraint condition, convenient for follow-up modeling, constant and the variable here needs used are explained.At first the relevant constant related in model is described:

descending category-A train set in adjusting stage, the high speed train that this linear velocity is higher, $N_D^H=\left|T_D^H\right|,$ $\&CenterDot;\&CenterDot;\&CenterDot;,N_D^H;$

descending B in adjusting stage ₁the set of class train, the high speed train that this linear velocity is lower, $N_D^M=\left|T_D^M\right|,$ $i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_D^M;$

descending B in adjusting stage ₂the set of class train, the cross-line high speed train,

T _d: down train set in the adjusting stage has $T_D=T_D^H\&cup;T_D^M\&cup;T_D^O,N_D=\left|T_D\right|,i=\mathrm{1,2},L,N_D;$

up category-A train set in adjusting stage, the high speed train that this linear velocity is higher, $N_U^H=\left|T_U^H\right|,$ $i=\mathrm{1,2},L,N_U^H;$

up B in adjusting stage ₁the set of class train, the high speed train that this linear velocity is lower, $N_U^M=\left|T_U^M\right|,$ $i=\mathrm{1,2},L,N_U^M;$

up B in adjusting stage ₂the set of class train, the cross-line high speed train, $N_U^O=\left|T_U^O\right|,$ $i=\mathrm{1,2},L,N_U^O;$

T _u: up train set in the adjusting stage has $T_U=T_U^{H}U{T}_U^{M}U{T}_U^O,N_U=\left|T_U\right|,i=\mathrm{1,2},L,N_U;$

T: train set in the adjusting stage, so T=T _duT _u, N=|T|, N=N _d+ N _u, i=1,2, L, N;

W _i: the priority valve of i row train in the adjusting stage,

meet

λ (i): i row type of train, λ (i)=1 represents the category-A train, λ (i)=2 represents B ₁the class train, λ (i)=3 represents B ₂the class train,

θ: type of train, θ=1 represents the category-A train, θ=2 represent B ₁the class train, θ=3 represent B ₂the class train;

S _d: the set of descending station is numbered K successively to each station by down direction _d=| S _d|, k=1,2, L, K _d;

B _d: the downlink interval set is numbered K successively to each interval by down direction _d-1=|B _d|, kk=1,2, L, K _d-1;

S _u: the set of up station is numbered K successively to each station by up direction _u=| S _u|, k=1,2, L, K _u;

B _u: the uplink interval set is numbered K successively to each interval by up direction _u-1=|B _u|, kk=1,2, L, K _u-1;

according to train operation plan, the operation pathway of i row down train on high speed railway---station phraseology, $G_D^i=\{{\mathrm{KD}}_1^i,{\mathrm{KD}}_2^i,L,{\mathrm{KD}}_t^{i}L,{\mathrm{KD}}_{K\left(i\right)}^i\},\&ForAll;i\&Element;T_D,G_D^i\&SubsetEqual;S_D;$

with mean respectively according to train operation plan the t of i row down train process and last station;

according to train operation plan, the operation pathway of the i row up train on high speed railway---station phraseology, $G_U^i=\{{\mathrm{KU}}_1^i,{\mathrm{KU}}_2^i,L,{\mathrm{KU}}_t^{i}L,{\mathrm{KU}}_{K\left(i\right)}^i\},\&ForAll;i\&Element;T_U,G_U^i\&SubsetEqual;S_U;$

with

mean respectively according to the row train operation plan t of i row up train process and last station;

the operation pathway of i row down train on high speed railway---station phraseology in adjusting stage, $J_D^i=\{{\mathrm{kd}}_1^i,{\mathrm{kd}}_2^i,L,{\mathrm{kd}}_t^{i}L,{\mathrm{kd}}_{K\left(i\right)}^i\},\&ForAll;i\&Element;T_D,J_D^i\&SubsetEqual;S_D;$

with

t and last station meaning respectively i row down train process in the adjusting stage;

the operation pathway of i row down train on high speed railway---interval phraseology in adjusting stage, $V_D^i=\{{\mathrm{kkd}}_1^i,{\mathrm{kkd}}_2^i,L,{\mathrm{kkd}}_t^{i}L,{\mathrm{kkd}}_{\mathrm{KK}\left(i\right)}^i\},\&ForAll;i\&Element;T_D,V_D^i\&SubsetEqual;B_D;$ with

t and last interval meaning respectively i row down train process in the adjusting stage;

the operation pathway of the i row up train on high speed railway---station phraseology in adjusting stage, $J_U^i=\{{\mathrm{ku}}_1^i,{\mathrm{ku}}_2^i,L,{\mathrm{ku}}_t^{i}L,{\mathrm{ku}}_{K\left(i\right)}^i\},\&ForAll;i\&Element;T_U,J_U^i\&SubsetEqual;S_U;$

with

t and last station meaning respectively i row up train process in the adjusting stage;

the operation pathway of the i row up train on high speed railway---interval phraseology in adjusting stage, $V_U^i=\{{\mathrm{kku}}_1^i,{\mathrm{kku}}_2^i,L,{\mathrm{kku}}_t^{i}L,{\mathrm{kku}}_{\mathrm{KK}\left(i\right)}^i\},\&ForAll;i\&Element;T_U,V_U^i\&SubsetEqual;B_U;$

with

t and last interval meaning respectively i row up train process in adjusting stage;

θ class down train is at the pure time of run of the minimum of kk downlink interval, $\&ForAll;i\&Element;T_D,$ $\&ForAll;\mathrm{kk}\&Element;B_D;$

the θ class up train is at the pure time of run of the minimum of kk uplink interval, $\&ForAll;i\&Element;T_U,$ $\&ForAll;\mathrm{kk}\&Element;B_U;$

θ class down train is in the cycling start additional period at k descending station,

θ class down train is in the parking additional period at k descending station,

the θ class up train is in the cycling start additional period at k up station,

the θ class up train is in the parking additional period at k up station,

u class down train and v class down train be at the interval time that sets out at k descending station, $\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;S_D;$

u class down train and v class down train be at the interarrival time at k descending station, $\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;S_D;$

the u class is up

Train and the v class up train be at the interval time that sets out at k up station,

the u class up train and the v class up train be at the interarrival time at k up station, $\&ForAll;i\&Element;T_U,\&ForAll;k\&Element;S_U;$

i row down train is in the minimum dwell time of the descending station of its k, if i row train starts at k station or during terminal, make its minimum dwell time equal 0,

the i row up train is in the minimum dwell time of the up station of its k,

k descending station to the hair line number,

k up station to the hair line number,

the skylight time opening of kk downlink interval,

the skylight concluding time of kk downlink interval,

the skylight time opening of kk uplink interval,

the skylight concluding time of kk uplink interval,

i row down train, in the plan time of advent at k descending station, if when i row train starts at k station, makes its plan equal to plan the time of departure time of advent,

i row down train is in the plan time of departure at k descending station, if when k station Zhongdao, making it plan time of departure, i row train equals to plan the time of advent,

the i row up train is in the plan time of advent at k up station,

the i row up train is in the plan time of departure at k up station,

the 0-1 constant, if i row down train need to be handled passenger's business at k descending station, be taken as 1, otherwise be 0, $\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;J_D^i;$

the 0-1 constant, if the i row up train need to be handled passenger's business at k up station, be taken as 1, otherwise be 0, $\&ForAll;i\&Element;T_U,\&ForAll;k\&Element;J_U^i;$

i is listed as descending cross-line B ₂the class train can allow earliest arrival time when arriving its high speed railway linking station (cross-line point),

i is listed as descending cross-line B ₂the class train can allow the time of advent the latest when arriving its high speed railway linking station (cross-line point),

i lists capable cross-line B ₂the class train can allow earliest arrival time when arriving its high speed railway linking station (cross-line point),

i lists capable cross-line B ₂the class train can allow the time of advent the latest when arriving its high speed railway linking station (cross-line point),

i train train connecting time standard that continues with it;

passenger's connecting time standard of i train and ii train;

M: an abundant large integer.

Secondly the correlated variables related in model is described:

i row down train is in the actual time of arrival at k descending station,

i row down train is in the actual time of departure at k descending station,

the i row up train is in the actual time of arrival at k up station,

the i row up train is in the actual time of departure at k up station,

the 0-1 variable, if i row down train entered kk downlink interval before j row down train, its value is taken as 0, otherwise is 1,

and i ≠ j,

the 0-1 variable, if the i row up train entered kk uplink interval before the j up train, its value is taken as 0, otherwise is 1,

and i ≠ j,

the 0-1 variable, if i row down train passes through at k descending station, be taken as 0, otherwise be 1, $\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;J_D^i;$

the 0-1 variable, if the i row up train passes through at k up station, be taken as 0, otherwise be 1, $\&ForAll;i\&Element;T_U,\&ForAll;k\&Element;J_U^i;$

The following describes its concrete constraint condition:

A. train interval time of run constraint

According to Train Stopping or by two in abutting connection with station, the section operation time need to consider that train plays additional time for stopping, that is:

$X_D^{i,\mathrm{kk}+1}\&GreaterEqual;Y_D^{i,\mathrm{kk}}+t_D^{\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}+{\mathrm{\&phi;}}_D^{i,\mathrm{kk}}{\mathrm{\&alpha;}}_D^{\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}+{\mathrm{\&phi;}}_D^{i,\mathrm{kk}+1}{\mathrm{\&beta;}}_D^{\mathrm{\&lambda;}\left(i\right),\mathrm{kk}+1};\&ForAll;i\&Element;T_D,\&ForAll;\mathrm{kk}\&Element;V_D^i$

$X_U^{i,\mathrm{kk}+1}\&GreaterEqual;Y_U^{i,\mathrm{kk}}+t_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}+{\mathrm{\&phi;}}_U^{i,\mathrm{kk}}{\mathrm{\&alpha;}}_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}+{\mathrm{\&phi;}}_U^{i,\mathrm{kk}+1}{\mathrm{\&beta;}}_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{kk}+1};\&ForAll;i\&Element;T_U,\&ForAll;\mathrm{kk}\&Element;V_U^i$

When above formula means that kk the downlink interval of i row down train on its operating path moved, it arrives the time at this station, the place ahead, interval and must not be less than interval minimum motion time from the time gap at this station, if train passes through at certain station, get the corresponding parking additional hours that rises and be divided into 0; When following formula means that kk the uplink interval of the i row up train on its operating path moves, it arrives the time at this station, the place ahead, interval and must not be less than interval minimum motion time from the time gap at this station.

B. train dwelling time-constrain

According to train operation plan, each train need to move part or all of station on pathway at it and handle passenger's business.For each train, at it, need to handle on the station of passenger's business, need to give the necessary dwell time, get on or off the bus or carry out necessary technical operation for the passenger, that is:

$Y_D^{i,k}\&GreaterEqual;X_D^{i,k}+s_D^{i,k};\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;J_D^i;$

$Y_U^{i,k}\&GreaterEqual;X_U^{i,k}+s_U^{i,k};\&ForAll;i\&Element;T_U,\&ForAll;k\&Element;J_U^i;$

Above formula means that the k of i row down train on its operating path the length of the halt on descending station must not be less than the minimum dwell time, and following formula means that the k of the i row up train on its operating path the length of the halt on up station must not be less than the minimum dwell time.

C. set out tracking interval constraint of train

When adjacent train is from same station in the same way, need meet certain tracking interval that sets out, that is:

$Y_D^{i,k}\&GreaterEqual;Y_D^{j,k}+p_D^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),k}$ Or $Y_D^{j,k}\&GreaterEqual;Y_D^{i,k}+p_D^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),k};$ Wherein

$\&ForAll;i,j\&Element;T_D$ And i ≠ j, $\&ForAll;k\&Element;J_D^i\&cap;J_D^j;$

$Y_U^{i,k}\&GreaterEqual;Y_U^{j,k}+p_U^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),k}$ Or $Y_U^{j,k}\&GreaterEqual;Y_U^{i,k}+p_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),k};$ Wherein

$\&ForAll;i,j\&Element;T_U$ And i ≠ j, $\&ForAll;k\&Element;J_U^i\&cap;J_U^j.$

Above formula means when adjacent in the same way i row set out with the k of j row down train in its identical pathway descending station to meet the tracking interval that sets out; Following formula means when adjacent in the same way i row set out with the k of the j row up train in its identical pathway up station to meet the tracking interval that sets out;

By introducing decision variable

above-mentioned nonlinear restriction can be converted to linear restriction, that is:

$Y_D^{i,\mathrm{kk}}\&GreaterEqual;Y_D^{j,\mathrm{kk}}+p_D^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}-(1-{\mathrm{\&chi;}}_D^{i,j,\mathrm{kk}})\mathrm{gM},\&ForAll;i,j\&Element;T_D$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_D^{i}I{V}_D^j;$

$Y_D^{j,\mathrm{kk}}\&GreaterEqual;Y_D^{i,\mathrm{kk}}+p_D^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),\mathrm{kk}}-{\mathrm{\&chi;}}_D^{i,j,\mathrm{kk}}\mathrm{gM},\&ForAll;i,j\&Element;T_D$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_D^{i}I{V}_D^j;$

$Y_U^{i,\mathrm{kk}}\&GreaterEqual;Y_U^{j,\mathrm{kk}}+p_U^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}-(1-{\mathrm{\&chi;}}_U^{i,j,\mathrm{kk}})\mathrm{gM},\&ForAll;i,j\&Element;T_U$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_U^{i}I{V}_U^j;$

$Y_U^{j,\mathrm{kk}}\&GreaterEqual;Y_U^{i,\mathrm{kk}}+p_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),\mathrm{kk}}-{\mathrm{\&chi;}}_U^{i,j,\mathrm{kk}}\mathrm{gM},\&ForAll;i,j\&Element;T_U$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_U^{i}I{V}_U^j.$

D. train arrives the tracking interval constraint

When adjacent train arrives same station in the same way, need meet certain arrival tracking interval, that is:

$X_D^{i,k}\&GreaterEqual;X_D^{j,k}+q_D^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),k}$ Or $X_D^{j,k}\&GreaterEqual;X_D^{i,k}+q_D^{\mathrm{\&lambda;}\left(i\right)\mathrm{\&lambda;}\left(j\right),k};$ Wherein $\&ForAll;i,j\&Element;T_D$ And i ≠ j, $\&ForAll;k\&Element;J_D^{i}I{J}_D^j;$

$X_U^{i,k}\&GreaterEqual;X_U^{j,k}+q_U^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),k}$ Or $X_U^{j,k}\&GreaterEqual;X_U^{i,k}+q_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),k},$ Wherein $\&ForAll;i,j\&Element;T_U$ And i ≠ j, $\&ForAll;k\&Element;J_U^i\&cap;J_U^j.$

Need meet the arrival tracking interval when wherein above formula means adjacent in the same way i row with the k of j row down train in arriving its identical pathway descending station, following formula means adjacent in the same way i row and need meet the arrival tracking interval during the individual up station of k in the j row up train arrives its identical pathway.

By above-mentioned decision variable

above-mentioned nonlinear restriction is converted to linear restriction, that is:

$X_D^{i,\mathrm{kk}+1}\&GreaterEqual;X_D^{j,\mathrm{kk}+1}+q_D^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}-(1-{\mathrm{\&chi;}}_D^{i,j,\mathrm{kk}})\mathrm{gM},\&ForAll;i,j\&Element;T_D$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_D^{i}I{V}_D^j;$

$X_D^{j,\mathrm{kk}+1}\&GreaterEqual;X_D^{i,\mathrm{kk}+1}+q_D^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),\mathrm{kk}}-{\mathrm{\&chi;}}_D^{i,j,\mathrm{kk}}\mathrm{gM},\&ForAll;i,j\&Element;T_D$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_D^{i}I{V}_D^j;$

$X_U^{i,\mathrm{kk}+1}\&GreaterEqual;X_U^{j,\mathrm{kk}+1}+q_U^{\mathrm{\&lambda;}\left(j\right),\mathrm{\&lambda;}\left(i\right),\mathrm{kk}}-(1-{\mathrm{\&chi;}}_U^{i,j,\mathrm{kk}})\mathrm{gM},\&ForAll;i,j\&Element;T_U$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_U^{i}I{V}_U^j;$

$X_U^{j,\mathrm{kk}+1}\&GreaterEqual;X_U^{i,\mathrm{kk}+1}+q_U^{\mathrm{\&lambda;}\left(i\right),\mathrm{\&lambda;}\left(j\right),\mathrm{kk}}-{\mathrm{\&chi;}}_U^{i,j,\mathrm{kk}}\mathrm{gM},\&ForAll;i,j\&Element;T_U$ And i ≠ j, $\&ForAll;\mathrm{kk}\&Element;V_U^{i}I{V}_U^j.$

E. need to handle the constraint at the time of departure of passenger's business station

$Y_D^{i,k}\&GreaterEqual;y_D^{i,k},\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;J_D^i$ And $c_D^{i,k}=1$

$Y_D^{i,k}\&GreaterEqual;y_D^{i,k},\&ForAll;i\&Element;T_D,\&ForAll;k\&Element;J_D^i$ And $c_D^{i,k}=1$

Above formula means that i row down train institute within its operation adjusting stage, when passenger's business need be handled in certain station, must not be less than figure its time of departure at this station and determine the time of departure; Following formula means that row up train institute within its operation adjusting stage, when passenger's business need be handled in certain station, must not be less than figure and determine the time of departure its time of departure at this station.

F. station is to the hair line constraint

The train number that rests on certain station in the same time must not be greater than the available hair line number that arrives in this station, that is:

$\underset{i\&Element;T_D}{\mathrm{\&Sigma;}}{M}_D(i,j,k)\&le;F_D^k,\&ForAll;k\&Element;J_D^{i}I{J}_D^j$

$\underset{i\&Element;T_U}{\mathrm{\&Sigma;}}{M}_U(i,j,k)\&le;F_U^k,\&ForAll;k\&Element;J_U^{i}I{J}_U^j$

MD(i, j, k) mean that the same time rests on the train number at k descending station (containing passing through train) and must not be greater than its available hair line number that arrives; MU (i, j, k) means that the same time rests on the train number at k up station (containing passing through train) and must not be greater than its available hair line number that arrives.

G. train groups connecting time constraint

While for two trains, sharing one group of motor train unit, in order not affect starting on schedule of train, after the deadline that motor train unit are served as last transportation burden should be not later than, a transportation burden train starts and deducts the connecting time of motor train unit necessity constantly, that is:

$X_D^{i,k}+T_z\&le;y_D^{j,k},\&ForAll;T_D,\&ForAll;i\&Element;T_{D}U{T}_U,\mathrm{i\&Xi;j}$

$X_U^{i,k}+T_z\&le;y_U^{j,k},\&ForAll;T_U,\&ForAll;i\&Element;T_{D}U{T}_U,i\mathrm{\&Xi;j}$

H. passenger transference time-constrain

In order to ensure passenger's travelling efficiency, reduce passenger's transfer wait time as far as possible, should ensure continuing between two trains of a large amount of passenger transferences, train be got to the station constantly be not later than the transfer train to set out and deduct the transfer connecting time of passenger's necessity constantly, that is:

$X_D^{i,k}+T_c\&le;y_D^{j,k},\&ForAll;T_D,\&ForAll;j\&Element;T_{D}U{T}_U,i\mathrm{\&Xi;j}$

$X_U^{i,k}+T_c\&le;y_U^{j,k},\&ForAll;T_U,\&ForAll;j\&Element;T_{D}U{T}_U,i\mathrm{\&Xi;j}$

I. overline train cross-line time-constrain

In order to make cross-line B2 class train and to be connected the operation of alignment car good continuing arranged, do not produce the propagation of late impact between circuit due to train, realize the coordination of road network train operation, the time of overline train when arriving high speed railway linking station (cross-line point) need to meet the specific time interval, that is:

$H_D^{i,K_{K\left(i\right)}^i}\&le;X_D^{i,K_{K\left(i\right)}^i}\&le;Q_D^{i,K_{K\left(i\right)}^i},\&ForAll;i\&Element;T_D^O$ And $K_{K\left(i\right)}^i\&Element;J_D^i$

$H_U^{i,K_{K\left(i\right)}^i}\&le;X_U^{i,K_{K\left(i\right)}^i}\&le;Q_U^{i,K_{K\left(i\right)}^i},\&ForAll;i\&Element;T_U^O$ And $K_{K\left(i\right)}^i\&Element;J_U^i$

Above formula means that i row cross-line B2 class train need to meet the specific time interval when arriving its high speed railway linking station; Following formula means that i lists capable cross-line B2 class train and need to meet the specific time interval when arriving its high speed railway linking station (cross-line point).

J. Window time constraint

In the regulation Window time, train can not carry out the skylight operation, that is:

$X_D^{i,\mathrm{kk}+1}\&le;L_D^{\mathrm{kk}}U{Y}_D^{i,\mathrm{kk}}\&GreaterEqual;R_D^{\mathrm{kk}},\&ForAll;i\&Element;T_D$ And $\mathrm{kk}\&Element;V_D^i$

$X_U^{i,\mathrm{kk}+1}\&le;L_U^{\mathrm{kk}}U{Y}_U^{i,\mathrm{kk}}\&GreaterEqual;R_U^{\mathrm{kk}},\&ForAll;i\&Element;T_U$ And $\mathrm{kk}\&Element;V_U^i$

Above formula means that i row down train can not arrive or set out in the Window time of kk downlink interval, and following formula means that the i row up train can not arrive or set out in the Window time of kk uplink interval.

K. logical constraint

Arriving when whether train does not stop by station sent out a time relationship:

$Y_D^{i,k}-X_D^{i,k}\&le;{\mathrm{\&phi;}}_D^{i,k}\mathrm{gM},\&ForAll;i\&Element;T_D,k\&Element;J_D^i$

$Y_U^{i,k}-X_U^{i,k}\&le;{\mathrm{\&phi;}}_U^{i,k}\mathrm{gM},\&ForAll;i\&Element;T_U,k\&Element;J_U^i$

Mathematical programming model and institute's Prescribed Properties that the high speed rail train operation of having set up before contact is adjusted, can learn that this mathematical programming model has following characteristics:

First: objective function be single-row train index and, do not have coupled relation between the index of different trains.For such optimization aim, the optimum equivalence of the optimum of single-row train performance figure and train overall performance index, take rational method, it is feasible that model is not decomposed by train, in other words, such objective function structure is conducive to model is decomposed, and for reducing the model solution difficulty, provides possibility.

Second: train interval time of run and train dwelling time-constrain all adopt the inequality form, meet operation and adjust strategy, and late train is recovered on schedule as far as possible.In the train operation adjustment process, by utilizing running chart redundancy time change train interval time of run and dwell time, late train is recovered on schedule, or the degree of alleviation Train delay is the normal strategy adopted; Interval pure time of run and station dwell time all adopt minimum value, can take full advantage of planned train graph elasticity, recover normal driving as far as possible.

The the 3rd: by introducing the 0-1 variable, train is set out and arrives the tracking interval nonlinear restriction to be converted to linear restriction, although reduced the difficulty that too much nonlinear restriction causes to model solution, but meanwhile, too much 0-1 variable adds model, has strengthened the difficulty of model solution simultaneously.

The the 4th: adjust problem for concrete train operation, bulk redundancy constraint condition is arranged.Within the concrete operation adjusting stage, every row train likely only has the part operating path to complete in this stage, between two row trains, also only need thus to consider that corresponding partially restrained condition gets final product, from this aspect, although in master model, constraint condition is a lot, but when problem is adjusted in concrete operation specific to each, exist a large amount of redundancy constraint condition, realize getting final product in the feasible zone that the constraint condition that optimization aim only need be considered at needs forms.

The the 5th: in order to determine T _i,kand T _{ii, k}need to determine the value of 5 state indexs of train operation, in these 5 indexs, train class, train route completeness, train just can obtain corresponding train i and the value of ii when standing k in 4 indexs such as station parking nargin, train interval operation nargin after train diagram is given, in a stage plan for adjustment, are one group of constants.And the horizontal index of train running on scheduled time need be according to the just late situation value of train arrival time, T _i,kand T _{ii, k}it is respectively the actual time of arrival at station k about train i and ii

with

function.

The 6th: the aftereffect conflict of conflict resolution is the summation of all kinds of conflicts in calculating each train follow-up operation process according to the conflict prediction method after determining in train arrival/time-division leaving from station.In carrying out the conflict prediction process, train interval motion time, the time-division of stopping, redundancy time etc. have all been determined after running chart is determined.Therefore, the aftereffect conflict of conflict resolution with respectively about the independent variable train arrival & leaving function in the moment.Wherein train interarrival time conflict and to use conflict to hair line be the function of train arrival time deviation, all the other conflicts are functions of train time deviation leaving from station.

The 7th: train operation conflict resolution Least-cost is objective function, and because the aftereffect conflict of train operation eigenwert and conflict resolution is all to obtain by interative computation, its value is subject to Train Dispatching person to dispatch preference to be affected littlely, has higher objectivity.The aftereffect conflict of train operation eigenwert and conflict resolution all can be tried to achieve by programmed process, and the key that solves of model is to determine that rational train operation plan departs from the time.

(5) scheme of clearing up that judges whether all conflicts determines, if determined the scheme of clearing up of all conflicts, and this conflict resolution process end, if do not determined the scheme of clearing up of all conflicts, repeating step (3)～(5).

After the mathematical programming model of setting up this high speed rail train operation adjustment, need to be solved.This model solution algorithm is more, but most of algorithm is accurate not, and the result solved is very not satisfactory.Conventional nonlinear optimization method normally requires the optimization problem continuously differentiable, and the General Requirements initial solution is feasible solution, and many intelligence computations obtain locally optimal solution, aspect global optimizing, has wretched insufficiency.For problem is adjusted in train operation, train arrives, sets out constantly as discrete, does not have the continuously differentiable feature, and the search of different operational plans is calculated as pattern shooting.The train operation adjustment problem of M station N train of take is example, needing definite train arrival & leaving moment number is 2MN, the variable of adjusting the arriving and leaving moment of train as train operation, this train operation adjustment problem has 2MN variable, its calculated amount is very big, and there are the Complex Constraints in train travelling process and the time of running the strong characteristics of logicality, therefore, use the conventional non-linear optimization technique to be difficult to construct simple high speed rail train operation efficiently and adjust practical algorithm.

Genetic algorithm (Genetic Algorithm is called for short GA) is based on the adaptive stochastic search optimized algorithm of Darwinian evolution " survival of the fittest, the survival of the fittest ".Genetic algorithm can be brought into play its global optimizing advantage solving aspect the multiple constraint nonlinear programming problem, in the widespread use of industrial technology field, optimizes field in railway traffic organization and has also obtained tremendous development and application.The key step of genetic algorithm is followed successively by initialization of population, individual evaluation, genetic manipulation and stops judgement, wherein initialization of population is to realize by genetic coding, individual evaluation is to obtain by the population at individual target function value is carried out to Adaptability Evaluation, genetic manipulation comprises selection, three work of crossover and mutation, the search condition that stops judgement and be by setting is stopped algorithm, and output has the optimum individual of maximum adaptation degree and variate-value thereof as optimal solution.

The characteristics that the present invention adjusts according to high speed rail train operation, design solves train operation and adjusts the genetic algorithm (Real-coded Genetic Algorithm, RGA) of problem based on real coding.RGA is used each train encode and directly carry out genetic manipulation in the arriving and leaving moment at each station, without carrying out specific decode procedure, reduce the complexity of algorithm, especially for high speed rail train operation, adjusted extensive, the multivariable optimization problem of this class, can improve the efficiency of algorithm.

The operation practice of the circuits such as the Beijing-Tianjin inter-city high ferro that China has put into effect, military wide high ferro and Beijing-Shanghai express railway shows: the running interval at China Express Railway operation initial stage is larger, train diagram redundancy time is large, it is large that elasticity is adjusted in train operation, and the train operation adjustment difficulty is less.But along with the development of high speed railway passenger transport market, high density, large volume of traffic will be the development tendencys of China Express Railway, and now Train Regulation Model research efficiently is very important.

The present embodiment builds the example of a high density plan of adjusting train operation according to the operation practice of China's high ferro.And the object lesson that utilizes the matlab7.8 high speed rail train operation adjustment genetic algorithm for solving program based on the train operation conflict resolution of having write to be adjusted the periodic plan (3h) that contains 5 stations, 10 trains, 260km section illustrates the flow process that solves of this genetic algorithm.

One, parameter setting

1. determine the essential information of train operation

M=5 is counted at station, station sequence number m=1,2 ..., 5.

Train is counted N=10, train sequence number m=1,2 ..., 10.

Interval number Q=4, interval sequence number q=1,2 ..., 4.

The plan table time of running of known one-phase plan is as shown in table 1.

Table 1

Wherein "-" means that train is for starting or the Zhongdao train.Train 1 and train 5 be for entering section and, at the train of section destination stop Zhongdao, train 2, train 3, train 4, train 7, train 8 and train 10 are the train of the Zhongdao of starting in section, and train 6 and train 9 are the train of outside the section Zhongdao of starting in section.

Therefore its example periodic plan running chart as shown in Figure 3.

The train class attribute of each train is as shown in table 2, and it is constant at the train class at each station to establish each train.

Table 2

Wherein " 3 " mean high-grade high speed train, and " 2 " mean the inferior grade high speed train.

The mileage number completed during to each station according to each train operation accounts for its omnidistance ratio, calculates each train as shown in table 3 in the route completeness at each station.

Table 3

Each train operation state index weights:

w=[0.2220.4510.1020.0620.163]

The section operation of each train and station parking Triangular Fuzzy Number time are respectively as shown in table 4 and table 5.

Table 4

Table 5

Table 6 is each train motor train unit connecting time table.

Table 6

Train 5 and train 10 have a large amount of passenger flows to change to other trains at station 5, and the transfer deadline of this two train is respectively 43980s and 46680s.

Overline train 6 and the 9 permission cross-line time intervals at station 5 are respectively [43980,45180] and [45720,46920].

Stand 1 and station 5 be the terminal station that starts, be set to hair line all enough, the 2～4 every station one directions of standing of standing all have two to arrive hair line.

Because the high speed railway skylight time of offering is 0～21600s, so this example does not relate to the problem that time conflict is offered in train operation and skylight.

Train plays additional time for stopping t _q=t _t=120s, train arrives, minimum tracking interval I sets out _min=180s.

All trains all possess the engineering factor that utilizes redundancy time to come at the right time and move, and the inferior grade high speed train is the shortest at the station length of the halt when coming at the right time operation is 120s, and high-grade high speed train is the shortest at the length of the halt of standing when coming at the right time operation is 60s.

Two, genetic parameter setting

Population scale NIND=60;

The variation Probability p _m=0.03;

Maximum genetic algebra MAXGEN=1500;

GGAP=0.1；

Variation step-length h=60s.

Three, the emulation sight one: the late 600s terminal point 1 of train 1

Train 1 and train 2 be in the station 2 interval time conflicts of setting out, simultaneously at station 3 with stand and 4 the interarrival time conflict all occurs and the interval time conflict of setting out.The row of advancing by leaps and bounds through liquidating obtains plan of adjusting train operation as shown in table 7 and Fig. 4 after clearing up and adjusting train operation plan, and its train operation conflict resolution cost optimization curve as shown in Figure 5.

Table 7

In 1 short period of train when late, only by change train 1 at the frequency at station 1 and train 2 frequency at station 2, and make train 1 take full advantage of the operation of coming at the right time of its redundancy time at interval and station, the strategy used has translation operation line and two kinds of single Strategy of Conflict Resolution of exchange travel line, eliminated the conflict before adjusting, operation to train 3 to 10 does not exert an influence, the conflict resolution cost that is adjusted plan is 2397, total late time of all train Zhongdao is kept to 180s, obtained this operation result iteration 990 generations, 86s consuming time, aspect effective, can satisfy the demands.

Four, the emulation sight two: the late 1800s terminal point 1 of train 1

If train 1 utilizes redundancy time to reduce the late time as far as possible, it will produce at station 1 interval time that sets out with train 5 and conflict, and all produce interval the conflict in each interval with train 5, at station 4, with the train 3 generations interval time that sets out, conflict.The row of advancing by leaps and bounds through liquidating obtains plan of adjusting train operation as shown in table 8 after clearing up and adjusting train operation plan.

Table 8

And obtain the late 1800s train operation of train 1 conflict resolution cost diagram of curves, as shown in Figure 6.

In 1 long period of train when late, train 1 whole process takes full advantage of the operation of coming at the right time of its redundancy time at interval and station, the late 360s that sets out of train 5, train 4 is at station 2 train 1 to be avoided and trains 5, train 1 is at station 3 trains 5 to be avoided, the translation operation line, exchange travel line and three kinds of single Strategy of Conflict Resolution of change overtaking scheme have all used, eliminated the conflict before adjusting, operation to other trains does not exert an influence, the conflict resolution cost that is adjusted plan is 21858, total late time of all train Zhongdao is kept to 1380s, obtained this operation result iteration 1006 generations, 86s consuming time.

According to above-described embodiment, just can realize well the present invention.

Claims (5)

1. the high speed rail train operation method of adjustment based on feedback regulation, is characterized in that, comprises the following steps:

(1) obtain the current time train running state, pass through a station and state of section;

(2) detect and whether have conflict occur or in future time, whether potential conflict arranged, if not, when future time arrives, upgrade data message, and repeat this step, otherwise, next step entered;

(3) carry out the train operation conflict resolution according to the train operation regulation rule, and result is adjusted the train operation scheme until train operation finishes by clearing up, otherwise, previous step returned to.

2. a kind of high speed rail train operation method of adjustment based on feedback regulation according to claim 1, is characterized in that, in described step (3), the method for conflict resolution is as follows:

(3a) carry out train operation conflict tolerance;

(3b) determine train operation conflict resolution order;

(3c), according to the Removing Tactics of single conflict, calculate one of them conflict resolution cost;

(3d) formulate the scheme of clearing up of this conflict;

(3e) judge whether all conflicts to clear up scheme definite, if determined the scheme of clearing up of all conflicts, this conflict resolution process finishes, if do not determined the scheme of clearing up of all conflicts, repeating step (3c)～(3e).

3. a kind of high speed rail train operation method of adjustment based on feedback regulation according to claim 2, is characterized in that, described definite train operation conflict resolution order is to sort from high to low according to the severity of conflict.

4. a kind of high speed rail train operation method of adjustment based on feedback regulation according to claim 3, is characterized in that, the Removing Tactics of described single conflict be the translation operation line, the exchange travel line, the change stop and overtaking at least one.

5. a kind of high speed rail train operation method of adjustment based on feedback regulation according to claim 4, is characterized in that, adjusts the constraint that train operation adjustment scheme is subject to following 11 aspects in described step (3):

(I) train interval time of run constraint;

(II) train dwelling time-constrain;

(III) set out tracking interval constraint of train;

(IV) train arrives the tracking interval constraint;

(V) need handle the constraint at the time of departure of passenger's business station;

(VI) station is to the constraint of hair line number;

(VII) motor train unit connecting time constraint;

(VIII) passenger transference time-constrain;

(IX) overline train cross-line time-constrain;

(X) Window time constraint;

(XI) logical constraint.

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