CN105355035B - The traffic dispersion method formulated based on traffic flow distribution distributional difference - Google Patents
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Abstract
The invention discloses a kind of traffic dispersion method formulated based on traffic flow distribution distributional difference for belonging to urban traffic control technical field.It is the traffic dispersion method being conceived under non-burst traffic behavior, this method is the actual traffic situation according to urban road, each section stopping state, determine Impedance Function scale factor, establish Impedance Function, user is matched somebody with somebody in the quantizing scale factor to Impedance Function, the system based on dynamic system optimal and dynamic user optimization carries out the calculating of scale factor with flow distribution；Compare the difference of flow result, by two-by-two than being worth to minimum difference；The scale factor in each section is updated, it is available for optimizing and revising until reaching optimization aim or scale factor without any section, being shunted to the system optimal situation of communications policy person for induction user's flow, improves the stable operation ability of system, realizes optimal traffic dispersion target.
Description
Patent field
The invention belongs to urban traffic control technical field, it is more particularly to a kind of based on traffic flow distribution distributional difference formulate
Traffic dispersion method.The traffic dispersion method being specifically conceived under non-burst traffic behavior, proposition will match somebody with somebody Flow Policy and quantify
Into the scale factor of Impedance Function, the system based on dynamic system optimal and dynamic user optimization with flow distribution carry out ratio because
The calculating of son.
Background technology
In traffic control system, Used in Dynamic Traffic Assignment (hereinafter referred to as DTA) is its theoretical foundation.DTA refers to according to one
Fixed optimization principles, algorithm transport need being assigned on section.
Optimization principles include：Dynamic user optimization (DUO) and dynamic system optimal (DSO).Wardrop proposes two within 1956
Bar criterion, first is user equilibrium (UE), i.e., user can not individually change the path planning of oneself to obtain lower trip generation
Valency, second is system optimal (SO), i.e., the cost sum of all users is minimum in total system.This two principle is to be based on static bar
What part put forward, develop into DUO and DSO respectively later, refer to respectively user when making policy decision optimal and system it is optimal.
One very natural conclusion is：DUO and DSO flow distribution results are usually what is differed.It is primarily due to
The reason for such：
1) difference of user's cost function：Trip cost of the user for oneself has different models, than if any user
Think that late cost weight is higher, some pursuit hourages are most short, and some pursuit roads are most short, etc.；
2) limitation of user's road conditions knowledge：User can not possess to hold to the overall situation of road conditions knowledge, is primarily due to go
Knowledge limitation in car way, while individual subscriber possesses the historical experience of personalization, so as to form the understanding to road conditions knowledge
Deviation；
3) constraint of road current conditions：After the upper road of user, it can not at any time be switched on the optimal path of oneself, have
It is probably because of constraint of the mutual constraint, weather in wagon flow traveling to the visual field, the constraint of pedestrian's non-motor vehicle, etc.；
Do not have at present some important technological inventions and documents how to induce user it is optimal with flow distribution to being
Optimal Distribution of uniting is close.Existing system for traffic guiding and technology typically just provide the path for meeting the specific travelling purpose of user
Planning function.
In addition, there is many literature research information to issue the influence to user behavior, transport information mainly specify that
Species, content and potential income and risk.
It is contemplated that DSO and DUO is linked up, by issuing certain induction information so that user enters according to DUO criterion
Row flow obtains the distribution close to DSO.Main Means include：
1) assume in the case of the concrete form of user's cost function is diversified, by the form of abstract function, derive
The correcting of system of users colony, to solve the above-mentioned first point flow difference brought.
2) second and third above-mentioned is control residual error for the Induction Control effect of traffic system, and negative-feedback is discussed
Inhibitory action.
3) think that the strategy of parametrization easily causes the overreaction (i.e. " overshoot ") of system, also discuss its solution in the lump
Scheme.
The content of the invention
The purpose of the present invention is to propose to a kind of traffic dispersion method formulated based on traffic flow distribution distributional difference, its feature exists
In comprising the following steps：
Step 1. each section stopping state, determines Impedance Function scale factor according to the actual traffic situation of urban road,
Establish Impedance Function form：
In formula, v represents the average observed speed of wagon flow, k1, k2, and k3 represents fitting constant.The step of fitting is：To observation
Obtained average speed and transit time, it is independent variable to take average speed, and transit time is dependent variable, generally with identical average
The vehicle of speed there will not be identical transit time on same section, therefore this formula can be fitted its top edge；
Step 2. establishes path scale factor matrix A to road network, to road network node i and j, by graph-theoretical algorithm, solves it
In all reachable paths, the scale factor for putting the path with minimum impedance value min is 1, the scale factor in remaining path
Multiple for respective impedance value to min, so as to obtain scale factor matrix A；
Step 3. proposes that object function is as follows, to solve：
Its restrictive condition is
In formula：(a) i is starting point；J is terminal；(b) Pi j are alternative total number of paths；(c) user group is at starting point i and end
Demand between point j is q；(d) m is the label in alternative path；(e)f′mIt is the initial roadlock of road of traffic administration person's issue, such as
Fruit is not added with any publishing policy and loyal issue, then f 'm=fm, but just lose proposed inducing effect；(f)umIt is
Integration variable, represent with the infinitesimal roadlock being carried in after system operation on road；(g) A represents the scale factor in alternative path
Matrix, a vector is deteriorated to herein；(h)Gm(A) division function is represented, its independent variable is scale factor matrix；So qGm
(A) what is represented is exactly the division number of colony；
Step 4. calculates globally optimal solution DSO, and calculates DUO, here using a norm, chooses optimization aim
In formula, FtThe road grid traffic traffic matrix for solving to come according to respective flow criterion is represented, therefore this optimizes
Target seeks to the difference between the derivable dynamic user optimization criterion of minimization and not derivable dynamic system optimal criterion
It is different；The definition of a wherein norm is the maximum of each column element absolute value sum of matrix；
Step 5. compares the difference of flow result, by two-by-two than being worth to minimum difference；To the ratio in each section
The factor is updated, and is to carry out subsequent time period using the scale factor matrix that last moment obtains as the initial value of subsequent time
Learning process, be available for optimizing and revising until reaching optimization aim or scale factor without any section, repeat this mistake
Journey terminates until system time.
Restrictive condition is in the step 3Its input is column vector scale factor, and output is capable
Vector, each representation in components select the ratio shared by the colony of each paths, and note isOften
The mapping of individual component is an injection, maps with the rising of scale factor and declines, that is, represents that the sign roadlock in the path is bigger,
User's ratio of selection accordingly reduces.
The step 3 chooses optimization aim and Impedance Function as primary condition, and initialization ratio was a upper period
Result of calculation.
Scale factor vector or matrix A in the step 3 are the parameters optimized and revised by traffic publisher, not
It is actual traffic network impedance scaling matrices, that is to say, that the essence of traffic guidance " soft " information is embodied in A matrix stacks
Marrow.
The beneficial effects of the invention are as follows propose by the use of the dimensionless scale factor of path impedance to be used as communication DUO and DSO
Bridge.By being modeled to all paths between OD pairs of each pair, the correcting mode of impedance function scale factor is proposed, is repaiied
Observation error of the driver of the positive effect rational that has been floating to every road.Said from the angle of user, without departing from certainly
The optimal property of right selection, says that object function declines effectively from the angle of system.It is possible thereby to find, it is such
Method has following two advantages：First, suitable for different Impedance Function models.Because be finally attributed to nondimensional ratio because
Submodel；2nd, increase the stability of system, predicted flow rate can be combined to improve the stability of a system to the system continuously run.
Being shunted to the system optimal situation of communications policy person for user's flow is induced in a word, improves the stable operation ability of system,
Realize optimal traffic dispersion target.
Embodiment
The specific algorithm of the present invention realizes that step is as follows：
Step 1, according to the actual traffic situation of urban road, each section stopping state, Impedance Function scale factor is determined,
Establish Impedance Function form：
In formula, v represents the average observed speed of wagon flow, k1, k2, and k3 represents fitting constant.The step of fitting is：To observation
Obtained average speed and transit time, it is independent variable to take average speed, and transit time is dependent variable, generally with identical average
The vehicle of speed there will not be identical transit time on same section, thus this formula can be fitted its average or
Lower edges.
Step 2, roadlock scale factor is randomly generated, it is met that restrictive condition isOr use
The optimum results of a upper period, as this initial value；
Step 3, DSO and DUO flow result is solved, the mode specifically solved is led to using classical newton descending method
Searching feasible descent direction is crossed, iterative user optimizes and the solution of system optimization；
Step 4, the difference of flow result is compared, by two-by-two than being worth to small difference；And it is set to 1；
Step 5, the mode of learning declined according to gradient, is updated, it is proposed that learning rate to the scale factor in each section
Δ=10-5, to the flow section more than globally optimal solution, it should increase its roadlock scale factor, be allowed to be more than 1, on the contrary should
Reduce, be less than 1；
Step 6, setting falling-threshold value is provided based on actual conditions, that is, can not be with true feelings when releasing news
Condition difference is too big, otherwise influences Consumer's Experience and confidence level；
Step 7, repeat step 3 to 6 is available for optimizing until reaching optimization aim or scale factor without any section
Adjustment.
Restrictive condition is in the step 3Its input is column vector scale factor, and output is capable
Vector, each representation in components select the ratio shared by the colony of each paths, and note isOften
The mapping of individual component is an injection, maps with the rising of scale factor and declines, that is, represents that the sign roadlock in the path is bigger,
User's ratio of selection accordingly reduces.
The step 3 chooses optimization aim and Impedance Function as primary condition, and initialization ratio was a upper period
Result of calculation.At the beginning of system brings into operation or emulates just, this initialization is random.
The unstability of the control of the inventive method is mainly derived between the difference of driver's roadlock model and driver
Competitive behavior.The former refers to：When actual operation, driver is not necessarily right according to the experience real-time update on upper road oneself
The observation impedance in path, such driver may not switch the path decision of oneself, it is also possible to because environmental constraints are driven
The person of sailing can not be switched in best decision in time, and in this case, system response is excessively slow, and the latter refers to：In real process,
Due to the game behavior between user, the shortest path of issue can be had a preference for by driver, can so as to cause " overshoot " to occur
To increase the renewal step-length of parameter by negative-feedback, can effectively solve.And the appearance of overshoot, except because driver's is inclined
It is also relevant with the precision of prediction of system outside love.Multi-scale prediction relies primarily on such a fact：Do not change in relative error
In the case of change, the absolute error of the traffic flow forecasting of small yardstick is smaller, and influence scale factor is absolute error, not
It is relative error, therefore scale factor is smaller by being influenceed；Meanwhile in the case where relative error is constant, continuously predict
Error can be that overgauge can also be minus deviation, and the adjustment for scale factor plays the role of positive.
Claims (4)
- A kind of 1. traffic dispersion method formulated based on traffic flow distribution distributional difference, it is characterised in that comprise the following steps：Step 1. each section stopping state, determines Impedance Function scale factor, established according to the actual traffic situation of urban road Impedance Function form：<mrow> <mi>f</mi> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <msup> <mi>ve</mi> <mrow> <mo>-</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <mi>v</mi> <mo>+</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> </mrow> </msup> </mrow>In formula, v represents the average observed speed of wagon flow, k1、k2、k3Represent fitting constant；The step of fitting is：Observation is obtained Average speed and transit time, it is independent variable to take average speed, and transit time is dependent variable, generally with identical average speed Vehicle identical transit time is there will not be on same section, therefore this formula can be fitted to the equal of transit time Value or onUnderEdge；Step 2. establishes path scale factor matrix A to road network, to road network node i and j, by graph-theoretical algorithm, solves therein All reachable paths, the scale factor for putting the path with minimum impedance value min is 1, and the scale factor in remaining path is each From impedance value to min multiple, so as to obtain scale factor matrix A；Step 3. proposes that object function is as follows, to solve：<mrow> <mi>min</mi> <msubsup> <mo>&Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </msubsup> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mrow> <msub> <mi>qG</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> </mrow> </msubsup> <mi>c</mi> <mrow> <mo>(</mo> <msubsup> <mi>f</mi> <mi>m</mi> <mo>&prime;</mo> </msubsup> <mo>+</mo> <msub> <mi>u</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>u</mi> <mi>m</mi> </msub> <msub> <mi>du</mi> <mi>m</mi> </msub> <mo>,</mo> </mrow>Its restrictive condition isIn formula：(a) i is starting point；J is terminal；(b)PijFor alternative total number of paths；(c) q is user group in starting point i and terminal j Between demand；(d) m is the label in alternative path；(e)f′mBe traffic administration person issue the initial roadlock of road, fmFor road True roadlock, the loyal issue if being not added with any publishing policy, then f 'm=fm, but just lose proposed induction effect Fruit；(f)umIt is integration variable, represents with the infinitesimal roadlock being carried in after system operation on road；(g) A represents alternative path Scale factor matrix, deteriorate to a vector herein；(h)Gm(A) division function is represented, its independent variable is scale factor square Battle array A；So qGm(A) what is represented is exactly the division number of colony；Step 4. calculates globally optimal solution DSO, and calculates DUO, here using a norm, chooses optimization aim<mrow> <mi>min</mi> <mo>|</mo> <mo>|</mo> <msubsup> <mi>F</mi> <mrow> <mi>D</mi> <mi>S</mi> <mi>O</mi> </mrow> <mi>t</mi> </msubsup> <mo>-</mo> <msubsup> <mi>F</mi> <mrow> <mi>D</mi> <mi>V</mi> <mi>O</mi> </mrow> <mi>t</mi> </msubsup> <mo>|</mo> <msub> <mo>|</mo> <mn>1</mn> </msub> </mrow>In formula, FtThe road grid traffic traffic matrix for solving to come according to respective flow criterion is represented, therefore this optimization aim is just It is to want the difference between the derivable dynamic user optimization criterion of minimization and not derivable dynamic system optimal criterion；Wherein The definition of one norm is the maximum of each column element absolute value sum of matrix；Step 5. compares the difference of flow result, by two-by-two than being worth to minimum difference；To the scale factor in each section It is updated, is subsequent time period is carried out using the scale factor matrix that last moment obtains as the initial value of subsequent time Habit process, it is available for optimizing and revising until reaching optimization aim or scale factor without any section, it is straight repeats this process Terminated to system time.
- A kind of 2. traffic dispersion method formulated based on traffic flow distribution distributional difference according to claim 1, it is characterised in that Restrictive condition is in the step 3Its input is column vector scale factor Gm(A), output is PijDimension Row vectorSubscript " 1 × Pij" represent row vector dimension, " 1 " it is preceding expression refer in particular to row vector, be different from row to Amount；Each representation in components selects the ratio shared by the colony of each paths；In the mapping of each component be One injection, maps with the rising of scale factor and declines, that is, represents that the sign roadlock in the path is bigger, user's ratio of selection It is corresponding to reduce.
- A kind of 3. traffic dispersion method formulated based on traffic flow distribution distributional difference according to claim 1, it is characterised in that The step 3 chooses optimization aim and Impedance Function as primary condition, and initialization ratio was the result of calculation of a upper period.
- A kind of 4. traffic dispersion method formulated based on traffic flow distribution distributional difference according to claim 1, it is characterised in that Scale factor vector or matrix A in the step 3 are the parameters optimized and revised by traffic publisher, are not actual Traffic network impedance scaling matrices, that is to say, that the marrow of traffic guidance " soft " information is embodied in A matrix stacks.
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