CN109035767A - A kind of tide lane optimization method considering Traffic Control and Guidance collaboration - Google Patents
A kind of tide lane optimization method considering Traffic Control and Guidance collaboration Download PDFInfo
- Publication number
- CN109035767A CN109035767A CN201810776917.9A CN201810776917A CN109035767A CN 109035767 A CN109035767 A CN 109035767A CN 201810776917 A CN201810776917 A CN 201810776917A CN 109035767 A CN109035767 A CN 109035767A
- Authority
- CN
- China
- Prior art keywords
- model
- intersection
- optimization
- population
- traffic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/04—Network management architectures or arrangements
- H04L41/044—Network management architectures or arrangements comprising hierarchical management structures
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
Abstract
The invention discloses a kind of tide lane optimization method of consideration Traffic Control and Guidance collaboration, user selects road network to be optimized and period to be optimized, the geometrical characteristic data of input research road network, and the OD data within the research period；The tide lane optimization problem for considering Traffic Control and Guidance collaboration is abstracted into tri-level programming problem, wherein, upper layer model is the tide lane Optimized model based on 0-1 nonlinear programming, middle layer model is the traffic control Optimized model based on multiple-objection optimization, and underlying model is the user equilibrium model for considering intersection delay；Establish a kind of solution of heuristic iteration optimization algorithms progress three-level programming model, wherein, upper layer model is solved using genetic algorithm, middle layer model is solved using non-dominated sorted genetic algorithm, underlying model is solved using iteration weighting method, and Traffic Control and Guidance collaboration is introduced into the optimization of tide lane by this method.
Description
Technical field
The invention belongs to traffic programme and management domain, the tide lane for being related to a kind of consideration Traffic Control and Guidance collaboration is excellent
Change method improves the traffic efficiency of urban road network under tidal congestion.
Background technique
Land use is to influence the principal element of transport need characteristic, residential area, employment area, matching facilities for life and public affairs
The spatial distribution of facility altogether determines Urban Residential Trip total amount, trip distance and line direction out.Currently, segregation phenomenon is lived in duty
Seriously, thus tidal phenomena generates, and brings traffic jam issue.Tidal congestion phenomenon embody dynamic traffic demand with it is quiet
Contradiction between state means of transportation.In conjunction with the tidal regime of road traffic flow, studying the optimization of tide lane is to solve above-mentioned contradiction
Important means.Currently, having had already appeared the application in many tide lanes both at home and abroad, still, theoretical research is perfect not enough, special
It is not how to establish tide lane Optimized model, formulates tide lane scheme.
The collaboration of Traffic Control and Guidance optimizes, and searching to the bottom is that Optimal Signals control is excellent with the combination of discrete traffic design
Change problem.However, only considering the collaboration optimization of Traffic Control and Guidance not for the road network obvious for tidal phenomena
It can solve traffic jam issue, need further to consider that tide lane optimizes.However, comparison Traffic Control and Guidance association
With the present Research of optimization method, discovery: some researchs are although it is contemplated that timing controlled, but model system has ignored intersection
The problems such as Signal Phase Design, different flow directions are delayed the influence to traffic assignation；Some research application induction controls, but model hypothesis
On the high side, the not more acquisition problem for considering practical application level dynamic data, in shortage of data, model is extremely difficult to
Desired effect；In addition, seldom considering that the modeling of tide lane optimization is asked in the research field of Traffic Control and Guidance collaboration optimization
Topic.
Summary of the invention
Goal of the invention of the invention is to devise a kind of tide lane optimization side of consideration Traffic Control and Guidance collaboration
Method provides a solution for tidal congestion phenomenon.
To achieve the above object, the technical solution adopted by the present invention is a kind of tide of consideration Traffic Control and Guidance collaboration
Lane optimization method, specific implementation process are as follows:
Step 1: prepare basic data
User can independently select road network to be optimized according to traffic flow data, traffic administration demand, managerial experiences etc., with
And the period to be optimized, to realize, different periods use different tide lane schemes in same road network one day.
User inputs the geometrical characteristic data of research road network, the vehicle including each intersection, each section in research road network
Road attribute and quantity；Data are from GIS-Geographic Information System, or make an on-the-spot survey investigation on the spot.
User inputs OD data of the research road network within the research period；Data from user autonomous OD estimate, or
From intelligent transportation system.
Step 2: founding mathematical models
The tide lane optimization problem for considering Traffic Control and Guidance collaboration is abstracted into tri-level programming problem, underlying model
For the user equilibrium model for considering intersection delay, decision variable is the magnitude of traffic flow in each section；It is indicated using virtual segment
Influence of the intersection delay to user equilibrium model regard intersection difference flow direction as virtual segment, indicates section using delay
Travel time function can be delayed formula using Robert Webster；Use the travel time function in BPR function representation section.Lower layer
Model may be expressed as:
In formula, JLFor the control performance function of traffic guidance optimization；A is the set in practical section；I is the collection of virtual segment
It closes；ta(xa) be section a travel time function；da(xa) be virtual segment travel time function；For departure place be r,
Destination is the volume of traffic on the kth paths between the OD of s；ψrsFor all paths between departure place is r, destination is s OD
Set；qrsFor the volume of traffic between departure place is r, destination is s OD；R is the set of departure place r；S is the set of destination s；For 0-1 variable, if section a on the kth paths between the OD that departure place is r, destination is s,Otherwise For the hourage of the kth paths between departure place is r, destination is s OD.
Middle layer model is the traffic control Optimized model based on multiple-objection optimization, comprehensively considers efficiency of management optimization and travelling
Impression optimization, is respectively adopted the overall performance index average traffic delay and broad sense saturation degree of intersection；Decision variable is that intersection is each
The green time of flow direction.Constraint condition includes traditional equisaturation constraint, maximum saturation constrains, the signal period constrains, most
Short green time constraint and phase pattern constraint.Middle layer model may be expressed as:
Cmin≤Cj≤Cmax
In formula, JMFor the control performance function of traffic control optimization；djFor the average traffic delay of intersection j；χjFor intersection j
Broad sense saturation degree；The delay of i is flowed to for intersection j；The flow of i is flowed to for intersection j；I is flowed to for intersection j
Saturation degree；The average staturation respectively flowed to for intersection j；χ0For the threshold value of saturation degree mean difference；B is the sum of flow direction；
χmaxFor the threshold value of maximum saturation；CminFor the most short cycle of signal control；CmaxFor the longest period of signal control；To hand over
Prong j flows to i green time；tgminFor the threshold value of Minimum Green Time.
By taking intersection shown in FIG. 1 as an example, illustrate that phase pattern constrains.Enabling ty is yellow time, and tr is complete red time, if
Right-hand rotation is not controlled by signal,Phase pattern constraint is as shown in the table.
The constraint of 1 phase pattern of table
Upper layer model is the tide lane Optimized model based on 0-1 nonlinear programming, is up to the current benefit of road network excellent
Change target, is measured with hourage；Decision variable is the flow direction in tide lane at section, is indicated with 0-1 variable；Definition is constrained to
At decision variable and section and the relationship of intersection number of track-lines.Underlying model may be expressed as:
In formula, JUFor the control performance function of tide lane optimization.
By taking the major urban arterial highway of 1 south-north direction shown in Fig. 2 as an example, illustrate that definition is constrained at decision variable and section
And the relationship of intersection number of track-lines.It enablesFor 0-1 variable, the tide lane attribute of section a and a ' is respectively indicated；For 0-1 variable, the tide lane attribute of intersection j entrance driveway p is indicated；Respectively indicate intersection j entrance driveway
Left turn lane number and Through Lane number at p；la,la′Respectively indicate the number of track-lines of section a and a '.Two-way 6 vehicle of the major urban arterial highway
Road, north and south side are connected with intersection, have following fundamental characteristics:
(1) intersection carries out widening for lane, so that entrance driveway, exit ramp respectively increase by 1 lane.
(2) section only allows 2 lanes to be tide lane, might as well introduce 0-1 variableIndicate section a's and a '
Tide lane attribute: for the section of south-north direction, enabling 1 expression tide lane is from the south orientation north to 0 indicates by north orientation south
To；For east-west section, enabling 1 expression tide lane is from east orientation west to 0 indicates direction from west to east.
(3) crossing inlet road at least 1 Through Lane, 1 right-turn lane, 1 left turn lane, with support vehicles
Normal straight-ahead operation, turns left to pass through at right-hand rotation；Exit ramp at least 3 lanes, to ensure that intersection interior vehicle normally sails out of intersection
Mouthful.
(4) intersection can be canalized, so that entrance driveway occupies 1 lane of exit ramp.ForValue,
The possible case of canalization is discussed: if When, the number of track-lines l of section aaThe number of track-lines l of=4, section a 'a′=2, into
Mouth road should not occupy the lane of exit ramp, the decision that intersection has 2 lanes to can be used for straight and turning left at this time；If When, la=2, la′=4, entrance driveway can occupy 1 lane of exit ramp, and intersection has 1 lane to can be used for directly at this time
Row and the decision turned left；For Or When, la=3, la′=3, entrance driveway can occupy outlet
1 lane in road, the decision that intersection has 2 lanes to can be used for straight and turning left at this time.Therefore, 0-1 variable might as well be introducedThe tide lane attribute for indicating intersection enables 1 expression Through Lane, and 0 indicates left turn lane.
(5)It is 0-1 variable, the decision for tide lane attribute.0-1 variable to determine
Plan process is simpler, but loses meaning in some cases.Such as: at section, It seldom deposits in practice
" being followed successively by 2 north orientation lanes, 1 south orientation north lane, 1 north orientation south lane, 2 south orientation north lane eastwards from west ", stillL represented by valuea=3, la′=3 exist；Intersection, Seldom there is " import in practice
Road is followed successively by eastwards 2 Through Lanes, 1 left turn lane, 1 Through Lane, 1 right-turn lane from west ", still
The left turn lane quantity that value indicatesThrough Lane quantityIn the presence of.Therefore, during model solution, do not have to about
BeamBetween relationship, obtain optimal solution after, calculatela、la′Value, it may be assumed that tide lane
Scheme.
Decision variable isDefinition constrains
4, the tide lane optimization method for requiring a kind of consideration Traffic Control and Guidance to cooperate with according to right 1,
It is characterized in that:
Establish a kind of solution of heuristic iteration optimization algorithms (see Fig. 3) progress three-level programming model, wherein upper layer model
It is solved using genetic algorithm (Genetic Algorithm, GA), middle layer model uses non-dominated sorted genetic algorithm (Non-
Dominated Sorted Genetic Algorithm II, NSGAII) it solves, underlying model uses iteration weighting method
(Method ofSuccessive Averages, MSA) is solved.Algorithm flow is as follows,
Step 1: initialization enables the number of iterations h=0, ignores the influence of virtual segment, tide lane；
Step 2: underlying model solves, and using iteration weighting method solving model, obtains each link flow of the h times iterationSpecific step is as follows,
Step 2.1: enabling the number of iterations m=0,Have entirely according to each section free flow running time complete
Without distribution, initial solution is obtained
Step 2.2: enabling the number of iterations m=m+1, updates Link Travel Time
Step 2.3: according to Link Travel TimeThe progress of the OD volume of traffic is had completely without distribution entirely, obtains each section
Additional traffic amount
Step 2.4: link flow is updated:
Step 2.5: if the result of iteration is not much different twice in succession, stops calculating, record final allocation result, i.e.,Otherwise Step 2.2 is returned；Select average absolute percentage error MAPE as convergence.
Step 3: middle layer model solution obtains each of the h times iteration using non-dominated sorted genetic algorithm solving model
Intersection signal timing scheme, specific step is as follows,
Step 3.1: parameter initialization determines gene number, population scale, mutation probability, maximum evolutionary generation；
Step 3.2: evolutionary generation n=0 is enabled, initial population is randomly generated；
Step 3.3: to population PnExecute selection, intersection, mutation operation generation progeny population Qn, calculating target function；
Step 3.4: if meeting termination condition, Step 3.8 is carried out, otherwise carries out Step 3.5；
Step 3.5: merging parent population and progeny population, obtains new population Rn=Pn∪Qn；
Step 3.6: non-dominated ranking, crowding distance calculating and population cut operation are executed, creates n+1 for population Pn+1；
Step 3.7: enabling n=n+1, returns to Step 3.3；
Step 3.8: drawing Pareto optimal solution distribution map, retains progeny population Qn；
Step 3.9: according to the rule of user's autonomous Design, optimal solution is obtained from Pareto optimal solution set.
Step 4: upper layer model solution obtains each section number of track-lines of h iteration using GA algorithm solving modela
∈ I ∪ A, core procedure include:
Step 4.1: initialization: carrying out 0-1 coding to the decision variable of model, and Individual Size is the quantity of decision variable,
Population Size, mutation probability, maximum evolution number etc. are set；
Step 4.2: Fitness analysis: for minimization problem, using the fitness reciprocal as individual of objective function
Value；
Step 4.3: selection operation: using roulette method, and defect individual is randomly choosed from old population and forms new kind
Group obtains next-generation individual with breeding；
Step 4.4: crossover operation: randomly choosing 2 individuals from population, is combined by the exchange of chromosome, father is gone here and there
Outstanding feature be hereditary to substring, to generate new excellent individual；
Step 4.5: mutation operation: randomly selecting 1 individual from population, and 1 point in selection individual is into row variation to produce
Raw more excellent individual.
Step 5: iterative calculation enables h=h+1, returns to Step 2；
Step 6: convergence judgement stops if the flow in each section of iteration is not much different twice in succession, records best side
Case；Otherwise Step 2 is returned；Select average absolute percentage error MAPE as convergence:
The utility model has the advantages that the objective function of the optimization of tide lane, traffic control optimization and traffic guidance optimization can indicate
At road network lane quantity (decision variable of tide lane optimization), (decision of traffic control optimization becomes signal phase split
Amount), the function of the road network path flow decision variable of optimization (traffic guidance), three's optimization inputs and constrains each other, determine
Three cooperates with the necessity of optimization.It is excellent to consider that the tide lane optimization method of Traffic Control and Guidance collaboration realizes three's collaboration
Change, has great importance for the solution of tidal congestion problem.
Detailed description of the invention
Fig. 1 is case intersection schematic diagram.
Fig. 2 is case tide lane schematic diagram.
Fig. 3 is heuristic iteration optimization algorithms flow chart.
Fig. 4 is case road network schematic diagram
Fig. 5 is virtual segment design drawing.
Fig. 6 is underlying model initialization result figure.
Fig. 7 is middle layer model initialization result figure.
Fig. 8 is upper layer model initialization result figure.
Fig. 9 is iteration optimization result figure.
Specific embodiment
The urban road network of setting signal control mostly uses grid formula, is to regard as with 12 node road network shown in Fig. 4
The basic building block of urban road network, illustrates specific implementation method.12 node road networks include: 8 origin and destination (i=1,2 ...,
8), 4 primary cross mouths (j=9,10,11,12), 24 sections, share 64 OD to (r=1,2 ..., 8, s=1,2 ...,
8).Virtual segment design is as shown in Figure 5.
In practical applications, the lane attribute of road network and quantity are generally provided by GIS-Geographic Information System, or by the spot
It the methods of makes an on-the-spot survey and to obtain.It is arranged referring to the lane of Beijing's road network, determines the lane attribute and quantity of 12 node road networks,
That is: 24 sections are two-way 6 lane, lane quantity that 4 primary cross mouths respectively flow to () shown in table 3.
3 lane quantity of table
It, can be using road section traffic volume flow data, mobile data and resident's survey data etc., estimation under big data background
The OD demand data of research object period.The OD demand data that example uses is as shown in table 4.
The OD demand (pcu/h) of 4 research object period of table
Founding mathematical models, and solved, detailed process is as follows,
(1) it initializes
The number of iterations h=0 is enabled, the influence of virtual segment, tide lane is ignored, it may be assumed that section is 3 lanes, intersection
For 1 left turn lane, 2 Through Lanes and 1 right-turn lane.
(2) underlying model initializes
Based on iteration weighting method, meet MAPE≤0.5% (as shown in Figure 6) by 52 iteration, completes underlying model and ask
Solution.
(3) middle layer model initialization
Based on non-dominated sorted genetic algorithm, the solution of layer model, the optimal angle distribution of the Pareto of 4 intersections in completion
As shown in Figure 7.User can set certain rule and select a solution from Pareto optimal solution set as most according to actual needs
Excellent solution, for example,
(4) upper layer model initialization
Based on GA algorithm, optimal solution is obtained within maximum 30 generation of genetic algebra, as shown in Figure 8.
(5) iteration optimization
By 3 iteration, three-level programming model converges to MAPE≤0.5%, and obtains optimal solution, as shown in Figure 9.
By calculating the number of track-lines, the green time that can get intersection and respectively flow to, the number of track-lines in each section, such as table 5,6,5
It is shown:
The number of track-lines that 5 intersection of table respectively flows to
The green time and period (s) that 6 intersection of table respectively flows to
The number of track-lines in each section of table 7
As shown in figure 9, the 0th iteration represents traffic control, traffic guidance, tide lane independent optimization, system is total at this time
Hourage longest；It with the increase of the number of iterations, gradually restrains, system overall travel time gradually reduces, this illustrates to pass through
Consider that the tide lane that control is cooperateed with induction optimizes, improves the efficiency of traffic system.In addition, present case is with average absolute hundred
Point error is as convergence judgment criteria, if changing convergence judgment criteria into maximum number of iterations, according to trend shown in Fig. 9,
System overall travel time continues to reduce.
Claims (4)
1. a kind of tide lane optimization method for considering Traffic Control and Guidance collaboration, which is characterized in that the step of this method such as
Under:
Step 1: prepare basic data
User selects road network to be optimized and period to be optimized, the geometrical characteristic data of input research road network, and is studying
OD data in period；
Step 2: founding mathematical models
The tide lane optimization problem for considering Traffic Control and Guidance collaboration is abstracted into three-level programming model, wherein upper layer mould
Type is the tide lane Optimized model based on 0-1 nonlinear programming, and middle layer model is that the traffic control based on multiple-objection optimization is excellent
Change model, underlying model is the user equilibrium model for considering intersection delay；
Step 3: derivation algorithm is proposed
Establish a kind of solution of heuristic iteration optimization algorithms progress three-level programming model, wherein upper layer model is calculated using heredity
Method solves, and middle layer model is solved using non-dominated sorted genetic algorithm, and underlying model is solved using iteration weighting method.
2. according to the tide lane optimization method that right 1 requires a kind of consideration Traffic Control and Guidance to cooperate with, feature
Be: the implementation process of step 1 is as follows,
According to traffic flow data, traffic administration demand, managerial experiences independently select road network to be optimized and it is to be optimized when
Section, to realize, different periods use different tide lane schemes in same road network one day；
User inputs the geometrical characteristic data of research road network, the lane category including each intersection, each section in research road network
Property and quantity；Data are from GIS-Geographic Information System, or make an on-the-spot survey investigation on the spot；
User inputs OD data of the research road network within the research period；Data are estimated from the autonomous OD of user, or come from
In intelligent transportation system.
3. according to the tide lane optimization method that right 1 requires a kind of consideration Traffic Control and Guidance to cooperate with, feature
It is:
The tide lane optimization problem for considering Traffic Control and Guidance collaboration is abstracted into tri-level programming problem；
Underlying model is the user equilibrium model for considering intersection delay, and decision variable is the magnitude of traffic flow in each section；Use void
Quasi- section indicates influence of the intersection delay to user equilibrium model, regard intersection difference flow direction as virtual segment, uses
Delay indicates Link Travel Time function, is delayed formula using Robert Webster；Use the travel time letter in BPR function representation section
Number；Underlying model indicates are as follows:
In formula, JLFor the control performance function of traffic guidance optimization；A is the set in practical section；I is the set of virtual segment；ta
(xa) be section a travel time function；da(xa) be virtual segment travel time function；It is r for departure place, destination
The volume of traffic on kth paths between the OD of s；ψrsFor the set in all paths between departure place is r, destination is s OD；
qrsFor the volume of traffic between departure place is r, destination is s OD；R is the set of departure place r；S is the set of destination s；For
0-1 variable, if section a on the kth paths between the OD that departure place is r, destination is s,Otherwise For the hourage of the kth paths between departure place is r, destination is s OD；
Middle layer model is the traffic control Optimized model based on multiple-objection optimization, comprehensively considers efficiency of management optimization and travelling impression
Optimization, is respectively adopted the overall performance index average traffic delay and broad sense saturation degree of intersection；Decision variable is that intersection respectively flows to
Green time；Constraint condition includes traditional equisaturation constraint, maximum saturation constrains, the signal period constrains, is most short green
Lamp time-constrain and phase pattern constraint；Middle layer model is expressed as:
Cmin≤Cj≤Cmax
In formula, JMFor the control performance function of traffic control optimization；djFor the average traffic delay of intersection j；χjFor the wide of intersection j
Adopted saturation degree；The delay of i is flowed to for intersection j；The flow of i is flowed to for intersection j；The full of i is flowed to for intersection j
And degree；The average staturation respectively flowed to for intersection j；χ0For the threshold value of saturation degree mean difference；B is the sum of flow direction；χmaxFor
The threshold value of maximum saturation；CminFor the most short cycle of signal control；CmaxFor the longest period of signal control；For intersection j
Flow to i green time；tgminFor the threshold value of Minimum Green Time；
Upper layer model is the tide lane Optimized model based on 0-1 nonlinear programming, is up to optimization mesh with the current benefit of road network
Mark, is measured with hourage；Decision variable is the flow direction in tide lane at section, is indicated with 0-1 variable；Definition is constrained to decision
At variable and section and the relationship of intersection number of track-lines；Underlying model indicates are as follows:
In formula, JUFor the control performance function of tide lane optimization.
4. according to the tide lane optimization method that right 1 requires a kind of consideration Traffic Control and Guidance to cooperate with, feature
It is:
Establish a kind of solution of heuristic iteration optimization algorithms progress three-level programming model, wherein upper layer model is calculated using heredity
Method solves, and middle layer model is solved using non-dominated sorted genetic algorithm, and underlying model is solved using iteration weighting method；Algorithm flow
It is as follows,
Step 1: initialization enables the number of iterations h=0, ignores the influence of virtual segment, tide lane；
Step 2: underlying model solves, and using iteration weighting method solving model, obtains each link flow of the h times iterationa
∈ I ∪ A, the specific steps are as follows:
Step 2.1: enabling the number of iterations m=0,According to each section free flow running time carry out entirely have completely without point
Match, obtains initial solution
Step 2.2: enabling the number of iterations m=m+1, updates Link Travel Time
Step 2.3: according to Link Travel TimeThe progress of the OD volume of traffic is had completely without distribution entirely, obtains the additional friendship in each section
Flux
Step 2.4: link flow is updated:
Step 2.5: if the result of iteration is not much different twice in succession, stops calculating, record final allocation result, i.e.,It is no
Then return to Step 2.2；Select average absolute percentage error MAPE as convergence；
Step 3: middle layer model solution obtains each intersection of the h times iteration using non-dominated sorted genetic algorithm solving model
Mouth signal time distributing conception, the specific steps are as follows:
Step 3.1: parameter initialization determines gene number, population scale, mutation probability, maximum evolutionary generation；
Step 3.2: evolutionary generation n=0 is enabled, initial population is randomly generated；
Step 3.3: to population PnExecute selection, intersection, mutation operation generation progeny population Qn, calculating target function；
Step 3.4: if meeting termination condition, Step 3.8 is carried out, otherwise carries out Step 3.5；
Step 3.5: merging parent population and progeny population, obtains new population Rn=Pn∪Qn；
Step 3.6: non-dominated ranking, crowding distance calculating and population cut operation are executed, creates n+1 for population Pn+1；
Step 3.7: enabling n=n+1, returns to Step 3.3；
Step 3.8: drawing Pareto optimal solution distribution map, retains progeny population Qn；
Step 3.9: according to the rule of user's autonomous Design, optimal solution is obtained from Pareto optimal solution set；
Step 4: upper layer model solution obtains each section number of track-lines of h iteration using GA algorithm solving modela∈I∪
A, core procedure include:
Step 4.1: initialization: 0-1 coding is carried out to the decision variable of model, Individual Size is the quantity of decision variable, setting
Population Size, mutation probability, maximum evolution number etc.；
Step 4.2: Fitness analysis: for minimization problem, using the fitness value reciprocal as individual of objective function；
Step 4.3: selection operation: using roulette method, and defect individual is randomly choosed from old population and forms new population, with
Breeding obtains next-generation individual；
Step 4.4: crossover operation: randomly choosing 2 individuals from population, is combined by the exchange of chromosome, the excellent of father's string
Elegant feature is hereditary to substring, to generate new excellent individual；
Step 4.5: mutation operation: randomly selecting 1 individual from population, and 1 point in selection individual is into row variation to generate more
Outstanding individual；
Step 5: iterative calculation enables h=h+1, returns to Step 2；
Step 6: convergence judgement stops if the flow in each section of iteration is not much different twice in succession, records preferred plan；It is no
Then return to Step 2；Select average absolute percentage error MAPE as convergence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810776917.9A CN109035767A (en) | 2018-07-13 | 2018-07-13 | A kind of tide lane optimization method considering Traffic Control and Guidance collaboration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810776917.9A CN109035767A (en) | 2018-07-13 | 2018-07-13 | A kind of tide lane optimization method considering Traffic Control and Guidance collaboration |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109035767A true CN109035767A (en) | 2018-12-18 |
Family
ID=64642542
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810776917.9A Pending CN109035767A (en) | 2018-07-13 | 2018-07-13 | A kind of tide lane optimization method considering Traffic Control and Guidance collaboration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109035767A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902864A (en) * | 2019-02-20 | 2019-06-18 | 吉林大学 | A kind of construction area Traffic Organization design method considering network prestowage equilibrium |
| CN110414708A (en) * | 2019-06-10 | 2019-11-05 | 上海旷途科技有限公司 | A kind of tide lane prioritization scheme selection method, device and storage medium |
| CN110412990A (en) * | 2019-08-27 | 2019-11-05 | 大连理工大学 | A kind of AGV collision prevention method under the environment of plant |
| CN110992697A (en) * | 2019-12-19 | 2020-04-10 | 江苏金晓电子信息股份有限公司 | High-precision and high-expansibility method for planning optimal traffic path |
| CN111768638A (en) * | 2020-05-25 | 2020-10-13 | 同济大学 | Lane distribution method for single-point signalized intersection |
| CN112906984A (en) * | 2021-03-24 | 2021-06-04 | 苏州蓝图智慧城市科技有限公司 | Road traffic state prediction method and device, storage medium and electronic equipment |
| CN113011627A (en) * | 2020-11-20 | 2021-06-22 | 华南理工大学 | Tide lane optimization method based on distribution estimation algorithm |
| CN113781768A (en) * | 2021-08-09 | 2021-12-10 | 同济大学 | Hot spot district traffic organization control cooperation method based on reserve traffic capacity |
| CN114333367A (en) * | 2021-12-24 | 2022-04-12 | 苏州安泰阿尔法交通科技发展有限公司 | Tide lane traffic facility and signal cooperative control method |
| CN115171371A (en) * | 2022-06-16 | 2022-10-11 | 海信集团控股股份有限公司 | Cooperative type road intersection passing method and device |
| CN116543562A (en) * | 2023-07-06 | 2023-08-04 | 银江技术股份有限公司 | Method and device for constructing trunk coordination optimization model |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102157072A (en) * | 2011-03-29 | 2011-08-17 | 北京航空航天大学 | Inducing device and inducing method of vehicle confluence at intersection based on vehicle and road collaboration |
| CN103106789A (en) * | 2013-01-10 | 2013-05-15 | 东南大学 | Synergy method for traffic guidance system and signal control system |
| CN103208194A (en) * | 2013-04-03 | 2013-07-17 | 昆明联诚科技有限公司 | Urban traffic signal cooperative control system |
| CN107591004A (en) * | 2017-11-01 | 2018-01-16 | 中原智慧城市设计研究院有限公司 | A kind of intelligent traffic guidance method based on bus or train route collaboration |
| CN107945539A (en) * | 2017-08-28 | 2018-04-20 | 北京工业大学 | A kind of intersection signal control method |
-
2018
- 2018-07-13 CN CN201810776917.9A patent/CN109035767A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102157072A (en) * | 2011-03-29 | 2011-08-17 | 北京航空航天大学 | Inducing device and inducing method of vehicle confluence at intersection based on vehicle and road collaboration |
| CN103106789A (en) * | 2013-01-10 | 2013-05-15 | 东南大学 | Synergy method for traffic guidance system and signal control system |
| CN103208194A (en) * | 2013-04-03 | 2013-07-17 | 昆明联诚科技有限公司 | Urban traffic signal cooperative control system |
| CN107945539A (en) * | 2017-08-28 | 2018-04-20 | 北京工业大学 | A kind of intersection signal control method |
| CN107591004A (en) * | 2017-11-01 | 2018-01-16 | 中原智慧城市设计研究院有限公司 | A kind of intelligent traffic guidance method based on bus or train route collaboration |
Non-Patent Citations (2)
| Title |
|---|
| SUN ZHI-YUAN等: "Tri-level programming model for combined urban traffic signal control and traffic flow guidance", 《SPRINGER》 * |
| 孙智源: "大数据驱动的城市交通控制与诱导协同优化方法", 《清华大学图书馆博士学位论文》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902864B (en) * | 2019-02-20 | 2022-06-17 | 吉林大学 | Construction area traffic organization scheme design method considering network load balancing |
| CN109902864A (en) * | 2019-02-20 | 2019-06-18 | 吉林大学 | A kind of construction area Traffic Organization design method considering network prestowage equilibrium |
| CN110414708A (en) * | 2019-06-10 | 2019-11-05 | 上海旷途科技有限公司 | A kind of tide lane prioritization scheme selection method, device and storage medium |
| CN110414708B (en) * | 2019-06-10 | 2023-04-18 | 上海旷途科技有限公司 | Tide lane optimization scheme selection method and device and storage medium |
| CN110412990A (en) * | 2019-08-27 | 2019-11-05 | 大连理工大学 | A kind of AGV collision prevention method under the environment of plant |
| CN110412990B (en) * | 2019-08-27 | 2021-06-18 | 大连理工大学 | AGV collision prevention method used in factory environment |
| CN110992697A (en) * | 2019-12-19 | 2020-04-10 | 江苏金晓电子信息股份有限公司 | High-precision and high-expansibility method for planning optimal traffic path |
| CN111768638A (en) * | 2020-05-25 | 2020-10-13 | 同济大学 | Lane distribution method for single-point signalized intersection |
| CN113011627A (en) * | 2020-11-20 | 2021-06-22 | 华南理工大学 | Tide lane optimization method based on distribution estimation algorithm |
| CN112906984A (en) * | 2021-03-24 | 2021-06-04 | 苏州蓝图智慧城市科技有限公司 | Road traffic state prediction method and device, storage medium and electronic equipment |
| CN112906984B (en) * | 2021-03-24 | 2023-06-30 | 苏州蓝图智慧城市科技有限公司 | Road traffic state prediction method and device, storage medium and electronic equipment |
| CN113781768A (en) * | 2021-08-09 | 2021-12-10 | 同济大学 | Hot spot district traffic organization control cooperation method based on reserve traffic capacity |
| CN114333367A (en) * | 2021-12-24 | 2022-04-12 | 苏州安泰阿尔法交通科技发展有限公司 | Tide lane traffic facility and signal cooperative control method |
| CN115171371A (en) * | 2022-06-16 | 2022-10-11 | 海信集团控股股份有限公司 | Cooperative type road intersection passing method and device |
| CN115171371B (en) * | 2022-06-16 | 2024-03-19 | 海信集团控股股份有限公司 | Cooperative road intersection passing method and device |
| CN116543562A (en) * | 2023-07-06 | 2023-08-04 | 银江技术股份有限公司 | Method and device for constructing trunk coordination optimization model |
| CN116543562B (en) * | 2023-07-06 | 2023-11-14 | 银江技术股份有限公司 | Method and device for constructing trunk coordination optimization model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109035767A (en) | A kind of tide lane optimization method considering Traffic Control and Guidance collaboration | |
| CN108470444B (en) | A kind of city area-traffic big data analysis System and method for based on genetic algorithm optimization | |
| CN111785045B (en) | Distributed traffic signal lamp combined control method based on actor-critic algorithm | |
| CN106951978B (en) | Urban concentrated charging station planning method based on improved K-means algorithm | |
| CN101777990B (en) | Method for selecting multi-objective immune optimization multicast router path | |
| CN112489426B (en) | Urban traffic flow space-time prediction scheme based on graph convolution neural network | |
| CN108256969B (en) | Public bicycle leasing point dispatching area dividing method | |
| CN110648022A (en) | Community public transport network and departure frequency synchronous optimization method considering station full coverage for connecting subways | |
| CN103116865A (en) | Multidimensional collaborative power grid planning method | |
| CN109919819B (en) | Construction, evaluation and optimization method of regional ecological network | |
| CN109269516B (en) | Dynamic path induction method based on multi-target Sarsa learning | |
| CN112489464B (en) | Crossing traffic signal lamp regulation and control method with position sensing function | |
| CN104766484A (en) | Traffic control and guidance system and method based on evolutionary multi-objective optimization and ant colony algorithm | |
| CN112146673A (en) | Expressway multi-point collaborative rescue path planning method based on improved ant colony algorithm | |
| CN108538065A (en) | A kind of major urban arterial highway control method for coordinating based on adaptive iterative learning control | |
| CN109584552A (en) | A kind of public transport arrival time prediction technique based on network vector autoregression model | |
| CN114117700A (en) | Urban public transport network optimization research method based on complex network theory | |
| Lin | Transportation and metropolitan development in China’s Pearl River Delta: The experience of Panyu | |
| CN109214580A (en) | Commercial land layout optimization method based on traffic system performance | |
| CN102867409A (en) | Road traffic cooperative control method for urban central area | |
| CN111126687B (en) | Single-point offline optimization system and method for traffic signals | |
| CN105225004A (en) | The construction method of group of cities inter-city passenger rail gauze | |
| CN115355922A (en) | Travel path planning method and system based on improved ant colony algorithm | |
| CN113868812A (en) | Medium voltage distribution network cable path planning method based on intelligent ant colony algorithm | |
| CN104821086A (en) | Method for positioning low-efficient road section combination in large-scale traffic network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181218 |
|
| RJ01 | Rejection of invention patent application after publication |