CN107341580A - A kind of new heuritic approach for the planning of urban traffic network emergency evacuation - Google Patents
A kind of new heuritic approach for the planning of urban traffic network emergency evacuation Download PDFInfo
- Publication number
- CN107341580A CN107341580A CN201710670436.5A CN201710670436A CN107341580A CN 107341580 A CN107341580 A CN 107341580A CN 201710670436 A CN201710670436 A CN 201710670436A CN 107341580 A CN107341580 A CN 107341580A
- Authority
- CN
- China
- Prior art keywords
- cellular
- represent
- network
- evacuation
- planning
- 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
- 238000005516 engineering process Methods 0.000 claims abstract description 6
- 238000010845 search algorithm Methods 0.000 claims abstract description 4
- 230000001413 cellular effect Effects 0.000 claims description 49
- 238000000034 method Methods 0.000 claims description 18
- 230000008859 change Effects 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 5
- 238000012804 iterative process Methods 0.000 claims description 4
- 238000011144 upstream manufacturing Methods 0.000 claims description 2
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of new heuritic approach for the planning of urban traffic network emergency evacuation, mainly comprise the following steps：1) city emergency evacuation network is established；2) Optimized model suitable for emergency evacuation network is established；3) optimized using Lagrange relaxation tabu search algorithm urgency evacuation planning problem；4) Lagrange relaxation problem is solved；Since one is evacuated network feasible solution, iterative network state is updated by searching for, until meeting stopping criterion, exports final result.The present invention combines Lagrangian relaxation technology with TABU search, applied in the emergency network evacuation planning based on Used in Dynamic Traffic Assignment, compared with existing emergency network evacuation planning, the travel behaviour of traveler can be described preferably, algorithm is more efficiently and general, can be used in large-scale traffic evacuation network.
Description
Technical field
The present invention relates to emergency evacuation planning field, specifically, it is related specifically to a kind of be used for urban traffic network
The new heuritic approach of emergency evacuation planning.
Background technology
In city, after disaster or accident occurs, emergency traffic evacuation is withdrawn for personnel in disaster site, answered
Anxious breakdown lorry, which rescue etc., plays vital effect.Especially personnel withdraw in disaster area, stressing practical results property.If
The prediction scheme of emergency evacuation can not be carried out in advance, in case of emergency established unimpeded emergency evacuation network in time, will led
Road congestion is caused, personnel can not effectively withdraw, and cause Loss of Life and property.
In order to implement traffic organization in order when emergency case occurs, improve evacuation efficiency, it is necessary to the pipe of urban transportation
Reason person carries out emergency traffic evacuation planning.Rationally effective emergency evacuation planning is only applied, network evacuation effect could be improved
Rate so that personnel withdraw in time, can also help vehicle to enter disaster area and carry out rescue and relief work.
Emergency traffic evacuation planning is to be directed to a certain specific region network under specific circumstances, according to specific emergency evacuation
The traffic programme that demand is carried out.The optimization method of emergency traffic evacuation planning refers to, is planning a certain network under specific circumstances
Emergency evacuation scheme when, we are abstracted transportation network and corresponding traveler data using the method for mathematical modeling, and
Mathematical modeling is solved by optimized algorithm, finally gives the planing method of evacuation planning scheme.
Emergency evacuation planning problem solves often through the method for emulation or optimization.Emergency evacuation planning based on emulation
Method is for evaluating existing programme so as to select best scheme, and the emergency evacuation planing method based on optimization is
Optimal programme is directly tried to achieve for finding.In the method for optimization, it is important to model will can reflect reality middle network with
And the characteristic of traveler, including evacuation demand, traveler withdraw optimizing paths etc..With emergency evacuation planing method not
Disconnected progress is developed more efficient, the more preferable emergency evacuation planning of operation result and calculated, it is necessary to according to reality and technological progress
Method.
The content of the invention
It is an object of the invention to for deficiency of the prior art, there is provided one kind is used for urban traffic network emergency evacuation
The new heuritic approach of planning, to solve problems of the prior art.
Technical problem solved by the invention can be realized using following technical scheme：
A kind of method of traffic assignment based on the heterogeneous time value of user Yu congestion expense budget, comprises the following steps：
1) city emergency evacuation network is established
Evacuation prediction scheme is carried out first against specific building or public place, it is determined that the regional extent evacuated, and according to
Emergency evacuation network is established according to regional extent；The emergency evacuation network has some beginning and ends, and evacuation personnel are by rising
Point is current to terminal, escapes the regional extent of evacuation；
2) Optimized model suitable for emergency evacuation network is established；
Bilayer model is established for emergency evacuation network, bilayer model includes upper layer model and underlying model；
The upper layer model is used to optimize overall evacuation time, and its process is as follows：
Object function：
Constraint to road colleague's capacity：
To the constraints of connector：
Connector constrains with road quantitative relation：
The underlying model is Dynamic traffic assignment model, and its process is as follows：
Object function：
Constraints
The implication of set, parameter and variable is as follows in above-mentioned formula：
Set
C represents section cellular set, in Used in Dynamic Traffic Assignment, if each section or crossing are a cellular；CSRepresent
Cellular set；CRThe cellular set of expression source, traffic flow are produced by source cellular；E represents connector set；ESRepresent terminal connector
Set；ERRepresent section connector set；EIRepresent intersection connector set；Represent sensing cellular ρ connector set；Represent the connector set from cellular ρ；Represent sensing cellular ρ upstream cellular set；Expression goes out from cellular ρ
The downstream cellular set that the connector of hair points to；T represents to carry out the split time section set of evacuation planning；
Parameter
cρRepresent the maximum vehicle number allowed in cellular ρ；qρRepresent that cellular ρ allows the maximum vehicle number of disengaging；nι κ, θ ρ
Section quantity on one section both direction ι → κ and θ → ρ；Represent the natural flow velocity rate of the cellular ρ in time interval t；
MtRepresent the cost of the generation optimal traffic flow of user in time interval t；ζρRepresent initial vehicle number in cellular ρ；Represent the time
In the t of section demand is evacuated from caused by cellular ρ；
Variable
nρRepresent section number in cellular ρ；nικRepresent the section quantity on cellular sequence ι → κ；Represent time interval t
Vehicle number in interior cellular ρ；Represent the vehicle number moved in time interval t from cellular θ to cellular ρ；yηιRepresent connector
Variable (the y of connection status between η → ιηι=1 or 0)；zικRepresent the variable (z of connection status between cellular ι → κικ=1 or 0)；
3) optimized using Lagrange relaxation-tabu search algorithm urgency evacuation planning problem
Relaxed first by Lagrangian relaxation technology, upper layer model is changed into：
s.t.constraints2-4,9-21
Wherein,It is Lagrange multiplier, yηι+yρσ- 1 represents the number of hits in each intersection,
It is the cost based on intersection point；
4) Lagrange relaxation problem is solved
First since one is evacuated network feasible solution, iterative network state is updated by searching for, until meeting that stopping is accurate
Then, final result is exported, its process is as follows：
4.1) an initial feasible solution g is randomly choosed in solution space S, makes g*=g, i=0, j=0, in iteration mistake
Cheng Zhong, use g*To represent currently to obtain best solution, i represents iterations, and j represents Local Search number；
4.2) from g obtained in the previous step*Set out, it is mobile to carry out once variation：Change g at random*Intersection
Connection, obtains present feasible solution g；
4.3) a neighborhood N (g) is generated near second step present feasible solution, is existed according to aspiration criterion and immediate cause memory
Generation a subset S in neighborhood N (g)*；
4.4) in S*One elite subset of middle selection, carries out local motion in elite subset, and updates aspiration criterion, draws
Ge Lang multipliers, immediate cause memory, frequency memory, present feasible solution g and history optimal solution g*；
4.5) if the stopping criterion of neighborhood search is satisfied, then is carried out in next step；Otherwise, repeat 4.4)；
4.6) if stopping criterion is satisfied, then is stopped search；Otherwise, jump to 4.1).
Compared with prior art, beneficial effects of the present invention are as follows：
Lagrangian relaxation technology is combined with TABU search, applied to the emergency network based on Used in Dynamic Traffic Assignment
In evacuation planning, compared with existing emergency network evacuation planning, the travel behaviour of traveler can preferably be described, algorithm is more
Increase effect and it is general, large-scale traffic evacuation network can be used in.
Brief description of the drawings
Fig. 1 is the sub-network exemplary plot in intersection of the present invention and section.
Fig. 2 is the example model figure of the transportation network of the present invention for needing to be evacuated.
Fig. 3 is network of the present invention during carrying out emergency evacuation to transportation network using new heuritic approach
One of change schematic diagram.
Fig. 4 is network of the present invention during carrying out emergency evacuation to transportation network using new heuritic approach
The two of change schematic diagram.
Fig. 5 is network of the present invention during carrying out emergency evacuation to transportation network using new heuritic approach
The three of change schematic diagram.
Fig. 6 is network of the present invention during carrying out emergency evacuation to transportation network using new heuritic approach
The four of change schematic diagram.
Fig. 7 is network of the present invention during carrying out emergency evacuation to transportation network using new heuritic approach
The five of change schematic diagram.
Fig. 8 is network of the present invention during carrying out emergency evacuation to transportation network using new heuritic approach
The six of change schematic diagram.
Embodiment
To be easy to understand the technical means, the inventive features, the objects and the advantages of the present invention, with reference to
Embodiment, the present invention is expanded on further.
Lagrange multiplier technique and tabu search algorithm are combined, we can be by being calculated traffic evacuation net
The result of network planning.
Referring to Fig. 1-Fig. 8, the present embodiment will illustrate the iterative process of algorithm using a mininet：
First, the topological structure such as Fig. 2 established according to evacuation network, we will be seen that, it is necessary to which that plans dredges from Fig. 2
Scattered network contains an evacuation terminal, an intersection and several connected sections.
How we are it is envisaged that to control the UNICOM between each cellular and direction so that all evacuations during evacuation
The overall evacuation time that demand reaches evacuation emphasis is minimum.The parameter of each source cellular such as table 1.For common cellular, we will
All common cellular parameters are arranged to identical：100 meters of cellular length ,/second of maximum stream flow 4, track quantity 1, congestion rate 0.2
/ meter, the natural meter per second of flow velocity degree 20, backpropagation speed 1.
| Source cell | Period 1 | Period 2 | Period 3 | … | Period tmax | Total |
| 21 | 5 | 10 | 0 | … | 0 | 15 |
| 22 | 5 | 10 | 0 | … | 0 | 15 |
| 23 | 2.5 | 5 | 0 | … | 0 | 7.5 |
| 24 | 2.5 | 5 | 0 | … | 0 | 7.5 |
| 25 | 15 | 30 | 0 | … | 0 | 45 |
| 26 | 10 | 20 | 0 | … | 0 | 30 |
| 27 | 5 | 10 | 0 | … | 0 | 15 |
| 28 | 5 | 10 | 0 | … | 0 | 15 |
| 29 | 10 | 20 | 0 | … | 0 | 30 |
| 30 | 10 | 20 | 0 | … | 0 | 30 |
For algorithm parameter, we are arranged such：First unit punishment cost (initial unit penalty cost)
=200, increment/decrement punishment cost (increment/decrement penalty cost)=0, avoid the phase (tabu
Tenure)=4, elite ability (elite capacity)=1, is up to improvement iterations=20.For this example, root
According to our iterative process, network state such as Fig. 3 to Fig. 8 of generation.Wherein the process of Used in Dynamic Traffic Assignment we use traffic
Planning software is realized.By this iteration progressively, we have overturn 13-15,17-419,3-7,1-5 and 9-11 in order respectively
Direction.The number of hits of intersection difference turn direction is respectively 5,0,1,1,2,0, only number of hits in iterative process several times
Feasible solution, therefore final solution such as Fig. 8 are only for 0.
First time iteration：Initial feasible solution such as Fig. 3 is, it is necessary to which the variate-value (cellular, connector) considered is as follows：
| Variable | y6,7 | y6,14 | y6,10 | y8,14 | y8,10 | y8,5 | y13,10 | y13,5 | y13,7 | y9,5 | y9,7 | y9,14 |
| Value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Variable | zIsosorbide-5-Nitrae | z2,6 | z3,2 | z4,8 | z5,1 | z7,3 | z10,12 | z11,9 | z12,18 | z14,16 | z15,13 | z17,11 |
| Value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Variable | z18,20 | z19,17 | ||||||||||
| Value | 1 | 1 |
Table 1
The overall evacuation time calculated accordingly is tTET(x, y, n)=5600, the object function of Lagrange relaxation problemThis solution is recorded as g*And make g=g*, repeat to change the direction in one of section for g, generation
One g neighborhood N (g), therefrom selects best solution and record and g*Compare, update g*。
We update Lagrange multiplier and new history optimal solution such as table 2 after an iteration, as a result as corresponding to Fig. 4
tTET(x, y, n)=3975,
| Variable | y6,7 | y6,14 | y6,10 | y8,14 | y8,10 | y8,5 | y13,10 | y13,5 | y13,7 | y9,5 | y9,7 | y9,14 |
| Value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Variable | zIsosorbide-5-Nitrae | z2,6 | z3,2 | z4,8 | z5,1 | z7,3 | z10,12 | z11,9 | z12,18 | z14,16 | z15,13 | z17,11 |
| Value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
| Variable | z18,20 | z19,17 | ||||||||||
| Value | 1 | 1 |
Table 2
Above step is repeated, we may finally obtain optimal solution such as Fig. 8, as a result such as table 3.Corresponding tTET(x, y, n)=
2925,
| Variable | y6,7 | y6,14 | y6,10 | y8,14 | y8,10 | y8,5 | y13,10 | y13,5 | y13,7 | y9,5 | y9,7 | y9,14 |
| Value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Variable | zIsosorbide-5-Nitrae | z2,6 | z3,2 | z4,8 | z5,1 | z7,3 | z10,12 | z11,9 | z12,18 | z14,16 | z15,13 | z17,11 |
| Value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
| Variable | z18,20 | z19,17 | ||||||||||
| Value | 1 | 1 |
Table 3
The general principle and principal character and advantages of the present invention of the present invention has been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the simply explanation described in above-described embodiment and specification is originally
The principle of invention, without departing from the spirit and scope of the present invention, various changes and modifications of the present invention are possible, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (1)
1. a kind of new heuritic approach for the planning of urban traffic network emergency evacuation, it is characterised in that including following step
Suddenly：
1) city emergency evacuation network is established
Evacuation prediction scheme is carried out first against specific building or public place, it is determined that the regional extent evacuated, and according to area
Domain scope establishes emergency evacuation network；The emergency evacuation network has some beginning and ends, and evacuation personnel are led to by starting point
Row to terminal, escapes the regional extent of evacuation；
2) Optimized model suitable for emergency evacuation network is established
Bilayer model is established for emergency evacuation network, bilayer model includes upper layer model and underlying model；
The upper layer model is used to optimize overall evacuation time, and its process is as follows：
Object function：
Constraint to road colleague's capacity：
To the constraints of connector：
Connector constrains with road quantitative relation：
The underlying model is Dynamic traffic assignment model, and its process is as follows：
Object function：
Constraints
The implication of set, parameter and variable is as follows in above-mentioned formula：
Set
C represents section cellular set, in Used in Dynamic Traffic Assignment, if each section or crossing are a cellular；CSRepresent cellular collection
Close；CRThe cellular set of expression source, traffic flow are produced by source cellular；E represents connector set；ESRepresent terminal connector set；ER
Represent section connector set；EIRepresent intersection connector set；Represent sensing cellular ρ connector set；Represent
From cellular ρ connector set；Represent sensing cellular ρ upstream cellular set；Represent the connection from cellular ρ
The downstream cellular set that device points to；T represents to carry out the split time section set of evacuation planning；
Parameter
cρRepresent the maximum vehicle number allowed in cellular ρ；qρRepresent that cellular ρ allows the maximum vehicle number of disengaging；On a road
Section quantity on section both direction ι → κ and θ → ρ；Represent the natural flow velocity rate of the cellular ρ in time interval t；MtRepresent
The cost of the optimal traffic flow of user is produced in time interval t；ζρRepresent initial vehicle number in cellular ρ；Represent in time interval t
Demand is evacuated from caused by cellular ρ；
Variable
nρRepresent section number in cellular ρ；nικRepresent the section quantity on cellular sequence ι → κ；Represent member in time interval t
Vehicle number in born of the same parents ρ；Represent the vehicle number moved in time interval t from cellular θ to cellular ρ；yηιRepresent connector η → ι
Between connection status variable (yηι=1 or 0)；zικRepresent the variable (z of connection status between cellular ι → κικ=1 or 0)；
3) optimized using Lagrange relaxation-tabu search algorithm urgency evacuation planning problem
Relaxed first by Lagrangian relaxation technology, upper layer model is changed into：
where(yηι+yρσ-1)+=max (0, yηι+yρσ-1)
s.t.constraints 2-4,9-21
Wherein,It is Lagrange multiplier, yηι+yρσ- 1 represents the number of hits in each intersection,It is base
In the cost of intersection point；
4) Lagrange relaxation problem is solved
Since one is evacuated network feasible solution, iterative network state is updated by searching for, until meeting stopping criterion, output is most
Terminate fruit, and its process is as follows：
4.1) an initial feasible solution g is randomly choosed in solution space S, makes g*=g, i=0, j=0, in an iterative process,
Use g*To represent currently to obtain best solution, i represents iterations, and j represents Local Search number；
4.2) from g obtained in the previous step*Set out, it is mobile to carry out once variation：Change g at random*An intersection company
Connect, obtain present feasible solution g；
4.3) a neighborhood N (g) is generated near second step present feasible solution, according to aspiration criterion and immediate cause memory in neighborhood
Generation a subset S in N (g)*；
4.4) in S*One elite subset of middle selection, carries out local motion, and it is bright to update aspiration criterion, glug in elite subset
Day multiplier, immediate cause memory, frequency memory, present feasible solution g and history optimal solution g*；
4.5) if the stopping criterion of neighborhood search is satisfied, then is carried out in next step；Otherwise, repeat 4.4)；
4.6) if stopping criterion is satisfied, then is stopped search；Otherwise, jump to 4.1).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710670436.5A CN107341580A (en) | 2017-08-08 | 2017-08-08 | A kind of new heuritic approach for the planning of urban traffic network emergency evacuation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710670436.5A CN107341580A (en) | 2017-08-08 | 2017-08-08 | A kind of new heuritic approach for the planning of urban traffic network emergency evacuation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107341580A true CN107341580A (en) | 2017-11-10 |
Family
ID=60216275
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710670436.5A Pending CN107341580A (en) | 2017-08-08 | 2017-08-08 | A kind of new heuritic approach for the planning of urban traffic network emergency evacuation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107341580A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107680380A (en) * | 2017-11-23 | 2018-02-09 | 上海交通大学 | A kind of intersection origin and destination flow optimization method for evacuation network design |
| CN108694278A (en) * | 2018-04-27 | 2018-10-23 | 东南大学 | A kind of city discrete network design problem method based on road load equilibrium |
| CN108776584A (en) * | 2018-05-11 | 2018-11-09 | 东南大学 | The R language implementation methods of the discrete transportation network active safety design in city |
| CN108848188A (en) * | 2018-07-16 | 2018-11-20 | 南京理工大学 | Caching places the modified Lagrange relaxation heuristic of optimization problem |
| CN109993349A (en) * | 2019-03-11 | 2019-07-09 | 同济大学 | A kind of optimization method and device of city refuge addressing |
| CN111680822A (en) * | 2020-05-12 | 2020-09-18 | 河海大学 | Reciprocating type bus evacuation path planning method based on non-fixed route |
| CN113408189A (en) * | 2021-05-27 | 2021-09-17 | 华南理工大学 | Urban multipoint circulating emergency evacuation and simulation deduction method based on variable cells |
| CN113689696A (en) * | 2021-08-12 | 2021-11-23 | 北京交通大学 | Multi-mode traffic collaborative evacuation method based on lane management |
| CN115018175A (en) * | 2022-06-20 | 2022-09-06 | 东南大学 | Short-term early warning evacuation path planning method based on Lagrange relaxation algorithm |
| CN116595766A (en) * | 2023-05-18 | 2023-08-15 | 北京化工大学 | Emergency personnel evacuation route design method for dangerous chemical park leakage accident |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105119289A (en) * | 2015-07-27 | 2015-12-02 | 华南理工大学 | Multi-region power system completely distributed dynamic and economical dispatching method |
| CN105243431A (en) * | 2015-09-10 | 2016-01-13 | 西安电子科技大学 | Traffic flow evacuation time estimation method |
| CN105760960A (en) * | 2016-02-29 | 2016-07-13 | 东南大学 | Park and ride facility optimal siting and capacity determining method based on rail transit |
-
2017
- 2017-08-08 CN CN201710670436.5A patent/CN107341580A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105119289A (en) * | 2015-07-27 | 2015-12-02 | 华南理工大学 | Multi-region power system completely distributed dynamic and economical dispatching method |
| CN105243431A (en) * | 2015-09-10 | 2016-01-13 | 西安电子科技大学 | Traffic flow evacuation time estimation method |
| CN105760960A (en) * | 2016-02-29 | 2016-07-13 | 东南大学 | Park and ride facility optimal siting and capacity determining method based on rail transit |
Non-Patent Citations (1)
| Title |
|---|
| CHI XIE等: "A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies", 《TRANSPORTATION RESEARCH PART E》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107680380A (en) * | 2017-11-23 | 2018-02-09 | 上海交通大学 | A kind of intersection origin and destination flow optimization method for evacuation network design |
| CN108694278A (en) * | 2018-04-27 | 2018-10-23 | 东南大学 | A kind of city discrete network design problem method based on road load equilibrium |
| CN108694278B (en) * | 2018-04-27 | 2022-05-27 | 东南大学 | Urban discrete traffic network design method based on road load balancing |
| CN108776584A (en) * | 2018-05-11 | 2018-11-09 | 东南大学 | The R language implementation methods of the discrete transportation network active safety design in city |
| CN108776584B (en) * | 2018-05-11 | 2021-11-12 | 东南大学 | R language implementation method for active safety design of urban discrete traffic network |
| CN108848188B (en) * | 2018-07-16 | 2020-11-17 | 南京理工大学 | Improved Lagrange relaxation heuristic method for cache placement optimization problem |
| CN108848188A (en) * | 2018-07-16 | 2018-11-20 | 南京理工大学 | Caching places the modified Lagrange relaxation heuristic of optimization problem |
| CN109993349A (en) * | 2019-03-11 | 2019-07-09 | 同济大学 | A kind of optimization method and device of city refuge addressing |
| CN111680822A (en) * | 2020-05-12 | 2020-09-18 | 河海大学 | Reciprocating type bus evacuation path planning method based on non-fixed route |
| CN111680822B (en) * | 2020-05-12 | 2022-08-19 | 河海大学 | Reciprocating type bus evacuation path planning method based on non-fixed route |
| CN113408189A (en) * | 2021-05-27 | 2021-09-17 | 华南理工大学 | Urban multipoint circulating emergency evacuation and simulation deduction method based on variable cells |
| CN113689696A (en) * | 2021-08-12 | 2021-11-23 | 北京交通大学 | Multi-mode traffic collaborative evacuation method based on lane management |
| CN115018175A (en) * | 2022-06-20 | 2022-09-06 | 东南大学 | Short-term early warning evacuation path planning method based on Lagrange relaxation algorithm |
| CN116595766A (en) * | 2023-05-18 | 2023-08-15 | 北京化工大学 | Emergency personnel evacuation route design method for dangerous chemical park leakage accident |
| CN116595766B (en) * | 2023-05-18 | 2023-10-27 | 北京化工大学 | Emergency personnel evacuation route design method for dangerous chemical park leakage accident |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107341580A (en) | A kind of new heuritic approach for the planning of urban traffic network emergency evacuation | |
| Silva et al. | Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities | |
| Gallo et al. | The transit network design problem with elastic demand and internalisation of external costs: An application to rail frequency optimisation | |
| CN103049799B (en) | A kind of Study on Power Grid Planning method based on multiple-objection optimization | |
| CN101702655B (en) | Layout method and system of network topological diagram | |
| Pérez-Rúa et al. | Electrical cable optimization in offshore wind farms—A review | |
| CN106570595A (en) | Subway station position selection method and system based on space big data | |
| CN109344555A (en) | A kind of collection transmission pipe network design method and device | |
| CN110909920B (en) | Power transmission network capacity planning optimization method and system considering multiple fault scenes | |
| CN105512472B (en) | Large-scale wind electricity base power collects system topology hierarchy optimization design method | |
| CN107566194A (en) | A kind of method for realizing the mapping of cross-domain virtual network network | |
| Wang et al. | Multi-objective site selection optimization of the gas-gathering station using NSGA-II | |
| Veeramachaneni et al. | Optimizing energy output and layout costs for large wind farms using particle swarm optimization | |
| CN104202176B (en) | Optical-fiber network topology computer method for auto constructing | |
| Weihs et al. | Steady-state design of CO2 pipeline networks for minimal cost per tonne of CO2 avoided | |
| CN108053037A (en) | A kind of power distribution network based on two net fusions repairs policy development method and device | |
| CN104217073A (en) | Network association gravitation-directed visualization layout method | |
| CN104050245B (en) | A kind of social network influence power maximization approach based on liveness | |
| CN105611502A (en) | Indoor positioning system and method based on WiFi Mesh network | |
| CN107357965A (en) | A kind of path planning design method of wind power plant collection electric line | |
| CN109325614B (en) | Bus stop site selection method based on GIS | |
| CN102946610B (en) | A kind of method and system of Microwave Network Planning | |
| CN106503832A (en) | Unmanned someone's cooperative information distribution transmission optimization method and system | |
| Ljubic et al. | Solving the prize-collecting Steiner tree problem to optimality | |
| CN104821086B (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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171110 |