CN109190790A - Evacuation optimizing paths model building method based on two patterns paste - Google Patents
Evacuation optimizing paths model building method based on two patterns paste Download PDFInfo
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Abstract
The present invention relates to evacuation optimizing paths modelings, more particularly to a kind of evacuation optimizing paths model building method based on two patterns paste, the present invention respectively obtains the optimizing paths model of panic crowd and calm crowd for the psychological characteristics of different crowd, and it is modeled based on the optimizing paths of two type fuzzy reasonings, after model construction success, utilize evacuee's optimizing paths situation under the conditions of emulation platform analog emergency evacuation appropriate, or it formulates the evacuation instruction for different target and evacuates management method, and validity and scientific evaluation are carried out with management method to prepared instruction, solves the traffic jam issue during emergency evacuation, improve evacuation efficiency, with very strong creativeness.
Description
Technical field
The present invention relates to evacuation optimizing paths modelings, and in particular to a kind of evacuation Path selection based on two patterns paste
Behavior model construction method.
Background technique
The frequent of natural calamity occurs to bring huge loss to the life and property of people.It is reported that
Wenchuan earthquake in 2008 directly results in dead 69200, and be missing 18195 and injury 274216.In order to alleviate nature calamity
The negative effect of the brings such as evil, the attack of terrorism, effective extensive emergency evacuation seem extremely necessary and crucial.Currently, China
Management method is evacuated also in reduced levels, is difficult to give evacuee after disaster burst correctly guide and be imitated with commander, evacuation
Rate is low, scene is chaotic.Wherein, traffic problems especially merit attention.During extensive emergency evacuation, traffic trip demand
It sharply increases in a short time, and the supply of path resource becomes extremely deficient because disaster is destroyed.Such imbalance of supply and demand
Relationship will lead to serious traffic congestion, or even increase the potential injures and deaths during evacuation.During coping with emergency evacuation
Traffic jam issue, improve evacuation efficiency, carry out effectively simulation to the behavior of evacuee and become to formulate rationally evacuation manager
The prerequisite of case.In this regard, psychological characteristics and environment of the present invention combination evacuee in path selection process influence, propose
A kind of optimizing paths model building method being based on two type fuzzy theorys (Type-II Fuzzy Theory).
Summary of the invention
In view of this, the embodiment of the present invention is designed to provide a kind of evacuation optimizing paths based on two patterns paste
Model building method, the present invention model multiple choices behavioural habits according to the psychology and character trait of evacuee respectively；
Next introduces influence of the external environment to evacuee's optimizing paths；Finally using two type fuzzy mathematics theories to cope with master
See the uncertainty in decision process.
It is as follows that the embodiment of the present invention solves technical solution used by above-mentioned technical problem:
A kind of evacuation optimizing paths model building method based on two patterns paste, the evacuation Path selection of the method
Behavioural characteristic includes that the routing line of panic crowd is characterized and cools down crowd, is characterized for evacuation routing line, this
Method is modeled based on the optimizing paths of two type fuzzy reasonings, after model construction success, is simulated using emulation platform appropriate
Evacuee's optimizing paths situation under the conditions of emergency evacuation, or evacuation instruction and evacuation management method for different target
It formulates, and validity and scientific evaluation is carried out with management method to prepared instruction.
Preferably, the estimated cost calculation formula of the optimizing paths of the panic crowd are as follows:
WLa=F (d, λ)
cqIt (t) is estimated cost of the t moment evacuee in intersection n by itself perception to path q； WLaDelegated path q
The weighted value of middle section a, value are codetermined at a distance from terminal s with parameter lambda traffic information grade by d evacuee.
Preferably, the path cost estimation formulas of the optimizing paths of the calm crowd is as follows:
WRq=F (i, l)
cqIt (t) is estimated cost of the t moment evacuee in intersection n by itself perception to path q； WRqDelegated path
Weight, value range be 0 to 1 between, and by the average number of track-lines in the intersection i quantity and the path l codetermine.
Preferably, a type-2 fuzzy sets are set in the model building method to be combined intoIt is defined on domain X, degree of membership
Function isThere is following expression formula:
Wherein, JxFor main degree of membership, the codomain of the secondary membership function at x is indicated, be a subset of [0,1]；Referred to as secondary membership；
Or type-2 fuzzy sets closeAre as follows:
Wherein, ∫ ∫ indicates the joint of all desirable x and u, then can be used ∑ to replace ∫ discrete domain；IfAll time be subordinate to
Category degree is 1, i.e.,Then the type-2 fuzzy sets are combined into a section type-2 fuzzy sets and close；Secondary membership function
Uncertainty be made of a bounded interval, as uncertain covering domainIts up-and-down boundary is respectively upper bound degree of membership
Function
With lower bound subordinating degree functionThat is:
Preferably, uncertain covering domain, upper bound subordinating degree function and lower bound subordinating degree function combination type-2 fuzzy sets are closed
Traffic information grade is obtained, evacuee is at a distance from emphasis and the conjunction of the type-2 fuzzy sets of section weight is respectively as follows:
Wherein, Λ, D and WL are respectively λ, d and WLaSet.
Preferably, λ ∈ [0,50] is set, andWhen the deviation of path costIt is greater than
25%, traffic information grade λ value be 10.0 when, there is 92.41% evacuee to select shortest path, by with reality
Lower evacuee selects the ratio in path, determines the value range of parameter lambda.
Preferably, the traffic information grade value is divided into five fuzzy sets, is respectively as follows: very between 0 to 10
Low VL, low L, medium M, high H, very high VH；
The evacuee is divided into the fuzzy set of five grades at a distance from terminal, is respectively as follows: very close VC, nearly C, in
Equal M, remote F, very remote F；
The value range of the section weight is divided into five fuzzy sets between 0 to 1, respectively very small VS, small
S, medium M, big L, very big VL.
Preferably, if traffic information grade is very low, panic evacuee tend to according to from itself closer section at
This progress Path selection decision, when section a increases at a distance from decision point, the weight WL in the sectionaValue can be from 1 sharply
Drop to 0, WLaValue it is bigger, panic evacuee is higher to the attention rate of section a.
Preferably, if traffic information grade is very high, owner can accurately estimate the cost in each path, and directly utilize
They carry out decision, and the weight in all sections is all close to 1, under the conditions of traffic information is wide-open, most of evacuee
Shortest path can be selected, if traffic information grade is in medium level, the weight in all sections is all fluctuated between 0 and 1.
The invention has the benefit that
The present invention respectively obtains the optimizing paths of panic crowd and calm crowd for the psychological characteristics of different crowd
Model is modeled based on the optimizing paths of two type fuzzy reasonings, after model construction success, is simulated using emulation platform appropriate
Evacuee's optimizing paths situation under the conditions of emergency evacuation, or evacuation instruction and evacuation management method for different target
It formulates, and validity and scientific evaluation is carried out with management method to prepared instruction, during solving emergency evacuation
Traffic jam issue, improve evacuation efficiency, have very strong creativeness.
Detailed description of the invention
Fig. 1 is system overview flow chart of the invention；
Fig. 2 is the reported in Tianhe district of Guangzhou road network figure in the embodiment of the present invention 1；
Fig. 3 is the optimizing paths figure of panic evacuee of the invention；
Fig. 4 is λ, d and WL of the inventionaSubordinating degree function figure；
Fig. 5 is i, l and WR of the inventionqSubordinating degree function figure.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
In order to be clearer and more clear technical problems, technical solutions and advantages to be solved, tie below
Drawings and examples are closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only
To explain the present invention, it is not intended to limit the present invention.
Embodiment one
The optimizing paths of overview flow chart as shown in Figure 1, evacuee are affected by numerous factors, this makes path
Housing choice behavior modeling become it is extremely complex with it is difficult.On the one hand, it is influenced by different educational background, living environment etc., evacuee's heart
The diversity of reason and personality is difficult to describe by single Unified Model；On the other hand, subjective factor involved by decision process
Result in the uncertainty of human behavior.These behavioural characteristics are reflected as the high complexity of Macro-traffic Flow, thus to evacuation
Efficiency generate tremendous influence.Therefore, for the diversity of behavior, evacuee's behavior to different mental feature is needed to carry out
Sort out and models respectively；For the uncertainty of behavior, during needing to portray human judgment using two type fuzzy theorys
Subjective characteristics.
It is special to select the Guangzhou as shown in Fig. 2 day in order to better describe the related definition and modeling process in the present invention
River reach road network is as case；
The following mathematical way of relief of traffic road network G is expressed:
G (N, A)=(aij)N×N (1)
Wherein, N is the number of nodes in road network G, i.e. intersection quantity；A is the connection arc a of each nodeijSet, when
aijWhen=1, it is adjacent with node j to represent node i, otherwise, aij=0.
Assuming that traveler is from starting point r, reach home s, and Path selection set of the t moment in intersection n can be by Qn
(t) it indicates.If using the cost in path as the factor of overriding concern when evacuation, then for any optional path q ∈ Qn
(t), the selection ratio x in the t moment pathq(t) it can be calculated by following logit model:
Wherein, cqIt (t) is estimated cost of the t moment evacuee in intersection n by itself perception to path q；cz(t) it is
The estimated cost of optional path z；For the costvariation of current path q and optional path z；A and A is respectively the road for forming path q
The set of section and these sections；λ is a scale factor, represents traffic information grade；Logical variable δaqThe value between 0 and 1,
When section, a belongs to path q, δaqIt is 1, otherwise, value 0；θa(t) indicate section a in the bona fide cost of t moment；εq(t) it is
The deviation of bona fide cost and estimated cost, i.e. perceptual error.
For any cz(t)-cq(t) >=0, if λ increases to just infinite, i.e. traffic information grade highest, then xq(t) will
Equal to 1.This means that evacuee does not have deviation to the perception of path cost and reality, they can select without doubt cost most
Low path.On the contrary, evacuee selects the probability of each paths equal when traffic information scarcity (λ is equal to 0).Then public
Formula (2) can not portray the psychological characteristics of optimizing paths in the case of emergency evacuation.According to the characteristic of formula (2), path is selected
Probability determined completely by path cost, that is to say, that the probability that the path of identical cost is selected be it is identical, this is being answered
It is unreasonable in the case of anxious evacuation.In face of burst fire-disaster, different evacuees can show different moods, such as panic,
It cools down, optimizing paths model is specifically contemplated that these psychological characteristics.
The present embodiment is directed to the diversity of behavior, sorts out to evacuee's behavior of different mental feature and builds respectively
Mould；For the uncertainty of behavior, the subjective characteristics during human judgment are portrayed using two type fuzzy theorys.
Embodiment two
When disaster happens suddenly, people can generate stress reaction naturally, and it is panic to show as, or even makes some irrational
Behavior, this is very important in behavioral study.According to the road network in Fig. 3, there are three optional paths to reach home s at node n,
Their path totle drilling cost is 200.It is not difficult to obtain, no matter how parameter lambda changes, and the probability that each path is selected is 1/
3.This with reality be not consistent, when traffic information scarcity, with the evacuee of panic mood can be more concerned about immediate interest without
It is overall interests.Since the section cost of adjacent node n is more easier to estimate, after comparing the cost in these sections, with
The evacuee of panic mood will be more likely to selection path q3.In other words, by the subjective factor institute during path estimation
Caused perceptual error εqIt (t) is not to be uniformly distributed in the length in path, i.e., when the cost of optional path is all the same, it
Perceptual error it is not necessarily identical (such as road network in Fig. 3).In reality, this species diversity is related with traffic information grade.
Traffic information higher grade, represents estimated path cost and the perceptual error of true path cost is smaller, on the contrary, then bigger.This
It is meant that when traffic information hierarchy level is higher, even panic evacuee can also make the selection of more rationality.
According to the above analysis, the estimated cost calculation formula in path is improved, one is added before each section
Weighted value, as follows:
WLa=F (d, λ) (6)
Wherein, WLaThe weighted value of section a in delegated path q, value is by d (evacuee is at a distance from terminal s) and parameter
λ (traffic information grade) is codetermined.
After undergoing emergency event, being able to maintain calm evacuee can be more by whole in Path selection decision process
Body road network characteristic influences, such as complexity, the traffic capacity in path in path etc..For the complexity of simplified model, only consider
Two more main factors, for describing the Path selection preference of calm evacuee.It is moved for this purpose, being added one to formula (4)
For state weight to adapt to calm psychological preference of the evacuee in decision process, which (represents road by the intersection quantity in path
Diameter complexity) and lane quantity (the delegated path traffic capacity) obtain.The value of the two influence factors is in emulation and reality
It is all easy to calculate, it is also particularly significant for the Path selection decision of emergency evacuation.Existing empiric observation obtains traveler and more inclines
To in selecting straight major trunk roads rather than curved path.The path cost estimation formulas of calm evacuee is as follows as a result:
WRq=F (i, l) (8)
Wherein, WRqThe weight of delegated path, value range be 0 to 1 between, and by i (intersection quantity) and l (path
Average number of track-lines) it codetermines.
In view of individual to the sensibility of traffic information and road network condition and understands that difference is very big, traditional decision model is difficult
To portray the uncertainty and complexity of Psychology and behavior, it is desirable to accurately calculate section weight WLaWith path weight value WRqIt is very tired
Difficult.Accordingly, it is considered to using the fuzzy reasoning method based on two type fuzzy theorys respectively to the power of panic crowd and calm crowd
Weight evaluation.
The diversity of traveler behavior and uncertain traffic flow generation and evolution under the conditions of emergency evacuation play
Highly important effect.The perception of the extra context of individual and traffic behavior has apparent subjective factor, this is dynamic to traffic
Mechanics also produces very important influence.For example, a traveler estimates the stroke distances between certain two intersection
When, his statement is usually " about 10 minutes " this kind of fuzzy expression rather than to be similar to " 15 seconds 12 minutes " this kind of accurate
Expression, and under same case, another traveler may then provide the conclusion of " about 15 minutes ".Although this subjectivity exists
It is determined fundamentally or by objective environment, but it is neither the result measured, it is also difficult to be described with probability or stochastic variable.
Therefore, Zadeh professor causes rapidly the concern in decision behavior modeling field in the type fuzzy theory that nineteen sixty-five proposes, should
The combination of subjectivity and objectivity is become possible by theory.Have multinomial research and one type fuzzy theory has been applied to traffic assignation
Field.
The present invention proposes the fuzzy inference system based on two type fuzzy theorys for dredging under the conditions of portraying emergency evacuation
Dissipate the diversity and uncertainty showed when person perceives path attribute according to different traffic information grades.The mould
Pasting inference system includes blurring, fuzzy reasoning and ambiguity solution these modules, by traffic information grade (λ) and evacuee and end
The distance (d) of point is mapped to section weight (WLa) on, by intersection quantity (i) step comprise determining that input (λ, d, i, l) and
Export (WLa,WRq) subordinating degree function, establish fuzzy rule.Center of gravity technology is utilized in ambiguity solution module.
In order to help to understand model building method proposed in the present invention, first have to provide type-2 fuzzy sets and section two
The related definition of type fuzzy set.The degree of membership that one type-2 fuzzy sets closes is also a fuzzy value between 0 and 1 rather than a type
Exact value as fuzzy set.Assuming that a type-2 fuzzy sets are combined intoIt is defined on domain X, subordinating degree function is
There is following expression formula:
Wherein, JxFor main degree of membership, the codomain of the secondary membership function at x is indicated, be a subset of [0,1]；Referred to as secondary membership.
Type-2 fuzzy sets closeIt can also be expressed by following manner:
Wherein, ∫ ∫ indicates the joint of all desirable x and u, then can be used ∑ to replace ∫ discrete domain.
IfAll secondary memberships be 1, i.e.,Then the type-2 fuzzy sets are combined into two pattern of a section paste
Set.The uncertainty of secondary membership function be made of a bounded interval, referred to as uncertain covering domain
(Footprint of Uncertainty, FOU), up-and-down boundary is respectively upper bound subordinating degree functionIt is subordinate to lower bound
Spend function
There are many type, the present invention to use Gaussian subordinating degree function, expression way for two type fuzzy membership functions are as follows:
Wherein, c is preset parameter, represents the average value of subordinating degree function；σ is uncertain standard deviation；WithσRespectively σ
The upper bound and lower bound.
By formula (11), (12), (13) bring formula (10) into, and variable is changed to the variable of previous definition, then traffic
Message level, evacuee is at a distance from emphasis and the conjunction of the type-2 fuzzy sets of section weight can be expressed as:
Wherein, Λ, D and WL are respectively λ, d and WLaSet.
According to the path Choice Model in formula (2), it is understood that it is by traffic that traveler, which selects the probability of a paths,
Message level (parameter lambda) and current path and other optional paths costvariation () codetermine.Traffic information grade
Value is difficult to quantify and usually be provided by personal experience.But have a little more clear: when traffic information grade highest,
All evacuees can select shortest path, and when traffic information grade is zero, evacuee selects the probability of each paths
It is equal.In order to determine effective value range of traffic information grade, need to analyze its withRelationship.It is arranged λ ∈ [0,50], andThe relationship of λ and Path selection probability can be calculated by formula (1).It is tested, works as path
The deviation of costGreater than 25%, when traffic information grade λ value is 10.0, there is 92.41% evacuee to select shortest path
Diameter.By selecting the ratio in path with evacuee under reality, we can substantially determine the value range of parameter lambda.Usually
For, traffic information grade value is between 0 to 10.It is five fuzzy by traffic information grade classification after determining value range
Set, respectively very low (VL), low (L), medium (M), high (H) is very high (VH).Corresponding fuzzy membership function needs
It is determined for the practical road network studied and by testing.
Embodiment three
Evacuee is equally divided into the fuzzy set of five grades with terminal distance d, respectively very close (VC), closely
(C), medium (M), remote (F), very remote (F).By estimating that sensing range these available fuzzy sets of people are subordinate to
Spend function.It is worth noting that, d can change according to the current location of evacuee.Therefore, section weight is also dynamic, right
In same a road section, when it belongs to different paths, its weight is also different.
Table 3-1 reasoning WLaFuzzy rule
For fuzzy inference system and optimizing paths modeling, WLaValue and distribution it is crucial.As discussed above
It mentions, when traffic information grade is very high, model proposed by the invention and classics logit model are not different.
Therefore, WL is setaValue range between 0 to 1, and be classified as five fuzzy sets, respectively very small (VS) is small
(S), medium (M), big (L), very big (VL).WLaDistribution determined by fuzzy rule.According to evacuee under the conditions of emergency evacuation
Psychological characteristics, for example panic evacuee more focuses on immediate interest, and table 3-1 lists 25 two outputs of inputs (λ and d) one
(WLa) fuzzy rule.
If traffic information grade is very low (as described in the third line in table 3-1), panic evacuee tends to according to from certainly
The closer section cost of body carries out Path selection decision.That is, when section a increases at a distance from decision point, the section
Weight WLaValue can drop sharply to 0 from 1.Note that WLaValue it is bigger, the attention rate of panic evacuee to section a
It is higher.This reasonability in reality can be explained from two angles.On the one hand, the traffic information of road is limited, evacuation
Person can not accurately estimate the bona fide cost in all sections；On the other hand, relative to remote section, evacuee can more easily be utilized
The perception of itself estimates section cost nearby.If traffic information grade is very high (as described in last line in table 3-1), institute
Someone can accurately estimate the cost in each path, and directly carry out decision using them, at this point, the weight in all sections is all close
In 1.In other words, under the conditions of traffic information is wide-open, the optimizing paths of emergency evacuation can be directly by formula
(2) it describes, i.e., most of evacuee can select shortest path.If traffic information grade is in medium level, all sections
Weight all fluctuated between 0 and 1.
Want quantized λ, d and WLaGaussian subordinating degree function be it is very difficult, especially the upper bound of its standard deviation with
Lower bound.Therefore, each parameter value of subordinating degree function is needed by a large amount of experiment to determine.Table 2-2 is determined after giving experiment
Each parameter value.λ, d and WLaSubordinating degree function figure as shown in Figure 4.WLaSpecific fuzzy reasoning process given by algorithm 3-1
Out.
Table 3-2 λ, d and WLaGaussian subordinating degree function parameter value
Formula (18), (19) and (20) respectively represent intersection quantity, the two patterns paste of average number of track-lines and path weight value
Set.
Wherein, I, L and W R are respectively i, l and WRqSet.
Before defining relevant parameter, the invention firstly uses analogue systems to the person in servitude of intersection quantity and average number of track-lines
Membership fuction is trained.By taking reported in Tianhe district of Guangzhou road network (Fig. 1-2) as an example, in 1586 optional paths in total, maximum is average
Number of track-lines is 5, and maximum intersection number is 22.Therefore, the range that l is arranged is 0 to 5, and is classified as 5 fuzzy sets, including
Very little (VS), small (S) medium (M), big (L), very big (VL).For ease of calculation, the range of intersection quantity i is arranged 0
To between 25, it is equally classified as 5 fuzzy sets, respectively very little (VS), small (S) medium (M), big (L), very big (VL).
Path weight value WRqWith section weight WLaIt is similar, only change between 0 and 1, its five fuzzy sets are very little (VS), small
(S) medium (M), big (L), very big (VL).
Table 3-3 gives 25 two inputs (l and i) one and exports (WRq) fuzzy rule.If very small (such as table 3- of the value of i
In 3 shown in last line), l changes from very small to be very big, path weight value WRqA reduced levels will be remained at.This
It is meant that excessive intersection quantity will lead to it is more potential turn to may with the waiting time increased because of signal lamp, because
This calm evacuee will be more focused on intersection quantity to path cost impact.On the contrary, if value it is very big (in such as table 3-3
Shown in the third line), no matter how l changes, WRqIt all will remain in a higher level, value range is between 0.5 to 1.About
I, l and WRqThe parameter value of subordinating degree function determine that method is identical with 3.2 sections, table 3-4 gives design parameter value.It is subordinate to
Category degree function is as shown in Figure 5.WRqFuzzy reasoning process and WLaIt is similar, details are not described herein again.
Table 3-3 reasoning WRqFuzzy rule
Table 3-4l, i and WRqGaussian subordinating degree function parameter value
The present invention describes the construction method of evacuee's optimizing paths model under the conditions of emergency evacuation in detail, utilizes two
Type fuzzy mathematics theory embodies the uncertainty of crowd behaviour, and the psychological characteristics of different crowd respectively obtains fear when for evacuation
The optimizing paths model of crowd and calm crowd.It has the advantage that
Advantage first is that model respectively for the psychological characteristics of different crowd under the conditions of emergency evacuation, rather than with single model generation
Table all groups embody the diversity of crowd behaviour, are conducive to formulate different crowd respectively targetedly evacuation instruction；
Advantage distinguishes evacuation optimizing paths with common path housing choice behavior second is that introducing such environmental effects
Come；
Advantage third is that using two type fuzzy mathematics theories embody path selection process in subjective decision uncertainty, make mould
Type is more in line with reality, is more advantageous to the effective evacuation management method of formulation.
Preferred embodiments of the present invention have been described above with reference to the accompanying drawings, not thereby limiting the scope of the invention.This
Field technical staff without departing from the scope and spirit of the invention in made by any modifications, equivalent replacements, and improvements, should all this
Within the interest field of invention.
Claims (9)
1. a kind of evacuation optimizing paths model building method based on two patterns paste, it is characterised in that: the method is dredged
Scattered routing line is characterized be characterized and the cool down routing line of crowd of the routing line including panic crowd and is characterized,
This method is modeled based on the optimizing paths of two type fuzzy reasonings, after model construction success, utilizes emulation platform mould appropriate
Evacuee's optimizing paths situation under the conditions of quasi- emergency evacuation, or formulation is for the evacuation instruction and evacuation management of different target
Method, and validity and scientific evaluation are carried out with management method to prepared instruction.
2. the evacuation optimizing paths model building method according to claim 1 based on two patterns paste, feature exist
In: the estimated cost calculation formula of the optimizing paths of the fear crowd are as follows:
WLa=F (d, λ)
cqIt (t) is estimated cost of the t moment evacuee in intersection n by itself perception to path q；WLaThe Road delegated path q
The weighted value of section a, value are codetermined at a distance from terminal s with parameter lambda traffic information grade by d evacuee.
3. the evacuation optimizing paths model building method according to claim 1 based on two patterns paste, feature exist
In: the path cost estimation formulas of the optimizing paths of the calm crowd is as follows:
WRq=F (i, l)
cqIt (t) is estimated cost of the t moment evacuee in intersection n by itself perception to path q；WRqThe power of delegated path
Weight, value range be 0 to 1 between, and by the average number of track-lines in the intersection i quantity and the path l codetermine.
4. the evacuation optimizing paths model building method according to claim 1 based on two patterns paste, feature exist
In:
A type-2 fuzzy sets are set in the model building method to be combined intoIt is defined on domain X, subordinating degree function isHave
Following expression formula:
Wherein, JxFor main degree of membership, the codomain of the secondary membership function at x is indicated, be a subset of [0,1]；Referred to as secondary membership；
Or type-2 fuzzy sets closeAre as follows:
Wherein, ∫ ∫ indicates the joint of all desirable x and u, then can be used ∑ to replace ∫ discrete domain；IfAll secondary memberships
It is 1, i.e.,Then the type-2 fuzzy sets are combined into a section type-2 fuzzy sets and close；Secondary membership function not
Certainty is made of a bounded interval, as uncertain covering domainIts up-and-down boundary is respectively upper bound subordinating degree functionWith lower bound subordinating degree functionThat is:
5. the evacuation optimizing paths model building method according to claim 4 based on two patterns paste, feature exist
In: uncertain covering domain, upper bound subordinating degree function and lower bound subordinating degree function combination type-2 fuzzy sets are closed to obtain traffic information
Grade, evacuee is at a distance from emphasis and the conjunction of the type-2 fuzzy sets of section weight is respectively as follows:
Wherein, Λ, D and WL are respectively λ, d and WLaSet.
6. the evacuation optimizing paths model building method according to claim 1 based on two patterns paste, feature exist
In: setting λ ∈ [0,50], andWhen the deviation of path costGreater than 25%, traffic information
When grade λ value is 10.0, there is 92.41% evacuee to select shortest path, by selecting with evacuee under reality
The ratio in path determines the value range of parameter lambda.
7. the evacuation optimizing paths model building method according to claim 6 based on two patterns paste, feature exist
In: the traffic information grade value is divided into five fuzzy sets between 0 to 10, is respectively as follows: very low VL, low L, in
Equal M, high H, very high VH；
The evacuee is divided into the fuzzy set of five grades at a distance from terminal, is respectively as follows: very close VC, nearly C, medium M,
Remote F, very remote F；
The value range of the section weight is divided into five fuzzy sets between 0 to 1, respectively very small VS, small S, in
Equal M, big L, very big VL.
8. the evacuation optimizing paths model building method according to claim 7 based on two patterns paste, feature exist
In: if traffic information grade is very low, panic evacuee tends to carry out path choosing according to from itself closer section cost
Decision is selected, when section a increases at a distance from decision point, the weight WL in the sectionaValue can drop sharply to 0, WL from 1a's
Value is bigger, and panic evacuee is higher to the attention rate of section a.
9. the evacuation optimizing paths model building method according to claim 8 based on two patterns paste, feature exist
In: if traffic information grade is very high, owner can accurately estimate the cost in each path, and directly be determined using them
Plan, the weight in all sections is all close to 1, and under the conditions of traffic information is wide-open, most of evacuee can select most short
Path, if traffic information grade is in medium level, the weight in all sections is all fluctuated between 0 and 1.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434958A (en) * | 2020-11-30 | 2021-03-02 | 上海应用技术大学 | Panic group emergency evacuation analysis method based on big data |
| CN112990573A (en) * | 2021-03-12 | 2021-06-18 | 东南大学 | Path selection method based on asymmetric discrete selection model |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160125645A1 (en) * | 2014-11-05 | 2016-05-05 | Umm Al-Qura University | Grading and monitoring of a geographical region |
| CN106845680A (en) * | 2016-12-20 | 2017-06-13 | 中铁第四勘察设计院集团有限公司 | A kind of rail line transferring methods of comparison and selection and device |
| CN107886720A (en) * | 2017-11-07 | 2018-04-06 | 长沙理工大学 | One kind closes festivals or holidays urban traffic blocking transfer analysis method based on section type-2 fuzzy sets |
-
2018
- 2018-07-25 CN CN201810828436.8A patent/CN109190790A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160125645A1 (en) * | 2014-11-05 | 2016-05-05 | Umm Al-Qura University | Grading and monitoring of a geographical region |
| CN106845680A (en) * | 2016-12-20 | 2017-06-13 | 中铁第四勘察设计院集团有限公司 | A kind of rail line transferring methods of comparison and selection and device |
| CN107886720A (en) * | 2017-11-07 | 2018-04-06 | 长沙理工大学 | One kind closes festivals or holidays urban traffic blocking transfer analysis method based on section type-2 fuzzy sets |
Non-Patent Citations (3)
| Title |
|---|
| 杜沅泽: "人群动画中融入情绪模型的实时路径规划算法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 梁俊: "面向应急疏散的基于二型模糊的路径选择行为建模及仿真", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 江超阳: "基于二型模糊集合理论的交通流长时预测方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434958A (en) * | 2020-11-30 | 2021-03-02 | 上海应用技术大学 | Panic group emergency evacuation analysis method based on big data |
| CN112990573A (en) * | 2021-03-12 | 2021-06-18 | 东南大学 | Path selection method based on asymmetric discrete selection model |
| CN112990573B (en) * | 2021-03-12 | 2021-11-09 | 东南大学 | Path selection method based on asymmetric discrete selection model |
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