CN106952475A - The personalized inductivity distribution method of dual path road network under user equilibrium principle - Google Patents
The personalized inductivity distribution method of dual path road network under user equilibrium principle Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- 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
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- 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
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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Abstract
The present invention relates to a kind of personalized inductivity distribution method of the dual path road network under user equilibrium principle, comprise the following steps:1) for the dual path road network to be regulated and controled, road network basic parameter is counted;2) GreenShields models and traffic flow theory are based on, the optimal separation rate under ideal conditions is asked for according to road network basic parameter, it is optimal to reach the overall traffic flow of road network;3) under the constraints of road network total optimization, the ratio compared with shortest path is travelled according to individual vehicle history, the basic constraints of personalized induction allocation strategy is set up;4) concept of vehicle priority is set up, according to this concept, using equal difference distribution principle, the personalized inductivity distribution numerical value under vehicle priority known case is calculated;5) according to step 4) determine distribution principle and vehicle history most ratio of greater inequality probability distribution, distribute induction information, meet the principle of user equilibrium to greatest extent under probability meaning, it is ensured that vehicle receive induction fairness.
Description
Technical field
The present invention relates to a kind of personalized inductivity distribution method of the dual path road network under user equilibrium principle, belong to traffic
Induce optimisation technique field.
Background technology
Traditional induction information issue is published to real time information according to section real-time status on certain reception medium, two
The medium that receives of class mainly is for VMS (variable information traffic sign) induced screens of colony's vehicle guidance and for personalization
The car-mounted terminal of vehicle guidance.In terms of road network traffic flow macroscopic perspective, the control core variable of two class induction strategies is vehicle
Separation rate in fork in the road, influence of the induction information to vehicle routing choice behavior is key issue therein.
Two class induction strategies have respective advantage and deficiency:VMS inductions can effectively regulate and control the magnitude of traffic flow of road network
Distribution, it is ensured that road network total optimization, but all vehicles receive identical induction information, lacked and vehicle individual is taken into account;It is individual
Propertyization induction can issue targetedly induction scheme according to the different characteristic information of individual vehicle, effectively meet the individual character of user
Change demand, but often fail to consider that a large amount of vehicles receive the feedback influence to traffic flow after personalized induction information, road network is global
It is optimal to be difficult to be guaranteed.
The content of the invention
Regarding to the issue above, it is an object of the invention to provide one kind based on road network is optimal, individual vehicle individual character is taken into account
The personalized inductivity distribution method of dual path road network under user equilibrium principle under the user equilibrium principle of demand.
To achieve the above object, the present invention takes following technical scheme:A kind of dual path road network under user equilibrium principle
There is an a sole inlet O and sole outlet D in personalized inductivity distribution method, described dual path road network, from entrance O
There are two optional paths A and B to outlet D, vehicle can receive induction information when reaching entrance A, it is characterised in that:Described lures
Conductance distribution method comprises the following steps:1) for the dual path road network to be regulated and controled, road network basic parameter is counted;2) it is based on
GreenShields models and traffic flow theory, the optimal separation rate under ideal conditions is asked for according to road network basic parameter, with
Reach that the overall traffic flow of road network is optimal;3) under the constraints of road network total optimization, travel more excellent according to individual vehicle history
The ratio in path, sets up the basic constraints of personalized induction allocation strategy;4) concept of vehicle priority is set up, is utilized
Poor distribution principle, calculates the personalized inductivity distribution numerical value under vehicle priority known case;5) according to step 4) determine
The probability distribution of distribution principle and vehicle history most ratio of greater inequality, distributes induction information, is met to greatest extent under probability meaning
The principle of user equilibrium, it is ensured that vehicle receives the fairness of induction.
The step 1) in, road network basic parameter include induction information update cycle T, magnitude of traffic flow N, current separation rate p,
Path A real-time vehicle numbers N1, path B actual vehicle numbers N2, path A length LA, path B length LB, path A vehicles averagely travel
Time TAreal, path B vehicles average hourage TBreal;Wherein, current separation rate p refers to vehicle access path A quantity percentage
Than.
The step 2) in, under " optimal " is the overall average hourage minimum criterion with vehicle, ideal conditions
Optimal separation rate to ask for process as follows:
GreenShields models are pointed out, in certain scope, have linear relationship between speed v and vehicle density ρ,
Its expression formula is as follows:
In formula, vfreeRepresent section Maximum speed limit, ρjamVehicle most high-density is represented, wherein:
NmaxBetween representing that the maximum appearance of vehicle amount of road, L represent that road section length, l represent that average length of car, d represent that vehicle is minimum
Away from for different paths, average length of car and vehicle minimum spacing are identicals, i.e. vehicle most high-density ρjamIt is solid
Definite value;
Further according to the definition of vehicle density
In formula, NtripPresent road vehicle number is represented,
It is final to draw vehicle average hourage TtripWith road section length L and present road vehicle number NtripRelation:
The new vehicle that reaches receives induction information and selected behind path, and two section vehicle numbers are expected
E(NA)=N1+Np-N1out
E(NB)=N2+N(1-p)-N2out
In formula, N represents the magnitude of traffic flow, NA、NBRepresent that the new vehicle that reaches receives induction information and access path A, path respectively
B vehicle number, E (NA)、E(NB) represent that the new vehicle that reaches receives induction information and access path A, path B vehicle number respectively
Expect, p represents separation rate, N1Represent path A current real-time vehicle number, N2Path B current real-time vehicle number is represented, wherein
N1out,N2outRepresent the vehicle flowrate left in an information updating cycle from two paths;
Expect expression formula according to two section vehicle numbers, obtain vehicle overall travel time on the paths of A, B two and expect expression
Formula
Wherein only p is variable;
Minimum is asked for the formula, optimal separation rate p is drawnbestExpression formula
Wherein
In formula, NfreeA、NfreeBFor intermediate variable, represent to assume that vehicle fully enters B path, now path A, path B points
The maximum vehicle number that can not accommodate, vfree1vfree2Path A and path B Maximum speed limit is represented respectively.
The step 3) in, the process for setting up the basic constraints of personalized induction allocation strategy is as follows:
A basic constraints of customized information issue is obtained according to the expression formula of optimal separation rate:
Wherein piRepresent that vehicle i receives the probability that path A is selected after personalized induction information, also referred to as vehicle i's
Inductivity;
Assuming that current time optimal path is A paths, above-mentioned condition can be formulated as:
In formula, piRepresent vehicle i inductivity, pjVehicle j inductivity is represented, rate (i) represents vehicle i history most
Ratio of greater inequality, rate (j) represents vehicle j history most ratio of greater inequality;
Above-mentioned two formula constitutes the basic constraints of personalized inductivity distribution.
The step 4) implementation process it is as follows:
First, the concept of priority is defined, if the rate (i) of i-th car is ordered as kth greatly in all vehicles,
The priority of the vehicle is called k, and the history most ratio of greater inequality of this car is accordingly denoted as rate (i(k)), in addition, priority is k vehicle
Inductivity apportioning cost is designated as pk, it is clear that vehicle priority is bigger, inductivity apportioning cost pkIt is bigger;
According to the concept of above-mentioned vehicle priority, according to priority k sizes, successively decreased determination inductivity, use with arithmetic progression
Formula is expressed as:
pk+1-pk=pk+2-pk+1=Δ
The expression formula of the comprehensive formula and optimal separation rate, is obtained:
Minimum inductivity p only need to be provided1, then tolerance Δ can be obtained, and then determine the inductivity of each priority.
The step 5) in, the determination process of the allocative decision of induction information is as follows:
Note n=N-i is does not reach vehicle number, and in this n+1 car, the probability that vehicle i priority is k is:
In formula, P (rate (i)) represents that any vehicle priority that do not reach is higher than the probability for having reached vehicle i, and this is one
Only with rate (i) about and with the unrelated values of j;
According to total-expectation formula, can obtain vehicle i should distribute the desired value of inductivity, under probability meaning, and this is most
Excellent allocative decision;It is formulated as:
Wherein E (pk| rate (i)=rate (i(k))) to represent vehicle i priority be inductivity apportioning cost p under conditions of kk's
Conditional expectation;
In step 4) described in arithmetic progression allocation rule under, inductivity apportioning cost pkIt is only relevant with vehicle priority k, i.e.,
Once it is determined that vehicle priority, pkJust it is to determine and known, therefore has:
E(pk| rate (i)=rate (i(k)))=pk
In the continuous arrival process of vehicle, respective path is selected respectively with vehicle has been reached, optimal separation rate is in
In being continually changing, it is assumed that when i-th car reaches crossing, preceding i-1 car selects the quantity of A paths and B path according to allocation rule
Respectively n1And n2, then obviously it is not changed into by the road network optimal constraint conditionses of crossing vehicle now:
According to arithmetic progression allocation rule, can obtain the vehicle guidance rate apportioning cost that priority is k should meet condition, use
Formula is expressed as:
Wherein Δ is constant, and for the inductivity tolerance of setting, this tolerance should ensure that the minimum inductivity and highest of vehicle
Inductivity in the reasonable scope, takes
Further solve, can show that the vehicle guidance rate apportioning cost that priority is k is:
According to above-mentioned solution, i-th car inductivity apportioning cost optimal under probability meaning is obtained:
Wherein:
The step 5) solution to P (rate (i)), when there is enough prioris, just according to statistical information pair
The optimal ratio distribution of history of vehicle is fitted, when priori is not enough, then it is assumed that the history of vehicle most ratio of greater inequality obeys normal state
Distribution, now:
Wherein μ, δ are the average and standard deviation of normal distribution, and x is the symbol of stochastic variable.
The present invention is due to taking above technical scheme, and it has advantages below:1st, the present invention is solved by traffic flow theory
Optimized overall Vehicles separation rate, the serious road sections part vehicle of congestion is adjusted to the lighter section of congestion level, it is ensured that road network is imitated
The maximization of rate simultaneously avoids congestion status from shifting repeatedly as far as possible.2nd, the present invention is on the premise of road network total optimization is ensured, simultaneously
Take into account the individual demand of individual vehicle, it is ensured that the harmony of individual consumer's trip.3rd, the present invention combines colony's induction and individual
The advantage of propertyization induction, it is to avoid the deficiency of colony's induction and personalized induction, both ensure that the personalization of induction, it is contemplated that
To the feedback effect of road network after vehicle guidance so that the advantage of two kinds of induction strategies is given full play to.
Brief description of the drawings
Fig. 1 is the topological structure schematic diagram of dual path road network.
Embodiment
The present invention is described in detail with reference to the accompanying drawings and examples.
The present invention proposes the personalized inductivity distribution method of dual path road network under a kind of user equilibrium principle, dual path
The topological structure of road network has two as shown in figure 1, road network has a sole inlet O and sole outlet D from entrance O to outlet D
Bar optional path A and B, vehicle can receive induction information when reaching entrance O points, and select path according to induction information.The present invention
Method comprises the following steps:
1) for the dual path road network to be regulated and controled, road network basic parameter is counted, including induction information update cycle T,
Magnitude of traffic flow N, current separation rate (vehicle access path A number percent) p, path A real-time vehicle numbers N1, path B it is actual
Vehicle number N2, path A length LA, path B length LB, path A vehicles average hourage TAreal, path B vehicles are when averagely travelling
Between TBreal。
In above-mentioned parameter, path A length LA, path B length LBClearly known constant, path A real-time vehicle numbers N1And road
Footpath B actual vehicle numbers N2Can by way of in road network setup of entrances and exits detector indirect gain, known quantity can also be considered as.
2) GreenShields models and traffic flow theory are based on, is asked for according to road network basic parameter under ideal conditions
Optimal separation rate, it is optimal to reach the overall traffic flow of road network.Wherein, " optimal ", is minimum with the overall average hourage of vehicle
For criterion.
GreenShields models are pointed out, in certain scope, have linear relationship between speed v and vehicle density ρ,
Its expression formula is as follows:
In formula, vfreeRepresent section Maximum speed limit, ρjamVehicle most high-density is represented, be can be generally considered as
NmaxBetween representing that the maximum appearance of vehicle amount of road, L represent that road section length, l represent that average length of car, d represent that vehicle is minimum
Away from for different paths, average length of car and vehicle minimum spacing may be considered identical, i.e. vehicle most high-density
ρjamFor fixed value.
Further according to the definition of vehicle density
In formula, NtripPresent road vehicle number is represented, vehicle average hourage T can be finally drawntripWith road section length
L and present road vehicle number NtripRelation:
The new vehicle that reaches receives induction information and selected behind path, and two section vehicle numbers are expected
E(NA)=N1+Np-N1out (5)
E(NB)=N2+N(1-p)-N2out (6)
In formula, N represents the magnitude of traffic flow, NA、NBRepresent that the new vehicle that reaches receives induction information and access path A, path respectively
B vehicle number, E (NA)、E(NB) represent that the new vehicle that reaches receives induction information and access path A, path B vehicle number respectively
Expect, p represents separation rate, N1Represent path A current real-time vehicle number, N2Path B current real-time vehicle number is represented, wherein
N1out,N2outThe vehicle flowrate left in an information updating cycle from two paths is represented, its expression formula can be according to traffic flow
Theory is solved and drawn.
According to (5), (6) formula, vehicle overall travel time on the paths of A, B two can be obtained and expect expression formula
Wherein only p is variable, and its residual value is considered known constant.
Minimum is asked for (7) formula, optimal separation rate p can be drawnbestExpression formula
Wherein
In formula, NfreeA、NfreeBFor intermediate variable, represent to assume that vehicle fully enters B path, now path A, path B points
The maximum vehicle number that can not accommodate, vfree1vfree2Path A and path B Maximum speed limit is represented respectively.
3) under the constraints of road network total optimization, the ratio compared with shortest path is travelled according to individual vehicle history, set up
The basic constraints of personalization induction allocation strategy.
Traffic flow obtained in previous step, in the optimal separation rate of fork in the road, can be crossing VMS induction informations
Issue provides a basic control parameter.In fact, for the road network of same structure, the issue of VMS information and customized information
The core control parameter of issue is the Vehicles separation rate at crossing, regardless of whether being which kind of control strategy, its purpose is by adjusting
Control the separation rate of vehicle so that the distribution of road network vehicle reaches optimal result in some sense.Wherein, the standard of optimal solution is
Vehicle average hourage reaches minimum.
When road network input flow rate is constant and known, optimal total separation rate and the VMS information of customized information issue are issued
Optimal separation rate it is identical, its expression formula is (8) formula.
Different from VMS colonies inductions, in personalization induction, each vehicle can be distributed according to the different situations of vehicle in itself
Different inductivities, control different vehicle selects path with different probability.The optimal separation rate tried to achieve according to (8) formula, can be obtained
The basic constraints issued to customized information:
Wherein piRepresent that vehicle i receives the probability that path A is selected after personalized induction information, also referred to as vehicle i's
Inductivity.
In recent years, with the development of intelligent bus or train route cooperative system, by the communication of vehicle and roadside device, dress can be obtained
It is loaded with the individual vehicle information of communication equipment, including vehicle running history record.Therefore, for the vehicle at each arrival crossing
I, ratio (hereinafter referred to as " history most ratio of greater inequality ") rate (i) that the optimal section number of times of its history traveling accounts for total degree may be considered
It is known.In this data basis, personalized induction information can be recorded according to the traveling of individual vehicle, and issue specific aim is lured
Lead information, it is ensured that vehicle selects the fairness of optimal path.I.e. vehicle history travel optimal section number of times account for total degree ratio compared with
Hour, personalized induction information should cause the optimal path that the vehicle has bigger probability to choose current time.Assuming that when current
Quarter, optimal path was A paths, and above-mentioned condition can be formulated as:
In formula, piRepresent vehicle i inductivity, pjVehicle j inductivity is represented, rate (i) represents vehicle i history most
Ratio of greater inequality, rate (j) represents vehicle j history most ratio of greater inequality.
Comprehensive (10) (11) two formula, the inductivity of reasonable distribution each car so that on the premise of road network total optimization, take into account
Individual vehicle selects the fairness of optimal path.(10) (11) two formulas constitute the basic constraint bar of personalized inductivity distribution
Part.
4) concept of vehicle priority is set up, according to this concept, using equal difference distribution principle, vehicle priority has been calculated
Personalized inductivity distribution numerical value in the case of knowing.
When information of vehicles is complete and accurate, the rate size orders of all vehicles are also unique and accurate.Define one
The concept of priority, if the rate (i) of i-th car is ordered as kth greatly in all vehicles, the priority of the vehicle is called
K, the history most ratio of greater inequality of this car is accordingly denoted as rate (i(k)), dictating otherwise the vehicle guidance rate apportioning cost that priority is k is
pk, it is clear that vehicle priority is bigger, inductivity apportioning cost pkIt is bigger.
According to the concept of above-mentioned vehicle priority, according to priority k sizes, successively decreased determination inductivity, use with arithmetic progression
Formula is expressed as:
pk+1-pk=pk+2-pk+1=Δ (12)
Comprehensive (8) (12) two formula, can be obtained:
Suitable minimum inductivity p need to only be provided1, then tolerance Δ can be obtained, and then determine the inductivity of each priority.
5) according to step 4) distribution principle that determines, according to the probability distribution of vehicle history most ratio of greater inequality, reasonable distribution induction
Information, meets the principle of user equilibrium to greatest extent under probability meaning, it is ensured that vehicle receives the fairness of induction.
Because colony's induction is the behavior for a large amount of vehicles, it can be considered that the history of all vehicles most ratio of greater inequality is only
The vertical stochastic variable with distribution, can be according to history most ratio of greater inequality of the statistical information to vehicle when there is enough prioris
Distribution is fitted, when priori is not enough, it is believed that the history of vehicle most ratio of greater inequality Normal Distribution.
Below with history most ratio of greater inequality Normal Distribution on the premise of solved, obey other points for history most ratio of greater inequality
The situation of cloth, will not produce any influence, therefore can be solved according to identical step to solution procedure.
When i-th (i < N) car reaches crossing, compared with not yet reaching any vehicle j (i < j≤N) at crossing, preferentially
The lower probability of level is:
Wherein μ, δ are the average and standard deviation of normal distribution, and x is the symbol of stochastic variable.
Obviously, this is one can be abbreviated as P (rate (i)) only with rate (i) about and with the unrelated values of j, represent times
Meaning does not reach vehicle priority and is higher than the probability for having reached vehicle i.
Due to the history most ratio of greater inequality independent same distribution of all vehicles, thus reached the information of vehicle and do not reach vehicle without
Close, can only consider that vehicle i does not reach vehicle with remaining, note n=N-i is does not reach vehicle number, in this n+1 car, vehicle i
Priority be k probability be:
According to total-expectation formula, can obtain vehicle i should distribute the desired value of inductivity, under probability meaning, and this is most
Excellent allocative decision.It is formulated as:
Wherein E (pk| rate (i)=rate (i(k))) to represent vehicle i priority be inductivity apportioning cost p under conditions of kk's
Conditional expectation.
In step 4) described in arithmetic progression allocation rule under, inductivity apportioning cost pkIt is only relevant with vehicle priority k, i.e.,
Once it is determined that vehicle priority, pkJust it is to determine and known, therefore has:
E(pk| rate (i)=rate (i(k)))=pk(17)
In the continuous arrival process of vehicle, respective path is selected respectively with vehicle has been reached, optimal separation rate is in
In being continually changing, it is assumed that when i-th car reaches crossing, preceding i-1 car selects the quantity of A paths and B path according to allocation rule
Respectively n1And n2, then obviously it is not changed into by the road network optimal constraint conditionses of crossing vehicle now:
According to arithmetic progression allocation rule, can obtain the vehicle guidance rate apportioning cost that priority is k should meet condition, use
Formula is expressed as:
Wherein Δ is constant, and for the inductivity tolerance of setting, this tolerance should ensure that the minimum inductivity and highest of vehicle
Inductivity in the reasonable scope, takes
Further solve, can show that the vehicle guidance rate apportioning cost that priority is k is:
According to above-mentioned analysis and solution, i-th car inductivity apportioning cost optimal under probability meaning has been obtained:
Wherein:
The various embodiments described above are only used for having carried out further specifically the purpose of the present invention, technical scheme and beneficial effect
It is bright, it is not intended to limit the invention, within the spirit and principles of the invention, any modifications, equivalent substitutions and improvements done
Deng should be included within the scope of the present invention.
Claims (7)
1. a kind of personalized inductivity distribution method of dual path road network under user equilibrium principle, described dual path road network is present
, there are two optional paths A and B from entrance O to outlet D, vehicle reaches entrance A in an one sole inlet O and sole outlet D
When can receive induction information, it is characterised in that:Described inductivity distribution method comprises the following steps:
1) for the dual path road network to be regulated and controled, road network basic parameter is counted;
2) GreenShields models and traffic flow theory are based on, is asked for according to road network basic parameter optimal under ideal conditions
Separation rate, it is optimal to reach the overall traffic flow of road network;
3) under the constraints of road network total optimization, the ratio compared with shortest path is travelled according to individual vehicle history, individual character is set up
Change the basic constraints of induction allocation strategy;
4) concept of vehicle priority is set up, using equal difference distribution principle, the personalization under vehicle priority known case is calculated
Inductivity distributes numerical value;
5) according to step 4) determine distribution principle and vehicle history most ratio of greater inequality probability distribution, distribute induction information, general
The principle of user equilibrium is met under rate meaning to greatest extent, it is ensured that vehicle receives the fairness of induction.
2. the personalized inductivity distribution method of dual path road network under user equilibrium principle as claimed in claim 1, its feature
It is:The step 1) in, road network basic parameter includes induction information update cycle T, magnitude of traffic flow N, current separation rate p, road
Footpath A real-time vehicle numbers N1, path B actual vehicle numbers N2, path A length LA, path B length LB, path A vehicles are when averagely travelling
Between TAreal, path B vehicles average hourage TBreal;Wherein, current separation rate p refers to vehicle access path A quantity percentage
Than.
3. the personalized inductivity distribution method of dual path road network under user equilibrium principle as claimed in claim 2, its feature
It is:The step 2) in, " optimal " be under the overall average hourage minimum criterion with vehicle, ideal conditions most
Excellent separation rate to ask for process as follows:
GreenShields models are pointed out, in certain scope, have linear relationship, its table between speed v and vehicle density ρ
It is as follows up to formula:
In formula, vfreeRepresent section Maximum speed limit, ρjamVehicle most high-density is represented, wherein:
NmaxThe maximum appearance of vehicle amount of road is represented, L represents road section length, and l represents average length of car, and d represents vehicle minimum spacing,
For different paths, average length of car and vehicle minimum spacing are identicals, i.e. vehicle most high-density ρjamFor fixation
Value;
Further according to the definition of vehicle density
In formula, NtripPresent road vehicle number is represented,
It is final to draw vehicle average hourage TtripWith road section length L and present road vehicle number NtripRelation:
The new vehicle that reaches receives induction information and selected behind path, and two section vehicle numbers are expected
E(NA)=N1+Np-N1out
E(NB)=N2+N(1-p)-N2out
In formula, N represents the magnitude of traffic flow, NA、NBRepresent that the new vehicle that reaches receives induction information and access path A, path B respectively
Vehicle number, E (NA)、E(NB) represent that the new vehicle that reaches receives induction information and access path A, the phase of path B vehicle number respectively
Hope, p represents separation rate, N1Represent path A current real-time vehicle number, N2Path B current real-time vehicle number is represented, wherein
N1out,N2outRepresent the vehicle flowrate left in an information updating cycle from two paths;
Expect expression formula according to two section vehicle numbers, obtain vehicle overall travel time on the paths of A, B two and expect expression formula
Wherein only p is variable;
Minimum is asked for the formula, optimal separation rate p is drawnbestExpression formula
Wherein
In formula, NfreeA、NfreeBFor intermediate variable, represent to assume that vehicle fully enters B path, now path A, path B respectively can
With the maximum vehicle number of receiving, vfree1vfree2Path A and path B Maximum speed limit is represented respectively.
4. the personalized inductivity distribution method of dual path road network under user equilibrium principle as claimed in claim 3, its feature
It is:The step 3) in, the process for setting up the basic constraints of personalized induction allocation strategy is as follows:
A basic constraints of customized information issue is obtained according to the expression formula of optimal separation rate:
Wherein piRepresent that vehicle i receives the induction of probability, also referred to as vehicle i that path A is selected after personalized induction information
Rate;
Assuming that current time optimal path is A paths, above-mentioned condition can be formulated as:
In formula, piRepresent vehicle i inductivity, pjVehicle j inductivity is represented, rate (i) represents vehicle i history most ratio of greater inequality,
Rate (j) represents vehicle j history most ratio of greater inequality;
Above-mentioned two formula constitutes the basic constraints of personalized inductivity distribution.
5. the personalized inductivity distribution method of dual path road network under user equilibrium principle as claimed in claim 4, its feature
It is:The step 4) implementation process it is as follows:
First, the concept of priority is defined, if the rate (i) of i-th car is ordered as kth greatly in all vehicles, claiming should
The priority of vehicle is k, and the history most ratio of greater inequality of this car is accordingly denoted as rate (i(k)), in addition, priority is k vehicle guidance
Rate apportioning cost is designated as pk, it is clear that vehicle priority is bigger, inductivity apportioning cost pkIt is bigger;
According to the concept of above-mentioned vehicle priority, according to priority k sizes, successively decreased determination inductivity with arithmetic progression, use formula
It is expressed as:
pk+1-pk=pk+2-pk+1=Δ
The expression formula of the comprehensive formula and optimal separation rate, is obtained:
Minimum inductivity p only need to be provided1, then tolerance Δ can be obtained, and then determine the inductivity of each priority.
6. the personalized inductivity distribution method of dual path road network under user equilibrium principle as claimed in claim 5, its feature
It is:The step 5) in, the determination process of the allocative decision of induction information is as follows:
Note n=N-i is does not reach vehicle number, and in this n+1 car, the probability that vehicle i priority is k is:
In formula, P (rate (i)) represents that any vehicle priority that do not reach is higher than the probability for having reached vehicle i, this be one only with
Rate (i) is about and with the unrelated values of j;
According to total-expectation formula, can obtain vehicle i should distribute the desired value of inductivity, under probability meaning, and this is optimal
Allocative decision;It is formulated as:
Wherein E (pk| rate (i)=rate (i(k))) to represent vehicle i priority be inductivity apportioning cost p under conditions of kkCondition
Expect;
In step 4) described in arithmetic progression allocation rule under, inductivity apportioning cost pkIt is only relevant with vehicle priority k, i.e., once
Determine vehicle priority, pkJust it is to determine and known, therefore has:
E(pk| rate (i)=rate (i(k)))=pk
In the continuous arrival process of vehicle, respective path is selected respectively with vehicle has been reached, optimal separation rate is in continuous
In change, it is assumed that when i-th car reaches crossing, preceding i-1 car selects the quantity of A paths and B path to distinguish according to allocation rule
For n1And n2, then obviously it is not changed into by the road network optimal constraint conditionses of crossing vehicle now:
According to arithmetic progression allocation rule, can obtain the vehicle guidance rate apportioning cost that priority is k should meet condition, use formula
It is expressed as:
Wherein Δ is constant, and for the inductivity tolerance of setting, this tolerance should ensure that the minimum inductivity of vehicle and highest are induced
Rate in the reasonable scope, takes
Further solve, can show that the vehicle guidance rate apportioning cost that priority is k is:
According to above-mentioned solution, i-th car inductivity apportioning cost optimal under probability meaning is obtained:
Wherein:
7. the personalized inductivity distribution method of dual path road network under user equilibrium principle as claimed in claim 6, its feature
It is:The step 5) solution to P (rate (i)), when there is enough prioris, just according to statistical information to vehicle
The optimal ratio distribution of history be fitted, when priori is not enough, then it is assumed that the history of vehicle most ratio of greater inequality Normal Distribution,
Now:
Wherein μ, δ are the average and standard deviation of normal distribution, and x is the symbol of stochastic variable.
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