CN107256632B - Traffic distribution method based on user heterogeneous time value and congestion cost budget - Google Patents

Traffic distribution method based on user heterogeneous time value and congestion cost budget Download PDF

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CN107256632B
CN107256632B CN201710684862.4A CN201710684862A CN107256632B CN 107256632 B CN107256632 B CN 107256632B CN 201710684862 A CN201710684862 A CN 201710684862A CN 107256632 B CN107256632 B CN 107256632B
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path
cost
congestion
traffic
budget
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CN107256632A (en
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白婷
谢驰
刘海洋
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Shanghai Jiaotong University
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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
    • 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

Abstract

The invention discloses a traffic distribution method based on user heterogeneous time value and congestion cost budget, which is characterized in that two economic parameters of travelers, namely the time value and the congestion cost budget, are added into a traffic distribution model, so that more objective and reasonable road network traffic is output. The invention simplifies the actual road network into an abstract traffic network, and constructs a path selection and traffic distribution combined model considering the heterogeneous time value and the congestion fee budget. The time value is a parameter describing the boundary cost, i.e. the capital cost the user is willing to pay for per unit time savings; congestion budget, the amount of cash a traveler can dominate over congestion costs. Based on consideration of heterogeneity of travelers, the travel target of the user can be changed from the traditional shortest travel time to the minimum comprehensive cost after considering the congestion expense budget, namely, the sum of the travel time and the congestion charge is minimum. Obviously, the travel behavior and the road network traffic of the user can be more reasonably and objectively depicted, and a reference is provided for path planning and congestion charging.

Description

Traffic distribution method based on user heterogeneous time value and congestion cost budget
Technical Field
The invention relates to the field of traffic distribution methods, in particular to a traffic distribution method based on user heterogeneous time value and congestion cost budget.
Background
In the planning of the traffic network, the traffic flow on the traffic network needs to be obtained by a simulation or numerical calculation method, this process is called traffic distribution, and the result of the traffic distribution can be used to determine whether the planning of the traffic network is reasonable. The conventional traffic distribution assumes that a road user selects a route with the shortest time when traveling,
but in objective practice this assumption is too coarse and there is a large error. The traveler often needs to consider some economic factors when making the selection.
First, if the income is high or the emergency happens, the traveler does not specially balance the time and the cost, but considers more to shorten the travel time; but travelers with lower revenues and more free time prefer to reduce costs.
Second, travel behavior is also limited by how much cash can be handled. The user's trade-off between congestion charging and time can be described by the parameter of time value; and the amount of the disposable funds can be regarded as the user trip budget.
From the above two points, the path selection of the travelers is influenced by personal income condition, travel purpose, expense condition and the like, different travelers have different time values and congestion cost budgets, namely, the time values and the congestion cost budget parameters have heterogeneity, so that the two parameters are more suitable to be described by using a continuous distribution function.
Disclosure of Invention
The invention aims to provide a traffic distribution method based on user heterogeneous time value and congestion cost budget aiming at the defects in the prior art, and the time value and the congestion cost budget are added into a traffic distribution model, so that the travel behaviors and the road network flow of a user can be more reasonably and objectively depicted, and the problems in the prior art are solved.
The technical problem solved by the invention can be realized by adopting the following technical scheme:
a traffic distribution method based on heterogeneous time value and congestion cost budget of users comprises the following steps:
1) establishing abstract traffic networks
Setting a plurality of paths from a starting point r to an end point s, wherein each path consists of a plurality of road sections with the starting point rs connected with the end point rs, the total number of people going out is known between the starting point rs, a represents a road section, A = { a } represents a set of the road sections a, H represents a path, and H = { H } represents a set of the path H;
5) travel cost of different travelers
The time cost of different travelers is different, so the travel cost of the travelers is different
Figure BDA0001376439660000021
Comprises the following steps:
Figure BDA0001376439660000022
in the formula, xaIs the flow of the section a, the time impedance function ta(xa) Is a continuous convex function, caRepresenting the congestion charge for road segment a, the time value α obeys a known continuous distribution, α ∈ [ α [ ]min，αmax]The time value alpha is used for reducing the travel costThe combination with the travel time represents the balance of the travelers on time and cost;
6) establishing budget constraints
Figure BDA0001376439660000023
In the formula (I), the compound is shown in the specification,
Figure BDA0001376439660000024
indicating the congestion charge for path h at origin-destination rs,
Figure BDA0001376439660000025
wherein the dirac function
Figure BDA0001376439660000031
Is a link-path association indicator if
Figure BDA0001376439660000032
Then a path h with its origin-destination rs travels through segment a, otherwise,
Figure BDA0001376439660000033
τ represents the congestion cost budget, τ ∈ [ ]min，τmax]；
Figure BDA0001376439660000034
Representing a traffic flow density variable on a path h with an origin-destination point rs, a time value alpha and a congestion cost budget tau;
7) establishing flow conservation constraint condition
Figure BDA0001376439660000035
In the formula (I), the compound is shown in the specification,
Figure BDA0001376439660000036
the representation shows traffic flow of travelers on all paths between origin and destination rs, only intersection on all pathsThrough flow and traffic trip demand qrsWhen the vehicle speed is equal, the traveling requirements of all the motor vehicles on the road network can be met;
here, the travel demand is considered based on the route, and in the actual road network, there is a constraint relationship between the route and the road segment, that is:
Figure BDA0001376439660000037
5) establishing an improved objective function
Improving the traditional traffic distribution model according to the definitions in the steps 1) to 3), and selecting a path with the minimum comprehensive travel cost by all travelers according to a network balance principle, so that an objective function is as follows:
Figure BDA0001376439660000038
wherein x represents a matrix of the road network traffic, w is an integrator, ρa(τ) represents the traffic flow density on the road segment a with a congestion cost budget τ,
Figure BDA0001376439660000039
8) deriving minimum cost paths under budget constraints
Step 1: and finding all 'pareto' optimal path sets K which do not exceed the highest budget limit by adopting a labeling method, and sorting the optimal path sets K according to congestion fees on paths, wherein K is { K ═ Kmin,kmin+1,...,kmax-said "pareto" optimal path indicates that this path is irreplaceable;
step 2: find out all 'convex pareto optimal' paths Kextreme；
And step 3: distributing the flow to a 'convex pareto optimal' path according to the traffic demand characteristic parameters;
9) solving and solving the improved traffic distribution model by adopting a Frank-Wolfe algorithm;
firstly, feasibility check, namely, if the lowest budget is lower than the path with the lowest cost for the beginning point rs, the requirement of a part of road users cannot be met, and unallocated flow is reported;
② traffic network initialization based on
Figure BDA0001376439660000041
Carrying out traffic distribution on the minimum cost path problem under the constraint of network budget to obtain an initial solution of the network flow density
Figure BDA0001376439660000042
Thirdly, updating traffic network
Figure BDA0001376439660000043
Fourthly, calculating gradient descending direction to obtain an auxiliary solution etaa (n)}；
Solving the following one-dimensional optimization problem by a bisection method to obtain an optimal step factor theta;
Figure BDA0001376439660000044
sixthly, updating the variable quantity of the network flow,
Figure BDA0001376439660000045
and (9) convergence judgment. If the difference value with the previous iteration result is less than the set convergence threshold value, the convergence condition is satisfied,
Figure BDA0001376439660000046
i.e. the optimal solution. Otherwise, go to step 1.
Further, the specific process of deriving the minimum cost path under the budget constraint is as follows:
step 1: initializing, setting a first label set of each node as each node i e of the network
Figure BDA0001376439660000047
Wherein
Figure BDA0001376439660000049
The last node is represented as a node of the previous node,
Figure BDA0001376439660000048
is a state identification parameter, if κ i0, the node is not checked, otherwise, the node is checked;
for the starting point r, its first set of labels is set to
Figure BDA0001376439660000051
Step 1.1: labels, set of labels T for each node iiIf κiWhen the value is 0, the following operations are carried out:
step 1.1.1, for each road section (i, j) epsilon O (i), creating a new label set for the node j
Figure BDA0001376439660000052
Step 1.1.2: if it is not
Figure BDA0001376439660000053
Will be provided with
Figure BDA0001376439660000054
Compared with the domination of the time cost and the congestion expense of all the label sets of the node j, if the existing label set of the node j is not superior to the existing label set of the node j
Figure BDA0001376439660000055
Then handle
Figure BDA0001376439660000056
Putting the node j into a set of a node j label set;
step 1.1.3. if there is a set of labels for node j
Figure BDA0001376439660000057
While
Figure BDA0001376439660000058
Is superior to the set
Figure BDA0001376439660000059
The set of labels is removed from the set of labels of node j
Figure BDA00013764396600000510
Step 1.1.4: order to
Figure BDA00013764396600000511
Step 1.2: backtracking, selecting one or K label sets with the least cost at the end point s, and tracing each label set forwards according to the information of the upstream node and the label set until the starting point r, thereby obtaining the optimal trip chain or chains K ═ { K ═ K }min,kmin+1,...,kmax}；
Step 2: find all "convex pareto optimal" paths Kextreme：
Step 2.1: since the minimum time cost path and the minimum cost path are definitely the optimal paths of 'convex pareto', the change rate is calculated by the method
Figure BDA00013764396600000512
Step 2.2: finding the remaining 'pareto optimal' path set and path kmin、kmaxThe path with the largest second-order norm of (1); namely:
Figure BDA00013764396600000513
adding the found paths into a 'convex pareto optimal' path set;
step 2.3: repeating this process according to the dichotomy;
and step 3: distributing the flow to the optimal route of the convex pareto according to the characteristic parameters of the traffic demand:
step 3.1: obtaining a time value boundary set according to the 'convex pareto optimal' path set;
step 3.2: and integrating the demand distribution function of the adjacent time value boundary interval, limiting the distribution function integration according to the demand budget in the congestion fee interval of the path, and distributing the flow to the 'convex pareto optimal' path according to the traffic demand characteristic parameters in a layering manner.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of simplifying an actual road network into an abstract traffic network while considering user economic indexes, and constructing a route selection and traffic distribution combined model considering heterogeneous time value and congestion fee budget. The time value is a parameter describing the boundary fee, i.e. the capital cost the user is willing to pay for saving per unit time. Through the time value, the travel target of the user can be changed from the shortest traditional travel time to the minimum comprehensive cost, namely the sum of the travel time and the congestion charge is the minimum, so that the travel time and the congestion charge are more in line with the actual situation.
The method can obtain the road section flow and path flow prediction data of the road network based on the specific scene, the data can provide sufficient basis for congestion charging, and the obtained path running time can also provide other travel services such as travel planning and the like for the user.
Drawings
Fig. 1 is an overall flowchart of a user traffic distribution method in consideration of budget restrictions.
Fig. 2 is a schematic diagram of a path in an embodiment.
FIG. 3 is a schematic diagram of a convex pareto path and a non-convex pareto path in an embodiment.
FIG. 4 is a schematic diagram of the time value and congestion cost budget distribution density in an embodiment.
FIG. 5 is a schematic diagram of a path traffic loading process in an embodiment.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Step 1: and extracting the network structure and parameters in the road traffic network.
The method comprises the steps of abstracting a road traffic network in reality, and then determining a traffic network structure and related parameters in a data extraction and processing mode.
Step 2: in the embodiment, a traffic path allocation method based on consideration of heterogeneous time values and congestion fee budgets of different users is provided, and the model is as follows
An objective function:
Figure BDA0001376439660000071
constraint conditions are as follows:
Figure BDA0001376439660000072
Figure BDA0001376439660000073
wherein:
Figure BDA0001376439660000074
Figure BDA0001376439660000075
Figure BDA0001376439660000076
Figure BDA0001376439660000077
wherein:
a represents a link, a ═ a } is a set of links a,
h represents a path, H ═ { H } is the set of paths H;
τ represents the congestion cost budget, τ ∈ [ ]min,τmax]；
Figure BDA0001376439660000081
Representing the congestion fee for path h at origin-destination rs,
Figure BDA0001376439660000082
in which dirac function
Figure BDA0001376439660000083
Is a link-path association indicator if
Figure BDA0001376439660000084
A path h with an origin-destination rs travels through a section a, otherwise,
Figure BDA0001376439660000085
Figure BDA0001376439660000086
the traffic flow on path h, whose origin-destination is rs, is a variable and output of the model.
ta(xa) Representing the road resistance function, i.e. the transit time on the road section a. As traffic flow increases, transit time obviously increases, creating congestion.
According to the network balance principle, all travelers can select the path with the minimum comprehensive cost for themselves.
The road network balancing condition may also be expressed as:
Figure BDA0001376439660000087
in the formula
Figure BDA00013764396600000813
Representing the congestion fee for path h at origin-destination rs,
Figure BDA0001376439660000088
in which dirac function
Figure BDA0001376439660000089
Is a link-path association indicator if
Figure BDA00013764396600000810
A path h with an origin-destination rs travels through a section a, otherwise,
Figure BDA00013764396600000811
τ represents the congestion cost budget, τ ∈ [ ]min,τmax]；
Figure BDA00013764396600000812
And a traffic flow density variable on a path h with an origin-destination point rs, a time value alpha and a congestion cost budget tau.
And step 3: and determining origin-destination data of the traffic network and time value distribution and congestion fee budget distribution of each origin-destination demand data, and inputting the data into the traffic distribution model.
The origin-destination data refers to the traffic traveling quantity between the origin and the destination, namely the number of passing vehicles. The time value distribution and the congestion fee budget distribution refer to user demand characteristics owned by a traveler, for example, the time value of a certain user is 10 yuan/min, and the travel cost is the sum of the value obtained after time cost conversion and the congestion fee; if the congestion fee budget is 50 yuan, the user does not select a route with a congestion fee exceeding 50 yuan. The user demand characteristics are closely related to the income level, the travel purpose and the travel frequency of the user. While high income users have relatively greater acceptance of higher use costs, low cost roads may be used if they are not urgent to travel this time, and choosing a routine, cost effective route for commuters may also allow them to take travel budget constraints into account.
And 4, step 4: in this step, we provide a network with one origin-destination pair and five paths as shown in fig. 2 to illustrate the patent. It should be noted that the examples are only given for the sake of clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
The travel time function and the congestion fee of the five paths are respectively
t0＝4x0+5，c0＝18
t1＝3x1+10，c1＝16
t2＝2x2+15，c2＝15
t3＝x3+17，c3＝8
t4＝x4+30，c4＝5
The time value and congestion fee constraints fit into two uniform distributions, see fig. 4. The time value distribution follows the uniform distribution with the low value of 0 and the high value of 20, and the unit is Yuan/min; the congestion fee budget distribution follows a uniform distribution with a low value of 6 and a high value of 20, in units of dollars. The traffic demand of the traveler was set to 280 persons.
Therefore, the traffic distribution model for obtaining the road section flow and path flow indexes based on the road network traffic flow distribution of the specific scene is as follows:
Figure BDA0001376439660000101
s.t.
(x0+x1+x2+x3+x4)＝280，
(τ-18)ρ0(τ)≥0
(τ-16)ρ1(τ)≥0
(τ-15)ρ2(τ)≥0
(τ-8)ρ3(τ)≥0
(τ-5)ρ4(τ)≥0
x0，x1，x2，x3，x4≥0，
frank-walf is an iterative algorithm, where the first iteration and the criterion for terminating the iteration are explained: the first iteration:
(1) and (5) checking feasibility. For each origin-destination, the path that charges the least is found. Where the least charged path is path h4Charging thereof c4And the lowest budget is τ 6, so there is no case of not meeting the requirement.
(2) And (5) initializing. Based on link time
Figure BDA0001376439660000102
Finding a minimum cost path under the constraints of heterogeneity time value and budget, and comprising the following steps:
step 1: the labeling method finds all 'pareto optimal' path sets K which do not exceed the highest budget limit and sorts the paths according to congestion fees on the paths, wherein the 'pareto optimal' path means that the path has irreplaceability, namely no other path can save time and reduce cost, because the current road network flow is 0, obviously 5 paths all have irreplaceability, and K is ═ { h ═ at the moment0，h1，h2，h3，h4}。
Step 2: find all "convex pareto optimal" paths KextremeHere, convex pareto optimal refers to a set with convex hull property in pareto optimal set, and can be specifically seen in fig. 3:
step 2.1: firstly, the minimum time cost path and the minimum cost path are determined to belong to a 'convex pareto optimal' path, and the change rate is calculated according to the path
Figure BDA0001376439660000111
Step 2.2: find the remaining "pareto optimal" roadPath set and path kmin、kmaxThe path with the largest second-order norm of (1); namely:
Figure BDA0001376439660000112
the found path is h3Add to the "convex pareto optimal" path set, the process is shown in fig. 3 (b).
Step 2.3: this process is repeated according to the dichotomy. The process is shown in FIG. 3(c), and the found "convex pareto optimal" path has { h0,h3,h4}。
And step 3: distributing the flow to the optimal route of the convex pareto according to the characteristic parameters of the traffic demand:
step 3.1: from the "convex pareto optimal" path set, a time-value boundary set, i.e., α shown in FIG. 3(d), is obtained1,α2They are numerically equal to the ratio of the congestion cost of the adjacent "convex pareto optimal" route to the difference in transit time, i.e. the absolute value of the intercept;
step 3.2: integrating the demand distribution function of the adjacent time value boundary interval, limiting the distribution function according to the demand budget in the congestion fee interval of the path, distributing the flow to the 'convex pareto optimal' path according to the traffic demand characteristic parameters in a layering way, as shown in figure 5,
to obtain x0＝38.334,x1＝x2＝0,x3＝198.894,x4＝42.772。
(3) And updating the passing time. Calculating the passing time of the road network section at the moment as t according to the flow initially distributed0＝4x0+158.336,t1＝3x1+10,t2＝2x2+15,t3＝x3+215.894,t4＝x4+68.334。
(4) And (5) iterating to find the direction. And re-searching the shortest path of each motor vehicle according to the updated network traffic time. Obtain an auxiliary solution { ya}，y0＝0,y1＝79.2,y2＝19.863,y3＝0,y4＝180.894。
(5) Solving an optimal step length factor eta:
Figure BDA0001376439660000121
η＝0.189
(6) according to xa＝xa+η(ya-xa) Updating road network flow xa：
X is then0＝31.089，x1＝14.969，x2＝3.754，x3＝161.139，x4＝68.877°
(7) And judging whether the iteration is ended or not. And if the difference value between the first iteration objective function and the initialization objective function value is smaller than the threshold value, stopping iteration. And outputting the road network section flow result. Here the difference is 38.207 and the iteration continues.
……
And (4) interpolating 0.002417 between the 18 th iteration result and the 17 th iteration result, stopping iteration and outputting the result when the interpolation is less than the set threshold value of 0.001. The results were:
x0＝9.816，x1＝10.691，x2＝28.725，x3＝69.451，x4＝161.317
the foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (2)

1. A traffic distribution method based on heterogeneous time value and congestion cost budget of users is characterized by comprising the following steps:
1) establishing abstract traffic networks
Setting a plurality of paths from a starting point r to an end point s, wherein each path consists of a plurality of road sections with the starting point rs connected with the end point rs, the total number of people going out is known between the starting point rs, a represents a road section, A ═ a } represents a set of the road section a, H represents a path, and H ═ H represents a set of the path H;
2) travel cost of different travelers
The time cost of different travelers is different, so the travel cost of the travelers is different
Figure FDA0001376439650000011
Comprises the following steps:
Figure FDA0001376439650000012
in the formula, xaIs the flow of the section a, the time impedance function ta(xa) Is a continuous convex function, caRepresenting the congestion charge for road segment a, the time value α obeys a known continuous distribution, α ∈ [ α [ ]min，αmax]The time value alpha is used for combining travel cost and travel time and representing the balance of a traveler on the time and the cost;
3) establishing budget constraints
Figure FDA0001376439650000013
In the formula (I), the compound is shown in the specification,
Figure FDA0001376439650000014
indicating the congestion charge for path h at origin-destination rs,
Figure FDA0001376439650000015
wherein the dirac function
Figure FDA0001376439650000016
Is a link-path association indicator if
Figure FDA0001376439650000017
Then a path h with its origin-destination rs travels through segment a, otherwise,
Figure FDA0001376439650000018
τ represents the congestion cost budget, τ ∈ [ ]min，τmax]；
Figure FDA0001376439650000019
Representing a traffic flow density variable on a path h with an origin-destination point rs, a time value alpha and a congestion cost budget tau;
4) establishing flow conservation constraint condition
Figure FDA0001376439650000021
In the formula (I), the compound is shown in the specification,
Figure FDA0001376439650000022
the representation shows the traffic flow of the travelers on all paths between the origin and destination rs, and only the traffic flow on all paths and the travel demand qrsWhen the vehicle speed is equal, the traveling requirements of all the motor vehicles on the road network can be met;
here, the travel demand is considered based on the route, and in the actual road network, there is a constraint relationship between the route and the road segment, that is:
Figure FDA0001376439650000023
5) establishing an improved objective function
Improving the traditional traffic distribution model according to the definitions in the steps 1) to 3), and selecting a path with the minimum comprehensive travel cost by all travelers according to a network balance principle, so that an objective function is as follows:
Figure FDA0001376439650000024
wherein x represents a matrix of the road network traffic, w is an integrator, ρa(τ) represents the traffic flow density on the road segment a with a congestion cost budget τ,
Figure FDA0001376439650000025
6) deriving minimum cost paths under budget constraints
Step 1: finding all 'pareto' optimal path sets K which do not exceed the highest budget limit by adopting a dual-standard label method, and sorting the optimal path sets according to congestion fees on the paths, wherein K is { K ═ Kmin,kmin+1,...,kmax-said "pareto" optimal path indicates that this path is irreplaceable;
step 2: find out all 'convex pareto optimal' paths Kextreme；
And step 3: distributing the flow to a 'convex pareto optimal' path according to the traffic demand characteristic parameters;
7) solving and solving the improved traffic distribution model by adopting a Frank-Wolfe algorithm;
firstly, feasibility check, namely, if the lowest budget is lower than the path with the lowest cost for the beginning point rs, the requirement of a part of road users cannot be met, and unallocated flow is reported;
② traffic network initialization based on
Figure FDA0001376439650000031
According to the step 6), carrying out traffic distribution on the minimum cost path problem under the constraint of network budget to obtain an initial solution of the network flow density
Figure FDA0001376439650000032
Thirdly, updating traffic network
Figure FDA0001376439650000033
Fourthly, calculating gradient descending direction to obtain an auxiliary solution etaa (n)}；
Solving the following one-dimensional optimization problem by a bisection method to obtain an optimal step factor theta;
Figure FDA0001376439650000034
sixthly, updating the variable quantity of the network flow,
Figure FDA0001376439650000035
and determining convergence, if the difference value with the previous iteration result is less than a set convergence threshold value, meeting the convergence condition, stopping iteration,
Figure FDA0001376439650000036
the optimal solution is obtained; otherwise, go to step 1.
2. The traffic distribution method based on user heterogeneous time value and congestion cost budgets according to claim 1, wherein the specific process of deriving the minimum cost path under the budget constraint is as follows:
step 1.0: initializing, setting a first label set of each node as N for each node i E N of the network
Figure FDA0001376439650000037
Wherein
Figure FDA0001376439650000038
The last node is represented as a node of the previous node,
Figure FDA0001376439650000039
is a state identification parameter, if κi0, the node is not checked, otherwise, the node is checked;
for the starting point r, its first reference numberSet as
Figure FDA00013764396500000310
Step 1.1: labels, set of labels T for each node iiIf κiWhen the value is 0, the following operations are carried out:
step 1.1.1, for each road section (i, j) epsilon O (i), creating a new label set for the node j
Figure FDA0001376439650000041
Step 1.1.2: if it is not
Figure FDA0001376439650000042
Will be provided with
Figure FDA0001376439650000043
Compared with the domination of the time cost and the congestion expense of all the label sets of the node j, if the existing label set of the node j is not superior to the existing label set of the node j
Figure FDA0001376439650000044
Then handle
Figure FDA0001376439650000045
Putting the node j into a set of a node j label set;
step 1.1.3. if there is a set of labels for node j
Figure FDA0001376439650000046
While
Figure FDA0001376439650000047
Is superior to the set
Figure FDA0001376439650000048
The set of labels is removed from the set of labels of node j
Figure FDA0001376439650000049
Step 1.1.4: order to
Figure FDA00013764396500000410
Step 1.2: backtracking, selecting one or K label sets with the least cost at the end point s, and tracing each label set forwards according to the information of the upstream node and the label set until the starting point r, thereby obtaining the optimal trip chain or chains K ═ { K ═ K }min,kmin+1,...,kmax}；
Step 2: find all "convex pareto optimal" paths Kextreme：
Step 2.1: since the minimum time cost path and the minimum cost path are definitely the optimal paths of 'convex pareto', the change rate is calculated by the method
Figure FDA00013764396500000411
Step 2.2: finding the remaining 'pareto optimal' path set and path kmin、kmaxThe path with the largest second-order norm of (1); namely:
Figure FDA00013764396500000412
adding the found paths into a 'convex pareto optimal' path set;
step 2.3: repeating this process according to the dichotomy;
and step 3: distributing the flow to the optimal route of the convex pareto according to the characteristic parameters of the traffic demand:
step 3.1: obtaining a time value boundary set according to the 'convex pareto optimal' path set;
step 3.2: and integrating the demand distribution function of the adjacent time value boundary interval, limiting the distribution function integration according to the demand budget in the congestion fee interval of the path, and distributing the flow to the 'convex pareto optimal' path according to the traffic demand characteristic parameters in a layering manner.
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