CN113781768A - Hot spot district traffic organization control cooperation method based on reserve traffic capacity - Google Patents

Hot spot district traffic organization control cooperation method based on reserve traffic capacity Download PDF

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CN113781768A
CN113781768A CN202110908433.7A CN202110908433A CN113781768A CN 113781768 A CN113781768 A CN 113781768A CN 202110908433 A CN202110908433 A CN 202110908433A CN 113781768 A CN113781768 A CN 113781768A
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fork
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CN113781768B (en
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马万经
郑喆
王玲
俞春辉
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Tongji University
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    • G—PHYSICS
    • G08—SIGNALLING
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    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention relates to a hot spot district traffic organization control coordination method based on reserve traffic capacity, which comprises the following steps: 1) acquiring basic traffic data of a road network of a hot spot district, and constructing a road network descriptive set of the hot spot district; 2) establishing a traffic organization control collaborative optimization model based on the maximization of the running benefit of the road network of the hot spot area, and realizing the collaborative optimization of the direction, the function and the signal timing of each lane of the road section and the intersection; 3) and solving the traffic organization control collaborative optimization model by adopting a heuristic algorithm to generate a targeted hot spot district traffic organization control combined optimization scheme. Compared with the prior art, the method has the advantages of adopting a double-layer collaborative optimization model for collaborative optimization, improving the overall operation efficiency, fully mining and effectively utilizing space-time resources and the like.

Description

Hot spot district traffic organization control cooperation method based on reserve traffic capacity
Technical Field
The invention relates to the technical field of regional traffic control, in particular to a hot spot district traffic organization control cooperation method based on reserve traffic capacity.
Background
The urban traffic jam problem is caused by the imbalance between traffic supply and traffic demand, the motorization speed of urban traffic is rapidly accelerated along with the rapid development of economy and society, and the traffic land resources are relatively short. Under the condition, the scientific and reasonable planning, construction and management of the urban road traffic system are the key points for relieving traffic jam.
The urban hot spot district is one of key control objects of an urban road network traffic system, and the main flow of construction and optimization comprises three stages of planning (spatial layout), designing (spatial optimization), management control (time optimization) and the like. Firstly, establishing a reasonable road network structure through adjustment and improvement of road infrastructure; then, the existing traffic system and facilities thereof are optimally designed, an optimal scheme for improving traffic is sought for specific problems, and the traffic system and the constituent elements thereof are determined in a refined manner; and finally determining the right of way, the time and the space of the traffic and a management scheme thereof. However, in implementation and application, it is difficult to ensure that the design of the road space for traffic control is the most reasonable, and meanwhile, various traffic management strategies (reversible lanes, one-way lines, flow direction prohibition, etc.) and the relationship between the traffic management strategies and the traffic control have mutual influence, so how to fully utilize the existing road traffic resources and simultaneously quickly deal with the randomness of traffic demands is an important means for improving the traffic control effect of the hot spot areas by comprehensively considering space and time.
The existing hot spot district traffic control method gives full play to the utilization rate of the existing road resources mainly through various technical means, but because the various optimization strategies have the relationship of mutual influence and restriction, the combined optimization needs to face the problem of being more complicated than the optimization of a single strategy in the process of establishing, solving and analyzing the model.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a hot spot district traffic organization control cooperative method based on reserve traffic capacity.
The purpose of the invention can be realized by the following technical scheme:
a hot spot district traffic organization control coordination method based on reserve traffic capacity comprises the following steps:
1) acquiring basic traffic data of a road network of a hot spot district, and constructing a road network descriptive set of the hot spot district;
2) establishing a traffic organization control collaborative optimization model based on the maximization of the running benefit of the road network of the hot spot area, and realizing the collaborative optimization of the direction, the function and the signal timing of each lane of the road section and the intersection;
3) and solving the traffic organization control collaborative optimization model by adopting a heuristic algorithm to generate a targeted hot spot district traffic organization control combined optimization scheme.
In step 1), the hot spot segment road network descriptive set includes:
(1) for any lane
Figure BDA0003202746190000021
Defining whether the flow direction w has the right of way on an intersection r, a fork i and a lane k as a binary control variable x for a lane setriwkWhen the value is 1, the passing is allowed, and when the value is 0, the passing is not allowed, and a binary variable delta is adoptedriwWhether the flow direction w is allowed to pass at the intersection r and the fork i is indicated, when the value is 1, the flow direction w is allowed to pass, and when the value is 0, the flow direction w is not allowed to pass;
(2) for arbitrary connection
Figure BDA0003202746190000022
Defining the number of lanes on the link as naThe number of lanes on the opposite connecting line is defined as na′The total number of lanes of the road section is naa′Using a binary variable deltaaThe traffic management measures of the reversible lanes are realized by arranging asymmetrical lane numbers on the connecting lines of different directions of the road sections, namely n isa≠na′One-way traffic is implemented by assigning all lanes of a road segment to a directional link, i.e. n a0 or na′＝0。
In the step 2), the traffic organization control cooperative optimization model of the hot spot area is described by a double-layer optimization model, wherein the decision variables comprise the number of the road sections connecting the lines, the traffic flow right of way, the control parameter of the cycle time of the intersection, the starting time of the green light of the single lane of the intersection, the green light ratio of the single lane of the intersection and the control parameter of the phase sequence of the signal phase of the intersection.
The upper layer model of the hot spot district traffic organization control cooperative optimization model takes the improvement of the traffic capacity of the road network of the hot spot district as a basic target of optimization control, the objective function is the maximization of the reserve traffic capacity of the road network, and the optimization problem of the maximum flow coefficient is that:
maxμ
wherein μ is a flow coefficient.
The constraint conditions of the upper layer model of the hot spot district traffic organization control collaborative optimization model comprise flow distribution constraint, lane function distribution constraint, signal control constraint and saturation constraint.
The flow distribution constraint specifically includes:
Figure BDA0003202746190000031
Figure BDA0003202746190000032
wherein Q iso,For each OD in the road network to (o, d) traffic demand, qo,dFor the traffic demand, q, after the flow coefficient mu has been adjusted by optimizationriwTraffic volume q for intersection r fork i to flow to wriwkThe traffic on lane k is distributed to the intersection r fork i flow w,
Figure BDA0003202746190000033
in order to be a set of starting points,
Figure BDA0003202746190000034
in order to be a set of points-of-value,
Figure BDA0003202746190000035
is a steering intersection of the fork iThe general set of the characters is that,
Figure BDA0003202746190000036
in order to be a fork set, the fork is provided with a fork set,
Figure BDA0003202746190000037
and the nodes are set at the intersection.
The lane function allocation constraint is specifically as follows:
Figure BDA0003202746190000038
Figure BDA0003202746190000039
Figure BDA00032027461900000310
Figure BDA00032027461900000311
Figure BDA00032027461900000312
Figure BDA00032027461900000313
Figure BDA00032027461900000314
Figure BDA00032027461900000315
Figure BDA00032027461900000316
Figure BDA00032027461900000317
1xriw(k+1)≥xriw′k
Figure BDA00032027461900000318
wherein n isaa′Is the total number of lanes of the road section (a, a'), naNumber of lanes, n, for link a ═ r, ra′The number of lanes on the opposite connecting line a '═ r', r),
Figure BDA00032027461900000319
in order to be a set of the connection lines,
Figure BDA00032027461900000320
is the total number of lanes at intersection r and fork i, nriThe number of the entrances at the intersection r and the intersection i, M is a great positive number,
Figure BDA00032027461900000321
is a set of lane numbers, (ri, r ' i ') represents a connecting line between an intersection r fork i and an intersection r ' fork i ', w ' is a traffic flow direction of an inlet lane w flowing to the same side,
Figure BDA00032027461900000322
the set of diverted traffic at fork j.
The signal control constraint specifically comprises:
Figure BDA0003202746190000041
Figure BDA0003202746190000042
Figure BDA0003202746190000043
Figure BDA0003202746190000044
M(1-xriwk)≥Yrik-yriw≥-M(1-xriwk)
Figure BDA0003202746190000045
M(1-xriwk)≥Λrik-λriw≥-M(1-xriwk)
Figure BDA0003202746190000046
Figure BDA0003202746190000047
yrjw′+Pr(iw,jw′)≥yriw+λriw+δriwIr(iw,jw′)ξ
Figure BDA0003202746190000048
wherein, CminIs the minimum cycle duration, CmaxIs the maximum period duration, ξrIs the reciprocal of the signal cycle duration, y, of intersection rriwAt the start of the green light when r fork i at the intersection flows to w, lambdariwGreen light green ratio, P, for the flow of i to w at r fork of intersectionr(iw,jw′)For the phase sequence of the signal at the intersection r, a value of 0 indicates that the green light starting time of the flow direction (j, w') is after the flow direction (i, w), otherwise, Pr(iw,jw′)＝1，Ir(iw,jw′)Flowing for a set of conflictsLength of time of emptying ΛrikGreen light green ratio, delta, for r-intersection lane k at intersectionriwThe system is a binary variable and is used for indicating whether management measures are implemented on the flow direction w of an intersection r fork i, implementation is indicated when the value is 1, non-implementation is indicated when the value is 0, and xi is the reciprocal of the signal period duration of the intersection.
The saturation constraint specifically includes:
Figure BDA0003202746190000049
M(2-xriwk-xriw(k+1))≥γri(k+1)-γrik≥-M(2-xriwk-xriw(k+1))
Figure BDA00032027461900000410
Figure BDA00032027461900000411
Figure BDA00032027461900000412
wherein q isriwkThe flow rate, s, distributed on the lane k for the intersection r to diverge i to flow wrikIs the saturation flow rate, gamma, of the r intersection i lane krikSaturation of i lanes k, y for r intersection rrikIs the flow ratio, ds, of r-fork i lane k at the intersectionmaxIs the maximum saturation allowed value.
The lower layer model of the hot spot district traffic organization control collaborative optimization model is used for reflecting the distribution of traffic demands in a road network and the path selection behavior to form an optimal road network traffic demand distribution result, and the main constraint conditions comprise:
Figure BDA0003202746190000051
Figure BDA0003202746190000052
Figure BDA0003202746190000053
Figure BDA0003202746190000054
Figure BDA0003202746190000055
wherein the content of the first and second substances,
Figure BDA0003202746190000056
is a set of steering at all intersections in the road network,
Figure BDA0003202746190000057
Figure BDA0003202746190000058
for each line flow q under the condition of a scheme etaaAnd the turning flow q of each intersectionw，
Figure BDA0003202746190000059
Is the traffic demand of the connection a,
Figure BDA00032027461900000510
traffic demand in flow direction w, v*For the operating speed, Ω (η) is the set of solutions η,
Figure BDA00032027461900000511
for the traffic OD to the traffic demand of (o, d) on the path z adjusted by the flow coefficient mu,
Figure BDA00032027461900000512
a set of paths for path z of (o, d),
Figure BDA00032027461900000513
the variables are binary variables which respectively indicate whether the path z of (o, d) passes through the road section connecting line a and the intersection turning w, the path z passes through when the value is 1, and the path z does not pass through when the value is 0.
Compared with the prior art, the invention has the following advantages:
1. the invention comprehensively considers the optimization result of the cooperation of various traffic organization strategies and traffic control strategies in a unified framework, and can improve the overall operation efficiency of the hot spot district road network compared with the conventional independent optimization.
2. The double-layer collaborative optimization model fully reflects the decision of the traffic manager to achieve the maximum network traffic capacity and the path selection behavior of the driver, and realizes the strategy collaboration of the traffic manager and the managed person.
3. The collaborative optimization strategy of the invention optimizes and adjusts the traffic space elements dynamically or quasi-dynamically, provides an optimization scheme with high cost performance, realizes the full excavation and effective utilization of traffic space-time resources, and is beneficial to the efficient, stable and sustainable control and management of urban hot spots.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a hot spot road network according to the present invention.
FIG. 3 is a flowchart of the hot spot patch variable structure control optimization algorithm of the present invention.
FIG. 4 is a layout diagram of an embodiment of the present invention.
Fig. 5 is a plan view of an optimization strategy according to an embodiment of the present invention, in which fig. 5a is a lane layout diagram of strategy 1, fig. 5b is a lane layout diagram of strategy 2, fig. 5c is a lane layout diagram of strategy 3, and fig. 5d is a lane layout diagram of strategy 4.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, the present invention provides a cooperative control method for hot spot district traffic organization based on reserve traffic capacity, which specifically includes the following steps:
step S1: acquiring basic traffic data of a road network of a hot spot district, and constructing a road network descriptive set of the hot spot district;
step S2: establishing a traffic organization control collaborative optimization model based on the maximization of the running benefit of the road network of the hot spot area, and realizing the collaborative optimization of the direction, the function and the signal timing of each lane of the road section and the intersection;
step S3: and solving an optimization model by adopting a heuristic algorithm, and making a targeted hot spot district traffic organization control combination optimization strategy.
Hot spot road network (as shown in FIG. 2)
Figure BDA0003202746190000061
By node assembly at intersections
Figure BDA0003202746190000062
And a set of wires connecting the two nodes
Figure BDA0003202746190000063
And (4) forming. Any intersection r is assembled by intersections
Figure BDA0003202746190000064
Each fork i consists of a set of diverted traffic
Figure BDA0003202746190000065
And lane set
Figure BDA0003202746190000066
And (4) forming. A pair of connecting lines a ═ (r, r ') and a ═ r', r in different directions between the intersections r and r 'constitute the links (a, a').
For any lane
Figure BDA0003202746190000067
Defining whether the w flow direction has the right of way on the i fork k lane at the r intersection as a binary control variable xriwk(1-allowed, 0-not allowed). On the basis of the above, a binary variable delta is adoptedriwIndicating whether the w flow is allowed to pass at the i fork at the r intersection (1-allowed, 0-not allowed). Therefore, traffic management measures for flow direction prohibition at the intersection can be realized by setting the right of way needing flow direction prohibition on any lane to be 0 (not permitted); traffic management measures for continuous flow intersections can be implemented by a reasonable combination of flow direction prohibition at the intersections.
For arbitrary connection
Figure BDA0003202746190000068
Defining the number of lanes on the link as naThe number of lanes on the opposite connecting line is defined as na′The total number of lanes of the road section is naa′. On the basis of the above, a binary variable delta is adoptedaIndicating whether the vehicle is allowed to pass on line a (1-allowed, 0-not allowed). Thus, traffic control measures for reversible lanes can be implemented by setting an asymmetric number of lanes on links in different directions of a road segment (n)a≠na′) (ii) a One-way traffic can be achieved by assigning all lanes of a road segment to a directional link (n)a0 or na′＝0)。
On-road network
Figure BDA0003202746190000071
In (1), define the traffic demand origin set as
Figure BDA0003202746190000072
Has a value set as
Figure BDA0003202746190000073
(o, d) represents each traffic OD pair, wherein
Figure BDA0003202746190000074
The initial traffic demand of OD to (o, d) is Qo,The traffic demand after the flow coefficient mu is adjusted is qo,d。(Set of paths of o, d)
Figure BDA00032027461900000712
Indicating, for an arbitrary path
Figure BDA00032027461900000713
It includes a series of road section connecting lines and cross turning. Binary variable
Figure BDA0003202746190000075
And
Figure BDA0003202746190000076
respectively, whether the path z of (o, d) passes through the link line a and the intersection turn w (1-pass, 0-no-pass). The traffic OD is the traffic demand of the (o, d) on the path z after the adjustment of the flow coefficient mu
Figure BDA0003202746190000077
In order to maximize the running benefit of a road network, a hot spot district traffic organization control cooperative optimization model is established, and meanwhile, the direction, the function and the signal timing of each lane of a road section and an intersection are optimized. The optimization model is described by a two-tier planning model, wherein the control variables include:
na-number of lanes of road section link a;
xriwk-whether the w flow has right of way at intersection r, i intersection k lane (1-permit, 0-prohibit);
ξr-r the inverse of the signal period duration at intersection (1/s);
Yrikr, starting the green light of the i-fork k lane at the intersection;
Λrikr, the green light green signal ratio of the i-fork k lane at the intersection;
Pr(iw,jw′)-r intersection signal phase sequence, 0 indicating that the green light start time of flow (j, w') is after (i, w), otherwise, Pr(iw,jw′)＝1。
These control variables combine to form a scheme η ═ n, x, ξ, Y, ΛP) of which
Figure BDA0003202746190000078
Figure BDA0003202746190000079
Figure BDA00032027461900000710
Figure BDA00032027461900000711
The upper layer problem of the model takes the improvement of road traffic capacity as a basic target of optimization control. Road capacity refers to the maximum sustainable reasonable expected traffic flow rate through a lane or a point on a road or a uniform section of road under certain road, geometric linear, traffic, environmental and regulatory conditions over a given period of time. The optimization goal of the model is to maximize the traffic capacity of the road network reserves, and the traffic capacity is converted into the optimization problem with the maximum flow coefficient, which is shown as the following formula:
maxμ (29)
the model constraints include:
1) flow distribution constraints
Flow rate Q of each OD pair (o, d) in the road networko,dFor model input conditions and in order to obtain the maximum traffic capacity, a flow coefficient mu is introduced during optimization, and each OD demand is adjusted in equal proportion to obtain the traffic demand q adopted in optimization calculationo,dAs shown by the constraint (30). The sum of the flows allocated to a flow direction on each lane should equal the flow direction traffic demand, as shown by the constraint (31), where qriwTraffic volume (veh/h), q representing the flow direction of i branch w at intersection rriwkIndicating that r intersection i fork w flows to the traffic volume (veh/h) allocated on k lanes.
Figure BDA0003202746190000081
Figure BDA0003202746190000082
2) Lane function assignment constraints
The sum of the number of lanes going back and forth two links on a link should equal the total number of lanes on the link, as shown by the constraint (32). The total number of lanes at each intersection should equal the sum of the number of lanes at the entrance and exit, as shown by the constraint (33) where
Figure BDA0003202746190000083
Indicates the total number of lanes at the i-branch at the r-intersection, nriThe number of the lanes at the i-fork entrance of the r intersection is represented, and the number of the lanes at the exit road is assumed to be the same as the number of the lanes on the road section in the model. At the intersection, in order to make full use of the respective access lanes, each access lane should allow at least one flow direction to pass, as indicated by the constraint (34). The constraints (35) reflect the flow direction prohibition traffic management measures: if a certain flow direction is forbidden, the number of lanes allowing the flow direction to pass should be 0, otherwise, at least 1 lane should allow the flow direction to pass, wherein M represents a sufficiently large positive number. Constraints (36) - (38) reflect one-way traffic management measures: if a certain road section connecting line is forbidden, the number of lanes of the road section connecting line is 0, as shown by a constraint condition (36); at the same time, all the flow directions of the inlet passage at the downstream of the connecting line of the road section and the flow directions of the inlet passage at the upstream of the connecting line of the road section are forbidden, as shown by the constraint conditions (37) and (38) respectively. In order to ensure smooth traffic, the number of the lanes corresponding to the exit in a certain flow direction is more than or equal to the number of the lanes allowing the traffic in the flow direction, as shown by a constraint condition (39). The constraints (40) avoid the collision of the allowable flow directions of the lanes in the entrance lane.
Figure BDA0003202746190000084
Figure BDA0003202746190000085
Figure BDA0003202746190000086
Figure BDA0003202746190000087
Figure BDA0003202746190000088
Figure BDA0003202746190000089
Figure BDA0003202746190000091
Figure BDA0003202746190000092
Figure BDA0003202746190000093
3) Signal control constraints
The constraint (41) defines the range of values of the signal period, and in order to ensure that the constraint can be described by a linear function, the inverse of the signal period is used as a control variable. The constraint (42) defines that the starting time of each flow to green should be in the range of 0 to 1 cycle time. The constraint (43) indicates that the flow direction split should take values between 0 and the cycle duration. But for the forbidden flow direction, its split should be equal to 0, as shown by the constraint (44). Constraints (45) and (46) define lane signal control parameters including lane green start time and green ratio. For any set of conflicting flow directions, the signal phase and phase sequence can be expressed by a successor function as shown by the constraint (47). The constraint (48) indicates that the interval between the start and end of any set of conflicting flows to the green light should at least meet the clearing timeIn which I isr(iw,jw′)Indicating the duration(s) of the flushing of a set of conflicting flow directions.
Figure BDA0003202746190000094
Figure BDA0003202746190000095
Figure BDA0003202746190000096
Figure BDA0003202746190000097
Figure BDA0003202746190000098
Figure BDA0003202746190000099
Figure BDA00032027461900000910
Figure BDA00032027461900000911
4) Saturation constraint
A constraint (49) defines the calculation of the lane flow ratio, where yrikDenotes the flow ratio, s, of the i-fork k lane at the r intersectionrikIndicating the saturation flow rate (veh/h) of the i intersection k lane at r intersection. The constraint (50) indicates that the flow ratio of any adjacent entry lanes should be equal if they allow a certain flow direction at the same time. The constraint conditions (51) and (52) respectively represent the vehicles entering at any intersectionThe saturation of the link between the road and the road section cannot exceed a limit value, so as to ensure the service level of the intersection, wherein dsmaxRepresenting the maximum saturation allowed value.
Figure BDA0003202746190000101
Figure BDA0003202746190000102
Figure BDA0003202746190000103
Figure BDA0003202746190000104
The lower layer problem describes the distribution of traffic demand in the road network and the routing behavior. For a road network scheme eta ═ (n, x, xi, Y, Λ, P), the driver will choose the path without violating the flow direction inhibition and signal control, then form the traffic demand distribution result in the road network, use the user balance principle to distribute the traffic, if the travel time is used as the impedance, when the network reaches the balance state, each used path of each OD pair has equal and minimum travel time, the travel time of the unused path is greater than or equal to the minimum travel time.
The travel time of the path can be obtained by accumulating the travel time of the connecting line of the road sections along the line and the turning at the intersection. As shown in equations (53) and (54), the BPR equation is used to calculate the travel time for the link and the intersection turn.
Figure BDA0003202746190000105
Figure BDA0003202746190000106
In the formula (I), the compound is shown in the specification,
Figure BDA0003202746190000107
and
Figure BDA0003202746190000108
respectively representing the free stream journey time of the connecting line a and the intersection r fork i turning w; dsaAnd dsriwRespectively representing the saturation of the connecting line a and the saturation of the turning w of the intersection r and the fork i; k is a radical ofa、ba、kw、bwAre model parameters.
Because the travel time of intersection turning is not only related to the flow of the current flow but also related to the flow of other flow directions of the intersection, and the influence among the flow directions is asymmetric, the asymmetric influence balance distribution problem can be described by a variational inequality model. The variation inequality (55) describes the relationship between the road network scheme and the traffic flow distribution
Figure BDA0003202746190000109
Figure BDA00032027461900001010
Figure BDA00032027461900001011
Figure BDA0003202746190000111
In the formula (I), the compound is shown in the specification,
Figure BDA0003202746190000112
representing a set of steering of all intersections in a road network;
Figure BDA0003202746190000113
Figure BDA0003202746190000114
represents the flow (q) of each link under the condition of a scheme etaa) And the turning flow rate (q) of each intersectionw)。
The cooperative optimization model for the traffic organization control in the hot spot district is a double-layer combined model, the upper layer model is a mixed integer nonlinear programming, and the lower layer model is a variational inequality model, so that an improved genetic algorithm is adopted for solving, and the algorithm flow comprises the following steps (as shown in figure 3):
1) initialization
First, an initial flow coefficient mu is generatedl(l ═ 0) and population consisting of α chromosomes
Figure BDA0003202746190000115
Figure BDA0003202746190000116
Each chromosome represents a network design that satisfies the constraints (30) - (48). Wherein l represents the number of iterations of the algorithm; n represents a genetic algebra; m represents the number of the stained individuals;
Figure BDA0003202746190000117
represents chromosome m in the genetic algebra n.
2) Cross variation
From chromosome population
Figure BDA0003202746190000118
Selecting excellent individuals, performing cross mutation operation based on a one-point cross method and mutation probability respectively, and generating next generation chromosome population satisfying constraint conditions (30) - (48)
Figure BDA0003202746190000119
3) Network traffic flow distribution
Solving the variational inequality (56) by adopting a diagonal method to obtain the design scheme of the road network
Figure BDA00032027461900001110
Under the condition, the traffic distribution of each road section connecting line and intersection turning flow is carried out according to the principle of user balance
Figure BDA00032027461900001111
In each diagonalization iteration process, a diagonal function is constructed, the diagonal function is converted into a decomposable user balanced distribution problem, and a Frank-Wolf algorithm is adopted for solving.
4) Saturation test
Although the design scheme corresponding to each chromosome meets the constraint conditions (30) - (48) through reasonable coding, the saturation constraint check needs to be performed on the scheme because the traffic flow of the road section connecting line and the intersection turning cannot be obtained before the lower-layer model completes traffic distribution. For each solution of the model
Figure BDA00032027461900001112
If the saturation constraints (49) - (52) are satisfied, the solution is a feasible solution, and the termination condition judgment is carried out, otherwise, the feasible solution is continuously searched.
5) Fitness evaluation
Chromosomes in genetic algebra n
Figure BDA00032027461900001113
May be determined by the calculation of equation (57). The fitness index is then determined by equation (58). Where h (v, η) represents an operation level evaluation value,
Figure BDA00032027461900001114
and
Figure BDA00032027461900001115
the maximum and minimum values of the run level evaluation in the chromosome population in the nth iteration are shown, respectively. The range of epsilon is (0,1), which ensures that the denominator of equation (58) is not 0, and at the same time, makes the chromosome selection somewhat random.
Figure BDA0003202746190000121
Figure BDA0003202746190000122
6) Breeding new population
Generating a new population containing alpha chromosomes by adopting a binary competition selection method based on the fitness evaluation result of the formula (58)
Figure BDA0003202746190000123
7) Updating flow coefficient
Updating the flow coefficient according to the following 3 principles: a) if a feasible solution can be found in the current iteration and each previous iteration, increasing the flow coefficient according to the step rho; b) if the iteration finds a feasible solution but an iteration step of not finding a feasible solution exists before, the flow coefficient is increased according to a dichotomy (half of the difference value of the flow coefficients in the current iteration and the last iteration); c) and if the iteration does not find a feasible solution, reducing the flow coefficient according to a dichotomy. The flow coefficient may be updated as shown in equation (59), where βlRepresenting a binary variable, in the l-th iteration, if there is a feasible solution (1-present, 0-not present).
Figure BDA0003202746190000124
8) Termination conditions
The algorithm termination condition is that the variation of the flow coefficient of the two iterations is smaller than a threshold value.
|μl-μl-1|≤ρmin (60)
In this embodiment, the regional road network includes 40 road segments (80 connecting lines and 32 nodes (16 nodes on the periphery are the origin-destination points of traffic demand), each connecting line has a length of 600(m), the number of lanes is 3, each node is a signal control intersection, as shown in fig. 4, wherein the OD demand of all origin-destination points is 100(vph), and the minimum and maximum signal periods areThe time lengths are respectively 60(s) and 120(s); the emptying time was 4(s); the saturation flow rate was 1800 (veh/h/ln); the saturation limit was 0.90. In the BPR equation, the parameter ka、ba、kwAnd bwSet to 0.15, 4.0, 20 and 3.5, respectively. In the genetic algorithm, the cross probability is 0.25, the mutation probability is 0.01, the maximum value of genetic algebra is 100, and the population number is 50.
To verify the optimization effect of the co-optimization strategy, it is compared with the schemes ( schemes 2, 3 and 4) frequently used in 3 practical applications:
strategy 2: the conventional optimization strategy only optimizes the signal timing and lane functions of the intersection without considering other traffic organization measures;
strategy 3: a left-forbidden traffic organization strategy;
strategy 4: and (4) a single-file traffic organization strategy.
Fig. 5 shows the lane arrangement of the above four optimization strategies in general. Table 1 shows the timing of the signalings at each intersection of the collaborative optimization strategy.
Table 1 collaborative optimization strategy (strategy 1) intersection signal timing
Figure BDA0003202746190000131
Note: l, T and R represent left turn, straight run and right turn, respectively.
The comparison results of the operational benefits are shown in table 2, and include the flow coefficient, the average travel distance, the maximum value, the average value, and the standard deviation of the saturation of the intersection and the road section.
TABLE 2 comparison of traffic operation benefits for different strategies
Figure BDA0003202746190000132
Figure BDA0003202746190000141
By comparing the schemes, the following steps are known:
(1) the collaborative optimization strategy (strategy 1) has remarkable advantages in the aspect of improving the network traffic capacity compared with other three strategies by reasonably selecting and combining various traffic organization strategies.
(2) For a conventional optimization strategy (strategy 2), the saturation of the intersection is basically the same as that of the strategy 1, and the saturation of the road section is obviously lower than that of the intersection, which shows that for the strategy 2, the traffic capacity of the road section is not fully utilized, so that the traffic pressure of the intersection is too high, and the intersection becomes a serious traffic bottleneck node. In contrast, the optimization model reduces the number of bottleneck nodes and improves the traffic capacity of the intersection by reducing the signal phase number of the intersection and using the road section with relatively low saturation as the detour path.
(3) For the left-forbidden traffic organization strategy (strategy 3), although the average saturation degree of the intersections is reduced, the traffic pressure of individual key intersections ( nodes 6, 9, 24 and 27) is increased due to the existence of a large number of detour vehicles, so that the overall traffic capacity of the network is reduced.
(4) For a single-way traffic organization strategy (strategy 4), the single-way traffic organization strategy can effectively reduce the saturation of the intersections and improve the traffic capacity of the road network, but the standard deviation of the saturation of the intersections is still obviously greater than that of the strategies 1 and 2, so that the traffic demand of the road network is unbalanced, and the individual key intersections bear heavier traffic pressure than other intersections, so that the overall traffic capacity of the network is lower than that of the strategy 1.
In conclusion, the invention is based on the idea of traffic organization control cooperation, adopts the basic idea of reserve traffic capacity, truly reflects the corresponding traffic flow when the traffic facilities are saturated after the traffic demand is increased or reduced in equal proportion according to the actual situation, considers the influence of traffic demand distribution, better meets the actual analysis requirements of engineering, and effectively utilizes the existing road resources and relieves the problem of road congestion of the hotspot regions through the ideal of space-time resource cooperative optimization.

Claims (10)

1. A hot spot district traffic organization control cooperative method based on reserve traffic capacity is characterized by comprising the following steps:
1) acquiring basic traffic data of a road network of a hot spot district, and constructing a road network descriptive set of the hot spot district;
2) establishing a traffic organization control collaborative optimization model based on the maximization of the running benefit of the road network of the hot spot area, and realizing the collaborative optimization of the direction, the function and the signal timing of each lane of the road section and the intersection;
3) and solving the traffic organization control collaborative optimization model by adopting a heuristic algorithm to generate a targeted hot spot district traffic organization control combined optimization scheme.
2. The cooperative control method for traffic organization of hotspot zones based on reserve traffic capacity of claim 1, wherein in the step 1), the descriptive set of hotspot zone network comprises:
(1) for any lane
Figure FDA0003202746180000011
Figure FDA0003202746180000012
Defining whether the flow direction w has the right of way on an intersection r, a fork i and a lane k as a binary control variable x for a lane setriwkWhen the value is 1, the passing is allowed, and when the value is 0, the passing is not allowed, and a binary variable delta is adoptedriwWhether the flow direction w is allowed to pass at the intersection r and the fork i is indicated, when the value is 1, the flow direction w is allowed to pass, and when the value is 0, the flow direction w is not allowed to pass;
(2) for arbitrary connection
Figure FDA0003202746180000013
Defining the number of lanes on the link as naThe number of lanes on the opposite connecting line is defined as na', the total number of lanes of the road section is naa', using a binary variable deltaaWhether the vehicles are allowed to pass on the connecting line a or not is shown, the vehicles are allowed to pass when the value is 1, the vehicles are not allowed to pass when the value is 0, and the traffic management measures of the reversible lanes are carried out through different road sectionsBy setting asymmetrical number of lanes on the connecting line in direction, i.e. na≠na', one-way traffic is realized by assigning all lanes of a road segment to a directional link, i.e. na0 or na′＝0。
3. The reserve traffic capacity-based cooperative control method for the traffic organization of the hot spot district, according to claim 2, wherein in the step 2), the cooperative optimization model for the traffic organization control of the hot spot district is described by a double-layer optimization model, wherein the decision variables include the number of links, the traffic flow right of traffic, an intersection cycle time control parameter, an intersection single-lane green light starting time, an intersection single-lane green light ratio and an intersection signal phase sequence control parameter.
4. The method according to claim 3, wherein the upper layer model of the cooperative optimization model for controlling hotspot traffic organization based on reserve traffic capacity takes improving traffic capacity of a hotspot traffic organization as a basic target of optimization control, an objective function of the method is to maximize the reserve traffic capacity of a road network, and an optimization problem of converting the reserve traffic capacity into a maximum flow coefficient includes:
maxμ
wherein μ is a flow coefficient.
5. The reserve traffic capacity-based hotspot regional traffic organization control cooperative method according to claim 4, wherein the constraint conditions of the upper model of the hotspot regional traffic organization control cooperative optimization model comprise a flow distribution constraint, a lane function distribution constraint, a signal control constraint and a saturation constraint.
6. The reserve traffic capacity-based hotspot parcel traffic organization control coordination method according to claim 5, wherein the flow distribution constraint specifically is:
Figure FDA0003202746180000021
Figure FDA0003202746180000022
wherein Q iso，dFor each OD in the road network to (o, d) traffic demand, qo，dFor the traffic demand, q, after the flow coefficient mu has been adjusted by optimizationriwTraffic volume q for intersection r fork i to flow to wriwkThe traffic on lane k is distributed to the intersection r fork i flow w,
Figure FDA0003202746180000023
in order to be a set of starting points,
Figure FDA0003202746180000024
in order to be a set of points-of-value,
Figure FDA0003202746180000025
for the set of diverted traffic at the fork i,
Figure FDA0003202746180000026
in order to be a fork set, the fork is provided with a fork set,
Figure FDA0003202746180000027
and the nodes are set at the intersection.
7. The reserve traffic capacity-based hot spot parcel traffic organization control coordination method according to claim 6, characterized in that said lane function assignment constraint is specifically:
Figure FDA0003202746180000028
Figure FDA0003202746180000029
Figure FDA00032027461800000210
Figure FDA00032027461800000211
Figure FDA00032027461800000212
Figure FDA00032027461800000213
Figure FDA00032027461800000214
Figure FDA00032027461800000215
Figure FDA00032027461800000216
Figure FDA0003202746180000031
1-xriw(k+1)≥xriw′k
Figure FDA0003202746180000032
wherein n isaa′Is the total number of lanes of the road section (a, a'), naNumber of lanes, n, for link a ═ r, ra′The number of lanes on the opposite connecting line a '═ r', r),
Figure FDA0003202746180000033
in order to be a set of the connection lines,
Figure FDA0003202746180000034
is the total number of lanes at intersection r and fork i, nriThe number of the entrances at the intersection r and the intersection i, M is a great positive number,
Figure FDA0003202746180000035
is a set of lane numbers, (ri, r ' i ') represents a connecting line between an intersection r fork i and an intersection r ' fork i ', w ' is a traffic flow direction of an inlet lane w flowing to the same side,
Figure FDA0003202746180000036
the set of diverted traffic at fork j.
8. The method according to claim 7, wherein the signal control constraints are specifically:
Figure FDA0003202746180000037
Figure FDA0003202746180000038
Figure FDA0003202746180000039
Figure FDA00032027461800000310
M(1-xriwk)≥Yrik-yriw≥-M(1-xriwk)
Figure FDA00032027461800000311
M(1-xriwk)≥Λrik-λriw≥-M(1-xriwk)
Figure FDA00032027461800000312
Figure FDA00032027461800000313
yrjw′+Pr(iw，jw′)≥yriw+λriw+δriwIr(iw，jw′)ξ
Figure FDA00032027461800000314
wherein, CminIs the minimum cycle duration, CmaxIs the maximum period duration, ξrIs the reciprocal of the signal cycle duration, y, of intersection rriwAt the start of the green light when r fork i at the intersection flows to w, lambdariwGreen light green ratio, P, for the flow of i to w at r fork of intersectionr(iw，jw′)For the phase sequence of the signal at the intersection r, a value of 0 indicates that the green light starting time of the flow direction (j, w') is after the flow direction (i, w), otherwise, Pr(iw，jw′)＝1，Ir(iw，jw′)Clearing duration, Λ, for a set of conflicting flow directionsrikGreen light green ratio, delta, for r-intersection lane k at intersectionriwIs a binary variable used for representing intersectionAnd whether the flow direction w of the intersection r fork i implements management measures or not is judged, implementation is shown when the value is 1, non-implementation is shown when the value is 0, and xi is the reciprocal of the signal period duration of the intersection.
9. The method according to claim 8, wherein the saturation constraint specifically comprises:
Figure FDA0003202746180000041
Figure FDA0003202746180000042
Figure FDA0003202746180000043
Figure FDA0003202746180000044
Figure FDA0003202746180000045
wherein q isriwkThe flow rate, s, distributed on the lane k for the intersection r to diverge i to flow wrikIs the saturation flow rate, gamma, of the r intersection i lane krikSaturation of i lanes k, y for r intersection rrikIs the flow ratio, ds, of r-fork i lane k at the intersectionmaxIs the maximum saturation allowed value.
10. The reserve traffic capacity-based hotspot regional traffic organization control cooperative method according to claim 9, wherein the lower layer model of the hotspot regional traffic organization control cooperative optimization model is used for reflecting the distribution of traffic demands in a road network and the path selection behavior to form an optimal road network traffic demand distribution result, and the main constraint conditions include:
Figure FDA0003202746180000046
Figure FDA0003202746180000047
Figure FDA0003202746180000048
Figure FDA0003202746180000049
Figure FDA00032027461800000410
wherein the content of the first and second substances,
Figure FDA00032027461800000411
is a set of steering at all intersections in the road network,
Figure FDA00032027461800000412
Figure FDA00032027461800000413
for each line flow q under the condition of a scheme etaaAnd the turning flow q of each intersectionw，
Figure FDA00032027461800000414
Is the traffic demand of the connection a,
Figure FDA00032027461800000415
traffic demand in flow direction w, v*For the operating speed, Ω (η) is the set of solutions η,
Figure FDA00032027461800000416
for the traffic OD to the traffic demand of (o, d) on the path z adjusted by the flow coefficient mu,
Figure FDA00032027461800000417
a set of paths for path z of (o, d),
Figure FDA00032027461800000418
the variables are binary variables which respectively indicate whether the path z of (o, d) passes through the road section connecting line a and the intersection turning w, the path z passes through when the value is 1, and the path z does not pass through when the value is 0.
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