CN113192318B - Data drive control regional traffic signal dynamic optimization method and system - Google Patents
Data drive control regional traffic signal dynamic optimization method and system Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
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- G—PHYSICS
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- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
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Abstract
The invention discloses a data-driven control regional traffic signal dynamic optimization method and a system, wherein the method comprises the following steps: analyzing a road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channels; obtaining traffic monitoring state indexes of analyzed traffic flow and control channels based on lane level state monitoring and the intersection attribute parameters; constructing an intersection storage and forwarding state space equation meeting the signal control adaptation requirement based on the traffic detection state index and the signal control scheme; an iterative learning control model meeting the requirements of balance control and convergence is constructed, iterative optimization is carried out on the intersection storage and forwarding state space equation, and real-time traffic signal optimization control is achieved; the regional traffic signal dynamic optimization method can adapt to different signal control design schemes and steering-based demand changes, realizes fine control of intersections, is high in automation degree, and is not easy to overflow.
Description
Technical Field
The invention relates to the technical field of regional traffic signal dynamic optimization, in particular to a data-driven control regional traffic signal dynamic optimization method and system.
Background
The dynamic optimization control of the traffic signals is a hot technology concerned by various domestic large signal control manufacturers and researchers in recent years, and can adjust a signal control scheme in real time according to the change trend of the traffic flow, adapt to the change condition of the traffic flow in a control range, improve the traffic efficiency and reduce the driving delay. However, the dynamic optimization control technology suitable for regional traffic signals has shortcomings in research and application.
The realization forms of signal control systems in various regions in China are different, but the actual control effect is difficult to meet the control requirement in scene application by using a regional dynamic optimization control technology taking a traffic model and intelligent calculation control as a core due to model applicability and calculation capability constraint; the data driving control method which is not based on the traditional mechanism mathematical modeling, is not traffic prediction control and directly takes actual traffic flow input and output data as timing optimization basis has lower requirements on model calibration and calculation capacity, has certain advantages on system stability and response capacity, and can meet the large-scale traffic signal dynamic optimization control requirements.
However, the technical scheme of the existing application data driving control method has the defects of insufficient practicability and applicability in the field application: (1) only the optimization control of a given intersection and a signal control scheme is supported, the application scene is single, and the automation degree of the system is low; (2) the phase or the road section is used as a state index carrier, so that the fine control requirement of the intersection cannot be met, and the overflow problem is easy to generate in practical application.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a data-driven control regional traffic signal dynamic optimization method and system, which have high automation degree and are not easy to overflow.
The invention provides a dynamic optimization method of data-driven control regional traffic signals, which comprises the following steps:
analyzing a road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channels;
obtaining a traffic detection state index of the intersection attribute parameters based on the lane level state monitoring and the intersection attribute parameters;
according to the traffic detection state index and the signal control scheme, constructing an intersection storage and forwarding state space equation meeting the signal control adaptation requirement;
and constructing an iterative learning control model meeting the requirements of balance control and convergence, and performing iterative optimization on the intersection storage and forwarding state space equation to realize the real-time optimization control of the traffic signals.
Further, the traffic detection state index for obtaining the intersection attribute parameter based on the lane-level state monitoring and the intersection attribute parameter includes:
converting traffic flow information of a lane level inside the intersection into traffic flow taking the traffic flow as a carrier, taking the traffic flow as the carrier as an input parameter under the control of a traffic signal, and acquiring the traffic flow information in real time through traffic detection equipment;
and obtaining the number of the queued vehicles of the control channel according to the lane detection queuing length, wherein the number of the queued vehicles is obtained in real time through traffic detection equipment.
Further, in the construction of an intersection store-and-forward state space equation meeting the signal control adaptation requirement according to the traffic detection state index of the intersection attribute parameter, the method comprises the following steps:
according to the store-and-forward model on the road section steering, the steering state equation belonging to the same control channel is accumulated to obtain a store-and-forward model based on the intersection control channel;
defining signal control elements by an intersection signal control scheme based on convergence constraint to obtain an intersection store-and-forward state space equation with an indefinite period;
and (4) considering the coordination control requirement, carrying out derivation and conversion of the given period and the green time loss time on the crossing store-and-forward state space equation with the indefinite period to obtain the crossing store-and-forward state space equation with the definite period.
Further, an iterative learning control model meeting the requirements of balance control and convergence is constructed, iterative optimization is carried out on the intersection storage and forwarding state space equation, and real-time traffic signal optimization control is realized, and the method comprises the following steps:
establishing an evaluation function taking the phase-to-phase queuing balance as a target, constructing a balance target function, and obtaining a P-type learning law based on ILC (constant-period balance control);
and constructing an iterative learning control model and a control flow based on a P-type learning law, a balanced objective function and a fixed-period intersection storage and forwarding state space equation.
Further, the traffic flow information of the lane level inside the intersection is converted into the intersection taking the traffic flow as a carrierIn the flux, the traffic volume of the intersection comprises an inlet traffic flow I A And exit traffic flow I B The concrete formula is as follows;
for import traffic flow I A ：
Wherein q is X Representing the amount of traffic in the respective steering stream, X representing a steering attribute, q Y,Z The detected traffic volume of the Z-th lane of the guidance Y is shown, Y is the lane guidance attribute, alpha is the steering proportion of the composite guidance to the left, beta is the steering proportion of the composite guidance to the right,
the steering proportion of the composite steering to the left and the right is shown, and gamma is the steering proportion of a three-way lane;
outlet traffic flow I B The traffic volume is imported by the traffic flow I A And (3) statistical acquisition:
(1) according to the intersection traffic flow data, a vehicle flow convergence relation matrix R ═ { R ═ R can be obtained ij |i∈[1,m],j∈[1,n]M denotes the set I B N represents the set I A The elements of the matrix R satisfy:
wherein pi (i) represents an import traffic flow set of an export traffic flow i;
(2) outlet traffic flow I B Traffic q B And import traffic flow I A Traffic q A Satisfies the following matrix relationship:
q B ＝R×q A
wherein q is B ＝{q n+1 ,q n+2 ,...,q n+m } T ，q A ＝{q 1 ,q 2 ,...,q n } T 。
Further, in the storage and forwarding model based on the intersection control channel obtained by accumulating the steering state equations of the same control channel according to the storage and forwarding model on the road section steering, the storage and forwarding model on the road section steering and the storage and forwarding model based on the intersection control channel are obtained by the following formulas respectively:
the store-and-forward model on the road section steering corresponds to the following formula:
x z,l (k+1)＝x z,l (k)+λ z,l T[q z (k)+ε z (k)]-Tu z,l (k)
ε z (k)＝d z (k)-s z (k)
wherein x is z,l (k +1) represents the number of queued vehicles on the road segment z out of turn l at the beginning of the (k +1) th time period, x z,l (k) Representing the number of queued vehicles on the road segment z out of turn/at the beginning of the kth time period; q. q.s z (k) Represents [ kT, (k +1) T]Traffic volume, e, during which the vehicle travels into the stretch z z (k) Represents [ kT, (k +1) T]Disturbance of the state of the section z itself during the period, λ z,l Is the proportion of the steering that the vehicle on the observable stretch z is driving out of the steering; u. of z,l (k) Is [ kT, (k +1) T]Traffic volume during which a section z is driven from a turn l, T is a statistical time interval, d z (k) Represents [ kT, (k +1) T]The demanded flow, s, generated by the section z itself during the period z (k) Represents [ kT, (k +1) T]Dissipation flow of the period section z;
the storage forwarding model based on the intersection control channel corresponds to the following formula:
q z,l (k)＝λ z,l (1+δ z )q z (k)
wherein x is N,f (k +1) indicates the queue controlling the exit of lanes N, f to turn l at the beginning of the (k +1) th time periodThe number of vehicles, U (N, f) represents the traffic flow at the upstream intersection of the control lanes N, f, q z,l (k) Represents [ kT, (k +1) T]During which the demanded flow, u, of the traffic flow is driven out of the turn l of the stretch z z,l (k) Represents [ kT, (k +1) T]During which the dissipated flow, delta, of the traffic flow is driven out of the turn/of the stretch z z Representing the proportion of disturbance of the demanded flow on the road section z, q N,f (k) Represents [ kT, (k +1) T]During which the required flow, lambda, of the channel N, f is controlled z,l Is the proportion of the steering, δ, at which the vehicle on the stretch z can be seen to be driven out of the steering l U(N,f) Representing the disturbance proportion q generated by the transmission of the upstream intersection traffic flow of the control channels N and f through the road section U(N,f) (k) Indicating the required flow generated by the traffic flow at the upstream intersection of the control channels N, f, and psi (N, f) indicating the steering traffic flow set contained in the control channels N, f.
Furthermore, defining signal control elements in an intersection signal control scheme based on convergence constraint to obtain an indefinite-period intersection store-and-forward state space equation,
the constraint comprises: (A) the right-turn control is normally green by default, and only the motor vehicle straight-going and left-turn control in the signal control is considered;
defining the signal control element includes: (C) a control set f represents a straight-going and left-turning control channel set of an intersection, and (D) a timing scheme p represents a control scheme of the intersection; wherein p is j ＝(pf j ,g j ) Pf represents a phase release channel set, g represents a phase green time;
the indefinite-period intersection store-and-forward state space equation is expressed by a matrix as follows:
wherein, the first and the second end of the pipe are connected with each other,
s＝diag{s 1 ,s 2 ,...,s n }，
ω is the matching matrix of the control channel and the phase scheme, ω ═ ω ij |i∈[1,m],j∈[1,n]}，g j (k) Represents [ kT, (k +1) T]Green time of period phase j, c is signal control period duration, s i Is a control channel f i Saturated flow rate of q i (k) Represents [ kT, (k +1) T]Duration control channel f i The required flow of (2);
the intersection storage forwarding state space equation with fixed period is as follows:
wherein, the first and the second end of the pipe are connected with each other,
sω＝[s 1 ω 11 (c-l),s 2 ω 21 (c-l),...,s n ω n1 (c-l)] T and s ω is a constant matrix generated at the time of the fixed-period conversion.
Further, establishing an evaluation function taking the queuing balance among the phases as a target, establishing a balance target function, and obtaining a P-type learning law based on the ILC fixed period balance control, wherein the P-type learning law comprises the steps of;
the construction formula of the balance objective function is as follows:
y(k)＝[y 2,1 (k),y 3,1 (k),...,y m,1 (k)] T
＝[z 2 (k)-z 1 (k),z 3 (k)-z 1 (k),...,z m (k)-z 1 (k)] T
wherein the phase queuing length
Indicating the predicted queue length, y m,l (k) Representing the difference between the phase m queue length and the reference phase queue length, y m,l (k)＝z m (k)-z 1 (k)；
The corresponding formula of the P-type learning law is as follows:
e k (k+1)＝y d -y k (k+1)
where, k represents the number of iterations,
is the green time produced after the kth iteration in the kth period, Γ is the iterative learning gain matrix, Γ satisfies
I is an identity matrix of (m-1) × (m-1), C is y (k) and
s is diag { s ═ d 1 ,s 2 ,...,s n }，y d ＝0，
Is a variation matrix form of omega in the fixed period model conversion;
a data-driven control regional traffic signal dynamic optimization system comprises an analysis module, a traffic data analysis module, an intersection store-and-forward state space equation construction module and an iterative learning control model construction module;
the analysis module is used for analyzing the road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channel analysis;
the traffic data analysis module is used for obtaining a traffic detection state index of the intersection attribute parameter based on the lane level state monitoring and the intersection attribute parameter;
the intersection storage and forwarding state space equation building module is used for building an intersection storage and forwarding state space equation meeting the signal control adaptation requirement according to the traffic detection state index of the intersection attribute parameters;
the iterative learning control model building module is used for building an iterative learning control model meeting the requirements of balance control and convergence, and performing iterative optimization on the intersection storage and forwarding state space equation to realize real-time optimization control of traffic signals.
A computer readable storage medium having stored thereon a number of acquisition and classification procedures for being invoked by a processor and performing the method for dynamic optimization of regional traffic signals according to claim 1.
The data-driven control regional traffic signal dynamic optimization method and the system provided by the invention have the advantages that: the dynamic optimization method and the system for the data-driven control regional traffic signals can adapt to different signal control design schemes and the change of the demand based on steering, realize fine control of intersections, have wide application scenes and high system automation degree, and are not easy to overflow; calculating through a road network topological structure to obtain an intersection store-and-forward state space equation; the intersection store-and-forward state space equation is subjected to iterative optimization through the iterative learning control model, so that the control of the intersection store-and-forward state space equation on the intersection traffic flow is improved, and the overflow prevention of the intersection traffic path is realized.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2(a) is a schematic view of traffic flow inside an intersection with a road network topology structure;
FIG. 2(b) is a schematic diagram of a road network topology structure of intersection control channels;
FIG. 3 is a schematic illustration of an intersection in which a store-and-forward model of out-of-turn is considered;
FIG. 4 is a schematic diagram of an iterative learning process;
FIG. 5(a) is a diagram of intersection connectivity;
FIG. 5(b) is a schematic view of traffic flow inside the intersection;
fig. 6 is a recursive relationship between lane-traffic-control lanes.
Detailed Description
The present invention is described in detail below with reference to specific embodiments, and in the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
As shown in fig. 1 to 6, the present invention provides a data-driven controlled regional traffic signal dynamic optimization method, including:
s1: analyzing a road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channels;
s11: according to the design scheme of crossing channel division, crossing traffic flow data and control channel data can be obtained by analysis, and the analysis result of the crossing channel division traffic flow and the control channel is shown in figure 2;
the traffic flow divides the traffic flow in the intersection into an inlet traffic flow and an outlet traffic flow (figure 2a) which are respectively turned into different directions according to the driving direction and the track of the vehicle, and sets I are respectively used A And I B . A data structure as in table 1 is established to store traffic flow information.
TABLE 1 traffic flow data Structure
| Crossing | Arrangement of crossing in system road networkNumber (C) |
| Branch of | Numbering of branches in intersections |
| Traffic flow | Numbering of traffic flows in intersections |
| Type (B) | Travel directions and turn types of traffic flows, including: straight, left-turn, right-turn and exit |
| Afflux traffic flow | Import traffic flow set of import traffic flow merging into export traffic flow, type effective when export |
In actual intersection channeling, the number of control lanes is less than the number of incoming traffic flows, and there are cases where one control lane contains multiple incoming traffic flows (fig. 2b), represented by set F. A data structure as in table 2 is established to store the control channel information.
TABLE 2 control channel data Structure
| Crossing | Numbering of intersections in system road network |
| Control channel | Control channel numbering inside intersections |
| Controlling traffic flow | Inlet traffic flow set of current intersection contained by control channel |
S12: analyzing the topology of the road network, and determining the upstream traffic flow information of the intersection control channel according to the communication relation of adjacent intersections in the road network. And expanding the data structure on the basis of the table 2 to obtain a road network data structure based on the control channels, and referring to the table 3.
Table 3 control channel-based road network connection data structure
| Crossing | Numbering of intersections in system road network |
| Control channel | Control channel numbering inside intersections |
| Upstream crossing | Upstream crossing for controlling direction of passage |
| Upstream traffic flow | Outlet traffic flow for generating traffic demand for control channel at upstream intersection |
Through S11 and S12, technical data analysis of the road network is realized, and the control traffic flow and the control channel are numbered.
S2: obtaining a traffic detection state index of the intersection attribute parameter based on the lane level state monitoring and the intersection attribute parameter;
the traffic detection equipment can acquire traffic flow information of the road surface in real time, such as flow, queue length and other key information.
S21: in order to support subsequent modeling, the traffic detection result of the lane level inside the intersection needs to be converted into the traffic volume taking the traffic flow as a carrier. For import traffic flow I A In other words, its value is obtained from the associated lane traffic statistics:
wherein q is X Representing the amount of traffic in the respective steering stream, X representing a steering attribute, q Y,Z The detected traffic volume of the Z-th lane of the guide Y is shown, Y is the lane guide attribute, alpha is the steering proportion of the composite guide straight to the left, beta is the steering proportion of the composite guide straight to the right,
the steering ratio of the composite steering to the left and right is shown, and γ is the steering ratio of the three-way lane.
Outlet traffic flow I B The traffic volume is imported by the traffic flow I A And (3) statistical acquisition:
(1) according to the intersection traffic flow data, a vehicle flow convergence relation matrix R ═ { R ═ R can be obtained ij |i∈[1,m],j∈[1,n]Is set I for m and n respectively B And I A The elements of the matrix R satisfy:
wherein Π (i) is an incoming traffic flow set of the outgoing traffic flow i.
(2) Outlet traffic flow I B Traffic q B And import traffic flow I A Traffic q A Satisfies the following matrix relationship:
q B ＝R×q A (2)
wherein q is B ＝{q n+1 ,q n+2 ,...,q n+m } T ，q A ＝{q 1 ,q 2 ,...,q n } T
S22: and counting according to the lane detection queuing length to obtain the data of the number of the queued vehicles of the control channel. According to the control channel data structure in table 2, the queuing length of the covered lane is counted as the index of the number of queued vehicles of the control lane through the corresponding relationship of the control channel, the traffic flow and the lane, and the queuing length calculation formula corresponds to the following formula:
wherein, qNum f Is the number of queued vehicles, qLen, for control channel f l Indicates the length of the queue of the lane L, L veh Representing the average vehicle occupancy length, lane i is controlled by lane f.
The control traffic flow on the control passage is calculated through steps S21 to S22 as the input of the intersection store-and-forward state space equation of step S3.
S3: according to the traffic detection state index and the signal control scheme, constructing an intersection storage and forwarding state space equation meeting the signal control adaptation requirement;
s31: for one directional branch z connecting the intersections M and N as shown in fig. 3, the vehicle is driven from the intersection M to the intersection N. Considering a store-and-forward model in which a branch z turns at an intersection N, the dynamics of the branch can be described as:
x z,l (k+1)＝x z,l (k)+λ z,l T[q z (k)+ε z (k)]-Tu z,l (k) (4)
ε z (k)＝d z (k)-s z (k)
wherein x is z,l (k +1) represents the number of queued vehicles on the road segment z out of turn l at the beginning of the (k +1) th time period, x z,l (k) Representing the number of queued vehicles on the road segment z out of turn/at the beginning of the kth time period; q. q.s z (k) Represents [ kT, (k +1) T]Traffic volume, e, during which the vehicle travels into the stretch z z (k) Represents [ kT, (k +1) T]Disturbance of the state of the section z itself during the period, λ z,l Is that the vehicle on the observable section z is turning froml the steering proportion of the outgoing vehicle; u. of z,l (k) Is [ kT, (k +1) T]Traffic volume during which a section z is driven from a turn l, T is a statistical time interval, d z (k) Represents [ kT, (k +1) T]The demanded flow, s, generated by the section z itself during the period z (k) Represents [ kT, (k +1) T]The dissipated flow of the section z during.
Taking into account disturbances epsilon z (k) Can be expressed in terms of demand and disturbance factor, i.e. ε z (k)＝δ z q z (k) And delta is the observable disturbance coefficient, equation (4) can be further simplified to
x z,l (k+1)＝x z,l (k)+Tq z,l (k)-Tu z,l (k) (5)
Wherein q is z,l (k)＝λ z,l (1+δ z )q z (k) And the lambda and the delta can be popularized as time change functions in a long-time operation system, and specific calibrated parameters are determined in practical application.
S32: the formula (5) is a store-and-forward model established on road section steering, the road section steering and the intersection steering have a corresponding relation, and the steering state equation belonging to the same control channel can obtain the store-and-forward model based on the intersection control channel through accumulation
q z,l (k)＝λ z,l (1+δ z )q z (k)
Based on the upstream intersection and traffic flow information of the control lane in Table 3, q in equation (6) N,f (k) Can be represented by the traffic volume of the upstream intersection and the traffic flow
Wherein x is N,f (k +1) represents the number of queued vehicles exiting the control lane N, f to turn to l at the beginning of the (k +1) th time period, U (N, f) represents the traffic flow at the upstream intersection of the control lane N, f, q z,l (k) Represents [ kT, (k +1) T]While leaving the traffic flow from the turn l of the stretch zRequired flow of (u) z,l (k) Represents [ kT, (k +1) T]During which the dissipated flow, delta, of the traffic flow is driven out of the turn/of the stretch z z Representing the proportion of disturbance of the demanded flow on the road section z, q N,f (k) Represents [ kT, (k +1) T]During which the demanded flow, lambda, of the channel N, f is controlled z,l Is the proportion of the steering, δ, at which the vehicle on the stretch z can be seen to be driven out of the steering l U(N,f) Representing the disturbance proportion q generated by the transmission of the traffic flow passing through the road section at the upstream intersection of the control channels N and f U(N,f) (k) Indicating the demanded flow generated by the traffic flow at the upstream intersection of the control channels N, f, and psi (N, f) indicating the set of diverted traffic flows contained in the control channels N, f.
S33: and (4) considering the actual signal control scheme of the intersection. In order to ensure the final convergence, the following two constraints (1) and (2) are made on the interface signal control:
(1) the default of the right-turn control is evergreen, and only the motor vehicle straight-going and left-turn control in the signal control scheme is considered in the model;
(2) the motor vehicle straight and left-turn control channels are controlled in only one phase, i.e. no overlapping phase of the straight and left-turn channels occurs.
Defining signal control elements:
A. control set f, f ═ f 1 ,f 2 ,...,f n ]Representing a straight-going and left-turning control channel set of a crossing;
B. timing scheme p, p ═ p [ p ] 1 ,p 2 ,...,p m ]Indicating a control scheme of the intersection; wherein p is j ＝(pf j ,g j ) Pf is a phase release channel set, g is a phase green time;
a matching matrix ω ═ ω { ω } exists between the control set and the phase scheme ij |i∈[1,m],j∈[1,n]And expressing the controlled relationship between the control channels and the phase scheme in the set, wherein the matrix element values satisfy the following relationship:
establishing a storage forwarding state space equation of elements in the intersection control set f based on the formula (6):
x i (k+1)＝x i (k)+Tq i (k)-Tu i (k) (7)
wherein the index i denotes the ith control channel f in the control set f i ，q i (k) And u i (k) Respectively a control channel f i Is in a near-saturation state, the dissipation flow u is considered to be i (k) Satisfy the requirement of
Wherein, g j (k) Represents [ kT, (k +1) T]Green time of period phase j, c is signal control period duration, s i Is a control channel f i The saturation flow rate of (c). If the statistical period length T is set to the signal control period c, equation (7) can be converted to
At this time, the original output u i (k) Is replaced by g j (k) The expressed output capability, so the state space transition result is a predicted value.
Therefore, the intersection store-and-forward state space equation of the whole intersection with the indefinite period is expressed by a matrix as follows:
wherein the content of the first and second substances,
s＝diag{s 1 ,s 2 ,...,s n }，
ω is the matching matrix of the control channel and the phase scheme, ω ═ ω { [ ω } [ ] ij |i∈[1,m],j∈[1,n]}，g j (k) Represents [ kT, (k +1) T]Green time of period phase j, c is signal control period duration, s i Is a control channel f i Saturated flow rate of q i (k) Represents [ kT, (k +1) T]Duration control channel f i The required flow rate of (c).
S34: in order to meet basic regional coordination control, intersection signal control can be required to keep the same period duration within a period of time.
According to
With period c and loss l known, the green time for phase 1 is represented by the other phase green time:
the intersection store-and-forward state space equation with a fixed period can be obtained by substituting the equation (9):
wherein the content of the first and second substances,
sω＝[s 1 ω 11 (c-l),s 2 ω 21 (c-l),...,s n ω n1 (c-l)] T the constant matrix is generated when the fixed period is converted.
Through steps S31 to S34, the traffic flow of the intersection control channel directly entering and exiting is converted into the traffic flow controlled by the signal, and whether the control cycle duration is set for the traffic flow controlled by the signal is set, so as to obtain the intersection store-and-forward state space equation with the indefinite cycle and the intersection store-and-forward state space equation with the definite cycle.
S4: and (3) an iterative learning control model based on balance control and convergence requirements is established, iterative optimization is carried out on the intersection storage and forwarding state space equation, and real-time traffic signal optimization control is realized.
S41: and establishing an evaluation function taking the queuing balance among the phases as a target. In each determined signal period, the phase (x) is selected i (k)+c·q i (k))/s i Predicted queue length for maximum lane
As the phase queuing length z j (k) And constructing a queuing balance objective function among phases. Taking phase 1 as a reference phase, and queuing difference y between other phases and the reference phase j,1 (k)＝z j (k)-z 1 (k) And (3) forming an equalization objective function:
y(k)＝[y 2,1 (k),y 3,1 (k),...,y m,1 (k)] T
＝[z 2 (k)-z 1 (k),z 3 (k)-z 1 (k),...,z m (k)-z 1 (k)] T
wherein the content of the first and second substances,
indicating the predicted queue length, y m,l (k) Representing the difference between the phase m queue length and the reference phase (phase 1) queue length, i.e. y m,l (k)＝z m (k)-z 1 (k) Due to z j (k) Is determined by (x) i (k) And q is i (k) Is deterministic), so y (k) can be written about
The linear relation of (c):
wherein C is an m-1 × n matrix with the element value { -1,0,1}, and the queuing balance means that the queuing lengths in all directions of the intersection are equal during the control period, so the desired target is y d 0, periodThe difference between the desired target and the actual target is defined as the deviation:
e k (k+1)＝y d -y k (k+1)
s41: through the modeling process, the P-type learning law based on the ILC constant-period balance control can be obtained:
where, k represents the number of iterations,
is the green time produced after the kth iteration in the kth period, Γ is the iterative learning gain matrix, Γ needs to meet
The method comprises the following specific steps: the value is given by
The value of (2) is determined; according to the results of the prior research, when the requirements are met
The gain matrix values of (a) are such that convergence of the model is guaranteed, wherein I is a matrix of (m-1) × (m-1), and C is y (k) and
s is diag { s ═ d 1 ,s 2 ,...,s n }，
Is the variation matrix form of omega in the fixed period model conversion. Will control in practical application
By selecting a value level of (e.g. selecting)
The gain matrix of (a) takes values.
The iterative learning input quantity is the green light time of each phase of the intersection, is limited by the minimum green, the maximum green and the fixed period duration, and is balanced and controlled in the fixed period, the green light time of the first phase is represented by other phases, periods and loss time, so that the green light time of the first phase is represented by other phases, periods and loss time
The following constraints are to be satisfied:
in the iterative learning allocation process, changes exceeding the maximum green, the minimum green and the fixed period limit occur, and the change value exceeding the limit is uniformly allocated and balanced.
S42: an iterative learning process. Each signal period generates the initial state x of the current intersection according to the signal control scheme of the previous period 0 (k)、
And demand q generated by adjacent crossing 0 (k) Continuously and iteratively updated according to the learning rate in equation (11)
Until an iteration ceiling or convergence threshold is met, i.e. | | e κ (k+1)|| 2 Sigma is less than or equal to. The iterative process from the iteration start time and the initial state is shown in fig. 4, where the current state quantity and requirement of the k-th iteration are strict repetitions of the initial state, i.e. x, in the same signal period κ (k)＝x 0 (k)，q κ (k)＝q 0 (k)。
And performing iterative optimization on the intersection store-and-forward state space equation through S41 to S42, and improving the optimization degree of the intersection store-and-forward state space equation.
Through steps S1 to S4, the application scenes are wide, the system automation degree is high, and overflow is not easy to generate.
As an example:
road network data analysis based on road network topological structure and intersection and channel planning design
Fig. 5a shows a simple road network structure, in which intersections 1-4 are all crossroads, intersection 5 is a T-shaped intersection with east-west connectivity, and the inside of the intersection is not restricted or restricted. Firstly, traffic flow analysis is set according to the type of the intersection and the channel division to obtain the traffic flow structure of each intersection in fig. 5b, and part of the traffic flow analysis content of the intersection 1 is given in table 4.
Table 4 example intersection traffic flow analysis results
Assuming that the internal channeling at intersection 1 is consistent with the channeling scheme in fig. 2a, and the communication relationship between adjacent intersections is combined, the control channel data analysis result at intersection 1 is shown in table 5.
Table 5 illustrates the intersection control channel resolution results
And secondly, constructing a detection data analysis module based on the traffic flow and the control channel. And taking the lane-level detection data as input, and obtaining dynamic data output required by the technical application embodiment according to the progressive attribution relation of the lane, the traffic flow and the control channel. The data processing flow is shown in fig. 6. And taking the lane-level detection data as input, and obtaining the dynamic data output required by the technical application embodiment according to the progressive attribution relation of the lane, the traffic flow and the control channel. The data processing flow is shown in fig. 6.
1. Traffic volume statistics
(1) And (3) statistics of the import traffic volume: on the basis of matching of the inlet traffic flow and the lane, the lane detection flow is counted according to the formula (1) to obtain the traffic volume counting results of all the inlet traffic flows in the intersection;
(2) go outStatistics of oral traffic volume: determining a traffic flow influx relation matrix according to intersection traffic flow data, taking intersection traffic flow data in table 4 as an example, I B ＝{13,14,15,16}，I A 1,2, 12, a relationship matrix
Further carrying out an inlet traffic flow I according to the formula (2) A Traffic flow to Exit I B Traffic volume conversion of
q B ＝R×q A
Wherein q is B ＝{q 13 ,q 14 ,q 15 ,q 16 } T ，q A ＝{q 1 ,q 2 ,...,q 12 } T 。
(3) Export traffic application: the exit traffic volume inside the intersection can be used as the demand traffic volume of the associated branch of the downstream intersection.
2. Queue length statistics
And on the basis of matching of the control channel and the lane, converting the output lane queuing length data into the queuing vehicle number data of the control channel according to the formula (3) and outputting the data, wherein the data can be used as the input of the formula (4).
Thirdly, intersection store-and-forward state space equation
1. And determining an intersection signal control scheme according to the actual traffic demand, and analyzing and obtaining signal control elements required by the state space equation by taking the signal control scheme of the intersection 1 given in the table 6 as an example.
Table 6 signal control scheme example
Note: the release channel information only comprises a direct-turning control channel and a left-turning control channel
(1) A control channel: f ═ f 1 ,f 2 ,...,f 7 }＝{2,3,5,6,8,10,11}；
(2) The timing scheme is as follows:
p＝{p 1 ,p 2 ,p 3 ,p 4 }＝{[pf 1 ,27],[pf 2 ,27],[pf 3 ,27],[pf 4 ,27]}; wherein, pf 1 ＝{f 1 ,f 2 }＝{2,3}，pf 2 ＝{f 5 }＝{8}，pf 3 ＝{f 3 ,f 6 }＝{5,10}，pf 4 ＝{f 4 ,f 7 }＝{6,11}；
(3) Matching a matrix:
2. determining the state space equation of the intersection 1 with the statistical interval of one signal cycle c according to the equation form given by the formula (10):
wherein the content of the first and second substances,
sω＝[108s 1 ,108s 2 ,0,0,0,0,0,0] T q (k) is the traffic demand generated upstream of the control channel, q (k) is the first control channel f 1 For example, the traffic demand of (A) is satisfied
q 1 (k)＝λ 1 (1+δ 2→1 )q 2→1 (k)＝λ 1 (1+δ 2→1 )q 2,15 (k)
Wherein λ is 1 Is f 1 Steering ratio of delta 2→1 Is the traffic flow disturbance coefficient of intersection 2 to 1;
the minimum green time, maximum green time constraints in table 6 are met:
fourthly, constructing an iterative learning control model
1. An equalization control target is established based on the phase. Taking intersection 1 as an example, the number of queued vehicles in the first phase is taken as a reference, and the difference value between the number of queued vehicles in other phases and the reference is taken as a balance control target
y(k)＝[y 2,1 (k),y 3,1 (k),y 4,1 (k)] T
＝[z 2 (k)-z 1 (k),z 3 (k)-z 1 (k),z 4 (k)-z 1 (k)] T
Selecting (x) in phase i (k)+c·q i (k))/s i The state prediction value of the larger control channel is taken as the phase-queued vehicle number, and the equalization control target is further converted into the form y (k) ═ cx (k) with respect to the control channel. Under the signal control conditions given by the example, C has eight values, each of which is a different choice of control channel in the phase: to be provided with
For the purpose of example only,
2. establishing iterative learning input
And output y d -y (k +1) P-type learning rate, with the k +1 th iteration as follows:
to ensure the convergence of the learning rate, the gain matrix Γ should satisfy
According to the eight values of C, the corresponding eight convergence strips can be obtained by searching through a particle swarm optimization algorithmAnd selecting determined gamma to participate in iterative calculation in each signal period by the gain matrix of the element.
3. An iterative learning process. Each signal period is based on the current intersection initial state x generated by the previous period signal control scheme 0 (k)、
And demand q generated by adjacent crossing 0 (k) According to x 0 (k) And q is 0 (k) Determining a coefficient matrix C (i.e. a linear relation matrix) and a gain matrix gamma of the balance target of the current signal period, and continuously and iteratively updating the learning rate in the formula (12)
Until an iteration ceiling or convergence threshold is met, i.e. | | e κ (k+1)|| 2 Sigma is less than or equal to. The iterative process from the iteration start time and the initial state is shown in fig. 4, where the current state quantity and requirement of the k-th iteration are strict repetitions of the initial state, i.e. x, in the same signal period κ (k)＝x 0 (k)，q κ (k)＝q 0 (k)。
It should be noted that the above turning ratios are all determined by observation.
A data-driven control regional traffic signal dynamic optimization system is characterized by comprising an analysis module, a traffic data analysis module, an intersection storage and forwarding state space equation construction module and an iterative learning control model construction module;
the analysis module is used for analyzing the road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channel analysis;
the traffic data analysis module is used for obtaining a traffic detection state index of the intersection attribute parameter based on the lane level state monitoring and the intersection attribute parameter;
the intersection storage and forwarding state space equation building module is used for building an intersection storage and forwarding state space equation meeting the signal control adaptation requirement according to the traffic detection state index of the intersection attribute parameters;
the iterative learning control model building module is used for building an iterative learning control model meeting the requirements of balance control and convergence, and performing iterative optimization on the intersection storage and forwarding state space equation to realize real-time optimization control of traffic signals.
A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon a plurality of acquisition and classification programs, and the plurality of acquisition and classification programs are used for being called by a processor and executing the regional traffic signal dynamic optimization method according to the present application.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered as the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (8)
1. A dynamic optimization method for data-driven control of regional traffic signals is characterized by comprising the following steps:
analyzing a road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channels;
obtaining a traffic detection state index of the intersection attribute parameter based on the lane level state monitoring and the intersection attribute parameter;
according to the store-and-forward model on the road section steering, the steering state equation belonging to the same control channel is accumulated to obtain a store-and-forward model based on the intersection control channel;
defining signal control elements by an intersection signal control scheme based on convergence constraint to obtain an intersection store-and-forward state space equation with an indefinite period;
considering the coordination control requirement, carrying out derivation and conversion of the given period and green time loss time on the crossing store-and-forward state space equation with the indefinite period to obtain a crossing store-and-forward state space equation with the fixed period;
establishing an evaluation function taking the phase-to-phase queuing balance as a target, constructing a balance target function, and obtaining a P-type learning law based on ILC (constant-period balance control);
an iterative learning control model and a control flow are constructed on the basis of a P-type learning law, a balanced objective function and a fixed-period intersection storage and forwarding state space equation, so that real-time optimal control of traffic signals is realized.
2. The data-driven regional traffic signal dynamic optimization method of claim 1, wherein the obtaining of the traffic detection state index of the intersection attribute parameter based on the lane-level state monitoring and the intersection attribute parameter comprises:
converting traffic flow information of a lane level inside the intersection into traffic flow taking the traffic flow as a carrier, taking the traffic flow as the carrier as an input parameter under the control of a traffic signal, and acquiring the traffic flow information in real time through traffic detection equipment;
and obtaining the number of the queued vehicles of the control channel according to the lane detection queuing length, wherein the number of the queued vehicles is obtained in real time through traffic detection equipment.
3. The method according to claim 2, wherein in converting the traffic flow information at the lane level inside the intersection into the traffic flow using the traffic flow as the carrier, the traffic flow at the intersection includes an inlet traffic flow I A And exit traffic flow I B The concrete formula is as follows;
for import traffic flow I A ：
Wherein q is X Representing the traffic volume of the respective steered traffic stream, X representing a steering attribute, q Y,Z The detected traffic volume of the Z-th lane of the guide Y is shown, Y is the lane guide attribute, alpha is the steering proportion of the composite guide straight to the left, beta is the steering proportion of the composite guide straight to the right,
the steering proportion of the composite steering to the left and the right is shown, and gamma is the steering proportion of a three-way lane;
outlet traffic flow I B The traffic flow is from the inlet traffic flow I A And (3) statistical acquisition:
(1) according to the intersection traffic flow data, a vehicle flow convergence relation matrix R ═ { R ═ R can be obtained ij |i∈[1,m],j∈[1,n]M denotes the set I B N represents the set I A The elements of the matrix R satisfy:
wherein pi (i) represents an import traffic flow set of an export traffic flow i;
(2) outlet traffic flow I B Traffic q B And import traffic flow I A Traffic q A Satisfies the following matrix relationship:
q B ＝R×q A
wherein q is B ＝{q n+1 ,q n+2 ,...,q n+m } T ，q A ＝{q 1 ,q 2 ,...,q n } T 。
4. The dynamic optimization method of regional traffic signals under data-driven control according to claim 1, wherein in the step of obtaining the store-and-forward model based on the intersection control channel by adding the steering state equations of the same control channel according to the store-and-forward model on the road section steering, the store-and-forward model on the road section steering and the store-and-forward model based on the intersection control channel are obtained by the following formulas respectively:
the store-and-forward model on the road section steering corresponds to the following formula:
x z,l (k+1)＝x z,l (k)+λ z,l Tpq z (k)+ε z (k)]-Tu z,l (k)
ε z (k)＝d z (k)-s z (k)
wherein x is z,l (k +1) represents the number of queued vehicles on the road segment z out of turn l at the beginning of the (k +1) th time period, x z,l (k) Representing the number of queued vehicles on the road segment z out of turn/at the beginning of the kth time period; q. q.s z (k) Represents [ kT, (k +1) T]Traffic volume, e, during which the vehicle travels into the stretch z z (k) Represents [ kT, (k +1) T]Disturbance of the state of the section z itself during the period, λ z,l Is the proportion of the steering that the vehicle on the observable stretch z is driving out of the steering; u. of z,l (k) Is [ kT, (k +1) T]Traffic volume during which a section z is driven from a turn l, T is a statistical time interval, d z (k) Represents [ kT, (k +1) T]The demanded flow, s, generated by the section z itself during the period z (k) Represents [ kT, (k +1) T]Dissipation flow of the section z during;
the storage forwarding model based on the intersection control channel corresponds to the following formula:
q z,l (k)＝λ z,l (1+δ z )q z (k)
wherein x is N,f (k +1) represents the number of queued vehicles exiting the control lane N, f to turn to l at the beginning of the (k +1) th time period, U (N, f) represents the traffic flow at the upstream intersection of the control lane N, f, q z,l (k) Represents [ kT, (k +1) T]Time slave pathThe direction of rotation l of the segment z driving out of the traffic flow demand, u z,l (k) Represents [ kT, (k +1) T]During which the dissipated flow, delta, of the traffic flow is driven out of the turn/of the stretch z z Representing the proportion of disturbance of the demanded flow on the road section z, q N,f (k) Represents [ kT, (k +1) T]During which the required flow, lambda, of the channel N, f is controlled z,l Is the proportion of the steering, δ, at which the vehicle on the stretch z can be seen to be driven out of the steering l U(N,f) Representing the disturbance proportion q generated by the transmission of the upstream intersection traffic flow of the control channels N and f through the road section U(N,f) (k) Indicating the required flow generated by the traffic flow at the upstream intersection of the control channels N, f, and psi (N, f) indicating the steering traffic flow set contained in the control channels N, f.
5. The data-driven regional traffic signal dynamic optimization method according to claim 1, wherein signal control elements are defined in an intersection signal control scheme based on convergence constraints, and an indefinite-period intersection store-and-forward state space equation is obtained in which:
the constraint comprises: (A) the right turn control is normally green by default, only the motor vehicle straight-going and left turn control in the signal control is considered, and (B) the motor vehicle straight-going and left turn control channel is controlled by only one phase stage;
defining the signal control element includes: (C) a control set f represents a straight-going and left-turning control channel set of an intersection, and (D) a timing scheme p represents a control scheme of the intersection; wherein p is j ＝(pf j ,g j ) Pf represents a phase release channel set, g represents a phase green time;
the indefinite-period intersection store-and-forward state space equation is expressed by a matrix as follows:
wherein the content of the first and second substances,
s＝diag{s 1 ,s 2 ,...,s n }，g(k)＝[g 1 (k),g 2 (k),...,g m (k)] T where ω is the matching matrix for the control channel and phase scheme, and ω is { ω ═ ω } ij |i∈[1,m],j∈[1,n]}，g j (k) Represents [ kT, (k +1) T]Green time of period phase j, c is signal control period duration, s i Is a control channel f i Saturated flow rate of (q) i (k) Represents [ kT, (k +1) T]Duration control channel f i The required flow of (2);
the intersection storage and forwarding state space equation with fixed period is as follows:
wherein the content of the first and second substances,
sω＝[s 1 ω 11 (c-l),s 2 ω 21 (c-l),...,s n ω n1 (c-l)] T and is a constant matrix generated during the fixed period conversion.
6. The dynamic optimization method of regional traffic signals under data-driven control according to claim 1, wherein in establishing an evaluation function targeting inter-phase queuing equalization, constructing an equalization objective function, and obtaining a P-type learning law based on ILC fixed-period equalization control, the method comprises the following steps;
the construction formula of the equalization objective function is as follows:
y(k)＝[y 2,1 (k),y 3,1 (k),...,y m,1 (k)] T
＝[z 2 (k)-z 1 (k),z 3 (k)-z 1 (k),...,z m (k)-z 1 (k)] T
wherein the phase queuing length
Indicating the predicted queue length, y m,l (k) Representing the difference between the phase m queue length and the reference phase queue length, y m,l (k)＝z m (k)-z 1 (k)；
The corresponding formula of the P-type learning law is as follows:
e k (k+1)＝y d -y k (k+1)
wherein, k represents the number of iterations,
is the green time produced after the kth iteration in the kth period, Γ is the iterative learning gain matrix, Γ satisfies
I is an identity matrix of (m-1) × (m-1), C is y (k) and
s is diag { s ═ d 1 ,s 2 ,...,s n }，y d ＝0，
Is a variation matrix form of omega in the fixed period model conversion;
7. a data-driven control regional traffic signal dynamic optimization system is characterized by comprising an analysis module, a traffic data analysis module, an intersection storage and forwarding state space equation construction module and an iterative learning control model construction module;
the analysis module is used for analyzing the road network topological structure to obtain intersection attribute parameters in an intersection control area, wherein the intersection attribute parameters comprise traffic flow analysis and control channel analysis;
the traffic data analysis module is used for obtaining a traffic detection state index of the intersection attribute parameter based on lane level state monitoring and the intersection attribute parameter;
the intersection storing and forwarding state space equation building module is used for obtaining a storing and forwarding model based on an intersection control channel by accumulating according to a storing and forwarding model on road section steering and a steering state equation belonging to the same control channel, defining a signal control element based on an intersection signal control scheme of convergence constraint to obtain an intersection storing and forwarding state space equation with an indefinite period, and carrying out derivation and conversion of given period and green time loss time on the intersection storing and forwarding state space equation with the indefinite period by considering coordination control requirements to obtain the intersection storing and forwarding state space equation with the fixed period;
the iterative learning control model building module is used for building an evaluation function which takes queuing balance among phases as a target, building a balance target function, obtaining a P-type learning law of fixed-period balance control based on ILC, building an iterative learning control model and a control flow on the basis of the P-type learning law, the balance target function and a fixed-period intersection storage and forwarding state space equation, and realizing real-time optimal control of traffic signals.
8. A computer-readable storage medium having stored thereon a plurality of acquisition and classification procedures for being invoked by a processor and performing the method for dynamic optimization of regional traffic signals according to claim 1.
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