CN113706896A - Traffic control method based on space-time resource dynamic allocation - Google Patents
Traffic control method based on space-time resource dynamic allocation Download PDFInfo
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Abstract
The invention relates to a traffic control method based on space-time resource dynamic allocation, which specifically comprises the following steps: s1 defines the intersection space-time resources, establishes an S2 intersection space-time resource dynamic distribution model, solves the space of the S3 intersection space-time resource dynamic distribution model, and adopts a double-layer optimization control algorithm of the S4 intersection space-time resource dynamic distribution model: the double-layer optimization control algorithm comprises an upper-layer lane control algorithm and a lower-layer phase control algorithm. The method constructs the dynamic distribution model of the time-space resources of the intersection by redefining the concept of the time-space resources for traffic control, so that the control variable dimension is high, the flexibility is strong, the method is convenient and practical, the signal control modeling of the urban road intersection can be quickly realized, meanwhile, the double-layer control algorithm is advanced, the different regulation and control frequencies of the control variables are fully considered, and the regulation and control requirements are more effectively adapted and the running stability of the intersection is ensured.
Description
Technical Field
The invention relates to the field of urban road intersection control, in particular to a traffic control method based on space-time resource dynamic allocation.
Background
With the continuous increase of the automobile holding amount, the urban road congestion condition becomes more and more serious. Road intersections are important components of urban traffic transportation and safe traffic, and gradually become origins and serious disaster areas of traffic jam.
Due to the problems of city planning, signal control design, driver driving behavior and the like, passive traffic control designed based on the traditional traffic control theory is insufficient in control variable dimensionality and control strategy flexibility.
Therefore, the dimensionality of the traffic control variable is expanded, the composition form of the traffic control model is redefined, the flexible traffic control strategy is designed, and the method has very important significance for relieving urban road traffic jam, keeping the traffic flow of a road network stable and guaranteeing the driving safety.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the defects of the current urban road intersection control in control variable dimension and control strategy flexibility, the traffic control method based on the dynamic distribution of the time-space resources is provided, an intersection time-space resource dynamic distribution model is constructed by redefining the concept of traffic control time-space resources, and meanwhile, a double-layer control algorithm is designed, so that more flexible and effective intersection control is realized.
The traffic control method based on space-time resource dynamic allocation comprises the following steps,
s1, defining the time-space resources of the intersection: from the angle of urban intersection traffic control, describing space-time resources as representing space-time variables related in traffic control in a resource form by taking intersections and road sections as a whole, thereby realizing the form combination and use of the resources;
establishing a space-time resource dynamic allocation model at an intersection of S2: on the basis of defining the intersection space-time resources in the step S1, a five-dimensional intersection space-time resource dynamic distribution model is established,
the road section j, a is an upstream road section, and the road section o is a downstream road section; n isj,a(k) Representing the number of vehicles, q, of the road section j, a in the sampling period kj,a,in(k) Representing the number of vehicles sent to the road section j, a by the upstream road section in the sampling period k;
the number of phases belonging to the section j, a in the phase combination can be obtained after a feasible solution is found in a solution space with constraint in a sampling period k;
indicates the number of identical genes after expression of the lane genes, Sj,aIndicating road section capacity, gj,a,o(k) A green time representing the phase of the segment j, a within the sampling period k, and having gj,a,o(k)≥gj,a,o,min；
Specifically, the establishment process and theory of formula 1 are as follows: on the basis of the definition of the intersection space-time resources, designing an intersection space-time resource dynamic distribution model with five dimensions of { lane, phase sequence, phase green light time, interval and loss time }, wherein the intersection consists of an internal conflict area and an upstream and downstream connecting road section; the state equation of the road sections j and a in the connecting road section set of the intersection is set as follows:
nj,a(k+1)＝nj,a(k)+qj,a,in(k)-qj,a,out(k)
the above equation represents the number of vehicles on the link j, a within the sampling period k +1, which is equal to the sum of the differences between the number of vehicles on the link j, a within the sampling period k and the number of vehicles flowing toward j, a and flowing out of j, a on the upstream link. Wherein n isj,a(k) Representing the number of vehicles of the road section j, a in the sampling period k; q. q.sj,a,in(k) Representing the number of vehicles sent to the road section j, a by the upstream road section in the sampling period k; q. q.sj,a,out(k) Representing the number of vehicles sent to the downstream road section by the road section j, a in the sampling period k;
in order to accurately represent the dynamic characteristics of the lane attributes, a lane genetic concept is proposed, namely, the steering attributes of the lane are described as control variables to be output. The turning attributes of the lane include left turn, straight run and right turn, which are respectively represented by L, T and R, i.e., the basic constituent units of the genes of the lane are L, T and R;
and the intersection entrance lane steering attribute and the downstream connecting road section form a minimum unit for dispatching the traffic flow at the intersection. The section j, a is an upstream section, and the section o is a downstream section; r for lane of road section j, aj,a1, 2.., m, where m represents the number of lanes contained in the link j, a; fj,a(k)＝{fr (j,a)(k)}r＝1,2,...,mRepresents the combination of expression of the genes of the lane within k of the sampling period, wherein fr (j,a)(k) Gene expression indicative of lane r;
wherein
Genes representing lanes r, one lane consisting of 3 genes, G1，G2，G3In which there is G1→ L, expressed as the first gene mapping to left turn, G2→ T, expressed as second gene mapping straight line, G3→ R, expressed as the third gene maps to the right turn, and there
By the above description, regulatory variables for lane genes can be established:
wherein, gamma isj,a(t) represents a set of regulatory variables;
expressed as a manipulated variable, which is a function of the number of lanes, and is derived from the above equation;
nj,a(k) representing the number of connections of a road segment j, a to a road segment o, wherein
A first gene representing a gene expression union of lane groups,
a second gene representing a gene expression union of lanes,
a third gene representing a gene expression union of lanes;
s.t.
in the formula:
representing the number of the same genes after the lane genes are expressed;
the control variable is substituted into an intersection storage and forwarding model to obtain an intersection space-time resource dynamic model:
wherein S isj,aIndicating road section capacity, gj,a,o(k) A green time representing the phase of the segment j, a within the sampling period k, and having gj,a,o(k)≥gj,a,o,min；
Space is solved to the space-time resource dynamic allocation model at S3 intersection: obtaining a solution space of the relationship among the lane genes, the phases and the phase sequences of the model according to a lane genome expression set of all direction inlet road sections of the intersection, a set of phase combinations of the lanes of all direction inlet road sections of the intersection when a certain fixed genome is expressed, and a set of fixed phase combinations of the lanes of all direction inlet road sections of the intersection when a certain fixed gene is expressed,
described as g in the sampling period kj,a,o(k) Finding a feasible solution in the above formula solution space, and applying the feasible solution to the model can complete the adjustment of the green light time and the regulation variable;
wherein,
indicating that after finding a feasible solution in the constrained solution space within the sampling period k, the number of phases belonging to the segment j, a in the phase combination can be obtained,
specifically, the establishment process and theory of formula 1 and formula 3 are as follows:
and (3) lane genome expression sets of all direction entrance road sections of the intersection:
Φx(k)＝{FΙ (x)(k)}Ι＝1,2,...,ε
set of phase combinations of lanes of all direction entrance road sections of the intersection when a certain fixed genome is expressed:
the method comprises the following steps of (1) collecting fixed phase combination time phase sequences obtained when lanes of all direction entrance road sections of a crossing are expressed by a certain fixed gene:
obtaining a solution space of the lane gene, phase and phase sequence relation of the model by the 3 sets:
described as g in the sampling period kj,a,o(k) A feasible solution is found in the above solution space, and the feasible solution is applied to the model to complete the adjustment of the green light time and the regulating variable.
Wherein,
after finding a feasible solution in the solution space of { gene, phase sequence } with constraint in the sampling period k, the number of phases belonging to the segment j, a in the phase combination can be obtained, as follows:
due to green time gj,a,o(k) And control variable
O in (b) is related to the number of phases and thus can be determined by
Represents;
the reason why the upstream and downstream link and the phase number are different is that:
i.e. upstream and downstream cannot be connected, but the phase may not be subordinate to the phase sequence within the sampling period k.
The double-layer optimization control algorithm of the dynamic space-time resource distribution model at the intersection of S4 is as follows: comprises an upper lane control algorithm and a lower phase control algorithm, and specifically comprises the following steps,
s41 initializes: the initialization scheme is set by the form, traffic composition and the like of the actual intersection;
s42 lane control operation: inserting lane control and inserting a full red phase at the end position of the initialization operation scheme, and simultaneously starting the judgment of the traffic capacity coefficient Js of the intersection: if J issIf the lane is not less than 0, the lane is kept unchanged, and phase control is performed; if J iss<0, and J are present for n consecutive control periodss<0, starting lane control, selecting an action in a lane gene expression set according to the index, finishing adjustment within interval time, and entering phase control;
after lane control adjustment is finished, a phase needs to be selected as an initial phase, and the selection principle of the initial phase is as follows: calculating the phase traffic demand after lane adjustment, and taking the phase with the maximum traffic flow as an initial phase;
s43 phase control operation: the method takes the flow and queue of an intersection when a certain phase is executed in a sampling period k as input, selects n phases matched with the currently executed phase in a solution space as candidates of the next executed phase, and selects m phases continuously executed by each candidate phase as a control chain. Constructing a target function with the minimum traffic capacity coefficient Jmin, executing n control chains by using a genetic algorithm as an optimization algorithm, sequencing the obtained traffic capacity coefficients J of the n control chains, obtaining a first phase of the control chain with the minimum J as a next execution phase of the current phase, and outputting the obtained interval time, the phase and the green light time.
Further, the time-space variables involved in the traffic control at the urban intersection in the step S1 include six types of variables, namely, lane, phase sequence, phase green time, interval and loss time, and vehicle speed.
Further, the five-dimensional intersection space-time resource dynamic allocation model in step S2 includes five dimensions, including lane, phase sequence, phase green time, interval, and loss time.
Further, the phase control operation described in step S43 specifically includes the following steps,
s431: executing the current phase and the green light time, locking the green light time when entering, and outputting the traffic flow input and queuing states of all road sections of the current intersection;
s432: and starting phase control chain prediction, selecting a compatible control chain scheme group of the current execution phase from the set phase control chain scheme groups, taking the traffic flow and the queuing state in the step S431 as input, taking the traffic flow and the queuing state as a traffic capacity coefficient Jmin objective function, taking a genetic algorithm as an optimization algorithm, respectively executing all schemes in the compatible phase control chain scheme group, ranking the executed control chains, and outputting the first phase, the green light time and the interval time in the compatible phase control chain scheme with the first ranking. Asynchronous multithread calculation is adopted in the process, and the calculation time is the time for locking the green light;
s433: and outputting the interval time, the phase and the green time obtained by the calculation in the step S432 to a main process, and executing the interval time, the phase and the green time obtained by the calculation after the time locking green time of the current phase is finished.
Further, the genetic algorithm described in step S43 is specifically,
the objective function is Jmin, and the fitness function is set to
The design comprises population scale, cross probability, mutation probability and optimized algebra.
The invention relates to a traffic control method based on space-time resource dynamic allocation, which overcomes the defect that the current urban road intersection control is insufficient in control variable dimension and control strategy flexibility, constructs an intersection space-time resource dynamic allocation model by redefining the concept of traffic control space-time resources, has high control variable dimension and strong flexibility, is convenient and practical, can quickly realize signal control modeling of the urban road intersection, and simultaneously utilizes advanced double-layer control algorithm to fully consider different control frequency of control variables, thereby more effectively adapting to the control requirement and ensuring the running stability of the intersection.
Drawings
The traffic control method based on space-time resource dynamic allocation of the present invention is further described with reference to the accompanying drawings:
FIG. 1 is a schematic diagram of an intersection of a traffic control method based on space-time resource dynamic allocation;
FIG. 2 is a diagram of a storage and forwarding model of the intersection in the traffic control method based on space-time resource dynamic allocation;
FIG. 3 is a diagram of the intersection lane genome of the traffic control method based on the dynamic allocation of space-time resources;
FIG. 4 is a connection diagram of lane genes and downstream road segments of the road segments j, a of the traffic control method based on space-time resource dynamic allocation;
FIG. 5 is a traffic control method based on space-time resource dynamic allocation, gj,a,o(k) A solution space diagram of (a);
FIG. 6 is a structural diagram of the double-layer control method of the traffic control method based on space-time resource dynamic allocation;
fig. 7 is a flow chart of the dynamic phase control of the traffic control method based on space-time resource dynamic allocation.
Detailed Description
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, it is to be understood that the terms "left", "right", "front", "back", "top", "bottom", "inner", "outer", etc., indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
The technical solution of the present invention is further described by the following specific examples, but the scope of the present invention is not limited to the following examples.
Embodiment 1: as shown in fig. 1 to 7, the traffic control method based on the dynamic allocation of space-time resources substantially comprises the steps of,
s1, defining the time-space resources of the intersection: from the angle of urban intersection traffic control, describing space-time resources as representing space-time variables related in traffic control in a resource form by taking intersections and road sections as a whole, thereby realizing the form combination and use of the resources;
establishing a space-time resource dynamic allocation model at an intersection of S2: on the basis of defining the intersection space-time resources in the step S1, a five-dimensional intersection space-time resource dynamic distribution model is established,
the road section j, a is an upstream road section, and the road section o is a downstream road section; n isj,a(k) Representing the number of vehicles, q, of the road section j, a in the sampling period kj,a,in(k) Representing the number of vehicles sent to the road section j, a by the upstream road section in the sampling period k;
the number of phases belonging to the section j, a in the phase combination can be obtained after a feasible solution is found in a solution space with constraint in a sampling period k;
indicates the number of identical genes after expression of the lane genes, Sj,aIndicating road section capacity, gj,a,o(k) A green time representing the phase of the segment j, a within the sampling period k, and having gj,a,o(k)≥gj,a,o,min；
Specifically, the establishment process and theory of formula 1 are as follows: on the basis of the definition of the intersection space-time resources, designing an intersection space-time resource dynamic distribution model with five dimensions of { lane, phase sequence, phase green light time, interval and loss time }, wherein the intersection is composed of an internal conflict area and an upstream and downstream connecting road section, as shown in fig. 1; the state equation of the road sections j and a in the connecting road section set of the intersection is set as follows:
nj,a(k+1)＝nj,a(k)+qj,a,in(k)-qj,a,out(k)
the above equation represents the number of vehicles on the link j, a within the sampling period k +1, which is equal to the sum of the differences between the number of vehicles on the link j, a within the sampling period k and the number of vehicles flowing toward j, a and flowing out of j, a on the upstream link. Wherein n isj,a(k) Representing the number of vehicles of the road section j, a in the sampling period k; q. q.sj,a,in(k) Representing the number of vehicles sent to the road section j, a by the upstream road section in the sampling period k; q. q.sj,a,out(k) Representing the number of vehicles sent to the downstream road section by the road section j, a in the sampling period k;
as shown in fig. 3, in order to accurately characterize the dynamic characteristics of the lane attributes, a lane genetic concept is proposed, that is, a concept of outputting by describing the steering attributes of the lane as control variables. The turning attributes of the lane include left turn, straight run and right turn, which are respectively represented by L, T and R, i.e., the basic constituent units of the genes of the lane are L, T and R;
as shown in fig. 4, the intersection ingress lane steering attributes and downstream connecting segments constitute the minimum unit of the intersection's dispatched traffic. The section j, a is an upstream section, and the section o is a downstream section; r for lane of road section j, aj,a1, 2.., m, where m represents the number of lanes contained in the link j, a; fj,a(k)＝{fr (j,a)(k)}r＝1,2,...,mRepresents the combination of expression of the genes of the lane within k of the sampling period, wherein fr (j,a)(k) Gene expression indicative of lane r;
wherein
Genes representing lanes r, one lane consisting of 3 genes, G1，G2，G3In which there is G1→ L, expressed as the first gene mapping to left turn, G2→ T, expressed as second gene mapping straight line, G3→ R, expressed as the third gene maps to the right turn, and there
By the above description, regulatory variables for lane genes can be established:
wherein, gamma isj,a(t) represents a set of regulatory variables;
expressed as a manipulated variable, which is a function of the number of lanes, and is derived from the above equation;
nj,a(k) representing the number of connections of a road segment j, a to a road segment o, wherein
A first gene representing a gene expression union of lane groups,
expression of lane Gene expressionThe second gene of (a) above (b),
a third gene representing a gene expression union of lanes;
s.t.
in the formula:
representing the number of the same genes after the lane genes are expressed;
substituting the regulation variable into the intersection store-and-forward model, as shown in fig. 2, the intersection space-time resource dynamic model can be obtained:
wherein S isj,aIndicating road section capacity, gj,a,o(k) A green time representing the phase of the segment j, a within the sampling period k, and having gj,a,o(k)≥gj,a,o,min；
Space is solved to the space-time resource dynamic allocation model at S3 intersection: obtaining a solution space of the relationship among the lane genes, the phases and the phase sequences of the model according to a lane genome expression set of all direction inlet road sections of the intersection, a set of phase combinations of the lanes of all direction inlet road sections of the intersection when a certain fixed genome is expressed, and a set of fixed phase combinations of the lanes of all direction inlet road sections of the intersection when a certain fixed gene is expressed,
described as g in the sampling period kj,a,o(k) Finding a feasible solution in the above formula solution space, and applying the feasible solution to the model can complete the adjustment of the green light time and the regulation variable;
wherein,
indicating that after finding a feasible solution in the constrained solution space within the sampling period k, the number of phases belonging to the segment j, a in the phase combination can be obtained,
specifically, the establishment process and theory of formula 1 and formula 3 are as follows:
and (3) lane genome expression sets of all direction entrance road sections of the intersection:
Φx(k)＝{FΙ (x)(k)}Ι＝1,2,...,ε
set of phase combinations of lanes of all direction entrance road sections of the intersection when a certain fixed genome is expressed:
the method comprises the following steps of (1) collecting fixed phase combination time phase sequences obtained when lanes of all direction entrance road sections of a crossing are expressed by a certain fixed gene:
obtaining a solution space of the lane gene, phase and phase sequence relation of the model by the 3 sets:
as shown in figure 5 of the drawings,described as g in the sampling period kj,a,o(k) A feasible solution is found in the above solution space, and the feasible solution is applied to the model to complete the adjustment of the green light time and the regulating variable.
Wherein,
after finding a feasible solution in the solution space of { gene, phase sequence } with constraint in the sampling period k, the number of phases belonging to the segment j, a in the phase combination can be obtained, as follows:
due to green time gj,a,o(k) And control variable
O in (b) is related to the number of phases and thus can be determined by
Represents;
the reason why the upstream and downstream link and the phase number are different is that:
i.e. upstream and downstream cannot be connected, but the phase may not be subordinate to the phase sequence within the sampling period k.
The double-layer optimization control algorithm of the dynamic space-time resource distribution model at the intersection of S4 is as follows: including an upper lane control algorithm and a lower phase control algorithm, as shown in fig. 6, specifically includes the following steps,
s41 initializes: the initialization scheme is set by the form, traffic composition and the like of the actual intersection;
s42 lane control operation: inserting lane control into the full red phase at the end position of the initial operation scheme, and simultaneously starting intersection accessJudging a line capability coefficient Js: if J issIf the lane is not less than 0, the lane is kept unchanged, and phase control is performed; if J iss<0, and J are present for n consecutive control periodss<0, starting lane control, selecting an action in a lane gene expression set according to the index, finishing adjustment within interval time, and entering phase control;
after lane control adjustment is finished, a phase needs to be selected as an initial phase, and the selection principle of the initial phase is as follows: calculating the phase traffic demand after lane adjustment, and taking the phase with the maximum traffic flow as an initial phase;
s43 phase control operation: the method takes the flow and queue of an intersection when a certain phase is executed in a sampling period k as input, selects n phases matched with the currently executed phase in a solution space as candidates of the next executed phase, and selects m phases continuously executed by each candidate phase as a control chain. Constructing a target function with the minimum traffic capacity coefficient Jmin, executing n control chains by using a genetic algorithm as an optimization algorithm, sequencing the obtained traffic capacity coefficients J of the n control chains, obtaining a first phase of the control chain with the minimum J as a next execution phase of the current phase, and outputting the obtained interval time, the phase and the green light time;
the phase control operation described in step S43 specifically includes the steps of, as shown in fig. 7,
s431: executing the current phase and the green light time, locking the green light time when entering, and outputting the traffic flow input and queuing states of all road sections of the current intersection;
s432: and starting phase control chain prediction, selecting a compatible control chain scheme group of the current execution phase from the set phase control chain scheme groups, taking the traffic flow and the queuing state in the step S431 as input, taking the traffic flow and the queuing state as a traffic capacity coefficient Jmin objective function, taking a genetic algorithm as an optimization algorithm, respectively executing all schemes in the compatible phase control chain scheme group, ranking the executed control chains, and outputting the first phase, the green light time and the interval time in the compatible phase control chain scheme with the first ranking. Asynchronous multithread calculation is adopted in the process, and the calculation time is the time for locking the green light;
s433: and outputting the interval time, the phase and the green time obtained by the calculation in the step S432 to a main process, and executing the interval time, the phase and the green time obtained by the calculation after the time locking green time of the current phase is finished.
The genetic algorithm described in step S43 is specifically,
the objective function is Jmin, and the fitness function is set to
The design comprises population scale, cross probability, mutation probability and optimized algebra.
Embodiment 2: the time-space variables involved in the urban intersection traffic control in the step S1 include six types of variables including lane, phase sequence, phase green time, interval, lost time, and vehicle speed. The remaining steps and the specific method are as described in embodiment 1, and the description will not be repeated.
Embodiment 3: the five-dimensional intersection space-time resource dynamic allocation model in the step S2 includes five dimensions of lane, phase sequence, phase green time, interval and loss time. The remaining steps and the specific method are as described in embodiment 1, and the description will not be repeated.
The traffic control method based on the dynamic distribution of the time-space resources overcomes the defect that the current urban road intersection control is insufficient in control variable dimensionality and control strategy flexibility, an intersection time-space resource dynamic distribution model is constructed by redefining the concept of traffic control time-space resources, so that the intersection time-space resource dynamic distribution model is high in control variable dimensionality, high in flexibility, convenient and practical, signal control modeling of the urban road intersection can be rapidly achieved, meanwhile, a double-layer control algorithm is advanced, the difference of control variable frequency is fully considered, and therefore regulation and control requirements are more effectively met and the running stability of the intersection is guaranteed.
The foregoing description illustrates the principal features, rationale, and advantages of the invention. It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments or examples, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The foregoing embodiments or examples are therefore to be considered in all respects illustrative and not restrictive. The scope of the invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (5)
1. A traffic control method based on space-time resource dynamic allocation is characterized in that: the control method comprises the following steps of,
s1, defining the time-space resources of the intersection: from the angle of urban intersection traffic control, describing space-time resources as representing space-time variables related in traffic control in a resource form by taking intersections and road sections as a whole, thereby realizing the form combination and use of the resources;
establishing a space-time resource dynamic allocation model at an intersection of S2: on the basis of defining the intersection space-time resources in the step S1, a five-dimensional intersection space-time resource dynamic distribution model is established,
the road section j, a is an upstream road section, and the road section o is a downstream road section; n isj,a(k) Representing the number of vehicles, q, of the road section j, a in the sampling period kj,a,in(k) Indicating that the upstream road section in the sampling period k is sent to the vehicle of the road section j, aCounting;
the number of phases belonging to the section j, a in the phase combination can be obtained after a feasible solution is found in a solution space with constraint in a sampling period k;
indicates the number of identical genes after expression of the lane genes, Sj,aIndicating road section capacity, gj,a,o(k) A green time representing the phase of the segment j, a within the sampling period k, and having gj,a,o(k)≥gj,a,o,min；
Space is solved to the space-time resource dynamic allocation model at S3 intersection: obtaining a solution space of the relationship among the lane genes, the phases and the phase sequences of the model according to a lane genome expression set of all direction inlet road sections of the intersection, a set of phase combinations of the lanes of all direction inlet road sections of the intersection when a certain fixed genome is expressed, and a set of fixed phase combinations of the lanes of all direction inlet road sections of the intersection when a certain fixed gene is expressed,
described as g in the sampling period kj,a,o(k) Finding a feasible solution in the above formula solution space, and applying the feasible solution to the model can complete the adjustment of the green light time and the regulation variable;
wherein,
indicating that after finding a feasible solution in the constrained solution space within the sampling period k, the number of phases belonging to the segment j, a in the phase combination can be obtained,
the double-layer optimization control algorithm of the dynamic space-time resource distribution model at the intersection of S4 is as follows: comprises an upper lane control algorithm and a lower phase control algorithm, and specifically comprises the following steps,
s41 initializes: the initialization scheme is set by the form, traffic composition and the like of the actual intersection;
s42 lane control operation: inserting lane control and inserting a full red phase at the end position of the initialization operation scheme, and simultaneously starting the judgment of the traffic capacity coefficient Js of the intersection: if J issIf the lane is not less than 0, the lane is kept unchanged, and phase control is performed; if J iss<0, and J are present for n consecutive control periodss<0, starting lane control, selecting an action in a lane gene expression set according to the index, finishing adjustment within interval time, and entering phase control;
after lane control adjustment is finished, a phase needs to be selected as an initial phase, and the selection principle of the initial phase is as follows: calculating the phase traffic demand after lane adjustment, and taking the phase with the maximum traffic flow as an initial phase;
s43 phase control operation: the method takes the flow and queue of an intersection when a certain phase is executed in a sampling period k as input, selects n phases matched with the currently executed phase in a solution space as candidates of the next executed phase, and selects m phases continuously executed by each candidate phase as a control chain. Constructing a target function with the minimum traffic capacity coefficient Jmin, executing n control chains by using a genetic algorithm as an optimization algorithm, sequencing the obtained traffic capacity coefficients J of the n control chains, obtaining a first phase of the control chain with the minimum J as a next execution phase of the current phase, and outputting the obtained interval time, the phase and the green light time.
2. The traffic control method based on dynamic allocation of space-time resources as claimed in claim 1, wherein: the time-space variables involved in the urban intersection traffic control in the step S1 include six types of variables including lane, phase sequence, phase green time, interval, lost time, and vehicle speed.
3. The traffic control method based on dynamic allocation of space-time resources as claimed in claim 1, wherein: the five-dimensional intersection space-time resource dynamic allocation model in the step S2 includes five dimensions of lane, phase sequence, phase green time, interval and loss time.
4. The traffic control method based on dynamic allocation of space-time resources as claimed in claim 1, wherein: the phase control operation described in step S43 specifically includes the following steps,
s431: executing the current phase and the green light time, locking the green light time when entering, and outputting the traffic flow input and queuing states of all road sections of the current intersection;
s432: and starting phase control chain prediction, selecting a compatible control chain scheme group of the current execution phase from the set phase control chain scheme groups, taking the traffic flow and the queuing state in the step S431 as input, taking the traffic flow and the queuing state as a traffic capacity coefficient Jmin objective function, taking a genetic algorithm as an optimization algorithm, respectively executing all schemes in the compatible phase control chain scheme group, ranking the executed control chains, and outputting the first phase, the green light time and the interval time in the compatible phase control chain scheme with the first ranking. Asynchronous multithread calculation is adopted in the process, and the calculation time is the time for locking the green light;
s433: and outputting the interval time, the phase and the green time obtained by the calculation in the step S432 to a main process, and executing the interval time, the phase and the green time obtained by the calculation after the time locking green time of the current phase is finished.
5. The traffic control method based on dynamic allocation of space-time resources as claimed in claim 1, wherein: the genetic algorithm described in step S43 is specifically,
the objective function is J min, and the fitness function is set to
The design comprises population scale, cross probability, mutation probability and optimized algebra.
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