CN115798226B - Signal control optimization method based on green light utilization rate - Google Patents
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Abstract
According to the signal control optimization method based on the green light utilization rate, from the angle of reducing green light empty, the green light waste at an intersection is minimized, namely, the method is equivalent to pursuing the maximum green light utilization rate, and an optimization target is set for a target; by setting the optimization target to minimize green light waste, reduce green light empty time, improve the matching degree of green lights and traffic demands of each flow direction, ensure that the saturation of the intersection can be improved within a reasonable range by the restriction of constraint conditions, correspondingly reduce the waiting time of invalidation of other flow directions, reduce the cycle time of the intersection, increase the turnover speed of the intersection, reduce the queuing length of each flow direction and improve the saturation of the intersection, thereby improving the operation efficiency of the intersection.
Description
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to a signal control optimization method based on green light utilization rate.
Background
The conventional fixed-point detector for traffic control has the problems of easy damage to equipment, low data precision and the like. With the development of technology, the installation and the use of urban road electronic monitoring equipment in China are more and more widespread in recent years. The electronic monitoring equipment is mainly arranged near a stop line of an intersection, and can capture instant information of all passing vehicles on a lane through automatic identification of the vehicles, namely electric alarm data; the electrical alert data includes: basic information of intersections, vehicle license plates, vehicle passing stop line time, vehicle types and other data. Compared with the traditional fixed-point detector, the data detection precision of the electronic monitoring equipment is high, the timeliness is good, the equipment is not easy to damage, and the data collected by the electronic monitoring equipment has great application value in the field of traffic control.
The existing research on traffic signal control is mostly designed based on the data collected by the traditional fixed-point detector, and in order to adapt to the characteristics of the data collected by the traditional fixed-point detector, many researches on traffic signal control have the problems of complex algorithm and low operation efficiency of intersections. In the existing research, the research based on the data of the electronic monitoring equipment is also available, but the research direction is mostly focused on the field of traffic state estimation; such as travel time estimation, OD matrix estimation, queue length estimation, etc. Although a small percentage of technicians are also discussing the use of electrical alarm data in adaptive signal control, such as: nie et al, discloses "sensing control based on electric alarm data". The method utilizes the electric warning data to calculate traffic parameters required by MinGT model and MaxGT model, but is limited by a framework of induction control, and the improvement of the running efficiency of the intersection is not obvious after the scheme is implemented.
Disclosure of Invention
In order to solve the problem that the effect of the control optimization method for the intersection signal control equipment is not obvious in the prior art, the invention provides the signal control optimization method based on the green light utilization rate, which can remarkably improve the operation efficiency of the intersection and is widely applied to the urban intersections where electronic police are arranged.
The technical scheme of the invention is as follows: the signal control optimization method based on the green light utilization rate is characterized by comprising the following steps of:
S1: based on the electronic monitoring equipment installed at the intersection to be optimized, acquiring the electric alarm data in the appointed time period to be optimized, and recording as: data to be analyzed;
setting: the intersection to be optimized comprises N flow directions, and all the flow directions are numbered;
s2: calculating the green light utilization rate of the intersection to be optimized;
The green light utilization rate is the proportion of the green light which is fully utilized in the phase green light; for any flow direction k, assuming that its green light utilization is GUR k, the green light utilization is calculated as follows:
GURk＝GTUk/gk
Wherein GUR k is the green light utilization rate; GTU k is the green light utilization effect, and the unit is s; g k is the green light time length of the flow direction k, and the unit is s;
s3: constructing a periodic green light utility model;
The periodic green light utility model is used for representing a functional relation between green light utilization utility and signal timing parameters, and is specifically as follows:
Wherein, GTU k is the green light utilization effect of flow direction k, which is the function of signal period length C and green light time g k; utility for green light utilization for queued vehicles flowing to k; /(I) Utility for green light utilization for non-queued vehicles flowing to k;
S4: optimizing the period green light utility model based on the signal timing parameters of the green light utilization rate GUR k to realize optimization of period length and green signal ratio; the method specifically comprises the following steps:
a1: constructing an optimization target of the periodic green light utility model, wherein the optimization target is as follows:
n is the total flow direction of the intersection to be optimized; GWR k is the minimum green light waste value of the flow direction k;
GWRk＝1-GURk
the average value of green light waste is minimized; /(I) Waste standard deviation for minimizing green light of each flow direction;
By adding So that the waste of the green lights of all the flow directions tends to be balanced;
a2: determining constraint conditions:
The constraint conditions include: phase structure constraints, maximum minimum green and cycle range constraints and non-saturation constraints determined by the original phase structure;
a3: and solving the optimization target based on constraint conditions to obtain an optimization result of the signal period length and the green light time, and optimizing the intersection to be optimized.
It is further characterized by:
in step S3, the construction of the periodic green light utility model includes the following steps:
b1: calculating and obtaining the average periodic flow in the time period to be optimized based on the data to be analyzed And average maximum queuing length/>
Wherein,Flow through to kth signal period; /(I)A unit vehicle is used for the average value of the signal period flow in the time period to be optimized of the flow direction k; /(I)Maximum queuing length for the j-th cycle of flow direction k; /(I)A unit vehicle is used as the average value of the maximum queuing length in the time period to be optimized of the flow direction k; j is the number of cycles included in the time period to be optimized;
b2: based on the periodic average flow rate And said average maximum queuing length/>The average arrival time interval/>, of the aggregate wave speed w k corresponding to the intersection to be optimized and the non-queuing vehiclesThe specific calculation method comprises the following steps:
Wherein h s is the saturated headway, unit s; d 0 is the average parking space in m; v k is the desired vehicle speed in flow direction k in m/s; g k is the green time to flow k; c 0 and g k 0 are the signal period length and the effective green time at the initial timing;
b3: respectively calculating the green light utilization utility of the queuing vehicle and the non-queuing vehicle;
Wherein, The green light of the queuing vehicle on the flow direction k is utilized, namely the time required for queuing dissipation; /(I)The utility for green light utilization for non-queuing vehicles; w k is the wave velocity of the collected waves corresponding to the intersection to be optimized; /(I)For the evanescent wave velocity, the unit is m/s; v k is the desired vehicle speed in flow direction k in m/s; /(I)Average arrival headway for non-queuing vehicles; h s is the saturated headway, and the unit is s;
The time corresponding to the maximum queuing length in the period;
b4: constructing the periodic green light utility model based on the green light utilization utility;
in the step a2, the unsaturated constraint needs to ensure that each phase is not queued for the second time;
the constraints are as follows:
Wherein, Dissipation time for queuing vehicles; max (0.9 g k,gk -3) is the upper bound given by the uncertainty in consideration of queuing dissipation;
In step a3, the optimization target is solved by adopting scipy.optimize.minisize in the python language;
In step a2, when the intersection to be optimized is a four-phase intersection, the constraint condition includes:
Phase structure constraint:
gk＝gk+4，k＝1，2，3，4
wherein, C is the signal period length, L is the total loss time;
minimum green light constraint:
gk≥gmin
cycle range constraint:
Cmax≥C≥Cmin
the unsaturated constraint needs to ensure that each phase is not queued for the second time, and the constraint is as follows:
According to the signal control optimization method based on the green light utilization rate, from the angle of reducing green light empty, the green light waste at an intersection is minimized, namely, the method is equivalent to pursuing the maximum green light utilization rate, and an optimization target is set for a target; by setting the optimization target to minimize green light waste, reduce green light empty time, improve the matching degree of green lights and traffic demands of each flow direction, ensure that the saturation of the intersection can be improved within a reasonable range by the restriction of constraint conditions, correspondingly reduce the waiting time of invalidation of other flow directions, reduce the cycle time of the intersection, increase the turnover speed of the intersection, reduce the queuing length of each flow direction and improve the saturation of the intersection, thereby improving the operation efficiency of the intersection.
Drawings
FIG. 1 is a schematic diagram of green light utilization calculation in the present application;
FIG. 2 is an embodiment of a bit structure of a 4-phase intersection phase;
FIG. 3 is an embodiment of a phase structure of a 6-phase intersection;
FIG. 4 is a diagram of an embodiment site satellite;
FIG. 5 is a schematic diagram of an embodiment initial timing scheme;
FIG. 6 is a schematic diagram of a comparative optimization scheme in an embodiment;
FIG. 7 is a schematic view of an embodiment of the present application;
FIG. 8 is a graph comparing maximum queue length results under various schemes of an example demonstration scenario;
FIG. 9 is a graph comparing saturation results under various schemes of an example demonstration scene;
FIG. 10 is a graph comparing delay results of various schemes of the embodiment demonstration scene;
Fig. 11 is a comparison chart of green light idle time results under various schemes of the embodiment demonstration scene.
Detailed Description
As shown in fig. 1, the invention comprises a signal control optimization method based on green light utilization rate, which comprises the following steps.
S1: based on the electronic monitoring equipment installed at the intersection to be optimized, acquiring the electric alarm data in the appointed time period to be optimized, and recording as: data to be analyzed;
setting: the intersection to be optimized comprises N flow directions, and all the flow directions are numbered.
The technical scheme of the application is applied to intersections with fixed phase structures and phase sequences, such as intersections with nema standard double-ring structures. Such as: the 8 flow directions of the cross-shaped intersection comprise: the left turn of the south entrance way, the straight going of the north entrance way, the left turn of the west entrance way, the straight going of the east entrance way, the left turn of the north entrance way, the straight going of the south entrance way, the left turn of the east entrance way and the straight going of the west entrance way. Each flow direction is numbered in sequence, and the data of the flow direction k is calculated subsequently, so that the data can correspond to a specific flow direction.
For the collection of data to be analyzed, in practical application, the time period to be optimized is most of the peak time period of an intersection, such as the early peak time period (7 to 9 points) or the late peak time period (17 to 19 points) of Monday; and setting an acquisition period as a period for optimizing the subsequent signal lamp based on the detection data in the detection period of a certain day or a plurality of days. Such as: taking monday as an example, the acquisition period is set to be 1 week, and the signal timing of the next monday period can be optimized based on the detection data (or historical data of a plurality of monday early peak periods) of the last monday early peak period, so that the optimization result of the application can be ensured to be in line with the traffic change condition of the intersection.
S2: calculating the green light utilization rate of the intersection to be optimized;
The green light utilization rate (Green Utilization Ratio, GUR) is the proportion of the green light which is fully utilized in the phase green light; for any flow direction k, assuming that its green light utilization is GUR k, the green light utilization is calculated as follows:
GURk＝GTUk/gk
Wherein GUR k is the utilization rate of the k flow direction green light and is dimensionless data; the GTU k is the green light utilization Utility (GREEN TIME Utility, GTU) of k flow directions, and the unit is s; g k is the green light time length of the flow direction k, and the unit is s.
S3: constructing a periodic green light utility model;
the periodic green light utility model is used for representing the functional relation between green light utilization utility and signal timing parameters, and is specifically as follows:
Wherein, GTU k is the green light utilization effect of flow direction k, which is the function of cycle length C and green light time g k; utility for green light utilization for queued vehicles flowing to k; /(I) The utility is utilized for green light flowing to the non-queued vehicles of k.
The construction of the periodic green light utility model comprises the following steps in detail.
B1: calculating and obtaining average periodic flow in a time period to be optimized based on data to be analyzedAnd average maximum queuing length/>
Wherein,Flow through to kth signal period; /(I)A unit vehicle is used for the average value of the signal period flow in the time period to be optimized of the flow direction k; /(I)Maximum queuing length for the j-th cycle of flow direction k; /(I)A unit vehicle is used as the average value of the maximum queuing length in the time period to be optimized of the flow direction k; j is the number of cycles included in the time period to be optimized.
Wherein the average cycle flow rateBased on the electric alarm data, the flow passing through each period can be directly obtained, and then the average flow is calculated to obtain the flow; with respect to average maximum queuing length/>The value of the maximum queuing length of the intersection to be optimized can be obtained according to the historical data, the queuing length of each lane in each period can be calculated through the electric alarm data set by the intersection to be optimized, and then the maximum queuing length is calculated, or the estimation of the maximum queuing length of each period is realized based on the method of the prior art, as disclosed in the patent: ZL202011078910.3, resulting in average periodic traffic and average maximum queuing length for the time period to be optimized (e.g., early and late peak time periods). According to the technical scheme, the average cycle flow of two input values/>And average maximum queuing length/>The method can be obtained by calculation only depending on the electric police data set at the intersection to be optimized, and does not need to rely on monitoring equipment of other intersections and track data of vehicles for calculation, so that the technical scheme of the method can be widely applied to urban intersections where electronic police is arranged.
B2: as shown in fig. 1, the average flow is based on the periodAnd average maximum queuing length/>By utilizing traffic wave theory, the average arrival time interval/>, of the aggregate wave speed wk corresponding to the intersection to be optimized and the non-queuing vehiclesThe specific calculation method comprises the following steps:
Wherein, The time interval is the unit s of the saturated headstock; d 0 is the average parking space in m; v k is the desired vehicle speed in flow direction k in m/s; g k is the green time to flow k; c 0,gk 0 is the signal period length and the effective green light time when the initial configuration is carried out; subscripts s, f are marking marks; s in h s corresponds to saturated, representing saturation; /(I)Wherein f represents free-flow, and represents free flow (non-queuing vehicles);
In the concrete calculation, h s is the time interval of queuing vehicles, and can be synchronously obtained when the electronic monitoring equipment acquires the queuing length in the electric alarm data; the average parking distance d 0 and the expected vehicle speed v k of the flow direction k are obtained based on the historical data of the intersection to be adjusted.
FIG. 1 is a schematic diagram of green light utilization effectiveness calculation in the invention, taking a parking line as a starting point, taking a horizontal axis unit as time, indicating the time of a vehicle passing through the parking line, and the red light starting time in a target flow direction signal period is 0; the longitudinal axis is the distance between the vehicle in the intersection and the parking line, and the parking line is taken as 0 point; the time of one signal period (0-period end time) can be decomposed into: red light time, time required for queuing vehicles to dissipateAnd the time of non-queuing vehicle passage (corresponding average arrival headway/>). Then, the integrated wave velocity w k is the point/>Slope of the slope line to point 0; evanescent wave velocity/>For the dot/>Slope of the slope to the end of the red light; the desired vehicle speed v k is point/>Time required for ending time of red light and vehicle dissipation in line/>Slope of the sum.
B3: respectively calculating the green light utilization utility of the queuing vehicle and the non-queuing vehicle;
Wherein, The green light of the queuing vehicle on the flow direction k is utilized, namely the time required for queuing dissipation; /(I)The utility for green light utilization for non-queuing vehicles; wk is the wave velocity of the collected waves corresponding to the intersection to be optimized; /(I)For the evanescent wave velocity, the unit is m/s; v k is the desired vehicle speed in flow direction k in m/s; /(I)Average arrival headway for non-queuing vehicles; h s is the saturated headway, and the unit is s;
c, g k is the period length of the signal to be optimized and the effective green time respectively, and is a decision variable in the function;
The time corresponding to the maximum queuing length in the period; wherein, the subscript m represents maximum and corresponds to the queuing evanescent wave velocity; subscript q represents queued, corresponding to a queued vehicle; the subscript nq represents non-queued, corresponding to a non-queued vehicle.
B4: and constructing a periodic green light utility model based on the green light utilization utility.
S4: optimizing a period green light utility model based on signal timing parameters of green light utilization rate GUR k, and optimizing period length and green signal ratio; the method specifically comprises the following steps:
a1: constructing an optimization target of a periodic green light utility model, wherein the optimization target is as follows:
Wherein GWR k is the minimum green light waste value of the flow direction k;
GWRk＝1-GURk
the average value of green light waste is minimized; /(I) Waste standard deviation for minimizing green light of each flow direction;
By adding So that the waste of the green lights of all the flow directions tends to be balanced; according to the technical scheme, the green light waste standard deviation of each flow direction is reduced by increasing and minimizing the green light waste standard deviation of each flow direction, namely, the green light waste standard deviation of each flow direction tends to be consistent by realizing the optimization goal of increasing and minimizing the green light waste standard deviation of each flow direction, so that the green light waste of each flow direction tends to be balanced;
a 2: determining constraint conditions:
The constraint conditions include: phase structure constraint, minimum green light constraint, cycle range constraint and unsaturated constraint determined by the original phase structure;
the unsaturated constraint needs to ensure that each phase is not subjected to secondary queuing;
the constraints are as follows:
Wherein, Dissipation time for queuing vehicles; max (0.9 g k,gk -3) is the upper bound given by the uncertainty in consideration of queuing dissipation;
a3: solving the optimization target based on the constraint condition to obtain an optimization result of the period length and the green light time, and realizing the optimization of the intersection to be optimized.
When solving the optimization target, the method is based on the solving package in the prior art, such as: python embedded, matlab embedded, and even business software gurobi are all possible, in this embodiment using scipy.optimize.miniize in python language to solve the optimization objective.
In step a2, when the intersection to be optimized is a four-phase intersection, as in the embodiment shown in fig. 2, the intersection signal control adopts a four-phase structure in a dual-ring structure, and the signal control flow direction numbers and flows, for example: a flow direction number k=1 turned from south to left, and a flow direction number k=2 going straight from north to south; the phase structure is: the signal cycle length C comprises 4 phases, and the phases are 1-4; phase 1 includes: the south entryway turns left (k=1) and the north entryway turns left (k=5).
The constraints corresponding to the example of fig. 2 are as follows:
Phase structure constraint:
gk＝gk+4，k＝1，2，3，4
wherein, C is the signal period length, L is the total loss time;
minimum green light constraint:
gk≥gmin
cycle range constraint:
Cmax≥C≥Cmin
the unsaturated constraint needs to ensure that each phase is not queued for the second time, and the constraint is as follows:
For other phase structures, the method is also applicable, and only the corresponding phase structure constraint needs to be adjusted; for example, for a dual ring structure with overlapping phases as shown in fig. 3, which is 6 phases at this time, the phase structure constraint can be written as:
g1+g2＝g5+g6
g3+g4＝g7+g8
And finally, solving by adopting a scipy.optimize.miniize solving packet in the python language, and optimizing the cycle length and the green light time.
In summary, the technical scheme of the application comprises the following steps: 1) Defining the utilization rate of the green light, and obtaining the utilization rate of the green light through conversion; 2) Constructing a green light utilization utility model, and deducing a functional relation between green light utilization utility and signal timing parameters; 3) And constructing a signal timing parameter optimization model based on the green light utilization rate, and optimizing the cycle length and the green signal ratio. Compared with the prior art, the application is suitable for an electric alarm system, has wide application range and good optimizing effect.
The intersection of the double-ring structure 4 phase shown in fig. 2 is used as an intersection to be optimized, and the application process and the optimization effect of the technical scheme of the invention are described.
The actual shape of the intersection to be optimized is shown in fig. 4. The input data are the electric warning data and the signal timing data of the intersection to be optimized on the whole day, and examples of the data are shown in the following tables 1 and 2. Based on the electric alarm data time stamp and the signal timing data phase starting time, the electric alarm detection vehicle can be divided into specific periods, so that the period flow is directly obtained, and the period queuing length is estimated based on the prior art. Furthermore, signal timing optimization can be realized according to the method, and a signal timing scheme of each period is output, wherein the signal timing scheme comprises a period length and a green light length of each phase.
Table 1 electric alarm data example
Table 2 signal timing examples
Based on the technical scheme of the application, in the calculation process, an example of the calculation result of the related parameters is used, as shown in the following table 3.
Table 3 parameter calculation result example
The comparison scheme selects an initial timing scheme and a scheme provided by a certain electronic engineering company as comparison optimization schemes. The initial schedule was issued for 2021, 10 months and 20 days, and the initial timing schedule is shown in fig. 5. The time of delivery of the comparative optimization protocol was 2021, 12 months and 1 day, and the delivery protocol is shown in fig. 6. The delivery time of the scheme of the invention is 2021, 12 months and 23 days, and the delivery scheme is shown in fig. 7.
Through the real monitoring video, a representative time period is selected, the green primary queuing length, the maximum queuing length, the parking delay, the saturation and the green light idle time length are manually extracted and compared, and the results are shown in fig. 8-11. The summary results are shown in table 4 below:
TABLE 4 field verification control benefits for intersections to be optimized (based on video extraction results)
The optimization scheme of the application can obviously reduce the maximum queuing length and vehicle delay of the intersection, improve the saturation of the intersection, reduce the green light free time and is obviously superior to the optimization method used by the comparison optimization scheme. Further comparing with the actual monitoring video, it can be known that the control benefit can be obviously improved by the optimization scheme of the application, as shown in fig. 9. Before optimization, the flow matching degree is low, the individual flow direction is seriously free, and after optimization, the green light waste of each entrance road is obviously reduced.
Claims (5)
1. The signal control optimization method based on the green light utilization rate is characterized by comprising the following steps of:
S1: based on the electronic monitoring equipment installed at the intersection to be optimized, acquiring the electric alarm data in the appointed time period to be optimized, and recording as: data to be analyzed;
setting: the intersection to be optimized comprises N flow directions, and all the flow directions are numbered;
s2: calculating the green light utilization rate of the intersection to be optimized;
The green light utilization rate is the proportion of the green light which is fully utilized in the phase green light; for any flow direction k, assuming that its green light utilization is GUR k, the green light utilization is calculated as follows:
GURk＝GTUk/gk
Wherein GUR k is the green light utilization rate; GTU k is the green light utilization effect, and the unit is s; g k is the green light time length of the flow direction k, and the unit is s;
s3: constructing a periodic green light utility model;
The periodic green light utility model is used for representing a functional relation between green light utilization utility and signal timing parameters, and is specifically as follows:
Wherein, GTU k is the green light utilization effect of flow direction k, which is the function of signal period length C and green light time g k; utility for green light utilization for queued vehicles flowing to k; /(I) Utility for green light utilization for non-queued vehicles flowing to k;
S4: optimizing the period green light utility model based on the signal timing parameters of the green light utilization rate GUR k to realize optimization of period length and green signal ratio; the method specifically comprises the following steps:
a1: constructing an optimization target of the periodic green light utility model, wherein the optimization target is as follows:
n is the total flow direction of the intersection to be optimized; GWR k is the minimum green light waste value of the flow direction k;
GWRk＝1-GURk
the average value of green light waste is minimized; /(I) Waste standard deviation for minimizing green light of each flow direction;
By adding So that the waste of the green lights of all the flow directions tends to be balanced;
a2: determining constraint conditions:
The constraint conditions include: phase structure constraints, maximum minimum green and cycle range constraints and non-saturation constraints determined by the original phase structure;
a3: and solving the optimization target based on constraint conditions to obtain an optimization result of the signal period length and the green light time, and optimizing the intersection to be optimized.
2. The signal control optimization method based on green light utilization rate according to claim 1, wherein: in step S3, the construction of the periodic green light utility model includes the following steps:
b1: calculating and obtaining the average periodic flow in the time period to be optimized based on the data to be analyzed And average maximum queuing length/>
Wherein,Flow through to kth signal period; /(I)A unit vehicle is used for the average value of the signal period flow in the time period to be optimized of the flow direction k; /(I)Maximum queuing length for the j-th cycle of flow direction k; /(I)A unit vehicle is used as the average value of the maximum queuing length in the time period to be optimized of the flow direction k; j is the number of cycles included in the time period to be optimized;
b2: based on the periodic average flow rate And said average maximum queuing length/>The average arrival time interval/>, of the aggregate wave speed w k corresponding to the intersection to be optimized and the non-queuing vehiclesThe specific calculation method comprises the following steps:
Wherein h s is the saturated headway, unit s; d 0 is the average parking space in m; v k is the desired vehicle speed in flow direction k in m/s; g k is the green time to flow k; c 0 and g k 0 are the signal period length and the effective green time at the initial timing;
b3: respectively calculating the green light utilization utility of the queuing vehicle and the non-queuing vehicle;
Wherein, The green light of the queuing vehicle on the flow direction k is utilized, namely the time required for queuing dissipation; /(I)The utility for green light utilization for non-queuing vehicles; w k is the wave velocity of the collected waves corresponding to the intersection to be optimized; /(I)For the evanescent wave velocity, the unit is m/s; v k is the desired vehicle speed in flow direction k in m/s; /(I)Average arrival headway for non-queuing vehicles; h s is the saturated headway, and the unit is s;
The time corresponding to the maximum queuing length in the period;
b4: and constructing the periodic green light utility model based on the green light utilization utility.
3. The signal control optimization method based on green light utilization rate according to claim 1, wherein: in the step a2, the unsaturated constraint needs to ensure that each phase is not queued for the second time;
the constraints are as follows:
Wherein, Dissipation time for queuing vehicles; max (0.9 g k,gk -3) is an upper bound given by consideration of uncertainty in queuing dissipation.
4. The signal control optimization method based on green light utilization rate according to claim 1, wherein: in step a2, when the intersection to be optimized is a four-phase intersection, the constraint condition includes:
Phase structure constraint:
gk＝gk+4,k＝1,2,3,4
wherein, C is the signal period length, L is the total loss time;
minimum green light constraint:
gk≥gmin
cycle range constraint:
Cmax≥C≥Cmin
the unsaturated constraint needs to ensure that each phase is not queued for the second time, and the constraint is as follows:
5. The signal control optimization method based on green light utilization rate according to claim 1, wherein: in step a3, the optimization objective is solved using scipy.optimize.minisize in python language.
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