CN118887816A - A real-time dynamic variable lane and trunk green wave collaborative optimization method - Google Patents

Abstract

The invention discloses a real-time dynamic variable lane and trunk green wave collaborative optimization method, which relates to traffic green wave control technology; the method solves the problems of reaction delay and strategy failure in emergency scenes of variable lane and green collaborative optimization based on historical data of the target intersection in the prior art. The invention predicts the flow of the target intersection based on the flow data of the first two periods of the upstream intersection, determines the direction of the variable lane according to the saturation of the target intersection corresponding to the lane direction, takes the minimum total delay of the lane group at the entrance of the key intersection as the target, optimizes the signal phase timing in the signal period, and thus realizes the cooperative control of the variable lane and traffic green wave; the control strategy of the target intersection can be quickly adjusted according to the dynamic change of the upstream intersection, so that the control strategy has flexibility, can cope with the data mutation caused by emergency, and improves the use value of the control strategy.

Description

A real-time dynamic variable lane and trunk green wave collaborative optimization method

Technical Field

The present invention relates to a traffic green wave control technology, in particular to a real-time dynamically variable lane and trunk green wave collaborative optimization method.

Background Art

With the acceleration of urbanization and the continuous growth of traffic demand, urban traffic congestion is becoming more and more serious. The traditional red and green lights with fixed time settings and fixed direction lanes can no longer meet the current traffic conditions.

Dynamic variable lane technology and trunk green wave technology have been proposed and applied in the prior art, which effectively alleviated traffic congestion; however, the existing traffic arterial control measures are basically based on the long-term historical data of the target intersection, and variable lanes and green wave control are performed on the target intersection. This method can achieve good results for regular traffic conditions, but once extreme weather conditions or traffic accidents occur, causing the traffic flow on some sections to disrupt the regularity, the optimization control scheme based on long-term historical data will be unable to cope with it, and even cause greater traffic congestion; for this reason, we propose a real-time dynamic variable lane and trunk green wave collaborative optimization method.

Summary of the invention

The purpose of the present invention is to provide a real-time dynamic variable lane and trunk green wave collaborative optimization method to solve the problem that the existing variable lane and green wave control cannot make real-time response according to irregular scenes, the reaction is slow, causing traffic congestion and control strategy failure.

The present invention can be implemented by the following technical solution: A real-time dynamic variable lane and trunk green wave collaborative optimization method, comprising the following steps:

Step 1: Collect traffic flow data of trunk intersections based on traffic flow detectors, including traffic flow volume, flow direction, vehicle speed and queue length at each intersection;

Step 2: Take the traffic flow data of the target intersection and its two upstream intersections in the first two cycles as input, and the traffic flow data of the target intersection in the next cycle as output, build a traffic flow prediction model for the target intersection and use support vector regression SVR for training;

Step 3: Calculate the predicted saturation of different directions of the intersection based on the predicted intersection flow data, and determine the variable lane direction of the next optimization cycle according to the predicted saturation;

Step 4: Use the Webster model to calculate the phase cycle duration of each intersection in the trunk green wave line, and select the intersection with the largest cycle duration as the key intersection for trunk green wave control;

Step 5: Optimize the signal phase timing of the green wave key intersections on the trunk line according to the direction of the variable lanes, so as to minimize the total delay of vehicles in all entrance lane groups of the key intersections;

Step 6: Solve the green wave speed and bandwidth of non-critical intersections based on the MAXBAND model to obtain the absolute phase difference between non-critical intersections and critical intersections;

Step 7: Accumulate the optimized signal phase cycle length. If the accumulated value exceeds the optimization cycle length, enter the next optimization cycle; the optimization cycle length is consistent with the cycle length of the data selected in step 2;

Step 8: According to the consistency of the variable lane directions of two adjacent optimization cycles, different switching strategies are formulated to match the real scene;

Step 9: Monitor and provide feedback on the implementation effect of the variable lane and trunk green wave coordinated optimization plan, and dynamically optimize and adjust the plan based on the feedback results.

A further technical improvement of the present invention is that the calculation process of the predicted saturation in step 3 is:

Among them, _αs and _αl are the saturation of the straight and left-turn directions respectively, _qs and _ql are the flow rates in the straight and left-turn directions respectively, _ns and _nl are the number of lanes in the straight and left-turn directions respectively, and _Qs and _Ql are the single-lane capacities in the straight and left-turn directions respectively.

A further technical improvement of the present invention is that the specific method for determining the variable lane direction of the next optimization period according to the predicted saturation is:

If α _s ≥ _{α l} and α _s ≥ 0.9, or α _s ≥ α _l and α _l ≤ 0.9, the direction of the variable lane is the straight direction;

If α _s ≤ _{α l} and α _l ≥ 0.9, or α _s ≥ _{α l} and α _s ≤ 0.9, the variable lane direction is the left turn direction.

A further technical improvement of the present invention is that the process of defining the minimum total delay of all vehicle lane groups on the entrance road of the key intersection in step 5 is as follows:

d _im ＝d _im1 ×PF+d _im2 +d _im3

d _im2 =900T(α _im -1)

Wherein, d _i is the average vehicle delay at the ith intersection, _dim and q _im are the average vehicle delay and flow of the mth lane group at the ith intersection, _dim1 , _dim2 and _dim3 are the average vehicle delay, incremental delay and initial queue delay of the mth lane group at the ith intersection, respectively, PF is the adjustment parameter for uniform control delay; λ _im is the green signal ratio of the mth lane group at the ith intersection, x _im is the saturation of the mth lane group at the ith intersection, and T is the analysis duration;

K is the incremental delay correction coefficient of induction control; I is the incremental delay correction coefficient; _Cim is the capacity of the mth lane group at the ith intersection; the lane group is a combination of intersection lanes with the same flow direction.

A further technical improvement of the present invention is that the constraints for the optimization goal of minimizing the total delay of vehicles in all entrance lane groups of the key intersection include:

Cycle duration constraint: C _min ≤C _i ≤C _max ;

C _min is the minimum cycle length, C _max is the maximum cycle length;

Green light duration constraint: g _min ≤ _{g i} ≤ g _max ;

g _min is the minimum green light duration, and g _max is the maximum green light duration.

The further technical improvement of the present invention is that the specific steps of solving the optimal green wave bandwidth based on the MAXBAND model in step 6 and obtaining the absolute phase difference between the uplink and downlink directions of intersection i and the key intersection include:

minb＝b'

Among them, _ri , ri _' , _ti , _ti ', l _i and l _i ' are known quantities, which need to be set according to the actual traffic conditions of the trunk intersection; _bi and _b'i are the green wave bandwidths in the up and down directions of intersection i, _ri and _ri ' are the red light durations in the up and down directions of intersection i, _ti and _ti ' are the travel times of vehicles from intersection i to intersection i+1 in the up and down directions, l _i and l _i ' are the left turn phase durations in the up and down directions of intersection i, respectively;

w _i , w _i ', o _i , o _i ', z _i , z _i ', δ _i , δ _i ' are variables to be solved, respectively; w _i and w _i ' are the time from the end of the red light of the coordinated phase in the up and down directions of intersection i to the center line of the green wave band, respectively; o _i and o _i ' are the absolute phase differences in the up and down directions of intersection i, respectively; z _i and z _i ' are integer variables in the up and down directions of intersection i, respectively; δ _i , δ _i ' are 0 and 1 variables, and when the value is 0, it means that the left turn phase in the up/down direction of intersection i is delayed, and when the value is 1, it means that the left turn phase in the up/down direction of intersection i is advanced, so that (δ _i ,δ _i ') forms four combination relationships (0,0), (0,1), (1,0), (1,1);

The calculation formula for the absolute phase difference between the up and down directions of intersection i and the key intersection is:

φ _i and φ _i ' are the absolute phase differences in the uplink and downlink directions of intersection i, respectively.

A further technical improvement of the present invention is that the method of switching the control strategy according to the consistency of the variable lane directions includes:

(1) If the variable lane direction in the current optimization cycle is the same as that in the next optimization cycle, the current variable lane and trunk green wave control strategy is maintained;

(2)① If they are not the same, and the end signal phase direction of the current optimization cycle is consistent with the variable lane direction of the next optimization cycle, the green light duration of the end signal phase of the optimization cycle is extended;

② If they are not the same, and the end signal phase direction of the current optimization cycle is inconsistent with the variable lane direction of the next optimization cycle, the green light duration of the end signal phase of the current optimization cycle will be shortened.

A further technical improvement of the present invention is that the extension time is defined as the time for clearing the variable lane length of the current cycle, i.e.

in, is the average saturated headway, _Lq is the distance between the variable lane guide sign and the entrance lane stop line, is the average headway between vehicles;

The shortened time is set to a manually set value of 5 or 10 seconds.

A further technical improvement of the present invention is that the monitoring results are obtained based on the vehicle queue length, vehicle delay, and travel time as evaluation indicators, thereby adjusting the signal period, green-to-signal ratio, and phase difference of the trunk green wave intersection.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention predicts the flow of the target intersection based on the flow data of the first two cycles of the upstream intersection, and determines the direction of the variable lane according to the saturation of the corresponding lane direction of the target intersection, and then optimizes the signal phase timing within the signal cycle with the goal of minimizing the total delay of the import lane group of the key intersection, thereby realizing the coordinated control of the variable lane and the traffic green wave; and decides whether to maintain the current control strategy based on whether the variable lane directions in adjacent optimization cycles are consistent, and at the same time, decides on the correction method of the green light duration of the terminal signal phase of the current optimization cycle based on the consistency between the terminal signal phase direction of the current optimization cycle and the variable lane direction of the next optimization cycle; it is related to the influence of the upstream intersection on the target intersection as a whole, and can quickly adjust the control strategy of the target intersection according to the dynamic changes of the upstream intersection, which is more flexible and can cope with data mutations caused by emergency situations, thereby improving the use value of the control strategy.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

FIG1 is a schematic diagram of the execution flow of the method of the present invention.

DETAILED DESCRIPTION

In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

Please refer to FIG1 , which shows a method for collaboratively optimizing real-time dynamically variable lanes and trunk green waves. The method specifically includes the following steps:

Step 1: Collect traffic flow data at trunk intersections

Based on the radar, video and other traffic flow detectors deployed at each intersection, traffic flow data such as traffic volume, flow direction, vehicle speed and queue length at each intersection of the trunk line are collected;

Step 2: Predict traffic flow at arterial intersections

For a target intersection, the traffic data of the previous two cycles and the traffic data of the previous two cycles of the two intersections upstream of the target intersection are selected from the historical traffic data as the input of the prediction model, and the support vector regression SVR is used to predict the traffic flow data of the target intersection in the next cycle. The cycle length can be set to 15 minutes or 30 minutes.

Specifically, the prediction steps are as follows:

S21. Input the traffic flow sample set and divide it

The sample set is defined as N = {(x ₁ ,y ₁ ),(x ₂ ,y ₂ ),...,(x _n , _yn )}, where x _i = {qi _-2 ,qi _-1 , _qi } ^t-2:t , y _i = _qi ^t:t+1 ; and _qi ^t-2:t represents the traffic data of the ith trunk intersection from the t-2th period to the tth period;

The above sample set is randomly divided into training set and test set in a ratio of 7:3;

S22. Use support vector regression SVR to train the prediction model

Find a linear function f(x) = (w, x) + b, so that the error between the function value in the training sample and the expected value is no greater than a given value ε; where w is the weight vector and b is the threshold. Adding the insensitive loss coefficient ε and the slack variables ζ _i and ζ _i *, SVR can be summarized as the following regression problem:

Among them, A is the penalty function.

Introducing Lagrange multipliers a and a*, the above optimization problem is transformed according to the duality theory:

Solve the planning problem to get a, and then calculate w:

The deviation b is calculated using the KKT Kuhn-Tucker condition:

The final regression function is:

S23. Input the time-series historical traffic data of the trunk intersection to be predicted, and predict the future traffic of the intersection based on the trained model.

Step 3: Optimize the configuration of variable lanes

Based on the predicted traffic flow data of each intersection on the trunk line, the predicted saturation of each intersection in different directions (straight/left turn) in the next optimization cycle is calculated. The calculation formula is as follows:

_αs and _αl are the saturation of the straight and left-turn directions respectively, _qs and _ql are the flow rates in the straight and left-turn directions respectively, _ns and _nl are the number of lanes in the straight and left-turn directions respectively, and _Qs and _Ql are the single-lane capacities in the straight and left-turn directions respectively.

According to the saturation of the straight/left turn direction, the variable lane direction of the next optimization cycle is determined:

If α _s ≥ _{α l} and α _s ≥ 0.9, or α _s ≥ α _l and α _l ≤ 0.9, the direction of the variable lane is the straight direction;

If α _s ≤ _{α l} and α _l ≥ 0.9, or α _s ≥ _{α l} and α _s ≤ 0.9, the variable lane direction is the left turn direction.

Step 4: Determine the key intersections of the trunk green wave

The Webster model is used to calculate the phase cycle duration C of each intersection in the trunk green wave line, and the intersection with the largest cycle duration is selected as the key intersection for trunk green wave control.

The calculation formula of the Webster model is:

Where: _Li is the loss time of the i-th intersection in a single signal cycle, such as the vehicle startup loss time; _Yi is the ratio of the traffic prediction value of the next optimization cycle of the i-th intersection to the saturated traffic; where the saturated traffic is the design traffic of the corresponding road intersection in unit time.

Step 5: Collaborative optimization of variable lanes and green wave signal control at key intersections

An optimization cycle generally includes multiple signal phase optimization cycles. According to the variable lane direction of the next optimization cycle, the signal phase timing of the green wave key intersection of the trunk line is optimized. The optimization goal is to minimize the total vehicle delay of all entrance lane groups at the key intersection, that is:

d _im ＝d _im1 ×PF+d _im2 +d _im3

d _im2 =900T(α _im -1)

Wherein, d _i is the average vehicle delay at the ith intersection, _dim and q _im are the average vehicle delay and flow of the mth lane group at the ith intersection, respectively; _dim1 , _dim2 and _dim3 are the average vehicle delay, incremental delay and initial queue delay of the mth lane group at the ith intersection, respectively; PF is the adjustment parameter for uniform control of delay; λ _im is the green-signal ratio of the mth lane group at the ith intersection, x _im is the saturation of the mth lane group at the ith intersection, and T is the analysis duration, which is generally 15 minutes.

K is the incremental delay correction coefficient of induction control, which is generally 0.5; I is the incremental delay correction coefficient, which is generally 1.0; C _im is the capacity of the mth lane group at the ith intersection. A lane group is a combination of intersection lanes with the same flow direction.

The constraints include signal cycle duration constraints and green light duration constraints.

Signal cycle duration constraint: C _min ≤C _i ≤C _max ; C _min is the minimum cycle duration, and C _max is the maximum cycle duration.

Green light duration constraint: g _{min ≤gi} ≤g _max ; g _min is the minimum green light duration, generally 15s, g _max is the maximum green light duration, generally 60s.

Intelligent algorithms such as genetic algorithm or particle swarm algorithm are used to solve the optimal signal cycle and green light duration of key intersections of the main green wave.

Step 6: Collaborative optimization of variable lanes and green wave signal control at non-critical intersections

The signal cycle duration of the trunk green wave non-critical intersections is consistent with that of the critical intersections. Based on the MAXBAND model, the maximum trunk green wave bandwidth is taken as the optimization goal to solve the optimal green wave bandwidth of adjacent intersections, and then the phase difference between non-critical intersections and critical intersections is obtained.

The specific steps for solving the green wave speed and bandwidth based on the MAXBAND model are as follows:

minb＝b'

Among them, _ri , ri _' , _ti , _ti ', _li and _li ' are known quantities and need to be set according to the actual traffic conditions of the trunk intersection. _{b i} and b' _i are the green wave bandwidths in the up and down directions of intersection i, _ri and _ri ' are the red light durations in the up and down directions of intersection i, _ti and _ti ' are the travel times of vehicles from intersection i to intersection i+1 in the up and down directions, and _li and _li ' are the left turn phase durations in the up and down directions of intersection i.

w _i , w _i ', o _i , o _i ', z _i , z _i ', δ _i , δ _i ' are variables to be solved respectively, w _i and w _i ' are the time from the end of the red light of the coordinated phase in the up and down directions of intersection i to the center line of the green wave band respectively, o _i and o _i ' are the absolute phase differences in the up and down directions of intersection i respectively, z _i and z _i ' are integer variables in the up and down directions of intersection i respectively, δ _i , δ _i ' are 0 and 1 variables, δ i ＝0, δ i '＝0 means that the left turn phase in the up and down directions of intersection i are both postponed, δ _i ＝0, δ _i '＝1 means that the left turn phase in the up direction of intersection i is postponed and the left turn phase in the down direction is advanced, δ _i ＝1, δ _{i '} ＝0 means that the left turn phase in the up direction of intersection i is advanced and the left turn phase in the down direction is postponed, δ _i ＝0, δ _{i '} ＝1 means that the left turn phase in the up direction of intersection i is postponed and the left turn phase in the down direction is advanced, '=0 means that the left turn phases in both the upstream and downstream directions of intersection i are advanced.

Substitute the solved variables into the phase difference calculation formula to obtain the absolute phase difference between the up and down directions of intersection i and the key intersection. The calculation formula is:

φ _i and φ _i ' are the absolute phase differences in the up and down directions of intersection i, respectively, that is, the minimum time difference between the coordination phase of intersection i and the start time of the critical intersection.

Step 7: Switch the signal control scheme for adjacent optimization cycles

When a round of signal cycle optimization strategy within the optimization cycle is completed, the length of the signal cycle of this round is accumulated. When the total duration of the optimized signal cycle is greater than the optimization cycle length, switch to the next optimization cycle and optimize the variable lane direction and trunk green wave strategy of the next optimization cycle; it should be noted that the optimization cycle length is consistent with the cycle length set in the sample set constructed in step 2.

Step 8: Formulate a control strategy switching strategy for adjacent optimization cycles

After the coordinated optimization of the variable lanes and trunk green wave schemes in the current optimization cycle is completed, it is necessary to coordinate the variable lanes and trunk green wave control strategies in the adjacent optimization cycles.

If the variable lane direction of the current optimization cycle is the same as that of the next optimization cycle, the variable lane and trunk green wave control strategy of the current and next optimization cycles will not be modified;

On the contrary, if they are different, the time it takes to clear existing vehicles in the variable lanes during the current optimization period should be considered.

If the end signal phase direction of the current optimization cycle is consistent with the variable lane direction of the next optimization cycle, the green light duration of the end signal phase of the optimization cycle is extended by the variable lane queue length clearing time of the current signal cycle, that is:

in, is the average saturated headway, _Lq is the distance between the variable lane guide sign and the entrance lane stop line, is the average headway.

If the end signal phase direction of the current optimization cycle is inconsistent with the variable lane direction of the next optimization cycle, the green light duration of the end signal phase of the current optimization cycle will be shortened, and the shortening time is generally 5s or 10s.

Step 9: Optimize and adjust the trunk green wave signal control scheme

Under variable lane conditions, the operation status and efficiency of trunk green wave traffic are continuously monitored, and the trunk green wave scheme is dynamically optimized and adjusted based on the optimized output of vehicle queue length, vehicle delay, travel time and other operation efficiency evaluation indicators; for example: under the current variable lane setting scenario, if the total queue length, total delay or travel time of vehicles at all intersections of several green wave lines increases, the signal period, green-to-signal ratio and phase difference of the trunk green wave intersection will be adjusted.

The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims (9)

1. A real-time dynamic variable lane and trunk green wave collaborative optimization method is characterized by comprising the following steps:

Step one, collecting traffic flow data of intersections of a trunk line based on a traffic flow detector, wherein the traffic flow data comprise traffic flow, flow direction, vehicle speed and queuing length of each intersection;

Secondly, taking the flow data of the first two periods of the target intersection and the two upstream intersections of the target intersection as input, taking the flow data of the next period of the target intersection as output, constructing a traffic flow prediction model of the target intersection, and carrying out training by using Support Vector Regression (SVR);

Calculating predicted saturation of different directions of the intersection based on predicted intersection flow data, and determining a variable lane direction of the next optimization period according to the predicted saturation;

Calculating the phase period duration of each intersection in the trunk line green wave line by adopting a Webster model, and selecting the intersection with the largest period duration as a key intersection for trunk line green wave control;

Optimizing signal phase timing of a main line green wave key intersection according to the variable lane direction, so that total delay of vehicles of all entrance lane groups of the key intersection is minimized;

step six, solving the optimal green wave bandwidth of the non-critical intersection based on MAXBAND model to obtain the absolute phase difference between the non-critical intersection and the critical intersection;

accumulating the optimized signal phase period length, and entering the next optimization period if the accumulated value exceeds the optimization period length; the optimization period duration is consistent with the period duration selected by the data in the second step;

Step eight, according to the consistency of the variable lane directions of two adjacent optimization periods, different switching strategies are formulated to match the actual scene;

And step nine, monitoring and feeding back the execution effect of the green wave collaborative optimization scheme of the variable lanes and the trunk line, and dynamically optimizing and adjusting the scheme according to the feedback result.

2. The method for collaborative optimization of real-time dynamically variable lanes and arterial green waves according to claim 1, wherein the calculating process of the predicted saturation in the third step is:

Wherein, α _s and α _l are respectively saturation in the straight-going direction and the left-turning direction, Q _s and Q _l are respectively flow in the straight-going direction and the left-turning direction, n _s and n _l are respectively lane numbers in the straight-going direction and the left-turning direction, and Q _s and Q _l are respectively single lane traffic capacity in the straight-going direction and the left-turning direction.

3. The real-time dynamic variable lane and trunk green wave collaborative optimization method according to claim 2, characterized in that the specific method for determining the direction of the variable lane of the next optimization cycle according to the predicted saturation is as follows:

If alpha _s≥α_l and alpha _s are more than or equal to 0.9, or alpha _s≥α_l and alpha _l are less than or equal to 0.9, the variable lane direction is a straight direction;

If alpha _s≤α_l and alpha _l are more than or equal to 0.9, or alpha _s≥α_l and alpha _s are less than or equal to 0.9, the direction of the changeable lane is the left turning direction.

4. The method for optimizing green wave cooperation of real-time dynamic variable lanes and trunk lines according to claim 1, wherein the defining process of the minimum total delay of vehicles in all entrance lane groups of the key intersection in the fifth step is as follows:

d_im＝d_im1×PF+d_im2+d_im3

d_im2＝900T(α_im-1)

Wherein d _i is the vehicle average delay of the ith intersection, d _im、q_im is the vehicle average delay and the flow of the ith intersection and the mth lane group, d _im1、d_im2、d_im3 is the vehicle average delay, the increment delay and the initial queuing delay of the ith intersection and the mth lane group, respectively, and PF is the adjustment parameter of the average control delay; lambda _im is the green-to-blue ratio of the mth lane group of the ith intersection, x _im is the saturation of the mth lane group of the ith intersection, and T is the analysis duration;

K is an induction control increment delay correction coefficient; i is an incremental delay correction coefficient; c _im is the traffic capacity of the mth lane group of the ith intersection; the lane groups are intersection lane combinations with the same flow direction.

5. The method of green wave collaborative optimization of real-time dynamically variable lanes and trunks according to claim 4, wherein the constraint of the optimization objective of minimizing total vehicle delay for all lane groups of the entrance to the critical intersection comprises:

cycle duration constraint: c _min≤C_i≤C_max;

C _min is the minimum period length, C _max is the maximum period length;

Green light duration constraint: g _min≤g_i≤g_max;

g _min is the minimum green light duration and g _max is the maximum green light duration.

6. The real-time dynamic variable lane and trunk green wave collaborative optimization method according to claim 1, wherein the specific steps of solving the optimal green wave bandwidth based on MAXBAND model and obtaining the absolute phase difference between the uplink and downlink directions of the intersection i and the key intersection in the sixth step comprise:

minb＝b'

wherein r _i、r_i'、t_i、t_i'、l_i and l _i' are known quantities respectively and need to be set according to the actual traffic conditions of the trunk intersections; b _i and b '_i are green wave bandwidths in the uplink direction and the downlink direction of the intersection i respectively, r _i and r _i' are red light durations in the uplink direction and the downlink direction of the intersection i respectively, t _i and t _i 'are travel time of the vehicle from the intersection i to the intersection i+1 in the uplink direction and the downlink direction respectively, and l _i and l _i' are left-turn phase durations in the uplink direction and the downlink direction of the intersection i respectively;

w _i、w_i'、o_i、o_i'、z_i、z_i'、δ_i、δ_i 'is a variable to be solved, w _i and w _i' are respectively time from the end time of the red light of the coordination phase to the central line of the green wave band in the up direction and the down direction of the intersection i, o _i and o _i 'are respectively absolute phase differences in the up direction and the down direction of the intersection i, z _i and z _i' are respectively integer variables in the up direction and the down direction of the intersection i, delta _i、δ_i 'is a 0 and 1 variable, the left turn phase of the intersection i in the up/down direction is postponed when the 0 value is taken, the left turn phase of the intersection i in the up/down direction is preposed when the 1 value is taken, and thus (delta _i,δ_i') forms four combination relations (0, 0), (0, 1), (1, 0), (1, 1);

the calculation formula of the absolute phase difference between the uplink and downlink directions of the intersection i and the key intersection is as follows:

Phi _i and phi _i' are absolute phase differences in the upstream and downstream directions of intersection i, respectively.

7. The method for collaborative optimization of real-time dynamic variable lanes and arterial green waves according to claim 1, wherein the manner of switching control strategies according to whether the variable lane direction is consistent or not comprises:

(1) If the direction of the current optimization period is the same as that of the variable lane of the next optimization period, the current variable lane and the main line green wave control strategy is kept;

(2) ① if the signal phase direction at the tail end of the current optimization period is different and is consistent with the variable lane direction of the next optimization period, prolonging the green light duration of the signal phase at the tail end of the optimization period;

② If the signal phase directions at the tail end of the current optimization period are different and are inconsistent with the variable lane direction of the next optimization period, the green light duration of the signal phase at the tail end of the current optimization period is shortened.

8. The method for collaborative optimization of real-time dynamically variable lanes and arterial green waves according to claim 7, wherein the extended time is defined as the current period variable lane queue length empty time, i.e.

Wherein, For average saturated headway, L _q is the distance between the variable lane guide plate and the entrance way stop line,Is the average locomotive spacing;

the shortening time is set as the set value, and 5 or 10 seconds is taken.

9. The method for optimizing green wave cooperation of a real-time dynamic variable lane and a trunk line according to claim 1, wherein the monitoring result is obtained based on a vehicle queuing length, a vehicle delay and a travel time as evaluation indexes, so as to adjust a signal period, a green signal ratio and a phase difference of a green wave intersection of the trunk line.