CN108538065B - Urban main road coordination control method based on adaptive iterative learning control - Google Patents
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Abstract
A city arterial road coordination control method based on adaptive iterative learning control comprises the following steps: a. determining a key intersection: for the controlled main road, determining an intersection with the largest traffic demand as a key intersection; b. initializing a common signal period, a green signal ratio and a phase difference; c. optimizing the green signal ratio of the key intersection; d. optimizing the green signal ratio of the non-critical intersection; e. and c, circulating, and repeating the steps c and d every 3-5 signal periods. The method takes the intersection as a control object, coordinates the intersection pairwise by utilizing the flow correlation of the upstream intersection and the downstream intersection, calculates the green time of each phase of a signal lamp by an iterative learning control method (ILC) according to the real-time road flow condition, and finally sets the parameters of the closed-loop controller in real time by a pseudo-elimination strategy. The invention reduces the real-time calculated amount of the main road control, improves the traffic efficiency of the main road, has better effect than the traditional timing control scheme, and provides an effective method for the coordination control of the urban main road.
Description
Technical Field
The invention relates to the technical field of traffic signal control, in particular to an urban main road coordination control method based on adaptive iterative learning control.
Background
With the development of social economy and improvement of the living standard of people in China, more and more automobiles enter common families, the problems of traffic accidents, traffic jam, environmental pollution, energy consumption and the like become more and more serious, and the travel time, the travel safety, the environmental quality and the living quality are all restricted by traffic conditions.
Urban road traffic signal control is an extremely important aspect in modern urban traffic management, and the quality of the management and control level of the urban road traffic signal control directly influences the effect of urban road traffic operation. In urban road networks, the main road bears huge traffic load, so that the realization of good urban main road traffic signal control is the key point of urban traffic unblocked measures.
The research of modern urban traffic signal control theory shows that the dynamic coordination control of urban main road traffic signals is realized, particularly, the traffic flow is regulated and controlled by realizing signal timing optimization conditions and is uniformly distributed in main roads, the traffic capacity of a road network is greatly improved, the traffic overflow phenomenon of the main traffic road and surrounding roads is improved, and the method is the optimal choice for controlling the traffic signals in the peak period of urban traffic.
As an efficient urban traffic coordination control mode, the urban main road coordination control method based on adaptive iterative learning control has the following characteristics: 1. the overall traffic flow balance of the main road is ensured, so that the overflow condition of the intersection of the main road is reduced; 2. the intersection green light has higher use efficiency, so that the traffic capacity of the main road is improved; 3. the timing scheme is adjusted according to the real-time data, so that the traffic demand change can be quickly coped with, and the stability of the road network is improved; 4. the main road can bear larger traffic demands, thereby reducing the pressure of the rest parts of the road network and improving the traffic condition of the whole road network.
The coordination control method for the traffic signals of the foreign main road has the following research results: the MAXBAND algorithm is firstly proposed by j.d.c. little, and a group of optimized traffic signal phase differences are given for urban main roads including n intersections S1. N.h. gartner proposes multi band on the basis of the MAXBAND method, and improves many important characteristics, such as setting of emptying time, control of left-turn vehicles, realization of different bandwidths for different road sections in a trunk line, and the like. However, these results of research do not effectively utilize the day-to-day characteristics of traffic demand changes, and the amount of calculation increases rapidly with the increase in the road network size.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a city main road coordination control method based on adaptive iterative learning control, so that the probability of congestion of the main road is reduced, and the city trip efficiency is improved.
The invention relates to a method for coordinating and controlling urban main roads based on adaptive iterative learning control, which is used for an urban road traffic area comprising a plurality of continuous adjacent intersections and comprises the following steps:
a. determining a key intersection: and for the controlled main road, determining the intersection with the largest traffic demand as a key intersection.
b. Initializing common signal period, split ratio, phase difference: for a key intersection, acquiring an intersection signal period according to a Webster method, and taking the intersection signal period as an intersection public signal period; each intersection adopts a four-phase timing scheme, and the initial green signal ratio is calculated according to the phase flow ratio of the intersection; and meanwhile, the phase difference between adjacent intersections of the main road is calculated by dividing the length of the road section by the average speed of the road section.
c. Optimizing the green signal ratio of the key intersection: and determining the closed-loop control rate of iterative learning control by using a pseudo-removing control method according to the real-time traffic data, and then optimizing the split green ratio by using the open-closed loop iterative learning control method by using the error of the previous iteration and the error of the current iteration.
d. Optimizing the green signal ratio of the non-critical intersection: and (3) from the adjacent intersections of the key intersections, coordinating every two intersections, taking the upstream intersection as a main intersection and the downstream intersection as a slave intersection, and performing master-slave coordination control design to sequentially complete the optimization of the green-to-letter ratio of the non-key intersections.
e. And c, circulating, and repeating the steps c and d every 3-5 signal periods.
The invention is used as a coordination control method for urban main roads, and the optimization targets of the steps c and d are limited control time [0, K ]]In the method, the road occupancy of key traffic flow of each phase of the key intersection is ensured
Tending towards the ideal occupancy odAnd making the road occupancy of traffic flow in the main road direction of the non-key intersection tend to the road occupancy of the corresponding flow direction of the main road, namely
In the step c, the algorithm for optimizing the split is as follows:
1) closed-loop controller for determining iterative learning control by utilizing de-counterfeiting strategy
First, a candidate controller parameter set is determined
And ensuring that the controller corresponding to each parameter ensures the convergence of the corresponding iterative learning control. Then, determining a calculation method of the virtual reference of the controller corresponding to each candidate parameter in the pseudo-removing strategy:
finally, determining the performance index of each candidate controller:
wherein α and β are the setting vectors, the largest is selected from the set of candidate controller parameters
Calculating its virtual reference to the controller
And performance index
And if the candidate controller meets the performance index, the controller is a non-pseudo controller and is added into the control system, and if the candidate controller does not meet the performance index, the largest one of the remaining candidate controller parameters is selected for calculation until the controller corresponding to the candidate parameter meets the performance index.
2) And calculating the green time of the key intersection according to the controller obtained by the pseudo-removing strategy, wherein the learning law of iterative learning control is set as follows:
wherein u isn(k) Green time for the kth sampling period of the nth iteration, en(k) For the error at the kth sampling instant of the nth iteration,
for the open-loop iterative learning control portion,
in order to perform the closed-loop iterative learning control part,
for closed loop learning control rate, koThe open loop learning control rate.
Aiming at the current situation that the urban traffic problem is more prominent, and the urban main road is used as the main artery of the urban traffic, the traffic load is continuously increased, the existing road infrastructure is reasonably utilized by coordinating the signal timing of the urban main road, and the probability of congestion is reduced. The method takes the intersection as a control object, coordinates the intersection pairwise by utilizing the flow correlation of the upstream intersection and the downstream intersection, calculates the green time of each phase of a signal lamp by an iterative learning control method (ILC) according to the real-time road flow condition, and finally sets the parameters of the closed-loop controller in real time by a pseudo-elimination strategy.
The invention has the advantages that: the method reduces the real-time calculated amount of the main road control, improves the traffic efficiency of the main road, has better effect than the traditional timing control scheme, and provides an effective method for the coordination control of the urban main road.
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FIG. 1 is a schematic illustration of a section of a main road of a city using the method of the present invention;
Detailed Description
The invention is further illustrated by the following figures and examples.
As shown in figure 1, the urban road traffic area comprising a plurality of continuous adjacent intersections using the method of the invention has 3 intersections, and is expressed as {1,2,3} by natural number sequence, wherein the east-west road is a main road, the south-north road is a secondary road or branch road, and the east-west flow is generally obviously greater than the south-north flow. The upward direction from west to east is defined as the main trunk, and the downward direction from east to west. The traffic flow phase at each intersection is divided as follows: the phase 1 is straight and right-turning in the east-west direction; phase 2 is east-west left and right turns; phase 3 is north-south straight and right-turn; phase 4 is a north-south left turn and a right turn.
The invention discloses a city arterial road coordination control method based on adaptive iterative learning control, which comprises the following steps:
a. determining a key intersection: and for the controlled main road, determining the intersection with the largest traffic demand as a key intersection.
b. Initializing common signal period, split ratio, phase difference: for a key intersection, obtaining an intersection signal period according to a Webster method, and taking the intersection signal period as an intersection public signal period; each intersection adopts a four-phase timing scheme, and the initial green signal ratio is calculated according to the phase flow ratio of the intersection; and meanwhile, the phase difference between adjacent intersections of the main road is calculated by dividing the length of the road section by the average speed of the road section.
c. Optimizing the green signal ratio of the key intersection: and determining the closed-loop control rate of iterative learning control by using a pseudo-removing control method according to the real-time traffic data, and then optimizing the split green ratio by using the open-closed loop iterative learning control method by using the error of the previous iteration and the error of the current iteration.
d. Optimizing the green signal ratio of the non-critical intersection: and (3) from the adjacent intersections of the key intersections, coordinating every two intersections, taking the upstream intersection as a main intersection and the downstream intersection as a slave intersection, and performing master-slave coordination control design to sequentially complete the optimization of the green-to-letter ratio of the non-key intersections.
e. And c, circulating, and repeating the steps c and d every 3-5 signal periods.
The selection steps of the key intersection in the step a are as follows:
and obtaining traffic demand data { Q1, Q2, Q3} of each intersection on the controlled road section, and selecting the largest one as a key intersection, wherein the key intersection of the control area is the intersection 1 shown in the figure 1.
In steps c and d, the optimization of the split is aimed at a limited control time 0, K]In the method, the road occupancy of key traffic flow of each phase of the key intersection is ensured
Tending towards the ideal occupancy odAnd making the road occupancy of traffic flow in the main road direction of the non-key intersection tend to the road occupancy of the corresponding flow direction of the main road, namely
The process of performing optimization calculation on the green signal ratio of the key intersection in the step c comprises the following steps:
1) closed-loop controller for determining iterative learning control by utilizing de-counterfeiting strategy
First, a candidate controller parameter set is determined
And ensuring that the controller corresponding to each parameter ensures the convergence of the corresponding iterative learning control. Then, determining a calculation method of the virtual reference of the controller corresponding to each candidate parameter in the pseudo-removing strategy:
finally, determining the performance index of each candidate controller:
wherein α and β are the setting vectors, the largest is selected from the set of candidate controller parameters
Calculating its virtual reference to the controller
And performance index
And if the candidate controller meets the performance index, the controller is a non-pseudo controller and is added into the control system, and if the candidate controller does not meet the performance index, the largest one of the remaining candidate controller parameters is selected for calculation until the controller corresponding to the candidate parameter meets the performance index.
2) And calculating the green time of the key intersection according to the controller obtained by the pseudo-removing strategy, wherein the learning law of iterative learning control is set as follows:
and d, optimizing the split ratio of the non-key intersection in the step d, namely coordinating every two intersections on the main road from the adjacent intersections of the key intersection, performing master-slave control by taking the upstream intersection as an ideal model of the downstream intersection, and sequentially finishing optimization of the split ratio, wherein the optimization step of the controlled area is {2,3} as shown in figure 1.
The specific embodiments described herein are merely illustrative of the spirit of the invention. And should not be taken as limiting the scope of the invention. In fact, for more complicated field conditions, such as the existence of a T-shaped intersection, the fact that part of lanes are one-way lanes, and the like, the method of the invention can be applied as well, and only the method of simply changing the flow calculation is considered.
Claims (1)
1. A city main road coordination control method based on adaptive iterative learning control is suitable for an urban road traffic area comprising a plurality of continuous adjacent intersections, and comprises the following steps:
a. determining a key intersection: for the controlled main road, determining an intersection with the largest traffic demand as a key intersection;
b. initializing common signal period, split ratio, phase difference: for a key intersection, acquiring an intersection signal period according to a Webster method, and taking the intersection signal period as an intersection public signal period; each intersection adopts a four-phase timing scheme, and the initial green signal ratio is calculated according to the phase flow ratio of the intersection; meanwhile, the phase difference between adjacent intersections of the main road is calculated by dividing the length of the road section by the average speed of the road section;
c. optimizing the green signal ratio of the key intersection: determining the closed-loop control rate of iterative learning control by using a pseudo-removing control method according to real-time traffic data, and then optimizing the split green ratio by using the open-closed loop iterative learning control method by using the error of the previous iteration and the error of the current iteration, wherein the optimization aims at the limited control time [0, K ]]In the method, the road occupancy of each phase key traffic flow at the key intersection is ensured
Tending towards the ideal occupancy odWherein
The subscript indicates the ith key phase of the key intersection j;
the steps for carrying out optimization calculation on the split green ratio are as follows:
1) determining a closed-loop controller for iterative learning control by utilizing a de-counterfeiting strategy;
first, a candidate controller parameter set is determined
Ensuring the convergence of corresponding iterative learning control by the controller corresponding to each parameter, and then determining a calculation method of virtual reference of the controller corresponding to each candidate parameter in the pseudo-removing strategy:
finally, determining the performance index of each candidate controller:
wherein α and β are set vectors, and the largest is selected from the set of candidate controller parameters
Calculating its virtual reference to the controller
And performance index
If the candidate controller meets the performance index, the controller is a non-pseudo controller, a control system is added, if the candidate controller does not meet the performance index, the largest one of the remaining candidate controller parameters is selected for calculation until the controller corresponding to the candidate parameter meets the performance index;
2) and calculating the green time of the key intersection according to the controller obtained by the pseudo-removing strategy, wherein the learning law of iterative learning control is set as follows:
wherein u isn(k) For the green light of the kth sampling period of the nth iterationM, en(k) For the error at the kth sampling instant of the nth iteration,
for the open-loop iterative learning control portion,
in order to perform the closed-loop iterative learning control part,
for closed loop learning control rate, koThe open loop learning control rate;
d. optimizing the green signal ratio of the non-critical intersection: starting from adjacent intersections of the key intersections, coordinating every two intersections, taking an upstream intersection as a main intersection and a downstream intersection as a slave intersection, carrying out master-slave coordination control design, and sequentially finishing the optimization of the green-to-green ratio of non-key intersections, wherein the optimization target is that the limited control time is [0, K ]]In the method, the road occupancy of traffic flow in the main road direction of the non-critical intersection tends to the road occupancy of the corresponding flow direction of the main road, namely
Wherein
Subscripts denote the ith phase of n, m at adjacent crossings;
e. and c, circulating, and repeating the steps c and d every 3-5 signal periods.
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| CN111445694B (en) * | 2020-03-04 | 2022-02-01 | 青岛海信网络科技股份有限公司 | Festival and holiday traffic scheduling method and device based on traffic flow prediction |
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| CN111951574A (en) * | 2020-07-29 | 2020-11-17 | 太原理工大学 | Traffic signal self-adaptive iterative learning control method based on attenuation memory false-removing control |
| CN113053120B (en) * | 2021-03-19 | 2022-03-22 | 宁波亮控信息科技有限公司 | Traffic signal lamp scheduling method and system based on iterative learning model predictive control |
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