CN108648446B - Road network traffic signal iterative learning control method based on MFD - Google Patents

Road network traffic signal iterative learning control method based on MFD Download PDF

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CN108648446B
CN108648446B CN201810374659.1A CN201810374659A CN108648446B CN 108648446 B CN108648446 B CN 108648446B CN 201810374659 A CN201810374659 A CN 201810374659A CN 108648446 B CN108648446 B CN 108648446B
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杨曦
黄青青
沈国江
刘志
朱李楠
刘端阳
阮中远
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Zhejiang University of Technology ZJUT
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Abstract

Aiming at the characteristics of large urban traffic volume, large urban scale, complex structure and the like in China, the invention considers the method for iterative learning control of road network traffic signals based on MFD. The method comprises the following steps: 1.1 acquiring traffic data of the sub-region MFD; 1.2 sub-region MFD fitting; 1.3 determine road ideal occupancy based on MFD. Step two: 2.1 open-close loop iterative learning control strategy; 2.2 establishing a state space equation; and 2.3, optimizing the signal timing of each intersection. The invention can make the whole structure of the road network relatively balanced, and improve the outflow vehicles of the subareas, thereby improving the traffic volume of the road network, providing an effective urban road network control means for traffic managers, and improving the traffic service level of the urban road network.

Description

Road network traffic signal iterative learning control method based on MFD
Technical Field
The invention relates to a traffic signal control problem of an urban road network, in particular to an MFD (traffic macroscopic basic graph) and an iterative learning control strategy.
Background
Modern urban traffic congestion remains one of the major social problems due to limitations in road resources and infrastructure. Signal control is the most important traffic control means, and has also been greatly developed with the continuous and deep research of traffic students.
Since the urban traffic system is an uncertain complex system, the scale is large, the system model parameters are difficult to determine, N.Geroliminiis and the like discover that a specific relation exists between the number of accumulated vehicles in an urban area and traffic flow through analysis of traffic data such as Nissan shorea and the like, and provide an MFD (macroscopic basic diagram) on the basis, thereby avoiding the defects in a traffic flow modeling analysis method based on a complex network Origin-Destination Matrix (Origin-Destination Matrix). And performing intersection signal control on the traffic network by using the road network traffic data and adopting a control strategy of iterative learning, wherein the intersection signal control comprises two parts of contents. The optimal accumulated vehicle obtained by fitting the road network sub-area through the MFD in the first part can be used as an ideal control target in the iterative learning signal, and the iterative learning control method is adopted in the second part, a large-scale urban road network is modeled through a traffic flow model, and iterative control is carried out on signals inside the sub-road network.
Iterative learning control is a data-driven method, a controller is designed by using only online and offline I/O data of a controlled system and knowledge obtained through data processing, and the method is widely applied to traffic signal control, such as ramp control, urban road network control and the like. Therefore, iterative learning control is combined with the number of the ideal road vehicles based on the MFD, the green-to-traffic ratio of the traffic signals is used as the input of the iterative learning control, a proper learning law is selected, the green light time of the traffic signals is adjusted, the vehicles in the road network reach an ideal set target, the overall structure of the road network is relatively balanced, the road network is in the optimal running state in the MFD characteristic, the outflow vehicles in the sub-area are improved, and the traffic volume of the road network is improved.
Disclosure of Invention
The invention provides an iterative learning signal control based on a hierarchical control structure to balance vehicles in a road network and enable the road network to be in the optimal running state of a macroscopic basic graph, so that vehicles flowing out of the road network are improved, and the traffic capacity of the road network is improved.
The invention discloses an MFD-based iterative learning city signal control method, which comprises the following steps:
1) acquiring an ideal road occupancy rate based on the MFD:
1.1 obtaining traffic data of the sub-region MFD: dividing a large-scale city road network to obtain a plurality of subareas RiThe algorithm for sub-area division is divided by adopting an Ncuts algorithm, a large-scale urban road network is divided into a plurality of 'homogeneous' sub-areas, and traffic data of each sub-area is obtained.
1.2 sub-region MFD fitting: fitting by using a 3-order polynomial according to the traffic data of each subarea, the accumulated vehicle number at different moments and the MFD (MFD) characteristic of the output flow of the subarea, and aiming at any subarea RiThe fit form is as follows:
Figure GDA0002481610490000021
wherein n isiIs a sub-region RiAccumulated number of vehicles of a1～a4Are fitting coefficients.
Determining a fitting coefficient in an empirical formula by using a least square method:
Figure GDA0002481610490000022
wherein, yiIs a sub-region RiActual output flow of G (n)i) Is a sub-region RiApproximate fitting curve of flow, minimizing data deviation according to the above equation
Figure GDA0002481610490000023
Thereby obtaining the fitting result of MFD and obtaining the extreme point of the fitting curve according to the fitting result
Figure GDA0002481610490000024
1.3, determining the ideal road occupancy rate: obtaining the optimal cumulative vehicle number of each subarea according to the MFD fitting result of the step 1.2
Figure GDA0002481610490000025
According to sub-region RiThe network structure of (2) weighting the vehicles in the sub-area to obtain the sub-area RiIdeal occupancy of each road:
Figure GDA0002481610490000026
wherein
Figure GDA0002481610490000031
In the step 1.2 sub-region RiThe optimal cumulative vehicle number, D, obtained by MFD fitting ofiDenotes the sub-region RiThe sum of the lengths of the various paths within,
Figure GDA0002481610490000032
ideal occupancy for road j (where j ∈ Ri) As a reference target for the system control design in step 2).
2) Controlling and optimizing intersection signal timing based on iterative learning:
2.1 open-close loop iterative learning control strategy: the open-closed loop iterative learning control structure can be represented in the form:
Figure GDA0002481610490000033
wherein u isn(k) For the control vector at the kth sampling instant of the nth iteration, en(k) For the error at the kth sampling instant, k, of the nth iterationcFor closed loop learning control rate, koThe open loop learning control rate.
2.2 establish the state space equation:
Figure GDA0002481610490000034
wherein
Figure GDA0002481610490000035
The state vector represents the number of vehicles included in each road segment in the road network. u (k) ═ g1(k),...,gN(k)]TThe control vector represents the green time for all phases in the road network. d (k) is a state disturbance vector representing the disturbance of each road section. y (k) ═ o1(k),...,oN(k)]TThe occupancy of each link in the road network is reflected for the system output. The input matrix B reflects the characteristics of the phase, the period, the saturation flow and the like of the road network; the output matrix C represents characteristics representing road capacity and vehicle length.
2.3 optimizing the signal timing of each intersection: the green light time u (k) in the traffic model is used as the control input of the iterative learning of the open-close loop, the number x (k) of vehicles in the road section is used as the control state variable, and the state output of the system is the same as the number of vehicles in the road section. Selecting a suitable learning rate kcAnd koAdjusting the green time of the intersection, controlling the road occupancy in the sub-area to make it track the ideal
Figure GDA0002481610490000036
Road occupancy.
2.4 repeating the step 2.3, iteratively adjusting the signal timing of each intersection until the number of vehicles in the road network reaches the ideal value set in the step 1)
Figure GDA0002481610490000037
And balancing the number of vehicles in the whole road network. I.e. the algorithm objective is achieved.
The invention has the beneficial effects that: aiming at the characteristics that a traffic system is an uncertain complex system, the scale is large, and system model parameters are difficult to determine, the method can reduce the calculated amount and dimensionality of a large-scale urban road network, achieve the purposes of balancing the traffic flow distribution of the road network, improving the traffic volume of the road network, reducing traffic delay and travel time, and has important significance for improving the traffic condition of the whole city.
Drawings
Fig. 1 is a schematic diagram of a city road network structure in the embodiment of the present invention.
FIG. 2 is a graph showing the MFD fitting effect in the embodiment of the present invention, wherein FIG. 2a shows a sub-region R1FIG. 2b shows the MFD-fitted curve of (A), and the subregion R2FIG. 2c shows the MFD-fitted curve of (A), and the subregion R3FIG. 2d shows the MFD-fitted curve of (A), and the subregion R4The MFD of (1) is fitted to the curve.
Detailed Description
The invention is further illustrated by the following figures and examples.
The present invention is directed to an urban road network with 34 intersections as shown in fig. 1, each intersection and road segment being equipped with real-time detection equipment for detecting the required traffic parameters. Two adjacent intersections are bidirectional lanes, each road has 2 lanes, the length of each road section is determined, and the road network has 21 input nodes.
The invention discloses an MFD-based iterative learning city signal control method, which comprises the following steps:
1) acquiring an ideal road occupancy rate based on the MFD:
1.1 obtaining traffic data of the sub-region MFD: dividing the urban road network of FIG. 1, and dividing by adopting an Ncuts algorithm to obtain 4 'homogeneous' sub-areas, wherein different colors represent different sub-areas, and R is1Comprising 8 intersections, R2Comprising 7 intersections, R3Comprising 7 intersections, R4Comprises 12And (6) each intersection. And obtaining a fitting curve of the MFD based on the traffic data of each subarea, and calculating the optimal operation state of the subarea.
1.2 sub-region MFD fitting: fitting by using a 3-order polynomial according to the traffic data of each subarea, the accumulated vehicle number at different moments and the MFD (MFD) characteristic of the output flow of the subarea, and aiming at any subarea RiThe fit form is as follows:
Figure GDA0002481610490000041
wherein n isiIs a sub-region RiAccumulated number of vehicles of a1～a4Are fitting coefficients.
Determining a fitting coefficient in an empirical formula by using a least square method:
Figure GDA0002481610490000042
wherein, yiIs a sub-region RiActual output flow of G (n)i) Is a sub-region RiApproximate fitting curve of flow, minimizing data deviation according to the above equation
Figure GDA0002481610490000051
Thereby obtaining the fitting result of MFD and obtaining the extreme point of the fitting curve according to the fitting result
Figure GDA0002481610490000052
1.3, determining the ideal road occupancy rate: obtaining the optimal cumulative vehicle number of each subarea according to the MFD fitting result of the step 1.2
Figure GDA0002481610490000053
According to sub-region RiThe network structure of (2) weighting the vehicles in the sub-area to obtain the sub-area RiIdeal occupancy of each road:
Figure GDA0002481610490000054
wherein
Figure GDA0002481610490000055
In the step 1.2 sub-region RiThe optimal cumulative vehicle number, D, obtained by MFD fitting ofiDenotes the sub-region RiThe sum of the lengths of the various paths within,
Figure GDA0002481610490000056
ideal occupancy for road j (where j ∈ Ri) As a reference target for the system control design in step 2).
2) Controlling and optimizing intersection signal timing based on iterative learning:
2.1 open-close loop iterative learning control strategy: the open-closed loop iterative learning control structure can be represented in the form:
Figure GDA0002481610490000057
wherein u isn(k) For the control vector at the kth sampling instant of the nth iteration, en(k) For the error at the kth sampling instant, k, of the nth iterationcFor closed loop learning control rate, koThe open loop learning control rate.
2.2 establish the state space equation:
Figure GDA0002481610490000058
wherein
Figure GDA0002481610490000059
The state vector represents the number of vehicles included in each road segment in the road network. u (k) ═ g1(k),...,gN(k)]TThe control vector represents the green time for all phases in the road network. d (k) is a state disturbance vector representing the disturbance of each road section. y (k) ═ o1(k),...,oN(k)]TThe occupancy of each link in the road network is reflected for the system output. The input matrix B reflects the phase, period and saturation of the road networkAnd flow rate; the output matrix C represents characteristics representing road capacity and vehicle length.
2.3 optimizing the signal timing of each intersection: the green light time u (k) in the traffic model is used as the control input of the iterative learning of the open-close loop, the number x (k) of vehicles in the road section is used as the control state variable, and the state output of the system is the same as the number of vehicles in the road section. Selecting a suitable learning rate kcAnd koAdjusting the green time of the intersection, controlling the road occupancy in the sub-area to make it track the ideal
Figure GDA0002481610490000061
Road occupancy.
2.4 repeating the step 2.3, iteratively adjusting the signal timing of each intersection until the number of vehicles in the road network reaches the ideal value set in the step 1)
Figure GDA0002481610490000062
And balancing the number of vehicles in the whole road network. I.e. the algorithm objective is achieved.
The specific embodiments described herein are merely illustrative of the present invention and do not limit the scope of the claims.

Claims (1)

1. A road network traffic iterative learning control method based on MFD is characterized in that the MFD is combined with the iterative learning control idea to control signals of a large-scale urban road network, and the steps are as follows:
1) acquiring an ideal road occupancy rate based on the MFD:
1.1 obtaining traffic data of the sub-region MFD: dividing a large-scale city road network to obtain a plurality of subareas RiWherein i ∈ {1,2,3. }, the algorithm for sub-area division adopts the Ncuts algorithm for division, and a large-scale urban road network is decomposed into a plurality of 'homogeneous' sub-areas to obtain traffic data of each sub-area;
1.2 sub-region MFD fitting: fitting by using a 3-order polynomial according to the traffic data of each subarea, the accumulated vehicle number at different moments and the MFD (MFD) characteristic of the output flow of the subarea, and aiming at any subarea RiThe fit form is as follows:
Figure FDA0002481610480000011
wherein n isiIs a sub-region RiAccumulated number of vehicles of a1～a4Is a fitting coefficient;
determining a fitting coefficient in an empirical formula by using a least square method:
Figure FDA0002481610480000012
wherein, yiIs a sub-region RiActual output flow of G (n)i) Is a sub-region RiApproximate fitting curve of flow, minimizing data deviation according to the above equationi 2So as to obtain the fitting result of MFD and obtain the extreme point of the fitting curve according to the fitting result
Figure FDA0002481610480000013
1.3, determining the ideal road occupancy rate: obtaining the optimal cumulative vehicle number of each subarea according to the MFD fitting result of the step 1.2
Figure FDA0002481610480000014
According to sub-region RiThe network structure of (2) weighting the vehicles in the sub-area to obtain the sub-area RiIdeal occupancy of each road:
Figure FDA0002481610480000015
wherein
Figure FDA0002481610480000021
In the step 1.2 sub-region RiThe optimal cumulative vehicle number, D, obtained by MFD fitting ofiDenotes the sub-region RiThe sum of the lengths of the various paths within,
Figure FDA0002481610480000022
ideal occupancy for road j (where j ∈ Ri) As a reference target for the system control design in step 2);
2) controlling and optimizing intersection signal timing based on iterative learning:
2.1 open-close loop iterative learning control strategy: the open-closed loop iterative learning control structure can be represented in the form:
Figure FDA0002481610480000023
wherein u isn(k) For the control vector at the kth sampling instant of the nth iteration, en(k) For the error at the kth sampling instant, k, of the nth iterationcFor closed loop learning control rate, koThe open loop learning control rate;
2.2 establish the state space equation:
Figure FDA0002481610480000024
wherein
Figure FDA0002481610480000025
A state vector representing the number of vehicles included in each road segment in the road network; u (k) ═ g1(k),...,gN(k)]TRepresenting the green light time of all phases in the road network as a control vector; d (k) is a state disturbance vector which represents the disturbance of each road section; y (k) ═ o1(k),...,oN(k)]TReflecting the occupancy of each road section in the road network for system output; the input matrix B reflects the phase, the period and the saturation flow of the road network; the output matrix C represents characteristics representing road capacity and vehicle length;
2.3 optimizing the signal timing of each intersection: taking the green light time u (k) in the traffic model as the control input of the iterative learning of the open-close loop, taking the number x (k) of vehicles on the road section as a control state variable, and taking the state output of the system to be the same as the number of vehicles on the road section;selecting a suitable learning rate kcAnd koAdjusting the green time of the intersection, controlling the road occupancy in the sub-area to make it track the ideal
Figure FDA0002481610480000026
Road occupancy;
2.4 repeating the step 2.3, iteratively adjusting the signal timing of each intersection until the number of vehicles in the road network reaches the ideal value set in the step 1)
Figure FDA0002481610480000027
And balancing the number of vehicles in the whole road network.
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