CN112258856B - Method for establishing regional traffic signal data drive control model - Google Patents

Method for establishing regional traffic signal data drive control model Download PDF

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CN112258856B
CN112258856B CN202010798058.0A CN202010798058A CN112258856B CN 112258856 B CN112258856 B CN 112258856B CN 202010798058 A CN202010798058 A CN 202010798058A CN 112258856 B CN112258856 B CN 112258856B
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张海波
王力
吉鸿海
潘彦斌
李丹阳
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North China University of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/081—Plural intersections under common control

Abstract

The invention relates to a method for establishing a regional traffic signal data drive control model, which is characterized in that floating car data (typical mobile detection data source) is taken as a basis, intersection flow parameters are obtained through an estimation method, a periodic vehicle number estimation model is established based on a storage-forwarding modeling method, constraint conditions for intersection green light time are adjusted, a time-varying control signal period C (k) is introduced, and brand-new green light time constraint conditions are obtained, so that green light time waste is avoided, the constraint conditions for breaking a fixed period are considered, and the space occupancy is ensured to always meet xi(k +1) is not less than 0, the established regional traffic signal control model can simultaneously describe three traffic state forms of undersaturation, critical saturation and supersaturation at the intersection, and a regional traffic signal data driving control model which is based on a multi-agent network and aims at space occupancy balance is provided.

Description

Method for establishing regional traffic signal data drive control model
Technical Field
The invention relates to the technical field of intelligent traffic signal control, in particular to a regional traffic signal data drive control model establishing method.
Background
With the development of social economy and urban traffic, the quantity of automobile reserves in cities in China is increased rapidly. At present, the road construction capacity of most cities is far behind the growth rate of motor vehicles, and the contradiction between the road construction capacity and the motor vehicles is mainly reflected in increasingly serious road traffic jam. Therefore, under the limited road space resources, the improvement of the road utilization rate and the travel efficiency by the intelligent traffic control method is an important task which must be considered by urban traffic managers.
With the continuous development of the ITS (ITS) technology, various advanced traffic detection devices are applied in a large range, great changes are brought to urban road traffic control, various high-precision and large-range detection data enable a traditional control algorithm based on a traffic flow model to be improved, the traditional traffic signal control model and control System using a fixed detector as a detection means cannot meet the control requirements of novel mobile traffic information acquisition modes such as a floating car and a mobile phone, the problems of difficult deployment, high fault rate, poor detection precision, high maintenance cost and the like and the problems of low occupancy rate of a mobile detection method are solved, and new problems and challenges are brought to urban traffic control. With the rapid development of the GPS technology, the floating car has been widely used in the traffic field as a new data acquisition mode. A traffic prediction model is established by means of GPS floating car data, so that real-time road traffic information can be obtained.
The main reason of the phenomenon is that the three major factors of people, vehicles and roads which determine the control strategy are not in a complete linear relationship, the three major factors are in a strong coupling relationship which is mutually related, and the behaviors of people are difficult to predict, so that the control models constructed aiming at the vehicles and the roads have errors with the reality.
Disclosure of Invention
Considering that urban regional traffic signal control is a complex control problem and comprises a series of practical problems of high modeling cost, high dynamic modeling difficulty, poor coordination control effect, low network expansibility and the like, the design of the urban regional traffic data driving control method based on floating car data has important theoretical and practical significance on the basis of the floating car data. In urban traffic, adding additional infrastructure to accommodate the increased number of vehicles is expensive and unsustainable due to limited road resources. A more socially feasible option is to optimize traffic signal timing in a data-driven manner. As urban traffic systems are more and more complex, establishing an accurate road network and even an intersection mechanical model is a difficult or impossible problem due to high-order, strong nonlinearity, non-stationarity and complex structure. In addition, it becomes easier to obtain traffic data regarding vehicle number, queue, occupancy, and traffic, collecting large amounts of online/offline data from secondary heterogeneous traffic sensors (e.g., inductive loop detectors, microwave detectors, video surveillance) on a daily basis. Therefore, the spatiotemporal relationship between traffic data should be considered when executing a data-driven intelligent traffic control system.
The data-driven control method applies relevant theories and methods based on data to the research of the traffic system, analyzes and understands rules and control modes of the traffic system through off-line and on-line data generated by the traffic system under the conditions that the internal mechanism of the traffic system cannot be completely acquired and an accurate traffic flow dynamics model is difficult to establish, designs a control method and makes a control strategy according to the rules, and plays an important role in relieving traffic jam.
With the rapid development of intelligent vehicles and internet traffic and communication technologies, the scale, quality, accuracy, instantaneity and the like of mobile detection data are greatly improved. The signal control method is based on floating car data (typical mobile detection data source), intersection flow parameters are obtained through an estimation method, a periodic vehicle number estimation model is built based on a store-forward modeling method, and a regional traffic signal data driving control model which is based on a multi-agent network and aims at space occupancy balance is provided.
In the traditional analysis of a store-and-forward model, generally, only the traffic signal timing problem in the oversaturated traffic state is considered, more green light time is allocated to a certain direction and a certain period of an undersaturated intersection, and the problem of green light time waste, namely the idle discharge phenomenon, exists; in fact, at this time, the traffic capacity can be guaranteed only by allocating less green light time. However, most traffic signal controls are periodic control, and the constraint of the maximum and minimum green time of the phase is also required to be met, so the adjustable range of the green time in the traditional signal control is limited, and the control problems of three traffic states of undersaturation, critical saturation and oversaturation cannot be perfectly compatible.
In order to solve the problem, in the establishment of a store-and-forward model, constraint conditions for green time of an intersection are adjusted, a time-varying control signal cycle C (k) is introduced, and a brand-new green time constraint condition is obtained, so that green time waste is avoided, the constraint conditions for breaking a fixed cycle are considered, and space occupation is guaranteedThe rate always satisfies xiAnd (k +1) is not less than 0, and the established regional traffic signal control model can describe three traffic state forms of undersaturation, critical saturation and supersaturation at the intersection at the same time, so that the applicability is stronger.
The technical scheme adopted by the invention is as follows:
the method comprises the following steps:
(1) creating a component-positive system model
Figure GDA0003388641810000031
Wherein x ism(k) The value is more than or equal to 0, m is 1, …, and N represents the state of the component system element m at the time k; a ismnM ≠ n denotes the state quantity transfer proportionality coefficient of the component system element m to the component system element n in the sampling period T > 0; i ism(k) Not less than 0 and Om(k) More than or equal to 0 respectively represents the input and the output of the component system element m in the sampling period;
describing the component system model into a vector form:
x(k+1)＝Ax(k)+I(k)
in the formula: x (k) ═ x1(k),…,xN(k)]T∈RNIs a system state vector;
I(k)＝[I1(k),…,IN(k)]T∈RNinputting for the outside of the system; a is an element of RN×NIs a system state matrix and has
Figure GDA0003388641810000032
Assuming that the current state quantity of any component is greater than or equal to the total quantity of state transitions in the sampling period, diagonal elements of the state matrix A are all non-negative, and the column sum satisfies:
Figure GDA0003388641810000033
(2) establishing multi-intersection variable-period multidirectional space occupancy model
Based on a positive system model, as shown in fig. 1, the multi-intersection variable-period multi-directional space occupancy balance control in a traffic area is implemented, and then a mathematical model is as follows:
lm,i(k+1)＝lm,i(k)+Cm(k)qm,i(k)-Sm,igm,i(k)
wherein M is 1, …, and M represents the M-th intersection of the area; the i is 1, …, N represents the ith direction of the intersection, and the N is 4, which represents that the intersection has 4 directions; cm(k) The kth control signal period of the mth intersection of the area is represented and is a variable period; lm,i(k) The number of queued vehicles in the kth period in the ith direction of the mth intersection of the area is shown; sm,iRepresenting the saturation flow rate of the m-th intersection in the i-th direction of the area; gm,i(k) Indicating the green time of the kth period of the mth intersection in the area in the ith direction; wherein q ism,i(k) The vehicle arrival rate of the ith cycle in the ith direction of the mth intersection of the area satisfies the following relation:
Figure GDA0003388641810000034
the green time and the control signal period of each direction of the mth intersection in the area meet the following constraint conditions:
Figure GDA0003388641810000041
wherein, tm,LRepresenting the total loss time of the mth intersection of the area;
defining the space occupancy of a certain direction of the mth intersection of the area as the ratio of the number of queued vehicles in the direction to the length of the road section of the mth intersection, and as follows:
Figure GDA0003388641810000042
wherein x ism,i(k) Represents the m-th of the areaSpace occupancy rate of k cycle in i direction of intersection, lm,i,maxThe length of a road section in the ith direction of the mth intersection of the area is represented;
the variable-period multidirectional space occupancy model of the mth intersection of the traffic area can be expressed in the following form:
Figure GDA0003388641810000043
the saturation of the ith cycle of the mth intersection in the area in the ith direction is defined as follows:
Figure GDA0003388641810000044
defining the green light time constraint and the control signal period constraint of the kth period in the ith direction of the mth intersection of the area as follows:
Figure GDA0003388641810000045
Figure GDA0003388641810000046
the traffic area multi-intersection variable-period multi-direction space occupancy model considering the green light time constraint is as follows:
Figure GDA0003388641810000047
drawings
FIG. 1 is a schematic diagram of the component model.
Detailed Description
The method comprises the following steps:
the method comprises the following steps: establishing a positive system model
Traffic control systems have the distinct feature that the traffic state is non-negative, with the initial non-negative stateSpace and traffic states are always kept non-negative in the evolution process, such as the number of vehicles in a road section, queuing length, traffic flow density, occupancy and the like, and a system with the properties is called a positive system. The positive system model component model is a very simplified model. The network traffic flow evolution is modeled by a component system, the network supersaturation characteristic with any control structure can be described, the non-negativity constraint of the system state is ensured by the self property of the system, the network traffic flow input is given, a network has a unique stable balance point by a non-negative matrix theory, a balance point analysis calculation formula is given, and the quantitative relation between the network steady state and the input is established. Therefore, a network steady-state signal control law is provided, and the control law is a state feedback control law when the intersection is green[129]. A system with n ≧ 2 comprest systems is shown in FIG. 1, where xm(k) The value is more than or equal to 0, m is 1, …, and N represents the state of the component system element m at the time k; a ismnM ≠ n denotes the state quantity transfer proportionality coefficient of the component system element m to the component system element n in the sampling period T > 0; i ism(k) Not less than 0 and Om(k) And the value is more than or equal to 0, which respectively represents the input and the output of the Compartment system element m in the sampling period. Thus, the state of the component system element m satisfies the following conservation equation:
Figure GDA0003388641810000051
further, the above equation is written in vector form:
x(k+1)＝Ax(k)+I(k)
in the formula: x (k) ═ x1(k),…,xN(k)]T∈RNIs a system state vector;
I(k)＝[I1(k),…,IN(k)]T∈RNinputting for the outside of the system; a is an element of RN×NIs a system state matrix and has
Figure GDA0003388641810000052
Suppose any component shouldAnd if the front state quantity is more than or equal to the total quantity of state transition in the sampling period, the diagonal elements of the state matrix A are all non-negative, and the column sum satisfies:
Figure GDA0003388641810000053
thus, the state matrix A is a non-negative matrix, so that the component system is a type of positive system, and the matrix A is referred to as the component matrix.
Step two: establishing multi-intersection variable-period multidirectional space occupancy model
Based on a positive system model, as shown in fig. 1, the multi-intersection variable-period multi-directional space occupancy balance control in a traffic area is implemented, and then a mathematical model is as follows:
lm,i(k+1)＝lm,i(k)+Cm(k)qm,i(k)-Sm,igm,i(k)
wherein M is 1, …, and M represents the M-th intersection of the area; the i is 1, …, N represents the ith direction of the intersection, and the N is 4, which represents that the intersection has 4 directions; cm(k) The kth control signal period of the mth intersection of the area is represented and is a variable period; lm,i(k) Represents the kth period ([ (k-1) C (k), kC (k) at the ith direction of the mth intersection of the area]In-time period) number of queued vehicles (veh); sm,iIndicating the saturation flow rate (veh/s) in the ith direction at the mth intersection of the zone; gm,i(k) And (3) indicating the green time(s) of the kth period in the ith direction of the mth intersection of the area.
Wherein q ism,i(k) The vehicle arrival rate of the ith cycle in the ith direction of the mth intersection in the area is usually related to the number of vehicles in each direction of adjacent intersections, and according to the vehicle conservation law, the following relationship is satisfied:
Figure GDA0003388641810000061
the green time and the control signal period of each direction of the mth intersection in the area meet the following constraint conditions:
Figure GDA0003388641810000062
wherein, tm,LRepresenting the total loss time for the mth intersection of the area.
The space occupancy rate of a certain direction at the m-th intersection of the area is defined as the ratio of the number of vehicles in line in the direction to the length of the road section (vehicle storage capacity), and the form is as follows:
Figure GDA0003388641810000063
wherein x ism,i(k) Represents the space occupancy rate, l, of the kth period in the ith direction of the mth intersection of the aream,i,maxIndicating the link length (vehicle storage capacity) in the ith direction at the mth intersection of the area (veh).
The variable-period multidirectional space occupancy model of the mth intersection of the traffic area can be expressed in the following form:
Figure GDA0003388641810000064
the saturation of the ith cycle of the mth intersection in the area in the ith direction is defined as follows:
Figure GDA0003388641810000065
the index is mainly used for judging three traffic states of undersaturation, critical saturation and supersaturation.
Defining the green light time constraint and the control signal period constraint of the kth period in the ith direction of the mth intersection of the area as follows:
Figure GDA0003388641810000071
Figure GDA0003388641810000072
the traffic area multi-intersection variable-period multi-direction space occupancy model considering the green light time constraint is as follows:
Figure GDA0003388641810000073
at the moment, the multi-intersection multi-direction constrained space occupancy model in the traffic area can describe three traffic state forms of undersaturation, critical saturation and supersaturation at the intersection at the same time.

Claims (1)

1. A method for establishing a regional traffic signal data drive control model is characterized in that,
(1) creating a component-positive system model
Figure FDA0003388641800000011
Wherein x ism(k) The value is more than or equal to 0, m is 1, …, and N represents the state of the component system element m at the time k; a ismnM ≠ n denotes the state quantity transfer proportionality coefficient of the component system element m to the component system element n in the sampling period T > 0; i ism(k) Not less than 0 and Om(k) More than or equal to 0 respectively represents the input and the output of the component system element m in the sampling period;
describing the component system model into a vector form:
x(k+1)＝Ax(k)+I(k)
in the formula: x (k) ═ x1(k),…,xN(k)]T∈RNIs a system state vector;
I(k)＝[I1(k),…,IN(k)]T∈RNinputting for the outside of the system; a is an element of RN×NIs a system state matrix and has
Figure FDA0003388641800000012
Assuming that the current state quantity of any component is greater than or equal to the total quantity of state transitions in the sampling period, diagonal elements of the state matrix A are all non-negative, and the column sum satisfies:
Figure FDA0003388641800000013
(2) establishing multi-intersection variable-period multidirectional space occupancy model
On the basis of a positive system model, the multi-intersection variable-period multi-direction space occupancy balance control in a traffic area is realized, and then the mathematical model is as follows:
lm,i(k+1)＝lm,i(k)+Cm(k)qm,i(k)-Sm,igm,i(k)
wherein M is 1, …, and M represents the M-th intersection of the area; the i is 1, …, N represents the ith direction of the intersection, and the N is 4, which represents that the intersection has 4 directions; cm(k) The kth control signal period of the mth intersection of the area is represented and is a variable period; lm,i(k) The number of queued vehicles in the kth period in the ith direction of the mth intersection of the area is shown; sm,iRepresenting the saturation flow rate of the m-th intersection in the i-th direction of the area; gm,i(k) Indicating the green time of the kth period of the mth intersection in the area in the ith direction; wherein q ism,i(k) The vehicle arrival rate of the ith cycle in the ith direction of the mth intersection of the area satisfies the following relation:
Figure FDA0003388641800000021
the green time and the control signal period of each direction of the mth intersection in the area meet the following constraint conditions:
Figure FDA0003388641800000022
wherein, tm,LRepresents the sameTotal loss time at the mth intersection of the area;
defining the space occupancy of a certain direction of the mth intersection of the area as the ratio of the number of queued vehicles in the direction to the length of the road section of the mth intersection, and as follows:
Figure FDA0003388641800000023
wherein x ism,i(k) Represents the space occupancy rate, l, of the kth period in the ith direction of the mth intersection of the aream,i,maxThe length of a road section in the ith direction of the mth intersection of the area is represented;
the variable-period multidirectional space occupancy model of the mth intersection of the traffic area can be expressed in the following form:
Figure FDA0003388641800000024
the saturation of the ith cycle of the mth intersection in the area in the ith direction is defined as follows:
Figure FDA0003388641800000025
defining the green light time constraint and the control signal period constraint of the kth period in the ith direction of the mth intersection of the area as follows:
Figure FDA0003388641800000026
the traffic area multi-intersection variable-period multi-direction space occupancy model considering the green light time constraint is as follows:
Figure FDA0003388641800000027
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