CN112133109A - Method for establishing single-cross-port multidirectional space occupancy balance control model - Google Patents

Method for establishing single-cross-port multidirectional space occupancy balance control model Download PDF

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CN112133109A
CN112133109A CN202010798059.5A CN202010798059A CN112133109A CN 112133109 A CN112133109 A CN 112133109A CN 202010798059 A CN202010798059 A CN 202010798059A CN 112133109 A CN112133109 A CN 112133109A
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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
    • 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
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing

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Abstract

The invention relates to a method for establishing a single-intersection multi-direction space occupancy balance control model, which is characterized in that a signal control method of the invention 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 established based on a store-forward modeling method, and the single-intersection traffic signal data driving control model which is based on a multi-agent network and aims at space occupancy balance is provided. In the method, in the establishment of a store-and-forward model, the constraint condition of green light time of an intersection is adjusted, a time-varying control signal period C (k) is introduced, and a brand-new green light time constraint condition is obtained, so that green light time waste is avoided, the constraint condition of breaking a fixed period is considered, and the space occupancy rate is ensured to meet x all the timei(k +1) is not less than 0, and the established single-intersection multidirectional constrained space occupancy model can simultaneously describe the undersaturation of the intersectionAnd three traffic state forms of harmony, critical saturation and supersaturation are adopted, so that the applicability is stronger.

Description

Method for establishing single-cross-port multidirectional space occupancy balance control model
Technical Field
The invention relates to the technical field of intelligent traffic signal control, in particular to a method for establishing a single-intersection multi-direction space occupancy balance control model based on multi-agent data driving.
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 exist, the problem of low occupancy rate of a mobile detection method (taking the floating car as an example) exists, and new problems and challenges are brought to urban traffic control.
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 of the invention is based on floating car data (typical mobile detection data source), obtains intersection flow parameters through an estimation method, constructs a periodic vehicle number estimation model based on a store-forward modeling method, and provides a single intersection traffic signal data driving control model based on a multi-agent network and aiming at space occupancy balance.
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 light time of an intersection are adjusted, a time-varying control signal period C (k) is introduced, and a brand-new green light time constraint condition is obtained, so that green light time waste is avoided, the constraint conditions for breaking a fixed period are considered, and the space occupancy rate is guaranteed to meet x all the timeiAnd (k +1) is not less than 0, the established single-intersection multi-direction constrained space occupancy model can describe three traffic state forms of undersaturation, critical saturation and supersaturation at the intersection at the same time, and the applicability is stronger. The technical scheme adopted by the invention is as follows:
the method comprises the following steps:
the method comprises the following steps: aiming at a four-phase fixed-period single intersection, modeling is carried out by adopting a store-and-forward method
The traffic flow dynamics of the branch m with sufficient traffic demand is represented as follows:
xm(k+1)＝xm(k)+T[qm(k)-um(k)+Im(k)-Om(k)]
Om(k)＝tm,0qm(k)
Figure BDA0002626385070000031
Figure BDA0002626385070000032
Figure BDA0002626385070000033
wherein m is a branch connecting intersections i and j, and T is a sampling time interval(s); x is the number ofm(k) Is the number of vehicles (veh) for branch m; q. q.sm(k) And um(k) Are respectively [ kT, (k +1) T]Input flow and output flow (veh/s) of the branch m in a time period; i ism(k) And Om(k) Respectively the demand flow and the dissipation flow (veh/s) of the branch m, and the dissipation rate tm,0Is a known constant value, tn,mIs the steering ratio, s, set by the branch n passing through the intersection j into the branch mmRepresents the saturation flow (veh/s), g, of branch mm(k) Represents the green time(s) of branch m;
step two: establishing a single intersection variable-period multidirectional space occupancy model
The single intersection variable-period multidirectional space occupancy model can be expressed in the following form:
Figure BDA0002626385070000034
Figure BDA0002626385070000035
Figure BDA0002626385070000036
wherein, i is 1, …, N represents the ith direction of the intersection, and N is 4 represents the 4 directions of the intersection; c (k) represents the k control signal period of the intersection, and the k control signal period is a variable period; li(k) Represents the kth period ([ (k-1) C (k), kC (k) in the ith direction of the intersection)]The number of vehicles in a time period), namely the number of vehicles queued behind a stop line at a periodic time; q. q.si(k) Indicating the vehicle arrival rate of the kth period in the ith direction of the intersection; siIndicating a saturation flow rate in the ith direction of the intersection;gi(k) a green time(s) representing the kth period in the ith direction of the intersection; t is tLRepresents the total loss time of the intersection, xi(k) The space occupancy rate of the kth period in the ith direction of the intersection is expressed, namely the ratio of the number of vehicles queued in the ith direction to the length of the road section of the ith direction, ii,maxA link length indicating an ith direction of the intersection;
step three: introducing a time-varying control signal period C (k) to obtain a brand new green light time constraint condition, thereby ensuring that the space occupancy always meets xi(k+1)≥0；
Defining the green light time constraint and the control signal period constraint of the kth period in the ith direction of the intersection as follows:
Figure BDA0002626385070000041
Figure BDA0002626385070000042
considering the single cross-intersection period-variable multidirectional space occupancy model with green light time constraint as follows:
Figure BDA0002626385070000043
drawings
Fig. 1 is a schematic diagram of an urban intersection.
FIG. 2 is a schematic diagram of single cross-port cycle-varying multi-directional space occupancy equalization.
Detailed Description
The invention realizes the following processes:
the method comprises the following steps: and aiming at the four-phase fixed-period single intersection, modeling is carried out by adopting a store-and-forward method.
As shown in fig. 1, a vehicle travels from intersection i to intersection j on a single branch m connecting intersections i and j.
The traffic flow dynamics of the branch m with sufficient traffic demand is represented as follows:
xm(k+1)＝xm(k)+T[qm(k)-um(k)+Im(k)-Om(k)] (4-1)
Om(k)＝tm,0qm(k)
Figure BDA0002626385070000044
Figure BDA0002626385070000045
Figure BDA0002626385070000046
wherein, T is sampling time interval(s); x is the number ofm(k) Is the number of vehicles (veh) for branch m; q. q.sm(k) And um(k) Are respectively [ kT, (k +1) T]Input flow and output flow (veh/s) of the branch m in a time period; i ism(k) And Om(k) Respectively the demand flow and the dissipation flow (veh/s) of the branch m, and the dissipation rate tm,0Is a known constant value, tn,mIs the steering ratio, s, set by the branch n passing through the intersection j into the branch mmRepresents the saturation flow (veh/s), g, of branch mm(k) Representing the green time(s) for leg m.
Determining a multi-direction space occupancy balance control mode with a variable cycle at a single intersection, wherein the specific control mode is described as follows:
(1) and releasing sequence: the control method comprises the steps of releasing according to the sequence of multiple intelligent agents, wherein the multiple intelligent agents are 1-2-3-4-1;
(2) green time: the green light time meets the time constraints of minimum and maximum green light time, the control method directly adjusts the green light time without concerning the green signal ratio, and the period is more flexible and variable;
(3) and phase control: multiple intelligent agents are designed at the intersection, one direction is used as one multiple intelligent agent, the multiple intelligent agents are released in different directions, conflict points are reduced, and release efficiency is improved;
(4) and a network structure: the network formed by the multiple intelligent agents is a full-link networking, and each multiple intelligent agent is in strong connection;
and the vehicles are released in sequence according to the releasing sequence shown in the figure 2, and vehicles turning left and going straight in all directions are released at the same time, so that phase conflict is avoided. The green time for each direction can be adjusted to meet the maximum and minimum green time constraints. And the cycle length is dynamically adjusted in real time in each cycle according to the space occupancy of each entrance lane, so that the green light time is prevented from being wasted. 1. The No. 2, 3 and 4 signal control systems form a multi-agent system, agents in four directions communicate in real time to form a network topology structure, and therefore the purposes of balancing space occupation rate and reducing delay are achieved.
Step two: and establishing a single intersection variable-period multidirectional space occupancy model.
The single intersection variable-period multidirectional space occupancy model can be expressed in the following form:
Figure BDA0002626385070000051
Figure BDA0002626385070000052
Figure BDA0002626385070000053
wherein, i is 1, …, N represents the ith direction of the intersection, and N is 4 represents the 4 directions of the intersection; c (k) represents the k control signal period of the intersection, and the k control signal period is a variable period; li(k) Represents the kth period ([ (k-1) C (k), kC (k) in the ith direction of the intersection)]Number of vehicles (veh) in a time period), i.e., the number of vehicles queued behind the stop line at the periodic time; q. q.si(k) A vehicle arrival rate (veh/s) indicating the k-th cycle in the ith direction of the intersection; siIndicating the saturation flow rate (veh/s) in the ith direction of the intersection; gi(k) Indicating the green time(s) of the kth cycle in the ith direction of the intersection. t is tLRepresents the intersectionTotal loss time of mouth, xi(k) The space occupancy rate of the ith cycle in the ith direction of the intersection, i.e. the ratio of the number of vehicles in line in the ith direction to the length of the section (vehicle storage capacity) |i,maxIndicating the link length (vehicle storage capacity) in the ith direction of the intersection (veh).
The mathematical model takes the period duration as the sampling duration to describe the dynamic change of the vehicles at the period time point, does not describe the formation and dissipation processes of vehicle queuing in the period, and is a signal control optimization model of the invention, wherein the green time is the control quantity to be optimized.
Step three: introducing a time-varying control signal period C (k) to obtain a brand new green light time constraint condition, thereby ensuring that the space occupancy always meets xi(k+1)≥0。
Defining the green light time constraint and the control signal period constraint of the kth period in the ith direction of the intersection as follows:
Figure BDA0002626385070000061
Figure BDA0002626385070000062
considering the single cross-intersection period-variable multidirectional space occupancy model with green light time constraint as follows:
Figure BDA0002626385070000063
at the moment, the single-intersection multi-direction constrained space occupancy model can describe three traffic state forms of undersaturation, critical saturation and supersaturation at the intersection at the same time, and the applicability is stronger.

Claims (1)

1. A method for establishing a single-intersection multi-direction space occupancy balance control model is characterized by comprising the following steps:
the method comprises the following steps: aiming at a four-phase fixed-period single intersection, modeling is carried out by adopting a store-and-forward method
The traffic flow dynamics of the branch m with sufficient traffic demand is represented as follows:
xm(k+1)＝xm(k)+T[qm(k)-um(k)+Im(k)-Om(k)]
Om(k)＝tm，0qm(k)
Figure FDA0002626385060000011
Figure FDA0002626385060000012
Figure FDA0002626385060000013
wherein m is a branch connecting intersections i and j, and T is a sampling time interval(s); x is the number ofm(k) Is the number of vehicles (veh) for branch m; q. q.sm(k) And um(k) Are respectively [ kT, (k +1) T]Input flow and output flow (veh/s) of the branch m in a time period; i ism(k) And Om(k) Respectively the demand flow and the dissipation flow (veh/s) of the branch m, and the dissipation rate tm，0Is a known constant value, tn，mIs the steering ratio, s, set by the branch n passing through the intersection j into the branch mmRepresents the saturation flow (veh/s), g, of branch mm(k) Represents the green time(s) of branch m;
step two: establishing a single intersection variable-period multidirectional space occupancy model
The single intersection variable-period multidirectional space occupancy model can be expressed in the following form:
Figure FDA0002626385060000014
Figure FDA0002626385060000015
Figure FDA0002626385060000016
wherein, i is 1, …, N represents the ith direction of the intersection, and N is 4 represents the 4 directions of the intersection; c (k) represents the k control signal period of the intersection, and the k control signal period is a variable period; li(k) Represents the kth period ([ (k-1) C (k), kC (k) in the ith direction of the intersection)]The number of vehicles in a time period), namely the number of vehicles queued behind a stop line at a periodic time; q. q.si(k) Indicating the vehicle arrival rate of the kth period in the ith direction of the intersection; siIndicating a saturation flow rate in the ith direction of the intersection; gi(k) A green time(s) representing the kth period in the ith direction of the intersection; t is tLRepresents the total loss time of the intersection, xi(k) The space occupancy rate of the kth period in the ith direction of the intersection is expressed, namely the ratio of the number of vehicles queued in the ith direction to the length of the road section of the ith direction, ii，maxA link length indicating an ith direction of the intersection;
step three: introducing a time-varying control signal period C (k) to obtain a brand new green light time constraint condition, thereby ensuring that the space occupancy always meets xi(k+1)≥0；
Defining the green light time constraint and the control signal period constraint of the kth period in the ith direction of the intersection as follows:
Figure FDA0002626385060000021
Figure FDA0002626385060000022
considering the single cross-intersection period-variable multidirectional space occupancy model with green light time constraint as follows:
Figure FDA0002626385060000023
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Application publication date: 20201225