CN114093178A - Traffic signal control method and device, electronic equipment and storage medium - Google Patents

Traffic signal control method and device, electronic equipment and storage medium Download PDF

Info

Publication number
CN114093178A
CN114093178A CN202210069135.8A CN202210069135A CN114093178A CN 114093178 A CN114093178 A CN 114093178A CN 202210069135 A CN202210069135 A CN 202210069135A CN 114093178 A CN114093178 A CN 114093178A
Authority
CN
China
Prior art keywords
traffic
control information
information
target
state
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210069135.8A
Other languages
Chinese (zh)
Other versions
CN114093178B (en
Inventor
姜伟浩
丁可
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Hikvision Digital Technology Co Ltd
Original Assignee
Hangzhou Hikvision Digital Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Hikvision Digital Technology Co Ltd filed Critical Hangzhou Hikvision Digital Technology Co Ltd
Priority to CN202210069135.8A priority Critical patent/CN114093178B/en
Publication of CN114093178A publication Critical patent/CN114093178A/en
Application granted granted Critical
Publication of CN114093178B publication Critical patent/CN114093178B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Traffic Control Systems (AREA)

Abstract

The embodiment of the application provides a traffic signal control method, a traffic signal control device, electronic equipment and a storage medium, which relate to the technical field of computers and comprise the following steps: determining expected traffic state information of the target traffic area according to the initial traffic state information; determining new control information of the traffic signals of the target traffic area, simulating by using the new control information and the flow information, predicting new traffic state information of the target traffic area, updating the initial control information into the new control information by using a second state error adjustment optimization algorithm, and returning to the step of determining the new control information of the traffic signals of the target traffic area until the iteration number reaches a preset number threshold; and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information. By the scheme provided by the embodiment of the application, the passing efficiency of the vehicle can be improved.

Description

Traffic signal control method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for controlling a traffic signal, an electronic device, and a storage medium.
Background
In the traffic field, in order to facilitate management of vehicles and pedestrians passing through a traffic intersection, a traffic light is generally arranged at the traffic intersection, and a red light, a green light and a yellow light are alternately lighted to control the vehicles or the pedestrians to pass through the intersection or wait at the intersection.
In the related art, the traffic lights are usually alternately lighted at regular time intervals, so that although traffic management can be realized, the traffic flow is larger because the traffic flow is different on the roads at different times, for example, at the peak of work in the morning or evening; and the traffic flow is smaller at night or at noon. If the traffic lights are controlled to be alternately turned on at fixed time intervals, the passing efficiency of the vehicles can be reduced, for example, in the case of low traffic flow, the vehicles still need to wait for the red lights at the intersection for a long time, so that the queuing density of the vehicles is high, and the delay time is long.
Disclosure of Invention
An object of the embodiments of the present application is to provide a traffic signal control method, a traffic signal control device, an electronic device, and a storage medium, so as to improve vehicle passing efficiency. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a traffic signal control method, where the method includes:
acquiring initial control information of traffic signals of a target traffic area in a road, and predicting flow information of a preset time period of the target traffic area;
simulating by using the initial control information and the flow information to predict the initial traffic state information of the target traffic area;
determining expected traffic state information of the target traffic area according to the initial traffic state information;
determining new control information of the traffic signal of the target traffic area by using an optimization algorithm based on the initial control information and the first state error, performing simulation by using the new control information and the traffic information, predicting new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is: an error of the expected traffic status information with respect to a last obtained traffic status information, wherein the second status error is: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information;
and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information.
In an embodiment of the present application, said adjusting said optimization algorithm using said second state error comprises:
and under the condition that the iteration times are more than or equal to 3, adjusting the current optimization algorithm by utilizing the second state error and the optimization algorithm adopted in the previous 3 iterations.
In an embodiment of the application, when the iteration number is greater than or equal to 3, adjusting the current optimization algorithm by using the second state error and the optimization algorithm used in the previous 3 iterations includes:
obtaining an adjusted optimization algorithm according to the following formula
Figure DEST_PATH_IMAGE001
：
Figure DEST_PATH_IMAGE003
Wherein, the ∂1Represents a first weight, said∂2Represents a second weight, said ∂3Represents a third weight, said
Figure 753593DEST_PATH_IMAGE004
Represents the optimization algorithm employed during the first 1 iteration, said
Figure DEST_PATH_IMAGE005
Represents the optimization algorithm employed during the first 2 iterations, said
Figure 370519DEST_PATH_IMAGE006
Represents the optimization algorithm adopted in the first 3 iterations, μ, ѯ represent preset regularization factors, Δ (k, l-1) represents the difference between the new control information adopted in the current iteration and the new control information adopted in the previous iteration, Δ
Figure DEST_PATH_IMAGE007
(k +1, l-1) represents the second error.
In an embodiment of the present application, the determining new control information of the traffic signal of the target traffic zone by using an optimization algorithm based on the initial control information and the first state error includes:
determining new control information u (k, l) for the traffic signal of the target traffic zone according to the following formula:
Figure DEST_PATH_IMAGE009
wherein u (k, l-1) represents the initial control information, p represents a preset learning rate, and
Figure 129528DEST_PATH_IMAGE001
representing the optimization algorithm, said JcritRepresents the desired traffic status information, the J (k +1, l-1) represents the last resulting traffic status information, the λ, the,
Figure 540918DEST_PATH_IMAGE010
Representing a preset regularization factor.
In an embodiment of the present application, the predicting the initial traffic state information of the target traffic region by performing simulation using the initial control information and the flow information includes:
and simulating by using the initial control information and the flow information, and predicting the vehicle queuing density variance and the vehicle average delay time of the target traffic area as initial traffic state information.
In an embodiment of the application, the determining, from the new control information obtained from each iteration, that the corresponding new traffic state information meets the target control information of the preset traffic state requirement includes:
aiming at new control information obtained by each iteration, calculating a first lifting ratio of corresponding new traffic state information relative to the variance of the vehicle queuing density in the initial traffic state information, calculating a second lifting ratio of corresponding new traffic state information relative to the average delay time of vehicles in the initial traffic state information, and performing weighted calculation on the first lifting ratio and the second lifting ratio to obtain the comprehensive lifting degree of the new control information;
and determining the new control information with the highest comprehensive lifting degree as target control information.
In an embodiment of the application, the predicting the variance of the vehicle queue density in the target traffic area by using the initial control information and the flow information for simulation includes:
simulating by using the initial control information and the flow information, and predicting the maximum queuing lengths of the target traffic area at different moments and different road sections during the preset time period;
aiming at each moment, calculating the vehicle queuing density at the moment by using the maximum queuing length at the moment and the length of the road section;
and calculating the variance between the vehicle queuing densities corresponding to different moments to obtain the vehicle queuing density variance of the target traffic area.
In a second aspect, an embodiment of the present application provides a traffic signal control device, including:
the information acquisition module is used for acquiring initial control information of traffic signals of a target traffic area in a road and predicting flow information of a preset time period of the target traffic area;
the traffic state prediction module is used for simulating by utilizing the initial control information and the flow information to predict the initial traffic state information of the target traffic area;
an expected traffic state determining module for determining expected traffic state information of the target traffic zone according to the initial traffic state information;
an optimization iteration module, configured to determine new control information of the traffic signal in the target traffic area by using an optimization algorithm based on the initial control information and the first state error, perform simulation by using the new control information and the traffic information, predict new traffic state information of the target traffic area, calculate a second state error, adjust the optimization algorithm by using the second state error, update the initial control information to the new control information, return to the step of determining the new control information of the traffic signal in the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, where the first state error is: an error of the expected traffic status information with respect to a last obtained traffic status information, wherein the second status error is: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information;
and the target control module is used for determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information.
In an embodiment of the application, the optimization iteration module is specifically configured to:
and under the condition that the iteration times are more than or equal to 3, adjusting the current optimization algorithm by utilizing the second state error and the optimization algorithm adopted in the previous 3 iterations.
In an embodiment of the application, the optimization iteration module is specifically configured to:
obtaining an adjusted optimization algorithm according to the following formula
Figure 821595DEST_PATH_IMAGE001
：
Figure 445474DEST_PATH_IMAGE003
Wherein, the ∂1Representing a first weight, said ∂2Represents a second weight, said ∂3Represents a third weight, said
Figure 511519DEST_PATH_IMAGE004
Represents the optimization algorithm employed during the first 1 iteration, said
Figure 890548DEST_PATH_IMAGE005
Represents the optimization algorithm employed during the first 2 iterations, said
Figure 284620DEST_PATH_IMAGE006
Represents the optimization algorithm adopted in the first 3 iterations, μ, ѯ represent preset regularization factors, Δ (k, l-1) represents the difference between the new control information adopted in the current iteration and the new control information adopted in the previous iteration, Δ
Figure 587557DEST_PATH_IMAGE007
(k +1, l-1) represents the second error.
In an embodiment of the application, the optimization iteration module is specifically configured to:
determining new control information u (k, l) for the traffic signal of the target traffic zone according to the following formula:
Figure 117895DEST_PATH_IMAGE009
wherein u (k, l-1) represents the initial control information, p represents a preset learning rate, and
Figure 995721DEST_PATH_IMAGE001
representing the optimization algorithm, said JcritRepresents the desired traffic status information, the J (k +1, l-1) represents the last resulting traffic status information, the λ, the,
Figure 673827DEST_PATH_IMAGE010
Representing a preset regularization factor.
In an embodiment of the application, the traffic status prediction module is specifically configured to:
and simulating by using the initial control information and the flow information, and predicting the vehicle queuing density variance and the vehicle average delay time of the target traffic area as initial traffic state information.
In one embodiment of the present application, the target control module includes:
the lifting degree calculation unit is used for calculating a first lifting ratio of the corresponding new traffic state information relative to the vehicle queuing density variance in the initial traffic state information, calculating a second lifting ratio of the corresponding new traffic state information relative to the vehicle average delay duration in the initial traffic state information, and performing weighted calculation on the first lifting ratio and the second lifting ratio to obtain the comprehensive lifting degree of the new control information;
and the control information determining unit is used for determining the new control information with the highest comprehensive lifting degree as the target control information.
In an embodiment of the application, the traffic status prediction module is specifically configured to:
simulating by using the initial control information and the flow information, and predicting the maximum queuing lengths of the target traffic area at different moments and different road sections during the preset time period;
aiming at each moment, calculating the vehicle queuing density at the moment by using the maximum queuing length at the moment and the length of the road section;
and calculating the variance between the vehicle queuing densities corresponding to different moments to obtain the vehicle queuing density variance of the target traffic area.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface complete communication between the memory and the processor through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of the first aspect when executing a program stored in the memory.
In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the method steps of any one of the first aspect.
Embodiments of the present application also provide a computer program product containing instructions, which when run on a computer, cause the computer to perform any of the above described traffic signal control methods.
The embodiment of the application has the following beneficial effects:
in the traffic signal control scheme provided by the embodiment of the application, the initial control information of the traffic signal of the target traffic area in the road can be obtained, and the traffic information of the preset time period of the target traffic area is predicted; performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area; determining expected traffic state information of the target traffic area according to the initial traffic state information; determining new control information of a traffic signal of a target traffic area by using an optimization algorithm based on the initial control information and a first state error, performing simulation by using the new control information and flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is as follows: an error of the expected traffic status information relative to the last obtained traffic status information, the second status error being: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information; and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information. Therefore, the traffic state of the target traffic area under the current initial control information can be deduced based on the initial control information and the predicted flow information, and then the control information of the traffic signal and the optimization algorithm can be continuously optimized by utilizing the first state error and the second state error to finally obtain the optimized target control information, so that the traffic state of the target traffic area can meet the preset traffic state requirement under the condition of controlling the traffic signal based on the target control information. Therefore, the scheme provided by the embodiment of the application can improve the passing efficiency of the vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and it is also obvious for a person skilled in the art to obtain other embodiments according to the drawings.
Fig. 1 is a schematic flow chart of a traffic signal control method according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of an optimization iteration process provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a traffic signal control device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the description herein are intended to be within the scope of the present disclosure.
In order to improve vehicle passing efficiency, embodiments of the present application provide a traffic signal control method, a traffic signal control apparatus, an electronic device, and a storage medium, which are described in detail below.
In an embodiment of the present application, a traffic signal control method is provided, where the method may be applied to electronic devices such as an electronic computer, a server, and a traffic signal controller, and the traffic signal control method includes:
acquiring initial control information of traffic signals of a target traffic area in a road, and predicting flow information of a preset time period of the target traffic area;
performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area;
determining expected traffic state information of the target traffic area according to the initial traffic state information;
determining new control information of a traffic signal of a target traffic area by using an optimization algorithm based on the initial control information and a first state error, performing simulation by using the new control information and flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is as follows: an error of the expected traffic status information relative to the last obtained traffic status information, the second status error being: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information;
and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information.
Therefore, the traffic state of the target traffic area under the current initial control information can be deduced based on the initial control information and the predicted flow information, and then the control information of the traffic signal and the optimization algorithm can be continuously optimized by utilizing the first state error and the second state error to finally obtain the optimized target control information, so that the traffic state of the target traffic area can meet the preset traffic state requirement under the condition of controlling the traffic signal based on the target control information. Therefore, the scheme provided by the embodiment can improve the passing efficiency of the vehicle.
The above-described traffic signal control method will be described in detail below.
Referring to fig. 1, fig. 1 is a schematic flow chart of a traffic signal control method provided in an embodiment of the present application, where the method includes the following steps S101 to S105:
s101, obtaining initial control information of traffic signals of a target traffic area in a road, and predicting flow information of a preset time period of the target traffic area.
Wherein, the target traffic area refers to: one intersection region in the road, or a collection of multiple interconnected intersection regions in the road.
The traffic signal refers to: traffic light signals in the intersection area in the road.
The control information of the traffic signal refers to: the green signal ratio is the ratio of the duration of the green light signal in one period of the traffic light signal to the total period.
In addition, the control information may further include one or more of the following information: the time length of one period of the traffic light signal, the difference value of the duration time lengths of different signals, the ratio of the duration time length of the red light signal to the total time length of the period, the ratio of the duration time length of the green light signal to the duration time length of the red light signal, and the like.
The length of the preset time period may be 30 minutes, 1 hour, 2 hours, etc.
The traffic information includes: the number of vehicles passing by.
Specifically, one intersection region or a plurality of interconnected intersection regions in the road may be used as a target traffic region, current control information of traffic signals in the target traffic region is obtained as initial control information, and then the number of vehicles passing through the target traffic region in a future preset time period is predicted to obtain flow information.
In one embodiment of the present application, when predicting the traffic information of a preset time period in a target traffic area, the traffic information may be set manually;
the traffic flow of different time intervals and different intersection areas can be counted to obtain a statistical result, and the traffic flow of the target traffic area in the preset time period is determined according to the statistical result based on the time interval to which the preset time period belongs and the number of intersection areas included in the target traffic area, so that the traffic flow information is obtained.
And S102, performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area.
Wherein the traffic status information is used to reflect: the traffic condition information may include, for example, at least one of the following information: the maximum delay time of the vehicles, the maximum queuing length of the vehicles, the average delay time of the vehicles and the like.
Specifically, the initial control information, the flow information, and the area information of the intersection area included in the target traffic area may be input into a preset simulation algorithm, and the simulation algorithm is used to simulate a vehicle passing condition of the target traffic area in a future preset time period under the condition that the traffic signal of the target traffic area is controlled by using the initial control information, so as to obtain the initial traffic state information.
The area information includes at least one of the following information: the number of intersection areas, the topological relation of the intersection areas, the number of lanes of the intersection areas and the like.
In an embodiment of the application, before the simulation is performed by using the initial control information and the flow information, parameter verification may be performed on the initial control information and the flow information, and when the verification passes, the simulation is performed by using the initial control information and the flow information.
Wherein, the parameter verification may include: whether the format of the input information meets the format requirement of the simulation algorithm, whether the initial control information contains the control information of the traffic signals of all intersection areas in the target traffic area, whether the traffic flow reflected by the flow information is in a preset flow interval and the like.
S103, determining expected traffic state information of the target traffic area according to the initial traffic state information.
In an embodiment of the present application, traffic state information of the initial traffic state information satisfying the above-mentioned degree of lift may be calculated according to a preset degree of lift, and used as the expected traffic state information.
For example, it is assumed that the traffic state information is an average delay time of a vehicle, the degree of increase is 20%, the initial traffic state information is 30 seconds, and in order to guarantee that the traffic state increases by 20%, the average delay time of the vehicle should decrease by 20%, that is, the traffic state information is expected to be 30 × (1-20%) =24 seconds.
In addition, the traffic state information of the initial traffic state information under the condition that the lifting threshold is met can be calculated according to a preset lifting threshold to serve as the expected traffic state information.
S104, determining new control information of the traffic signals of the target traffic area by using an optimization algorithm based on the initial control information and the first state error, simulating by using the new control information and the flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, and returning to the step of determining the new control information of the traffic signals of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold.
Wherein the first state error is: an error in the desired traffic status information relative to the last obtained traffic status information. Specifically, in the first iteration, the first state error is: an error of the expected traffic state information relative to the initial traffic state information, at a second and subsequent iteration, the first state error being: an error in the traffic state information is expected relative to the new traffic state information obtained at the last iteration.
The second state error is: the degree of improvement of the current traffic state information relative to the expected traffic state information and the degree of improvement of the last traffic state information relative to the expected traffic state information. Specifically, the degree of increase of the currently obtained traffic state information with respect to the expected traffic state information may be calculated as a first degree of increase, the degree of increase of the traffic state information obtained last time with respect to the expected traffic state information may be calculated as a second degree of increase, and then an error of the first degree of increase with respect to the second degree of increase may be calculated as a second state error.
The optimization algorithm may be a pseudo gradient matrix, which is also called a jacobian matrix, and is a matrix in which pseudo partial derivatives are arranged in a certain manner to implement linear optimization. Besides, the optimization algorithm may be other algorithms, and the embodiment of the present application is not limited to this.
Referring to fig. 2, fig. 2 is a schematic diagram of an optimization iterative process provided in an embodiment of the present application, where the optimization iterative process shown in the above step S104 includes the following steps S1041-S1044:
s1041, determining new control information of the traffic signal of the target traffic area by using an optimization algorithm based on the initial control information and the first state error.
Specifically, the initial control information and the first state error may be substituted into a preset optimization algorithm, and the control information of the traffic signal in the target traffic area is optimized by using the optimization algorithm, so as to obtain new control information.
And S1042, performing simulation by using the new control information and the flow information, and predicting new traffic state information of the target traffic area.
Specifically, the new control information, the flow rate information, and the area information of the intersection area included in the target traffic area may be input to the simulation algorithm, and the simulation algorithm may be used to simulate the vehicle passing condition of the target traffic area within a future preset time period when the traffic signal of the target traffic area is controlled by the new control information, so as to obtain the new traffic state information.
And S1043, calculating a second state error, and adjusting an optimization algorithm by using the second state error.
Specifically, an error between a degree of increase of the current traffic state information with respect to the expected traffic state information and a degree of increase of the last traffic state information with respect to the expected traffic state information may be calculated as a second state error, and the second state error may reflect: under the control of the current control information, the difference between the vehicle passing condition of the target traffic area and the vehicle passing condition of the previous control information can further reflect the error of the current control information of the traffic signal of the target traffic area, and the optimization algorithm for optimizing the control information can be adjusted based on the error, so that the current control information can be continuously optimized based on the adjusted optimization algorithm.
And S1044, judging whether the iteration frequency reaches a preset frequency threshold, if not, updating the initial control information into new control information, and returning to the step S1041.
The number threshold may be 100, 500, 2000, or the like.
Specifically, the execution times of the optimization iteration process may be recorded as iteration times, and whether the iteration times reach a time threshold is determined, if so, the iteration process may be ended to obtain a plurality of new control information and new traffic state information corresponding to each new control information;
if not, the initial control information can be updated to new control information, the step S1041 is returned to, the new control information and the first state error are substituted into the adjusted optimization algorithm, and the control information of the traffic signal of the target traffic area is optimized by using the adjusted optimization algorithm, so as to obtain new control information.
And S105, determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information.
Specifically, the traffic state requirement may be preset, after the iteration is finished, a plurality of new control information and new traffic state information corresponding to each new control information may be obtained, the control information whose corresponding new traffic state information satisfies the traffic state requirement is selected from the new control information as the target control information, and then the traffic signal of the target traffic area may be controlled by using the target control information, so that the vehicle passing condition of the target traffic area satisfies the traffic state requirement.
In the traffic signal control scheme provided by the embodiment, initial control information of a traffic signal of a target traffic area in a road can be obtained, and flow information of a preset time period of the target traffic area is predicted; performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area; determining expected traffic state information of the target traffic area according to the initial traffic state information; determining new control information of a traffic signal of a target traffic area by using an optimization algorithm based on the initial control information and a first state error, performing simulation by using the new control information and flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is as follows: an error of the expected traffic status information relative to the last obtained traffic status information, the second status error being: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information; and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information. Therefore, the traffic state of the target traffic area under the current initial control information can be deduced based on the initial control information and the predicted flow information, and then the control information of the traffic signal and the optimization algorithm can be continuously optimized by utilizing the first state error and the second state error to finally obtain the optimized target control information, so that the traffic state of the target traffic area can meet the preset traffic state requirement under the condition of controlling the traffic signal based on the target control information. Therefore, the scheme provided by the embodiment can improve the passing efficiency of the vehicle.
In an embodiment of the present application, when the error adjustment optimization algorithm is used in step S104, if the number of iterations is greater than or equal to 3, the current optimization algorithm is adjusted by using the second state error and the optimization algorithm used in the previous 3 iterations.
Specifically, when the iteration number is greater than or equal to 3, it is indicated that at least 3 sub-optimization algorithms are adopted, and there is a difference between the adopted 3 sub-optimization algorithms, and when the optimization algorithm is adjusted this time, the current optimization algorithm can be adjusted by using the second state error and the optimization algorithm adopted in the previous 3 iterations, so as to reduce the difference between the adjusted optimization algorithm and the previous optimization algorithm, and ensure that the optimized control information is smoother when the adjusted optimization algorithm is adopted to optimize the control information.
In one embodiment of the present application, the adjusted optimization algorithm can be obtained according to the following formula
Figure 170668DEST_PATH_IMAGE001
：
Figure 194993DEST_PATH_IMAGE003
Wherein, ∂1Denotes a first weight, ∂2Denotes a second weight, ∂3It is indicated that the third weight is,
Figure 587929DEST_PATH_IMAGE004
showing the optimization algorithm used in the previous 1 iteration,
Figure 81227DEST_PATH_IMAGE005
showing the optimization algorithm used in the previous 2 iterations,
Figure 116179DEST_PATH_IMAGE006
represents the optimization algorithm adopted in the first 3 iterations, μ, ѯ represent preset regularization factors, Δ (k, l-1) represent the difference between the new control information adopted in the current iteration and the new control information adopted in the previous iteration, Δ
Figure 762055DEST_PATH_IMAGE007
(k +1, l-1) represents a second error.
In one embodiment of the present application, when calculating the new control information for the above step S104, the new control information u (k, l) of the traffic signal of the target traffic zone may be determined according to the following formula:
Figure 591471DEST_PATH_IMAGE009
where u (k, l-1) represents initial control information, ρ represents a preset learning rate,
Figure 572065DEST_PATH_IMAGE001
representation optimization algorithm, JcritRepresents the expected traffic state information, J (k +1, l-1) represents the traffic state information obtained last, lambda,
Figure 410708DEST_PATH_IMAGE010
Representing a preset regularization factor.
In one embodiment of the present application, for the step S102, when predicting the initial traffic state information, the following steps may be performed:
and simulating by using the initial control information and the flow information, and predicting the vehicle queuing density variance and the vehicle average delay time of the target traffic area as the initial traffic state information.
Wherein the vehicle queue density variance refers to: the variance between the vehicle queue densities at different times, where the vehicle queue densities refer to: the ratio of the length of the queue of vehicles on the lane to the length of the lane.
The above-mentioned average delay time period of the vehicle means: average of the length of delay of a vehicle due to waiting in line for a traffic signal.
Specifically, the traffic state information may include a vehicle queuing density variance and a vehicle average delay time, the initial control information, the flow information, and area information of an intersection area included in the target traffic area may be input into a preset simulation algorithm, the simulation algorithm may be used to simulate a vehicle passing condition, such as a vehicle queuing condition and a vehicle delay condition, of the target traffic area in a future preset time period under the condition that the traffic signal of the target traffic area is controlled by using the initial control information, the vehicle queuing density variance may be obtained based on the vehicle queuing condition, and the vehicle average delay time may be calculated based on the vehicle delay condition, so that the vehicle queuing density variance and the vehicle average delay time are used as the initial traffic state information.
In one embodiment of the application, when the vehicle queuing density variance is predicted, initial control information and flow information can be utilized for simulation, and the maximum queuing lengths of different road sections at different moments in a preset time period in a target traffic area are predicted; aiming at each moment, calculating the vehicle queuing density at the moment by using the maximum queuing length at the moment and the length of the road section; and calculating the variance between the corresponding vehicle queuing densities at different moments to obtain the vehicle queuing density variance of the target traffic area.
Specifically, the initial control information, the flow information, and the area information of the intersection area included in the target traffic area may be input into a preset simulation algorithm, the simulation algorithm is used to simulate the maximum queuing lengths of different time points and different road sections of the target traffic area in a preset time period in the future under the condition that the traffic signal of the target traffic area is controlled by using the initial control information, and for each time point, the vehicle queuing density at the time point is calculated by using the maximum queuing length at the time point and the length of the road section; after the vehicle queue densities corresponding to different moments are obtained, the variance among the vehicle queue densities can be calculated, and the vehicle queue density variance in the target traffic area is obtained.
In one embodiment of the present application, the vehicle queue density variance may be calculated using the following formula
Figure DEST_PATH_IMAGE011
：
Figure DEST_PATH_IMAGE013
Figure DEST_PATH_IMAGE015
Wherein I represents a lane, N represents a link included in the target traffic zone, N represents a total number of links included in the target traffic zone, and InRepresenting a set of lanes on a road section n,/nIndicates the length of the link n, qitThe maximum queuing length of the vehicles on the lane i is shown, T represents the number of the vehicles at different moments, and the vehicle queuing density is counted according to a preset time intervalIn the case of degrees, T may represent a ratio between a preset time period and a preset time interval, QntIndicating vehicle queue density, above
Figure 878468DEST_PATH_IMAGE016
And the average value of the vehicle queuing density of each road section is shown.
In one embodiment of the present application, the vehicle average delay period D may be calculated using the following formula:
Figure 268998DEST_PATH_IMAGE018
wherein M represents a total number of vehicles passing through the target traffic zone,
Figure DEST_PATH_IMAGE019
indicating the delay period of the vehicle m.
In an embodiment of the present application, when the traffic state information includes the vehicle queue density variance and the vehicle average delay time, and when the target control information is selected in step S105, for new control information obtained in each iteration, a first lifting ratio of the corresponding new traffic state information with respect to the vehicle queue density variance in the initial traffic state information may be calculated, a second lifting ratio of the corresponding new traffic state information with respect to the vehicle average delay time in the initial traffic state information may be calculated, and the first lifting ratio and the second lifting ratio are weighted and calculated to obtain a comprehensive lifting degree of the new control information; and determining the new control information with the highest comprehensive lifting degree as target control information.
Specifically, the above-mentioned comprehensive lift J may be calculated according to the following formula:
Figure DEST_PATH_IMAGE021
wherein a represents a preset fourth weight, b represents a preset fifth weight, x0 represents a vehicle queue density variance in the initial traffic state information, x represents a vehicle queue density variance in the new traffic state information, y0 represents a vehicle average delay time period in the initial traffic state information, and y represents a vehicle average delay time period in the new traffic state information.
In the traffic signal control scheme provided by the embodiment, initial control information of a traffic signal of a target traffic area in a road can be obtained, and flow information of a preset time period of the target traffic area is predicted; performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area; determining expected traffic state information of the target traffic area according to the initial traffic state information; determining new control information of a traffic signal of a target traffic area by using an optimization algorithm based on the initial control information and a first state error, performing simulation by using the new control information and flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is as follows: an error of the expected traffic status information relative to the last obtained traffic status information, the second status error being: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information; and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information. Therefore, the traffic state of the target traffic area under the current initial control information can be deduced based on the initial control information and the predicted flow information, and then the control information of the traffic signal and the optimization algorithm can be continuously optimized by utilizing the first state error and the second state error to finally obtain the optimized target control information, so that the traffic state of the target traffic area can meet the preset traffic state requirement under the condition of controlling the traffic signal based on the target control information. Therefore, the scheme provided by the embodiment can improve the passing efficiency of the vehicle.
Corresponding to the traffic state control method, the embodiment of the application also provides a traffic state control device, which is described in detail below.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a traffic signal control device according to an embodiment of the present application, where the traffic signal control device includes:
the information obtaining module 301 is configured to obtain initial control information of a traffic signal of a target traffic area in a road, and predict traffic information of a preset time period of the target traffic area;
a traffic state prediction module 302, configured to perform simulation using the initial control information and the flow information to predict initial traffic state information of the target traffic area;
an expected traffic state determining module 303, configured to determine expected traffic state information of the target traffic region according to the initial traffic state information;
an optimization iteration module 304, configured to determine new control information of the traffic signal in the target traffic area by using an optimization algorithm based on the initial control information and the first state error, perform simulation by using the new control information and the traffic information, predict new traffic state information of the target traffic area, calculate a second state error, adjust the optimization algorithm by using the second state error, update the initial control information to the new control information, return to the step of determining the new control information of the traffic signal in the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, where the first state error is: an error of the expected traffic status information with respect to a last obtained traffic status information, wherein the second status error is: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information;
and the target control module 305 is configured to determine, from the new control information obtained in each iteration, target control information that the corresponding new traffic state information meets a preset traffic state requirement, and control the traffic signal of the target traffic area by using the target control information.
In an embodiment of the present application, the optimization iteration module 304 is specifically configured to:
and under the condition that the iteration times are more than or equal to 3, adjusting the current optimization algorithm by utilizing the second state error and the optimization algorithm adopted in the previous 3 iterations.
In an embodiment of the present application, the optimization iteration module 304 is specifically configured to:
obtaining an adjusted optimization algorithm according to the following formula
Figure 894145DEST_PATH_IMAGE001
：
Figure 661113DEST_PATH_IMAGE022
Wherein, the ∂1Representing a first weight, said ∂2Represents a second weight, said ∂3Represents a third weight, said
Figure 937373DEST_PATH_IMAGE004
Represents the optimization algorithm employed during the first 1 iteration, said
Figure 374171DEST_PATH_IMAGE005
Represents the optimization algorithm employed during the first 2 iterations, said
Figure 318905DEST_PATH_IMAGE006
Represents the optimization algorithm adopted in the first 3 iterations, μ, ѯ represent preset regularization factors, Δ (k, l-1) represents the difference between the new control information adopted in the current iteration and the new control information adopted in the previous iteration, Δ
Figure 499351DEST_PATH_IMAGE007
(k +1, l-1) represents the second error.
In an embodiment of the present application, the optimization iteration module 304 is specifically configured to:
determining new control information u (k, l) for the traffic signal of the target traffic zone according to the following formula:
Figure DEST_PATH_IMAGE023
wherein u (k, l-1) represents the initial control information, p represents a preset learning rate, and
Figure 426856DEST_PATH_IMAGE001
representing the optimization algorithm, said JcritRepresents the desired traffic status information, the J (k +1, l-1) represents the last resulting traffic status information, the λ, the,
Figure 644341DEST_PATH_IMAGE010
Representing a preset regularization factor.
In an embodiment of the present application, the traffic status prediction module 302 is specifically configured to:
and simulating by using the initial control information and the flow information, and predicting the vehicle queuing density variance and the vehicle average delay time of the target traffic area as initial traffic state information.
In one embodiment of the present application, the target control module 305 includes:
the lifting degree calculation unit is used for calculating a first lifting ratio of the corresponding new traffic state information relative to the vehicle queuing density variance in the initial traffic state information, calculating a second lifting ratio of the corresponding new traffic state information relative to the vehicle average delay duration in the initial traffic state information, and performing weighted calculation on the first lifting ratio and the second lifting ratio to obtain the comprehensive lifting degree of the new control information;
and the control information determining unit is used for determining the new control information with the highest comprehensive lifting degree as the target control information.
In an embodiment of the present application, the traffic status prediction module 302 is specifically configured to:
simulating by using the initial control information and the flow information, and predicting the maximum queuing lengths of the target traffic area at different moments and different road sections during the preset time period;
aiming at each moment, calculating the vehicle queuing density at the moment by using the maximum queuing length at the moment and the length of the road section;
and calculating the variance between the vehicle queuing densities corresponding to different moments to obtain the vehicle queuing density variance of the target traffic area.
In the traffic signal control scheme provided by the embodiment, initial control information of a traffic signal of a target traffic area in a road can be obtained, and flow information of a preset time period of the target traffic area is predicted; performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area; determining expected traffic state information of the target traffic area according to the initial traffic state information; determining new control information of a traffic signal of a target traffic area by using an optimization algorithm based on the initial control information and a first state error, performing simulation by using the new control information and flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is as follows: an error of the expected traffic status information relative to the last obtained traffic status information, the second status error being: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information; and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information. Therefore, the traffic state of the target traffic area under the current initial control information can be deduced based on the initial control information and the predicted flow information, and then the control information of the traffic signal and the optimization algorithm can be continuously optimized by utilizing the first state error and the second state error to finally obtain the optimized target control information, so that the traffic state of the target traffic area can meet the preset traffic state requirement under the condition of controlling the traffic signal based on the target control information. Therefore, the scheme provided by the embodiment can improve the passing efficiency of the vehicle.
The embodiment of the present application further provides an electronic device, as shown in fig. 4, which includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404,
a memory 403 for storing a computer program;
the processor 401 is configured to implement the steps of the traffic signal control method when executing the program stored in the memory 403.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In yet another embodiment provided by the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the traffic signal control methods described above.
In yet another embodiment provided by the present application, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform any of the traffic signal control methods of the above embodiments.
In the traffic signal control scheme provided by the embodiment, initial control information of a traffic signal of a target traffic area in a road can be obtained, and flow information of a preset time period of the target traffic area is predicted; performing simulation by using the initial control information and the flow information, and predicting the initial traffic state information of the target traffic area; determining expected traffic state information of the target traffic area according to the initial traffic state information; determining new control information of a traffic signal of a target traffic area by using an optimization algorithm based on the initial control information and a first state error, performing simulation by using the new control information and flow information, predicting the new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is as follows: an error of the expected traffic status information relative to the last obtained traffic status information, the second status error being: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information; and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information. Therefore, the traffic state of the target traffic area under the current initial control information can be deduced based on the initial control information and the predicted flow information, and then the control information of the traffic signal and the optimization algorithm can be continuously optimized by utilizing the first state error and the second state error to finally obtain the optimized target control information, so that the traffic state of the target traffic area can meet the preset traffic state requirement under the condition of controlling the traffic signal based on the target control information. Therefore, the scheme provided by the embodiment can improve the passing efficiency of the vehicle.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, apparatus embodiments, electronic device embodiments, computer program product embodiments, and computer-readable storage medium embodiments are substantially similar to method embodiments, so that the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
The above description is only for the preferred embodiment of the present application and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims (10)

1. A traffic signal control method, characterized in that the method comprises:
acquiring initial control information of traffic signals of a target traffic area in a road, and predicting flow information of a preset time period of the target traffic area;
simulating by using the initial control information and the flow information to predict the initial traffic state information of the target traffic area;
determining expected traffic state information of the target traffic area according to the initial traffic state information;
determining new control information of the traffic signal of the target traffic area by using an optimization algorithm based on the initial control information and the first state error, performing simulation by using the new control information and the traffic information, predicting new traffic state information of the target traffic area, calculating a second state error, adjusting the optimization algorithm by using the second state error, updating the initial control information into the new control information, returning to the step of determining the new control information of the traffic signal of the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, wherein the first state error is: an error of the expected traffic status information with respect to a last obtained traffic status information, wherein the second status error is: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information;
and determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information.
2. The method of claim 1, wherein said adjusting said optimization algorithm using said second state error comprises:
and under the condition that the iteration times are more than or equal to 3, adjusting the current optimization algorithm by utilizing the second state error and the optimization algorithm adopted in the previous 3 iterations.
3. The method according to claim 2, wherein in the case that the number of iterations is greater than or equal to 3, adjusting a current optimization algorithm by using the second state error and an optimization algorithm adopted in the previous 3 iterations comprises:
obtaining an adjusted optimization algorithm according to the following formula
Figure DEST_PATH_IMAGE002
：
Figure DEST_PATH_IMAGE004
Wherein, the ∂1Representing a first weight, said ∂2Represents a second weight, said ∂3Represents a third weight, said
Figure DEST_PATH_IMAGE006
Represents the optimization algorithm employed during the first 1 iteration, said
Figure DEST_PATH_IMAGE008
Represents the optimization algorithm employed during the first 2 iterations, said
Figure DEST_PATH_IMAGE010
Represents the optimization algorithm adopted in the first 3 iterations, μ, ѯ represent preset regularization factors, Δ (k, l-1) represents the difference between the new control information adopted in the current iteration and the new control information adopted in the previous iteration, Δ
Figure DEST_PATH_IMAGE012
(k +1, l-1) represents the second error.
4. The method of claim 1, wherein determining new control information for the traffic signal for the target traffic zone using an optimization algorithm based on the initial control information and the first state error comprises:
determining new control information u (k, l) for the traffic signal of the target traffic zone according to the following formula:
Figure DEST_PATH_IMAGE014
wherein u (k, l-1) represents the initial control information, p represents a preset learning rate, and
Figure 568798DEST_PATH_IMAGE002
representing the optimization algorithm, said JcritRepresents the desired traffic status information, the J (k +1, l-1) represents the last resulting traffic status information, the λ, the,
Figure DEST_PATH_IMAGE016
Representing a preset regularization factor.
5. The method according to any one of claims 1-4, wherein the predicting initial traffic status information of the target traffic zone using the initial control information, flow information for simulation, comprises:
and simulating by using the initial control information and the flow information, and predicting the vehicle queuing density variance and the vehicle average delay time of the target traffic area as initial traffic state information.
6. The method of claim 5, wherein determining the target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained from each iteration comprises:
aiming at new control information obtained by each iteration, calculating a first lifting ratio of corresponding new traffic state information relative to the variance of the vehicle queuing density in the initial traffic state information, calculating a second lifting ratio of corresponding new traffic state information relative to the average delay time of vehicles in the initial traffic state information, and performing weighted calculation on the first lifting ratio and the second lifting ratio to obtain the comprehensive lifting degree of the new control information;
and determining the new control information with the highest comprehensive lifting degree as target control information.
7. The method of claim 5, wherein the predicting the variance of the vehicle queue density for the target traffic zone using the initial control information, flow information for the simulation, comprises:
simulating by using the initial control information and the flow information, and predicting the maximum queuing lengths of the target traffic area at different moments and different road sections during the preset time period;
aiming at each moment, calculating the vehicle queuing density at the moment by using the maximum queuing length at the moment and the length of the road section;
and calculating the variance between the vehicle queuing densities corresponding to different moments to obtain the vehicle queuing density variance of the target traffic area.
8. A traffic signal control apparatus, characterized in that the apparatus comprises:
the information acquisition module is used for acquiring initial control information of traffic signals of a target traffic area in a road and predicting flow information of a preset time period of the target traffic area;
the traffic state prediction module is used for simulating by utilizing the initial control information and the flow information to predict the initial traffic state information of the target traffic area;
an expected traffic state determining module for determining expected traffic state information of the target traffic zone according to the initial traffic state information;
an optimization iteration module, configured to determine new control information of the traffic signal in the target traffic area by using an optimization algorithm based on the initial control information and the first state error, perform simulation by using the new control information and the traffic information, predict new traffic state information of the target traffic area, calculate a second state error, adjust the optimization algorithm by using the second state error, update the initial control information to the new control information, return to the step of determining the new control information of the traffic signal in the target traffic area by using the optimization algorithm based on the initial control information and the first state error until the iteration number reaches a preset number threshold, where the first state error is: an error of the expected traffic status information with respect to a last obtained traffic status information, wherein the second status error is: the error between the degree of promotion of the current traffic state information relative to the expected traffic state information and the degree of promotion of the last traffic state information relative to the expected traffic state information;
and the target control module is used for determining target control information of which the corresponding new traffic state information meets the preset traffic state requirement from the new control information obtained by each iteration, and controlling the traffic signals of the target traffic area by using the target control information.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 7 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
CN202210069135.8A 2022-01-21 2022-01-21 Traffic signal control method and device, electronic equipment and storage medium Active CN114093178B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210069135.8A CN114093178B (en) 2022-01-21 2022-01-21 Traffic signal control method and device, electronic equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210069135.8A CN114093178B (en) 2022-01-21 2022-01-21 Traffic signal control method and device, electronic equipment and storage medium

Publications (2)

Publication Number Publication Date
CN114093178A true CN114093178A (en) 2022-02-25
CN114093178B CN114093178B (en) 2022-05-06

Family

ID=80309244

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210069135.8A Active CN114093178B (en) 2022-01-21 2022-01-21 Traffic signal control method and device, electronic equipment and storage medium

Country Status (1)

Country Link
CN (1) CN114093178B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106157643A (en) * 2015-04-07 2016-11-23 中国科学院沈阳自动化研究所 A kind of traffic signal optimization alternative manner based on message matrix gridding
CN106683441A (en) * 2016-12-28 2017-05-17 安徽科力信息产业有限责任公司 Intersection signal timing plan evaluating method
CN109767632A (en) * 2019-03-02 2019-05-17 太原理工大学 A kind of traffic signals mixing control method based on iterative learning and Model Predictive Control
CN110517510A (en) * 2019-08-27 2019-11-29 华侨大学 Based on the Intersections timing designing method for improving Webster function and genetic algorithm
KR102051071B1 (en) * 2018-11-08 2019-12-02 아주대학교 산학협력단 Method and apparatus for diagnosing traffic signal system
CN111710167A (en) * 2020-08-24 2020-09-25 深圳市城市交通规划设计研究中心股份有限公司 Single-point optimization control method and device based on online traffic simulation

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106157643A (en) * 2015-04-07 2016-11-23 中国科学院沈阳自动化研究所 A kind of traffic signal optimization alternative manner based on message matrix gridding
CN106683441A (en) * 2016-12-28 2017-05-17 安徽科力信息产业有限责任公司 Intersection signal timing plan evaluating method
KR102051071B1 (en) * 2018-11-08 2019-12-02 아주대학교 산학협력단 Method and apparatus for diagnosing traffic signal system
CN109767632A (en) * 2019-03-02 2019-05-17 太原理工大学 A kind of traffic signals mixing control method based on iterative learning and Model Predictive Control
CN110517510A (en) * 2019-08-27 2019-11-29 华侨大学 Based on the Intersections timing designing method for improving Webster function and genetic algorithm
CN111710167A (en) * 2020-08-24 2020-09-25 深圳市城市交通规划设计研究中心股份有限公司 Single-point optimization control method and device based on online traffic simulation

Also Published As

Publication number Publication date
CN114093178B (en) 2022-05-06

Similar Documents

Publication Publication Date Title
WO2022121510A1 (en) Stochastic policy gradient-based traffic signal control method and system, and electronic device
CN111243299B (en) Single cross port signal control method based on 3 DQN-PSER algorithm
CN103700255B (en) A kind of traffic flow forecasting method based on spacetime correlation data mining
CN112289034A (en) Deep neural network robust traffic prediction method based on multi-mode space-time data
CN108648457B (en) Method, device and computer readable storage medium for speed prediction
WO2021129086A1 (en) Traffic prediction method, device, and storage medium
Guthrie Real options analysis of climate-change adaptation: investment flexibility and extreme weather events
CN1737502A (en) Quasi dynamic route optimization method of vehicle-mounted guiding system for evading delaying risk
CN113762578A (en) Training method and device of flow prediction model and electronic equipment
CN114093178B (en) Traffic signal control method and device, electronic equipment and storage medium
CN115691165A (en) Traffic signal lamp scheduling method, device and equipment and readable storage medium
CN117540938B (en) Integrated building energy consumption prediction method and system based on TD3 reinforcement learning optimization
WO2022258943A1 (en) Traffic control system
CN113537563A (en) Pollution emergency management and control effect evaluation method and device
Cheng et al. Efficient scenario analysis for optimal adaptation of bridge networks under deep uncertainties through knowledge transfer
CN107688862A (en) Insulator equivalent salt density accumulation rate Forecasting Methodology based on BA GRNN
CN114091357B (en) Traffic flow prediction method, traffic flow prediction device, electronic device, and storage medium
CN112446550B (en) Short-term building load probability density prediction method
CN115081920A (en) Attendance check attendance check scheduling management method, system, equipment and storage medium
Siu et al. Applying regression analysis to predict and classify construction cycle time
CN114048886A (en) Effective berthage combination prediction method based on neural network, terminal and storage medium
CN112686416A (en) Wind power probability prediction method and system
Tijskens et al. Predicting the hygrothermal behaviour of building components using neural networks
Hu et al. Modeling bus bunching: A comprehensive approach accounting for transit demand and traffic flows
CN117708716B (en) Regression and time sequence fusion-based photovoltaic power generation power prediction method and equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant