CN117671977B - Signal lamp control method, system, device and medium for traffic trunk line - Google Patents
Signal lamp control method, system, device and medium for traffic trunk line Download PDFInfo
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Abstract
The application relates to a signal lamp control method, a system, a device and a medium for a traffic trunk, wherein the method comprises the following steps: acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk line; constructing a trunk simulation platform of a preset traffic trunk based on intersection physical information and intersection traffic flow information; and training a trunk signal lamp control strategy of a preset traffic trunk through a kernel orthogonal gradient algorithm based on the given ideal state of the traffic trunk and the trunk simulation platform, wherein the trunk signal lamp control strategy is used for controlling signal lamps of all intersections in the preset traffic trunk. The application solves the problem of how to efficiently control the traffic lights at all intersections of the traffic trunk, reduces the calculated amount and the iteration times of the control strategy based on the orthogonality of the kernel orthogonal gradient algorithm, improves the control efficiency of the traffic lights at all intersections in the traffic trunk, and can effectively control the traffic flow in the large-scale traffic trunk.
Description
Technical Field
The application relates to the technical field of traffic control, in particular to a traffic signal lamp control method, a traffic signal lamp control system, a traffic signal lamp control device and a traffic signal lamp control medium for a traffic trunk line.
Background
Traffic control is required in some situations such as large sporting events, green tunnels, and emergency handling. In order to simultaneously ensure traffic control and reduce the impact on the traffic routes and to quickly resume the traffic capacity of the routes after the traffic control is completed, efficient traffic signal control schemes are required to be implemented.
At present, no effective solution is proposed for the problem of how to efficiently control traffic signals at all intersections of a traffic trunk in the related art.
Disclosure of Invention
The embodiment of the application provides a signal lamp control method, a system, a device and a medium for a traffic trunk line, which at least solve the problem of how to efficiently control the traffic signal lamps at all intersections of the traffic trunk line in the related technology.
In a first aspect, an embodiment of the present application provides a method for controlling a signal lamp of a traffic trunk, where the method includes:
acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk line;
constructing a trunk simulation platform of the preset traffic trunk based on the intersection physical information and the intersection traffic flow information;
And training a trunk signal lamp control strategy of the preset traffic trunk through a kernel orthogonal gradient algorithm based on the given ideal state of the traffic trunk and the trunk simulation platform, wherein the trunk signal lamp control strategy is used for controlling signal lamps of all intersections in the preset traffic trunk.
In some of these embodiments, training the trunk signal control strategy of the preset traffic trunk by a kernel orthogonal gradient algorithm based on a given traffic trunk ideal state and the trunk simulation platform comprises:
Repeatedly executing the preset training steps until the execution times reach the training iteration times M, and obtaining a trunk signal lamp control strategy pi θ after training is completed;
The preset training step comprises the following steps:
generating N traffic running tracks of the preset traffic trunk line through the trunk line simulation platform;
By passing through Calculating to obtain a parameter w i of an orthogonal Gaussian kernel function of each traffic running track, wherein K (e i, ei+1)=exp(-0.5||ei,-ei+1||2) is the orthogonal Gaussian kernel function of the traffic running track, i epsilon N, N is the number of the traffic running tracks, e i =(si,ai) is a traffic trunk simulation state set of the ith traffic running track, s i and a i are included, s i is a traffic trunk flow simulation state, and a i is a signal lamp simulation phase;
the parameters θ of the trunk signal lamp control strategy pi θ are iteratively updated based on the given traffic trunk ideal state, and the parameters w i of the orthogonal gaussian kernel function for each traffic trace.
In some of these embodiments, iteratively updating the parameters θ of the trunk signal control strategy pi θ based on the given traffic trunk ideal state, and the parameters w i of the orthogonal gaussian kernel function for each traffic trace, includes:
setting a reference function as a (e) = R (e) -V(s), wherein R (e) = - |e * -e| is a reward function, e * is a given ideal state of the traffic trunk, e is a simulation state of the traffic trunk, and V(s) is a parameterized value function obtained through a general gradient algorithm;
By passing through Iteratively updating parameters theta of a trunk signal lamp control strategy pi θ, wherein alpha is a learning rate, w i is a parameter of an orthogonal Gaussian kernel function, i is a serial number of a traffic running track,Gamma is discount factor, t is time in traffic running track, s is traffic main line traffic flow simulation state, a is signal lamp simulation phase,/>，/>A is a reference function.
In some embodiments, acquiring intersection physical information of each intersection in the preset traffic trunk includes:
And acquiring intersection physical information of each intersection in a preset traffic trunk line from actual road network layout information, wherein the intersection physical information comprises intersection position information, intersection signal lamp distribution information and trunk line entrance information.
In some embodiments, obtaining intersection traffic information for each intersection in the preset traffic trunk includes:
and acquiring intersection traffic flow information of each intersection in a preset traffic trunk line through traffic equipment, wherein the intersection traffic flow information comprises trunk line entrance traffic flow information, trunk line exit traffic flow information and intersection vehicle running speed information.
In some embodiments, collecting intersection traffic information for each intersection in a preset traffic trunk by a traffic device includes:
The method comprises the steps of collecting intersection traffic flow information of all intersections in a preset traffic trunk line in different periods through traffic equipment, wherein the different periods comprise rush hour, traffic control period and holiday period.
In some embodiments, the trunk signal control strategy is used for controlling signal lights of various intersections in the preset traffic trunk, and the method comprises the following steps:
acquiring traffic flow state information and signal lamp phase information of the preset traffic trunk line in real time;
And controlling signal lamp phase switching of each intersection through the main signal lamp control strategy based on the traffic flow state information and the signal lamp phase information of the preset traffic main line.
In a second aspect, an embodiment of the present application provides a signal lamp control system for a traffic trunk, where the system includes a data acquisition module, a model simulation module, and a signal lamp control module;
The data acquisition module is used for acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk;
the model simulation module is used for building a trunk simulation platform of the preset traffic trunk according to the intersection physical information and the intersection traffic flow information;
the signal lamp control module is used for training a main signal lamp control strategy of the preset traffic main line through a kernel orthogonal gradient algorithm according to the given ideal state of the traffic main line, wherein the main signal lamp control strategy is used for controlling signal lamp phases based on the main simulation platform.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as described in the first aspect above.
Compared with the related art, the signal lamp control method, system, device and medium for the traffic trunk provided by the embodiment of the application have the advantages that intersection physical information and intersection traffic flow information of all intersections in the preset traffic trunk are obtained; constructing a trunk simulation platform of a preset traffic trunk based on intersection physical information and intersection traffic flow information; based on the given ideal state of the traffic trunk and the trunk simulation platform, training a trunk signal lamp control strategy of a preset traffic trunk through a kernel orthogonal gradient algorithm, wherein the trunk signal lamp control strategy is used for controlling signal lamps of all intersections in the preset traffic trunk, solving the problem of how to efficiently control the traffic signal lamps of all intersections of the traffic trunk, realizing the reduction of the calculated amount and the iteration times of the control strategy based on the orthogonality of the kernel orthogonal gradient algorithm, improving the signal lamp control efficiency of all intersections in the traffic trunk, and effectively controlling traffic flow in a large-scale traffic trunk.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
Fig. 1 is a flow chart of steps of a traffic signal control method for a traffic trunk according to an embodiment of the present application;
FIG. 2 is a block diagram of a traffic signal control system for a traffic trunk according to an embodiment of the present application;
Fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.
The attached drawings are identified: 21. a data acquisition module; 22. a model simulation module; 23. and the signal lamp control module.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
Example 1
The embodiment of the application provides a signal lamp control method of a traffic trunk, and fig. 1 is a step flow chart of the signal lamp control method of the traffic trunk according to the embodiment of the application, as shown in fig. 1, and the method comprises the following steps:
Step S102, acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk line;
step S102 specifically further includes the steps of:
Step S1021, obtaining intersection physical information of each intersection in a preset traffic trunk line from actual road network layout information, wherein the intersection physical information comprises intersection position information, intersection signal lamp distribution information and trunk line entrance information.
Step S1022, collecting intersection traffic flow information of all intersections in a preset traffic trunk line through traffic equipment, wherein the intersection traffic flow information comprises trunk line entrance traffic flow information, trunk line exit traffic flow information and intersection vehicle running speed information.
In particular, step S1022 collects intersection traffic flow information of each intersection in the preset traffic trunk under different periods by the traffic device, wherein the different periods include rush hour, traffic control period and holiday period.
It should be noted that, the intersection traffic flow information of each intersection in the traffic trunk with complex traffic conditions such as the early and late peak, holidays, temporary traffic control and the like is collected, so that the trunk simulation platform is built in the subsequent step S104, so that the signal lamp phase under various complex traffic conditions can be effectively controlled based on the signal lamp control strategy trained by the trunk simulation platform.
Step S104, setting up a trunk simulation platform of a preset traffic trunk based on intersection physical information and intersection traffic flow information;
step S104 is to build a trunk simulation platform of a preset traffic trunk through a micro simulation software VISSIM based on the intersection physical information and the intersection traffic flow information.
Step S106, training a trunk signal lamp control strategy of a preset traffic trunk through a kernel orthogonal gradient algorithm based on a given ideal state of the traffic trunk and a trunk simulation platform, wherein the trunk signal lamp control strategy is used for controlling signal lamps of all intersections in the preset traffic trunk.
Step S106 specifically further includes the steps of:
Step S1061, repeating the preset training step until the execution times reach the training iteration times M, and obtaining a main signal lamp control strategy pi θ after training is completed; the preset training steps include the following steps S1062 to S1064.
Step S1062, generating N traffic running tracks of a preset traffic trunk through a trunk simulation platform;
step S1063, pass Calculating to obtain a parameter w i of an orthogonal Gaussian kernel function of each traffic running track, wherein K (e i, ei+1)=exp(-0.5||ei,-ei+1||2) is the orthogonal Gaussian kernel function of the traffic running track, i epsilon N, N is the number of the traffic running tracks, e i =(si,ai) is a traffic trunk simulation state set of the ith traffic running track, s i and a i are included, s i is a traffic trunk flow simulation state, and the state can be any traffic flow parameter such as flow, speed, density and the like or a combination of the traffic flow parameters, and a i is a signal lamp simulation phase;
Step S1064, iteratively updating the parameter θ of the trunk signal lamp control policy pi θ based on the given ideal state of the traffic trunk and the parameter w i of the orthogonal gaussian kernel function of each traffic track.
Step S1064 specifically sets the reference function as a (e) = R (e) -V (S), where R (e) = - |e * -e| is the reward function, e * is the ideal state of the given traffic trunk, e is the simulation state of the traffic trunk, and V (S) is the parameterized value function obtained by the general gradient algorithm;
By passing through Iteratively updating parameters theta of a trunk signal lamp control strategy pi θ, wherein alpha is a learning rate, w i is a parameter of an orthogonal Gaussian kernel function, i is a serial number of a traffic running track,Gamma is discount factor, t is time in traffic running track, s is traffic main line traffic flow simulation state, a is signal lamp simulation phase,/>，/>A is a reference function.
Further, in step S106, the trunk signal lamp control strategy for controlling the signal lamps of the respective intersections in the preset traffic trunk includes:
Acquiring traffic flow state information and signal lamp phase information of a preset traffic trunk line in real time; and controlling signal lamp phase switching of each intersection through a main signal lamp control strategy based on the traffic flow state information and the signal lamp phase information of the preset traffic main line.
Through the steps S102 to S106 in the embodiment of the application, the problem of how to efficiently control the traffic lights at all intersections of the traffic trunk is solved, the calculated amount and the iteration times of the control strategy are reduced based on the orthogonality of the kernel orthogonal gradient algorithm, the control efficiency of the traffic lights at all intersections in the traffic trunk is improved, and the traffic flow in the large-scale traffic trunk can be effectively controlled. In other words, the embodiment of the application creatively combines the trunk simulation platform with the kernel orthogonal gradient algorithm, so that the signal lamp control strategy obtained by training can effectively control the traffic flow in a large-scale traffic trunk.
Example 2
The specific embodiment of the application provides a signal lamp control method of a traffic trunk, which comprises the following steps:
step S1, for a given urban traffic trunk line, collecting distributed relevant data of each intersection, including physical information such as intersection positions, signal lamp distribution, traffic flow inlets and outlets and the like;
it should be noted that, in step S1, the intersection information of the trunk line should be similar to the actual road network, for example, the auxiliary road of the trunk line, the position and distribution of the tunnel, and the simulation model should be approximately restored in the construction.
Step S2, for the trunk line, traffic flow information at the intersection of the trunk line, such as time-division traffic flow, running speed distribution and the like of each entrance, is counted through a monitoring camera;
S3, constructing a microscopic simulation platform according to the collected data, restoring the vehicle inflow distribution of the intersection at the traffic inlet of the trunk line according to traffic information, setting simulation signal lamps according to the distribution positions of the signal lamps, and switching the signal lamps;
it should be noted that, in the step S3, the traffic flow distribution of the dry road network should be emphasized in consideration of the time when the traffic pressure is high, such as the early and late peak, holidays, temporary traffic control, etc., and this typical time can reflect the traffic performance of the road network.
And step S4, giving the expected state of the road network flow, namely the ideal state of the traffic trunk line, so that the traffic of vehicles in a certain period accords with the expected state. For example, the ideal state of the trunk line in the early peak period is that the phase of the intersection takes priority of the phase of the traffic flow direction of the peak, so that the problem of congestion of the traffic flow direction of the peak is relieved; under the holiday period, the ideal traffic state of the trunk line is the shortest average waiting time of vehicles in unit time, and the maximum traffic number of the vehicles is the optimal index; the ideal traffic state under temporary traffic control is the optimal state according to the traffic state under the control measures set by the traffic control department, and the traffic quantity of each phase vehicle in the control time period accords with the control measures. According to the provided expected state, training a signal lamp control strategy based on kernel orthogonal gradient optimization training a main line network;
It should be noted that the policy training step in step S4 is specifically as follows:
S41, first, a parameter description of the training section is given: the control strategy of the trunk signal lamp is recorded as pi θ, the number of strategy training iterations M is set as N, the simulation training firstly generates N running tracks of the trunk traffic, the tracks comprise road network states s and signal lamp phases a, and the tracks are recorded as The road network state corresponds to a bonus function of R (e) = - |e * -e|, where the bonus function definition is associated with a given road network ideal state e *, the closer to the ideal state e *, the larger the bonus function. Let the policy optimization reference function be a (e) =r (e) -V(s), where V(s) represents an estimate of the parameterized value function, which can be derived from common gradient optimization algorithms. The positive definite kernel function of the track is marked as K (e, e '), the weighted N-point subset (i.e. the N-point subset e' obtained by weighting e) approximates the empirical measure of the N-point experimental measurement, the process is completed by Gaussian regression, and the orthogonal Gaussian kernel function is output as the parameter w of the kernel function.
S42, repeatedly executing the strategy training iteration times M times of S43-S47.
S43, generating N tracks based on the trunk simulation platform.
S44, according to kernel functionThe parameter w of the kernel function is estimated.
S45, calculating a reward function R and a reference function A.
S46, for each track, parameters θ of the rail signal control strategy pi θ:
Wherein, alpha=0.01 is the learning rate, ，/>Γ=0.9 is a discount factor, and t represents a time within one traffic trajectory.
S47, for each track, optimizing a reference function by using an inner product update strategy of the estimated kernel function parameters。
S48, the final strategy is the obtained main signal lamp control strategy based on the kernel orthogonal strategy optimization algorithm.
And S5, applying the strategy for controlling the signal lamp phase of the given urban trunk line, and testing.
In the actual application of the urban trunk, the expected traffic flow e * needs to be set first, and the traffic flow needs to be in accordance with the actual situation, and is generally obtained from the road network state at a specific moment, and cannot be obtained on assumption. However, during the verification process, some limit states may be assumed to test the robustness of the control strategy, for example: 1) Carrying out full-road-section supersaturated vehicle flow simulation on the trunk line, setting the vehicle flow to be larger than the intersection bearing capacity, and detecting whether a control strategy can stably run; 2) And simulating the supersaturated traffic flow of the local road section of the trunk line, wherein the entrance vehicles of the local road section are larger than the bearing capacity of the road junction, and detecting whether the control strategy can take precedence over the traffic efficiency of the supersaturated traffic flow road junction and whether the operation is stable.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
Example 3
The embodiment of the application provides a signal lamp control system of a traffic trunk, and fig. 2 is a structural block diagram of the signal lamp control system of the traffic trunk according to the embodiment of the application, and as shown in fig. 2, the system comprises a data acquisition module 21, a model simulation module 22 and a signal lamp control module 23;
the data acquisition module 21 is used for acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk;
the model simulation module 22 is configured to build a trunk simulation platform of a preset traffic trunk according to the intersection physical information and the intersection traffic flow information;
The signal lamp control module 23 is used for training a main signal lamp control strategy of a preset traffic main line through a kernel orthogonal gradient algorithm according to the given ideal state of the traffic main line, wherein the main signal lamp control strategy is used for controlling signal lamp phases based on a main simulation platform.
The data acquisition module 21, the model simulation module 22 and the signal lamp control module 23 in the embodiment of the application solve the problem of how to efficiently control the traffic signals at all intersections of the traffic trunk, realize the reduction of the calculation amount and the iteration times of the control strategy based on the orthogonality of the kernel orthogonal gradient algorithm, improve the signal lamp control efficiency of all intersections in the traffic trunk and effectively control the traffic flow in a large scale of traffic trunk.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Example 4
The present embodiment also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and this embodiment is not repeated herein.
In addition, in combination with the traffic signal control method of the traffic trunk in the above embodiment, the embodiment of the present application may be implemented by providing a storage medium. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements the traffic light control method of any of the above-described embodiments.
Example 5
In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of controlling a traffic light of a traffic trunk. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Example 6
In one embodiment, fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 3, an electronic device, which may be a server, is provided, and an internal structure diagram thereof may be as shown in fig. 3. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus, where the non-volatile memory stores an operating system, computer programs, and a database. The processor is used for providing computing and control capabilities, the network interface is used for communicating with an external terminal through a network connection, the internal memory is used for providing an environment for the operation of an operating system and a computer program, and the computer program is executed by the processor to realize a traffic signal control method of a traffic trunk line, and the database is used for storing data.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be understood by those skilled in the art that the technical features of the above-described embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above-described embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A method of controlling a traffic signal on a traffic trunk, the method comprising:
acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk line;
constructing a trunk simulation platform of the preset traffic trunk based on the intersection physical information and the intersection traffic flow information;
Repeatedly executing the preset training steps until the execution times reach the training iteration times M, and obtaining a trunk signal lamp control strategy pi θ after training is completed;
The preset training step comprises the following steps:
generating N traffic running tracks of the preset traffic trunk line through the trunk line simulation platform;
By passing through Calculating to obtain a parameter w i of an orthogonal Gaussian kernel function of each traffic running track, wherein K (e i, ei+1)=exp(-0.5|| ei,-ei+1||2) is the orthogonal Gaussian kernel function of the traffic running track, i epsilon N, N is the number of the traffic running tracks, e i =(si, ai) is a traffic trunk simulation state set of the ith traffic running track, s i and a i are included, s i is a traffic trunk flow simulation state, and a i is a signal lamp simulation phase;
the reference function is set to a (e) =r (e) -V(s), wherein, As a reward function,/>For a given ideal state of the traffic trunk, e is a simulation state of the traffic trunk, and V(s) is a parameterized value function obtained through a general gradient algorithm;
By passing through Iteratively updating parameters theta of a trunk signal lamp control strategy pi θ, wherein alpha is a learning rate, w i is a parameter of an orthogonal Gaussian kernel function, i is a serial number of a traffic running track,Gamma is discount factor, t is time in the traffic running track, s t is traffic main flow simulation state at time t, a t is signal lamp simulation phase at time t,/>，/>And A is a reference function, wherein the trunk signal lamp control strategy is used for controlling signal lamps of all intersections in the preset traffic trunk.
2. The method of claim 1, wherein obtaining intersection physical information for each intersection in the preset traffic trunk comprises:
And acquiring intersection physical information of each intersection in a preset traffic trunk line from actual road network layout information, wherein the intersection physical information comprises intersection position information, intersection signal lamp distribution information and trunk line entrance information.
3. The method of claim 1, wherein obtaining intersection traffic flow information for each intersection in the pre-set traffic trunk comprises:
and acquiring intersection traffic flow information of each intersection in a preset traffic trunk line through traffic equipment, wherein the intersection traffic flow information comprises trunk line entrance traffic flow information, trunk line exit traffic flow information and intersection vehicle running speed information.
4. A method according to claim 3, wherein collecting, by the traffic device, intersection traffic information for each intersection in the preset traffic trunk comprises:
The method comprises the steps of collecting intersection traffic flow information of all intersections in a preset traffic trunk line in different periods through traffic equipment, wherein the different periods comprise rush hour, traffic control period and holiday period.
5. A method according to claim 3, wherein the trunk signal control strategy for controlling the signal of each intersection in the preset traffic trunk comprises:
acquiring traffic flow state information and signal lamp phase information of the preset traffic trunk line in real time;
And controlling signal lamp phase switching of each intersection through the main signal lamp control strategy based on the traffic flow state information and the signal lamp phase information of the preset traffic main line.
6. The signal lamp control system of the traffic trunk is characterized by comprising a data acquisition module, a model simulation module and a signal lamp control module;
The data acquisition module is used for acquiring intersection physical information and intersection traffic flow information of each intersection in a preset traffic trunk;
the model simulation module is used for building a trunk simulation platform of the preset traffic trunk according to the intersection physical information and the intersection traffic flow information;
the signal lamp control module is used for repeatedly executing the preset training steps until the execution times reach the training iteration times M, and obtaining a main signal lamp control strategy pi θ after training is completed;
The preset training step comprises the following steps:
generating N traffic running tracks of the preset traffic trunk line through the trunk line simulation platform; by passing through Calculating to obtain a parameter w i of an orthogonal Gaussian kernel function of each traffic running track, wherein K (e i, ei+1)=exp(-0.5|| ei,-ei+1||2) is the orthogonal Gaussian kernel function of the traffic running track, i epsilon N, N is the number of the traffic running tracks, e i =(si, ai) is a traffic trunk simulation state set of the ith traffic running track, s i and a i are included, s i is a traffic trunk flow simulation state, and a i is a signal lamp simulation phase;
the reference function is set to a (e) =r (e) -V(s), wherein, As a reward function,/>For a given ideal state of the traffic trunk, e is a simulation state of the traffic trunk, and V(s) is a parameterized value function obtained through a general gradient algorithm;
By passing through Iteratively updating parameters theta of a trunk signal lamp control strategy pi θ, wherein alpha is a learning rate, w i is a parameter of an orthogonal Gaussian kernel function, i is a serial number of a traffic running track,Gamma is discount factor, t is time in the traffic running track, s t is traffic main flow simulation state at time t, a t is signal lamp simulation phase at time t,/>，/>And A is a reference function, wherein the trunk signal lamp control strategy is used for controlling signal lamp phase based on the trunk simulation platform.
7. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 1 to 5.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 5.
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