Fixed-time area traffic signal optimization method and system

Abstract

The application provides a fixed time area traffic signal optimization method and system, and belongs to the technical field of traffic signal control. Road network data of an area to be optimized is acquired. The road network data comprises intersection positions, lane flow directions, lane numbers, signal light phases and sequences, and vehicle input and output of each entrance and exit in a unit hour. The acquired road network data of the area to be optimized is processed by using a constructed optimization model to obtain new signal timing of the area to be optimized. The optimization model is constructed according to the relationship between signal light phases of the area to be optimized and vehicle average delay, and the optimization target is to obtain as small vehicle average delay time as possible. The application can be well applied to large-scale area road networks by using a proxy model to assist evolutionary optimization algorithms, and can compensate for the problem that existing signal timing optimization algorithms are difficult to be applied to large-scale road networks due to large search spaces and limited calculation budgets to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic signal control, and in particular to a method and system for optimizing traffic signals in a fixed time area. Background Art

[0002] There are two main categories of methods used for regional traffic signal timing optimization: reinforcement learning-based methods and optimization-based methods. Numerous studies have demonstrated, in various ways, that nature-inspired algorithms can solve challenging optimization problems in practice. Drawing inspiration from natural phenomena such as species evolution and animal intelligence, metaheuristic algorithms possess strong optimization capabilities and flexibility in complex environments. Genetic algorithms and particle swarm optimization algorithms are two of the most popular. Rouphail et al. attempted to use a classical genetic algorithm to optimize traffic signals in an urban traffic network with nine intersections to reduce link delay and total network queuing time. Sánchez-Medina et al. combined a genetic algorithm with a cellular automaton-based microscopic simulator to develop a signal optimization platform and implemented it on a BeowulfCluster multi-machine system. The performance of the optimization platform was tested on a traffic network with seven intersections. Kachroudi and Bhouri combined a multi-objective particle swarm optimization algorithm with model predictive control to optimize two conflicting objectives in a virtual urban network with 16 intersections. Hu et al. proposed a signal timing scheduling optimization (TSO) algorithm based on a quantum particle swarm optimization algorithm. They mapped an urban traffic network with 15 intersections into an enhanced Biham, Middleton, and Levine (BML) traffic model to test the performance of TSO. Garcia-Nieto et al. used an improved particle swarm optimization algorithm to solve large-scale traffic signal optimization problems and showed that the algorithm can find attractive signal schemes for traffic networks with up to 40 intersections. Celtek et al. proposed a swarm-based heuristic optimization algorithm to optimize traffic signal control problems. They numerically demonstrated the success of the proposed method and proved that the particle swarm optimization algorithm and its variance algorithm can be applied to traffic signal control optimization in actual traffic.

[0003] In addition to genetic algorithms and particle swarm optimization, there are other methods. Jovanoviki used a bee colony optimization algorithm to optimize signal timing for a traffic network with nine intersections. Experimental results showed that the algorithm outperformed a simulated annealing algorithm in reducing total vehicle travel time. Liang et al. introduced two improved distribution estimation algorithms to reduce total vehicle delay time. Experiments conducted on a traffic network with 11 intersections showed that these two improved distribution estimation algorithms outperformed several existing metaheuristic algorithms. Gao et al., with the goal of reducing total vehicle delay time, comprehensively studied the effectiveness of genetic algorithms, artificial bee colony algorithms, discrete harmonic search algorithms, Jaya algorithms, and water cycle algorithms on a set of traffic networks with 9 to 400 intersections. They also proposed three local search operators and their sets based on the characteristics of the problem. Liu Changyuan et al. proposed a regional traffic signal timing optimization method based on an improved firefly algorithm. With the goal of minimizing total regional delay, they added Cauchy mutation to the standard firefly algorithm to improve global optimization capabilities. Lin et al. established an average vehicle delay model, an average queue length model, a total delay calculation model, and a vehicle exhaust emission model at intersections, and obtained an optimization model for a new traffic artery coordinated control system. They also combined fuzzy control theory with an adaptive sequential mutation multi-objective differential evolution algorithm to propose a new method for optimizing traffic signal control at urban intersections.

[0004] Although metaheuristic algorithms have demonstrated certain advantages in traffic signal optimization and have been successfully applied to small- and medium-scale traffic signal optimization problems, they are rarely applied to large-scale traffic networks. On the one hand, as the number of traffic signals increases, the solution space for signal optimization problems grows exponentially, and traffic flows within the network become more complex. On the other hand, the signal optimization process relies on traffic simulation to model traffic flow dynamics and evaluate the effectiveness of candidate signal solutions. This process is computationally intensive, necessitating only a small number of simulations. To address this issue, some researchers have introduced surrogate models to aid in the optimization process, thereby conserving computational resources.

[0005] Timothy Tay et al. proposed a Bayesian optimization (BO) method using a Gaussian process (GP) model to solve high-dimensional transportation problems. They optimized a large-scale traffic network with 97 intersections and proposed formulas for the GP's prior mean and covariance functions, enabling BO to incorporate problem-specific transportation information while maintaining computational tractability. Liang et al. proposed an agent-assisted collaborative signal optimization (SCSO) method to optimize a large-scale traffic network with 43 intersections. The method decomposes the traffic network into a set of manageable subnetworks, which are then collaboratively optimized using an agent-assisted optimizer to implement signal settings. The agent-assisted optimizer significantly reduces the computational burden by reducing the number of expensive traffic simulations.

[0006] In large-scale traffic signal optimization, the solution space for the problem grows exponentially as the number of traffic signals increases, and traffic flows in the network become increasingly complex, a phenomenon known as the "curse of dimensionality." Furthermore, the signal optimization process relies on traffic simulation models to simulate traffic flow dynamics and evaluate the effectiveness of candidate signal solutions. This process is computationally expensive, exacerbated by the increasing size of the network, and only a small number of simulations are possible. Currently, relatively little research has been conducted on the application of surrogate model-assisted optimization algorithms to large-scale traffic signal optimization.

[0007] Furthermore, since most real-world traffic lights operate under fixed signal timing schemes, traffic flows tend to repeat similar morning and evening rush hour patterns. Therefore, developing efficient fixed-time traffic signal optimization methods is of great practical significance. However, due to the large search space and limited computational budget, the search performance of existing signal timing optimization algorithms for large-scale traffic signal optimization has generally declined, making most existing technologies difficult to apply to large-scale road networks. Summary of the Invention

[0008] The object of the present invention is to provide a method and system for optimizing traffic signals in a fixed time area, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] In a first aspect, the present invention provides a method for optimizing traffic signals in a fixed time zone, comprising:

[0011] Obtaining road network data for the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour for each entrance and exit;

[0012] The constructed optimization model is used to process the acquired road network data of the area to be optimized to obtain a new signal timing for the area to be optimized. The optimization model is constructed by taking into account the relationship between the signal light phase and the average vehicle delay in the area to be optimized. The optimization goal is to minimize the average vehicle delay time.

[0013] As a further limitation of the first aspect of the present invention, the relationship between the signal light phase and the average vehicle delay in the area to be optimized is used to construct the optimization model, including: the number of T-intersections in the area to be optimized is a, each T-intersection has three phases, the number of cross intersections is b, each cross intersection has four phases, and the decision variable x=(x 11 , x 12 , x 13 , x 21 ,...,x pq ,...,x (a+b)4 ) is the duration of each phase at each intersection, x pq represents the duration of the qth phase of the pth intersection and is an integer. The dimension of x is d = 3 × a + 4 × b. The objective function y is the average delay time of vehicles. The results are output through SUMO simulation. The optimization goal is to obtain the smallest possible y value through iteration.

[0014] As a further limitation of the first aspect of the present invention, the auxiliary optimization training algorithm for the optimization model includes: initializing the population by Latin hypercube sampling to generate a population X = [x1, x2, ..., x N ], the number of individuals in the population is N, each individual represents a decision variable, that is, a signal scheme; all individuals in the population are simulated by SUMO, and the result output is y=y1, y2, ..., y N . All signal schemes x and their corresponding average vehicle delay time y are recorded in the database, and the optimal solution y is recorded. best and its corresponding individual x best .

[0015] As a further limitation of the first aspect of the present invention, the following steps are performed for each individual in a loop: a radial basis function proxy model is constructed using the first 2N individuals with smaller y values ​​in the database, and if the number of individuals in the database is less than 2N, all individuals are used; k new particles are obtained by diffusion, that is, k signal schemes, and pre-fitness values ​​of these k schemes are obtained through the radial basis function proxy model, and the scheme x with the smallest pre-fitness value is selected as the first particle. tem Perform SUMO real simulation, simulation results f tem Enter the database; if f tem Better than y i , then x tem Replace x iIf f tem Better than y best , then x tem Replace x best .

[0016] As a further limitation of the first aspect of the present invention, the agent-assisted first update is performed: the update probability of N signal schemes is calculated; for each dimension of each individual, if its update probability value is less than the random number ε, it is updated, otherwise it remains unchanged; for the updated population V = [v1, v2, ..., v N ], the corresponding pre-fitness values ​​are obtained through the radial basis function proxy model like Then for v i Execute the SUMO real simulation, and the simulation result is recorded as f i And enter into the database; if f i <y i , then v i Replace x i If f i <y best , then v i Replace x best .

[0017] As a further limitation of the first aspect of the present invention, a second proxy-assisted update is performed: a mutation operation is performed on each individual in the population, and then a crossover operation is performed, each dimension of each particle is processed, and the first 2N individuals with smaller y values ​​in the database are selected to construct a Gaussian process proxy model. If the number of individuals in the database is less than 2N, all individuals are used; after the expected improvement calculation, the first Ns particles with larger expected improvement values ​​are selected, and a SUMO real simulation is performed, and the results are recorded in the database; if the i-th simulation result is less than y i , then it replaces x i ; If the result of the i-th simulation is less than y best , then it replaces x best .

[0018] In a second aspect, the present invention provides a fixed-time area traffic signal optimization system, comprising:

[0019] An acquisition module is used to obtain road network data of the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour at each entrance and exit;

[0020] The processing module is used to use the constructed optimization model to process the acquired road network data of the area to be optimized to obtain a new signal timing for the area to be optimized. The optimization model is constructed based on the relationship between the signal light phase and the average vehicle delay in the area to be optimized. The optimization goal is to obtain the smallest possible average vehicle delay time.

[0021] In a third aspect, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the fixed-time area traffic signal optimization method as described in the first aspect is implemented.

[0022] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the fixed-time area traffic signal optimization method as described in the first aspect.

[0023] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the fixed-time area traffic signal optimization method as described in the first aspect.

[0024] Glossary: ​​Phase: The display state of a signal group corresponding to one or more traffic flows that are granted the right of way simultaneously. Cycle: The time it takes for a traffic light to complete one cycle. Surrogate model: An approximate mathematical model that can replace more complex and time-consuming numerical analysis in optimization design.

[0025] The beneficial effects of the present invention are as follows: the evolutionary optimization algorithm is assisted by the proxy model and can be better applied to large-scale regional road networks, which to a certain extent makes up for the problem that the existing signal timing optimization algorithm is difficult to apply to large-scale road networks due to the large search space and limited computing budget.

[0026] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a flow chart of a fixed-time area traffic signal optimization method based on an agent-assisted optimization algorithm according to an embodiment of the present invention.

[0029] Figure 2 This is a flowchart of the SUMO file construction described in an embodiment of the present invention.

[0030] Figure 3 Schematic diagram of three phases of a T-intersection according to an embodiment of the present invention.

[0031] Figure 4 This is a phase diagram of an intersection according to an embodiment of the present invention.

[0032] Figure 5 This is a flow chart of the agent-assisted optimization algorithm described in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0034] Those skilled in the art will understand that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0035] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0036] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0037] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise contradictory.

[0038] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0039] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0040] Example 1

[0041] In this embodiment 1, a fixed-time area traffic signal optimization system is first provided, comprising: an acquisition module for acquiring road network data of the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour at each entrance and exit; a processing module for processing the acquired road network data of the area to be optimized using a constructed optimization model to obtain a new signal timing for the area to be optimized; wherein the optimization model is constructed based on the relationship between the signal light phase and the average vehicle delay in the area to be optimized, and the optimization goal is to obtain the smallest possible average vehicle delay time.

[0042] In this embodiment, the above-mentioned system is used to implement a fixed-time area traffic signal optimization method, including: obtaining road network data for the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output of each entrance and exit per unit hour; using a constructed optimization model to process the obtained road network data for the area to be optimized to obtain a new signal timing for the area to be optimized; wherein the optimization model is constructed based on the relationship between the signal light phase and the average vehicle delay in the area to be optimized, and the optimization goal is to obtain the smallest possible average vehicle delay time.

[0043] The optimization model is constructed based on the relationship between the signal phase and the average vehicle delay in the area to be optimized, including: the number of T-intersections in the area to be optimized is a, each T-intersection has three phases, the number of cross intersections is b, each cross intersection has four phases, and the decision variable x = (x 11, x 12 , x 13 , x 21 ,...,x pq ,...,x (a+b)4 ) is the duration of each phase at each intersection, x pq represents the duration of the qth phase of the pth intersection and is an integer. The dimension of x is d = 3 × a + 4 × b. The objective function y is the average delay time of vehicles. The results are output through SUMO simulation. The optimization goal is to obtain the smallest possible y value through iteration.

[0044] The auxiliary optimization training algorithm for the optimization model includes: initializing the population by Latin hypercube sampling method, generating a population X = [x1, x2, ..., x N ], the number of individuals in the population is N, each individual represents a decision variable, that is, a signal scheme; all individuals in the population are simulated by SUMO, and the result output is y=y1, y2, ..., y N . All signal schemes x and their corresponding average vehicle delay time y are recorded in the database, and the optimal solution y is recorded. best and its corresponding individual x best .

[0045] Execute the proxy-assisted diffusion step, specifically: perform the following steps for each individual through a loop: use the first 2N individuals with smaller y values ​​in the database to build a radial basis function proxy model. If the number of individuals in the database is less than 2N, use all individuals; diffuse to obtain k new particles, that is, k signal schemes, obtain the pre-fitness values ​​of these k schemes through the radial basis function proxy model, and use the scheme x with the smallest pre-fitness value as the first particle. tem Perform SUMO real simulation, simulation results f tem Enter the database; if f tem Better than y i , then x tem Replace x i If f tem Better than y best , then x tem Replace x best .

[0046] Perform the first proxy-assisted update: calculate the update probability of N signal schemes; for each dimension of each individual, if its update probability value is less than the random number ε, then update it, otherwise keep it unchanged; for the updated population V = [v1, v2, ..., v N ], the corresponding pre-fitness values ​​are obtained through the radial basis function proxy model like Then for v i Execute the SUMO real simulation, and the simulation result is recorded as fi And enter into the database; if f i <y i , then v i Replace x i If f i <y best , then v i Replace x best .

[0047] Perform the second proxy-assisted update: perform a mutation operation on each individual in the population, then perform a crossover operation, process each dimension of each particle, select the first 2N individuals with the smallest y value in the database to build a Gaussian process proxy model, and use all individuals if the number in the database is less than 2N; after calculating the expected improvement, select the first Ns particles with the largest expected improvement value, perform a SUMO real simulation, and record the results in the database; if the i-th simulation result is less than y i , then it replaces x i ; If the result of the i-th simulation is less than y best , then it replaces x best .

[0048] If the termination condition is not met (the number of SUMO real simulations reaches the set number), it returns to the step of executing agent-assisted diffusion and starts a new round of iteration. If the termination condition is met, it outputs x best The output x best New signal timing for this area. Optimization complete.

[0049] Example 2

[0050] like Figure 1 As shown, in this embodiment 2, a fixed-time area traffic signal optimization method based on a proxy-assisted optimization algorithm is provided. This method addresses large-scale traffic signal optimization problems. Due to the large search space and limited computational budget, the search performance of existing signal timing optimization algorithms has generally declined, making most existing technologies difficult to apply to large-scale road networks. This embodiment utilizes a proxy model for auxiliary computation, enabling application to large-scale road networks with limited computing power.

[0051] For areas requiring optimization, we downloaded road network data from OpenStreetMap, retaining the roads used for motor vehicle travel, including intersection locations, lane flow, number of lanes, and signal phases and sequences, while removing other road data. We then obtained the hourly vehicle inflow and outflow for each entrance and exit of the regional road network.

[0052] like Figure 2As shown, the relevant SUMO files are created based on the road network data, including .nod files, .edg files, .con files, and .additional files. The .nod file includes information about intersections and road network entrances and exits. The intersection information includes id, coordinates, and whether it is controlled by a signal. The road network entrance and exit information includes id and coordinates. The .edg file includes information about the roads connecting two intersections and between intersections and entrances and exits, specifically including id, starting point (id in the .nod file), end point (id in the .nod file), number of lanes, and maximum speed limit of the road (unit: m / s). The .con file includes information about the lanes traveled when passing through each intersection, specifically including the starting point (id in the .edg file), end point (id in the .edg file), starting lane (0, 1, 2... from right to left), end lane (0, 1, 2... from right to left), and the id of the signal light that controls the intersection. The .additional file includes information about each signal light, specifically including id, type (fixed time in this invention), duration of each phase, and signal status of each exit lane of the intersection in that phase.

[0053] Generate a .net file using the .nod, .edg, .con, and .additional files. Create a .trip file using the hourly vehicle input and output for each entrance and exit. This file contains the traffic flow ID, start time, end time, vehicle input, start point (ID from the .edg file), and end point (ID from the .edg file). Generate a .rou file using the .net and .trip files. Generate a .sumocfg file using the .net and .rou files.

[0054] A mathematical model is constructed to model the relationship between the signal phase and the average vehicle delay in this area. Figure 3 、 Figure 4 As shown, the number of T-intersections in the area to be optimized is a, each T-intersection has three phases, the number of cross intersections is b, each cross intersection has four phases, and the decision variable x = (x 11 ,x 12 ,x 13 ,x 21 ,…,x pq ,…,x (a+b)4 ) is the duration of each phase at each intersection, x pq It represents the duration of the qth phase of the pth intersection and is an integer. The dimension of x is d=3×a+4×b.

[0055] The objective function y is the average vehicle delay time, which can be output through SUMO simulation. The optimization goal is to obtain the smallest possible y value through iteration. The relationship between the two is: y = f(x).

[0056] like Figure 5 As shown, the agent-assisted optimization algorithm is executed according to the following steps.

[0057] Step 1: Initialize the population by Latin hypercube sampling and generate a population of X = [x1, x2, ..., x N ], the number of individuals in the population is N, and each individual represents a decision variable (a signal scheme).

[0058] Step 2: Perform a real simulation of all individuals in the population through SUMO, and the output is y=y1,y2,…,y N . All signal schemes x and their corresponding average vehicle delay time y are recorded in DB (Database), and the optimal solution y is recorded. best and its corresponding individual x best .

[0059] Step 3: Perform agent-assisted diffusion.

[0060] Perform the following steps for each individual in a loop:

[0061] The first 2N individuals with smaller y values ​​in DB are used to construct the RBF (Radial Basis Function) surrogate model. If the number of individuals in DB is less than 2N, all individuals are used.

[0062] Diffusion is performed through formula (1) to obtain k new particles (k signal schemes). The pre-fitness values ​​of these k schemes are obtained through the RBF proxy model. The scheme x with the minimum pre-fitness value is selected as tem Perform SUMO real simulation, simulation results f tem Included in DB. If f tem Better than y i , then x tem Replace x i If f tem Better than y best , then x tem Replace x best .

[0063] GW=Gaussian(x best ,σ)+(ε1×x best -ε2×x i )#(1)

[0064] Among them, ε1 and ε2 are random numbers between [0,1], and σ can be obtained by formula (2).

[0065]

[0066] Where t is the current iteration of the algorithm.

[0067] Step 4: Perform the agent-assisted first update.

[0068] Use formula (3) to calculate the update probability of N signal schemes. i ) represents the corresponding sorting of all particles after they are arranged in descending order according to their fitness values.

[0069]

[0070] For each dimension of each individual, if its Pa i If the value is less than the random number ε, it is updated according to formula (4), otherwise it remains unchanged. t (pq) and x r (pq) is the pq-th dimension of two randomly selected particles in the population, and ε is a random number between [0, 1].

[0071] v i (pq) = x t (pq)-ε×(xr(pq)-x i (pq))#(4)

[0072] Use the first 2N individuals with smaller y values ​​in DB to build the RBF proxy model. If the number of individuals in DB is less than 2N, use all individuals. N ], the corresponding pre-fitness values ​​are obtained through the RBF model like Then for v i Execute the SUMO real simulation, and the simulation result is recorded as f i And included in DB. If f i <y i , then v i Replace x i If f i <y best , then v i Replace x best .

[0073] Step 5: Perform a proxy-assisted second update.

[0074] Perform mutation operation on each individual in the population through formula (5) to obtain W = [W1, W2, ..., w N ]. Among them, x t1 、x t2 、x t3 、x t4 are particles randomly selected from the population, and F is a scaling factor that controls the magnitude of the variation.

[0075] w i =x best +F×(x t1 -x t2 )+F×(x t3 -x t4 )#(5)

[0076] By performing the crossover operation through formula (6), each dimension of each particle is processed to obtain U = [u1, u2, ..., u N ]. Among them, r and CR are random numbers between [0, 1], pq rand is a random integer between [1, d].

[0077]

[0078] The first 2N individuals with the smallest y values ​​in DB are selected to construct the GP (Gaussian Processes) surrogate model. If the number of individuals in DB is less than 2N, all individuals are used. After calculating EI (Expected Improvement), the first Ns particles with the largest EI values ​​are selected to perform SUMO real simulation, and the results are included in DB. If the result of the i-th simulation is less than y i , then it replaces x i If the result of the i-th simulation is less than y best , then it replaces x best .

[0079] Step 6: If the termination condition is not met (the number of SUMO real simulations reaches the set number), return to step 3 and start a new round of iteration. If the termination condition is met, output x best .

[0080] Output x best New signal timing for this area. Optimization complete.

[0081] In summary, in this embodiment, before using SUMO for real-world simulation, the objective function fitness value is first obtained through a proxy model, and only promising decision variables (signaling schemes) are simulated. Using multiple proxy models overcomes the potential bias that can occur when using only a single proxy model. Python interacts with SUMO through the traci and sumolib interfaces, enabling parallel simulation and optimization. Using proxy models to assist evolutionary optimization algorithms can be well applied to large-scale regional road networks, to some extent addressing the difficulty of existing signal timing optimization algorithms in applying them to large-scale road networks due to their large search space and limited computational budget.

[0082] Example 3

[0083] This embodiment 3 provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the fixed-time area traffic signal optimization method described above is implemented. The method includes:

[0084] Obtaining road network data for the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour for each entrance and exit;

[0085] The constructed optimization model is used to process the acquired road network data of the area to be optimized to obtain a new signal timing for the area to be optimized. The optimization model is constructed by taking into account the relationship between the signal light phase and the average vehicle delay in the area to be optimized. The optimization goal is to minimize the average vehicle delay time.

[0086] Example 4

[0087] This embodiment 4 provides a computer device including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the above-described fixed-time area traffic signal optimization method, the method comprising:

[0088] Obtaining road network data for the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour for each entrance and exit;

[0089] The constructed optimization model is used to process the acquired road network data of the area to be optimized to obtain a new signal timing for the area to be optimized. The optimization model is constructed by taking into account the relationship between the signal light phase and the average vehicle delay in the area to be optimized. The optimization goal is to minimize the average vehicle delay time.

[0090] Example 5

[0091] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the above-described fixed-time area traffic signal optimization method, which includes:

[0092] Obtaining road network data for the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour for each entrance and exit;

[0093] The constructed optimization model is used to process the acquired road network data of the area to be optimized to obtain a new signal timing for the area to be optimized. The optimization model is constructed by taking into account the relationship between the signal light phase and the average vehicle delay in the area to be optimized. The optimization goal is to minimize the average vehicle delay time.

[0094] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0098] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing traffic signals in a fixed time zone, characterized in that: include: Obtaining road network data for the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output per hour for each entrance and exit; The constructed optimization model is used to process the acquired road network data of the area to be optimized to obtain the new signal timing of the area to be optimized; wherein, the relationship between the signal light phase and the average vehicle delay in the area to be optimized is used to construct the optimization model, and the optimization goal is to obtain the smallest possible average vehicle delay time; wherein, the auxiliary optimization training algorithm for the optimization model includes: initializing the population through the Latin hypercube sampling method, and generating a population of , the number of individuals in the population is N, each individual represents a decision variable, that is, a signal scheme; all individuals in the population are simulated by SUMO, and the result output is ; All signal schemes x and their corresponding average vehicle delay time y are recorded in the database, and the optimal solution is recorded and its corresponding individuals ; Perform the following steps for each individual through a loop: Use the first 2N individuals with smaller y values ​​in the database to build a radial basis function proxy model. If the number of individuals in the database is less than 2N, use all individuals; Diffusion obtains k new particles, that is, k signal schemes, and obtains the pre-fitness value of these k signal schemes through the radial basis function proxy model. The scheme x with the smallest pre-fitness value is used as the tem Perform SUMO real simulation, simulation results f tem Enter the database; if f tem Better than y i , then x tem Replace x i If f tem Better than y best , then x tem Replace x best ; Perform the agent-assisted first update: calculate the update probability of N signal schemes; for each dimension of each individual, if its update probability value is less than the random number , then continue to update, otherwise remain unchanged; if the new particle after the update The fitness value is better than , then replace Become a new particle in the population; for the updated population All individuals in the , get the corresponding pre-fitness value through the radial basis function proxy model ,like , then Execute the SUMO real simulation, and the simulation result is recorded as And included in the database; if ,but replace ;like ,but replace ; Perform the second proxy-assisted update: perform a mutation operation on each individual in the population, then perform a crossover operation, process each dimension of each particle, select the first 2N individuals with the smallest y value in the database to build a Gaussian process proxy model, if the number of individuals in the database is less than 2N, use all individuals; after the expected improvement calculation, select the first Ns particles with the largest expected improvement value, perform SUMO real simulation, and record the results in the database; if the i-th simulation result is less than , then it replaces ; If the result of the i-th simulation is less than , then it replaces .

2. The method for optimizing traffic signals in a fixed time zone according to claim 1, characterized in that: The optimization model is constructed based on the relationship between the signal phase and the average vehicle delay in the area to be optimized, including: the number of T-intersections in the area to be optimized is a, each T-intersection has three phases, the number of cross intersections is b, each cross intersection has four phases, and the decision variables are is the duration of each phase at each intersection, represents the duration of the qth phase of the pth intersection and is an integer. The dimension of x is d = 3 × a + 4 × b. The objective function y is the average delay time of vehicles. The results are output through SUMO simulation. The optimization goal is to obtain the smallest possible y value through iteration.

3. A fixed time area traffic signal optimization system based on the method according to claim 1 or 2, characterized in that: include: An acquisition module is used to obtain road network data of the area to be optimized; wherein the road network data includes the location of each intersection, lane flow direction, number of lanes, signal light phase and sequence, and vehicle input and output of each entrance and exit per unit hour; The processing module is used to use the constructed optimization model to process the acquired road network data of the area to be optimized to obtain a new signal timing for the area to be optimized. The optimization model is constructed based on the relationship between the signal light phase and the average vehicle delay in the area to be optimized. The optimization goal is to obtain the smallest possible average vehicle delay time.

4. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the fixed-time area traffic signal optimization method according to claim 1 or 2 is implemented.

5. A computer device, characterized in that: The method comprises a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the fixed-time area traffic signal optimization method according to claim 1 or 2.

6. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to implement the fixed time area traffic signal optimization method as described in claim 1 or 2.

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