A method for setting machine non-isolation based on macro basic graph

Abstract

The present invention provides a method for setting up non-isolation between vehicles and roads based on a macro basic diagram, comprising the following steps: step 10) collecting basic data of a road network; step 20) building a non-interference cellular transmission model for vehicles and roads according to the basic data of the road network; step 30) using a genetic algorithm according to the non-interference cellular transmission model for vehicles and roads to obtain a new non-isolation setting scheme for vehicles and roads for the road network, and drawing a new macro basic diagram; step 40) substituting the original non-isolation setting scheme for vehicles and roads for the road network into the non-interference cellular transmission model for vehicles and roads to draw the original macro basic diagram; step 50) comparing the new macro basic diagram with the original macro basic diagram to determine the non-isolation setting scheme for vehicles and roads. The method for setting up non-isolation between vehicles and roads based on a macro basic diagram of the present invention can find a non-isolation scheme for vehicles and roads for a suitable section of a road, reasonably construct non-isolation hard isolation facilities for vehicles and roads, and reduce unnecessary waste of resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban traffic planning, and in particular relates to a method for setting up machine-non-machine isolation based on a macro basic graph. Background Art

[0002] Currently, most secondary trunk roads and branch roads in domestic cities have sections where motor vehicle lanes and non-motor vehicle lanes share a common paving and are separated by markings, known as soft-separation sections. Because there is no physical separation, or hard separation, between motor vehicles and non-motor vehicles, non-motor vehicles often overtake vehicles on motor vehicle lanes by crossing the markings as traffic increases. This impacts the operation of surrounding motor vehicles, increases vehicle delays, reduces road capacity, and ultimately affects the performance of the entire road network. Installing hard separations between non-motor vehicle lanes and motor vehicle lanes can effectively reduce conflicts between motor vehicles and non-motor vehicles on road sections. However, installing hard separations requires significant construction funding, and some sections have fewer motor vehicles or non-motor vehicles, making the addition of hard separations ineffective. Therefore, when hard separations are not necessary, they are generally not installed. Therefore, in order to more reasonably construct hard isolation facilities for motor vehicles and non-motor vehicles and reduce unnecessary waste of resources, we established a cellular transmission model of motor vehicle and non-motor vehicle interference. Taking the maximum passenger flow through the cross-section of a motor vehicle road section as the goal, we selected a motor vehicle and non-motor vehicle isolation scheme suitable for the section. Finally, using the macro basic map as an evaluation method, we evaluated the impact of the new scheme on the performance of the original road network from a macro perspective, and then determined the feasibility of the new scheme. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for setting up vehicle-non-vehicle isolation based on a macro basic map, which can find a vehicle-non-vehicle isolation solution for a suitable road section, reasonably construct vehicle-non-vehicle hard isolation facilities, and reduce unnecessary waste of resources.

[0004] To solve the above technical problems, an embodiment of the present invention provides a method for setting up non-isolated machines based on a macro basic graph, comprising the following steps:

[0005] Step 10) Collecting basic road network data;

[0006] Step 20) Building a non-interference cellular transmission model based on the road network basic data;

[0007] Step 30) Based on the non-interference cellular transmission model, a genetic algorithm is used to obtain a new non-isolation setting scheme for the road network and draw a new macro basic diagram;

[0008] Step 40) Substituting the original non-isolated setting scheme of the road network into the non-interference cellular transmission model, and drawing the original macro basic diagram;

[0009] Step 50) Compare the new macro basic diagram with the original macro basic diagram to determine the machine non-isolation setting plan.

[0010] As a further improvement of the embodiment of the present invention, the step 20) includes:

[0011] Step 201) fitting a function of the number of times a vehicle or non-vehicle crosses the line based on the basic road network data;

[0012] Step 202) Based on the basic road network data, the multi-valued cellular model and the cellular transmission model are combined through the vehicle-non-interference cellular transmission model through the vehicle-non-interference function.

[0013] As a further improvement of the embodiment of the present invention, the function of the number of times the machine does not cross the line is as shown in formula (1):

[0014] η=β0+β1x1+β2width 非机 Formula (1)

[0015] In the formula, η represents the number of times non-motor vehicles cross the line per unit distance, x1 represents the number of non-motor vehicles per unit distance, width 非机 represents the width of the non-motorized vehicle lane, β0 represents the first parameter, β1 represents the second parameter, and β2 represents the third parameter.

[0016] As a further improvement of the embodiment of the present invention, the multi-valued cellular model adopts the EBCA1 road section model, and the cellular transmission model adopts the CTM node model.

[0017] As a further improvement of the embodiment of the present invention, the step 202) specifically includes:

[0018] Step 2021) Use equation (2) to map the cell lengths of the EBCA1 road segment model to the cell lengths of the CTM node model:

[0019]

[0020] Where λ represents the λth cell of the CTM node model a section, Represents the length of a single cell of the CTM node model a section, represents the length of a single cell in the EBCA1 road section model;

[0021] Step 2022) Use equation (3) to map the simulation step size of the EBCA1 road section model to the simulation step size of the CTM node model:

[0022] ξ=z×τ Formula (3)

[0023] Where ξ represents the ξth simulation step of the EBCA1 road section model, z represents the zth simulation step of the CTM node model, and τ represents the time taken for one step of the CTM node model, in seconds.

[0024] Step 2023) Extract the EBCA1 road section model at time ξ To The number of non-motor vehicles in a cell and the width of non-motor vehicles in a road section Substitute this into the function of the number of times non-motor vehicles cross the line, and calculate the number of times non-motor vehicles cross the line at time ξ, η(ξ);

[0025] Step 2024) Use formula (4) to calculate the maximum carrying capacity of the λth cell of the CTM node model a section at time ξ under the influence of non-motor vehicles crossing the line

[0026]

[0027] In the formula, fix() represents the rounding function to 0, represents the maximum carrying capacity of the λth cell of the CTM node model section a at time ξ when there is no impact of non-motor vehicles crossing the line;

[0028] Step 2025) Introduced into the calculation expression of the CTM node model, such as formula (5):

[0029]

[0030] Where, It represents the number of motor vehicles that the λth cell of the road section a can accept from the upstream cell λ-1 to the downstream cell λ at the maximum carrying capacity at time ξ. represents the maximum inflow of the λth cell of section a at time ξ, represents the maximum carrying capacity of the λth cell of section a at time ξ, represents the number of vehicles in the λth cell of road section a at time ξ;

[0031] The non-interference cellular transmission model of the machine is obtained.

[0032] As a further improvement of the embodiment of the present invention, in step 30), a new non-isolation setting scheme for the road network is obtained using a genetic algorithm based on the non-interference cellular transmission model of the machine, specifically including:

[0033] Step 301) Initialize the population code and number the road sections of the road network; the genetic algorithm uses 0-1 coding, where 0 represents the road section with non-soft isolation and 1 represents the road section with non-hard isolation, to generate an N×A population matrix; the nth row of the population matrix represents the nth road network non-isolation scheme, and the ath column represents the ath road section;

[0034] Step 302) The maximum sum of the maximum passenger flow of the penultimate cell b of each road section at all times is used as the fitness function. The fitness function is expressed as formula (6):

[0035]

[0036] Where f(max) represents the fitness function, The penultimate cell b of section a starts from the first step of the CTM node model to the end of the simulation z = z max The sum of the maximum number of people that can flow in, m1 represents the maximum number of passengers that a motor vehicle can carry, and m2 represents the maximum number of passengers that a non-motor vehicle can carry;

[0037] Substitute N individuals in the population matrix into the non-interference cellular transmission model for simulation, obtain the fitness value of each individual, and record the individual with the largest fitness function value;

[0038] Step 303) The individuals in the population are selected by the roulette wheel method to perform crossover and mutation operations. The probability of an individual being selected in the roulette wheel method is shown in formula (7):

[0039]

[0040] Where f represents the fitness function value;

[0041] Select N parent individuals;

[0042] Step 304) Crossover the selected parent individuals in pairs, randomly selecting the crossover position for crossover operation, and forming two offspring individuals after crossover;

[0043] Step 305) In the generated offspring, the mutation probability p is set 变异 Randomly select offspring individuals and randomly determine the mutation position to change the coding number; generate a total of N offspring individuals through crossover and mutation, and return to step 302) to recalculate until the maximum number of iterations is reached or the maximum value of the fitness function is found, then output the optimal individual, and the output optimal individual is the non-isolated setting scheme for the new machine in the road network.

[0044] As a further improvement of the embodiment of the present invention, in step 30), drawing a new macro basic graph specifically includes:

[0045] The average vehicle density of each motor vehicle lane in each section of the road network at the time ξ and the maximum passenger flow that can pass through the cross-section of the motor vehicle lane in each section under the new non-isolated setting scheme of the road network are extracted, and a new macro basic map is obtained by fitting.

[0046] As a further improvement of the embodiment of the present invention, the step 50) specifically includes:

[0047] Calculate the area S1 enclosed by the curve and coordinates of the new macro basic diagram, and calculate the area S2 enclosed by the curve and coordinates of the original macro basic diagram; calculate the influence coefficient If S>10%, the new non-isolation setting plan of the road network will be adopted as the non-isolation setting plan; if S≤10%, the original non-isolation setting plan of the road network will be adopted as the non-isolation setting plan.

[0048] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: the embodiment of the present invention provides a method for setting up vehicle and non-motorized vehicle separation based on a macro-basic graph, establishes a vehicle and non-motorized vehicle interference cellular transmission model, selects a vehicle and non-motorized vehicle separation scheme suitable for the motor vehicle road section, and finally uses the macro-basic graph as an evaluation method to evaluate the impact of the new scheme on the original road network performance from a macro perspective, thereby determining the feasibility of the new scheme. Compared with other mixed traffic flow simulation models, the vehicle and non-motorized vehicle interference cellular transmission model established by the method of the embodiment of the present invention has the advantage that each cell of the multi-valued cellular model and cellular transmission model used can accommodate multiple non-motorized vehicles or motor vehicles. There is no need to display lane-changing and following rules for vehicles within the cell, and only the dynamic allocation of non-motorized vehicle and motor vehicle flows needs to be focused. This can easily model mixed vehicle and non-motorized vehicle traffic flows on large road networks, is convenient to build, and has higher operation efficiency. The method of the embodiment of the present invention effectively selects vehicle and non-motorized vehicle separation schemes through a genetic algorithm, which can avoid the waste of manpower, financial resources, and material resources caused by the blind addition of vehicle and non-motorized vehicle hard separation facilities, thereby reducing losses. The method of the embodiment of the present invention uses a macro basic graph to evaluate the solutions, selects the solution that most significantly improves the road network performance, and maximizes the improvement of the road network performance while reducing vehicle-non-vehicle conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of machine-non-soft isolation;

[0050] Figure 2 This is a schematic diagram of machine-non-hard isolation;

[0051] Figure 3 This is a flow chart of a method for setting non-isolation of a machine based on a macro basic graph according to an embodiment of the present invention;

[0052] Figure 4 A diagram showing the functional relationships of various modules in a machine-non-interference cellular transmission model in a machine-non-isolation setting method based on a macro basic graph according to an embodiment of the present invention;

[0053] Figure 5 A schematic diagram of a road network according to a specific embodiment of the present invention;

[0054] Figure 6 A macro basic diagram of an original road network according to a specific embodiment of the present invention;

[0055] Figure 7 A macro basic diagram of a new road network according to a specific embodiment of the present invention;

[0056] Figure 8 for Figure 6 and Figure 7 . DETAILED DESCRIPTION

[0057] The technical solution of the present invention is described in detail below.

[0058] from Figure 1 The machine non-soft isolation diagram shown and Figure 2 As shown in the diagram of hard separation between vehicles and non-motorized vehicles, if a road section has a high volume of both vehicles and non-motorized vehicles, significant interference between vehicles and non-motorized vehicles will occur, resulting in reduced safety and increased delays. In this case, soft separation between vehicles and non-motorized vehicles should be avoided and hard separation between vehicles and non-motorized vehicles should be added to reduce interference. If some sections have low vehicle traffic, hard separation between vehicles and non-motorized vehicles would result in a waste of space resources.

[0059] Based on this, the embodiment of the present invention proposes a method for setting non-isolated machines based on a macro basic graph, such as Figure 3 As shown, the following steps are included:

[0060] Step 10) Collect basic road network data. This includes the geometry of each road segment, traffic flow within the segment, free-flow speed of motor vehicles within the segment, reverse propagation speed of traffic waves during congestion, lane occupancy of motor vehicles, lane occupancy of non-motor vehicles, and the number of non-motor vehicles per unit distance. Unit distance represents the distance traveled by a motor vehicle at free-flow speed in a cellular transport model simulation step.

[0061] Step 20) Based on the basic road network data, a non-interference cellular transmission model is constructed.

[0062] Step 30) Based on the machine-non-interference cellular transmission model, a genetic algorithm is used to obtain a road network machine-non-isolation setting scheme.

[0063] The method for obtaining a motor vehicle and non-motor vehicle isolation setting scheme provided by the embodiment of the present invention establishes a motor vehicle and non-motor vehicle interference cellular transmission model, takes the maximum passenger flow through the motor vehicle road section cross section as the goal, and selects a motor vehicle and non-motor vehicle isolation scheme suitable for the road section. The motor vehicle and non-motor vehicle interference cellular transmission model established by the method of the embodiment of the present invention uses a multi-valued cellular model and each cell of the cellular transmission model can accommodate multiple non-motor vehicles or motor vehicles. There is no need to display the lane-changing and following rules of the vehicles in the cell. It only needs to focus on the dynamic allocation of non-motor vehicle flow and motor vehicle flow. It can easily model the motor vehicle and non-motor vehicle mixed traffic flow of large road networks. The model is easy to build and has higher operation efficiency. The method of the embodiment of the present invention substitutes the genetic algorithm into the motor vehicle and non-motor vehicle interference cellular transmission model to effectively screen out motor vehicle and non-motor vehicle isolation schemes, which can avoid the waste of manpower, financial resources and material resources caused by the blind addition of motor vehicle and non-motor vehicle hard isolation facilities, and reduce losses.

[0064] As a preferred example, the step 20) includes:

[0065] Step 201) According to the basic road network data, a function of the number of times a vehicle or non-vehicle crosses the line is fitted.

[0066] Specifically, the function of the number of times non-motor vehicles cross the lane is related to the width of the non-motorized vehicle lane, the number of non-motorized vehicles per unit distance, and the number of times non-motorized vehicles cross the lane per unit distance. The function of the number of times non-motor vehicles cross the lane is shown in formula (1):

[0067] η=β0+β1x1+β2width 非机 Formula (1)

[0068] In the formula, η represents the number of times non-motor vehicles cross the line per unit distance, x1 represents the number of non-motor vehicles per unit distance, width 非机 represents the width of the non-motorized vehicle lane, β0 represents the first parameter, β1 represents the second parameter, and β2 represents the third parameter.

[0069] According to the number of non-motor vehicles per unit distance x1 and the width of the non-motor vehicle lane width obtained in step 10) 非机 , the number of non-motor vehicle crossing the line per unit distance η, is substituted into the curve fitting toolbox of MATLAB2019a for fitting, and the first parameter β0, the second parameter β1 and the third parameter β2 are obtained, thereby obtaining the function of the number of non-motor vehicle crossing the line.

[0070] Step 202) Based on the basic data of the road network, the multi-valued cellular model and the cellular transmission model are combined through the vehicle-non-interference number function to establish a vehicle-non-interference cellular transmission model, such as Figure 4 Preferably, the multi-valued cellular model adopts the EBCA1 road section model, and the cellular transmission model adopts the CTM node model.

[0071] In the embodiment method of the present invention, a multi-valued cellular model is used to simulate non-motor vehicle traffic, and a cellular transmission model is used to simulate macroscopic motor vehicle traffic. A function for the number of times non-motor vehicles cross the line is introduced to convert the non-motor vehicle traffic simulated by the multi-valued cellular model into the number of times non-motor vehicles cross the line and bring it into the cellular transmission model, thereby changing the maximum carrying capacity of the cells in the cellular transmission model and characterizing the traffic phenomenon of non-motor vehicles occupying motor vehicle lanes after crossing the line. The function for the number of times non-motor vehicles cross the line receives the number of non-motor vehicles transmitted in the multi-valued cellular model, calculates the corresponding number of times they cross the line, and then connects to the cellular transmission model to input the number of times they cross the line into the cells of the cellular transmission model, thereby changing the maximum carrying capacity of the cells. The multi-valued cellular model adopts the EBCA1 road section model, which has the advantage that vehicles can move at two different speeds, 1 and 2, while in the traditional BCA model, vehicles can only move one cell per step. The EBCA1 road section model is more consistent with actual traffic behavior. The cellular transmission model adopts the CTM node model, which has the advantage of being able to capture the discontinuous changes in road network traffic flow, clearly describe the physical effects of queuing, and at the same time consider signal control and channelization of road section exits, which can well characterize the propagation process of traffic flow between road sections.

[0072] As a preferred example, the step 202) specifically includes:

[0073] Step 2021) Use equation (2) to map the cell lengths of the EBCA1 road segment model to the cell lengths of the CTM node model:

[0074]

[0075] Where λ represents the λth cell of the CTM node model a section, Represents the length of a single cell of the CTM node model a section, Represents the length of a single cell in the EBCA1 road segment model.

[0076] Step 2022) Use equation (3) to map the simulation step size of the EBCA1 road section model to the simulation step size of the CTM node model:

[0077] ξ=z×τ Formula (3)

[0078] Where ξ represents the ξth simulation step of the EBCA1 section model, z represents the zth simulation step of the CTM node model, and τ represents the time taken for one step of the CTM node model, in seconds.

[0079] Step 2023) Extract the EBCA1 road section model at time ξ To The number of non-motor vehicles in a cell and the width of non-motor vehicles in a road section Substituting this into the function of the number of times non-motor vehicles cross the line, the number of times non-motor vehicles cross the line at time ξ η(ξ) is calculated.

[0080] Step 2024) Use formula (4) to calculate the maximum carrying capacity of the λth cell of the CTM node model a section at time ξ under the influence of non-motor vehicles crossing the line

[0081]

[0082] In the formula, fix() represents the rounding function to 0, It represents the maximum carrying capacity of the λth cell of the CTM node model section a at time ξ when there is no impact of non-motor vehicles crossing the line.

[0083] Step 2025) Introduced into the calculation expression (5) of the CTM node model:

[0084]

[0085] Where, It represents the number of motor vehicles that the λth cell of the road section a can accept from the upstream cell λ-1 to the downstream cell λ at the maximum carrying capacity at time ξ. represents the maximum inflow of the λth cell of section a at time ξ, represents the maximum carrying capacity of the λth cell of section a at time ξ, It represents the number of vehicles in the λth cell of road section a at time ξ, 0 represents soft isolation of vehicles and non-vehicles, and 1 represents hard isolation of vehicles and non-vehicles.

[0086] Thus, the non-interference cellular transmission model is obtained.

[0087] As a preferred example, the step 30) specifically includes:

[0088] Step 301) Initialize the population code and number the road sections of the road network. The genetic algorithm uses a 0-1 encoding, where 0 indicates that the road section is not softly isolated and 1 indicates that the road section is not hard isolated. This generates an N×A population matrix. The nth row of the population matrix represents the nth road network non-isolated solution, and the ath column represents the ath road section.

[0089] In step 302, the maximum sum of the maximum passenger flow of the penultimate cell b of each road section at all times is used as the fitness function. The fitness function is expressed as formula (6):

[0090]

[0091] Where f(max) represents the fitness function, The second-to-last cell b of section a starts from the first step of the CTM node model (i.e., the τth moment of the EBCA1 section model) to the end of the simulation z = z max (i.e. ξ max =z max ×τ), m1 represents the maximum number of passengers that can flow in at the moment of m1 ×τ, and its preferred value is 5; m2 represents the maximum number of passengers that can be carried by non-motor vehicles, and its preferred value is 2.

[0092] Substitute N individuals in the population matrix into the non-interference cellular transmission model for simulation, obtain the fitness value of each individual, and record the individual with the largest fitness function value.

[0093] Step 303) The individuals in the population are selected by the roulette wheel method to perform crossover and mutation operations. The probability of an individual being selected in the roulette wheel method is shown in formula (7):

[0094]

[0095] Where f represents the fitness function value.

[0096] Thus, N parent individuals are selected.

[0097] Step 304) Crossover the selected parent individuals in pairs, randomly select crossover positions for crossover operation, and form two offspring individuals after crossover.

[0098] Step 305) In the generated offspring, the mutation probability p is set 变异 Randomly select offspring individuals and randomly determine the mutation position to change the coding number. After crossover and mutation, a total of N offspring individuals are generated, and return to step 302) to recalculate until the maximum number of iterations is reached or the maximum value of the fitness function is found, then output the optimal individual. The output optimal individual is the non-isolated setting scheme for the new machine in the road network. Among them, the preset mutation probability p 变异 The value should be 0.1 or less.

[0099] In step 30), a variety of optimization algorithms can be used. The method of the embodiment of the present invention uses a genetic algorithm. The genetic algorithm has the following advantages over other algorithms:

[0100] (1) Genetic algorithms start their search from a set of solutions to a problem, rather than from a single solution. This is a significant difference between genetic algorithms and traditional optimization algorithms. Traditional optimization algorithms iterate from a single initial value to find the optimal solution, which can easily lead to local optimal solutions. Genetic algorithms start their search from a set of solutions, covering a wide area and facilitating global optimization.

[0101] (2) Genetic algorithms process multiple individuals in a population simultaneously, that is, they evaluate multiple solutions in the search space, which reduces the risk of falling into a local optimal solution. At the same time, the algorithm itself is easy to parallelize.

[0102] (3) Genetic algorithms basically do not use knowledge of the search space or other auxiliary information, but only use the fitness function value to evaluate individuals and perform genetic operations on this basis. The fitness function is not only not constrained to be continuously differentiable, but its domain of definition can be set arbitrarily.

[0103] (4) Binary coding can be used in genetic algorithms. One bit can represent the information of two states, which is well consistent with the two states of machine-non-isolation, namely, 0 for machine-non-soft isolation and 1 for machine-non-hard isolation. Therefore, a sufficiently long binary chromosome can represent the machine-non-isolation state of all road sections in a road network.

[0104] As a preferred example, in step 30), drawing a new macro basic diagram specifically includes:

[0105] The average vehicle density of each motor vehicle lane in each section of the road network at the time ξ and the maximum passenger flow that can pass through the cross-section of the motor vehicle lane in each section under the new non-isolated setting scheme of the road network are extracted, and a new macro basic map is obtained by fitting.

[0106] As a preferred example, the step 50) specifically includes:

[0107] Use the int() function of MATLAB2019a to calculate the area S1 enclosed by the curve and coordinates of the new macro basic graph, and calculate the area S2 enclosed by the curve and coordinates of the original macro basic graph; calculate the influence coefficient If S>10%, the new non-isolation setting plan of the road network will be adopted as the non-isolation setting plan; if S≤10%, the original non-isolation setting plan of the road network will be adopted as the non-isolation setting plan.

[0108] In addition to calculating areas to compare macro-basic diagrams, we can also use manual comparison. The advantage of this method is that the entire process, from simulation to determining the non-isolation configuration, is performed by a computer, and the advantages and disadvantages of the methods are quantitatively judged, eliminating manual labor and ensuring accurate calculations. Manual comparison relies on observing the ups and downs of the curves to qualitatively judge the quality of the methods. Since there are no rigid quantitative standards, the selection of solutions is more flexible.

[0109] The following is a specific example of the method of the present invention, taking part of the road network in Luoyang City as an example. Figure 5 shown.

[0110] Step 10) Collect the geometric conditions of each road section in the road network, the traffic flow of the road section, the free flow speed of motor vehicles on the road section, the reverse propagation speed of traffic waves during traffic congestion, the occupancy rate of motor vehicle lanes, the occupancy rate of non-motor vehicle lanes, and the number of non-motor vehicles per unit distance.

[0111] Step 20) Determine the number of non-motor vehicle crossing the line function using MATLAB 2019a based on the collected data, and compile the non-interference cellular transmission model of the vehicle. The relationship between the non-interference cellular transmission modules is as follows: Figure 4 shown.

[0112] Step 30) Embed the genetic algorithm into the non-interference cellular transmission model, initialize the population, and iterate the calculation to obtain a new non-isolated road network configuration scheme. Extract the average vehicle density of each road section at the time ξ under the new non-isolated road network configuration scheme and the maximum passenger flow that can pass through the cross-section of each road section. Based on the data, a new macro basic diagram is fitted, as shown in the following example: Figure 7 shown.

[0113] Step 40) Substitute the original non-isolated setting scheme of the road network into the non-interference cellular transmission model, extract the average vehicle density of each section of the motor vehicle lane at time ξ under the original non-isolated setting scheme of the road network, and the maximum passenger flow that can pass through the cross section of the motor vehicle lane of each section, draw a flow density point-to-point diagram, and fit the macro basic diagram based on the data, as shown in the figure below: Figure 6 shown.

[0114] Step 50) Figure 8 As shown in the figure, by comparing the new macro basic map with the original macro basic map, it can be seen that the point pairs in the new macro basic map are relatively concentrated, and the road network density distribution is relatively uniform. The fitting curve shows that the maximum passenger flow that can pass through the road network under the new scheme is larger, which has obvious advantages over the original road network vehicle and non-vehicle separation scheme. Therefore, we choose the new scheme to replace the original vehicle and non-vehicle separation scheme of the case road network.

[0115] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the specific embodiments described above. The specific embodiments and descriptions in the specification are merely intended to further illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for setting up non-isolation of machines based on a macro basic graph, characterized in that: The following steps are involved: Step 10) Collecting basic road network data; Step 20) Building a non-interference cellular transmission model based on the basic road network data; Step 30) According to the non-interference cellular transmission model, a genetic algorithm is used to obtain a new non-isolation setting scheme for the road network and draw a new macro basic diagram; Step 40) Substitute the original non-isolation setting scheme of the road network into the non-interference cellular transmission model of the machine to draw the original macro basic diagram; Step 50) Compare the new macro basic diagram with the original macro basic diagram to determine the machine non-isolation setting scheme; The step 20) comprises: Step 201) fitting a function of the number of times a vehicle or non-vehicle crosses the line based on the basic road network data; Step 202) Based on the basic road network data, the multi-valued cellular model and the cellular transmission model are combined through the vehicle-non-interference cellular transmission model through the vehicle-non-interference function. The function of the number of times the machine crosses the line is as shown in formula (1): η=β0+β1x1+β2width 非机 formula(1) In the formula, η represents the number of non-motor vehicles crossing the line per unit distance, x1 represents the number of non-motor vehicles per unit distance, width 非机 represents the width of the non-motorized vehicle lane, β0 represents the first parameter, β1 represents the second parameter, and β2 represents the third parameter; The multi-valued cellular model adopts the EBCA1 road section model, and the cellular transmission model adopts the CTM node model; The step 202) specifically includes: Step 2021) Use equation (2) to make a one-to-one correspondence between the cell length of the EBCA1 section model and the cell length of the CTM node model: Where λ represents the λth cell of the CTM node model a section. represents the length of a single cell of the CTM node model a section, represents the length of a single cell in the EBCA1 section model a; Step 2022) Use equation (3) to make the simulation step length of the EBCA1 section model correspond to the simulation step length of the CTM node model one by one: ξ=z×τ Formula (3) Where ξ represents the ξ-th simulation step of the EBCA1 section model, z represents the z-th simulation step of the CTM node model, and τ represents the time taken for one step of the CTM node model, in seconds; Step 2023) extract the first To The number of non-motor vehicles in a cell and the width of non-motor vehicles in a road section Substitute it into the function of the number of times non-motor vehicles cross the line, and calculate the number of times non-motor vehicles cross the line at time ξ η(ξ); Step 2024) Calculate the maximum carrying capacity of the λth cell of the CTM node model a section under the influence of non-motor vehicle crossing the line at time ξ using formula (4) In the formula, fix() represents the rounding function to 0, It represents the maximum carrying capacity of the λth cell of the CTM node model section a at time ξ without the influence of non-motor vehicles crossing the line; Step 2025) Introduced into the calculation expression of the CTM node model, as shown in formula (5): In the formula, It represents the number of motor vehicles that the λth cell of the road section a can accept from the upstream cell λ-1 to the downstream cell λ at the maximum carrying capacity at time ξ. represents the maximum inflow of the λth cell of section a at time ξ, represents the maximum carrying capacity of the λth cell of section a at time ξ, represents the number of vehicles in the λth cell of road section a at time ξ; The non-interference cellular transmission model of the machine is obtained.

2. The method for setting non-isolated machines based on a macro basic graph according to claim 1, characterized in that: In the step 30), according to the non-interference cellular transmission model of the machine, a genetic algorithm is used to obtain a new non-isolation setting scheme for the road network, which specifically includes: Step 301) Initialize the population code and number the road sections of the road network; the genetic algorithm uses 0-1 coding, 0 represents the road section is not softly isolated, 1 represents the road section is not hard isolated, and generate an N×A population matrix; the nth row of the population matrix represents the nth road network non-isolation scheme, and the ath column represents the ath road section; Step 302) The maximum sum of the maximum passenger flow of the second to last cell b of each road section at all times is used as the fitness function. The fitness function is expressed as formula (6): In the formula, f(max) represents the fitness function, Indicates that the second to last cell b of section a starts from the first step of the CTM node model to the end of the simulation z = z max The sum of the maximum number of people that can flow in, m1 represents the maximum number of passengers that a motor vehicle can carry, and m2 represents the maximum number of passengers that a non-motor vehicle can carry; Substitute the N individuals in the population matrix into the non-interference cellular transmission model for simulation, obtain the fitness value of each individual, and record the individual with the largest fitness function value; Step 303) The individuals in the population are selected by the roulette method to perform crossover and mutation operations. The probability of an individual being selected in the roulette method is shown in formula (7): In the formula, f represents the fitness function value; Select N parent individuals; Step 304) Crossover the selected parent individuals in pairs, randomly select the crossover position for crossover operation, and form two offspring individuals after crossover; Step 305) In the generated offspring, the mutation probability p is set to 变异 Randomly select offspring individuals, and randomly determine the mutation position to change the coding number; generate N offspring individuals in total through crossover and mutation, and return to step 302) to recalculate until the maximum number of iterations is calculated or the maximum value of the fitness function is found, then output the optimal individual, and the output optimal individual is the non-isolated setting plan for the new machine in the road network.

3. The method for setting non-isolated machines based on a macro basic graph according to claim 1, characterized in that: In the step 30), drawing a new macro basic diagram specifically includes: The average vehicle density of each motor vehicle lane in each section of the road network at the time ξ and the maximum passenger flow that can pass through the cross-section of the motor vehicle lane in each section under the new non-segregated setting scheme of the road network are extracted, and a new macro basic map is obtained by fitting.

4. The method for setting non-isolated machines based on a macro basic graph according to claim 1, characterized in that: The step 50) specifically includes: Calculate the area S1 enclosed by the curve and coordinates of the new macro basic diagram, and calculate the area S2 enclosed by the curve and coordinates of the original macro basic diagram; calculate the influence coefficient If S>10%, the new non-segregation setting scheme of the road network is adopted as the non-segregation setting scheme; if S≤10%, the original non-segregation setting scheme of the road network is adopted as the non-segregation setting scheme.

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