Method, device and equipment for controlling disturbance event of expressway and medium

Abstract

The invention provides an expressway disturbance event control method, device and equipment and a medium, and relates to the technical field of traffic control. The method includes: acquiring actual road section information; inputting the actual road section information into a cellular transmission model to obtain a disturbance cellular section which is a road section where a disturbance event occurs in a simulated actual road section; controlling the disturbance cellular section, constructing a variable speed limit and ramp coordination control model, and performing optimization solution on the variable speed limit and ramp coordination control model based on a space reduction genetic algorithm to obtain an optimal variable speed limit value and an optimal ramp control rate; and controlling the actual road section according to the optimal variable speed limit value and the ramp control rate to obtain the optimal traffic flow. The problem that the actual road section cannot be controlled according to the real-time state is solved, and the travel efficiency is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and in particular to a method, device, equipment and medium for controlling disturbance events on a highway. Background Art

[0002] In the existing traffic control field, the actual road section is generally coordinated and controlled through a single speed limit or ramp, and it is impossible to change the control method's control capability range according to the real-time traffic conditions of the road, dynamically adjust the model parameters according to the occurrence of disturbance events, and obtain a more effective control strategy, which leads to the problem that the actual road section cannot be controlled according to the real-time status. Therefore, there is an urgent need for a control method, device, equipment and medium for highway disturbance events, which solves the problem that the actual road section cannot be controlled according to the real-time status and ensures the efficiency of travel. Summary of the invention

[0003] The object of the present invention is to provide a method, device, equipment and medium for controlling disturbance events on a highway to improve the above-mentioned problems. In order to achieve the above-mentioned object, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a method for controlling a highway disturbance event, comprising:

[0005] Get actual road section information;

[0006] Inputting the actual road section information into the cellular transmission model to obtain a disturbance cellular segment, wherein the disturbance cellular segment is a road section simulating a disturbance event occurring in the actual road section;

[0007] The disturbance cell segment is controlled to construct a variable speed limit and ramp coordinated control model, and the variable speed limit and ramp coordinated control model is optimized and solved based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate;

[0008] The actual road section is controlled according to the optimal variable speed limit value and ramp control rate to obtain an optimal traffic flow.

[0009] In a second aspect, the present application also provides a control device for a highway disturbance event, comprising:

[0010] The first acquisition module is used to obtain actual road section information;

[0011] A first calculation module is used to input the actual road section information into the cellular transmission model to obtain a disturbance cellular segment, where the disturbance cellular segment is a road section simulating a disturbance event occurring in the actual road section;

[0012] A second calculation module is used to control the disturbance cell segment, construct a variable speed limit and ramp coordination control model, and optimize and solve the variable speed limit and ramp coordination control model based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate;

[0013] The third calculation module is used to control the actual road section according to the optimal variable speed limit value and ramp control rate to obtain an optimal traffic flow.

[0014] In a third aspect, the present application further provides a control device for a highway disturbance event, comprising:

[0015] Memory for storing computer programs;

[0016] A processor is used to implement the steps of the method for controlling highway disturbance events when executing the computer program.

[0017] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned control method based on highway disturbance events are implemented.

[0018] The beneficial effects of the present invention are:

[0019] The present invention adopts a variable speed limit and ramp coordinated control model and a space-reduced genetic algorithm. The variable speed limit and ramp coordinated control model can more accurately predict the traffic state when a disturbance event occurs, and realize more effective variable speed limit and ramp coordinated control. The space-reduced genetic algorithm reduces the amount of calculation for solving the variable speed limit and ramp coordinated control strategy. The common setting of the variable speed limit and ramp coordinated control model and the space-reduced genetic algorithm solves the problem that the actual road section cannot be controlled according to the real-time state, thereby ensuring the travel efficiency.

[0020] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 It is a flow chart of a method for controlling a highway disturbance event according to an embodiment of the present invention;

[0023] Figure 2 Schematic diagram of the structure of the control device for highway disturbance events described in an embodiment of the present invention.

[0024] Markings in the figure: 800, control device for highway disturbance events; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] Embodiment 1:

[0028] This embodiment provides a method for controlling a highway disturbance event.

[0029] See also Figure 1 , the figure shows that the method includes steps S1 to S4, including:

[0030] S1: Obtaining actual road section information;

[0031] The actual road section information includes the geometric characteristics of the actual road section, the traffic status of the actual road section in the current period and the remaining traffic capacity of the actual road section in the current period. The traffic status includes traffic flow, traffic density and average speed of the road section.

[0032] In order to clarify the specific method of obtaining the actual road section information, step S1 includes S11 to S14, which are specifically:

[0033] S11: Obtain the total number of vehicles passing through the actual road section in the current period;

[0034] S12: Obtaining the traffic flow of the actual road section in the current period according to the total number of vehicles passing through the actual road section in the current period;

[0035] In this step, the traffic volume of the actual road section during the current period is:

[0036]

[0037] In the above formula (1), q represents the traffic volume; N represents the total number of vehicles passing through the actual road section in the current period; and T represents the observation time interval.

[0038] S13: Calculating based on the length of the actual road section and the total number of vehicles passing through the actual road section in the current time period to obtain a traffic density;

[0039] In this step, the traffic density is:

[0040]

[0041] In the above formula (2), ρ represents the traffic density; L represents the length of the actual road section; and N represents the total number of vehicles passing through the actual road section during the current period.

[0042] S14: Calculate based on the length of the actual road section and the average travel time of all vehicles passing through the road section during the observation time to obtain the average speed of the road section.

[0043] In this step, the average speed of the passing section is:

[0044]

[0045] In the above formula (3), v represents the average speed of the road section; t represents the average travel time of all vehicles passing through the road section during the observation time; and d represents the length of the actual road section.

[0046] S2: inputting the actual road section information into the cellular transmission model to obtain a disturbance cellular segment, wherein the disturbance cellular segment is a road section simulating a disturbance event occurring in the actual road section;

[0047] In order to clarify the specific method of obtaining the perturbed cell segment, step S2 includes S21 to S22, which are:

[0048] S21: inputting the geometric characteristics of the actual road section into the cellular transmission model to obtain a road section cellular model;

[0049] In this step, the geometric characteristics of the actual road section include parameters such as the length, width, curve radius and slope of the road section.

[0050] S22: Inputting the traffic state and the remaining traffic capacity into the road section cellular model to obtain a disturbance cellular segment, where the disturbance cellular segment simulates a road section where a disturbance event occurs in an actual road section.

[0051] In order to clarify the specific method of obtaining the perturbed cell segment, step S22 includes S221 to S226, specifically:

[0052] S221: Obtain the accident reduction coefficient and actual road capacity of the actual road section.

[0053] In this step, the accident reduction factor is the ratio of the possible reduction of the accident occurrence;

[0054] S222: Calculate the speed drop value of the adjacent section according to the average speed of the current main line cell and the average speed of the next main line cell;

[0055] The adjacent section speed drop values ​​include a first section speed drop value and a second section speed drop value;

[0056] In this step, the speed drop value of the first section is:

[0057] U 1 =v(i,t)-v(i+1,t)(4)

[0058] In the above formula (4), U 1 represents the speed drop value of the first section; v(i,t) represents the average speed of the main line cell i in the t time step; v(i+1,t) represents the average speed of the main line cell i+1 in the t time step.

[0059] The average speed of the mainline cell i at time step t represents the average speed of the current mainline cell; the average speed of the mainline cell i+1 at time step t represents the average speed of the next mainline cell.

[0060] In this step, the speed reduction value of the second section is:

[0061]

[0062] In the above formula (5), U 2 represents the speed drop value of the second section; v(i,t) represents the average speed of the main line cell i in the t time step; v(i+1,t) represents the average speed of the main line cell i+1 in the t time step.

[0063] The average speed of the mainline cell i at time step t represents the average speed of the current mainline cell; the average speed of the mainline cell i+1 at time step t represents the average speed of the next mainline cell.

[0064] S223: Calculating based on the accident reduction coefficient of the actual road section and the actual road section traffic capacity to obtain the remaining traffic capacity of the actual road section in the current period;

[0065] In this step, the remaining traffic capacity of the actual road section in the current period is:

[0066] Q S = b·q m (6)

[0067] In the above formula (6), Q S represents the remaining traffic capacity of the actual road section at the current time period; b represents the accident reduction coefficient of the actual road section; q m Indicates the actual road section capacity.

[0068] S224: judging the speed drop value of the adjacent section based on a preset road section speed threshold to obtain the location of the congested area;

[0069] In this step, the congestion area position includes the end position of the congestion queue and the head position of the congestion queue, and the road section speed threshold includes a first speed threshold and a second speed threshold;

[0070] In this step, the tail position of the congested queue is determined as:

[0071]

[0072] In the above formula (7), U 1 Indicates the speed drop value of the first section; U 2 Indicates the speed drop value of the second section; W 1 represents the first speed threshold; W 2 Indicates the second speed threshold.

[0073] In this step, the position of the head of the congested queue is determined as:

[0074]

[0075] In the above formula (8), U 1 Indicates the speed drop value of the first section; U 2 Indicates the speed drop value of the second section; W 1 represents the first speed threshold; W 2 Indicates the second speed threshold.

[0076] S225: judging the location of the congested area according to the traffic status, and obtaining the flow status of the location of the congested area;

[0077] S226: judging the road section cellular model according to the flow state of the congested area and the remaining traffic capacity of the actual road section in the current period, and obtaining a disturbance cellular segment.

[0078] In this step, the disturbance cell segment is a road segment simulating a disturbance event point occurring in an actual road segment.

[0079] In order to clarify the specific acquisition method before the preset rule controls the disturbance cell segment, step S2 includes S23 to S24, which are specifically:

[0080] S23: updating the road section cellular model according to the remaining traffic capacity and a preset updating rule to obtain an updated road section cellular model;

[0081] In order to clarify the specific method of obtaining the updated road section cellular model, step S23 includes S231 to S234, which are:

[0082] S231: Obtain the traffic capacity of the current main line cell;

[0083] S232: Calculate based on the density of the current mainline cell at the current time step, the traffic capacity of the current mainline cell and the speed limit value of the current mainline cell at the current time step to obtain a flow update rule;

[0084] In order to clarify the specific method of obtaining the traffic update rule, step S232 includes S2321 to S2324, specifically:

[0085] S2321: Calculate based on the density of the current mainline cell at the current time step, the traffic capacity of the current mainline cell and the speed limit value of the current mainline cell at the current time step to obtain a maximum transmission capacity model of the current cell;

[0086] In this step, the maximum sending capacity model of the current cell is:

[0087]

[0088] In the above formula (9), qs(i,t) represents the maximum sending capacity of cell i at time step t; v f represents the free flow speed; v lim (i,t) represents the speed limit of cell i at time step t; ρ(i,t) represents the density of cell i at time step t; q m (i) represents the traffic capacity of cell i.

[0089] The maximum sending capacity of the cell i at time step t represents the maximum sending capacity of the current cell; the density of the cell i at time step t represents the density of the current mainline cell at the current time step; the traffic capacity of the cell i represents the traffic capacity of the current mainline cell; the speed limit value of the cell i at time step t represents the speed limit value of the current mainline cell at the current time step.

[0090] S2322: Calculating based on the density of the current mainline cell at the current time step, the traffic capacity of the current mainline cell and the congestion density value, obtaining a maximum receiving capacity model of the current cell, wherein the congestion density value is a value for simulating the degree of congestion of vehicles on an actual road section;

[0091] In this step, the maximum receiving capacity model of the current cell is:

[0092]

[0093] In the above formula (10), qr(i,t) represents the maximum receiving capacity of cell i at time step t; q m (i) represents the capacity of cell i; ωb represents the reverse wave velocity; ρ j represents the congestion density value; ρ(i,t) represents the density of cell i at time step t.

[0094] The maximum receiving capacity of the cell i at time step t represents the maximum receiving capacity of the current cell; the traffic capacity of the cell i represents the traffic capacity of the current mainline cell; the density of the cell i at time step t represents the density of the current mainline cell at the current time step.

[0095] S2323: Calculate based on the capacity of the current mainline cell, the density of the previous mainline cell at the current time step, and the speed limit value of the previous mainline cell at the current time step to obtain an inflow flow model of the current cell;

[0096] In this step, the inflow flow model of the current cell is:

[0097]

[0098] In the above formula (11), q(i,t) represents the inflow flow of cell i at time step t; ρ(i-1,t) represents the density of cell i-1 at time step t; v lim (i-1,t) represents the speed limit of cell i-1 at time step t; v f represents the free flow speed; q m (i) represents the traffic capacity of cell i; ρ j represents the congestion density value; ρ(i,t) represents the density of cell i at time step t; ωb represents the reverse wave velocity.

[0099] The inflow flow of the cell i at time step t represents the inflow flow of the current cell; the density of the cell i-1 at time step t represents the density of the previous mainline cell at the current time step; the speed limit value of the cell i-1 at time step t represents the speed limit value of the previous mainline cell at the current time step; the capacity of the cell i represents the capacity of the current mainline cell; the density of the cell i at time step t represents the density of the current mainline cell at the current time step.

[0100] S2324: Calculate according to the maximum sending capability model of the current cell, the maximum receiving capability model of the current cell, and the inflow flow model of the current cell to obtain a flow update rule.

[0101] S233: Calculating based on the density of the current mainline cell in the previous time step, the preset inflow flow of the current cell in the previous time step, and the preset cell transmission update period to obtain a density update rule;

[0102] In this step, the density update rule is:

[0103]

[0104] In the above formula (12), ρ(i,t) represents the density of cell i at time step t; ρ(i,t-1) represents the density of cell i at time step t-1; q(i,t-1) represents the inflow flow of cell i at time step t-1; q(i+1,t-1) represents the inflow flow of cell i+1 at time step t-1, L represents the cell length; T represents the cell transmission update period.

[0105] The density of cell i at time step t represents the density of the current mainline cell at the current time step; the density of cell i at time step t-1 represents the density of the current mainline cell at the previous time step; the inflow flow of cell i at time step t-1 represents the inflow flow of the current cell at the previous time step; the inflow flow of cell i+1 at time step t-1 represents the inflow flow of the next cell at the previous time step.

[0106] S234: updating the road section cellular model according to the remaining traffic capacity, the flow update rule and the density update rule to obtain an updated road section cellular model.

[0107] S24: Based on the preset critical condition and the updated road section cellular model, determine whether to control the disturbance cellular segment.

[0108] In order to clarify the specific judgment content of whether to control the disturbance cell segment, step S24 includes S241 to S243, which are:

[0109] S241: Obtaining the actual flow rate of the actual road section in the current period according to the traffic status;

[0110] The traffic status includes traffic flow, traffic density and average speed of the traffic section;

[0111] S242: Determine in real time the remaining traffic capacity of the simulated road section in the updated road section cellular model and the size of the actual traffic volume;

[0112] S243: When the remaining capacity of the simulated road section in the updated road section cellular model is less than the actual flow rate, the disturbance cellular section is controlled.

[0113] In this step, when q m ' 2 , the disturbance cell segment is controlled. In this step, the actual road segment is jointly controlled by the variable speed limit and ramp coordination.

[0114] In this step, it also includes: when q m '∈(q m ,q m ), there is no need to control the speed limit equipment on the road. m '∈(q 2 ,q in ) can avoid congestion by controlling the speed limit devices on the road individually.

[0115] q m ' represents the remaining traffic of the simulated road section; q 2 Indicates the actual flow rate; q m Indicates the actual road section capacity; q in Indicates the actual road section capacity.

[0116] S3: Controlling the disturbance cell segment, constructing a variable speed limit and ramp coordinated control model, and optimizing and solving the variable speed limit and ramp coordinated control model based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate;

[0117] In this step, controlling the disturbance cellular segment includes controlling the cellular segment where the disturbance event occurs and the cellular segment upstream of the disturbance event. The cellular segment upstream of the disturbance event is an upstream segment where the disturbance event occurs in the simulated actual road segment.

[0118] In order to clarify the specific method of obtaining the optimal variable speed limit value and ramp control rate, step S3 includes S31 to S33, specifically:

[0119] ​S31: optimizing and solving the speed limit scheme and ramp control rate in each time period in a preset control time domain based on a model predictive control algorithm, a preset optimization calculation model and a preset speed limit control constraint condition, and obtaining a variable speed limit and ramp coordinated control model, wherein the preset control time domain includes a time period for obtaining a disturbance cell segment;

[0120] In order to clarify the specific construction method of the optimization calculation model, step S31 includes S311 to S316, specifically:

[0121] S311: Obtaining the total number of mainline cells, the average speed of the current mainline cells, and the density of the current ramp cells, wherein the mainline cells include the previous mainline cells and the current mainline cells;

[0122] S312: Calculating based on the total number of mainline cells, the average speed of the current mainline cells, and the average speed of the previous mainline cells to obtain a safety optimization target model;

[0123] In this step, the security optimization target model is:

[0124]

[0125] In the above formula (13), Z 1 represents the safety optimization target; N represents the total number of mainline cells; Np represents the number of prediction steps of the short-term prediction model; k represents the current time step in which the rolling optimization is in progress; v(i,t) represents the average speed of mainline cell i in time step t; v(i+1,t) represents the average speed of mainline cell i+1 in time step t.

[0126] The average speed of the mainline cell i at time step t represents the average speed of the current mainline cell; the average speed of the mainline cell i+1 at time step t represents the average speed of the previous mainline cell.

[0127] S313: Calculating based on the average speed of the current main line cells and the density of the current main line cells to obtain a traffic efficiency optimization target model;

[0128] In this step, the traffic efficiency optimization target model is:

[0129]

[0130] In the above formula (14), Z 2 represents the traffic efficiency optimization target; N represents the total number of mainline cells; Np represents the number of prediction steps of the short-term prediction model; k represents the current time step in which the rolling optimization is in progress; v(i,t) represents the average speed of mainline cell i at time step t; ρ(i,t) represents the density of cell i at time step t; T represents the cell transmission update period.

[0131] The density of the cell i at time step t represents the density of the current main line cells; the average speed of the main line cell i at time step t represents the average speed of the current main line cells.

[0132] S314: performing calculation based on the density of the current ramp cell and a preset ramp performance weight coefficient to obtain a ramp performance optimization target model;

[0133] In this step, the ramp performance weight coefficient is:

[0134]

[0135] In the above formula (15), C r (i) is the ramp performance weight coefficient; c is the ramp performance weight impact model parameter; N r is the number of cell segments divided into by the ramp; i represents cell i.

[0136] The ramp performance optimization target model is:

[0137]

[0138] In the above formula (16), Z 3 represents the ramp performance optimization target; N r is the number of cell segments divided for the ramp; Np represents the number of prediction steps of the short-term prediction model; k represents the current time step of the rolling optimization; ρ r (i,t) represents the density of ramp cell i at time step t; C r (i) is the ramp performance weight coefficient.

[0139] The density of the ramp cell i at time step t represents the density of the current ramp cell.

[0140] S315: Calculating based on the total number of mainline cells and the density of the current mainline cells to obtain a mainline bottleneck smoothness optimization target model;

[0141] In this step, the optimization target model of the main line bottleneck patency is:

[0142] Z 4 =max[ρ(i,t)],t∈[k,k+Np),i∈[1,N-1) (17)

[0143] In the above formula (17), Z 4 It represents the optimization target of the mainline bottleneck smoothness; ρ(i,t) represents the density of cell i at time step t; Np represents the number of prediction steps of the short-term prediction model; k represents the current time step in which the rolling optimization is in progress; N represents the total number of mainline cells.

[0144] The density of the cell i at time step t represents the density of the current main line cells.

[0145] S316: Calculate according to the safety optimization target model, the traffic efficiency optimization target model, the ramp performance optimization target model, the main line bottleneck smoothness optimization target model and the preset optimization weight coefficient to obtain an optimization calculation model.

[0146] S32: optimizing the preset search constraint conditions according to the space reduction genetic algorithm to obtain a search space reduction range for the speed limit strategy;

[0147] In order to clarify the specific method of obtaining the optimization space reduction range of the speed limit strategy, step S32 includes S321 to S325, specifically:

[0148] S321: Obtain the speed limit value of the current main line cell at the current time step;

[0149] S322: performing rolling optimization of the traffic state and the speed limit scheme and ramp control rate for each time period in the preset control time domain according to the model predictive control algorithm to obtain an optimal speed limit strategy for the last update cycle, where the last update cycle is the most recently completed optimization cycle;

[0150] S323: Calculate based on the optimal speed limit strategy of the previous update cycle, the maximum change of the speed limit value in the adjacent time period and the variable speed limit cellular segment to obtain the search range of the decision variable, wherein the adjacent time period is a time period adjacent to the time period when the disturbance event occurs, and the variable speed limit cellular segment is a road section in which a speed limit device capable of adjusting the speed limit value is provided in the simulated actual road section;

[0151] In this step, the optimal speed limit strategy of the previous update cycle is the speed limit value of cell i at time step t-1.

[0152] The last update cycle is the most recently completed optimization cycle:

[0153] [v lim (i,t-1)-Δv m ,v lim (i,t-1)+Δv m ],i∈I lim (18)

[0154] In the above formula (18), v lim (i,t-1) represents the speed limit of cell i at time step t-1; Δv m Indicates the maximum acceptable change in the speed limit value during adjacent time periods on the same road section; I lim Indicates a controlled road segment with a variable speed limit.

[0155] The maximum acceptable change in the speed limit values ​​in adjacent time periods of the same road section represents the maximum change in the speed limit values ​​in adjacent time periods; the controlled road section with variable speed limit represents a variable speed limit cellular section.

[0156] S324: Calculate based on the speed limit value of the current main line cell at the current time step, the statutory minimum speed limit value of the actual road section, and the statutory maximum speed limit value of the actual road section to obtain the optimization space of the optimal speed limit strategy in the current update cycle;

[0157] In this step, the optimization space of the optimal speed limit strategy of the current update cycle is:

[0158] v LegalMin ≤v lim (i,t)≤v LegalMax (19)

[0159] In the above formula (19), v LegalMin Indicates the legal minimum speed limit of the actual road section; v lim (i,t) represents the speed limit of cell i at time step t; v LegalMax Indicates the legal maximum speed limit on the actual road section.

[0160] The speed limit value of the cell i at time step t represents the speed limit value of the current main line cell at the current time step.

[0161] S325: Calculate according to the search range of the decision variable and the optimization space of the optimal speed limit strategy in the current update cycle to obtain a reduced range of the optimization space of the speed limit strategy.

[0162] In this step, the optimization space of the speed limit strategy is reduced to:

[0163] max[v lim (i,t-1)-Δv m ,v LegalMin ]≤v lim (i,t)≤min[v lim (i,t-1)+Δv m ,v LegalMax ] (20)

[0164] In the above formula (20), v lim (i,t-1) represents the speed limit of cell i at time step t-1; Δv m Indicates the maximum acceptable change in the speed limit value during adjacent time periods on the same road section; v LegalMin Indicates the legal minimum speed limit of the actual road section; v lim (i,t) represents the speed limit of cell i at time step t; v lim (i,t-1) represents the speed limit of cell i at time step t-1; vLegalMax Indicates the legal maximum speed limit on the actual road section.

[0165] S33: Calculating according to the variable speed limit and ramp coordinated control model and the optimization space reduction range of the speed limit strategy to obtain the optimal variable speed limit value and ramp control rate.

[0166] S4: Controlling the actual road section according to the optimal variable speed limit value and ramp control rate to obtain an optimal traffic flow.

[0167] Embodiment 2:

[0168] This embodiment provides a control device for highway disturbance events, the device comprising:

[0169] The first acquisition module is used to obtain actual road section information;

[0170] A first calculation module is used to input the actual road section information into the cellular transmission model to obtain a disturbance cellular segment, where the disturbance cellular segment is a road section simulating a disturbance event occurring in the actual road section;

[0171] To clarify the specific method of obtaining the perturbation cell segment:

[0172] A first calculation unit, used for inputting the geometric characteristics of the actual road section into the cellular transmission model to obtain a road section cellular model;

[0173] The second calculation unit is used to input the traffic state and the remaining traffic capacity into the road section cellular model to obtain a disturbance cellular segment, where the disturbance cellular segment simulates the road section where the disturbance event occurs in the actual road section.

[0174] A second calculation module is used to control the disturbance cell segment, construct a variable speed limit and ramp coordination control model, and optimize and solve the variable speed limit and ramp coordination control model based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate;

[0175] To clarify the specific method of obtaining the optimal variable speed limit value and ramp control rate:

[0176] A first construction unit is used to optimize and solve the speed limit scheme and ramp control rate in each time period in a preset control time domain based on a model predictive control algorithm, a preset optimization calculation model and a preset speed limit control constraint condition, so as to obtain a variable speed limit and ramp coordinated control model, wherein the preset control time domain includes a time period for obtaining a disturbance cell segment;

[0177] A second construction unit is used to optimize the preset search constraint conditions according to the space reduction genetic algorithm to obtain a search space reduction range of the speed limit strategy;

[0178] The third construction unit is used to calculate according to the variable speed limit and ramp coordinated control model and the optimization space reduction range of the speed limit strategy to obtain the optimal variable speed limit value and ramp control rate.

[0179] The third calculation module is used to control the actual road section according to the optimal variable speed limit value and ramp control rate to obtain an optimal traffic flow.

[0180] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0181] Embodiment 3:

[0182] Corresponding to the above method embodiment, this embodiment also provides a control device for a highway disturbance event. The control device for a highway disturbance event described below and the control method for a highway disturbance event described above can refer to each other.

[0183] Figure 2 FIG. 8 is a block diagram of a control device 800 for a highway disturbance event according to an exemplary embodiment. Figure 2 As shown, the control device 800 for the highway disturbance event may include: a processor 801, a memory 802. The control device 800 for the highway disturbance event may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0184] The processor 801 is used to control the overall operation of the control device 800 of the highway disturbance event to complete all or part of the steps in the control method of the highway disturbance event. The memory 802 is used to store various types of data to support the operation of the control device 800 of the highway disturbance event, and these data may include, for example, instructions for any application or method operated on the control device 800 of the highway disturbance event, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the control device 800 of the highway disturbance event and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0185] In an exemplary embodiment, the control device 800 for highway disturbance events can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned control method for highway disturbance events.

[0186] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the control method of the above-mentioned highway disturbance event are implemented. For example, the computer-readable storage medium can be the memory 802 including the program instructions, and the program instructions can be executed by the processor 801 of the control device 800 of the highway disturbance event to complete the control method of the above-mentioned highway disturbance event.

[0187] Embodiment 4:

[0188] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the method for controlling a highway disturbance event described above can refer to each other.

[0189] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for controlling a highway disturbance event in the above method embodiment.

[0190] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0191] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0192] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for controlling a highway disturbance event, characterized in that: include: Get actual road section information; Inputting the actual road section information into the cellular transmission model to obtain a disturbance cellular segment, wherein the disturbance cellular segment is a road section simulating a disturbance event occurring in the actual road section; The disturbance cell segment is controlled to construct a variable speed limit and ramp coordinated control model, and the variable speed limit and ramp coordinated control model is optimized and solved based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate; The actual road section is controlled according to the optimal variable speed limit value and ramp control rate to obtain an optimal traffic flow.

2. The control method for highway disturbance events according to claim 1 is characterized in that the actual road section information is input into the cellular transmission model to obtain the disturbance cellular segment, and the actual road section information includes the geometric characteristics of the actual road section, the traffic state of the actual road section in the current period, and the remaining traffic capacity of the actual road section in the current period, including: Inputting the geometric characteristics of the actual road section into the cellular transmission model to obtain a road section cellular model; The traffic state and the remaining traffic capacity are input into the road section cellular model to obtain a disturbance cellular segment, where the disturbance cellular segment simulates a road section where a disturbance event occurs in an actual road section.

3. The control method for highway disturbance events according to claim 1 is characterized in that the disturbance cell segment is controlled to construct a variable speed limit and ramp coordination control model, and the variable speed limit and ramp coordination control model is optimized and solved based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate, including: Obtaining an optimization calculation model; Based on a preset model predictive control algorithm, the optimization calculation model and the preset speed limit control constraint condition, the speed limit scheme and ramp control rate in each time period in a preset control time domain are optimized and solved to obtain a variable speed limit and ramp coordination control model, wherein the preset control time domain includes a time period for obtaining a disturbance cell segment; Optimizing the preset search constraint conditions according to the space reduction genetic algorithm to obtain the optimization space reduction range of the speed limit strategy; The optimal variable speed limit value and ramp control rate are obtained by calculating the variable speed limit and ramp coordinated control model and the optimization space reduction range of the speed limit strategy.

4. The control method for highway disturbance events according to claim 3 is characterized in that the construction method of the optimization calculation model, the road section cell model includes main line cells and ramp cells, including: Obtaining the total number of mainline cells, the average speed of the current mainline cells, and the density of the current ramp cells, wherein the mainline cells include the previous mainline cells and the current mainline cells; Calculating based on the total number of mainline cells, the average speed of the current mainline cells and the average speed of the previous mainline cells, to obtain a safety optimization target model; Calculating based on the average speed of the current main line cells and the density of the current main line cells to obtain a traffic efficiency optimization target model; Calculating based on the density of the current ramp cell and a preset ramp performance weight coefficient to obtain a ramp performance optimization target model; Calculate based on the total number of main line cells and the density of the current main line cells to obtain a main line bottleneck smoothness optimization target model; The optimization calculation model is obtained by performing calculations based on the safety optimization target model, the traffic efficiency optimization target model, the ramp performance optimization target model, the main line bottleneck smoothness optimization target model and preset optimization weight coefficients.

5. The control method for highway disturbance events according to claim 3 is characterized in that the preset search constraint conditions are optimized according to the genetic algorithm of space reduction to obtain the optimization space reduction range of the speed limit strategy, including: Get the speed limit value of the current main line cell at the current time step; According to the preset model predictive control algorithm, the traffic state and the speed limit scheme and ramp control rate of each time period in the preset control time domain are rolled optimized to obtain the optimal speed limit strategy of the last update cycle, wherein the last update cycle is the most recently completed optimization cycle; The search range of the decision variable is obtained by calculating based on the optimal speed limit strategy of the last update cycle, the maximum change of the speed limit value in the preset adjacent time periods, and the preset variable speed limit cellular segment, wherein the adjacent time period is a time period adjacent to the time period when the disturbance event occurs, and the variable speed limit cellular segment is a road section in which a speed limit device capable of adjusting the speed limit value is set in a simulated actual road section; Calculate based on the speed limit value of the current main line cell at the current time step, the preset legal minimum speed limit value of the actual road section, and the preset legal maximum speed limit value of the actual road section, to obtain the optimization space of the optimal speed limit strategy of the current update cycle; A calculation is performed based on the search range of the decision variable and the optimization space of the optimal speed limit strategy in the current update cycle to obtain a reduced range of the optimization space of the speed limit strategy.

6. A control device for highway disturbance events, characterized in that: include: The first acquisition module is used to obtain actual road section information; A first calculation module is used to input the actual road section information into the cellular transmission model to obtain a disturbance cellular segment, where the disturbance cellular segment is a road section simulating a disturbance event occurring in the actual road section; A second calculation module is used to control the disturbance cell segment, construct a variable speed limit and ramp coordination control model, and optimize and solve the variable speed limit and ramp coordination control model based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate; The third calculation module is used to control the actual road section according to the optimal variable speed limit value and ramp control rate to obtain an optimal traffic flow.

7. The control device for highway disturbance events according to claim 6 is characterized in that the actual road section information is input into the cellular transmission model to obtain the disturbance cellular segment, and the actual road section information includes the geometric characteristics of the actual road section, the traffic state of the actual road section in the current period, and the remaining traffic capacity of the actual road section in the current period, including: A first calculation unit, used for inputting the geometric characteristics of the actual road section into the cellular transmission model to obtain a road section cellular model; The second calculation unit is used to input the traffic state and the remaining traffic capacity into the road section cellular model to obtain a disturbance cellular segment, where the disturbance cellular segment simulates the road section where the disturbance event occurs in the actual road section.

8. The control device for highway disturbance events according to claim 6 is characterized in that the disturbance cell segment is controlled to construct a variable speed limit and ramp coordination control model, and the variable speed limit and ramp coordination control model is optimized and solved based on a space-reduced genetic algorithm to obtain an optimal variable speed limit value and ramp control rate, including: A first construction unit is used to optimize and solve the speed limit scheme and ramp control rate in each time period in a preset control time domain based on a preset model predictive control algorithm, the optimization calculation model and the preset speed limit control constraint condition, so as to obtain a variable speed limit and ramp coordinated control model, wherein the preset control time domain includes a time period for obtaining a disturbance cell segment; A second construction unit is used to optimize the preset search constraint conditions according to the space reduction genetic algorithm to obtain a search space reduction range of the speed limit strategy; The third construction unit is used to calculate according to the variable speed limit and ramp coordinated control model and the optimization space reduction range of the speed limit strategy to obtain the optimal variable speed limit value and ramp control rate.

9. A control device for highway disturbance events, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the method for controlling highway disturbance events as claimed in any one of claims 1 to 5 when executing the computer program.

10. A readable storage medium, characterized in that The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for controlling a highway disturbance event as described in any one of claims 1 to 5 are implemented.

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