Double-layer cooperative control method considering ramp bottleneck capacity falling in network connection environment

Abstract

The invention discloses a double-layer cooperative control method for improving the traffic capacity of an uphill road section in a network connection environment, which is suitable for a road basic road section containing a ramp bottleneck, and comprises the following steps: 1, obtaining a current traffic flow state, calculating an equivalent time headway and mixed traffic flow basic diagram parameters, and predicting a traffic state of a next time period; 2, calculating the entrance sending capacity of the upslope bottleneck road section and the supply capacity and falling capacity of the downstream straight road section, and determining the upper flow limit of the upslope bottleneck road section; and 3, when the regional transfer total demand flow exceeds the regional transfer total supply and the regional transfer total supply flow is greater than the falling capacity, judging that the capacity falling is about to occur, activating the double-layer cooperative control framework, and performing rolling optimization to obtain a variable speed limit value and a special road layout scheme, and issuing and implementing the variable speed limit value and the special road layout scheme. According to the method, modeling and management and control requirements of a ramp capacity falling mechanism are considered, generation of capacity falling can be inhibited, the bottleneck queuing length of the ramp is reduced, and the traffic capacity and the traffic efficiency of the basic road section of the road are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of intelligent traffic management and control, and specifically relates to a traffic cooperative management control method of dynamically deploying a connected vehicle special lane on part of the main line of an upstream straight section and variable speed limit (VSL) control in a mixed connected traffic environment with connected human-driven vehicles (CHV) and connected autonomous vehicles (CAV), which comprehensively considers the traffic flow information and road geometric properties of the uphill bottleneck section and its upstream and downstream straight sections. BACKGROUND

[0002] There are a large number of uphill sections on actual roads, especially in the mountainous areas of western China. In Japan, nearly 60% of traffic congestion occurs on uphill sections. In addition, the non-smooth acceleration curve of vehicles on a slope will cause additional fuel consumption and emissions. However, unlike bottlenecks caused by factors such as lane reduction, ramp merging, and traffic accidents, uphill bottlenecks are the result of the combined action of the geometric characteristics of the uphill and the driving behavior of the driver - in order to cope with the gravitational effect of the slope, the driver usually needs to adjust the throttle or brake pedal, which, although it can partially offset the impact of the uphill, will also cause the vehicle's longitudinal acceleration to be limited and unstable, thereby increasing the vehicle headway and traffic density, reducing traffic flow and passing efficiency. Therefore, it is of great theoretical significance and practical application value to deeply study the formation mechanism and control strategy of uphill bottlenecks.

[0003] Existing research focuses on straight sections or ordinary bottlenecks, and most studies fail to fully consider the impact of the geometric characteristics of the uphill and the driving behavior on the uphill bottleneck section on traffic flow. Currently, existing traffic control strategies, such as lane allocation and speed limit control, mostly use static or single-dimensional considerations, which are difficult to meet the complex needs of uphill bottlenecks. Although variable speed limit (VSL) control can alleviate traffic congestion in some cases, it usually focuses on longitudinal speed control and is difficult to effectively coordinate the differences between different lanes and vehicle types, especially in the uphill bottleneck section area, the adaptability of VSL control is poor, and it fails to fundamentally improve the passing capacity of the uphill bottleneck section. Therefore, existing control strategies have not completely solved the traffic flow efficiency problem caused by uphill bottlenecks, and a new traffic management method is needed to address this challenge. SUMMARY

[0004] To overcome the shortcomings of existing technologies, this invention proposes a two-layer collaborative control method to improve the traffic capacity of uphill road sections in a connected environment. By dynamically deploying dedicated CAV (Connected Autonomous Vehicle) lanes and combining them with lane variable speed limit (VSL) control, this method can address the modeling and control needs of uphill capacity fall mechanisms and flexibly adjust traffic flow. This helps to suppress capacity fall, reduce queue length at uphill bottlenecks, and effectively improve the traffic capacity and efficiency of uphill bottleneck sections, thereby achieving more efficient and safer traffic management.

[0005] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The present invention discloses a two-layer cooperative control method for improving the traffic capacity of uphill sections in a connected environment. The method is characterized by its application in a hybrid connected traffic environment containing both connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), and by using a basic road segment containing an uphill bottleneck section as the control object. This basic road segment, along the vehicle's direction of travel, sequentially includes an upstream straight section, an uphill bottleneck section, and a downstream straight section. A segment of the upstream straight section is selected as the control area. The two-layer cooperative control method proceeds according to the following steps: Step 1: Along the vehicle's direction of travel, the basic road section... The lanes are divided into two equal parts, one from the inside out. Each region, and constitutes Let the first two-dimensional cell, the second The first region The two-dimensional cell containing each lane is denoted as ,in, For the index of the region, For lane indexing; Collect current control cycle Lower two-dimensional cells density ,speed ,flow Total number of vehicles Number of CAV vehicles CHV vehicle numbers And calculate the current control cycle. Lower two-dimensional cells CAV penetration rate CAV traffic CHV flow ; Step 2: Classify the car-following combination types of mixed traffic flow in the uphill bottleneck section into CAV car-following CAV, CAV car-following CHV, and CHV car-following any type of vehicle. according to Combined with the car-following combination type, calculate the current control cycle. Lower two-dimensional cells Equivalent average headway of mixed traffic flow and based on Calculate the current control cycle Lower two-dimensional cells Mixed traffic flow parameters: critical density , traffic capacity and congestion wave speed ;in, Representing a two-dimensional cell Horizontal position; Step 3, Construct the current control cycle A lane-level CTM model considering capacity drop at uphill bottleneck sections is used to obtain the next control cycle. Lower two-dimensional cells Mixed traffic flow parameters; Step 4, based on the next control cycle Lower two-dimensional cells Based on the mixed traffic flow parameters, construct the next control cycle. The lower-level variable speed limit control model and the upper-level CAV dedicated lane layout optimization model form the next control cycle. The two-layer collaborative control model is as follows; Step 5, proceed to the next control cycle. The lower control region will control the rate limit value of each cell within the control region. ; and the index of the first area of ​​the dedicated lane within the control area. Index of the last area of ​​the dedicated lane Number of dedicated lanes They are all encoded into a single chromosome, allowing a genetic algorithm to be used for the next control cycle. The two-layer cooperative control model is solved iteratively to obtain the next control cycle. The optimal solution for the two-layer cooperative control includes: the next control cycle. The optimal speed limit and optimal layout plan for CAV dedicated lanes were determined and distributed to variable speed limit signs, lane indicator signs, and vehicle terminals; among them, Two-dimensional cells for the control region The set, This is the index of the first region of the control region. This is the index of the last region in the control region; To control the cycle Lower control area Each two-dimensional cell The speed limit value; Step 6, Assign to ,like Reaching the time threshold If the condition is met, control will stop; otherwise, return to step 1 and execute sequentially.

[0006] The characteristic of the two-layer cooperative control method described in this invention is that, in step 2, the current control cycle is calculated using equation (1). Lower two-dimensional cells Equivalent average headway : (1) In equation (1), This indicates the average headway between the CAV and the CAV. This indicates the average headway between the CAV and the CHV. Let CHV represent the average headway when following any vehicle, and we have: (2) In equation (2), The length of the uphill bottleneck section. This represents the average headway of the CHV vehicle on the upstream straight section of the road. This represents the average headway of the CHV vehicle on the downstream straight section of road. This represents the change in average headway after the CHV passes through the uphill bottleneck section. Calculate using equations (3), (4), and (5) respectively. , and : (3) (4) (5) In equations (3), (4), and (5), The free flow velocity of each two-dimensional cell, The crowding density of each two-dimensional cell.

[0007] Furthermore, step 3 includes: Step 3.1, based on , and Calculate the next control cycle Lower two-dimensional cells transmission capacity and acceptance ability And calculate the next control cycle. Lower two-dimensional cells Vehicles of type medium enter the first The first region The two-dimensional cell containing the lane lane change probability This allows us to obtain the actual transfer flow of vehicles of type `type`. ; ; Step 3.2, Construct the current control cycle Constrained acceleration model Used to calculate the next control cycle Fall capacity of uphill and downhill bottleneck sections ; Step 3.3, record the index of the last region in the upstream straight road segment as... The index of the first region of the uphill bottleneck section is... ; The index of the last region in the bottleneck section of the uphill road is: Then the index of the first region of the downstream straight road segment is ; Calculate the next control cycle The entrance to the bottleneck section on the uphill / downhill road is located at the first Total regional transfer demand flow in each region and the next control cycle The first straight section of the downstream road entrance is located at the first Total regional transfer supply flow in each region Thus, the next control cycle is obtained. The entrance to the uphill bottleneck section is located at the first Traffic limit for each region ; Step 3.4, based on Calculate the next control cycle respectively Vehicles of type below from the first The first region The two-dimensional cell upstream of the lane Two-dimensional cells flowing downstream Total flow and the In each control cycle, vehicle type 2 starts from the upstream two-dimensional cell. Flow into the first The first region Two-dimensional cells downstream of the lane Total flow ;when season Total flow For a fixed value, when season Total flow For fixed values, , ,when season ,when season ; Step 3.5, based on and To obtain the next control cycle Lower two-dimensional cells Mixed traffic flow parameters.

[0008] Furthermore, step 3.1 includes: Step 3.1.1, calculate the next control cycle using equation (6). Lower two-dimensional cells Downstream two-dimensional cells transmission capacity : (6) In equation (6), For the current control cycle Lower two-dimensional cells The speed limit value; Step 3.1.2, calculate the next control cycle using equation (7). Lower two-dimensional cells vehicles enter the The first region The two-dimensional cell containing the lane Lane change benefits : (7) In equation (7), This is the lane-changing delay parameter; For the current control cycle Lower two-dimensional cells average speed, Current control cycle Lower two-dimensional cells average speed, Indicates the time interval between adjacent control cycles; Step 3.1.3, calculate the next control cycle using equation (8). Lower two-dimensional cells Vehicles of type medium enter the first The first region The two-dimensional cell containing the lane lane change probability : (8) In equation (8), Let be the lane-changing feasibility constraint factor for vehicle type 'type', where 'type' represents the vehicle category. When 'type' is CHV, and the '...' is the lane-changing feasibility constraint factor... When each lane is a dedicated lane for CAVs, Otherwise, let ; This represents the lane-changing sensitivity coefficient; Step 3.1.4, calculate the next control cycle using equation (9). Next All vehicles in the region for the first The first region The two-dimensional cell containing the lane Total demand flow for lane shifting : (9) In equation (9), Indicates the next control cycle Lower two-dimensional cells China CHV enters the 1st The first region The two-dimensional cell containing the lane The probability of lane changing; Indicates the next control cycle Lower two-dimensional cells CAV enters the 1st The first region The two-dimensional cell containing the lane The probability of lane changing; Step 3.1.5, calculate the next control cycle using equation (12). Lower two-dimensional cells Downstream two-dimensional cells reception capability : (10) In formula (10) , , and These represent the current control cycle. Next The first region CAV penetration rate, congestion wave velocity, traffic capacity and density of each lane; Step 3.1.6, use equation (11) to calculate the next control cycle. Next All vehicles in the region for the first The first region The two-dimensional cell containing the lane Total supply flow shift : (11) Step 3.1.7, calculate the next control cycle using equation (12). Lower two-dimensional cells Scaling of total demand for lane changeover : (12) Step 3.1.8, calculate the next control cycle using equation (13). Lower two-dimensional cells CAV enters the 1st The first region The two-dimensional cell containing the lane Actual transfer flow and CHV enter the first The first region The two-dimensional cell containing the lane Actual transfer flow : (13) Step 3.1.9, calculate the next control cycle using equation (14). Lower two-dimensional cells vehicles enter the The first region The two-dimensional cell containing the lane Actual total transfer flow : (14).

[0009] Furthermore, step 3.2 includes: Step 3.2.1, calculate the current control cycle using equation (15). Constrained acceleration model : (15) In equation (15), Indicates the vehicle's maximum acceleration; Represents gravitational acceleration; Indicates the slope of the ramp; Step 3.2.2, calculate using equation (16) : (16) In formula (15) It represents the spatial change rate of the equivalent average headway.

[0010] Furthermore, step 3.3 includes: Step 3.3.1, calculate using equation (17) : (17) Step 3.3.2, calculate using equation (18) : (18) Step 3.3.3, calculate using equation (19) : (19).

[0011] Furthermore, step 3.4 includes: Step 3.4.1, calculate the next control cycle using equation (20). Next Vehicles in the first area The actual total transfer flow in each region : (20) Step 3.4.2, calculate the next control cycle using equation (21). Capacity drop scaling factor : (twenty one) Step 3.4.3, calculate using equation (22) : (twenty two) Step 3.4.4, calculate using equations (23) and (24) respectively. and : (twenty three) (twenty four).

[0012] Furthermore, step 3.5 includes: Step 3.5.1, calculate the next control cycle using equation (25). Lower two-dimensional cells Number of vehicles of type medium : (25) In equation (25), This indicates the time interval between two adjacent control cycles; Step 3.5.2, calculate the next control cycle using equation (26). Lower two-dimensional cells CAV penetration rate : (26) In equation (26), Indicates the next control cycle Lower two-dimensional cells The number of CAVs in the data; Step 3.5.3, calculate the next control cycle using equation (27). Lower two-dimensional cells average speed : (27).

[0013] Furthermore, step 4 includes: Step 4.1: Construct the next control cycle using equations (28) and (29) respectively. The objective function of the lower-level variable speed limit control model The objective function of the optimization model for the layout of dedicated CAV lanes in the upper layer : (28) (29) In equations (28) and (29), The duration of each control cycle, , These are the weighting coefficients for the two lower-level controls. , This is represented by the weighting coefficients of two upper-level controls. The length of the cell, To control the cycle Two-dimensional cells with dedicated lanes deployed in the lower control area The set, ,in To control the cycle The collection of lanes with dedicated underground lanes. For the next control cycle The number of lanes for dedicated underground lanes, For the next control cycle Down The Middle Two-dimensional cells with dedicated lanes on each lane The index of the first region, For the next control cycle Down The Middle Two-dimensional cells with dedicated lanes on each lane The index of the last region; Step 4.2, construct the next control cycle using equation (30) Constraints of the two-level cooperative control model: (30) In equation (30), This is the minimum speed limit. This is the maximum speed limit. For control area The threshold for the rate limit difference between adjacent control cycles for the same two-dimensional cell. Control region under the same control cycle The threshold for the rate difference between adjacent two-dimensional cells. For the current control cycle Lower control area Inner two-dimensional cell The speed limit value, For the next control cycle Lower control area Inner two-dimensional cell The speed limit value.

[0014] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program supporting the processor in performing the method described therein, and the processor is configured to execute the program stored in the memory.

[0015] Compared with existing technologies, the beneficial effects of this invention are reflected in: 1. This invention introduces "equivalent average headway related to uphill position" and "hybrid traffic flow parameters" in the uphill bottleneck road segment scenario, so that the key traffic flow parameters, such as capacity, critical density and congestion wave velocity, can be adaptively updated according to the classification of uphill position and car-following combination type. This avoids the prediction bias caused by treating the impact of uphill bottleneck as a fixed parameter in the prior art, and realizes interpretable modeling of the law of uphill bottleneck impact changing with position.

[0016] 2. This invention combines the constrained acceleration model with the calculation of the upper limit of the flow rate at the entrance of the uphill bottleneck section introduced by the lane-level CTM to form an explicit constraint mechanism of "fall capacity - flow rate upper limit". When the sending capacity exceeds the supply capacity and the supply capacity is greater than the fall capacity, it determines that a capacity fall has occurred and triggers a two-layer control method for the upstream straight section. This avoids the drawbacks of ignoring the capacity fall phenomenon or simply calibrating a capacity fall coefficient in the prior art. Instead, it can conditionally predict and suppress the occurrence of the capacity fall phenomenon in advance.

[0017] 3. This invention constructs a multi-lane lane-changing model that combines speed benefit drive, CAV / CHV vehicle type differences, and CHV access restrictions on CAV dedicated lanes, and is coupled with the transmission / reception capabilities of lane-level CTM. Compared with existing technologies that only use single-lane conservation or rely solely on static lane-changing rules, this model more realistically reflects the lateral traffic exchange and competition relationships between lanes under dedicated lane deployment conditions.

[0018] 4. Within the same rolling control cycle, the upper layer uses the CAV dedicated lane layout scheme as the decision variable, and the lower layer uses the lane variable speed limit as the decision variable, and implements collaborative optimization according to a unified performance index. Compared with directly mixing lane control, dedicated lane layout and ramp bottleneck constraints into a single-layer high-dimensional optimization problem, this invention can realize the decomposition modeling and collaborative solution of the "lateral organization (dedicated lane layout) - longitudinal speed regulation (VSL control)" mechanism, thereby improving the pertinence and feasibility of the control strategy. 5. This invention uses CAV penetration rate and hybrid car-following combination structure as inputs for equivalent headway and traffic flow parameters. The control strategy can automatically adjust speed limits and dedicated lane layout according to changes in penetration rate. Therefore, compared with existing technologies that are only applicable to a single vehicle type or have a fixed penetration rate assumption, it has better compatibility and scalability. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a schematic diagram of the control scene; Figure 3 This is a schematic diagram of a two-dimensional cell; Figure 4 This is a schematic diagram of a car-following combination; Figure 5 This is a schematic diagram illustrating the mechanical analysis of a car on a slope. Detailed Implementation

[0020] In this embodiment, a two-layer cooperative control method for improving the traffic capacity of uphill road sections in a connected environment is characterized by its application in a hybrid connected traffic environment including connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), and the control object being a basic road segment containing an uphill bottleneck section; the basic road segment sequentially includes, along the vehicle travel direction, an upstream straight section, an uphill bottleneck section, and a downstream straight section; wherein, a section of the upstream straight section is selected as the control area; the two-layer cooperative control method is performed according to the following steps: Figure 1As shown, the two-layer collaborative control method proposes a two-layer control framework: a lower-layer lane-level variable speed limit control combined with an upper-layer dynamic CAV (Continuous Access Control) lane layout. The causes of ramp capacity reduction and capacity drop are intrinsically characterized in the traffic flow model. This optimizes the capacity of uphill bottleneck sections while improving the stability and efficiency of mixed traffic flow. Specifically, the method proceeds as follows: Step 1, as follows Figure 2 As shown, along the direction of vehicle travel, the basic road sections are... The lanes are divided into two equal parts, one from the inside out. Each region, and constitutes A two-dimensional cell, such as Figure 3 As shown, let the first The first region The two-dimensional cell containing each lane is denoted as ,in, For the index of the region, For lane indexing; Collect current control cycle Lower two-dimensional cells density ,speed ,flow Total number of vehicles Number of CAV vehicles CHV vehicle numbers And calculate the current control cycle. Lower two-dimensional cells CAV penetration rate CAV traffic CHV flow .

[0021] Step 2: Classify the car-following combination types of mixed traffic flow in the uphill bottleneck section into CAV car-following CAV, CAV car-following CHV, and CHV car-following any type of vehicle. according to Combined with the car-following combination type, calculate the current control cycle. Lower two-dimensional cells Equivalent average headway of mixed traffic flow and based on Calculate the current control cycle Lower two-dimensional cells Mixed traffic flow parameters: critical density , traffic capacity and congestion wave speed ;in, Representing a two-dimensional cell The horizontal position.

[0022] Calculate the current control cycle using equation (1) Lower two-dimensional cells Equivalent average headway : (1) In formula (1), such as Figure 4 As shown, This indicates the average headway between the CAV and the CAV. This indicates the average headway between the CAV and the CHV. Let CHV represent the average headway when following any vehicle, and we have: (2) In equation (2), The length of the uphill bottleneck section. This represents the average headway of the CHV vehicle on the upstream straight section of the road. This represents the average headway of the CHV vehicle on the downstream straight section of road. This represents the change in average headway after a CHV vehicle passes through an uphill bottleneck section; it reflects the driver's subconscious tendency to increase headway even when maintaining the same speed as on a straight road, leading to an increase in effective headway as position increases, thus causing traffic capacity to gradually decrease with position.

[0023] Calculate using equations (3), (4), and (5) respectively. , and : (3) (4) (5) In equations (3), (4), and (5), The free flow velocity of each two-dimensional cell, The crowding density of each two-dimensional cell.

[0024] Step 3, Construct the current control cycle A lane-level CTM model considering capacity drop at uphill bottleneck sections is used to obtain the next control cycle. Lower two-dimensional cells Mixed traffic flow parameters; Step 3.1, based on , and Calculate the next control cycle Lower two-dimensional cells transmission capacity and acceptance ability And calculate the next control cycle. Lower two-dimensional cells Vehicles of type medium enter the first The first region The two-dimensional cell containing the lane lane change probability This allows us to obtain the actual transfer flow of vehicles of type `type`. ; .

[0025] Step 3.1.1, calculate the next control cycle using equation (6). Lower two-dimensional cells Downstream two-dimensional cells transmission capacity : (6) In equation (6), For the current control cycle Lower two-dimensional cells The speed limit value.

[0026] Step 3.1.2, calculate the next control cycle using equation (7). Lower two-dimensional cells vehicles enter the The first region The two-dimensional cell containing the lane Lane change benefits : (7) In equation (7), This is the lane-changing delay parameter; For the current control cycle Lower two-dimensional cells average speed, Current control cycle Lower two-dimensional cells average speed, This indicates the time interval between adjacent control cycles; lane change benefit is the reason why vehicles choose to change lanes, and in this implementation method, it is defined as any type of vehicle tending to gain speed benefits when changing lanes.

[0027] Step 3.1.3, calculate the next control cycle using equation (8). Lower two-dimensional cells Vehicles of type medium enter the first The first region The two-dimensional cell containing the lane lane change probability : (8) In equation (8), Let be the lane-changing feasibility constraint factor for vehicle type 'type', where 'type' represents the vehicle category. When 'type' is CHV, and the '...' is the lane-changing feasibility constraint factor... When each lane is a dedicated lane for CAVs, Otherwise, let ; This represents the lane-changing sensitivity coefficient; This reflects the lane management strategy, which states that CAV vehicles can use any lane on the main road segment, while CHVs are prohibited from using the CAV-only lane.

[0028] Step 3.1.4, calculate the next control cycle using equation (9). Next All vehicles in the region for the first The first region The two-dimensional cell containing the lane Total demand flow for lane shifting : (9) In equation (9), Indicates the next control cycle Lower two-dimensional cells China CHV enters the 1st The first region The two-dimensional cell containing the lane The probability of lane changing; Indicates the next control cycle Lower two-dimensional cells CAV enters the 1st The first region The two-dimensional cell containing the lane Lane-changing probability; total demand flow shifted by lane changing This represents the desired flow of vehicles within a two-dimensional cell based on lane-changing benefits, but is subject to the actual road capacity. Constraints.

[0029] Step 3.1.5, calculate the next control cycle using equation (12). Lower two-dimensional cells Downstream two-dimensional cells reception capability : (10) In formula (10) , , and These represent the current control cycle. Next The first region CAV penetration rate, congestion wave velocity, traffic capacity and density of each lane; receiving capacity. Constrained the total demand flow for lane changing and transfer .

[0030] Step 3.1.6, use equation (11) to calculate the next control cycle. Next All vehicles in the region for the first The first region The two-dimensional cell containing the lane Total supply flow shift : (11) Step 3.1.7, calculate the next control cycle using equation (12). Lower two-dimensional cells Scaling of total demand for lane changeover : (12).

[0031] Step 3.1.8, calculate the next control cycle using equation (13). Lower two-dimensional cells CAV enters the 1st The first region The two-dimensional cell containing the lane Actual transfer flow and CHV enter the first The first region The two-dimensional cell containing the lane Actual transfer flow : (13) Step 3.1.9, calculate the next control cycle using equation (14). Lower two-dimensional cells vehicles enter the The first region The two-dimensional cell containing the lane Actual total transfer flow : (14).

[0032] Step 3.2, Construct the current control cycle Constrained acceleration model Used to calculate the next control cycle Fall capacity of uphill and downhill bottleneck sections ; Step 3.2.1, calculate the current control cycle using equation (15). Constrained acceleration model : (15) In equation (15), Indicates the vehicle's maximum acceleration; Represents gravitational acceleration; Indicates the slope of the ramp; such as Figure 5 As shown, vehicles are affected by gravity at the bottleneck section of a ramp. The constrained acceleration model quantifies this effect and provides a basis for calculating the macroscopic traffic flow phenomenon of capacity drop at the bottleneck section of a ramp. Different constrained acceleration models can be used to better fit the capacity drop phenomenon of different bottleneck sections of ramps, depending on local conditions.

[0033] Step 3.2.2, calculate using equation (16) : (16) In formula (15) It represents the spatial change rate of the equivalent average headway.

[0034] Step 3.3, record the index of the last region in the upstream straight road segment as... The index of the first region of the uphill bottleneck section is... ; The index of the last region in the bottleneck section of the uphill road is: Then the index of the first region of the downstream straight road segment is ; Calculate the next control cycle The entrance to the bottleneck section on the uphill / downhill road is located at the first Total regional transfer demand flow in each region and the next control cycle The first straight section of the downstream road entrance is located at the first Total regional transfer supply flow in each region Thus, the next control cycle is obtained. The entrance to the uphill bottleneck section is located at the first Traffic limit for each region ; Step 3.3.1, calculate using equation (17) : (17) Step 3.3.2, calculate using equation (18) : (18) Step 3.3.3, using equation (19) : (19) In equation (19), when At this time, upstream demand is not greater than downstream supply, and there is no continuous queue upstream; when At this point, upstream demand exceeds downstream supply, but downstream supply itself is below the falling capacity, meaning the capacity fall is not a constraining factor; when At this point, there is both upstream queuing and a sufficiently large downstream supply, making the limited acceleration constraint the decisive factor.

[0035] Step 3.4, based on Calculate the next control cycle respectively Vehicles of type below from the first The first region The two-dimensional cell upstream of the lane Two-dimensional cells flowing downstream Total flow and the In each control cycle, vehicle type 2 starts from the upstream two-dimensional cell. Flow into the first The first region Two-dimensional cells downstream of the lane Total flow ;when season Total flow For a fixed value, when season Total flow For fixed values, , ,when hour, ,when hour, This prevents the index from going out of bounds.

[0036] Step 3.4.1, calculate the next control cycle using equation (20). Next Vehicles in the first area The actual total transfer flow in each region : (20) Step 3.4.2, calculate the next control cycle using equation (21). Capacity drop scaling factor : (twenty one) Step 3.4.3, calculate using equation (22) : (twenty two).

[0037] Step 3.4.4, calculate using equations (23) and (24) respectively. and : (twenty three) (twenty four).

[0038] Step 3.5, based on and To obtain the next control cycle Lower two-dimensional cells Mixed traffic flow parameters: Step 3.5.1, calculate the next control cycle using equation (25). Lower two-dimensional cells Number of vehicles of type medium : (25) In equation (25), This indicates the time interval between two adjacent control cycles.

[0039] Step 3.5.2, calculate the next control cycle using equation (26). Lower two-dimensional cells CAV penetration rate : (26) In equation (26), Indicates the next control cycle Lower two-dimensional cells The number of CAVs in the data.

[0040] Step 3.5.3, use equation (27) to calculate the first... Two-dimensional cell under each control cycle average speed : (27).

[0041] Step 4, based on the next control cycle Lower two-dimensional cells Based on the mixed traffic flow parameters, construct the next control cycle. The lower-level variable speed limit control model and the upper-level CAV dedicated lane layout optimization model form the next control cycle. The two-layer collaborative control model is as follows; Step 4.1: Construct the objective function of the lower-level variable speed limit control model using equations (28) and (29) respectively. and: (28) (29) In equations (28) and (29), The duration of each control cycle, , These are the weighting coefficients for the two lower-level controls. , This is represented by the weighting coefficients of two upper-level controls. The length of the cell. Two-dimensional cells for the control region The set, The index of the first region of the control region. This is the index of the last region in the control area; To control the cycle Lower control area Each two-dimensional cell The speed limit value; To control the cycle Two-dimensional cells with dedicated lanes deployed in the lower control area The set, ,in To control the cycle The collection of lanes with dedicated underground lanes. To control the cycle The number of lanes for dedicated underground lanes, To control the cycle Next set The Middle Two-dimensional cells with dedicated lanes on each lane The index of the first region, To control the cycle Next set The Middle Two-dimensional cells with dedicated lanes on each lane The index of the last region.

[0042] Step 4.2, use equation (30) to construct the constraints of the two-layer cooperative control model: (30) In formula (30) This is the minimum speed limit. This is the maximum speed limit. For control area The threshold for the rate limit difference between adjacent control cycles for the same two-dimensional cell. Control region under the same control cycle The threshold for the rate difference between adjacent two-dimensional cells. To control the cycle Lower control area Inner two-dimensional cell The speed limit value, To control the cycle Lower control area Inner two-dimensional cell The speed limit value. In the... In each control cycle, the lower-level control controls the area. Rate limiting value of each two-dimensional cell As a control variable, in the first In each control cycle, the upper-level control is taken as... The two-dimensional cell within is the control variable; let the total travel time of the lower and upper layers in their respective prediction time domains be the control variable. Deviation from speed index The weighted sum is used as a unified performance index.

[0043] Step 5, proceed to the next control cycle. The lower control region will control the rate limit value of each cell within the control region. ; and the index of the first area of ​​the dedicated lane within the control area. Index of the last area of ​​the dedicated lane Number of dedicated lanes They are all encoded into a single chromosome, allowing a genetic algorithm to be used for the next control cycle. The two-layer cooperative control model is solved iteratively to obtain the next control cycle. The optimal solution for the two-layer cooperative control includes: the next control cycle. The optimal speed limit and optimal layout plan for CAV dedicated lanes were determined and distributed to variable speed limit signs, lane indicator signs, and vehicle terminals; among them, Two-dimensional cells for the control region The set, This is the index of the first region of the control region. This is the index of the last region in the control region; To control the cycle Lower control area Each two-dimensional cell The speed limit value; Step 6, Assign to ,like Reaching the time threshold If the condition is met, control will stop; otherwise, return to step 1 and execute sequentially.

[0044] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0045] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A double-layer cooperative control method for improving the capacity of uphill road sections in a connected environment, characterized in that, The application is applied to a mixed network-connected traffic environment containing network-connected automatic driving vehicles (CAVs) and network-connected manual driving vehicles (CHVs), and a basic road section containing an uphill bottleneck section is taken as a control object. The basic road section contains an upstream straight section, an uphill bottleneck section and a downstream straight section in sequence along the driving direction of the vehicle, wherein a section of the upstream straight section is selected as a control area; the double-layer cooperative control method is performed according to the following steps: Step 1, divide the road basic section in the driving direction of the vehicle into stripes, and divide each stripe into areas from inside to outside, and form two-dimensional cells, let the two-dimensional cell where the th area and the th lane are located be recorded as , where is the index of the area, is the index of the lane; Collect current control cycle Lower two-dimensional cells density ,speed ,flow Total number of vehicles Number of CAV vehicles CHV vehicle numbers And calculate the current control cycle. Lower two-dimensional cells CAV penetration rate CAV traffic CHV flow ; Step 2, the following-following types of the mixed traffic flow in the uphill bottleneck section are divided into CAV-following-CAV, CAV-following-CHV and CHV-following-any type vehicle; According to With the combination type of car following, the current control period The lower two-dimensional cell The equivalent average vehicle headway in mixed traffic flow , and based on , the current control period The lower two-dimensional cell The parameters of mixed traffic flow in the cell: critical density , traffic capacity And congestion wave speed ; wherein Indicates the lateral position of the two-dimensional cell ​ Step 3, build current control cycle The lane-level CTM model considering the capacity drop of uphill bottleneck links is used to obtain the next control cycle The two-dimensional cell of mixed traffic flow parameters; Step 4, based on the next control cycle Lower two-dimensional cell The traffic flow parameters of the next control cycle The lower variable speed limit control model and the upper CAV special lane layout optimization model in the next control cycle The double-layer collaborative control model in the next control cycle; Step 5, the next control cycle The speed limit value of each cell in the control area ; and the index of the first region of the special lane in the control area , the index of the last region of the special lane , the number of special lane lanes are jointly coded into a chromosome, so that the next control cycle The double-layer collaborative control model is solved by rolling iteration using a genetic algorithm, and the optimal scheme of the double-layer collaborative control in the next control cycle , including: the optimal speed limit value in the next control cycle and the optimal layout scheme of the CAV special lane, and is issued to the variable speed limit sign, the lane indication sign and the vehicle terminal; wherein, is the set of two-dimensional cells of the control area, is the index of the first region of the control area, is the index of the last region of the control area; is the control cycle The speed limit value of each two-dimensional cell in the control area . Step 6, set to , if the arrival time threshold is reached, stop control, otherwise, return to step 1 for sequential execution.

2. The dual layer coordinated control method of claim 1, wherein, The step 2 is to calculate the current control period using formula (1) Lower two-dimensional cell Medium effective average headway : (1) In formula (1), denotes the average time headway of a CAV following a CAV, denotes the average time headway of a CAV following a CHV, denotes the average time headway of a CHV following any vehicle, and has: (2) In formula (2), is the length of the uphill bottleneck section, is the average headway of CHVs on the upstream straight section, is the average headway of CHVs on the downstream straight section, is the change in average headway of CHVs after passing the uphill bottleneck section. (3), (4) and (5) respectively , and : (3) (4) (5) in formula (3), formula (4), and formula (5), is the free flow speed for each two-dimensional cell, is the congestion density for each two-dimensional cell.

3. The dual layer coordinated control method of claim 2, wherein, The step 3 includes: Step 3.1, based on , and Calculate the next control cycle Lower two-dimensional cells transmission capacity and acceptance ability And calculate the next control cycle. Lower two-dimensional cells Vehicles of type medium enter the first The first region The two-dimensional cell containing the lane lane change probability This allows us to obtain the actual transfer flow of vehicles of type `type`. ; ; Step 3.2, building the current control period under the limited acceleration model for calculating the next control period of the uphill bottleneck section ; Step 3.3, record the index of the last region in the straight section upstream as Then the index of the first region of the uphill bottleneck section is ; The index of the last region in the uphill bottle neck section is The index of the first region of the downstream flat section is ; calculating the next control period the total demand flow of the zone where the entrance of the uphill bottleneck link is located in the next control period the total supply flow of the zone where the entrance of the downstream flat link is located in the next control period and the next control period the total supply flow of the zone where the entrance of the downstream flat link is located in the next control period the total supply flow of the zone where the entrance of the downstream flat link is located in the next control period ; thereby obtaining the flow upper limit of the zone where the entrance of the uphill bottleneck link is located in the next control period the total supply flow of the zone where the entrance of the downstream flat link is located in the next control period the total supply flow of the zone where the entrance of the downstream flat link is located in the next control period the total supply flow of the zone where the entrance of the downstream flat link is located in the next control period Step 3.4, based on Calculate the next control period The total flow of type vehicles from the second lane of the first region upstream of the two-dimensional cell flowing into the two-dimensional cell downstream The total flow of type vehicles from the second control period upstream of the two-dimensional cell flowing into the second lane of the first region downstream of the two-dimensional cell ; when , let the total flow of type vehicles from the second lane of the first region upstream of the two-dimensional cell flowing into the two-dimensional cell downstream The total flow of type vehicles from the second control period upstream of the two-dimensional cell flowing into the second lane of the first region downstream of the two-dimensional cell ; when , let the total flow of type vehicles from the second lane of the first region upstream of the two-dimensional cell flowing into the two-dimensional cell downstream The total flow of type vehicles from the second control period upstream of the two-dimensional cell flowing into the second lane of the first region downstream of the two-dimensional cell ; when , let , when ; Step 3.5, based on and , resulting in the next control cycle The mixed traffic flow parameters of the next two-dimensional cell .

4. The dual layer coordinated control method of claim 3, wherein, The step 3.1 includes: Step 3.1.1, calculating the next control period using equation (6) Downstream two-dimensional cells Downstream two-dimensional cells Transmitting capability : (6) In formula (6), is the current control period lower two-dimensional cell speed limit value; Step 3.1.2, calculating the next control period with formula (7) next two-dimensional cell the vehicle enters the first lane of the first two-dimensional cell of the vehicle entering the first lane of the first (7) In formula (7), is a lane change delay parameter; is the current control period the lower two-dimensional cell the average speed, the current control period the lower two-dimensional cell the average speed, denotes the time interval between adjacent control periods; Step 3.1.3, calculate the next control cycle using equation (8). Lower two-dimensional cells Vehicles of type medium enter the first The first region The two-dimensional cell containing the lane lane change probability : (8) In formula (8), is a lane-changing feasibility constraint factor for a type vehicle, type represents the type of the vehicle, when type is CHV, and the first lane is a CAV exclusive lane, let , otherwise, let ; ; represents a lane-changing sensitivity coefficient; Step 3.1.

4. Calculate next control period using equation (9) Next region lane total demand flow of lane change in two-dimensional cell : (9) In formula (9), the next control period the next control period the next control period the next control period the next control period the next control period the next control period the next control period the next control period the next control period the next control period the next control period Step 3.1.5, using equation (12) to calculate the next control period Downstream two-dimensional cells Receiving capacity of downstream two-dimensional cells Downstream two-dimensional cells : (10) In formula (10) , , and These represent the current control cycle. Next The first region CAV penetration rate, congestion wave velocity, traffic capacity and density of each lane; Step 3.1.6, calculate the next control period using equation (11) Next region The total supply flow of lane-changing transitions for the first lane of the first region The total supply flow of lane-changing transitions for the first lane of the first region The total supply flow of lane-changing transitions for the first lane of the first region The total supply flow of lane-changing transitions for the first lane of the first region (11) Step 3.1.7, calculation of next control period using equation (12) Lower two-dimensional cell Scaling of total demand for lane change maneuvers : (12) Step 3.1.8, calculating next control period using equation (13) Next two-dimensional cell CAV enters the first lane of the second region Actual transition flow of the two-dimensional cell where the CAV enters the first lane of the second region Actual transition flow of the two-dimensional cell where the CHV enters the first lane of the second region : (13) Step 3.1.9, calculate the next control cycle using equation (14). Lower two-dimensional cells vehicles enter the The first region The two-dimensional cell containing the lane Actual total transfer flow : (14)。 5. The dual layer coordinated control method of claim 3, wherein, The step 3.2 includes: Step 3.2.1, calculating the current control period using equation (15) the limited acceleration model under : (15) In formula (15), represents the maximum acceleration of the vehicle; represents the acceleration of gravity; represents the slope of the ramp; Step 3.2.2, calculation using formula (16) : (16) In formula (15) denotes the spatial rate of change of the equivalent average headway.

6. The dual layer coordinated control method of claim 5, wherein, The step 3.3 includes: Step 3.3.1, calculation using formula (17) : (17) Step 3.3.2, calculation using formula (18) : (18) Step 3.3.3, calculation using formula (19) : (19)。 7. The dual layer coordinated control method of claim 6, wherein, The step 3.4 includes: Step 3.4.1, calculating the next control period using equation (20) The next area The actual total flow of vehicles from the first area to the second area : (20) Step 3.4.2, use equation (21) to calculate the capacity drop scaling factor for the next control period :​ (21) Step 3.4.3, calculation using formula (22) : (22) Step 3.4.4, calculate using equation (23) and equation (24) respectively and : (23) (24)。 8. The dual layer coordinated control method of claim 7, wherein, The step 3.5 includes: Step 3.5.1, using equation (25) to calculate the next control period lower two-dimensional cell number of vehicles of the type type : (25) In formula (25), denotes the time interval between two adjacent control periods; Step 3.5.2, using equation (26) to calculate the next control period the CAV permeability of the CAV : (26) In formula (26), indicates the next control period the number of CAVs in the next two-dimensional cell Step 3.5.3, calculating next control period using equation (27) lower two-dimensional cell average velocity : (27)。 9. The dual layer coordinated control method of claim 1, wherein, The step 4 includes: Step 4.1, construct the target function of the lower variable speed limit control model in the next control period with formula (28) and formula (29) respectively and the target function of the upper CAV special lane layout optimization model :​ (28) (29) In equations (28) and (29), The duration of each control cycle, , These are the weighting coefficients for the two lower-level controls. , This is represented by the weighting coefficients of two upper-level controls. The length of the cell, To control the cycle Two-dimensional cells with dedicated lanes deployed in the lower control area The set, ,in To control the cycle The collection of lanes with dedicated underground lanes. For the next control cycle The number of lanes for dedicated underground lanes, For the next control cycle Down The Middle Two-dimensional cells with dedicated lanes on each lane The index of the first region, For the next control cycle Down The Middle Two-dimensional cells with dedicated lanes on each lane The index of the last region; Step 4.2, constructing the next control period with formula (30) The constraint condition of the double-layer cooperative control model under (30) In formula (30), is the minimum speed limit value, is the maximum speed limit value, is the control region of the same two-dimensional cell in adjacent control periods, is the speed limit difference threshold of adjacent two-dimensional cells in the control region in the same control period, is the speed limit value of the two-dimensional cell in the control region in the current control period, is the speed limit value of the two-dimensional cell in the control region in the next control period, is the speed limit value of the two-dimensional cell in the control region.

10. An electronic device comprising a memory and a processor, characterized in that The memory is used for storing a program supporting the processor to execute the method in any one of claims 1-9, and the processor is configured to execute the program stored in the memory.

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