A system and method for cooperative control of expressway ramp signal lights and intelligent network-connected vehicles

Abstract

The application relates to a coordinated control system and method of expressway ramp signal lights and intelligent networked vehicles, which comprises the following steps: deploying a plurality of roadside units to collect vehicle operation information of expressway ramps, and sending the information to a terminal server to receive and process traffic flow parameters and vehicle operation information, generate expressway ramp signal light control strategies and intelligent networked vehicle driving strategies; establishing an expressway ramp signal light control model, so as to adjust the signal light control duration by using the feedback information of the main line downstream occupancy rate provided by the road test unit; establishing an expressway intelligent networked vehicle driving model, so as to reduce ramp merging area vehicle conflicts, improve road section traffic capacity and reduce fuel consumption by controlling the lane changing driving and speed guidance of the intelligent networked vehicle; and establishing a road section pollutant emission model, so as to measure the effect of the application from the perspective of emission amount.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a collaborative control system and method for expressway entrance ramp traffic lights and intelligent connected vehicles. Background Technology

[0002] Expressways, as vital urban arteries, typically experience high traffic volumes and relatively high vehicle emissions, significantly impacting urban air quality. Expressway entrance ramps are critical nodes for vehicles entering the expressway, and effectively controlling traffic flow and reducing vehicle emissions has become a major challenge for urban traffic management. To address this issue, a control system for expressway entrance ramps based on emission reduction is needed. This system monitors traffic flow and air quality at ramp entrances to achieve intelligent regulation, controlling the number and speed of vehicles entering the expressway, thereby reducing vehicle emissions and improving urban air quality.

[0003] Current research on expressway entrance ramp control largely focuses on individual traffic light control or intelligent connected vehicle control. However, as a system, expressway entrance ramps involve traffic lights and vehicles as two key interdependent elements. Optimizing only one element cannot achieve overall optimality. For example, in high-traffic scenarios, optimizing only vehicle speed trajectories may worsen the efficiency of expressway entrance ramps. Furthermore, current optimization goals for expressway entrance ramp control primarily focus on improving ramp capacity. However, with the increasing number of vehicles on the road, pollutant emissions are becoming increasingly serious. How to reduce pollutant emissions while ensuring the efficiency of expressway entrance ramps is a significant challenge facing current expressway entrance ramp control. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by proposing a collaborative control method for traffic lights at expressway entrance ramps and intelligent connected vehicles. This method ensures efficient traffic flow at expressway ramp merging points while reducing vehicle fuel consumption, thereby alleviating current urban traffic congestion, environmental pollution, and resource shortages, and achieving green and sustainable development.

[0005] The technical problem solved by this invention is achieved through the following technical solution:

[0006] 1. A collaborative control system for expressway entrance ramp traffic lights and intelligent connected vehicles, characterized in that: it includes several roadside units, a terminal server communicating with the several roadside units, and an expressway entrance ramp traffic light control unit connected to the terminal server.

[0007] The roadside unit is used to collect vehicle operation information at the entrance ramps of the expressway and on the main line near the ramps, and send the vehicle operation information to the terminal server;

[0008] The terminal server is used to receive vehicle operation information from the roadside unit and process it to generate different expressway entrance ramp control strategies;

[0009] The expressway entrance ramp signal light control unit is used to receive the expressway entrance ramp control strategy, generate signal light control commands, and control the operation of the signal lights.

[0010] 2. The coordinated control system for expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: the roadside unit includes a geomagnetic vehicle detector and a roadside unit communication module;

[0011] The geomagnetic vehicle detector is used to collect vehicles in each lane at the entrance ramp of the expressway and on the main line near the ramp, and to calculate the occupancy rate of the generated lanes.

[0012] The roadside unit communication module is used to send vehicle operation information to the terminal server and receive commands sent by the terminal server.

[0013] 3. The coordinated control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 2, characterized in that: the roadside unit communication module includes dedicated short-range communication, 5G communication, camera, millimeter-wave radar and lidar.

[0014] 4. The coordinated control system for expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: the terminal server includes a data storage module, a simulation module and a server communication module;

[0015] The data storage module is used to store vehicle operation information sent by the roadside unit;

[0016] The simulation module is used to build a traffic control model for expressway entrance ramps and to process and optimize the generation of expressway entrance ramp control strategies.

[0017] The server communication module is used to communicate with the roadside unit and the traffic light control unit of the expressway entrance ramp.

[0018] 5. The coordinated control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 4, characterized in that: the simulation module is embedded with VISSIM traffic simulation software that has been secondary developed using Visual C#, used to build a traffic model of the expressway entrance ramp, and connected to the terminal server through the DPI interface to transmit the expressway entrance ramp control strategy.

[0019] 6. The coordinated control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: the expressway ramp traffic light control unit is connected to the terminal server through a DPI interface.

[0020] 7. A method for coordinated control of traffic lights at expressway entrance ramps and intelligent connected vehicles, characterized by the following steps:

[0021] S1. Determine the location and deployment of roadside units;

[0022] S2. Determine the delineation of control zones for expressway entrance ramps;

[0023] The expressway entrance ramp control area is divided into a core control area, a pre-control area, a vehicle merging area, and a normal driving area.

[0024] S3. Obtain information on intelligent connected vehicles within the communication range of the expressway entrance ramp. The intelligent connected vehicle information includes: current speed, acceleration, lane location, and distance between the current location and the stop line.

[0025] S4. Establish a traffic light control model for expressway entrance ramps, and then use the feedback information on the downstream occupancy of the mainline provided by the road test unit to adjust the traffic light control duration;

[0026] The control model for the traffic lights at the entrance ramp of the expressway is shown in Equation (1):

[0027]

[0028] In equation (1): r k and r k-1 These are the ramp control rates for the k-th and k-1-th cycles, in vehicles / h; k r The adjustment rate parameter is vehicles / hour; The occupancy rate threshold corresponding to when downstream traffic volume on the main line reaches capacity; O k-1 This represents the real-time downstream occupancy rate in the (k-1)th cycle.

[0029] S5. Establish a lane-changing model for vehicles on the main line of the expressway, thereby reducing vehicle conflicts in the merging zone of ramps, improving road capacity, and reducing fuel consumption by controlling the lane-changing behavior of intelligent connected vehicles.

[0030] S6. Establish a vehicle speed guidance model for expressways, thereby improving road capacity, reducing emergency acceleration, deceleration or emergency stopping of vehicles, and reducing fuel consumption by controlling the speed of intelligent connected vehicles.

[0031] S7. Based on the expressway ramp signal light model in S4, the vehicle lane change control in S5, and the vehicle speed guidance model in S6, realize the coordinated control of expressway entrance ramp signal lights and intelligent connected vehicles.

[0032] S8. Establish a road segment pollutant emission model to measure the effectiveness of the invention from the perspective of emissions.

[0033] 8. The method for coordinated control of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 7, characterized in that: step S5, establishing a lane-changing model for vehicles on the expressway mainline, specifically includes the following sub-steps:

[0034] S51. Before entering the pre-control zone, vehicles on the main line shall complete lane changing within the pre-control zone. Lane changing is mainly based on the overall traffic environment of the main line. When the service level of the inner lane of the main line is greater than that of the outer lane and is not saturated, connected vehicles will change from the outer lane to the inner lane. The conditions for lane changing at this time should be met as follows:

[0035]

[0036] S52. After the lane-changing conditions are met, if the uncontrollable vehicle changes lanes and the traffic volume in the inner lane is still less than or equal to that in the outer lane, and the time spent in the inner lane is longer than that in the outer lane, then the following conditions must be met:

[0037]

[0038] S53. Considering the number of controllable vehicles, the number of vehicles that can be controlled in a single operation is:

[0039]

[0040] In equations (2), (3), and (4): Q ramp —Ramp merging traffic volume (pcu / h); —Maximum capacity of the outer lane of the main line (pcu / h); —Controllable vehicle traffic volume (pcu / h) of the outer lane of the main line; Q Li —Traffic volume (pcu / h) of each lane on the main line;

[0041] 9. The method for coordinated control of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 7, characterized in that: step S6 is as follows:

[0042] S61. Calculate the earliest arrival time at the parking line.

[0043] When a vehicle accelerates through a ramp or main road intersection, the earliest arrival time at the stop line is calculated using equation (5).

[0044]

[0045] When a vehicle passes through a ramp or main road intersection at a constant speed, the earliest arrival time at the stop line can be calculated using equation (6).

[0046]

[0047] When a vehicle decelerates through a ramp or main road intersection, the earliest arrival time at the stop line is calculated using equation (7).

[0048]

[0049] In equations (5), (6), and (7), Let i be the distance of vehicle i in lane L from the stop line at the intersection. Let i be the speed of vehicle i in lane L. Let v be the acceleration of vehicle i on lane L. tar The target speed for the vehicle;

[0050] S62. If Within the green light area, calculate the minimum speed V that can be passed during this green light period. min When the vehicle is in the lane, if the connected vehicle speed V0 > V min If the speed is constant, then the speed is accelerated; otherwise, the speed is accelerated.

[0051] S63. If If the area is outside the green light zone, calculate the minimum speed V that allows passage during the next green light period. min and maximum speed V max Then, during the next green light period, the range that can be passed is [V]. min V max If V min ≤V0≤V max Guided to uniform speed, if V0 <V min Then, acceleration is guided if V0 > V. max This will guide the deceleration.

[0052] 10. The method for coordinated control of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 7, characterized in that: the specific steps of S8 in establishing the road segment pollutant emission model include the following sub-steps:

[0053] S81. Based on the extraction of road type, pollutant type, average speed of road segment, motor vehicle type classification, vehicle age distribution, fuel information and traffic information of the collected area, the pollutant emission factors at different speeds of the collected road segment are obtained;

[0054] S82. Based on the acquisition of road network motor vehicle traffic parameters and vehicle emission factors, pollutant emissions are analyzed based on the emission intensity of road segments and the overall emission volume of the road network. The emission intensity of road segments refers to the spatial distribution of pollutant emission intensity on the road network by considering only the vehicle type composition and traffic flow of each vehicle type on the road segment, excluding the influence of different road segment lengths.

[0055] The emission intensity of the road section is calculated as follows:

[0056] IE ik =∑e ik ×Q i (8)

[0057] In the formula, IE ik - During the study period, the emission intensity (in g / km) of pollutant type k on road segment i, k = 1, 2, 3, 4, corresponding to CH, CO, NO, and CO2, respectively; e ijk ---Emission factor (in g / km) of pollutant type k for vehicle j under driving conditions on road segment i; Q i --Traffic volume (vehicles) on road segment i;

[0058] S83. The calculation of the overall emissions of the road network is the sum of the emissions of motor vehicles on each road segment, and the calculation method is as follows:

[0059] E total-k =∑e ik ×Q i ×l i (9)

[0060] In the formula, E total-k ---Total emissions of pollutant k from the road network, in g; l i ---Length of segment i (in km), other parameters are the same as above.

[0061] The advantages and positive effects of this invention are:

[0062] 1. In the context of vehicle-to-everything (V2X) communication, this invention establishes an optimization model for expressway traffic lights and a collaborative control model for connected vehicles, thereby achieving collaborative control of traffic lights at expressway entrance ramps and connected vehicles. Compared to single traffic light or intelligent connected vehicle control, the overall optimization effect of collaborative control is better, thus effectively improving the traffic efficiency of intersections and reducing pollutant emissions.

[0063] 2. In the application of the algorithm on the terminal server, this system adopts the CAV-ALINEA algorithm, which has higher traffic efficiency and lower pollutant emissions than previous expressway ramp control algorithms. Attached Figure Description

[0064] Figure 1 This is a layout diagram of a coordinated control system for expressway entrance ramp traffic lights and intelligent connected vehicles according to the present invention.

[0065] Figure 2 This is a flowchart of a collaborative control algorithm between expressway entrance ramp traffic lights and intelligent connected vehicles according to the present invention;

[0066] Figure 3 This is an architecture diagram of a collaborative control system for expressway entrance ramp traffic lights and intelligent connected vehicles according to the present invention.

[0067] Figure 4 This is a simulation module diagram of the terminal server unit in this invention. Detailed Implementation

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0069] In this embodiment, the control time for the target expressway entrance ramp is set to the morning peak hours of 7:00-8:00. At the main line of the target expressway entrance ramp, there are three lanes (e.g., Figure 1 (As shown).

[0070] As shown in the figure, the present invention provides a coordinated control system for expressway entrance ramp traffic lights and intelligent connected vehicles, including...

[0071] Several roadside units are used to collect vehicle operation information at the entrance ramps of the expressway and on the main line near the ramps, and send the vehicle operation information to the terminal server;

[0072] Each roadside unit includes a geomagnetic vehicle detector and a roadside unit communication module;

[0073] The geomagnetic vehicle detector is used to collect data on vehicles in each lane at the entrance ramp of the expressway and on the main line near the ramp, and to calculate the generated lane occupancy rate.

[0074] The roadside unit communication module is used to send the vehicle operation information to the terminal server and receive commands sent by the terminal server;

[0075] The roadside unit communication module includes: Dedicated Short Range Communication (DSRC), 5G communication, cameras, millimeter-wave radar, lidar and other equipment.

[0076] The terminal server is used to receive vehicle operation information from the roadside unit, process and generate different traffic light control schemes, and calculate traffic light control commands and intelligent connected vehicle driving commands.

[0077] The terminal server includes a data storage module, an simulation module, and a server communication module;

[0078] The data storage module is used to store vehicle operation information sent by the roadside unit;

[0079] The simulation module is used to build a traffic model for controlling expressway entrance ramps and to process and optimize the generation of expressway entrance ramp control strategies.

[0080] The server communication module is used to communicate with the roadside unit and the traffic light control unit of the expressway entrance ramp;

[0081] The expressway entrance ramp traffic light control unit is used to receive the expressway entrance ramp control strategy, generate traffic light control commands, and control the operation of the traffic lights.

[0082] Each main lane of the road testing unit is equipped with a geomagnetic detector located upstream and downstream, and a geomagnetic detector is located at each ramp. In this embodiment, there are a total of seven geomagnetic detectors.

[0083] The expressway ramp signal light control unit connects to the terminal server via a DPI interface to obtain expressway ramp control strategies and optimize timing.

[0084] The CAV-ALINEA algorithm is embedded in the terminal server.

[0085] Both the roadside unit communication module and the server communication module are 5G-level signal transmission modules.

[0086] The simulation module is embedded with VISSIM traffic simulation software, which has been developed using Visual C#. It is used to build traffic models of expressway entrance ramps and connects to the terminal server through the DPI interface to transmit the control strategies of the expressway entrance ramps.

[0087] The control method for the coordinated control system of the aforementioned expressway entrance ramp traffic lights and intelligent connected vehicles includes the following steps:

[0088] S1. Determine the location and deployment of roadside units;

[0089] like Figure 1 As shown, there is one geomagnetic detector at each of the upstream and downstream sides of each main lane, and one geomagnetic detector at each ramp, for a total of seven geomagnetic detectors.

[0090] S2. Determine the delineation of control zones for expressway entrance ramps;

[0091] The expressway entrance ramp control area is divided into a mainline core control area, a pre-control area, a vehicle merging area, a ramp core control area, and a normal driving area. The specific location and length information of the expressway entrance ramp control area are as follows: Figure 1 As shown:

[0092] The core control area of ​​the ramp is located 700m away from the ramp parking line;

[0093] The merging zone is located 200m downstream of the ramp entrance;

[0094] The mainline core control area is located 1500m upstream of the vehicle merging area;

[0095] The pre-control zone is located 500m upstream of the main line core control zone;

[0096] S3. Obtain information on intelligent connected vehicles within the communication range of the expressway entrance ramp; the intelligent connected vehicle information includes: current speed, acceleration, lane location, and distance between the current location and the stop line;

[0097] like Figure 1 As shown, the roadside unit acquires information about intelligent connected vehicles within the communication range of the expressway entrance ramp through dedicated short-range communication (DSRC), 5G communication, camera, radar and other V2I communication and sensor technologies, in order to optimize the expressway entrance ramp control strategy.

[0098] S4. Establish a traffic light control model for expressway entrance ramps, and then use the feedback information on the downstream occupancy of the mainline provided by the road test unit to adjust the traffic light control duration;

[0099] The control model for the traffic lights at the entrance ramp of the expressway is shown in Equation (1):

[0100]

[0101] In equation (1): r k and r k-1 These are the ramp control rates for the k-th and k-1-th cycles, in vehicles / h; k r The adjustment rate parameter is vehicles / hour; The occupancy rate threshold corresponding to when downstream traffic volume on the main line reaches capacity; O k-1 This represents the real-time downstream occupancy rate in the (k-1)th cycle.

[0102] S5. Establish a lane-changing model for vehicles on the main line of the expressway, thereby reducing vehicle conflicts in the ramp merging area, improving road capacity, and reducing fuel consumption by controlling the lane-changing behavior of intelligent connected vehicles. This includes the following steps:

[0103] S51. Before mainline vehicles enter the pre-control area, i.e., the core control area upstream of the merging zone, lane changing is completed in the pre-control area. Lane changing is mainly based on the overall traffic environment of the mainline. When the service level of the inner lanes of the mainline is greater than that of the outer lanes and is not saturated, i.e., the time that mainline vehicles spend traveling in the two inner lanes is less than the time spent traveling in lane 3, connected vehicles will change from lane 3 to the inner lanes. The conditions for lane changing at this time should be met as follows:

[0104]

[0105] S52. After the lane-changing conditions are met, the number of vehicles that can change lanes should be determined. That is, after uncontrollable vehicles change lanes, the traffic volume in the inner lane should still be less than or equal to that in the outer lane, and the time taken by vehicles in the inner lane should be longer than that taken in the outer lane. In this case, the following conditions should be met:

[0106]

[0107] S53. Considering the number of controllable vehicles, the number of vehicles that can be controlled in a single operation is:

[0108]

[0109] In equations (2), (3), and (4): Q ramp —Ramp merging traffic volume (pcu / h); —Maximum capacity of lane 3 on the main line (pcu / h); —Controllable vehicle traffic volume (pcu / h) for lane 3 of the main line; Q Li —Traffic volume (pcu / h) of each lane on the main line;

[0110] S6. Establish a vehicle speed guidance model for expressways to improve road capacity, reduce emergency acceleration / deceleration or emergency stops, and lower fuel consumption by controlling the speed of intelligent connected vehicles. (See diagram below for steps.) Figure 3 As shown:

[0111] S61. Calculate the earliest arrival time at the parking line.

[0112] When a vehicle accelerates through a ramp or main road intersection, the earliest arrival time at the stop line is calculated using equation (5).

[0113]

[0114] When a vehicle passes through a ramp or main road intersection at a constant speed, the earliest arrival time at the stop line can be calculated using equation (6).

[0115]

[0116] When a vehicle decelerates through a ramp or main road intersection, the earliest arrival time at the stop line is calculated using equation (7).

[0117]

[0118] In equations (5), (6), and (7), Let i be the distance of vehicle i in lane L from the stop line at the intersection. Let i be the speed of vehicle i in lane L. Let v be the acceleration of vehicle i on lane L. tar The target speed for the vehicle;

[0119] S62. If Within the green light area, calculate the minimum speed V that can be passed during this green light period. min When the vehicle is in the lane, if the connected vehicle speed V0 > V min If the speed is constant, then the speed is accelerated; otherwise, the speed is accelerated.

[0120] S63. If If the area is outside the green light zone, calculate the minimum speed V that allows passage during the next green light period. min and maximum speed V max Then, during the next green light period, the range that can be passed is [V]. min V max If V min ≤V0≤V max Guided to uniform speed, if V0 <V min Then, acceleration is guided if V0 > V. max This will guide the deceleration.

[0121] S7. Based on the expressway ramp signal light model in S4, the vehicle lane change control in S5, and the vehicle speed guidance model in S6, realize the coordinated control of expressway entrance ramp signal lights and intelligent connected vehicles.

[0122] S8. Establish a road segment pollutant emission model to measure the effectiveness of the invention from the perspective of emissions. This specifically includes the following steps:

[0123] S81. Based on the extraction of road type, pollutant type, average speed of road segment, motor vehicle type classification, vehicle age distribution, fuel information and traffic information of the collected area, the pollutant emission factors at different speeds of the collected road segment are obtained.

[0124] S82. Based on the acquisition of road network motor vehicle traffic parameters and vehicle emission factors, pollutant emissions are analyzed based on the emission intensity of road segments and the overall emission volume of the road network. Road segment emission intensity refers to the spatial distribution of pollutant emission intensity on the road network, considering only the vehicle type composition and traffic flow of each vehicle type on the road segment, excluding the influence of different road segment lengths.

[0125] The emission intensity of the road section is calculated as follows:

[0126] IE ik =∑e ik ×Q i (17)

[0127] In formula (8), IE ik - During the study period, the emission intensity (in g / km) of pollutant type k on road segment i, k = 1, 2, 3, 4, corresponding to CH, CO, NO, and CO2, respectively; e ijk ---Emission factor (in g / km) of pollutant type k for vehicle j under driving conditions on road segment i; Q i --Traffic volume (vehicles) on road segment i.

[0128] S83. The calculation of the overall emissions of the road network is the sum of the emissions of motor vehicles on each road segment, and the calculation method is as follows:

[0129] E total-k =∑e ik ×Q i ×l i (18)

[0130] In equation (9), E total-k ---Total emissions of pollutant k from the road network, in g; l i ---Length of segment i (in km), other parameters are the same as above.

[0131] The following is a specific example:

[0132] A control model for the entrance ramp of the expressway was built in VISSIM (traffic simulation software). The collaborative control strategy combining ALINEA, vehicle lane changing, and vehicle speed guidance was programmed in Visual C#. The simulation time was set to 3600s and the simulation time step was 100s.

[0133] This invention compares the simulation test results of vehicle travel time and average vehicle speed at expressway entrance ramps with the simulation test results of pollutant emissions at expressway entrance ramps. The results show that, compared with the ramp control method with timed traffic lights, the method proposed in this invention can effectively improve the traffic capacity of intersections by up to 12.7% and reduce pollutant emissions by up to 20.0%.

[0134] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A collaborative control method for a collaborative control system between traffic lights at expressway entrance ramps and intelligent connected vehicles, characterized in that, The collaborative control system includes several roadside units, a terminal server that communicates with these roadside units, and a traffic light control unit for the expressway entrance ramps connected to the terminal server. The roadside unit is used to collect vehicle operation information at the entrance ramps of the expressway and on the main line near the ramps, and send the vehicle operation information to the terminal server; The terminal server is used to receive vehicle operation information from the roadside unit and process it to generate different expressway entrance ramp control strategies; The expressway entrance ramp signal light control unit is used to receive the expressway entrance ramp control strategy, generate signal light control commands, and control the operation of the signal lights; The collaborative control method includes the following steps: S1. Determine the location and deployment of roadside units; S2. Determine the delineation of the control zone for expressway entrance ramps; The expressway entrance ramp control area is divided into a core control area, a pre-control area, a vehicle merging area, and a normal driving area. S3. Obtain information on intelligent connected vehicles within the communication range of the expressway entrance ramp. The intelligent connected vehicle information includes: current speed, acceleration, lane location, and distance between the current location and the stop line. S4. Establish a traffic light control model for expressway entrance ramps, and then use the feedback information on the downstream occupancy of the mainline provided by the road test unit to adjust the traffic light control duration; The signal light control model for the expressway entrance ramp is shown in equation (1): In formula (1): and These are the ramp control rates for the k-th and k-1th cycles, respectively, in vehicles / h; The adjustment rate parameter is vehicles / h; This refers to the critical occupancy rate value corresponding to the downstream traffic volume of the main line reaching the capacity. This represents the real-time downstream occupancy rate in the (k-1)th cycle. S5. Establish a lane-changing model for vehicles on the main line of the expressway, thereby reducing vehicle conflicts in the ramp merging area, improving road capacity, and reducing fuel consumption by controlling the lane-changing behavior of intelligent connected vehicles; Step S5 specifically includes the following sub-steps: S51. Before entering the pre-control zone, vehicles on the main line shall complete lane changing within the pre-control zone. Lane changing is mainly based on the overall traffic environment of the main line. When the service level of the inner lane of the main line is greater than that of the outer lane and is not saturated, connected vehicles will change from the outer lane to the inner lane. The conditions for lane changing at this time should be met as follows: S52. After the lane-changing conditions are met, if the uncontrollable vehicle changes lanes and the traffic volume in the inner lane is still less than or equal to that in the outer lane, and the time spent in the inner lane is longer than that in the outer lane, then the following conditions must be met: S53. Considering the number of controllable vehicles, the number of vehicles that can be controlled in a single operation is: In equations (2), (3), and (4): —Traffic volume waiting to merge at ramps; —Maximum capacity of lane 3 on the main line; —Controllable vehicle traffic volume in lane 3 of the main line; —Traffic volume in each lane of the main line; S6. Establish a vehicle speed guidance model for expressways, thereby improving road capacity, reducing emergency acceleration, deceleration or emergency stopping of vehicles, and reducing fuel consumption by controlling the speed of intelligent connected vehicles. S7. Based on the expressway ramp signal light model in S4, the vehicle lane change control in S5, and the vehicle speed guidance model in S6, realize the coordinated control of expressway entrance ramp signal lights and intelligent connected vehicles. S8. Establish a pollutant emission model for road sections.

2. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: The roadside unit includes a geomagnetic vehicle detector and a roadside unit communication module; The geomagnetic vehicle detector is used to collect vehicles in each lane at the entrance ramp of the expressway and on the main line near the ramp, and to calculate the occupancy rate of the generated lanes. The roadside unit communication module is used to send vehicle operation information to the terminal server and receive commands sent by the terminal server.

3. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 2, characterized in that: The roadside unit communication module includes dedicated short-range communication, 5G communication, camera, millimeter-wave radar, and lidar.

4. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: The terminal server includes a data storage module, an simulation module, and a server communication module; The data storage module is used to store vehicle operation information sent by the roadside unit; The simulation module is used to build a traffic control model for expressway entrance ramps and to process and optimize the generation of expressway entrance ramp control strategies. The server communication module is used to communicate with the roadside unit and the traffic light control unit of the expressway entrance ramp.

5. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 4, characterized in that: The simulation module is embedded with VISSIM traffic simulation software, which has been developed using Visual C#. It is used to build traffic models of expressway entrance ramps and connects to the terminal server through the DPI interface to transmit expressway entrance ramp control strategies.

6. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: The expressway ramp traffic light control unit is connected to the terminal server via a DPI interface.

7. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: Step S6 is as follows: S61. Calculate the earliest arrival time at the parking line. , When a vehicle accelerates through a ramp or main road intersection, the earliest arrival time at the stop line is calculated using equation (5). : When a vehicle passes through a ramp or main road intersection at a constant speed, the earliest arrival time at the stop line can be calculated using equation (6). : When the vehicle decelerates through the ramp or main road intersection, the earliest arrival time at the stop line is calculated using equation (7). : In equations (5), (6), and (7), Let i be the distance of vehicle i in lane L from the stop line at the intersection. Let i be the speed of vehicle i in lane L. Let be the acceleration of vehicle i on lane L. The target speed for the vehicle; S62. If Within the green light area, calculate the minimum speed V that can be passed during this green light period. min When the vehicle is in the lane, if the connected vehicle speed V0 > V min If the speed is constant, then the speed is accelerated; otherwise, the speed is accelerated. S63. If If the area is outside the green light zone, calculate the minimum speed V that allows passage during the next green light period. min and maximum speed V max Then, during the next green light period, the range that can be passed is [V]. min V max If V min ≤V0≤V max Guided to uniform speed, if V0 <V min Then, acceleration is guided if V0 > V. max This will guide the deceleration.

8. The collaborative control method for the collaborative control system of expressway entrance ramp traffic lights and intelligent connected vehicles according to claim 1, characterized in that: The specific steps for establishing the pollutant emission model for the road section in S8 include the following: S81. Based on the extraction of road type, pollutant type, average speed of road segment, motor vehicle type classification, vehicle age distribution, fuel information and traffic information of the collected area, the pollutant emission factors at different speeds of the collected road segment are obtained; S82. Based on the acquisition of road network motor vehicle traffic parameters and vehicle emission factors, pollutant emissions are analyzed based on the emission intensity of road segments and the overall emission volume of the road network. The emission intensity of road segments refers to the spatial distribution of pollutant emission intensity on the road network by considering only the vehicle type composition and traffic flow of each vehicle type on the road segment, excluding the influence of different road segment lengths. The emission intensity of the road section is calculated as follows: In the formula, - During the study period, the emission intensity of pollutant of type k on road segment i, k=1, 2, 3, 4, corresponds to CH, CO, NO and CO2 respectively; ---The emission factor of pollutant type k for vehicle j under the driving conditions of road segment i; --Traffic volume of vehicle type j on section i; S83. The calculation of the overall emissions of the road network is the sum of the emissions of motor vehicles on each road segment, and the calculation method is as follows: In the formula, ---Total emissions of pollutant k from the road network, in grams; ---The length of segment i, other parameters are the same as above.

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