Road traffic management and control system and method based on mixed traffic flow and weather environment

Abstract

The invention discloses a road traffic control system and method based on a mixed traffic flow and a weather environment, and relates to the technical field of traffic control, and the method comprises the steps: employing an SUMO simulation platform to simulate the mixed traffic flow of intelligent network connection vehicles and traditional vehicles in a weather scene, building a simulation scene, building a vehicle operation model library, and carrying out the simulation of the mixed traffic flow. Associating the meteorological data with the vehicle behavior; key traffic indexes are obtained through simulation data, the influence of intelligent network connection vehicle permeability and weather factors on traffic flow is analyzed, and the change trend of traffic flow characteristics is displayed through a data visualization technology; on the basis of traffic flow characteristic analysis, management and control measures such as speed limiting, flow management and control and lane use optimization are provided from the two perspectives of safety and efficiency. Meanwhile, a feedback learning mechanism is established, a management and control scheme is adjusted in real time, and high efficiency and adaptability of traffic management are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and in particular to a road traffic control system and method based on mixed traffic flow and weather environment. Background Art

[0002] With the rapid development of intelligent connected vehicles, there will be a large number of mixed traffic flow scenarios on the roads in the future, that is, human-driven vehicles and intelligent connected vehicles driving in parallel. Under this new development trend, traditional traffic control methods face new challenges in terms of the mixed traffic flow characteristics under weather scenarios (such as rain, fog, snow, etc.). Among them:

[0003] Analysis of mixed traffic flow characteristics: Existing studies have analyzed the flow, speed, density and other characteristics of mixed traffic of intelligent connected vehicles and traditional vehicles through models and simulations. For example, Guo et al. (2023) studied mixed traffic flow based on a cellular automaton model, and explored the impact of human driving behavior and the communication range of intelligent connected vehicles on traffic flow. They proposed a heterogeneous traffic flow model and pointed out that intelligent connected vehicles can increase road capacity by reducing the distance between vehicles. Although it provides some theoretical basis for the characteristics of mixed traffic flow, it fails to fully consider the traffic flow dynamics under weather scenarios.

[0004] Traffic control in weather scenarios: The impact of weather on road traffic flow was analyzed. For example, the impact of rainy days on road speed and capacity was analyzed, the NaSch model in rainy days was constructed, and speed limit strategies based on different rainfall intensities were proposed. However, the penetration rate of intelligent connected vehicles and the characteristics of mixed traffic flow were not taken into consideration, and no targeted control solutions were designed specifically for mixed traffic flow in weather scenarios.

[0005] At present, most of them focus on single traffic flow characteristics or weather impact analysis, lacking an integrated control system that can combine the penetration rate of intelligent connected vehicles, weather changes, traffic flow characteristics and control solutions. In particular, how to monitor and adjust mixed traffic flows in real time, improve control efficiency, and reduce the accident rate is still an urgent problem to be solved.

[0006] In view of this, the present invention proposes a road traffic control system and method based on mixed traffic flow and weather environment. Summary of the invention

[0007] The purpose of the present invention is to provide a road traffic control system and method based on mixed traffic flow and weather environment. By introducing a simulation-based mixed traffic flow model and combining traffic flow analysis under weather scenarios, a traffic control plan that can be adjusted in real time is formulated.

[0008] In a first aspect, the present invention provides a road traffic control system based on mixed traffic flow and weather environment, including a perception layer, a decision layer and a response layer; each layer is connected by wire and / or wireless;

[0009] The perception layer uses roadside equipment to collect traffic flow data and meteorological data in real time within the target road section, and transmits the traffic flow data and meteorological data as input data to the decision layer;

[0010] The decision-making layer, based on the traffic flow data and meteorological data transmitted by the perception layer, uses simulation technology to model the mixed traffic flow of meteorological data under the corresponding weather scenario, outputs the simulation results of the mixed traffic flow under the current weather scenario, and formulates corresponding control measures based on the simulation results of the mixed traffic flow;

[0011] The response layer realizes the visual interface interaction of the management and control solutions of the decision-making layer through roadside equipment, traffic lights and lane change control systems.

[0012] As a preferred solution of the first aspect of the present invention, the roadside equipment includes traffic sensors and meteorological monitoring equipment; the roadside equipment includes traffic sensors and meteorological monitoring equipment; traffic flow data is obtained based on the traffic sensors, the traffic flow data includes the flow, density and vehicle type corresponding to the vehicles on the current road section, the vehicle types include traditional driving vehicles and autonomous driving vehicles, the comprehensive flow status of different types of vehicles is marked as mixed traffic flow, the mixed traffic flow is collected in real time, and the penetration rate is analyzed based on the mixed traffic flow, and the penetration rate is the proportion of the number of autonomous driving vehicles to the total number of vehicles; meteorological data corresponding to weather conditions are collected in real time based on the meteorological sensing equipment, and weather scenes are constructed based on the meteorological data.

[0013] As a preferred solution of the first aspect of the present invention, the hybrid traffic flow simulation model adopts traffic simulation software, combines the coexistence of intelligent connected vehicle traffic flow data and traditional human-driven vehicle traffic flow data, and simulates the traffic flow characteristics of road traffic flow data under different weather scenarios.

[0014] As a preferred solution of the first aspect of the present invention, the construction logic of the mixed traffic flow simulation model is:

[0015] Simulation scenario construction: Based on the actual road network layout of the target area, the road network layout includes key road nodes corresponding to different road types, including urban roads, highways and rural roads. Key road nodes include main sections, intersections, ramps and bridges, and the road network layout is modeled in detail;

[0016] Vehicle and traffic flow construction: Set up driving models for different types of vehicles based on the simulation software, adjust the size and speed of traffic flow in the simulation according to the traffic flow and density in the input traffic flow data, and calculate the penetration rate based on the vehicle type. The penetration rate can be adjusted by setting different vehicle type ratios in the road network and vehicle input files;

[0017] Association of meteorological data with vehicle behavior: The vehicle behavior parameters in the simulation model are dynamically adjusted using meteorological data collected in real time by meteorological sensing equipment.

[0018] As a preferred solution of the first aspect of the present invention, the logic for obtaining the mixed traffic flow simulation result is:

[0019] Configure the simulation environment according to the actual road network information, use the configuration file to establish a vehicle operation simulation model library, and simulate the behavior of different types of vehicles; adjust the penetration rate of various types of vehicles and build a mixed traffic flow model that adapts to different weather conditions;

[0020] Based on the constructed simulation scenario model, we simulated traffic flow under different weather scenarios;

[0021] After the simulation is finished, the estimated hourly traffic flow characteristics detected by the detector are used, and the traffic flow characteristics include density, average speed and road occupancy rate of each road section to obtain a mixed traffic flow simulation result.

[0022] As a preferred solution of the first aspect of the present invention, the mixed traffic flow simulation results are visualized, and the result visualization includes a flow-density basic graph.

[0023] In a second aspect, the present invention provides a road traffic control method based on mixed traffic flow and weather environment, based on the implementation of the first aspect, comprising the following steps:

[0024] Step S1: Establish a mixed traffic flow simulation model: Use the SUMO simulation platform to simulate the mixed traffic flow of intelligent connected vehicles and traditional vehicles under weather scenarios, including setting up simulation scenarios, constructing vehicle and traffic flow models, and associating meteorological data with vehicle behavior;

[0025] Step S2: Analyze mixed traffic flow characteristics: obtain key traffic indicators through simulation data, analyze the impact of intelligent connected vehicle penetration rate and weather factors on traffic flow, and display the changing trend of traffic flow characteristics through data visualization technology;

[0026] Step S3: Formulate traffic control plan: Based on the analysis of traffic flow characteristics, from the perspectives of safety and efficiency, propose control measures such as speed limit, flow control and lane optimization. At the same time, establish a feedback learning mechanism to adjust the control plan in real time to ensure the efficiency and adaptability of traffic management.

[0027] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0028] Through simulation analysis and intelligent management and control strategies, the present invention effectively improves the safety and efficiency of mixed traffic flows under weather scenarios, optimizes traffic management decisions, enhances the responsiveness of intelligent transportation systems, reduces management costs, and supports applications under a variety of road and weather conditions, providing comprehensive technical support for traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a diagram of the road traffic control system architecture based on mixed traffic flow and weather environment of the present invention;

[0031] Figure 2 It is the basic flow-density diagram of the mixed traffic flow of the present invention under the light rain scenario with a permeability of 60%;

[0032] Figure 3 It is the basic flow-density diagram of different permeabilities under light rain of the present invention;

[0033] Figure 4 It is the basic flow-density diagram of the mixed traffic flow of the present invention under the condition of 60% permeability and different weather conditions;

[0034] Figure 5 It is a flow chart of the road traffic control method for mixed traffic flow and weather environment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] See also Figure 1 As shown, the road traffic control system based on mixed traffic flow and weather environment described in this embodiment includes a perception layer, a decision layer and a response layer; each layer is connected by wire and / or wireless;

[0038] The perception layer uses roadside equipment to collect traffic flow data and meteorological data in real time within the target road section, and transmits the traffic flow data and meteorological data as input data to the decision layer;

[0039] It should be noted that: roadside equipment includes traffic sensors and meteorological monitoring equipment; traffic flow data is obtained based on traffic sensors, and the traffic flow data includes the flow, density and vehicle type corresponding to the current road section. The vehicle type includes traditional driving vehicles and self-driving vehicles. The comprehensive flow state of different types of vehicles is marked as mixed traffic flow, and the mixed traffic flow is collected in real time. The penetration rate is analyzed based on the mixed traffic flow. The penetration rate refers to the proportion of self-driving vehicles in the total traffic flow, that is, the proportion of the number of self-driving vehicles to the total number of vehicles. It helps to analyze the interaction between self-driving vehicles and traditional vehicles and make targeted adjustments in traffic control; the calculation formula for the penetration rate is:

[0040]

[0041] Changes in penetration rate reflect the popularity of autonomous vehicles in traffic flow and are critical for predicting and regulating system responses.

[0042] Meteorological data corresponding to weather conditions are collected in real time based on meteorological sensing equipment. The meteorological data include temperature, humidity, wind speed and rainfall. The specific situations in which meteorological data affects traffic flow under specific conditions are marked as weather scenes, that is, weather scenes are matched based on meteorological data.

[0043] The decision-making layer, based on the traffic flow data and meteorological data transmitted by the perception layer, models the mixed traffic flow under the weather scenario through simulation technology, outputs the simulation results of the mixed traffic flow under the current weather scenario, and formulates corresponding control measures based on the simulation results of the mixed traffic flow;

[0044] It should be noted that: the hybrid traffic flow simulation model uses traffic simulation software, combined with the coexistence of intelligent connected vehicles and traditional human-driven vehicles, to simulate the traffic flow characteristics such as flow, speed, density, etc. of traffic flow data on the road under different weather scenarios; the weather scenario is a scenario-based modeling based on actual meteorological data and preset environmental conditions; the weather scenarios include rainy day scenes, dense fog scenes and snowfall scenes, among which the rainy day scenes include normal weather, light rain, moderate rain and heavy rain, and so on. This embodiment is mainly illustrated by the rainy day scene in the following text.

[0045] Specifically, the construction logic of the mixed traffic flow simulation model is:

[0046] Simulation scenario construction: Based on the actual road network layout of the target area, the road network layout includes key road nodes corresponding to different road types, including urban roads, highways and rural roads. Key road nodes include main sections, intersections, ramps and bridges. The road network layout is modeled in detail, taking into account factors such as road width, number of lanes, speed limit and traffic signal system;

[0047] Vehicle and traffic flow model building: Set up driving models for different types of vehicles based on the simulation software, select the intelligent driver model (IDM) to build a common vehicle model, and the collaborative adaptive cruise control model (CACC) to build an autonomous driving vehicle model. Then, adjust the size and speed of traffic flow in the simulation according to the traffic flow and density in the input traffic flow data, and calculate the penetration rate based on the vehicle type. The penetration rate can be adjusted by setting different vehicle type ratios in the road network and vehicle input files.

[0048] Meteorological data is associated with vehicle behavior: The system dynamically adjusts the vehicle behavior parameters in the simulation model based on the meteorological data collected in real time by the meteorological sensing device. For example, in rainy or snowy weather, the vehicle's maximum speed, front sight distance, acceleration and other parameters will be adjusted to simulate the mixed traffic flow characteristics under different weather conditions.

[0049] Obtain the road network information of the target road section based on the map data, and use simulation technology to model the mixed traffic flow under different weather scenarios in the traffic simulation software to simulate the road network model;

[0050] It should be noted that: after determining the control location, use the API or download tool (such as osmnx library) provided by the freely edited and used global map database (such as OpenStreetMap) to obtain the road network data of the target section, including information such as roads, intersections, and number of lanes. Use the open source website to obtain the road network file of the corresponding section, and adjust the road network file according to the actual situation of the road; convert the downloaded data into a format suitable for traffic simulation software (such as SUMO), which may require the use of conversion tools or scripts. Construct a simulation road network model in the simulation software based on the OpenStreetMap data, including road geometry, traffic lights, lane divisions, etc.

[0051] Define different types of vehicles in the simulation road network model, configure a following model for each type of vehicle, set the probability of occurrence of different types of vehicles to simulate the operation of vehicles with different traffic and penetration rates of intelligent connected vehicles on the road, and superimpose the vehicle operation simulation on the simulation road network model to generate a vehicle operation simulation model library;

[0052] It should be noted that vehicles include traditional human-driven vehicles and intelligent connected vehicles with different levels of automation; the following models include the IDM model and the CACC model; among them: the IDM model is a model based on human driving behavior, and the CACC model is a collaborative adaptive cruise control model suitable for autonomous driving behavior, which can realize communication and coordination between vehicles.

[0053] Specifically, the roadside equipment can collect information such as the composition of traffic on the road, while the meteorological sensing equipment can realize the real-time collection of weather conditions. Assuming that the data of the roadside equipment and meteorological sensing equipment can be transmitted and shared for real-time traffic control, the traffic simulation can set the basic information of the vehicle and the type of following model based on such information. In the experiment, we mainly selected two following models, IDM (intelligent driver) and CACC (cooperative adaptive cruise control), as examples. By setting the probability of occurrence of different types of vehicles, the simulation of different traffic and penetration rates of intelligent connected vehicles on the road is realized.

[0054] The logic for obtaining the mixed traffic flow simulation results is as follows:

[0055] Traffic flow monitors are installed at key nodes of the road network (such as intersections, entry and exit ramps, lane junctions, etc.) to identify areas where traffic flow suddenly decreases, vehicle speeds drop significantly, or traffic jams by real-time monitoring of traffic density, vehicle speeds, vehicle distances, and other data. These areas are potential traffic bottlenecks, usually accompanied by traffic density exceeding a certain threshold (such as vehicle density exceeding a certain number of vehicles per kilometer, or vehicle speeds falling below a certain limit), indicating that there is a traffic congestion problem in the area.

[0056] After the simulation, the density, average speed, road occupancy rate and other data of each road section are exported, and the estimated hourly traffic volume, average time speed, average spatial speed and other data detected by the detector are exported, so as to analyze the mixed traffic flow characteristics of the road under the weather scenario and obtain the mixed traffic flow simulation results.

[0057] Visualize the simulation results and analyze the traffic flow characteristics intuitively based on the results visualization;

[0058] Specifically, the visualization of the results includes the basic flow-density graph, which shows the classic "human" shape. Before the critical density, the flow and density show an obvious linear relationship; after the critical density, the relationship between the flow and density becomes more difficult to observe; Figure 1 As shown, the basic flow-density diagram under the scenario of 60% infiltration rate and light rain;

[0059] The data of the same weather scene and different permeability are integrated and curve fitting is performed. The flow-density fitting curve is as follows: Figure 3As shown, the following conclusions can be drawn: the higher the penetration rate of intelligent connected vehicles, the higher the apex of the curve, that is, the greater the maximum traffic capacity of the road; the higher the penetration rate of intelligent connected vehicles, the density corresponding to the apex of the curve gradually shifts to the right, that is, the greater the optimal density.

[0060] After integrating the data of different rainy weather scenarios under the same permeability and performing curve fitting, the flow-density fitting curve is as follows: Figure 4 As shown in the figure, we can also draw the following conclusions: Under the same penetration rate of intelligent connected vehicles, the traffic capacity of different rainy day scenes is ranked as normal weather > light rain > moderate rain > heavy rain; among them, the traffic capacity in light rain is almost the same as that in normal weather, the traffic capacity in moderate rain is slightly different from that in heavy rain, and the traffic capacity change from light rain to moderate rain is particularly obvious. This is consistent with the fact that in light rain, the driving speed and flow of vehicles are not much affected, but from moderate rain onwards, the driver's driving psychology and decision-making change significantly, and the road flow decreases.

[0061] By integrating and analyzing all the data files, it can be found that the maximum capacity under different weather and penetration scenarios is different, which is combined into a table as shown in Table 1. The higher the penetration rate of intelligent connected vehicles, the smaller the impact of weather on capacity.

[0062] Table 1: Maximum capacity (veh / hr / ln) at different permeabilities in different weather scenarios

[0063]

[0064] According to the simulation results, adjust parameters such as vehicle type, penetration rate, weather conditions, etc. to optimize the simulation model; perform multiple simulations to verify the stability of the model and the reliability of the results. Use the simulation software's visualization tools or external tools (such as MATLAB, Python's matplotlib library) to display the simulation results, and write a technical report based on the simulation results to summarize the findings and make suggestions for improvement.

[0065] Through these steps, traffic flow simulation under different intelligent connected vehicle penetration rates and weather scenarios on highways can be achieved, providing a scientific basis for traffic management and planning.

[0066] Specifically, the construction logic of the simulation scene model is:

[0067] In the simulation software, we first built a road scene and configured the simulation environment according to the actual road network information. Then, we used the configuration file to establish a vehicle operation simulation model library to simulate the behavior of different types of vehicles (including traditional vehicles and intelligent connected vehicles). On this basis, we adjusted the penetration rate of various types of vehicles and constructed a mixed traffic flow model that adapts to different weather conditions, providing a basis for subsequent simulation experiments;

[0068] Based on the constructed simulation scenario model, we simulated traffic flow under different weather scenarios. By analyzing the simulation results, we extracted the key traffic flow characteristics of mixed traffic flow, such as flow rate, vehicle speed, traffic density, etc. According to the impact of different weather conditions on traffic flow, we can clearly identify traffic bottlenecks and potential safety hazards, and then provide data support for subsequent control strategies. ;

[0069] Based on the analysis results of mixed traffic flow characteristics under weather scenarios, we propose corresponding road control solutions from the perspectives of safety and efficiency. For example, in severe weather conditions, measures such as speed limits, lane management, or intelligent traffic signal adjustments may be proposed. After implementing these control measures, roadside equipment will monitor traffic flow parameters in real time and evaluate the effectiveness of the control measures. By collecting evaluation results, the system can feedback and update the control plan to form a closed-loop feedback mechanism. This mechanism helps us continuously optimize control strategies based on real-time data and actual performance, and improve traffic management efficiency under similar weather conditions in the future.

[0070] Through the analysis of traffic flow data and simulation results under weather scenarios, corresponding control measures are formulated. First, the mixed traffic flow under weather scenarios is modeled by simulation technology. Different from the traditional single traffic flow model, the SUMO simulation platform is used to combine the coexistence of intelligent connected vehicles (CAVs) and traditional human-driven vehicles (HVs) to simulate the traffic flow characteristics such as flow, speed, density, etc. of the two on the road in different weather conditions (such as rainy days, dense fog, snowfall, etc.); secondly, through the analysis of simulation data, the speed, flow, vehicle distance, vehicle spacing and other key indicators of mixed traffic flow under weather scenarios are obtained, and the impact of the penetration rate of intelligent connected vehicles on traffic flow and the specific impact of weather factors on traffic efficiency and safety are analyzed; finally, based on the results of the analysis of mixed traffic flow characteristics, the present invention proposes two control schemes: one is to propose speed limit and other measures from a safety perspective, and the other is to propose flow control, lane use optimization and other schemes from an efficiency perspective.

[0071] The logic of formulating the control plan is:

[0072] The control plan includes a safety control plan and a driving efficiency control plan;

[0073] (1) The logic of the safety control plan is as follows:

[0074] By analyzing a large number of simulation results, we found that the number of vehicle collisions and emergency braking during the simulation process is strongly correlated with the road speed limit and traffic density. At the same time, the different penetration rates of intelligent connected vehicles will also affect the number of accidents. Therefore, it is necessary to limit the speed of vehicles on rainy days. For example, based on the simulation results, in moderate to heavy rain weather scenarios, the safe speeds under different penetration rates and traffic densities are shown in Table 2;

[0075] Table 2 Safe speeds at different penetration rates and traffic densities

[0076]

[0077] (2) Driving efficiency control plan is formulated based on driving efficiency;

[0078] The "Road Traffic Congestion Evaluation Method" (GAT 115-2020) states that for roads with a speed limit of 120km / h, when the average speed of vehicles is 30km / h to 50km / h, it is considered moderate congestion; less than 30km / h is considered severe congestion. Based on the data on vehicle flow, vehicle density, average speed, etc. collected by roadside equipment, according to different control objectives, when moderate or severe congestion occurs on the road, we can query the maximum capacity under the corresponding weather scenario and permeability from the simulation result database, and perform corresponding flow control on the entrance ramp.

[0079] Through roadside equipment, we can collect traffic flow data after the implementation of the control plan. Based on this data, we can correct the maximum capacity output by simulation and continuously improve the accuracy of the control plan. Therefore, we added a feedback mechanism design to our integrated management system prototype.

[0080] In the context of the rapid development of intelligent connected vehicles, this embodiment conducts an in-depth analysis of mixed traffic flows (human driving and intelligent connected vehicles) under weather scenario conditions. By utilizing the data of roadside equipment and meteorological sensing equipment and combining SUMO software, we have established a simulation model library for mixed road traffic flows, studied and analyzed the characteristics of traffic flows under weather scenarios, formulated safe and efficient traffic control plans, and considered the penetration rate of intelligent connected vehicles in the control plan to form a highly integrated integrated traffic management system prototype; and based on the feedback of the control plan effect and the model self-correction mechanism, the accuracy and practicality of the control plan are improved, providing new ideas and methods for traffic management in the era of intelligent connected vehicles.

[0081] By accurately simulating and analyzing mixed traffic flow characteristics, it can significantly improve road use efficiency and driving safety, especially reducing the probability of traffic accidents under severe weather conditions. It not only directly reduces the related maintenance and control response costs, but also indirectly reduces the economic losses caused by traffic delays.

[0082] The response layer implements the control plan of the decision-making layer through roadside equipment, traffic lights and lane change control systems to realize visual interface interaction, which is real-time and effective for traffic control interaction.

[0083] The implementation method of the present invention combines intelligent traffic management and simulation technology, and forms an efficient traffic control system through three major parts: perception layer, decision layer and response layer. The perception layer collects traffic flow, vehicle speed and meteorological data in real time through roadside equipment and meteorological perception equipment. The decision layer analyzes the characteristics of mixed traffic flow based on the SUMO simulation platform and formulates traffic control plans, including measures such as speed limit and flow control. The response layer is responsible for executing the control plan and adjusting the plan in real time through the feedback mechanism to optimize the traffic flow. Under weather scene conditions, such as rain, dense fog, etc., the system can improve road safety and traffic efficiency through dynamic speed limit, flow control and other measures. In addition, the system also has a visual interface to facilitate traffic management personnel to monitor and control traffic conditions. The present invention significantly improves the efficiency and safety of traffic management under weather scenarios through precise simulation, real-time feedback and intelligent decision-making.

[0084] Here is an example: Suppose that on a section of road A in a certain city, in heavy rain, the traffic density is too high, and traditional vehicles and intelligent connected vehicles are mixed. The system first obtains information such as traffic volume, vehicle speed, and meteorological data of the section through the perception layer, and inputs the data into the decision layer. Based on the simulation results, the decision layer decides to reduce the speed limit of the section to 60km / h, and at the same time control the flow of the entrance ramp to avoid further congestion. The feedback mechanism monitors the effectiveness of the strategy in real time and adjusts the speed limit or flow control plan when necessary. Simulation data shows that the incidence of traffic accidents after control has decreased by 20%, and the road traffic efficiency has increased by 15%.

[0085] Example 2

[0086] See also Figure 5 As shown, the road traffic control method based on mixed traffic flow and weather environment described in this embodiment includes the following steps:

[0087] Step S1: Establish a mixed traffic flow simulation model: Use the SUMO simulation platform to simulate the mixed traffic flow of intelligent connected vehicles and traditional vehicles under weather scenarios, including setting up simulation scenarios, constructing vehicle and traffic flow models, and associating meteorological data with vehicle behavior.

[0088] Specifically, simulation technology is used to model mixed traffic flows under weather scenarios. Different from the traditional single traffic flow model, this paper uses the SUMO simulation platform, combined with the coexistence of intelligent connected vehicles (CAVs) and traditional human-driven vehicles (HVs), to simulate the traffic flow characteristics of the two on the road in different weather conditions (such as rain, fog, snow, etc.), such as flow, speed, density, etc. The specific steps are as follows:

[0089] Simulation scenario construction: Based on the actual road network and meteorological data, build a road traffic simulation environment under the weather scenario, taking into account factors such as road type, number of lanes, and traffic signals.

[0090] Vehicle and traffic flow model construction: Based on the simulation software, driving models of different types of vehicles are set up, and the intelligent driver model (IDM) and collaborative adaptive cruise control (CACC) are selected as representatives to simulate traffic flows under different penetration rates of intelligent connected vehicles.

[0091] Association between meteorological data and vehicle behavior: Meteorological data is collected through meteorological sensing equipment, and combined with simulation models to adjust parameters such as vehicle speed, vehicle distance, reaction time, etc. under different weather conditions to simulate mixed traffic flows under different weather scenarios.

[0092] Step S2: Analyze mixed traffic flow characteristics: obtain key traffic indicators through simulation data, analyze the impact of intelligent connected vehicle penetration rate and weather factors on traffic flow, and display the changing trend of traffic flow characteristics through data visualization technology.

[0093] Specifically, by analyzing the simulation data, we can obtain key indicators such as speed, flow, vehicle distance, and vehicle spacing of mixed traffic flow under weather scenarios, analyze the impact of the penetration rate of intelligent connected vehicles on traffic flow, and the specific impact of weather factors on traffic efficiency and safety. The specific steps are as follows:

[0094] Data collection: Obtain data such as density, average speed, road occupancy, and traffic volume of each road section from the simulation model.

[0095] Characteristic analysis: Based on the impact of weather conditions on traffic flow, analyze the performance of intelligent connected vehicles at different penetration rates and the changes in their traffic capacity under different weather scenarios.

[0096] Data visualization: Through data visualization technology, charts are generated to help analyze the changing trends of traffic flow characteristics under different weather conditions.

[0097] Step S3: Formulate traffic control plan: Based on the analysis of traffic flow characteristics, from the perspectives of safety and efficiency, propose control measures such as speed limit, flow control and lane optimization. At the same time, establish a feedback learning mechanism to adjust the control plan in real time to ensure the efficiency and adaptability of traffic management.

[0098] Specifically, based on the results of mixed traffic flow characteristics analysis, two control solutions are proposed: one is to propose speed limit and other measures from the perspective of safety, and the other is to propose flow control, lane use optimization and other solutions from the perspective of efficiency. The specific steps are as follows:

[0099] Safety perspective control plan:

[0100] In the case of weather scenarios, the safety of intelligent connected vehicles and traditional vehicles in different weather scenarios is analyzed. According to the simulation results, speed limit measures are formulated under different weather conditions, such as appropriately reducing the speed limit in low visibility conditions such as rainy days and dense fog.

[0101] By analyzing the relationship between the frequency of vehicle collisions and emergency braking and traffic density, control measures to reduce traffic accidents are proposed.

[0102] Efficiency control solution:

[0103] Based on the standards in the "Road Traffic Congestion Evaluation Method" (GAT 115-2020), the road capacity under different weather conditions is analyzed and targeted traffic control solutions are proposed.

[0104] Based on the traffic volume, vehicle density, average speed and other data collected in real time by roadside equipment, the traffic volume and lane usage of the entrance ramp are dynamically adjusted to avoid road congestion and ensure efficient traffic.

[0105] Feedback learning mechanism:

[0106] After the control plan is implemented, the roadside equipment is used to collect traffic flow data and adjust the control plan in real time to ensure the continuous optimization of the control effect. Through the feedback mechanism, the control strategy is revised according to the actual traffic conditions and the evaluation of the control results to improve the accuracy and practicality of the control plan.

[0107] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0108] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0111] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A road traffic control system based on mixed traffic flow and weather environment, characterized by: It includes the perception layer, decision layer and response layer; each layer is connected by wired and / or wireless; The perception layer uses roadside equipment to collect traffic flow data and meteorological data in real time within the target road section, and transmits the traffic flow data and meteorological data as input data to the decision layer; The decision-making layer, based on the traffic flow data and meteorological data transmitted by the perception layer, uses simulation technology to model the mixed traffic flow of meteorological data under the corresponding weather scenario, outputs the simulation results of the mixed traffic flow under the current weather scenario, and formulates corresponding control measures based on the simulation results of the mixed traffic flow; The response layer realizes the visual interface interaction of the management and control solutions of the decision-making layer through roadside equipment, traffic lights and lane change control systems.

2. The road traffic control system and method based on mixed traffic flow and weather environment according to claim 1 is characterized by: Roadside equipment includes traffic sensors and meteorological monitoring equipment. Traffic flow data is obtained based on traffic sensors. The traffic flow data includes the flow, density and vehicle type corresponding to the vehicles on the current road section. The vehicle types include traditional driving vehicles and autonomous driving vehicles. The comprehensive flow status of different types of vehicles is marked as mixed traffic flow. The mixed traffic flow is collected in real time, and the penetration rate is analyzed based on the mixed traffic flow. The penetration rate is the proportion of autonomous driving vehicles to the total number of vehicles.

3. The road traffic control system based on mixed traffic flow and weather environment according to claim 2 is characterized by: Based on meteorological sensing equipment, meteorological data corresponding to weather conditions are collected in real time, and weather scenes are matched based on the meteorological data.

4. The road traffic control system based on mixed traffic flow and weather environment according to claim 3 is characterized by: The hybrid traffic flow simulation model uses traffic simulation software to combine the coexistence of intelligent connected vehicle traffic flow data and traditional human-driven vehicle traffic flow data to simulate the traffic flow characteristics of road traffic flow data under different weather scenarios.

5. The road traffic control system based on mixed traffic flow and weather environment according to claim 4 is characterized in that: The construction logic of the mixed traffic flow simulation model is: Simulation scenario construction: Based on the actual road network layout of the target area, the road network layout includes key road nodes corresponding to different road types, including urban roads, highways and rural roads. Key road nodes include main sections, intersections, ramps and bridges, and the road network layout is modeled in detail; Vehicle and traffic flow construction: Set up driving models for different types of vehicles based on the simulation software, adjust the size and speed of traffic flow in the simulation according to the traffic flow and density in the input traffic flow data, and calculate the penetration rate based on the vehicle type. The penetration rate can be adjusted by setting different vehicle type ratios in the road network and vehicle input files; Association of meteorological data with vehicle behavior: The vehicle behavior parameters in the simulation model are dynamically adjusted using meteorological data collected in real time by meteorological sensing equipment.

6. The road traffic control system based on mixed traffic flow and weather environment according to claim 5 is characterized by: The logic for obtaining the mixed traffic flow simulation results is as follows: Configure the simulation environment according to the actual road network information, use the configuration file to establish a vehicle operation simulation model library, and simulate the behavior of different types of vehicles; adjust the penetration rate of various types of vehicles and build a mixed traffic flow model that adapts to different weather conditions; Based on the constructed simulation scenario model, we simulated traffic flow under different weather scenarios; After the simulation is finished, the estimated hourly traffic flow characteristics detected by the detector are used, and the traffic flow characteristics include density, average speed and road occupancy rate of each road section to obtain a mixed traffic flow simulation result.

7. The road traffic control system based on mixed traffic flow and weather environment according to claim 6 is characterized by: The mixed traffic flow simulation results are visualized, and the result visualization includes a flow-density basic graph.

8. A road traffic control method based on mixed traffic flow and weather environment, based on the implementation of a road traffic control system based on mixed traffic flow and weather environment according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step S1: Establish a mixed traffic flow simulation model: Use the SUMO simulation platform to simulate the mixed traffic flow of intelligent connected vehicles and traditional vehicles under weather scenarios, including setting up simulation scenarios, constructing vehicle and traffic flow models, and associating meteorological data with vehicle behavior; Step S2: Analyze mixed traffic flow characteristics: obtain key traffic indicators through simulation data, analyze the impact of intelligent connected vehicle penetration rate and weather factors on traffic flow, and display the changing trend of traffic flow characteristics through data visualization technology; Step S3: Formulate traffic control plan: Based on the analysis of traffic flow characteristics, from the perspectives of safety and efficiency, propose control measures such as speed limit, flow control and lane optimization. At the same time, establish a feedback learning mechanism to adjust the control plan in real time to ensure the efficiency and adaptability of traffic management.

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