A method, system, device and medium for detecting and evaluating highway traffic flow

Abstract

The present application relates to the technical field of traffic control, and in particular to a method, system, device, and medium for detecting and evaluating highway traffic flow. The present application first utilizes video detectors, radar detectors, and coil detectors to collaboratively collect parameters such as vehicle type distribution, vehicle speed distribution, and headway spacing. Based on these data, the application then calculates a traffic flow heterogeneity index, which includes a large vehicle impact coefficient, a vehicle speed difference coefficient, and a following safety factor. Furthermore, the application analyzes the traffic flow operating status to obtain the traffic capacity attenuation characteristics, ultimately achieving an evaluation of the traffic efficiency of different road sections. This allows for a more comprehensive and accurate reflection of the operating quality of mixed traffic flows.

Description

Technical Field

[0001] The present application relates to the technical field of traffic control, and in particular to a method, system, device and medium for detecting and evaluating highway traffic flow. Background Art

[0002] As my country's expressway network continues to improve and vehicle ownership continues to grow, the mixing of large and small vehicles has become the norm. Performance differences between different vehicle types have led to uneven traffic speed distribution and complex car-following behaviors, posing severe challenges to expressway efficiency and safe operation.

[0003] Existing technologies only consider the static characteristics of a single space-time section and fail to reflect the dynamic interaction between different vehicle types. This makes it difficult to accurately depict the operating quality of mixed traffic flows, and this situation needs further improvement. Summary of the Invention

[0004] To address the problem that existing traffic detection fails to reflect the dynamic interaction between different types of vehicles and is difficult to accurately characterize the operating quality of mixed traffic flows, this application provides a highway traffic flow detection and evaluation method, system, device, and medium, which adopt the following technical solutions:

[0005] In a first aspect, the present application provides a method for detecting and evaluating highway traffic flow, comprising the following steps:

[0006] Collecting vehicle operation data through video detectors, radar detectors, and coil detectors, wherein the vehicle operation data includes vehicle type distribution, vehicle speed distribution, and headway between vehicles;

[0007] Calculating a traffic flow heterogeneity index based on the vehicle operation data, the traffic flow heterogeneity index including a large vehicle influence coefficient, a vehicle speed difference coefficient, and a following safety degree;

[0008] Determine the traffic flow operation state according to the traffic flow heterogeneity index and obtain the traffic capacity attenuation characteristics under the mixed traffic conditions of different vehicle types;

[0009] Based on the traffic capacity attenuation characteristics, traffic efficiency evaluation results of different sections are determined.

[0010] By adopting the above-mentioned technical solution, the present application first utilizes video detectors, radar detectors and coil detectors to collaboratively collect parameters such as vehicle type distribution, vehicle speed distribution and vehicle head spacing, and then calculates the traffic flow heterogeneity index including large vehicle impact coefficient, vehicle speed difference coefficient and following safety based on these data, and then analyzes the traffic flow operation status to obtain the traffic capacity attenuation characteristics, and finally realizes the evaluation of the traffic efficiency of different road sections; it can more comprehensively and accurately reflect the operation quality of mixed traffic flow.

[0011] Optionally, a traffic flow heterogeneity index is calculated based on the vehicle operation data, where the traffic flow heterogeneity index includes a large vehicle influence coefficient, a vehicle speed difference coefficient, and a car-following safety factor, specifically comprising the following steps:

[0012] Determine the proportion of large vehicles based on the distribution of vehicle types, and calculate the large vehicle impact coefficient based on the proportion of large vehicles;

[0013] Calculate the vehicle speed difference coefficient based on the dispersion degree between the actual driving speed and the average speed of different vehicle models;

[0014] The following safety degree is calculated based on the ratio of the actual headway between adjacent vehicles to the safe headway.

[0015] By adopting the above technical solution, this application first calculates the large vehicle impact coefficient based on the vehicle model distribution data to reflect the basic impact of large vehicles on traffic flow, and obtains the speed difference coefficient reflecting the stability of traffic flow by analyzing the degree of dispersion of the actual driving speed and average speed of different vehicle models; finally, the following safety degree is introduced to quantify the safety degree of vehicle following behavior by the ratio of the actual headway to the safe headway.

[0016] Optionally, determining the proportion of large vehicles based on the distribution of vehicle types and calculating the large vehicle impact coefficient based on the proportion of large vehicles specifically includes the following steps:

[0017] Based on the vehicle classification standard, vehicles are divided into large vehicles, medium vehicles and small vehicles;

[0018] Count the cumulative number of various types of vehicles passing through during the inspection period and calculate the proportion of large vehicles in the total traffic flow;

[0019] Determine the weight coefficient of large vehicle impact based on the number of lanes and the lateral distribution characteristics of large vehicles;

[0020] The large vehicle impact coefficient is calculated based on the proportion of the large vehicle number and the large vehicle impact weight coefficient.

[0021] By adopting the above technical solution, the traditional method only considers the proportion of large vehicles and ignores their spatial distribution characteristics, resulting in a large deviation between the evaluation results and the actual situation. For example, in a four-lane highway section, even with the same 25% large vehicle proportion, when large vehicles are concentrated in the two outer lanes, their impact on traffic flow is much more serious than when they are evenly distributed in each lane. This application first divides vehicles into three categories: large, medium and small according to the vehicle type classification standards, and then counts the number of each type of vehicles passing through during the inspection period to obtain the basic value of the large vehicle proportion. Then, the lateral distribution law of large vehicles is analyzed to determine the weight coefficient. Finally, the number proportion is combined with the distribution weight to obtain a more accurate large vehicle impact coefficient.

[0022] Optionally, based on the number of lanes and the lateral distribution characteristics of large vehicles, a large vehicle impact weight coefficient is determined, specifically including the following steps:

[0023] According to the lane type, the lanes are divided into emergency lanes, outermost lanes, middle lanes, and innermost lanes to obtain the lane function classification results;

[0024] Based on the lane function classification results, the proportion of large vehicles occupying time on each lane is counted to obtain lane occupancy characteristics;

[0025] Determining a baseline weight value for each lane based on the lane function classification result and the lane occupancy characteristics;

[0026] Based on the lane occupancy characteristics, the distribution of large vehicles in adjacent lanes is calculated to obtain an inter-lane interference coefficient;

[0027] The large vehicle impact weight coefficient is calculated based on the reference weight value and the lane-to-lane interference coefficient.

[0028] By adopting the above technical solution, when large vehicles occupy the two outermost lanes at the same time, it not only directly affects the traffic efficiency of these two lanes, but also significantly reduces the service level of the adjacent middle lane through the "ripple effect", thereby reducing the traffic capacity of the entire section; this application first classifies the lanes according to their functional characteristics, then counts the characteristics of the large vehicle occupancy time of each lane, and then determines the benchmark weight value based on the functional positioning, analyzes the large vehicle distribution correlation between adjacent lanes to obtain the interference coefficient, and finally combines the benchmark weight with the interference effect to calculate the final impact weight coefficient; thus, a systematic evaluation of the impact of large vehicles is achieved.

[0029] Optionally, the vehicle speed difference coefficient is calculated based on the degree of dispersion between the actual driving speed and the average speed of different vehicle models, specifically including the following steps:

[0030] Based on the vehicle speed distribution data, the average speed of large vehicles, medium vehicles and small vehicles is calculated to obtain the speed characteristics of each vehicle type;

[0031] Based on the speed characteristics of each vehicle type, a weighted average speed of the entire vehicle flow is calculated to obtain a reference speed value;

[0032] Calculating the speed deviation of each vehicle type according to the speed characteristics of each vehicle type and the reference speed value to obtain a speed dispersion characteristic;

[0033] Determining a speed fluctuation weight coefficient based on the speed discrete characteristics and the proportion of each vehicle type;

[0034] A vehicle speed difference coefficient is calculated according to the speed discrete characteristic and the speed fluctuation weight coefficient.

[0035] By adopting the above technical solution, the present application first calculates the average speed characteristics of large, medium and small vehicles respectively, and then determines the weighted average reference speed based on the number of each vehicle model. Then, the degree of deviation between the actual speed of each vehicle model and the reference speed is analyzed to obtain the discrete characteristics. Then, the fluctuation weight coefficient is determined according to the vehicle model composition ratio. Finally, the speed discrete characteristics are combined with the fluctuation weight to obtain the final difference coefficient; thereby scientifically evaluating the speed stability in mixed traffic flow.

[0036] Optionally, the vehicle operation data further includes traffic flow, queue length, and vehicle occupancy rate. Determining the traffic flow operation state based on the traffic flow heterogeneity index and obtaining the traffic capacity attenuation characteristics under mixed traffic conditions of different vehicle types specifically includes the following steps:

[0037] Calculating a mixed traffic impact coefficient according to the traffic flow heterogeneity index;

[0038] determining a road saturation based on the traffic flow and vehicle occupancy;

[0039] Obtaining a traffic capacity attenuation characteristic according to the mixed traffic influence coefficient and the road saturation;

[0040] The capacity attenuation characteristic is corrected according to the queue length variation trend.

[0041] By adopting the above technical solution, the existing method only evaluates based on static indicators, ignoring the dynamic evolution characteristics of traffic flow, resulting in the evaluation results being unable to reflect the actual operating conditions; this application first calculates the mixed traffic impact coefficient based on the traffic heterogeneity index, and then determines the road saturation degree based on traffic flow and vehicle occupancy rate, and then obtains the preliminary traffic capacity attenuation characteristics, and finally introduces the dynamic change trend of queue length for correction, thereby realizing a traffic capacity evaluation system that is closer to reality.

[0042] Optionally, based on the traffic capacity attenuation characteristics, determining traffic efficiency evaluation results of different sections specifically includes the following steps:

[0043] According to the capacity attenuation characteristics, obtaining a section-improved capacity index and a section-reduced capacity index;

[0044] Obtain the benchmark traffic capacity of the section based on the road geometry characteristics and traffic operation environment;

[0045] By combining the section improvement capacity index, section reduction capacity index and section benchmark capacity, the traffic efficiency evaluation results of different sections are obtained.

[0046] By adopting the above technical solution, this application first quantifies the improvement and reduction indicators of each section based on the capacity attenuation characteristics, then determines the baseline capacity according to the road geometry characteristics and operating environment, and finally comprehensively evaluates these three types of indicators to obtain a more accurate section traffic efficiency; by considering the influencing factors in both positive and negative directions, a differentiated evaluation of section traffic efficiency is achieved.

[0047] In a second aspect, the present application provides a highway traffic flow detection and evaluation system, comprising:

[0048] A vehicle operation data acquisition module is used to collect vehicle operation data through a video detector, a radar detector, and a coil detector. The vehicle operation data includes vehicle type distribution, vehicle speed distribution, and headway between vehicles;

[0049] a traffic flow heterogeneity index calculation module, configured to calculate a traffic flow heterogeneity index based on the vehicle operation data, wherein the traffic flow heterogeneity index includes a large vehicle influence coefficient, a vehicle speed difference coefficient, and a following safety degree;

[0050] A traffic capacity attenuation characteristic acquisition module is used to determine the traffic flow operation state according to the traffic flow heterogeneity index and obtain the traffic capacity attenuation characteristics under the mixed traffic conditions of different vehicle types;

[0051] The traffic efficiency evaluation result determination module is used to determine the traffic efficiency evaluation results of different sections based on the traffic capacity attenuation characteristics.

[0052] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned highway traffic flow detection and evaluation method when executing the computer program.

[0053] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned highway traffic flow detection and evaluation method.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. This application first uses video detectors, radar detectors, and coil detectors to collaboratively collect parameters such as vehicle type distribution, vehicle speed distribution, and headway distance. Based on this data, it then calculates a traffic flow heterogeneity index, which includes the large vehicle impact coefficient, vehicle speed difference coefficient, and following safety. This then analyzes the traffic flow status to derive capacity degradation characteristics, ultimately enabling an evaluation of traffic efficiency across different road sections. This approach can more comprehensively and accurately reflect the operational quality of mixed traffic flows.

[0056] 2. Traditional methods only consider the proportion of large vehicles and ignore their spatial distribution characteristics, resulting in significant deviations from actual conditions. For example, on a four-lane highway section, even with the same 25% large vehicle ratio, when large vehicles are concentrated in the two outer lanes, their impact on traffic flow is much more severe than when they are evenly distributed across all lanes. This application first divides vehicles into three categories: large, medium, and small based on vehicle type classification standards. Then, the number of vehicles of each type passing during the inspection cycle is counted to obtain a baseline value for the proportion of large vehicles. The lateral distribution of large vehicles is then analyzed to determine a weight coefficient. Finally, the proportion of large vehicles is combined with the distribution weight to obtain a more accurate large vehicle impact coefficient.

[0057] 3. This application first calculates the average speed characteristics of large, medium and small vehicles respectively, then determines a weighted average baseline speed based on the number of each vehicle model, then analyzes the degree of deviation between the actual speed of each vehicle model and the baseline speed to obtain discrete characteristics, and then determines the fluctuation weight coefficient based on the vehicle model composition ratio. Finally, the speed discrete characteristics and the fluctuation weight are combined to obtain the final difference coefficient; thereby scientifically evaluating the speed stability in mixed traffic flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0059] Figure 2 This is a flow chart of step S200 in a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0060] Figure 3 This is a flow chart of step S210 in a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0061] Figure 4 This is a flow chart of step S213 in a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0062] Figure 5 This is a flow chart of step S220 in a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0063] Figure 6 This is a flow chart of step S300 in a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0064] Figure 7 This is a flow chart of step S400 in a method for detecting and evaluating highway traffic flow according to an embodiment of the present application;

[0065] Figure 8 This is a module diagram of a highway traffic flow detection and evaluation system according to an embodiment of the present application;

[0066] Figure 9 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0067] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0068] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0069] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0070] In the first aspect, the present application provides a method for detecting and evaluating highway traffic flow, referring to Figure 1 , including the following steps:

[0071] S100: Collect vehicle operation data through a video detector, a radar detector, and a coil detector. The vehicle operation data includes vehicle type distribution, vehicle speed distribution, and headway between vehicles.

[0072] This embodiment utilizes three different types of detectors working in concert. The video detector is primarily used for vehicle type recognition and counting, extracting vehicle features through a deep learning algorithm. The radar detector uses the Doppler effect to obtain real-time vehicle speeds. The coil detector, embedded in the road surface, collects basic data such as vehicle passing times and occupancy times.

[0073] Specifically, the system collects data every 5 minutes. For vehicle type distribution, vehicles are divided into large vehicles, medium-sized vehicles and small vehicles according to their length; for vehicle speed distribution, the instantaneous speed of each vehicle when passing through the detection section is recorded; for the distance between vehicle heads, it is calculated by combining the time difference between adjacent vehicles and the vehicle speed.

[0074] S200. Calculate a traffic flow heterogeneity index based on vehicle operation data. The traffic flow heterogeneity index includes a large vehicle impact coefficient, a vehicle speed difference coefficient, and a following safety degree.

[0075] The Traffic Flow Heterogeneity Index is a comprehensive indicator reflecting traffic flow complexity, taking into account the influence of vehicle type composition, speed distribution, and vehicle spacing. The Large Vehicle Impact Coefficient primarily considers the impact of large vehicles on spatial and dynamic characteristics; the Vehicle Speed Difference Coefficient reflects the degree of speed difference between different vehicle types; and the Car-Following Safety Factor represents the safety margin between adjacent vehicles.

[0076] Specifically, the system first counts the proportion of large vehicles and calculates the large vehicle impact coefficient based on their lateral distribution characteristics; then it analyzes the speed distribution characteristics of different vehicle models and quantifies the speed differences through discrete units; finally, it compares the actual headway with the theoretical safe distance to obtain the following safety degree.

[0077] S300: Determine the traffic flow operating state based on the traffic flow heterogeneity index and obtain the traffic capacity attenuation characteristics under mixed traffic conditions of different vehicle types.

[0078] Among them, based on the traffic heterogeneity index threshold method, the operating status is divided into three types: stable flow, unstable flow and congested flow.

[0079] Specifically, when the traffic flow heterogeneity index is in different ranges, it corresponds to different operating states: an index less than 0.3 indicates stable flow, 0.3-0.7 indicates unstable flow, and greater than 0.7 indicates congested flow.

[0080] S400: Determine traffic efficiency evaluation results for different sections based on traffic capacity attenuation characteristics.

[0081] Among them, the traffic efficiency evaluation adopts a segment-by-segment and time-period evaluation method, dividing the road into several characteristic sections, considering the road section characteristics and operating environment of each section, and establishing differentiated evaluation standards.

[0082] In one embodiment, referring to Figure 2 In step S200, based on the vehicle operation data, the traffic flow heterogeneity index is calculated. The traffic flow heterogeneity index includes the large vehicle influence coefficient, the vehicle speed difference coefficient and the following safety degree. Specifically, the steps include:

[0083] S210. Determine the proportion of large vehicles based on the distribution of vehicle models, and calculate the large vehicle impact coefficient based on the proportion of large vehicles.

[0084] S220. Calculate a vehicle speed difference coefficient based on the degree of dispersion between the actual driving speeds and average speeds of different vehicle models.

[0085] S230: Calculate a following safety degree based on a ratio of an actual headway between adjacent vehicles to a safe headway.

[0086] This embodiment evaluates the safe operation level of traffic flow by comparing the actual headway with the theoretical safe headway. The safe headway increases with increasing vehicle speed, and different vehicle type combinations have different safety distance requirements.

[0087] Specifically, the system calculates the actual headway between adjacent vehicles based on detection data and determines a safe headway standard based on the vehicle types and speeds of both vehicles. For example, if the leading vehicle is a large car and the following vehicle is a small car, and the actual headway is 60 meters, while the safe headway under the current operating conditions is 40 meters, the following safety factor is 1.5, indicating a sufficient safety margin. By statistically analyzing the following characteristics of multiple vehicle pairs, a following safety factor index is derived that represents the overall safety level.

[0088] In one embodiment, referring to Figure 3 In step S210, the proportion of large vehicles is determined based on the distribution of vehicle types, and the large vehicle impact coefficient is calculated based on the proportion of large vehicles, which specifically includes the following steps:

[0089] S211. Based on the vehicle classification standards, vehicles are divided into large vehicles, medium vehicles and small vehicles.

[0090] This embodiment uses a multi-level classification method based on vehicle dimensions, extracting vehicle characteristic parameters through video image processing technology. The system pre-establishes a vehicle type recognition model that incorporates multi-dimensional features such as vehicle length, height, and width, enabling rapid and accurate classification of different vehicle types.

[0091] S212. Count the cumulative number of various types of vehicles that pass through during the inspection period, and calculate the proportion of large vehicles in the total traffic flow.

[0092] By setting different time windows, both short-term fluctuation characteristics and long-term change trends can be obtained. The system counts each type of vehicle passing through during each statistical period and calculates the corresponding composition ratio.

[0093] Specifically, the system records each vehicle's passing time and vehicle type, using a 15-minute statistical cycle. For example, between 8:00 and 8:15, a section detected 450 vehicles, including 145 large vehicles, 85 medium vehicles, and 220 small vehicles. Therefore, large vehicles accounted for 32.2% of the total number of vehicles during this period.

[0094] S213. Determine the large vehicle impact weight coefficient based on the number of lanes and the lateral distribution characteristics of large vehicles.

[0095] This embodiment uses a lane-by-lane weighted evaluation method to consider the varying impacts of large vehicles on traffic flow across different lanes. By analyzing the lateral distribution of large vehicles, a weighted calculation model is established that takes into account lane position and adjacent lane interference.

[0096] S214. Calculate the large vehicle impact coefficient based on the proportion of large vehicles and the large vehicle impact weight coefficient.

[0097] In this embodiment, a nonlinear combination model is used to calculate the large vehicle influence coefficient, and a coupling relationship between the large vehicle proportion and the influence weight is established to obtain an evaluation result that is more in line with reality.

[0098] Specifically, a piecewise function is used to calculate the gantry influence coefficient. When the gantry ratio is less than 20%, the influence coefficient is primarily determined by the ratio. When the gantry ratio is between 20% and 50%, the influence of the weight coefficient gradually increases. When the gantry ratio exceeds 50%, the influence of the weight coefficient reaches its maximum. For example, if the gantry ratio in a certain section is 35% and the weight coefficient is 1.35, the final gantry influence coefficient obtained after nonlinear combination calculation is 0.65.

[0099] In one embodiment, referring to Figure 4 In step S213, the weight coefficient of large vehicle impact is determined based on the number of lanes and the lateral distribution characteristics of large vehicles, which specifically includes the following steps:

[0100] S2131. According to lane type, the lanes are divided into an emergency lane, an outermost lane, a middle lane, and an innermost lane to obtain a lane function classification result.

[0101] This embodiment uses a lane classification method based on traffic function, taking into account the usage characteristics and management requirements of different lanes. The emergency lane is primarily used in emergencies; the outermost lane is the main truck lane; the middle lane is a mixed traffic lane; and the innermost lane is a fast overtaking lane.

[0102] Specifically, for a four-lane highway section, the lanes are numbered from right to left as the emergency lane, lane 1 (outermost), lane 2 (middle), and lane 3 (innermost). For example, a standard road section is 5 kilometers long, with each lane 3.75 meters wide and the emergency lane 2.5 meters wide. Traffic markings and signs clearly define the function of each lane. The system pre-creates a classification database based on lane numbers and functional characteristics.

[0103] S2132. Based on the lane function classification results, calculate the proportion of time occupied by large vehicles on each lane to obtain lane occupancy characteristics.

[0104] This embodiment uses a time occupancy analysis method, where detectors record the cumulative time large vehicles occupy each lane. The system uses a five-minute basic statistical cycle to record the time large vehicles pass through and stay in each lane, and calculate the occupancy ratio.

[0105] Specifically, vehicle data is collected using loop detectors or video detectors to calculate the time occupied by large vehicles in each lane. For example, within a 5-minute period, if the total observation time in Lane 1 is 300 seconds and the cumulative occupancy time of large vehicles is 120 seconds, then the occupancy rate of large vehicles in that lane is 40%. Similarly, the occupancy rates of other lanes are calculated to form a complete lane occupancy profile.

[0106] S2133. Determine a baseline weight value for each lane based on the lane function classification results and lane occupancy characteristics.

[0107] Among them, this embodiment comprehensively considers the lane functional attributes, design speed and traffic capacity, and sets differentiated benchmark weights for different lanes to reflect the differences in the degree of impact of large vehicles on each lane.

[0108] Specifically, the system sets the baseline weight range based on lane characteristics: emergency lane 1.5-2.0, outermost lane 1.2-1.5, middle lane 1.0-1.2, and innermost lane 0.8-1.0. For example, the baseline weight values for lanes on a certain road section might be: emergency lane 1.8, lane 1 1.3, lane 2 1.1, and lane 3 0.9.

[0109] S2134. Based on the lane occupancy characteristics, calculate the distribution of large vehicles in adjacent lanes to obtain the inter-lane interference coefficient.

[0110] In this embodiment, an interference assessment method based on neighbor relationships is used to analyze the coupling effect of large vehicles distributed on adjacent lanes. By establishing an inter-lane interference model, the interference degree under different distribution combinations is calculated.

[0111] Specifically, when large vehicles are present in two adjacent lanes simultaneously, the interference coefficient increases as the occupancy rate of large vehicles in both lanes increases. For example, if the occupancy rate of large vehicles in lanes 1 and 2 is 40% and 25%, respectively, the interference coefficient between the two lanes may reach 1.4. However, if the occupancy rate of large vehicles in lanes 2 and 3 is 25% and 10%, respectively, the interference coefficient may be 1.2.

[0112] S2135. Calculate the large vehicle impact weight coefficient based on the benchmark weight value and the lane-to-lane interference coefficient.

[0113] In this embodiment, a weighted comprehensive calculation method is adopted to perform a nonlinear combination of the reference weight value of each lane and the corresponding interference coefficient.

[0114] In one embodiment, referring to Figure 5 In step S220, the speed difference coefficient is calculated based on the dispersion degree between the actual driving speed and the average speed of different vehicle models, which specifically includes the following steps:

[0115] S221. Calculate the average driving speeds of large vehicles, medium vehicles, and small vehicles based on the vehicle speed distribution data to obtain the speed characteristics of each vehicle type.

[0116] In this embodiment, a classification statistical analysis method is used to collect vehicle speed data through a multi-point radar detector. The system sets up an independent data processing channel for each vehicle type and uses a sliding time window method to extract speed features.

[0117] S222. Calculate the weighted average speed of the entire vehicle flow based on the speed characteristics of each vehicle type to obtain a reference speed value.

[0118] In this embodiment, a weighted calculation method that takes into account the number of vehicles and vehicle type characteristics is adopted to determine the reference speed by establishing a multi-level weight system.

[0119] Specifically, the system first calculates the proportion of each type of vehicle within a 15-minute period and then calculates a weighted value based on their average speed. For example, if small cars account for 50% of the traffic during a certain period, medium-sized cars account for 30%, and large cars account for 20%, and after factoring in the vehicle type correction factor, the calculated weighted average speed of the entire traffic flow is 88 km / h, which serves as the evaluation baseline.

[0120] S223. Calculate the speed deviation of each vehicle type based on the speed characteristics of each vehicle type and the reference speed value to obtain a speed dispersion characteristic.

[0121] Among them, this embodiment calculates the deviation degree of the actual speed of each vehicle model, establishes a hierarchical deviation calculation model, and reflects the speed differences within and between vehicle models.

[0122] S224. Determine a speed fluctuation weight coefficient based on the speed discrete characteristics and the proportion of each vehicle type.

[0123] In this embodiment, the weight coefficient is determined based on the number distribution of each vehicle type and the speed dispersion. When the number of a certain type of vehicle is large and the speed dispersion is large, its weight coefficient will be increased accordingly.

[0124] S225. Calculate a vehicle speed difference coefficient based on the speed discrete characteristic and the speed fluctuation weight coefficient.

[0125] Specifically, the system uses a piecewise function to calculate the difference coefficient. When the speed deviation is less than 10km / h, the influence of the fluctuation weight is mainly considered; when the deviation is between 10-20km / h, the two have equivalent effects; when the deviation exceeds 20km / h, the discrete feature plays a dominant role.

[0126] In one embodiment, the vehicle operation data also includes traffic flow, queue length and vehicle occupancy rate; Figure 6 In step S300, the traffic flow operation state is determined according to the traffic flow heterogeneity index, and the traffic capacity attenuation characteristics under the mixed traffic conditions of different vehicle types are obtained, which specifically includes the following steps:

[0127] S310. Calculate the mixed traffic impact coefficient based on the traffic flow heterogeneity index.

[0128] In this embodiment, the large vehicle impact coefficient, the vehicle speed difference coefficient, and the car-following safety factor are combined to obtain the mixed traffic impact coefficient.

[0129] S320: Determine road saturation based on traffic flow and vehicle occupancy rate.

[0130] In this embodiment, road saturation is evaluated by combining traffic flow and occupancy rate. Traffic flow refers to the number of vehicles passing through a certain section per unit time, and the unit is vehicles / hour; vehicle occupancy rate refers to the ratio of the time that the detector is occupied by vehicles to the total observation time, expressed as a percentage.

[0131] Specifically, traffic flow and occupancy are calculated over a 15-minute statistical period. For example, if 900 vehicles pass through a section within 15 minutes, and the detector is occupied by vehicles for 225 seconds out of a 900-second observation period, the traffic flow during this period is converted to 3,600 vehicles per hour, and the occupancy rate is 25%. By combining these two parameters, we can more accurately determine the actual operating status of the road.

[0132] S330: Obtain a traffic capacity attenuation characteristic based on the mixed traffic influence coefficient and the road saturation.

[0133] In this embodiment, the system establishes a three-level attenuation evaluation standard: when the mixed traffic impact coefficient is less than 0.5 and the saturation is less than 0.8, it is slight attenuation; when one of the two indicators reaches the warning value, it is moderate attenuation; when both indicators exceed the warning value, it is severe attenuation.

[0134] S340. Correct the traffic capacity attenuation characteristics according to the queue length change trend.

[0135] In this embodiment, a dynamic correction method based on queuing theory is adopted to correct the attenuation assessment results by monitoring the spatiotemporal evolution characteristics of the queue length, so that assessment deviations can be discovered and handled in a timely manner, thereby improving the reliability of the results.

[0136] In one embodiment, referring to Figure 7 In step S400, based on the traffic capacity attenuation characteristics, the traffic efficiency evaluation results of different sections are determined, which specifically includes the following steps:

[0137] S410. Obtain a section-improved capacity index and a section-reduced capacity index based on the capacity attenuation characteristics.

[0138] S420. Obtain the benchmark traffic capacity of the section based on the road geometric characteristics and traffic operating environment.

[0139] S430. Combine the section improvement capacity index, the section reduction capacity index and the section baseline capacity to obtain traffic efficiency evaluation results for different sections.

[0140] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0141] In a second aspect, the present application provides a highway traffic flow detection and evaluation system. The highway traffic flow detection and evaluation system of the present application is described below in combination with the above-mentioned highway traffic flow detection and evaluation method.

[0142] Reference Figure 8 , a highway traffic flow detection and evaluation system, comprising:

[0143] The vehicle operation data acquisition module is used to collect vehicle operation data through video detectors, radar detectors and coil detectors. The vehicle operation data includes vehicle type distribution, vehicle speed distribution and headway between vehicles;

[0144] The traffic flow heterogeneity index calculation module is used to calculate the traffic flow heterogeneity index based on vehicle operation data. The traffic flow heterogeneity index includes the large vehicle impact coefficient, vehicle speed difference coefficient and following safety degree;

[0145] The capacity attenuation characteristic acquisition module is used to determine the traffic flow operation status based on the traffic heterogeneity index and obtain the capacity attenuation characteristics under the mixed traffic conditions of different vehicle types;

[0146] The traffic efficiency evaluation result determination module is used to determine the traffic efficiency evaluation results of different sections based on the traffic capacity attenuation characteristics.

[0147] In one embodiment, the traffic flow heterogeneity index calculation module includes:

[0148] A large vehicle impact coefficient calculation unit is used to determine the proportion of large vehicles based on the distribution of vehicle types and calculate the large vehicle impact coefficient based on the proportion of large vehicles;

[0149] A vehicle speed difference coefficient calculation unit, used to calculate the vehicle speed difference coefficient based on the degree of dispersion between the actual driving speed and the average speed of different vehicle models;

[0150] The following safety calculation unit is used to calculate the following safety based on the ratio of the actual headway between adjacent vehicles to the safe headway.

[0151] In one embodiment, the vehicle influence coefficient calculation unit includes:

[0152] A vehicle classification module is used to classify vehicles into large vehicles, medium vehicles, and small vehicles based on vehicle classification standards;

[0153] The large vehicle ratio calculation module is used to count the cumulative number of various types of vehicles passing through during the detection period and calculate the proportion of large vehicles in the total traffic flow;

[0154] The weight coefficient determination module is used to determine the large vehicle impact weight coefficient based on the number of lanes and the lateral distribution characteristics of large vehicles;

[0155] The influence coefficient calculation module is used to calculate the large vehicle influence coefficient based on the proportion of large vehicles and the large vehicle influence weight coefficient.

[0156] In one embodiment, the weight coefficient determination module includes:

[0157] A lane classification unit is used to divide lanes into emergency lanes, outermost lanes, middle lanes, and innermost lanes according to lane types, and obtain lane function classification results;

[0158] An occupancy feature statistics unit is used to calculate the proportion of time that large vehicles occupy each lane based on the lane function classification results to obtain lane occupancy features;

[0159] A reference weight determination unit, configured to determine a reference weight value for each lane based on the lane function classification result and the lane occupancy characteristics;

[0160] An interference coefficient calculation unit is used to calculate the distribution of large vehicles in adjacent lanes based on lane occupancy characteristics to obtain an interference coefficient between lanes;

[0161] The weight coefficient calculation unit is used to calculate the large vehicle impact weight coefficient based on the reference weight value and the lane interference coefficient.

[0162] In one embodiment, the vehicle speed difference coefficient calculation unit includes:

[0163] The speed feature acquisition module is used to calculate the average driving speed of large vehicles, medium vehicles and small vehicles based on the vehicle speed distribution data, and obtain the speed characteristics of each vehicle type;

[0164] The benchmark speed calculation module is used to calculate the weighted average speed of the entire vehicle flow based on the speed characteristics of each vehicle type to obtain the benchmark speed value;

[0165] The discrete feature calculation module is used to calculate the speed deviation of each vehicle type based on the speed characteristics of each vehicle type and the reference speed value to obtain the speed discrete feature;

[0166] The weight coefficient determination module is used to determine the speed fluctuation weight coefficient based on the speed discrete characteristics and the proportion of each vehicle type;

[0167] The difference coefficient calculation module is used to calculate the vehicle speed difference coefficient based on the speed discrete characteristics and the speed fluctuation weight coefficient.

[0168] In one embodiment, the vehicle operation data further includes traffic flow, queue length, and vehicle occupancy rate; and the traffic capacity attenuation feature acquisition module includes:

[0169] A mixed traffic influence coefficient calculation unit is used to calculate the mixed traffic influence coefficient based on the traffic heterogeneity index;

[0170] a road saturation determination unit, configured to determine a road saturation based on traffic flow and vehicle occupancy;

[0171] An attenuation characteristic calculation unit is used to obtain a capacity attenuation characteristic based on a mixed traffic influence coefficient and a road saturation;

[0172] The characteristic correction unit is used to correct the capacity attenuation characteristic according to the queue length change trend.

[0173] In one embodiment, the traffic efficiency evaluation result determination module includes:

[0174] A capacity index acquisition unit is used to acquire a section-improved capacity index and a section-reduced capacity index according to the capacity attenuation characteristics;

[0175] The benchmark capacity determination unit is used to obtain the benchmark capacity of the section based on the road geometry characteristics and traffic operation environment;

[0176] The efficiency evaluation unit is used to combine the section improvement capacity index, the section reduction capacity index and the section benchmark capacity to obtain the traffic efficiency evaluation results of different sections.

[0177] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown. The electronic device includes a processor, a memory, and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for detecting and evaluating highway traffic flow is implemented.

[0178] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0179] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0180] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0181] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for detecting and evaluating highway traffic flow, characterized in that: The steps include: Collecting vehicle operation data through video detectors, radar detectors, and coil detectors, wherein the vehicle operation data includes vehicle type distribution, vehicle speed distribution, and headway between vehicles; Calculating a traffic flow heterogeneity index based on the vehicle operation data, the traffic flow heterogeneity index including a large vehicle influence coefficient, a vehicle speed difference coefficient, and a following safety degree; Determine the traffic flow operation state according to the traffic flow heterogeneity index and obtain the traffic capacity attenuation characteristics under the mixed traffic conditions of different vehicle types; Determining traffic efficiency evaluation results of different sections based on the traffic capacity attenuation characteristics; Calculating a traffic flow heterogeneity index based on the vehicle operation data includes a large vehicle impact coefficient, a vehicle speed difference coefficient, and a car-following safety factor, specifically including the following steps: Determine the proportion of large vehicles based on the distribution of vehicle types, and calculate the large vehicle impact coefficient based on the proportion of large vehicles; Calculate the vehicle speed difference coefficient based on the dispersion degree between the actual driving speed and the average speed of different vehicle models; The following safety degree is calculated based on the ratio of the actual headway between adjacent vehicles to the safe headway; The proportion of large vehicles is determined based on the distribution of vehicle types, and the large vehicle impact coefficient is calculated based on the proportion of large vehicles, which specifically includes the following steps: Based on the vehicle classification standard, vehicles are divided into large vehicles, medium vehicles and small vehicles; Count the cumulative number of various types of vehicles passing through during the inspection period and calculate the proportion of large vehicles in the total traffic flow; Determine the weight coefficient of large vehicle impact based on the number of lanes and the lateral distribution characteristics of large vehicles; Calculate the large vehicle impact coefficient based on the proportion of the large vehicle number and the large vehicle impact weight coefficient; The weight coefficient of large vehicle impact is determined based on the number of lanes and the lateral distribution characteristics of large vehicles. The specific steps include: According to the lane type, the lanes are divided into emergency lanes, outermost lanes, middle lanes, and innermost lanes to obtain the lane function classification results; Based on the lane function classification results, the proportion of large vehicles occupying time on each lane is counted to obtain lane occupancy characteristics; Determining a baseline weight value for each lane based on the lane function classification result and the lane occupancy characteristics; Based on the lane occupancy characteristics, the distribution of large vehicles in adjacent lanes is calculated to obtain an inter-lane interference coefficient; The large vehicle impact weight coefficient is calculated based on the reference weight value and the lane-to-lane interference coefficient.

2. The highway traffic flow detection and evaluation method according to claim 1, characterized in that: The speed difference coefficient is calculated based on the degree of dispersion between the actual driving speed and the average speed of different vehicle models, specifically including the following steps: Based on the vehicle speed distribution data, the average speed of large vehicles, medium vehicles and small vehicles is calculated to obtain the speed characteristics of each vehicle type; Based on the speed characteristics of each vehicle type, a weighted average speed of the entire vehicle flow is calculated to obtain a reference speed value; Calculating the speed deviation of each vehicle type according to the speed characteristics of each vehicle type and the reference speed value to obtain a speed dispersion characteristic; Determining a speed fluctuation weight coefficient based on the speed discrete characteristics and the proportion of each vehicle type; A vehicle speed difference coefficient is calculated according to the speed discrete characteristic and the speed fluctuation weight coefficient.

3. The highway traffic flow detection and evaluation method according to claim 1, characterized in that: The vehicle operation data also includes traffic flow, queue length and vehicle occupancy rate; according to the traffic flow heterogeneity index, the traffic flow operation state is determined to obtain the traffic capacity attenuation characteristics under the mixed traffic conditions of different vehicle types, specifically The steps include: Calculating a mixed traffic impact coefficient according to the traffic flow heterogeneity index; determining a road saturation based on the traffic flow and vehicle occupancy; Obtaining a traffic capacity attenuation characteristic according to the mixed traffic influence coefficient and the road saturation; The capacity attenuation characteristic is corrected according to the queue length variation trend.

4. The highway traffic flow detection and evaluation method according to claim 1, characterized in that: Determining the traffic efficiency evaluation results of different sections based on the traffic capacity attenuation characteristics specifically includes the following steps: According to the capacity attenuation characteristics, obtaining a section-improved capacity index and a section-reduced capacity index; Obtain the benchmark traffic capacity of the section based on the road geometry characteristics and traffic operation environment; By combining the section improvement capacity index, section reduction capacity index and section benchmark capacity, the traffic efficiency evaluation results of different sections are obtained.

5. A highway traffic flow detection and evaluation system, characterized in that: The method for detecting and evaluating highway traffic flow according to any one of claims 1 to 4 is applied, comprising: A vehicle operation data acquisition module is used to collect vehicle operation data through a video detector, a radar detector, and a coil detector. The vehicle operation data includes vehicle type distribution, vehicle speed distribution, and headway between vehicles; a traffic flow heterogeneity index calculation module, configured to calculate a traffic flow heterogeneity index based on the vehicle operation data, wherein the traffic flow heterogeneity index includes a large vehicle influence coefficient, a vehicle speed difference coefficient, and a following safety degree; A traffic capacity attenuation characteristic acquisition module is used to determine the traffic flow operation state according to the traffic flow heterogeneity index and obtain the traffic capacity attenuation characteristics under the mixed traffic conditions of different vehicle types; The traffic efficiency evaluation result determination module is used to determine the traffic efficiency evaluation results of different sections based on the traffic capacity attenuation characteristics.

6. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the highway traffic flow detection and evaluation method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the highway traffic flow detection and evaluation method according to any one of claims 1 to 4 are implemented.

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