A road network digitization cloud scheduling method and system based on vehicle-road cooperation

Abstract

This invention belongs to the field of digital cloud scheduling technology for road networks, specifically relating to a method and system for digital cloud scheduling of road networks based on vehicle-road cooperation. The method includes: collecting traffic flow data using roadside units and identifying vehicle information; analyzing the vehicle information to obtain the vehicle spatial interference index and vehicle behavior interference index of unconnected vehicles, and combining these to generate a penetration impact index for unconnected vehicles; using the penetration impact index of unconnected vehicles to obtain a green wave attenuation index through green wave traffic interference analysis; using the green wave attenuation index to obtain road saturation through road capacity assessment; and invoking the corresponding green wave coordination strategy based on the road saturation. This invention achieves quantitative and dynamic differentiated scheduling of interference from unconnected vehicles, effectively improving the efficiency of green wave traffic and the overall operational efficiency of the road network in mixed traffic flow environments.

Description

Technical Field

[0001] This invention belongs to the field of digital cloud scheduling technology for road networks based on vehicle-road cooperation, and specifically relates to a method and system for digital cloud scheduling of road networks based on vehicle-road cooperation. Background Technology

[0002] In recent years, vehicle-road-cloud integrated systems, with cloud computing at their core, have continued to evolve, and the coverage of roadside sensing devices and intelligent vehicle terminals has gradually expanded. Against this backdrop, dynamic green wave traffic, as an important means to improve the traffic efficiency of urban arterial roads, provides high-precision and personalized speed guidance to vehicles in the vehicle-road-cloud system through real-time two-way data interaction between the systems, thereby forming a continuous green wave, reducing parking delays, and improving the overall operational efficiency of the road network.

[0003] However, in real-world road environments, the penetration rate of intelligent connected vehicles has not yet reached 100%, and many vehicles on the road still lack intelligent in-vehicle terminals. These vehicles cannot connect to the vehicle-road-cloud (V2X) system as nodes in the V2X collaboration network. Although these non-connected vehicles can obtain green wave traffic reference information through commercial navigation software, this software primarily serves the efficiency of individual vehicle travel. More importantly, commercial navigation software cannot achieve deep collaboration with roadside units such as traffic signal controllers, and cannot dynamically adjust signal timing at downstream intersections to offset green wave disturbances caused by non-connected vehicles' arbitrary lane changes and low-speed driving. This means that a large number of non-connected vehicles remain an uncertain source of disturbance affecting the continuity and stability of the green wave in mixed traffic flows.

[0004] Therefore, there is an urgent need for a digital cloud scheduling method and system for road networks based on vehicle-road cooperation, which can achieve efficient green wave traffic and effective scheduling by identifying vehicles that are not connected to the network. Summary of the Invention

[0005] (1) Technical problems to be solved The purpose of this invention is to provide a digital cloud scheduling method and system for road networks based on vehicle-road cooperation, so as to solve the problem that existing vehicle-road cloud systems are difficult to achieve effective scheduling when performing green wave traffic scheduling due to the influence of vehicles not connected to the network.

[0006] (2) Technical solution To achieve the above objectives, on the one hand, the present invention provides a digital cloud scheduling method for road networks based on vehicle-road cooperation, the method comprising: Traffic flow data within the target road segment is collected using roadside multi-source sensors; a three-dimensional dynamic image of the target road segment is generated based on the traffic flow data and uploaded to the roadside unit; the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment.

[0007] By analyzing the vehicle information, the vehicle space interference index and vehicle behavior interference index of the unconnected vehicles are obtained; based on the vehicle behavior interference index and the vehicle space interference index, the comprehensive interference index of the unconnected vehicles is calculated; by analyzing the comprehensive interference index, the penetration impact index of the unconnected vehicles is obtained.

[0008] The penetration impact index of the unconnected vehicles is used to obtain the green wave traffic attenuation index through green wave traffic interference analysis; the green wave traffic attenuation index is used to obtain the road saturation through road capacity assessment.

[0009] The corresponding green wave coordination strategy is invoked based on the road saturation.

[0010] Furthermore, the method by which the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment includes: Vehicle appearance features are obtained by recognizing three-dimensional dynamic images of the target road segment; the confidence level of the connected vehicle is obtained by comparing the vehicle appearance features with the connected vehicle feature database; vehicles with a confidence level not lower than a preset connected vehicle confidence level threshold are classified as connected vehicles; vehicles with a confidence level lower than the preset connected vehicle confidence level threshold are classified as vehicles to be authenticated.

[0011] The roadside unit broadcasts a detection signal to the vehicle to be authenticated; vehicles that respond to the detection signal are identified as connected vehicles; vehicles that do not respond to the detection signal are identified as non-connected vehicles; and the connected and non-connected vehicles are remapped onto the three-dimensional dynamic image of the target road segment to obtain vehicle information.

[0012] Furthermore, the method for obtaining the vehicle spatial interference index and vehicle behavioral interference index of unconnected vehicles by analyzing the vehicle information includes: Obtain historical traffic congestion data for the target road segment; analyze the historical congestion data of the target road segment to obtain the congestion nodes of the target road segment; merge the congestion nodes and the road traffic nodes of the target road segment to generate the traffic nodes of the target road segment; obtain the location of unconnected vehicles based on the vehicle information.

[0013] Based on the location of the unconnected vehicle, traffic nodes within the preset vehicle influence range of the target road segment are identified to obtain the traffic nodes causing vehicle interference. The static interference index of the unconnected vehicle is then calculated based on these traffic nodes. The vehicle static interference index The calculation formula is: .

[0014] in, For parameters affecting traffic nodes; This refers to the node distance attenuation parameter. This refers to the distance from unconnected vehicles to the traffic nodes on the target road segment; The total number of traffic nodes in the target road segment. Indicates the first One traffic node; This is a reference distance parameter.

[0015] A local traffic impact area is selected based on the preset vehicle impact range and the location of the unconnected vehicles; within a sliding time window, vehicle information in the local traffic impact area is collected using roadside multi-source sensors; image analysis is performed based on the vehicle information in the local traffic impact area to obtain the number of vehicles and the vehicle road space occupancy rate in the local traffic impact area; a local road saturation rate in the local traffic impact area is obtained by linear weighting based on the number of vehicles and the vehicle road space occupancy rate; and a vehicle space interference index is calculated based on the vehicle static interference index and the local road saturation rate. The vehicle space interference index The calculation formula is: .

[0016] in, This is the saturation influence coefficient; This represents the local road saturation.

[0017] When the speed of an unconnected vehicle is lower than the green wave speed of the target road segment, a three-dimensional dynamic image of the unconnected vehicle within the preset time window is extracted based on the vehicle information. The motion trajectory data of the unconnected vehicle and the motion trajectory data of the vehicles in front and behind are obtained based on the three-dimensional dynamic image of the target road segment. The motion intensity of the unconnected vehicle is obtained by calculating the vehicle's motion acceleration based on the motion trajectory data. The motion flow coordination degree of the unconnected vehicle is obtained by calculating the average vehicle speed and average acceleration based on the motion trajectory data of the unconnected vehicle and the motion flow coordination degree of the vehicles in front and behind. The motion intensity and motion flow coordination degree of the unconnected vehicle are normalized and linearly weighted to obtain the vehicle behavior interference index of the unconnected vehicle.

[0018] Furthermore, the method for calculating the comprehensive interference index of unconnected vehicles based on the vehicle behavior interference index and the vehicle spatial interference index includes: The comprehensive interference index of the unconnected vehicle is calculated based on the vehicle behavior interference index and the vehicle space interference index. The comprehensive interference index The calculation formula is: .

[0019] in, This is the vehicle behavior interference index.

[0020] Furthermore, the method for obtaining the penetration impact index of unconnected vehicles by analyzing the comprehensive interference index includes: Based on the comprehensive interference index of all unconnected vehicles in the target road segment The total effect equivalent was calculated. The total impact equivalent The calculation formula is: .

[0021] in, The location of the unconnected vehicle is the stop line position at the downstream key signal intersection; This is the road segment disturbance attenuation coefficient; This represents the number of vehicles not connected to the network in the target road segment. The target road segment length; This is the road segment location modulation coefficient.

[0022] Obtain the actual driving distance of the connected vehicles; calculate the vehicle offset distance by comparing the actual driving distance with the theoretical distance traveled at the cloud-recommended green wave speed; and obtain the vehicle coordination degree by linearly weighting and normalizing the offset distances of all connected vehicles. According to the vehicle coordination degree With the total impact equivalent The penetration disturbance index was calculated. The penetration disturbance index The formula is: .

[0023] in, The total number of vehicles on the target road segment; This represents the theoretical maximum value of the comprehensive interference index; To prevent division by zero of constants.

[0024] Furthermore, the method for obtaining the green wave attenuation index by analyzing the penetration impact index of the unconnected vehicles through green wave traffic interference includes: Divide the target road segment into sections along the direction of traffic flow. A series of continuous discrete observation road segments are identified; vehicle speed data within each discrete observation road segment is obtained based on vehicle information; a vehicle speed probability distribution for each discrete observation road segment is generated based on the vehicle speed data; and the relationships between the vehicle speed probability distributions of all adjacent discrete observation road segments are calculated based on the vehicle speed probability distribution of each discrete observation road segment. The dispersion was calculated, and the platoon dispersion of the target road segment was obtained by arithmetic averaging; based on the penetration impact index of the unconnected vehicles... Dispersion of the vehicle fleet The green wave attenuation index was calculated. The green wave attenuation index Calculation formula: .

[0025] in, This represents the vehicle dispersion parameter.

[0026] Furthermore, the method for obtaining road saturation by assessing the road capacity using the green wave traffic attenuation index includes: Obtain the theoretical traffic capacity of the target road segment Based on the theoretical traffic capacity of the target road segment and Green Wave Attenuation Index Calculate the effective throughput capacity ; By analyzing effective traffic capacity Road saturation was calculated. ;in, This is for real-time traffic flow.

[0027] Furthermore, the method of invoking the corresponding green wave coordination strategy based on the road saturation includes: Road saturation levels are defined based on road saturation, including first road saturation, second road saturation, and third road saturation; the corresponding green wave coordination strategy is invoked based on the road saturation level.

[0028] Based on the same inventive concept, this invention also provides a road network digital cloud scheduling system based on vehicle-road cooperation, the system comprising: The vehicle recognition module is used to collect traffic flow data within the target road segment through roadside multi-source sensors; generate a three-dimensional dynamic image of the target road segment based on the traffic flow data, and upload it to the roadside unit; the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment.

[0029] The vehicle penetration index calculation module is used to obtain the vehicle spatial interference index and vehicle behavior interference index of unconnected vehicles by analyzing the vehicle information; to calculate the comprehensive interference index of unconnected vehicles based on the vehicle behavior interference index and the vehicle spatial interference index; and to obtain the penetration impact index of unconnected vehicles by analyzing the comprehensive interference index.

[0030] The road saturation calculation module is used to obtain the green wave traffic attenuation index by analyzing the penetration impact index of the non-networked vehicles through green wave traffic interference; and to obtain the road saturation by evaluating the road traffic capacity using the green wave traffic attenuation index.

[0031] The green wave traffic strategy invocation module is used to invoke the corresponding green wave coordination strategy based on the road saturation.

[0032] (3) Beneficial effects Compared with the prior art, the beneficial effects of the present invention are: 1. Rapid identification of connected and non-connected vehicles was achieved through multi-source perception and communication verification of roadside units, and the impact of non-connected vehicles on green wave traffic was quantified from multiple perspectives, including spatial location, behavioral characteristics, and traffic flow status.

[0033] 2. Based on the disturbance level caused by the infiltration of unconnected vehicles, a hierarchical scheduling strategy is dynamically matched and executed, which effectively alleviates the problem of green wave attenuation under mixed traffic flow and improves the overall traffic efficiency of the road network. Attached Figure Description

[0034] Figure 1 This is a flowchart of a road network digital cloud scheduling method based on vehicle-road cooperation, according to Embodiment 1 of the present invention.

[0035] Figure 2 This is a schematic diagram of the module composition of a road network digital cloud scheduling system based on vehicle-road cooperation according to Embodiment 2 of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Before giving examples, it is necessary to describe the application scenario of the present invention. Imagine a six-lane, two-way main road running through the city center, approximately 3 kilometers long. There are five signal-controlled intersections along the route. A complete set of vehicle-road cooperative roadside equipment (including multi-source sensors) has been deployed along this section of road. While traffic flow includes traffic guidance screens, approximately 30% of vehicles during the morning rush hour are still non-connected vehicles without intelligent onboard units. For example, during the morning rush hour, a non-connected freight vehicle, failing to observe the recommended green wave speed on the guidance screen, maintained a low speed as it approached the intersection. This forced a group of connected vehicles following behind, which were proceeding at the coordinated green wave speed, to collectively slow down, disrupting the planned convoy rhythm and causing some vehicles to fail to pass through the intersection within a single green light cycle, resulting in a chain reaction of delays.

[0038] Example 1: As Figure 1 As shown, this embodiment provides a digital cloud scheduling method for road networks based on vehicle-road cooperation, the method including: Traffic flow data within the target road segment is collected using roadside multi-source sensors; a three-dimensional dynamic image of the target road segment is generated based on the traffic flow data and uploaded to the roadside unit; the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment.

[0039] By analyzing the vehicle information, the vehicle space interference index and vehicle behavior interference index of the unconnected vehicles are obtained; based on the vehicle behavior interference index and the vehicle space interference index, the comprehensive interference index of the unconnected vehicles is calculated; by analyzing the comprehensive interference index, the penetration impact index of the unconnected vehicles is obtained.

[0040] The penetration impact index of the unconnected vehicles is used to obtain the green wave traffic attenuation index through green wave traffic interference analysis; the green wave traffic attenuation index is used to obtain the road saturation through road capacity assessment.

[0041] The corresponding green wave coordination strategy is invoked based on the road saturation.

[0042] The method by which the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment includes: Vehicle appearance features are obtained by recognizing three-dimensional dynamic images of the target road segment; the confidence level of the connected vehicle is obtained by comparing the vehicle appearance features with the connected vehicle feature database; vehicles with a confidence level not lower than a preset connected vehicle confidence level threshold are classified as connected vehicles; vehicles with a confidence level lower than the preset connected vehicle confidence level threshold are classified as vehicles to be authenticated.

[0043] The roadside unit broadcasts a detection signal to the vehicle to be authenticated; vehicles that respond to the detection signal are identified as connected vehicles; vehicles that do not respond to the detection signal are identified as non-connected vehicles; and the connected and non-connected vehicles are remapped onto the three-dimensional dynamic image of the target road segment to obtain vehicle information.

[0044] For example, the roadside unit's multi-source sensors collect traffic flow data within the target road segment and generate a three-dimensional dynamic image reflecting the real-time traffic status of the segment. This three-dimensional dynamic image includes the spatial position, outline, and velocity vector of each vehicle. The roadside unit identifies the three-dimensional dynamic image of the target road segment. An older truck is identified, and its vehicle appearance features are obtained. The truck's appearance features are compared with a networked vehicle feature database, resulting in a networked vehicle confidence score of 0.4. A networked vehicle confidence score threshold of 0.5 is set, based on a comprehensive consideration of the completeness of the networked vehicle feature database, the accuracy of roadside sensor identification, and the typical mixed traffic flow of the target road segment. Since 0.4 is less than 0.5, this truck is classified as a vehicle awaiting authentication. Simultaneously, a newer car model is identified. A comparison with the networked vehicle feature database yields a networked vehicle confidence score of 0.7, higher than the threshold of 0.5. Therefore, it is directly and initially classified as a networked vehicle without proceeding to the subsequent communication verification stage. It should be noted that the connected vehicle feature database is constructed based on vehicle model data publicly released by automakers that have pre-installed intelligent connected vehicle units. The connected vehicle confidence level is determined by the degree of matching between the vehicle appearance features identified in the 3D dynamic image of the target road segment collected and generated by roadside sensors and the vehicle appearance features in the connected vehicle feature database. The higher the matching degree, the higher the confidence level in determining that the vehicle is a connected vehicle.

[0045] For vehicles classified as pending certification, the roadside unit broadcasts a detection signal. Within the set 200-millisecond response time window, the truck did not return any response signal and was therefore ultimately determined to be an unconnected vehicle. Another vehicle also classified as pending certification... (With a confidence level of 0.48), the roadside unit also broadcasts a detection signal to it. A response signal was returned within the response time window, thus correcting the vehicle's classification as a connected vehicle. The identified connected vehicle and the non-connected vehicle, along with their respective attribute tags, were remapped and updated in the 3D dynamic image of the target road segment. This provides the roadside unit with a vehicle information list containing information such as vehicle type (connected or non-connected) and precise location, providing reliable data support for subsequent green wave scheduling under mixed traffic flow in the cloud. It should also be noted that rapid initial screening using visual features allows for the direct classification of high-confidence connected vehicles, avoiding unnecessary communication detection of all vehicles on the road and saving communication resources and processing time for the roadside unit.

[0046] The method for obtaining the vehicle spatial interference index and vehicle behavioral interference index of unconnected vehicles by analyzing the vehicle information includes: Obtain historical traffic congestion data for the target road segment; analyze the historical congestion data of the target road segment to obtain the congestion nodes of the target road segment; merge the congestion nodes and the road traffic nodes of the target road segment to generate the traffic nodes of the target road segment; obtain the location of unconnected vehicles based on the vehicle information.

[0047] Based on the location of the unconnected vehicle, traffic nodes within the preset vehicle influence range of the target road segment are identified to obtain the traffic nodes causing vehicle interference. The static interference index of the unconnected vehicle is then calculated based on these traffic nodes. The vehicle static interference index The calculation formula is: .

[0048] in, For parameters affecting traffic nodes; This refers to the node distance attenuation parameter. This refers to the distance from unconnected vehicles to the traffic nodes on the target road segment; The total number of traffic nodes in the target road segment. Indicates the first One traffic node; This is a reference distance parameter.

[0049] A local traffic impact area is selected based on the preset vehicle impact range and the location of the unconnected vehicles; within a sliding time window, vehicle information in the local traffic impact area is collected using roadside multi-source sensors; image analysis is performed based on the vehicle information in the local traffic impact area to obtain the number of vehicles and the vehicle road space occupancy rate in the local traffic impact area; a local road saturation rate in the local traffic impact area is obtained by linear weighting based on the number of vehicles and the vehicle road space occupancy rate; and a vehicle space interference index is calculated based on the vehicle static interference index and the local road saturation rate. The vehicle space interference index The calculation formula is: .

[0050] in, This is the saturation influence coefficient; This represents the local road saturation.

[0051] For example, taking a main urban road as an example, the main road is 2 kilometers long and has 4 signalized intersections. There are a certain number of vehicles on the road that are not connected to the network. By analyzing historical traffic congestion data from the past month, it was found that there is a bus bay on the main road. The frequent entry and exit of buses often causes temporary lane bottlenecks in the middle of the road section, merging with another area formed by narrowing road geometry. These points are areas with a history of high rates of rear-end collisions and sudden braking. These two points will be designated as congestion points. Additionally, the main road itself has intersections. and intersection As a road traffic node, it will become a congestion point. and road traffic nodes Merge and generate traffic nodes for the target road segment. .

[0052] A vehicle not connected to the network is traveling on the main road. Its location, determined through vehicle information, is approximately [distance] from the target road segment traffic node. For 200 Distance from the target road segment traffic node For 800 Distance from the target road segment traffic node 1200 Distance from the target road segment traffic node 1800 Traffic node impact parameters These are: traffic nodes of the target road segment The value is 0.4, and the traffic node of the target road segment is... The value is 0.3, and the traffic node of the target road segment is... The value is 0.2, and the traffic node of the target road segment is... The traffic node impact parameter is set to 0.1. This parameter is determined by analyzing historical traffic congestion data of the target road segment. For each traffic node, based on historical congestion records, a value reflecting the degree of interference the traffic node causes to traffic flow on the road segment is calculated. The node distance attenuation parameter λ is used to quantify the rate attenuation of the traffic node's impact with distance; its value is related to the road type and the average traffic flow speed. In this embodiment, the node distance attenuation parameter is determined through historical traffic flow trajectory data analysis. Using an empirical value of 0.005m -1 The reference distance parameter 1 Used to make function Dimensionless. The calculated static interference index for the vehicle is approximately 0.154.

[0053] To reasonably assess the impact of unconnected vehicles on local traffic flow, the boundary of the local traffic impact area is determined based on the effective detection range (250) of roadside sensing units (such as millimeter-wave radar). The boundaries are defined. Furthermore, to more accurately reflect the propagation characteristics of traffic disturbances, the local traffic impact area is divided into zones centered on the vehicle and extending 180 degrees upstream (behind). , to downstream (front) 30 Within a sliding time window of 1 minute, vehicle information for the area is collected by roadside multi-source sensors, indicating a vehicle count of 20 vehicles and a road space occupancy rate of 30%. The designed saturation vehicle count for the road segment is 40 vehicles, with a maximum road space occupancy rate of 80%, resulting in a normalized vehicle count of 0.5 and a road space occupancy rate of 0.375. A local road saturation of 0.45 is obtained through linear weighting. It should be noted that the saturation influence coefficient is used to adjust the nonlinear amplification intensity of the local road saturation's effect on traffic node suppression, adapting to the traffic flow characteristics of different roads. For example, for urban arterial roads, with dense intersections and frequent vehicle starts and stops, the disturbance propagation effect of local congestion is significant, therefore a larger coefficient is required. Value (e.g.) =0.8) to enhance the amplification effect; while for urban expressways, the traffic flow is more continuous and stable, and the disturbance under the same saturation is relatively low, so a smaller value can be set. Value (e.g.) =0.5). The saturation influence coefficient can be obtained through regression analysis of historical traffic data of the target road segment or micro-traffic simulation optimization. When the saturation influence coefficient... With a value of 0.8, the calculated vehicle spatial interference index is approximately 0.054. This final vehicle spatial interference index is lower than the vehicle static interference index (0.153). This reflects that at a moderate saturation level (0.45), the inhibitory effect of unconnected vehicles on green wave traffic is somewhat suppressed.

[0054] When the speed of an unconnected vehicle is lower than the green wave speed of the target road segment, a three-dimensional dynamic image of the unconnected vehicle within the preset time window is extracted based on the vehicle information. The motion trajectory data of the unconnected vehicle and the motion trajectory data of the vehicles in front and behind are obtained based on the three-dimensional dynamic image of the target road segment. The motion intensity of the unconnected vehicle is obtained by calculating the vehicle's motion acceleration based on the motion trajectory data. The motion flow coordination degree of the unconnected vehicle is obtained by calculating the average vehicle speed and average acceleration based on the motion trajectory data of the unconnected vehicle and the motion flow coordination degree of the vehicles in front and behind. The motion intensity and motion flow coordination degree of the unconnected vehicle are normalized and linearly weighted to obtain the vehicle behavior interference index of the unconnected vehicle.

[0055] For example, when the roadside unit detects that a van's real-time speed is 30... The current green wave guidance speed is 40 km / h lower than the speed dynamically issued by the cloud for the target road segment. The system triggers behavior analysis, extracting a continuous 3D dynamic image sequence of the van within the most recent 5-second time window. From this image sequence, the van's trajectory is precisely calculated. Simultaneously, based on the rule of being in the same lane and having a headway of 2-5 seconds, two cars—one in front and one behind—are selected as reference vehicles, and their trajectory data is extracted to form a trajectory dataset for coordination analysis. Next, the intensity of motion is quantified. Based on the van's own trajectory data, its instantaneous acceleration sequence within the entire time window is calculated. The intensity of motion is calculated by normalizing the variance of the acceleration sequence, resulting in a intensity of 0.8, indicating that the van's acceleration and deceleration behavior is very frequent and unstable. Then, based on the trajectory data of the van and the reference vehicles in front and behind, the average speed of the three vehicles within the time window is calculated. The coordination degree of the moving traffic flow is measured by the relative value of the difference between the van's average speed and the average speed of the reference vehicles, and after normalization, a coordination degree of 0.3 is obtained, indicating poor coordination between the van and the surrounding traffic flow. The vehicle behavior interference index of the unconnected van was obtained by linearly weighting and fusing the motion intensity (0.8) and the traffic flow coordination degree (0.3) to a value of 0.55. This assessment of the unconnected vehicle's driving behavior interference quantitatively evaluates the potential interference of its driving behavior to the green wave traffic flow and will serve as a key input parameter for subsequent calculation of the comprehensive interference index.

[0056] The method for calculating the comprehensive interference index of unconnected vehicles based on the vehicle behavior interference index and the vehicle spatial interference index includes: The comprehensive interference index of the unconnected vehicle is calculated based on the vehicle behavior interference index and the vehicle space interference index. The comprehensive interference index The calculation formula is: .

[0057] in, This is the vehicle behavior interference index.

[0058] For example, on a busy urban arterial road with numerous intersections and frequent congestion during the evening rush hour, a roadside unit observed an unconnected truck and calculated its vehicle behavior interference index to be 0.26, due to its relatively small speed fluctuations; the vehicle spatial interference index was 0.175, because it was parked in a bus bay only 50 meters from the downstream signalized intersection and the local traffic flow was relatively sparse. The overall interference index, calculated using the formula, was approximately 0.411. This formula, by combining the nonlinear coupling effect of behavioral and spatial interference, ensures that even with relatively stable driving behavior, the overall interference index still reaches a high level due to the truck's location in a critical position, accurately quantifying the potential interference of the unconnected vehicle to the green wave convoy passing through the target road segment during peak hours.

[0059] The method for obtaining the penetration impact index of unconnected vehicles by analyzing the comprehensive interference index includes: Based on the comprehensive interference index of all unconnected vehicles in the target road segment The total effect equivalent was calculated. The total impact equivalent The calculation formula is: .

[0060] in, The location of the unconnected vehicle is the stop line position at the downstream key signal intersection; This is the road segment disturbance attenuation coefficient; This represents the number of vehicles not connected to the network in the target road segment. The target road segment length; This is the road segment location modulation coefficient.

[0061] Obtain the actual driving distance of the connected vehicles; calculate the vehicle offset distance by comparing the actual driving distance with the theoretical distance traveled at the cloud-recommended green wave speed; and obtain the vehicle coordination degree by linearly weighting and normalizing the offset distances of all connected vehicles. According to the vehicle coordination degree With the total impact equivalent The penetration disturbance index was calculated. The penetration disturbance index The formula is: .

[0062] in, The total number of vehicles on the target road segment; This represents the theoretical maximum value of the comprehensive interference index; To prevent division by zero of constants.

[0063] For example, the comprehensive interference indices of the two unconnected vehicles in the target road segment are 0.45 and 0.3, respectively, and their distances from the stop line of the downstream key signal intersection are 300. 800 The downstream key signalized intersection refers to the next signalized intersection where the vehicle queue is expected to pass continuously in the current green wave coordination scheme; the road segment disturbance attenuation coefficient is 0.003. The disturbance attenuation coefficient of the road segment is calibrated based on the historical traffic flow fluctuation propagation characteristics of the road segment, and is used to quantify the exponential attenuation rate of vehicle impact with distance; the target road segment length is 1500. The road segment location modulation coefficient is 0.1. This coefficient is used to adjust the modulation intensity of the vehicle location distribution on the total impact equivalent. It is determined based on the analysis of historical traffic data of the road segment and reflects the degree of difference in traffic disturbance at different road locations. The calculated total impact equivalent is approximately 0.887. The actual driving distance sequence of 13 networked vehicles in the target road segment was collected within a 5-second time window. (unit: (and the cloud-recommended unified theoretical driving distance of 86.5) The average offset level is obtained by taking the linear average of the normalized offset distances of all connected vehicles. The calculation formula is: Vehicle Coordination. =1 - Average Offset Level, calculated to obtain vehicle coordination degree. Approximately 0.90; total number of vehicles on the road section is 15; theoretical maximum value of the comprehensive interference index. The theoretical maximum value of the comprehensive interference index is 1. This is the theoretical upper limit of the comprehensive interference index, used to normalize the total impact equivalent, and determined by the formula for calculating the comprehensive interference index. Since the comprehensive interference index is mapped to the [0,1) interval through the arctangent function, the theoretical maximum value can be considered as 1. In the calculation of the penetration disturbance index, it serves as a normalization benchmark to ensure the comparability of the penetration disturbance index under different scenarios; a constant is used to prevent division by zero. The value is 0.001. The penetration disturbance index is calculated. The index, approximately 0.401, is nonlinearly coupled with the vehicle coordination degree, which reflects the anti-interference resilience of connected vehicles. This results in a comprehensive penetration disturbance index. This overcomes the limitations of assessing unconnected vehicles in isolation, dynamically reflecting the traffic flow's ability to absorb and resist interference. The calculated penetration disturbance index indicates that despite the presence of unconnected vehicles, the high level of coordination among connected vehicle groups suppresses the spread of interference from unconnected vehicles, keeping the overall disturbance caused by unconnected vehicles at a moderate level. This provides crucial and reliable quantitative evidence for accurately assessing green wave attenuation and triggering corresponding scheduling strategies.

[0064] The method for obtaining the green wave attenuation index by analyzing the penetration impact index of the unconnected vehicles through green wave traffic interference includes: Divide the target road segment into sections along the direction of traffic flow. A series of continuous discrete observation road segments are identified; vehicle speed data within each discrete observation road segment is obtained based on vehicle information; a vehicle speed probability distribution for each discrete observation road segment is generated based on the vehicle speed data; and the relationships between the vehicle speed probability distributions of all adjacent discrete observation road segments are calculated based on the vehicle speed probability distribution of each discrete observation road segment. The dispersion was calculated, and the platoon dispersion of the target road segment was obtained by arithmetic averaging; based on the penetration impact index of the unconnected vehicles... Dispersion of the vehicle fleet The green wave attenuation index was calculated. The green wave attenuation index Calculation formula: .

[0065] in, This represents the vehicle dispersion parameter.

[0066] For example, a target road segment approximately 1.8 kilometers long is uniformly divided into four consecutive discrete observation segments along the traffic flow direction (sequentially labeled Discrete Observation Segment 1, Discrete Observation Segment 2, Discrete Observation Segment 3, and Discrete Observation Segment 4). The division of these discrete observation segments is based on the geometric topology and traffic characteristics of the target road segment, typically using key traffic nodes (such as signalized intersections and ramp merging points) or fixed intervals (such as every 400-500 meters) as cross-sectional markers. By setting continuous discrete observation segments, the evolution of speed distribution during the propagation of the convoy from upstream to downstream is captured, which is a core prerequisite for quantifying the spatial consistency and structural stability of the green wave band. Based on real-time vehicle information uploaded by roadside units, the vehicle speed data of all vehicles in each discrete observation segment is obtained. Subsequently, a vehicle speed probability distribution for each discrete observation segment is generated: the vehicle speed range (e.g., 20 km / h) is divided into... Up to 80 ) with 5 The interval is divided into continuous discrete intervals. The number of vehicle speeds falling into each interval within each discrete observation segment is counted, and the frequency (number of vehicles / total number of vehicles in the discrete observation segment) is calculated to construct the probability distribution of discrete observation segment 1. Probability distribution of discrete observation segment 2 Probability distribution of discrete observation segment 3 And the probability distribution of discrete observation segment 4 Next, the distribution between cross-section 1 and cross-section 2 is calculated. Divergence, the process of which involves first calculating the probability distribution. With probability distribution Arithmetic mean distribution Then calculate the probability distribution separately. Arithmetic mean distribution of divergence ,as well as and of divergence ( || ),but Similarly, calculate in sequence. and Calculations yielded It is 0.12. It is 0.18. The value is 0.22. Then, for all adjacent observation segments, the dispersion is... The dispersion of the vehicle fleet on the target road segment is obtained by arithmetically averaging the divergence. The value is 0.173. This value indicates that the difference in convoy speed distribution gradually increases from upstream to downstream of the target road segment, showing a clear dispersion trend overall. The convoy dispersion of the target road segment reflects the dynamic attenuation of the coordination of green wave convoys during traffic movement and is a key state indicator for assessing the ability to maintain green wave traffic flow.

[0067] Meanwhile, the penetration impact index of unconnected vehicles The value is 0.48. (Fleet dispersion parameter) The vehicle dispersion parameter is 0.6. This is used to adjust the amplification intensity of the green wave attenuation caused by the vehicle dispersion. Its value is calibrated by analyzing the correlation between the green wave effect and the vehicle dispersion in historical traffic data; based on the green wave attenuation index... The green wave attenuation index is calculated using the formula. It is approximately 0.52. The green wave attenuation index combines the systemic disturbances caused by the infiltration of unconnected vehicles with the structural dispersion exhibited by the vehicle fleet itself during its movement, reflecting the comprehensive attenuation risk faced by the ideal green wave effect under the current mixed traffic flow conditions.

[0068] The method for obtaining road saturation by assessing road capacity using the green wave traffic attenuation index includes: Obtain the theoretical traffic capacity of the target road segment Based on the theoretical traffic capacity of the target road segment and Green Wave Attenuation Index Calculate the effective throughput capacity ; By analyzing effective traffic capacity Road saturation was calculated. ;in, This is for real-time traffic flow.

[0069] For example, the theoretical traffic capacity of the target road section as a two-way four-lane arterial road. The capacity is 5800 vehicles / hour; to maintain consistency with the 5-second calculation cycle, the theoretical capacity is converted to a cycle capacity of approximately 8.06 vehicles / cycle; Green wave attenuation index. The value is 0.8; the real-time number of vehicles passing through the roadside unit in the most recent 5-second calculation cycle is 5.6 (this value can be obtained by averaging or filtering over multiple cycles to smooth single-cycle fluctuations). The effective capacity within the cycle is obtained by combining the theoretical capacity within the cycle with the green wave attenuation index. Approximately 6.45 vehicles per cycle; thus, the road saturation is calculated and output. The value is approximately 0.87. This result indicates that, under the current mixed traffic flow conditions, the traffic demand on the road segment has reached 87% of its effective capacity, approaching saturation. This road saturation level... Updated at the end of each 5-second calculation cycle, this serves as the direct decision-making basis for the cloud-based scheduling system to invoke different levels of green wave coordination strategies, achieving second-level precise perception and dynamic response to road network load. It should be noted that to avoid system instability caused by frequent strategy switching, the cloud-based scheduling system continuously monitors road saturation and evaluates it based on road saturation over multiple consecutive calculation cycles. A minimum strategy evaluation window is set (e.g., three consecutive calculation cycles, totaling 15 seconds), and only when the road saturation exceeds a preset proportion within this window... The corresponding green wave coordination strategy is only triggered and updated when the road saturation reaches the road saturation level threshold for each cycle. At the same time, a minimum execution duration is set for the activated strategy (e.g., 6 calculation cycles, a total of 30 seconds). During this period, even if the road saturation fluctuates, the current strategy remains unchanged to ensure the continuity of traffic guidance and driver adaptability.

[0070] The method for invoking the corresponding green wave coordination strategy based on the road saturation includes: Road saturation levels are defined based on road saturation, including first road saturation, second road saturation, and third road saturation; the corresponding green wave coordination strategy is invoked based on the road saturation level.

[0071] For example, when The current road saturation level is at its highest. At this point, the first green wave coordination strategy is invoked: basic guidance commands are output to control the roadside. The display screen publishes the basic green wave recommended speed at a low frequency (e.g., every 30 seconds). The existing signal timing and cloud-based green wave scheme are maintained without any active intervention.

[0072] when The second road saturation level is reached. At this point, the second green wave coordination strategy is invoked: a collaborative buffering command is output, displaying the recommended green wave speed and recommended lane markings. Roadside guidance information is upgraded to lane-level dynamic guidance, with the update frequency increased to a medium frequency (e.g., 15 seconds / time). For vehicle speed deviations exceeding a preset threshold (based on ±10 of the recommended green wave speed), [further action is taken]. For unconnected vehicles, localized audio-visual alerts are triggered at their location. It should be noted that these localized audio-visual alerts are non-intrusive attention reminders sent via roadside directional acoustic devices, rather than strong audio-visual interference with the public environment. Simultaneously, personalized speed adjustment suggestions are pushed to connected vehicles within the disturbance area to smooth out traffic fluctuations.

[0073] when The third road saturation level is reached. At this point, the third green wave coordination strategy is invoked: a high-frequency (e.g., 5-8 seconds / time) and high-contrast dynamic emphasis mode is used to release comprehensive guidance information including optimal green wave speed, signal countdown, and alternative lanes. Coordinated avoidance instructions are sent to related network vehicles, guiding them to adjust lanes or distances to create passage windows for key unconnected vehicles. Dynamic signal priority is triggered, and based on the real-time trajectories of unconnected vehicles, real-time adjustments are made to downstream intersection traffic lights, such as extending green lights or prematurely ending red lights. It should be noted that when… When the value exceeds 1.2, green wave traffic fails, and a global congestion mitigation strategy is activated, with simplified instructions such as "follow the vehicle in front" displayed on roadside units. Green wave optimization is suspended, prioritizing intersection clearance and convoy dispersal; cross-segment coordination is initiated, restricting inbound traffic flow through upstream intersection signal control. Through this tiered response mechanism—from information prompts to vehicle coordination and then to proactive signal control—the maximization of green wave traffic benefits and the overall stability of the road network are ensured under different traffic loads.

[0074] Example 2: Based on the same inventive concept, such as Figure 2 As shown, this embodiment also provides a road network digital cloud scheduling system based on vehicle-road cooperation, including: The vehicle recognition module is used to collect traffic flow data within the target road segment through roadside multi-source sensors; generate a three-dimensional dynamic image of the target road segment based on the traffic flow data, and upload it to the roadside unit; the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment.

[0075] The penetration impact index calculation module is used to obtain the vehicle space interference index and vehicle behavior interference index of the non-networked vehicle by analyzing the vehicle information; to calculate the comprehensive interference index of the non-networked vehicle based on the vehicle behavior interference index and the vehicle space interference index; and to obtain the penetration impact index of the non-networked vehicle by analyzing the comprehensive interference index.

[0076] The road saturation calculation module is used to obtain the green wave traffic attenuation index by analyzing the penetration impact index of the non-networked vehicles through green wave traffic interference; and to obtain the road saturation by evaluating the road traffic capacity using the green wave traffic attenuation index.

[0077] The green wave traffic strategy invocation module is used to invoke the corresponding green wave coordination strategy based on the road saturation.

[0078] It should be noted that the specific ways in which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0079] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A digital cloud scheduling method for road networks based on vehicle-road cooperation, characterized in that, The method includes: Traffic flow data within the target road segment is collected using roadside multi-source sensors; a three-dimensional dynamic image of the target road segment is generated based on the traffic flow data and uploaded to the roadside unit; the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment. By analyzing the vehicle information, the vehicle space interference index and vehicle behavior interference index of the unconnected vehicles are obtained; based on the vehicle behavior interference index and the vehicle space interference index, the comprehensive interference index of the unconnected vehicles is calculated; by analyzing the comprehensive interference index, the penetration impact index of the unconnected vehicles is obtained. The penetration impact index of the unconnected vehicles is used to obtain the green wave traffic attenuation index through green wave traffic interference analysis; the green wave traffic attenuation index is used to obtain the road saturation through road capacity assessment. The corresponding green wave coordination strategy is invoked based on the road saturation.

2. The road network digital cloud scheduling method based on vehicle-road cooperation according to claim 1, characterized in that, The method by which the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment includes: Vehicle appearance features are obtained by recognizing three-dimensional dynamic images of the target road segment; the confidence level of the connected vehicle is obtained by comparing the vehicle appearance features with the connected vehicle feature database; vehicles with a confidence level not lower than a preset connected vehicle confidence level threshold are classified as connected vehicles; vehicles with a confidence level lower than the preset connected vehicle confidence level threshold are classified as vehicles to be certified. The roadside unit broadcasts a detection signal to the vehicle to be authenticated; vehicles that respond to the detection signal are identified as connected vehicles; vehicles that do not respond to the detection signal are identified as non-connected vehicles; and the connected and non-connected vehicles are remapped onto the three-dimensional dynamic image of the target road segment to obtain vehicle information.

3. The method for digital cloud scheduling of road networks based on vehicle-road cooperation according to claim 2, characterized in that, The method for obtaining the vehicle spatial interference index and vehicle behavioral interference index of unconnected vehicles by analyzing the vehicle information includes: Obtain historical traffic congestion data for the target road segment; analyze the historical congestion data of the target road segment to obtain the congestion nodes of the target road segment; merge the congestion nodes of the target road segment and the road traffic nodes of the target road segment to generate the traffic nodes of the target road segment; The location of the unconnected vehicle is obtained based on the vehicle information; traffic nodes on the target road segment within the preset vehicle influence range are identified based on the location of the unconnected vehicle to obtain the traffic nodes causing vehicle interference; and the static interference index of the unconnected vehicle is calculated based on the traffic nodes causing vehicle interference. The vehicle static interference index The calculation formula is: ； in, For parameters affecting traffic nodes; This refers to the node distance attenuation parameter. This refers to the distance from unconnected vehicles to the traffic nodes on the target road segment; The total number of traffic nodes in the target road segment. Indicates the first One traffic node; For reference distance parameters; A local traffic impact area is selected based on the preset vehicle impact range and the location of the unconnected vehicles; within a sliding time window, vehicle information in the local traffic impact area is collected using roadside multi-source sensors; image analysis is performed based on the vehicle information in the local traffic impact area to obtain the number of vehicles and the vehicle road space occupancy rate in the local traffic impact area; a local road saturation rate in the local traffic impact area is obtained by linear weighting based on the number of vehicles and the vehicle road space occupancy rate; and a vehicle space interference index is calculated based on the vehicle static interference index and the local road saturation rate. The vehicle space interference index The calculation formula is: ； in, This is the saturation influence coefficient; This refers to local road saturation. When the speed of an unconnected vehicle is lower than the green wave speed of the target road segment, a three-dimensional dynamic image of the unconnected vehicle within the preset time window is extracted based on the vehicle information. The motion trajectory data of the unconnected vehicle and the motion trajectory data of the vehicles in front and behind are obtained based on the three-dimensional dynamic image of the target road segment. The motion intensity of the unconnected vehicle is obtained by calculating the vehicle's motion acceleration based on the motion trajectory data. The motion flow coordination degree of the unconnected vehicle is obtained by calculating the average vehicle speed and average acceleration based on the motion trajectory data of the unconnected vehicle and the motion flow coordination degree of the vehicles in front and behind. The motion intensity and motion flow coordination degree of the unconnected vehicle are normalized and linearly weighted to obtain the vehicle behavior interference index of the unconnected vehicle.

4. The road network digital cloud scheduling method based on vehicle-road cooperation according to claim 3, characterized in that, The method for calculating the comprehensive interference index of unconnected vehicles based on the vehicle behavior interference index and the vehicle spatial interference index includes: The comprehensive interference index of the unconnected vehicle is calculated based on the vehicle behavior interference index and the vehicle space interference index. The comprehensive interference index The calculation formula is: ； in, This is the vehicle behavior interference index.

5. A road network digital cloud scheduling method based on vehicle-road cooperation according to claim 4, characterized in that, The method for obtaining the penetration impact index of unconnected vehicles by analyzing the comprehensive interference index includes: Based on the comprehensive interference index of all unconnected vehicles in the target road segment The total effect equivalent was calculated. The total impact equivalent The calculation formula is: ； in, The location of the unconnected vehicle is the stop line position at the downstream key signal intersection; This is the road segment disturbance attenuation coefficient; This represents the number of vehicles not connected to the network in the target road segment. The target road segment length; This refers to the road segment location modulation coefficient; Obtain the actual driving distance of the connected vehicles; calculate the vehicle offset distance by comparing the actual driving distance with the theoretical distance traveled at the cloud-recommended green wave speed; and obtain the vehicle coordination degree by linearly weighting and normalizing the offset distances of all connected vehicles. According to the vehicle coordination degree With the total impact equivalent The penetration disturbance index was calculated. The penetration disturbance index The formula is: ； in, The total number of vehicles on the target road segment; This represents the theoretical maximum value of the comprehensive interference index; To prevent division by zero of constants.

6. The method for digital cloud scheduling of road networks based on vehicle-road cooperation according to claim 5, characterized in that, The method for obtaining the green wave attenuation index by analyzing the penetration impact index of the unconnected vehicles through green wave traffic interference includes: Divide the target road segment into sections along the direction of traffic flow. A series of continuous discrete observation road segments are analyzed; vehicle speed data within the discrete observation road segments are obtained based on vehicle information; a vehicle speed probability distribution for the discrete observation road segments is generated based on the vehicle speed data; and the relationships between the vehicle speed probability distributions of all adjacent discrete observation road segments are calculated based on the vehicle speed probability distribution of the discrete observation road segments. The dispersion was calculated, and the platoon dispersion of the target road segment was obtained by arithmetic averaging; based on the penetration impact index of the unconnected vehicles... Dispersion of the fleet The green wave attenuation index was calculated. The green wave attenuation index Calculation formula: ； in, This represents the vehicle dispersion parameter.

7. A road network digital cloud scheduling method based on vehicle-road cooperation according to claim 6, characterized in that, The method for obtaining road saturation by assessing road capacity using the green wave traffic attenuation index includes: Obtain the theoretical traffic capacity of the target road segment Based on the theoretical traffic capacity of the target road segment Green wave attenuation index Calculate the effective throughput capacity ; By analyzing effective traffic capacity Road saturation was calculated. ;in, This is for real-time traffic flow.

8. A road network digital cloud scheduling method based on vehicle-road cooperation according to claim 7, characterized in that, The method for invoking the corresponding green wave coordination strategy based on the road saturation includes: Road saturation levels are defined based on road saturation, including first road saturation, second road saturation, and third road saturation; the corresponding green wave coordination strategy is invoked based on the road saturation level.

9. A digital cloud scheduling system for road networks based on vehicle-road cooperation, characterized in that, The system includes: The vehicle recognition module is used to collect traffic flow data within a target road segment through roadside multi-source sensors; generate a three-dimensional dynamic image of the target road segment based on the traffic flow data, and upload it to the roadside unit; the roadside unit obtains vehicle information based on the three-dimensional dynamic image of the target road segment. The penetration impact index calculation module is used to obtain the vehicle space interference index and vehicle behavior interference index of the non-networked vehicle by analyzing the vehicle information; to calculate the comprehensive interference index of the non-networked vehicle based on the vehicle behavior interference index and the vehicle space interference index; and to obtain the penetration impact index of the non-networked vehicle by analyzing the comprehensive interference index. The road saturation calculation module is used to obtain the green wave traffic attenuation index from the penetration impact index of the unconnected vehicles through green wave traffic interference analysis; and to obtain the road saturation from the green wave attenuation index through road traffic capacity assessment. The green wave traffic strategy invocation module is used to invoke the corresponding green wave coordination strategy based on the road saturation.

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