52 results about "Traffic delay" patented technology

The invention discloses a road section delay loss estimation method based on ponding depth and traffic flow probability density. The method comprises the following steps: S1, establishing a relation model of the ponding depth and the distance of a road section; s2, drawing up a probability density function of the maximum ponding depth based on the maximum ponding depth of the road section acquired by forecasting the historical record of the same rainfall; s3, drawing up a probability density function of the road section traffic flow based on the road section traffic flow collected by forecasting the same rainfall historical record; s4, calculating the free flow speed, calculating the average vehicle speed according to the road section traffic flow, and calculating the average passing time of the vehicle under the road section water accumulation condition; and S5, calculating the average delay time of the vehicle in the ponding road section, and estimating the total traffic delay loss amount of the road section according to the average delay time and the passenger number probability distribution. According to the method, the scientific delay loss estimation model is established by comprehensively based on the uncertainty of the ponding depth, the traffic flow and the number of passengers, and an important basis is provided for prevention and treatment of traffic congestion caused by urban waterlogging ponding.

The invention discloses a global traffic signal control method based on a regional context enhanced large language model, and the method comprises the steps: dividing a target region into K traffic signal lamp control regions according to a traffic network structure of the target region; global real-time traffic information of the target area is collected and converted into global traffic text information; and inputting the global traffic text information into the large language model after LoRA fine tuning, and outputting a global traffic signal lamp control strategy of the target area, namely a traffic signal lamp phase of each intersection. In the fine tuning process of the large language model, an optimal phase decision is generated by using a reinforcement learning agent, a decision result is converted into text data, a region-level optimal phase decision data set is constructed according to an adjacent region division strategy, fine tuning is performed on the model by using the data set, region loss is calculated, and the large language model is optimized through the loss. According to the invention, optimization of global traffic signal lamp control can be realized, the overall traffic delay is reduced, and the traffic efficiency is improved.

The invention discloses a BIM-based highway construction period traffic guide and change optimization system, and relates to the technical field of road traffic management. According to the BIM-based highway construction period traffic guide and change optimization system, traffic management in the construction period is dynamically adjusted in real time through space-time modeling, data fusion and optimization decision. The system generates a road network space-time model, matches vehicle trajectory data with lane space data, counts the number and speed of vehicles, and optimizes traffic flow. According to the optimization result, the system outputs the tailgating length, the number of lanes and the speed limit value and generates a corresponding control instruction and a speed limit issuing instruction, traffic management of the highway construction period is accurately optimized through space-time modeling and real-time data fusion based on BIM, the system can obtain vehicle track data in real time, the lanes and the speed limit strategy can be dynamically adjusted, and the speed limit efficiency is improved. Traffic delay in the construction period is minimized, and traffic safety and smoothness of a construction section are effectively guaranteed by generating an accurate lane control and speed limiting instruction.

The invention discloses an electric vehicle charging demand prediction method considering a charging management strategy, belongs to the technical field of electric vehicle charging demand prediction, and solves the aggregation effect of electric vehicle charging loads and the uncertainty of high coupling between the charging loads. And the safe and stable operation of the power grid and the reliability of power supply are severely influenced by the peak load. The travel space and time characteristics of an electric vehicle user are modeled by constructing an electric vehicle travel chain model, so that the travel behavior of the electric vehicle user is analyzed; constructing an urban traffic network and a delay model, assigning an edge weight of a traffic network topology by using road traffic delay, and constructing a minimum road delay time objective function; constructing a road-power grid coupling node aggregation model; constructing an electric vehicle charging load model, and simulating electric vehicle user charging demand space-time distribution by adopting Monte Carlo; the method can achieve the accurate prediction of the charging demands of the electric vehicle users, and optimizes the planning and operation of the power distribution network.

The invention provides a flight delay prediction method and system, computer equipment and a storage medium, and belongs to the technical field of traffic delay prediction.The method comprises the steps that firstly, a bacterial foraging algorithm (BFA) is optimized through a genetic algorithm (GA) so as to improve the global search ability and convergence speed of the algorithm; secondly, optimizing the structure and parameters of a deep neural network DNN by using the optimized BFA algorithm, and constructing a deep neural network model based on double hidden layers; according to the model, an attention mechanism and a residual block are introduced, so that the nonlinear mapping capability and the generalization capability are improved, and model overfitting is effectively prevented. In addition, the flight data and the weather condition data are combined, so that the prediction accuracy is further improved. The performance of the model is evaluated through multiple indexes, the result shows that the method can effectively solve the problem of DNN structure and parameter selection, and the training efficiency and generalization ability of the model are remarkably improved.

The invention relates to the technical field of traffic signal control, and provides a traffic accident surrounding traffic signal response method and system, and the method comprises the steps: obtaining main road traffic flow parameters and secondary road exit vehicle existence information; judging whether the trunk road is in a trunk road congestion state or not; judging whether the secondary road is in a secondary road exit abnormal retention state or not; identifying a key exit passive blocking event; a control instruction is sent to an upstream intersection of the target intersection corresponding to the key exit passive blocking event, so that the upstream intersection of the target intersection controls a main road direction signal lamp to be red, and a traffic gap is created; turning the signal lamp in the main road direction of the target intersection into green light, and emptying vehicles in the main road area in front of the key exit of the secondary road; after it is confirmed that the vehicle is emptied, the vehicle at the key exit is released; and after the release is completed, judging that the emergency condition is relieved and recovering traffic signal control. The method has the advantage of avoiding traffic delay and potential public safety risks.

The invention belongs to the field of intelligent traffic, and relates to a multi-intersection IVCPS cooperative control test case generalization generation method, which comprises the following steps: S1, constructing a mixed traffic flow fundamental diagram model, and deducing the relationship between traffic flow density and flow under different permeability by combining IDM and CACC models; s2, expanding an IVCPS scene body, and designing a multi-intersection road network scene; s3, adopting an upper-layer and lower-layer distributed cooperative control architecture, wherein the upper layer dynamically adjusts signal timing based on traffic pressure information, and the lower layer provides an optimal path and driving speed suggestion for the CAV; s4, constructing a scene parameter extraction module, and extracting a control algorithm, a vehicle model ratio, traffic flow and CAV permeability; S5, constructing a key calculation module, and calculating a parameter key index; and S6, constructing a test case generation module, and generating a combined test case set by adopting an AETG-S algorithm. According to the invention, the traffic efficiency of a multi-intersection road network can be effectively improved, traffic delay and energy consumption are reduced, and a scientific basis is provided for testing and optimization of an intelligent traffic system.

The present invention relates to the technical field of traffic control, and specifically to an intelligent traffic signal parameter configuration method and system, which includes the following steps: obtaining vehicle passing behaviors, extracting the lateral offset and longitudinal delay time series before and after the turning action, identifying the signal behavior delay section, extracting the waiting duration and release completion time point of the vehicle at the end of the lane, calculating the time gap, detecting the longitudinal distance of the queued vehicles in the real-time cycle, updating the control cycle of the end section, and obtaining the regulation result of the green light duration at the end section. In the present invention, through the detailed identification and analysis of vehicle behaviors and position characteristics, the signal scheduling is optimized, the signal light cycle is dynamically adjusted through real-time data, traffic congestion caused by untimely response is reduced, and for the calculation of the waiting duration and release time of the vehicle at the end, a variable green light time period is allocated for each lane, so that the green light time at the end section can be flexibly regulated according to actual needs, further reducing traffic delays and improving road utilization efficiency.

The invention relates to the technical field of traffic safety analysis, provides a driving delay degree multi-dimensional feature analysis system of an urban traffic accident site, and aims at extracting road dimensional features when an accident occurs based on driving delay data of the urban traffic accident site, and providing a targeted data filling scheme in combination with the characteristics of missing features. And according to statistical distribution, redundant feature screening is completed, and structured traffic modeling data of four dimension features of weather, road, POI and time is constructed. The traffic delay degree caused by urban traffic accidents is systematically modeled and analyzed from the three aspects of individual heterogeneity, group transferability and group heterogeneity, and the influence effect of significant variables in multi-dimensional influence factors is explored, so that a traffic control department is helped to take preventive measures and emergency response countermeasures; and a theoretical basis is provided for design optimization and management decision of the road, so that the influence caused by traffic accidents is relieved, the operation risk level of an accident road section is improved, secondary accidents are avoided, and traffic pollution is reduced.

The invention provides a method, a device and equipment for evaluating traffic toughness and delay cost under rainfall and waterlogging, and a storage medium. The method comprises the following steps: acquiring precipitation data of a target area; according to the precipitation data, runoff production and confluence simulation is carried out, and the ponding depth is obtained; according to the ponding depth and a speed-water depth mapping model, a safe driving speed corresponding to the ponding depth is obtained; the speed-water depth mapping model determines a safe driving speed on the premise of ensuring the vehicle control stability, and the safe driving speed is obtained according to the road waterlogging depth and a scaling coefficient; obtaining a travel demand matrix according to the population spatial distribution condition of the target area; determining traffic delay time according to the safe driving speed and a travel demand matrix; and obtaining the traffic delay cost according to the traffic delay time. According to the invention, the efficiency and accuracy of traffic toughness and delay cost evaluation under rainfall and waterlogging can be improved.

The invention provides an intersection left-turn traffic organization scheme recommendation method, and relates to the technical field of traffic organizations, and the recommendation method specifically comprises the steps: data loading and preprocessing, construction of a prediction model, recommendation of an optimization logic scheme, intelligent recommendation of a suitable intersection left-turn traffic organization optimization scheme, construction of an interactive Web interface, and realization of interaction with a user. The predicted delay time and the recommended optimization scheme are clearly presented to the user; through integration of data processing, a machine learning model and a user interface, prediction of traffic delay time and intelligent recommendation of an intersection left-turn traffic organization scheme are realized, and the underlying logic covers data-driven prediction and rule-Based scheme recommendation, so that comprehensive decision support is provided for traffic management and planning, and the traffic delay time and the intersection left-turn traffic organization scheme are recommended intelligently. A user can input data through a visual Web interface to obtain an accurate prediction result and a reasonable optimization suggestion, so that the operation efficiency and safety of a traffic system are effectively improved.

The invention relates to a machine learning-based pavement performance prediction and maintenance decision optimization method, which comprises the following steps of: quantifying carbon emission cost, traffic delay cost and economic cost of asphalt pavement maintenance, and obtaining pavement life prediction age limit, asphalt pavement service data and environmental parameters of a road section to be detected; and through the asphalt pavement service performance prediction model and the asphalt pavement maintenance decision model, obtaining the optimal maintenance measure of the road section and the benefit-cost ratio of considering the carbon emission and the delay cost, cyclically carrying out the above process, and updating the maintenance measure. Compared with the prior art, the method is suitable for decision analysis of the asphalt pavement maintenance problem with a multi-source heterogeneous asphalt pavement data set needing to consider carbon emission and delay cost, a scientific pavement maintenance decision can be quickly generated, the maintenance efficiency and the resource utilization rate are improved, and traffic safety and smoothness are guaranteed.

The embodiment of the application provides a traffic congestion discrimination method and device based on map coordinates and vehicle big data, fully utilizes high-precision coordinate information provided by modern map services and massive vehicle driving data, and realizes rapid and accurate discrimination of a traffic congestion state through comprehensive analysis of multidimensional data such as vehicle driving directions and speeds, thereby providing timely and reliable decision support for a traffic management department, optimizing traffic flow management, improving road passing efficiency, and reducing traffic delay and environmental pollution. The method innovatively combines map geometric analysis and vehicle big data processing, overcomes many limitations of traditional methods, and has remarkable application value and popularization prospect.

The invention relates to the field of intelligent traffic signal control, and discloses a lightweight intersection traffic signal coordination control method, which comprises the following steps that: a central control module generates a coordination scheme based on traffic flow data, decouples steering pairs, and determines the optimal phase difference of each intersection; the edge signal control unit receives the coordination instruction and dynamically adjusts signal lamp timing; in the bus priority mode, the system calculates green wave countdown by monitoring the position of a bus in real time, and adjusts the green light time to guarantee the priority passage of the bus; when no coordination request exists, the system automatically enters a single-intersection induction control mode, the green signal ratio and phase conversion are adaptively adjusted according to the real-time flow, and the signal control flexibility is improved. The multi-source data fusion and intelligent optimization algorithm is combined, the combination of regional signal coordination and single-point adaptive control is realized, the traffic delay is effectively reduced, and the road traffic capacity and the traffic management intelligent level are improved.

The application relates to the technical field of traffic signal control, and provides a traffic accident surrounding traffic signal response method and system, which comprises the following steps: acquiring main road traffic flow parameters and secondary road exit vehicle existence information; judging whether the main road is in a main road congestion state; judging whether the secondary road is in a secondary road exit abnormal stagnation state; identifying a key exit passive obstruction event; sending a control instruction to a crossroad upstream of a target intersection corresponding to the key exit passive obstruction event, so that the crossroad upstream of the target intersection controls a main road direction signal lamp to be red to create a traffic gap; making the main road direction signal lamp of the target intersection turn to green to empty vehicles in a main road area in front of the key exit of the secondary road; after confirming that the emptying is completed, releasing the vehicles of the key exit; and after the releasing is completed, judging that an emergency condition is removed and traffic signal control is restored. The application has the advantages of avoiding traffic delay and potential public safety risks.

The present invention discloses a flying car road traffic system, comprising a virtual lane, a parking deck, a functional parking area, and a rooftop service area. The virtual lane is a flight space and path constructed for flying cars; the parking deck is arranged in layers within a high-rise building according to the height of the virtual lane, and a transfer elevator is provided within the high-rise building to transfer pedestrians and flying cars to different floors; the parking deck functional area includes a flying car landing area that extends outside the high-rise building for takeoff and landing; and the rooftop service area. The present invention focuses on planning spatial traffic paths and fully utilizing urban airspace resources. The airspace above urban streets is divided into multiple layers according to height. At lane intersections, flying cars can migrate to other layers by turning left or right, ensuring straight flight at intersections. Traffic control is not required, and vehicles flow naturally throughout the entire process, reducing traffic delays. The height advantage of high-rise buildings is also utilized to establish service facilities such as parking decks, further improving the efficiency of flying car travel.

The present application relates to the technical field of intelligent transportation, in particular to a method and system for intelligent judgment of low-altitude parking violation target based on deep learning. It includes: traffic flow field anomaly diagnosis step, unmanned aerial vehicle collects road video data, generates vehicle motion vector field, and autonomously determines traffic obstruction area; obstruction root space-time backtracking step, through the attention network of stop state propagation graph and reverse propagation graph, the initial stationary vehicle as the obstruction root is locked; parking violation influence quantification and comprehensive judgment step, the additional traffic delay quantification index is calculated and whether parking violation is judged comprehensively according to the spatial relationship; dynamic causal evidence chain generation step, encapsulating the structured evidence file containing traffic flow evolution process, space-time backtracking path and quantification index. The present application realizes the paradigm upgrade from "passive single-point detection" to "active obstruction source tracing", greatly improves the intelligent level and disposal efficiency of low-altitude traffic law enforcement.

The application discloses a road section delay loss estimation method based on waterlogging depth and traffic flow probability density, comprising the following steps: S1, establishing a road section waterlogging depth and distance relationship model; S2, formulating a maximum waterlogging depth probability density function based on the maximum waterlogging depth of the road section collected from the historical records of the same rainfall forecast; S3, formulating a road section traffic flow probability density function based on the road section traffic flow collected from the historical records of the same rainfall forecast; S4, calculating a free flow speed, calculating a vehicle average speed according to the road section traffic flow, and calculating a vehicle average passing time under the road section waterlogging condition; S5, calculating a vehicle average delay time on the waterlogging road section, and estimating a road section traffic delay loss total amount according to the average delay time and the passenger number probability distribution. The application establishes a scientific delay loss estimation model by comprehensively considering the uncertainty of the waterlogging depth, the traffic flow and the passenger number, and provides an important basis for preventing and treating the traffic congestion caused by the urban waterlogging.

This application relates to a method, device, equipment, and medium for intelligent control of highway traffic flow. The method includes: acquiring real-time and historical traffic flow data and topology of the road network; generating dynamic road network map data containing node and edge association features through spatiotemporal feature fusion; inputting the data into a pre-trained spatiotemporal fusion prediction model to obtain preliminary prediction results including road segment flow, temporal sequence, spatial distribution, and key road segments; dynamically weighting key road segments and adjusting their spatiotemporal impact weights on the surrounding road network to generate future spatiotemporal distribution prediction results for road network flow; designating key road segments with flow exceeding the dynamic capacity threshold as independent intelligent agents; calculating traffic delay values ​​and road network load balance, and linearly weighting and fusing them to generate a joint reward value; and optimizing strategies through a multi-agent learning algorithm to generate a dynamic traffic collaborative control instruction set. This method can adapt to dynamic changes in traffic flow in real time, achieving accurate prediction and efficient control, improving road network operation efficiency and travel experience.

The invention discloses a smart park intelligent dynamic road management system, and relates to the technical field of smart park road management, and the system comprises a collection and feedback module which collects original data, constructs a traffic track matrix to carry out road flow prediction, obtains a flow prediction value, carries out the real-time feedback, and obtains a final prediction value; the construction and detection module is used for constructing a simulation model, recognizing a road bottleneck in combination with the final predicted value, and then calculating the queuing amount for optimization detection to obtain a detection result; the clustering and optimizing module is used for carrying out trajectory clustering based on the detection result and then optimizing total delay in combination with a BPR function and a Frank-Wolfe algorithm to obtain a final optimization result; according to the method, the flow distribution proportion of each road can be dynamically adjusted, so that the overall traffic delay of the park roads is reduced on the premise of meeting the traffic demand.

The invention provides a traffic delay integrated prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring user daily traffic time sequence data and user information feature data; performing graph structured processing on the daily flow time series data to obtain graph structured data; predicting flow prediction time sequence data of a future time period based on the graph structure data; and on the basis of the traffic prediction time sequence data of the future time period and the user information feature data, predicting whether the traffic of the user at the end of month is delayed or not. According to the method, graph structuring is carried out on the daily flow time sequence data of the user, the flow prediction time sequence data of the future time period is predicted based on the obtained graph structure data, and the flow prediction time sequence data is combined with the user information feature data to judge and predict whether the flow at the end of the month of the user is delayed or not. Namely, the dynamic characteristics and the static characteristics in the traffic data are predicted and judged, so that the accuracy of traffic delay prediction is improved.

The application relates to the field of intelligent traffic signal control, and discloses a lightweight intersection traffic signal coordination control method, which comprises the following steps: a center control module generates a coordination scheme based on traffic flow data, and performs decoupling processing on a turning pair to determine the optimal phase difference of each intersection; an edge signal control unit receives a coordination instruction and dynamically adjusts signal lamp timing; in a bus priority mode, the system calculates green wave countdown by monitoring the position of a bus in real time, adjusts the green light time to ensure bus priority, and automatically enters a single intersection induction control mode when there is no coordination request, and adaptively adjusts the green ratio and phase conversion according to real-time flow, thereby improving signal control flexibility. The application combines multi-source data fusion and intelligent optimization algorithms, realizes the combination of regional signal coordination and single-point adaptive control, effectively reduces traffic delay, and improves road traffic capacity and the intelligent level of traffic management.

The application discloses a kind of urban traffic planning multidimensional fusion method and system, it is related to urban traffic planning technical field, using the data acquisition module of traffic planning system, real-time collection current traffic planning related traffic data and historical traffic data, using evaluation calculation module, the traffic delay seconds JY that there is correlation influence between each other is calculated in turn output, delay seconds DM, the comprehensive influence value CH of traffic planning to traffic accident, using visual analysis module, the trend of online linear graph of traffic planning comprehensive influence value CH to traffic accident is evaluated, the current multidimensional fusion traffic status is fed back by linear graph in the application, and then the traffic delay condition of different time periods, different regions, vehicle delay condition and the change trend of traffic accident frequency are directly seen visually, according to this, urban traffic planning scheme can be more comprehensively evaluated and optimized, the accuracy and effectiveness of planning scheme are improved, and strong support is provided for the sustainable development of urban traffic.

This invention relates to a method for identifying key vehicles based on roadside group trajectories and intervening through navigation, comprising the following steps: S1, acquiring real-time traffic information and vehicle motion parameters of the road segment through radar trajectory data; S2, setting a threshold for the interaction and influence distance between vehicles to determine the impact of surrounding vehicles; S3, obtaining different vehicle group types according to the vehicle group identification model process; S4, completing the classification and division of vehicles across the entire road area, verifying the vehicle composition of each vehicle group, and performing vehicle group fusion; S5, constructing an evaluation index system, comparing the deviation of vehicle motion parameters within the vehicle group from the overall motion parameters, and identifying key vehicles based on preset thresholds; S6, verifying the navigation binding status of key vehicles and monitoring and correcting their behavior. This invention can identify different vehicle group types in traffic flow through roadside trajectories, identify key vehicles in the vehicle group that affect traffic safety or efficiency, and manage them through real-time navigation alerts, effectively improving driving safety or efficiency and reducing traffic accident rates and traffic delays.

This invention relates to a multi-level monitoring and evaluation method for green wave traffic on arterial roads. It solves the problem of poor coordinated control of green wave traffic on urban arterial roads in existing technologies. It includes the following steps: S1, acquiring traffic operation data and basic road network information data for urban green wave arterial roads; S2, constructing a multi-dimensional indicator system; S3, calculating the results based on the multi-dimensional indicator system; S4, analyzing problems with the green wave traffic on arterial roads based on the calculation results, evaluating the operational efficiency level, and coordinating control parameters; S5, outputting the coordinated control parameters of the green wave traffic on arterial roads as signals and transmitting them to the traffic signal controllers at intersections, thereby controlling and adjusting the traffic lights at the corresponding intersections. The advantages of this invention are: constructing a multi-dimensional indicator system at three levels—micro, meso, and macro—and calculating, monitoring, and evaluating the operational status of green wave traffic on arterial roads, promptly identifying problems and proposing optimization solutions, thereby improving the operational efficiency of green wave traffic on arterial roads, reducing traffic delays and parking rates, and optimizing road traffic.

The embodiment of the invention provides a logistics fleet intelligent scheduling method based on Beidou high-precision positioning. The logistics fleet intelligent scheduling method comprises the steps of obtaining continuous high-precision track point data of logistics vehicles; recognizing a congested road section and a potential passing time delay area based on the continuous high-precision track point clustering analysis result; predicting the traffic efficiency of each road section in different time periods based on a historical traffic time delay model, and constructing a dynamic traffic capacity map; and according to the dynamic traffic capacity map and the vehicle real-time position information, carrying out dynamic redistribution regulation and control on a distribution time period window. According to the scheme of the embodiment of the invention, dynamic redistribution regulation and control can be carried out on the distribution period window so as to solve the problem of low time fulfillment rate of the end node.

The present invention discloses a channel area boundary control method for trunk green wave control, comprising the following steps: step one, establishing a green wave belt model for the target trunk; step two, establishing a trunk correlation model for describing the degree of correlation between the channel area section and the trunk; step three, based on a channel area division algorithm oriented to the target trunk, obtaining a channel area oriented to the trunk; step four, establishing a channel area boundary control model and method oriented to the trunk based on reinforcement learning technology. Compared with the prior art, the positive effect of the present invention is that the present invention dynamically controls the channel area boundary intersection through the combination of macro and micro: at the macro level, the overall number of vehicles in the control area is stable, and at the micro level, the internal traffic flow distribution in the control area is reasonable, thereby not only optimizing and improving the trunk traffic conditions, reducing negative indicators such as trunk traffic delays and the number of stops, but also taking into account the normal operation of the channel area traffic.

This invention relates to the field of traffic safety analysis technology, providing a multi-dimensional feature analysis system for the degree of driving delay at urban traffic accident sites. Based on driving delay data at urban traffic accident sites, it extracts road-dimensional features at the time of the accident, proposes targeted data imputation schemes based on the characteristics of missing features, and completes redundant feature screening according to statistical distribution, constructing structured traffic modeling data with four dimensions: weather, road, POI, and time. It systematically models and analyzes the degree of driving delay caused by urban traffic accidents from three aspects: individual heterogeneity, group transferability, and group heterogeneity, exploring the influence of significant variables among the multi-dimensional influencing factors. The aim is to help traffic management departments take preventive measures and emergency response strategies, provide a theoretical basis for road design optimization and management decisions, thereby mitigating the impact of traffic accidents, improving the operational risk level of accident-prone road sections, avoiding secondary accidents, and reducing traffic pollution.

This invention discloses a TSN network bandwidth parameter pre-allocation method based on network calculation, comprising: obtaining a set of TSN network parameter information; the TSN network is a time-sensitive network; performing traffic modeling on the set of parameter information to obtain an aggregate arrival curve and a residual service curve for AVB traffic; and solving the aggregate arrival curve and the residual service curve to obtain bandwidth allocation parameters for AVB traffic in the TSN network. This method can allocate the minimum feasible bandwidth resources at the node level while ensuring traffic delay bounds. It is highly efficient and scalable, and can be applied to network topologies of various sizes.

This invention discloses an AVB traffic delay analysis method for TSN networks in incremental scenarios. Based on network calculus, this method overcomes the shortcomings of traditional analysis methods that rely on the global GCL scheduling table. First, the method characterizes the inhibitory effect of TT traffic on AVB flow transmission. Without parsing the full scheduling table, it constructs a fast TT arrival curve using only the maximum number and length of TT traffic slots. Second, it combines the hop-by-hop reshaping characteristics of the ATS shaper and the credit constraint mechanism of the CBS shaper to construct the arrival and service curve models of the AVB flow at the output port. Finally, it calculates the worst-case delay time at the port using the maximum horizontal distance between the arrival and service curves of the AVB flow, and accumulates the values ​​hop-by-hop along the AVB flow transmission path to obtain the end-to-end worst-case delay time. Compared with traditional network calculus methods, the incremental delay analysis method proposed in this invention does not rely on the global traffic information of the TSN network and can reduce computational complexity.