21 results about "Traffic delay" patented technology

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 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 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 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.

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.

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 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.

The application discloses a kind of heterogeneous mobile energy storage coupling power system optimization method and system considering traffic delay, comprising: establishing the unified space-time coupling model of power system and traffic system;Establish heterogeneous mobile energy storage unit model, including initial condition constraint, electric quantity evolution equation, energy capacity limit constraint, the constraint that end electric quantity returns to initial state, heterogeneity characteristics;Establish power system operation model, including generator capacity constraint, climbing rate constraint, logic consistency and anti-simultaneity constraint, minimum start-up / shut-down time constraint, DC power flow constraint;Build joint optimization framework, with the minimum power system operation cost as objective function, obtain the optimal mobile path, stop plan and charge-discharge strategy of each heterogeneous mobile energy storage unit by mixed integer linear programming solution.Utilizing the application, the flexible adjustment capacity of mobile energy storage can be fully exerted, and the adaptability of power system to renewable energy fluctuation is improved.

A method for minimizing packet retransmission, performed by a user device, includes determining a foreground application event associated with a foreground application; determining a background bandwidth limit associated with at least one background application; determining whether the background bandwidth limit exceeds a threshold value; based on the background bandwidth limit exceeding the threshold value, identifying a transmission protocol associated with the at least one background application; and based on the identified transmission protocol, controlling packet transmission by delaying egress traffic or adjusting an advertise window size. The method applies different control mechanisms for UDP and TCP protocols, with UDP using egress traffic delay and TCP using advertise window size adjustment. The system monitors streaming parameters and performs prioritization operations based on streaming status to optimize network performance.

The invention relates to a road maintenance operation area traffic organization strategy generation method and equipment considering carbon emission cost. The method comprises the following steps: collecting actual road data; the method comprises the following steps: constructing a road maintenance operation area model based on actual road data, performing a traffic simulation orthogonal experiment of a maintenance operation area, performing delay rule analysis based on simulation data, fusing traffic volume, cart ratio and traffic delay three-dimensional indexes, and obtaining a management and control threshold value of a closed lane in a maintenance project; taking the management and control threshold value and the minimum construction window period as constraints, generating a plurality of maintenance work area traffic organization strategies, and calculating the owner cost, the user cost and the environment cost of each scheme; and establishing an importance weight matrix based on different decision targets, obtaining the total score of the traffic organization strategy of each maintenance work area through weighted summation, and outputting the traffic organization strategy of the maintenance work area with the optimal total score. Compared with the prior art, the lane closing strategy, the speed limit value and the working area length are dynamically optimized, and the lowest comprehensive cost is achieved.

This invention discloses an algorithm-based method for evaluating the utilization efficiency of green lights in urban road traffic, comprising the following steps: obtaining a standardized dataset; performing lane matching for vehicles to obtain vehicle passage records for each lane; dividing the time interval and calculating the average flow rate of each lane within the target time period; calculating the saturation flow rate of each lane; obtaining the corrected saturation flow rate of each lane; using an improved Kuramoto model to output the total flow rate ratio of signal phases; obtaining low flow rate ratio scenarios and high flow rate ratio scenarios; using the Webster algorithm to calculate the signal cycle and the green light time of each signal phase, constructing an optimization model with the goal of minimizing the total delay at the intersection, and optimizing the solution for the signal cycle and the green light time of each signal phase; outputting signal timing parameters and corresponding traffic operation evaluation results to improve green light utilization efficiency, reduce traffic delay, and enhance the capacity and smoothness of the intersection.

This invention provides a method, apparatus, equipment, and storage medium for assessing traffic resilience and delay costs under rainfall-induced urban flooding. The method includes: acquiring rainfall data for a target area; performing runoff generation and confluence simulations based on the rainfall data to obtain the water depth; determining a safe driving speed corresponding to the water depth based on the water depth and a speed-depth mapping model; the speed-depth mapping model determining a safe driving speed while ensuring vehicle handling stability, wherein the safe driving speed is obtained based on the road water depth and a scaling factor; obtaining a travel demand matrix based on the spatial distribution of the population in the target area; determining traffic delay time based on the safe driving speed and the travel demand matrix; and obtaining traffic delay costs based on the traffic delay time. This invention can improve the efficiency and accuracy of assessing traffic resilience and delay costs under rainfall-induced urban flooding.

The application discloses a kind of apron intersection special vehicle control method and system, it is related to multi-agent reinforcement learning and traffic control technical field, including the following steps: the observation information of each special vehicle in apron intersection area at current time is collected, the observation information of each special vehicle at current time is input to pre-trained policy network, corresponding action instruction is generated, action instruction includes acceleration and deceleration action, front wheel angle action and desired speed action;The path trajectory of each special vehicle in future set time is generated by the environment state information and action instruction at current time;Each special vehicle travels according to the path trajectory in future set time, and the motion state of each special vehicle is updated after traveling, and the environment state information at next time is generated.The application can significantly reduce the average waiting time and traffic delay in intersection area.

The application discloses a kind of intersection accident influence under automatic driving vehicle trajectory planning method, it belongs to automatic driving vehicle trajectory planning control field.The application solves the problem of low traffic efficiency of intersection after accident.The application quantifies the influence of various factors on vehicle by accurately perceiving and analyzing intersection environment after accident, constructing driving risk field by expected trajectory field, accident repulsion field and vehicle motion field, and providing real-time path adjustment guide for automatic driving vehicle by calculating lateral and longitudinal driving force and acceleration, which avoids long-time retention and traffic congestion of vehicle caused by accident, effectively improves vehicle traffic efficiency under intersection accident scene, reduces traffic delay, enhances driving safety, ensures the safety of vehicle passing through complex intersection environment, and reduces the risk of secondary accident.The application method can be applied to automatic driving vehicle trajectory planning under intersection accident influence.

A road section delay loss estimation method based on a ponding depth probability density and a traffic flow probability density. The method comprises: S1, establishing a model for the relationship between the ponding depth of a road section and distance; S2, on the basis of the maximum ponding depth of the road section collected from a historical record with a forecasted equivalent rainfall, formulating a probability density function for the maximum ponding depth; S3, on the basis of the traffic flow of the road section collected from the historical record with the forecasted equivalent rainfall, formulating a probability density function for the traffic flow of the road section; S4, calculating free-flow vehicle speeds, and on the basis of the traffic flow of the road section, calculating the average vehicle speed of a vehicle, and calculating the average travel time of the vehicle under a road-section ponding condition; and S5, calculating the average delay time of the vehicle in a ponded road section, and on the basis of the average delay time and the probability distribution of passenger occupancy, estimating the total traffic delay loss of the road section. By means of taking the uncertainty based on the ponding depth, the traffic flow and the passenger occupancy into consideration, a scientific delay loss estimation model is established, thereby providing an important basis for the management of traffic congestion caused by urban flooding and ponding.

The invention provides an airspace traffic delay prediction method based on three-branch fusion and feature weighting, and aims to solve the problems that a traditional method in terminal area flight delay prediction is difficult to effectively express complex airspace dynamics and multi-modal information fusion is insufficient. Harbor entering and departure multi-channel situation images are constructed, and data such as flight quantity, average flight speed and height are converted into air traffic multi-modal images to be input. A port entry and departure dual-branch convolutional neural network is designed, cross-branch spatial-temporal feature interaction and weighted fusion are realized through an attention mechanism and fusion branches, and the perception ability of spatial domain dynamic change is enhanced. Global dependency modeling based on Transform is introduced, and long-distance spatial-temporal feature expression is improved. And in combination with multi-scale feature learning and an image enhancement strategy, the generalization performance of the model is further enhanced. In terminal area flight delay classification prediction, the accuracy and robustness of the method are obviously better than those of a traditional structured feature model, and the method is suitable for intelligent flight scheduling and air traffic control auxiliary decision making.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent network connection vehicle multi-mode traffic control method, which comprises the following steps of: constructing a combined working condition, aligning a vehicle speed curve of the combined working condition with a signal lamp SPAT information phase period, and establishing an urban road scene simulation model; constructing a traffic density-vehicle speed coupling model based on a cellular transmission model, screening typical traffic density working conditions in combination with a VISSIM model, and generating a dynamic vehicle speed interval map; a two-stage distance threshold is preset to divide a control interval, a multi-mode control model containing PID, MPC and CRUISE is constructed, and an algorithm is dynamically switched according to the distance between a vehicle and a signal lamp and the real-time state. The method realizes three-dimensional coupling of traffic density, signal lamp phase and multi-mode control, adapts to urban and rural mixed road scenes, reduces vehicle start-stop and traffic delay, improves traffic efficiency and driving smoothness, reduces energy consumption and emission, adapts to existing vehicle-mounted hardware, and is easy to popularize and apply.