13 results about "Traffic wave" patented technology

The invention discloses a traffic jam detection and early warning method based on an unmanned aerial vehicle video stream, and belongs to the technical field of intelligent traffic. The system is combined with a floating vehicle triggering model, collects road videos in real time through an unmanned aerial vehicle, and realizes high-precision vehicle detection and tracking in combination with improved YOLOv8 and DeepSort algorithms; unmanned aerial vehicle data and floating vehicle GPS data are fused, three elements of traffic flow are quantified, and a dual-mechanism congestion detection model is constructed; respectively utilizing a GRU cooperative traffic wave theory to predict the congestion dissipation duration and utilizing an improved GRU + GCN model to predict the traffic situation; the signal lamps are dynamically regulated and controlled through multi-agent reinforcement learning, and active congestion relieving is achieved. The method has the advantages of wide-area coverage, high-precision perception and intelligent response, and the urban traffic control efficiency is remarkably improved.

The invention discloses a traffic control subarea optimization and self-adaptive adjustment method and system, relates to the technical field of subarea optimization, and is used for solving the problem that the coordination effect in the peak period of traffic flow is poor due to the lack of dynamic response to real-time flow data when an existing traffic signal coordination system carries out regulation and control based on experience or a fixed algorithm. The coordination effect is quantified by calculating the overlapping length and the directional delay, so that the optimization process is more objective, the coordination effect of different initial phase differences can be quantified, the optimal initial phase difference is selected, a group of coordination control schemes is generated by considering the coordination of non-adjacent intersections, the problem of global incoordination caused by local optimization is reduced, and the optimization efficiency is improved. According to the method, factors such as path length and traffic flow weight are introduced, coordination deviation between local optimum and global optimum is calculated, coordination loss is quantified more accurately, a smooth transition strategy is adopted, and sudden traffic fluctuation caused by overlarge phase difference change is avoided.

This invention relates to the field of intelligent transportation technology, and provides a method and system for dynamic control of emergency lanes in highway accident scenarios. The method includes: acquiring accident reporting information; using a deep learning network model to predict the accident handling time; based on the accident handling time, using a traffic wave model to predict the queue length and dissipation time under the impact of the accident; using the queue length under the impact of the accident as the total length of the controlled road segment and discretizing it into segments; using the dissipation time under the impact of the accident as the control duration and intervalizing it; constructing a binary control strategy combining time windows and spatial segments; constructing an objective function with the goal of minimizing the weighted average of total travel time and total collision exposure time; solving the objective function using a Kriging surrogate model; and obtaining the dynamic control strategy for the emergency lane based on the binary control strategy. This improves the response speed of accident handling.

The invention discloses a multi-mode traffic flow active regulation and control method for a bottleneck area of an expressway. The method comprises the following steps: constructing a hierarchical trigger mechanism based on a dynamic fluctuation matrix; cAV vehicles are screened through a conflict resolution model, a motion wave absorption control strategy is constructed, a control position and head vehicle selection are dynamically optimized, and traffic fluctuation energy is absorbed; a dynamic speed limiting model based on CAV permeability is established according to the hysteresis characteristic of the manually-driven vehicle, and the traffic flow state is adjusted in real time; a motion wave absorption control strategy and a dynamic speed limiting strategy are fused, a state space partition control method is provided, and layered treatment of free flow, a transition area and a congestion area is achieved; double-loop cascade feedback control is adopted to ensure the stability of the system, and an improved NSGA-III algorithm is combined to optimize control parameters to form an optimization-control-feedback self-closed loop mechanism. The method effectively improves the safety and passing efficiency of the mixed traffic flow through macro-micro cooperative regulation and control, and is suitable for a complex traffic scene in an intelligent network connection environment.

The invention discloses a signal lamp duration estimation method based on depth image prior guided by physical information, which comprises the following steps: constructing a space-time grid at a target entrance lane, and mapping vehicle position and speed information into a sparse queue contour image; constructing a depth image prior model, taking the sparse image as input, performing iterative optimization through fusion reconstruction, traffic wave physical constraint and signal control information composite loss, and repairing to obtain a complete queue contour; identifying a signal lamp state based on a stop line queue time sequence, combining continuous time periods, and calculating a red light, a green light and a period duration; according to the method, signal lamp time length estimation is reconstructed into sparse queue contour image restoration, a depth image prior model guided by physical information is combined, and periodic signal lamp time length estimation can be realized in fixed timing and dynamic timing scenes without depending on a large amount of historical annotation information.

The application discloses a highway traffic state estimation method and system fusing a traffic flow model and a graph neural network, and the method comprises the following steps: discretizing a highway into cells with a fixed length, and representing the traffic state of each cell by three parameters of flow, density and speed; determining the active influence area of the cell in a time period and the area to be affected by using the traffic wave speed between any two cells, and establishing a cell traffic state space-time evolution model; converting the discretized cell traffic state and adjacent information into graph structure data, and designing a graph neural network to update the traffic state of all cells; and based on the updated cell traffic state, using the gradient descent method to optimize the cell traffic state space-time evolution model, and obtaining the highway traffic state estimation result. The application can effectively improve the explainability, accuracy and generalization ability of the highway traffic flow space-time evolution modeling, and accurately grasp the highway traffic flow space-time evolution mechanism.

The invention belongs to the crossing field of an electric data processing technology and traffic and transportation engineering, and particularly relates to a macro-micro fusion trajectory reconstruction method applied to emission estimation. Comprising the following steps: S1, generating a regional macroscopic velocity field datum reference; s2, the operation condition of the floating car is corrected; s3, determining a candidate track set of a non-floating vehicle; s4, determining a final track of the non-lane-changing vehicle in the non-floating vehicles based on the candidate track; s5, determining a final track of the lane changing vehicle in the non-floating vehicles based on the candidate track; and S6, integrating the two types of tracks, verifying the operation condition, and generating a complete vehicle track. According to the method, trajectory evolution and traffic wave propagation trends can be effectively aligned, the space-time fineness of the reconstructed trajectory is remarkably improved, and accurate input is provided for high-resolution emission evaluation; meanwhile, the common unpractical speed or acceleration sudden change phenomenon in a traditional deterministic car-following model is effectively avoided; and meanwhile, the method has the advantages of efficient utilization of multi-source data, space-time continuity optimization and the like.

This invention relates to the field of smart city management technology and discloses an intelligent early warning method for a smart sanitation platform. The method acquires multi-source data from sanitation facilities, including sensor data, pedestrian flow data, meteorological data, and traffic data. It constructs an urban sanitation topology network, defining downstream nodes using pedestrian movement direction vectors and dynamically assigning edge weights. A spatiotemporal prediction model is used to aggregate features of the dynamic topology network to predict the time of garbage overflow. When the garbage enters the warning range, a vision-sensor mutual verification mechanism is activated, using a semantic segmentation algorithm to verify the accuracy of sensor data to generate a definitive warning. Finally, meteorological data is combined to calculate the garbage decomposition rate factor, and traffic fluctuations are considered to calculate the optimal interception time window to generate dynamic dispatch instructions. This invention can predict overflow risks in advance based on pedestrian flow trends, reduce false alarm rates, and achieve precise garbage collection and dispatch based on environmental conditions.

This invention belongs to the interdisciplinary field of electrical data processing technology and transportation engineering, specifically relating to a macro-micro fusion trajectory reconstruction method applied to emissions estimation. It includes the following steps: S1. Generating a regional macroscopic velocity field reference; S2. Correcting the floating car operating conditions; S3. Determining a candidate trajectory set for non-floating vehicles; S4. Determining the final trajectory of non-floating vehicles that have not changed lanes based on the candidate trajectories; S5. Determining the final trajectory of non-floating vehicles that have changed lanes based on the candidate trajectories; S6. Integrating the two types of trajectories, verifying the operating conditions, and generating complete vehicle trajectories. This method can effectively align trajectory evolution with traffic wave propagation trends, significantly improving the spatiotemporal precision of the reconstructed trajectory and providing accurate input for high-resolution emissions assessment; it also effectively avoids unrealistic speed or acceleration abrupt changes common in traditional deterministic car-following models; and it has advantages such as efficient utilization of multi-source data and optimization of spatiotemporal continuity.

The application discloses a signal lamp duration estimation method based on physical information guided depth image prior, which comprises the following steps: constructing a space-time grid at a target entrance, mapping vehicle position and speed information into a sparse queue profile image; constructing a depth image prior model, taking the sparse image as input, iteratively optimizing through a composite loss of fusion reconstruction, traffic wave physical constraints and signal control information, and repairing to obtain a complete queue profile; identifying the signal lamp state based on the queue time sequence at the stop line, merging the continuous time period and calculating the red lamp, green lamp and cycle duration; the signal lamp duration estimation is reconstructed as a sparse queue profile image repair problem, combined with the depth image prior model guided by physical information, without relying on a large amount of historical labeled information, the cycle level signal lamp duration estimation can be realized in the fixed timing and dynamic timing scenes.

The invention relates to a nonlinear hybrid traffic optimal interference suppression method based on strategy iteration, and belongs to the technical field of automatic driving, and the method comprises the following steps: S1, building an affine nonlinear dynamic model used for describing a hybrid traffic system through employing a leading cruise control (LCC) frame; s2, providing an optimal robust control formula for the nonlinear mixed traffic flow with external interference so as to optimize the attenuation performance; and S3, solving the optimal robust control formula of the mixed traffic flow proposed in the step S2 by solving the numerical value of the converted zero sum game. According to the invention, traffic waves can be effectively attenuated.

The application discloses a double-lane highway local section single-lane alternate traffic signal dynamic timing method, which comprises the following steps: determining the local section traffic influence range of an event; collecting traffic flow data and analyzing traffic flow characteristics; calculating the equivalent amount of small vehicles based on delay and the adverse influence factor of large vehicles; and constructing a signal dynamic timing method for the alternate traffic of the event influence section of the double-lane highway. The application constructs a signal timing optimization model based on dynamic traffic flow characteristics, collects the traffic flow data of the event influence section in real time, including the actual traffic time, the vehicle type ratio and the actual arrival traffic volume, dynamically adjusts the signal cycle and the phase timing scheme, and is suitable for the special scene of the double-lane highway alternate traffic section. The method can quickly adapt to the traffic fluctuation of the alternate traffic section, reduces the vehicle delay under manual command, improves the vehicle traffic efficiency, and enhances the flexibility and dynamic adaptability of the signal timing of the double-lane highway alternate traffic section.