4results about How to "Reduce queue length" patented technology

The application relates to the technical field of traffic signal control, and particularly discloses an adaptive traffic signal control method based on reinforcement learning and a self-attention mechanism, which comprises the following steps: establishing a Markov decision process model and a policy neural network based on a deep reinforcement learning algorithm; building a deep Q network and a self-attention mechanism based on the Markov decision process model; evaluating the policy and updating the neural network of the policy neural network, an encoder and a decoder; performing multiple iterative training on the neural network; obtaining a trained neural network model; and generating real-time traffic signal control signals according to the trained neural network model. The method can gradually learn an optimal policy in a complex traffic flow. In addition, the algorithm adopts an encoder-decoder structure, stores historical trajectories in a replay buffer, and uses proximal policy optimization to update parameters. The method solves the problem that the existing adaptive traffic signal control method needs a large amount of data for training and requires a large amount of computing resources.

The invention discloses a traffic signal control method based on machine visual perception and space-time sequence prediction, and relates to the technical field of intelligent traffic signal control. The traffic signal control method based on machine vision perception and time-space sequence prediction comprises the following steps: S1, machine vision perception: acquiring real-time image data of a traffic intersection through image acquisition equipment, preprocessing the image data, and extracting traffic flow key parameters; and S2, space-time sequence prediction: constructing a PCA-LSTM traffic flow prediction model based on principal component analysis PCA and a long short-term memory neural network LSTM, inputting the preprocessed traffic flow historical data, and outputting a short-term traffic flow prediction result. The traffic signal control method based on machine visual perception and time-space sequence prediction is comprehensive in data coverage and low in acquisition cost; and the prediction performance is better: through abnormal data processing and dynamic principal component selection of the PCA-LSTM model, the prediction precision is improved by 2%-5% compared with the traditional LSTM, and the calculation efficiency is improved by 15.01%.

The invention discloses an urban traffic flow prediction and signal optimization method based on a space-time diagram neural network. The method specifically comprises the following steps: S1, constructing an urban road network topological graph; s2, multi-source heterogeneous data fusion and feature extraction; s3, establishing a space-time diagram neural network prediction model; s4, a signal optimization algorithm based on a prediction result; s5, model training and online strategy updating; s6, integration and real-time reasoning optimization are carried out; according to the method, through constructing topological graph representation of an urban road network, fusing multi-source heterogeneous traffic data and adopting a deep learning architecture combining a graph convolutional network and a time sequence prediction model, accurate short-term prediction of traffic flows of intersections and road sections is realized, and a dynamic signal optimization algorithm is designed based on a prediction result, so that the traffic flow prediction efficiency is improved. The adaptive adjustment of traffic signal timing is realized, so that the traffic efficiency of a road network is improved, and the vehicle delay is reduced.

The application relates to a right-turn vehicle separation method for a roundabout intersection under an intelligent network connection environment. The application aims to solve the problem that the existing roundabout intersection is significantly congested and diffused during a peak period, and the main entrance is oversaturated, leading to low traffic efficiency of the roundabout intersection during the peak period. The process is as follows: step one, relying on a roadside unit (RSU) to collect vehicle basic information, road state and other data in real time through V2X communication and video detection; presetting a roundabout density threshold value, a vehicle speed threshold value and a queue length threshold value; when the real-time collected roundabout density is greater than the roundabout density threshold value, or the collected vehicle speed is greater than the vehicle speed threshold value, or the collected queue length is greater than the queue length threshold value, step two is executed; step two, a right-turn vehicle separation strategy is executed; and the application is used in the field of roundabout traffic control.