Research And Implementation Of Traffic Flow Prediction Algorithm Based On Deep Learning

As a crucial research content of intelligent transportation system,traffic prediction is the premise and foundation of modern urban traffic control and guidance.Accurate traffic prediction can help the transportation department grasp the future development trend of traffic flow,and alleviate the negative social impact caused by traffic congestion.However,traffic forecasting is affected by the complexity of multi-source spatio-temporal data and faces severe challenges.Specifically,traffic data often show strong temporal and spatial correlation,have complex road network structure,are largely affected by factors such as time,location,emergencies,and weather changes,and have high nonlinearity,randomness,and complex change patterns.In addition,the lack of data greatly affects the performance of traffic prediction.In recent years,the rapid development of deep learning algorithms and technology,especially the rise of graph neural networks,provides a new idea and research direction for the field of traffic prediction.However,there are still deficiencies in the ability of existing methods to obtain the complex spatio-temporal dynamic changes and the performance of completing spatio-temporal series data.This paper makes an in-depth study of the above problems,as follows:In this paper,a traffic prediction model based on dynamic graph neural network is proposed.Using sequence-to-sequence as the main architecture,graph neural networks and recurrent neural networks are used to model the characteristics of two dimensions of spatio-temporal.In order to effectively capture the dynamic characteristics of traffic data,the spatio-temporal dynamic and static fusion network is designed,and the dynamic graph generation is synchronized with the process of cyclic extraction of timing dependence.Among them,the given node topology for the existing method has a limited representation of the node spatial relationship,so the dynamic graph generation module is designed at multiple angles.And a new topology is constructed at different time steps to supplement the road network topology,so as to obtain the potential information contained in the spatio-temporal data.What’s more,the situation of missing data is common.There’s no special treatment is given to the missing data in the existing forecasting task,and the existing data completion method does not take into account the complex spatio-temporal relationship,so the completion effect is not good when used for traffic spatio-temporal series data.Therefore,this paper proposes a new generation model to fill in the missing values in spatio-temporal sequence data.This model fully combines the information of temporal and spatial dimensions to obtain more reliable filling values and uses the missing data set for downstream prediction tasks after effective filling.Considering the complex spatial dependence of road network and the spatio-temporal dynamic characteristics of traffic data,the spatio-temporal generator and spatio-temporal discriminator network are designed.By combining the generated countermeasure network and graph convolution neural network to obtain the potential spatio-temporal characteristics in the traffic data,the generator can learn the complex high-dimensional feature distribution from the observed data in the game process,so as to generate data similar to the real data distribution,which can be used to supplement the information of missing nodes in the road network graph.The model combines generative loss and completion loss with the training generator to weigh the effects of completion loss.This paper designs rich comparative experiments on the above models on two real data sets(highway sensor monitoring data).The experimental results show that the model designed in this paper achieves the best performance compared with the benchmark model.In addition,the effectiveness of the dynamic topology building module is fully proved by comparing the proposed model with its variants.Finally,in order to further show the effect of traffic prediction,traffic prediction is integrated into the visualization system for display.