Research On Analysis And Forecasting Of Dynamic Spatio-Temporal Features Of Traffic Flow

With the rapid growth of the automobile industry,road traffic has increased substantially.Unfortunately,this has led to frequent occurrenc es of traffic congestion and accidents,resulting in reduced travel efficiency for individuals.To tackle these issues,many countries are now dedicated to building intelligent transportation systems(ITS)to alleviate traffic problems.Traffic flow predic tion is a crucial component of ITS.The timely and precise prediction of traffic flow holds immense value in traffic flow regulation,traffic control,and travel route planning,ultimately leading to enhanced road capacity and reduced duration and severity of traffic congestion.The accurate prediction of traffic flow is of paramount importance for the successful implementation of intelligent transportation systems.Traffic flow forecasting is a prominent spatio-temporal data forecasting problem that involves recording traffic flow at specific locations and times in a continuous space.It is evident that adjacent locations and times have interdependencies and exhibit strong dynamic spatio-temporal correlations.To achieve accurate and timely traffic flow forecasting,it is essential to effectively extract spatio-temporal features of traffic flow dynamics and model their correlations.This paper focuses on researching traffic flow prediction using dynamic spatio-temporal convolutional networks.The goal is to comprehensively explore the spatio-temporal correlations of traffic flow dynamics,extract their features,establish a prediction model based on these features,and enhance the accuracy of traffic flow prediction.The outcomes of this research will contrib ute to the advancement of intelligent transportation systems.The specific contents of the paper include:(1)Traffic network modeling and spatio-temporal feature analysis of traffic flow.The topological structure of sensors distributed on the traffic road n etwork is selectively deleted to remove redundant information on the structure and simplify the connection relationship between the structures,so as to retain the topological structure that can represent the state of traffic flow.Based on graph theory,it is abstracted into the form of a graph,and the graph structure of the road network is generated,and then the topological relationship of the road network is modeled.Then through the real traffic flow data to further analyze the spatio-temporal features of traffic flow,deeply analyze its dynamic spatio-temporal correlation,and provide the basis for subsequent traffic flow dynamic spatio-temporal correlation modeling.(2)A dynamic spatio-temporal convolutional network-based traffic flow forecasting model(DSTCN)is constructed to model the dynamic and non-linear spatio-temporal correlation of traffic flow.The model constructs a dynamic graph building block that generates an adjacency matrix to represent the spatial relationship of the traffic graph for the current time period based on the data of the current time period.Then a dynamic graph convolutional network is proposed to extract the spatial features of traffic flow dynamics based on the dynamically generated adjacency matrix and its corresponding data.A gated temporal convolutional network s and a multi-head temporal attention mechanism are also constructed,which work together to learn the dynamic temporal dependencies of traffic flow between different times and extract temporal features of traffic flow dynamics.The model can fully extract the spatio-temporal features of traffic flow dynamics by combining dynamic graph convolution and dynamic t emporal convolution networks,and more accurately predict future traffic flow conditions.(3)To further improve the accuracy of traffic flow prediction,an urban traffic flow prediction model based on multi-head spatiotemporal attention and adaptive graph convolutional network(MSTA-GCN)is constructed to extract the spatio-temporal features of traffic flow dynamics.The model consists of a temporal convolutional networks,a gated adaptive graph convolutional network s,a multi-head spatio-temporal attention mechanism,and a spatio-temporal feature fusion module.Temporal convolutional networks and multi-head temporal attention mechanisms work together to model the temporal dependence of traffic flow data dynamics.A multi-head spatial attention mechanism and a gated adaptive graph convolutional network work together to model the dynamic spatial correlation of traf fic flow data.Finally,the spatio-temporal feature fusion module fuses the extracted high-dimensional spatio-temporal features to obtain the final prediction result.The joint work of these modules can effectively extract the dynamic spatio-temporal features of traffic flow data and improve the prediction accuracy of traffic flow.