Abstract
Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction.
Highlights
Traffic flow prediction is to predict the future traffic flow of the road according to the historical traffic flow data
Autoregressive Integrated Moving Average (ARIMA) [12, 13]: traffic flow data are treated as random time series. e non-stationary data are transformed into stationary series data through multiple differential calculations, and the traffic prediction value is obtained by using Autoregressive Moving Average (ARMA) [44]
SVR [45]: Support Vector Regression (SVR) uses regression analysis to solve the problem of traffic flow prediction based on the principle of Support Vector Machine (SVM) [19, 20]. e traffic parameters such as vehicle speed inputs the trained SVR and outputs the traffic flow prediction results in the corresponding period. e kernel function that has been selected is the key to using SVR. e kernel function used is a linear kernel in this paper
Summary
Traffic flow prediction is to predict the future traffic flow of the road according to the historical traffic flow data. To better learn the complex spatial dependence and temporal correlation of traffic flow data and predict traffic flow more accurately, this paper proposes a spatio-temporal traffic flow prediction model based on a new Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). (1) Aiming at the spatial dependence of traffic flow data, the Graph Convolution Network (GCN) is introduced and improved, and a new spatio-temporal traffic flow prediction model is proposed based on GNN. E GNN-based method utilizes various graph formulations, so it has been extended to other transportation modes Based on this background, this paper proposes a new Deep Learning model on GNN [43], which can capture complex spatio-temporal characteristics from traffic flow data to further improve the accuracy of prediction
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