STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

Abstract

As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.

Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks

“…Previous studies related to the GCN can be classified into two types: temporal and spatial interdependence. First, temporal interdependence is considered as temporal blocks such as temporal self-attention [11], an LSTM module [18], or a gated recurrent unit (GRU) module [19].…”

“…Integrating weather data into traffic volume prediction models could significantly enhance their precision and reliability. Recent works have used the multi-layer perception network (MLP) [8], long short-term memory (LSTM) [9], graph convolution network (GCN) [10,11], and graph attention network (GAN) [7] to predict traffic volumes. Despite the important role of and progress in neural network applications, the intensive study of long-term traffic volume prediction incorporating meteorological data remains limited.…”