AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors

Traffic flow prediction is a fundamental part of ITS (Intelligent Transportation System). Since the correlations of traffic data are complicated and are affected by various factors, traffic flow prediction is a challenging task. Existing traffic flow prediction methods generally take limited static factors (e.g., the distance between sensors and road network topological structure) into consideration and model the correlations of the traffic data separately to predict the future traffic. In this paper, we propose AARGNN (Attentive Attributed Recurrent Graph Neural Network), a GNN (graph neural network) based method considering multiple dynamic factors to predict short-term traffic flow. With multi-source urban data (e.g., POI, road network, incident, weather, etc.), AARGNN considers both static factors and dynamic factors (e.g., spatial distance, semantic distance, road characteristic, road situation, and global context) to predict the short-term traffic flow. Specifically, AARGNN constructs an attributed graph and encodes various factors into the attributes. The correlations of the traffic data are modeled by utilizing the GNN combined with LSTM (long short-term memory). In addition, AARGNN specifies the contributions of each factor based on attention mechanism. Experiments on real-world datasets show that the proposed method outperforms all baseline methods.