Abstract
Traffic flow prediction is a key challenge in intelligent transportation, and the ability to accurately forecast future traffic flow directly affects the efficiency of urban transportation systems. However, existing deep learning-based prediction models suffer from the following issues: First, CNN- or RNN-based models are limited by their architecture and unsuitable for modeling long-term sequences. Second, most Transformer-based methods focus solely on the traffic flow data itself during embedding, neglecting the implicit information behind the traffic data. This implicit information includes behavioral trends, community and surrounding traffic patterns, urban weather, semantic information, and temporal periodicity. Third, methods using the original multi-head self-attention mechanism calculate attention scores point by point in the temporal dimension without utilizing contextual information, which to some extent leads to less accurate attention computation. Fourth, existing methods struggle to capture long and short-range spatial dependencies simultaneously. To address these four issues, we propose an IEEAFormer technique (Implicit-information Embedding and Enhanced Spatial-Temporal Multi-Head Attention Transformer). First, it adopts a Transformer architecture and incorporates an embedding layer to capture implicit information in the input. Secondly, the method replaces the traditional multi-head self-attention with time-environment-aware self-attention in the temporal dimension, enabling each node to perceive the contextual environment. Additionally, the technique uses two unique graph mask matrices in the spatial dimension. It employs a novel parallel spatial self-attention architecture to capture both long-range and short-range dependencies in the data simultaneously. The results verified on four real-world traffic datasets show that the proposed IEEAFormer outperforms most existing models regarding prediction performance.