## 2 Related Works  
Traffic flow forecasting is a crucial foundation for traffic management and transportation planning. Traffic flow prediction is vital for both traffic managers and participants. It optimizes traffic signal control, mitigates congestion, enhances road capacity, and provides precise road condition information, guiding travel mode and path selection and improving the travel experience. Consequently, traffic flow prediction significantly enhances urban transportation efficiency and service quality. Deep learning technology is currently extensively applied in this domain, with numerous scholars proposing various traffic flow prediction models leveraging deep learning, see Supplementary Materials Table S1. [PERSON] and colleagues [16] employed the random forest algorithm to extract multiple bootstrap samples from the original dataset, constructing a decision tree for each to develop a short-term traffic flow prediction model. This model effectively minimizes prediction error and variance, enhancing the accuracy and efficiency of short-term flow predictions, thereby surmounting traditional methodological constraints. [PERSON] et al. [17] applied the spatio-temporal feature selection algorithm (STFSA) to identify the optimal time lag and spatial road section count for the input data through correlation analysis, subsequently extracting relevant spatio-temporal traffic flow features and converting them into two-dimensional matrices embedded with spatio-temporal information. Subsequently, CNNs are utilized to extract and learn features from these two-dimensional matrices, facilitating the construction of a prediction model. This approach leads to the proposal of a short-term traffic flow prediction model that integrates spatio-temporal analysis within a CNN-based deep learning framework. [PERSON] et al. [18] leveraged a deep learning framework combining irregular convolutional residual networks with LSTM units to propose an urban traffic flow prediction model based on a Deep Irregular Convolutional Residual LSTM Network (DST-ICRL). [PERSON] et al. [19] introduced a trainable location matrix within the graph convolutional network framework to facilitate adaptive learning of spatial patterns in graph structures. They proposed a Location-Based Graph Convolutional Network (Location-GCN) and amalgamated Long Short-Term Memory (LSTM) with the Location-GCN to devise an end-to-end traffic prediction model. As deep learning technology advances, numerous innovative frameworks have emerged, with the Transformer model proposed by [PERSON] et al. [20] of Google standing out as particularly notable. This model eschews traditional CNN and RNN techniques, adopting Attention as its core method for representation learning. Furthermore, it integrates techniques like Positional Encoding, Feed-Forward Networks, Residual Networks, and Regularization to enhance its generalization capabilities and computational efficiency. The model performs excellently across various Natural Language Processing (NLP) tasks. Given the Transformer architecture's significant impact on deep learning, numerous researchers have focused on its enhancement and expansion. Their objectives include improving computational efficiency and generalization capacity, mitigating overfitting or underfitting risks, and boosting adaptability to diverse tasks and datasets. Innovations such as Autoformer, FEDformer, BERT [21], and Pyraformer [22] exemplify the continual evolution and optimization of the Transformer model, underscoring the research community's dedication to advancing model performance. Throughout this evolution, the attention mechanism--a technique for flexible information selection and synthesis--has emerged as a focal point of research. Its development has spurred the creation of numerous variants, such as multi-head Attention [20], bottleneck attention [23], cross-modality Attention [24], and graph attention [25]. These variants showcase the attention mechanisms' robust adaptability and innovation potential across various application contexts.