## 2 Related Work  
Over the past few decades, traffic prediction has been extensively studied. Early statistical methods for traffic prediction were simple time series models, containing Autoregressive Integrated Moving Average (ARIMA) [4] and its variant [17], vector autoregression (VAR) [5], etc. These methods rely on data stationary assumption, thus have limited ability to model complex traffic data. Later, models based on traditional machine learning methods, such as k-nearest neighbors (KNN) [6], support  
Figure 3: Effects of holidays and weather in San Francisco Bay Area. vector regression (SVR) [7], and Kalman filtering (KF) [8], were applied to traffic prediction to model more complex data. However, these methods cannot capture non-linearity in traffic data effectively, and barely utilize spatial correlations [18]. Moreover, they need more detailed feature engineering. Recently, methods based on deep learning have been applied in many fields and achieved success, which has inspired the study of traffic prediction to use deep-learning-based methods modeling the complex spatial-temporal dependencies of the traffic data [19]. [PERSON] et al. [20] utilized a stacked autoencoder (SAE) to predict the traffic status of different nodes. [PERSON] et al. [21] integrated KNN and LSTM [22] to predict traffic flow. [PERSON] et al. [23] combined LSTM networks with SAE to predict traffic status in extreme conditions. [PERSON] et al. [24] proposed a LSTM-based network composed of bidirectional ones and unidirectional ones for traffic prediction. In addition, [PERSON] et al. [25] transformed the road network into a regular 2D grid and used convolutional neural network to predict citywide crowd flows. [PERSON] et al. [26] used fully-connected neural networks and improved residual network to predict bus traffic flow. Later, the authors of [9; 10; 11] combined convolutional neural networks (CNNs) with recurrent neural networks (RNNs) and its variants for traffic forecasting. However, the main limitation of the above models is that conventional convolution operations can only capture the spatial characteristics of regular grid structures but do not work for data points with irregular topologies. Therefore, they fail to make an effective use of the topological structure of the traffic network to capture complex spatial correlations. To extract the spatial correlations of traffic data with complex topologies, extending neural networks to process graph-structured data has attracted widespread attention [27]. A series of studies has extended traditional convolution to model arbitrary graphs on spectral [28; 29; 30] or spatial [31; 32; 33] domain. Spectral-based methods use a graph spectral filter to smooth the input signals of nodes. Spatial-based methods extract high-level representations of nodes by gathering feature information of neighbors. Other studies focus on graph embedding, which learns low-dimensional representations for vertices that preserve the graph-structured information [34; 35]. To overcome the limitation of conventional convolution and capture more complex spatial-temporal dependencies, [PERSON] et al. [12] proposed a framework that combines the diffusion convolutional with the recurrent neural network to forecast traffic conditions. [PERSON] et al. [36] proposed Global Spatial-Temporal Network (GSTNet) for traffic flow prediction. GSTNet employs tensor casual convolution and global correlated mechanism for extracting dynamic temporal dependencies and global spatial correlations. [PERSON] et al. [13] proposed the Spatio-Temporal GCN (ST-GCN), which uses a full convolution structure combining graph convolution with 1D convolution. In ST-GCN, the graph convolution is used to obtain the spatial correlation, and the 1D convolution is used to extract the temporal dependencies. STGCN is much more computationally efficiently than the above-mentioned models using RNNs.