## 2 Literature Review  
The ARIMA is a widely used approach to forecasting time series data [13; 14]. It is acceptable for forecasting the stationary time series data. Despite its widespread use, the ARIMA's applicability to traffic volume forecasting is limited due to the non-stationary nature of traffic data. The non-stationary nature can exhibit unpredictable variations influenced by external traffic flows. With the help of machine learning techniques, researchers can explore faster and finer traffic predictions with a variety of ranges [15]. Recent deep learning techniques for traffic volume prediction first considered the MPN [8] and the recurrent neural network [16]. This deep learning technique contains several hidden, fully connected layers to predict nonlinear and nonstationary relationships. The LSTM is a recurrent neural network model to strengthen the long-term and short-term memories in a neural network [9]. The LSTM is powerful in predicting periodic time-series data. The GCN considers a graph structure to identify the interdependence between data [10]. It is powerful to consider the interdependence between data. It is widely used in time-series data prediction or spatial dependence [17]. The GAN uses an attention mechanism in the GCN [7]. The attention mechanism focuses on the most related features from the input data. The attention mechanism has been utilized in various areas due to its flexibility and high predictability with dependencies. For the systematic reviews on traffic volume prediction with artificial intelligence, please refer to this article [4].  
This paper intensively looks into GCN approaches most widely used to predict long-term traffic volume. The GCN is one of the emerging techniques to account for the interdependence between data using a graph structure. 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].  
On the other hand, previous works have used the GCN to identify the spatial dependence between traffic volume or speed data. Table 1 represents the literature reviews on the short-term traffic volume predictions with the GCN. First, [PERSON] et al. (2020) utilized the GCN with meteorological information to predict bite trips [12]. [PERSON] et al. (2020) focused on spatial dependence and covered more than one year. Second, [PERSON] et al. (2019) also described a similar model but with multiple parallel layers in the model to predict the bike-sharing volumes [20]. They considered three different cities to predict the traffic volume. Three to six months have been extensively utilized to train the model. [PERSON] et al. (2020) utilized the Manhattan area to predict the traffic volume [2]. They formed the main and minor areas and subjected the information to deep learning in advance [2]. The last study utilized the GCN and attention mechanisms unlike the GAN. The model performed better with spatial dependence.  
From the literature review section, we find out that there is no work on long-term traffic volume prediction and no work considering both meteorological information and temporal dependence. Furthermore, no study has used data spanning more than two years to identify the yearly impacts and the other impacts. Therefore, this study aimed to develop long-term traffic volume prediction techniques to utilize meteorological information and data spanning more than 2 years to obtain stable results.