## 2 Related Works  
The previous research for short-term traffic speed prediction can be grouped into three categories: statistical, machine learning, and deep learning methods. The most representative example of classic statistics machine learning models is autoregressive integrated moving average (ARIMA), which should follow the stationary assumption that the time-series characteristics of data must be time-invariant. ARIMA is proposed for the short-term prediction of traffic flow in urban arterials [22], and several variable models were subsequently presented for improved accuracy [23; 24]. Another algorithm,the Kalman filter, was also applied for traffic flow prediction [25; 26]. Similar to ARIMA, Kalman assumes time-invariant and suggests that states at specific points in time have a linear relationship with states at previous points [27]. The statistical approach has advantages, such as ease of implementation, minimal computation, and small storage demand. However, these algorithms can be significantly degraded if the data are nonlinear or do not follow the suggested assumptions, including an outlier. Unfortunately, the collected data barely satisfy the assumptions. Furthermore, there is always a possibility of including some outliers. Thus, obtaining reasonable results by applying this classic statistics model to urban road network tasks is challenging. Non-parametric methods can alleviate this problem with mathematical approaches for inference that do not consider the underlying assumptions about probability distribution shapes. The most common non-parametric approaches are k-nearest neighbor (kNN) and support vector machine (SVM), which have been used for short-term traffic forecasting. kNN outperformed ARIMA and simple-structured neural networks [28], and SVM performed better than the Kalman filter and ARIMA [29]. With the advent of big data, these data-driven approaches garnered the attention of researchers as high-quality data were produced. However, the exponentially growing amount of data revealed not only the limitations of classical static approaches but also the limitations of kNN and SVM. Particularly, it showed a difference in performance compared to deep learning methods that have demonstrated high performance in various fields [30]. However, the limitation of these approaches is that they do not reflect the temporal-spatial features of the data. As deep learning approaches have been evaluated as having high potential in the traffic field since the early 1990s [31], their use and development in related fields have been rapidly achieved [15; 16; 17; 18]. Deep learning approaches did not assume a specific distribution of data. Compared to conventional methods, they are robust to outliers, missing data, and nonlinear data, showing higher performance and a structure suitable for handling nonlinearities in data. Moreover, it is relatively easy to apply such deep learning algorithms to real-world data. RNN is a deep learning algorithm applied for traffic flow prediction and has a structure that can infer the current state by referring to the past state. These characteristics are suitable to reflect the temporal features of traffic data and were applied for prediction [32; 33]. However, RNNs must determine the length of the sequence, reflecting the previous state in advance, and as the time lag increases, there is a problem with vanishing/exploding gradients. Long short-term memory (LSTM) was proposed to compensate for the shortcomings of these RNNs [34]. In LSTM, unlike primary neural networks, the basic unit of the hidden layer is a memory block, and the input gate and output gate control the input and output of each block. For inputs at time \(T\), as time passes, new inputs overwrite the hidden layer, resulting in the network forgetting the original inputs. LSTM maintains its memory as long as the input gate is closed and can output without affecting the contents of the cell by opening and closing the output gate [35].