## 3 Data Processing and Methodology  
### Study Area and Data Collection  
In this study, we predicted the speed of a road network located in Daegu Metropolitan City, Republic of Korea. Mobileye sensors, which were installed in approximately 500 taxis, were utilized to collect the taxi movement data. Each point of the taxi trip record indicates the taxi location information in Figure 1.  
The sensors recorded the taxi movement information using the file transfer protocol server in the controller area network (CAN) message format every 60-100 ms. CAN messaging allows the car modules to communicate with each other. The sensor data are hexadecimal and comprise the location information, speed, time, brake, and TSR of each taxi.  
In this study, we use speed, brake, and TSR data to predict traffic flow considering the environment of the road network, graph-based relationship, and intention of the driver. Particularly, the TSR data represent a number from 0 to 7, i.e., speed limit classified by Mobileye. A value of 0 indicates that the current speed is equal to or lower than the speed indicated by the road traffic sign, 1 denotes 0-5 km/h over the speed limit, 2 denotes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Strengths** & **Weaknesses** \\ \hline \multirow{3}{*}{ARIMA} & Easy implementation, minimal computation, small storage demand & Struggles with nonlinear data, outliers, and assumptions \\ \hline \multirow{2}{*}{Kalman Filter} & Linear relationship, effective for time-invariant states & Same as ARIMA, limited by linear assumptions \\ \hline \multirow{3}{*}{RNN} & Captures temporal dependencies & Issues with \\  & Captures temporal dependencies & vanishing/exploding gradients, limited sequence length \\ \hline \multirow{2}{*}{LSTM} & Long-term memory, robust to vanishing gradient problem & Complex structure, requires more computation \\ \hline \multirow{2}{*}{CNN} & Captures spatial features, short learning time & No long-term temporal dependencies \\ \hline \multirow{2}{*}{GNN} & Encodes non-Euclidean relationships & Complex implementation, computationally intensive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of traffic speed prediction models.  
Figure 1: Mobileye point data in Daegu Metropolitan City in the Republic of Korea, 1 November 2020.  
5-10 km/h over the speed limit, and 7 denotes 30-35 km/h or more over the speed limit. Although we cannot assign weights based on the degree of speeding when TSR is 0, it is not a problem because the model also receives speed data.  
Brake data are a Boolean data type, at 1 when stopped and 0 when not. The temporal range of the point data was approximately two months (61 days), from 1 November 2020 to 31 December 2020. The data collection period was during the early stages of the COVID-19 pandemic. Due to the impact of the COVID-19 pandemic, public transportation usage in Daegu likely decreased while taxi usage increased, leading to an increase in data. Alternatively, overall mobility might decrease, reducing the traffic data volume. However, Daegu implemented advisories to avoid public transportation and mandatory mask wearing without strict lockdowns. Our model was designed to handle these changes in traffic patterns and maintained high predictive accuracy during the pandemic. Therefore, despite the pandemic's impact on traffic volume, our focus on traffic speed prediction ensured that our study results were not significantly affected.