## 3 Data Descriptions  
The dataset used in this study encompasses a comprehensive collection of information relevant to traffic volume and weather conditions within the spatial scope of the Korea University of Transportation, Chung-ju campus, from 2019 to 2022. Each row in the dataset represents an hourly observation, providing a detailed view of various attributes, including the week day, traffic volume, temperature, rainfall, wind speed, wind direction, humidity, air pressure, hour, week, and holiday indicators. We trimmed the dataset to align all row observations to be the same as the other datasets. Therefore, the trimmed dataset covered a period from 7 January 2019 to 29 December 2019, a period from 6 January 2020 to 27 December 2020, a period from 4 January 2021 to 26 December 2021, and a period from 3 January 2022 to 25 December 2022. Note that this dataset contained the COVID-19 period, such as 2020 to 2021. We used the 2019, 2020, and 2021 datasets to train the model, and we used the 2022 dataset to validate and test the model. We collected this publicly available meteorological information from the Open MET Data Portal ([[https://data.kma.go.kr/](https://data.kma.go.kr/)]([https://data.kma.go.kr/](https://data.kma.go.kr/)) (accessed on 1 March 2024)) using the Automated Synoptic Observing System (ASOS) and publicly available hourly traffic volume from Traffic Monitoring Systems ([[https://road.re.kr](https://road.re.kr)]([https://road.re.kr](https://road.re.kr)) (accessed on 1 March 2024)). The spatial scope of these data is limited to the Korea University of Transportation, Chung-ju campus. This dataset offers a comprehensive view of year-round traffic and weather patterns. The temporal granularity of this dataset allows for an in-depth examination of how these patterns change over different hours and days. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (Year)** & **Methodology** & **Spatial Scope** & **Weather** & **Spatial** **Dependence** & **Temporal Scope** \\ \hline [PERSON] et al. (2020) [12] & GCN & Boston in the U.S. & O & O & 406 days \\ [PERSON] et al. (2019) [20] & GCN, STG2 Seq & Shenyang and Beijing in China & X & X & 3 to 6 months \\ [PERSON] et al. (2020) [2] & GCN & Manhattan in the U.S. & X & X & 1 year \\ [PERSON] et al. (2023) [10] & PDFormer (GCN + attention) & Beijing in China & X & O & 2 months to 1 year \\ This study & GCN & Chung-ju si in South Korea & O & X & 4 years \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature Review. Figure 1 represents the traffic information. Figure 1a,c illustrate the weekly average traffic volumes at the upper and lower bounds, respectively, from 2019 to 2022. The datasets imply a seasonal trend, with heightened traffic volumes during the summer months (from week 22 to week 40) compared to those in winter (from week 0 to week 10 and from week 49 to week 52), suggesting a correlation between traffic patterns and seasonal variations. Figure 1b,d depict the hourly average traffic volumes at the upper and lower bounds for the same period. These figures elucidate a consistent hourly pattern over 2019 to 2022 marked by a morning peak at around 7 a.m. at the upper bound (Figure 1b) and an evening peak at around 5 p.m. at the lower bound (Figure 1d). This bimodal distribution indicates the conventional rush hours associated with morning and evening commutes. Figure 2a presents the average weekly air temperature from 2019 to 2022, representing a consistent annual pattern with a pronounced peak around week 31, indicative of the climatic zenith typically observed during the midsummer period. Figure 2b illustrates the average weekly rainfall within the same period. The data reveal a notable peak in precipitation around week 33, implying a temporal concentration of rainfall corresponding to regional and seasonal weather patterns. Figure 2c depicts the weekly wind speed averages over the same time period. The graphical representation indicates a relatively stable wind speed pattern irrespective of the annual variations.