###### Abstract  
Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.  
traffic volume prediction; graph convolutional networks; meteorological information +
Footnote †: journal: Applied  
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network methodologies has opened new avenues for traffic volume prediction. These contemporary techniques, especially those driven by neural networks, have shown promising results in predicting traffic speeds across road networks [7]. Neural network models can incorporate diverse data types, including meteorological conditions, such as precipitation, temperature, and humidity, to improve predictions. Integrating weather data into traffic volume prediction models could significantly enhance their precision and reliability. Recent works have used the multi-layer perception network (MLP) [8], long short-term memory (LSTM) [9], graph convolution network (GCN) [10; 11], and graph attention network (GAN) [7] to predict traffic volumes. Despite the important role of and progress in neural network applications, the intensive study of long-term traffic volume prediction incorporating meteorological data remains limited. This gap is attributable to the intricate interactions among traffic flows, temporal patterns, and weather conditions.  
Exploring long-term traffic volume prediction has been limited, primarily due to challenges associated with data acquisition. Precise long-term forecasting requires datasets spanning over a year to capture weekly, monthly, seasonal, and holiday impacts adequately. Previous studies have utilized up to 2 years of datasets for traffic volume or speed predictions [12] to explain seasonal effects carefully.  
The main aim of this study was to determine whether meteorological information significantly advances long-term traffic volume prediction. The contributions of this study are three-fold. First, this paper proposes a temporal GCN-based approach to one-year traffic volume prediction by the one-week traffic volume prediction format to analyze the impacts of temporal dependencies and meteorological information. Second, we propose a neural network structure for a prediction performance that is better than that of other types in previous studies. Third, this study used meteorological information to predict the impact of weather information.  
The outline of this paper is as follows. Section 2 presents some related studies on traffic volume prediction. Section 3 describes datasets on traffic volume and meteorological information. Section 4 presents the proposed model's framework, describing the graph's structure and layers. Section 5 shows the training and prediction results. Section 6 summarizes the findings of this study and proposes directions for the next steps.