###### Abstract
(2017) emphasized the potential of data management and transportation forecasting in smart cities, and these studies show that recurrent neural network-based methodologies have been dominant in transportation flow forecasting, including bus arrival time prediction [9; 10]. However, these methodologies suffer from the limitation that they do not sufficiently address the problem of long-term dependencies, and various studies have been conducted utilizing artificial intelligence (AI) and machine learning (ML) techniques to address this issue [11]. [PERSON] et al. (2017) developed a deep spatio-temporal residual network to emphasize the potential of deep learning models to process complex spatiotemporal data [12]. Recently, [PERSON] et al. (2022) proposed a network structure that combines CNN and Bi-LSTM in parallel to improve the performance of train travel time prediction. The model combines the feature-extraction capabilities of CNN with the long-term dependency learning capabilities of Bi-LSTM to effectively handle time series data, improving the accuracy of the transportation-prediction model and demonstrating its real-time applicability [13]. [PERSON] et al. (2022) presented a method to improve bus arrival time prediction in urban transportation conditions through different neural network architectures [14]. In addition, [PERSON] et al. (2019) improved transportation flow-prediction accuracy using a spatial-temporal graph convolutional network, and [PERSON] et al. (2019) demonstrated the effectiveness of the attention mechanism in enhancing sequence data processing in time-series prediction [15; 16]. These studies show that attention mechanism-based models, which are at the heart of Transformer, provide better prediction accuracy and real-time performance than traditional recurrent neural networks, and are playing an important role in transportation forecasting. In this study, we utilized public data platforms such as Seoul Open Data Plaza and Public data portal for bus arrival time prediction to obtain real-time transportation information, bus route information, and service records, which are various factors that can affect arrival times. In the data-preprocessing stage, we applied normalization and one-hot encoding techniques to handle missing values and maintain data consistency to build a high-quality dataset for model training, and to improve prediction performance, we applied resampling techniques to address the data imbalance problem proposed by [PERSON] et al. (2021) [17]. Data imbalance occurs when data are skewed toward certain paths or time periods, which negatively impacts model training, and we used resampling techniques to even out the distribution of the data. [PERSON] and [PERSON] (2021) highlighted that data augmentation plays a significant role in improving model generalization in time-series classification. Considering these references, we recognize the positive impact of applying data-resampling techniques on model performance ([PERSON] and [PERSON], 2021). We used these data-resampling techniques to address data imbalance and further improve the performance of our model. We also introduced a learnable position encoding to better capture temporal dynamics and specific context. In the original transformer model, a fixed location encoding is pre-computed based on a sine function and is maintained during training, but in this work, we introduce a learnable location encoding that adapts during training to better capture temporal dynamics and specific context to improve performance. These learnable location encodings evolve through training to encode optimal location information, which is essential for understanding the different factors that influence bus delays. Several studies have begun to introduce learnable positional encodings that can adapt to the data during training and have proven essential. [PERSON] et al.