Accurate traffic forecasting is challenging due to the complex interdependencies of large road networks and abrupt speed changes caused by unexpected events. Recent work has focused on spatial modeling with adaptive graph embedding or graph attention but has paid less attention to the temporal characteristics and effectiveness of in-situ modeling. In this paper, we propose the time-enhanced spatio-temporal attention model (TESTAM) to better capture recurring and nonrecurring traffic patterns with mixture-of-experts model with three experts for temporal modeling, spatio-temporal modeling with a static graph, and spatio-temporal dependency modeling with a dynamic graph. By introducing different experts and properly routing them, TESTAM better captures traffic patterns under various circumstances, including cases of spatially isolated roads, highly interconnected roads, and recurring and non-recurring events. Experimental results on three public traffic network datasets, METR-LA, PEMS-BAY, and EXPY-TKY, demonstrate that TESTAM outperforms 13 existing methods in terms of accuracy due to its better modeling of recurring and non-recurring traffic patterns. Spatio-temporal modeling in non-Euclidean space has received considerable attention since it can be widely applied to many real-world problems, such as social networks and human pose estimation. Traffic forecasting is a representative real-world problem, which is particularly challenging due to the difficulty of identifying innate spatio-temporal dependencies between roads. Moreover, such dependencies are often influenced by numerous factors, such as weather, accidents, and holidays (Park et al., 2020; Lee et al., 2020; Lee et al., 2022). To overcome the challenges related to spatio-temporal modeling, many deep learning models have been proposed, including graph convolutional networks (GCNs), recurrent neural networks (RNNs), and Transformer. Li et al. (2018) have introduced DCRNN, which injects graph convolution into recurrent units, while Yu et al. (2018) have combined graph convolution and convolutional neural networks (CNNs) to model spatial and temporal features, outperforming traditional methods, such as ARIMA. Although effective, GCN-based methods require prior knowledge of the topological characteristics of spatial dependencies. In addition, as the pre-defined graph relies heavily on the Euclidean distance and empirical laws (Tobler's first law of geography), ignoring dynamic changes in traffic (e.g., rush hour and accidents), it is hardly an optimal solution (Jiang et al., 2023).

To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the proposed models have been remarkably improved in recent years, there are still questions that need to be answered before deploying models. For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments. It is also difficult to determine which models would work when traffic conditions change abruptly (e.g., rush hour). In this work, we conduct two experiments to answer the two questions. In the first experiment, we conduct an experiment with the state-of-the-art models and the identical public datasets to compare model performance under a consistent experiment environment. We then extract a set of temporal regions in the datasets, whose speeds change abruptly and use these regions to explore model performance with difficult intervals. The experiment results indicate that Graph-WaveNet and GMAN show better performance in general. We also find that prediction models tend to have varying performances with data and intervals, which calls for in-depth analysis of models on difficult intervals for real-world deployment.