Accurate traffic forecasting plays a critical role in modern transportation systems by enabling effective congestion management and route optimization. Although recent deep learning-based models have shown substantial progress in modeling complex spatiotemporal dependencies, most existing methods overlook the challenges posed by anomalous traffic conditions. To address this gap, we propose a two-stage anomaly-aware forecasting correction framework. The first stage employs an unsupervised auto-regressive anomaly detector that combines representation learning and spatiotemporal attention to capture normal traffic patterns and filter anomalous inputs through error thresholding. In the second stage, an anomaly optimization predictor leverages a memory module and a sparsity regularization learning strategy to enhance the representation of normal patterns and suppress noise. It models spatiotemporal dependencies using an information propagation layer composed of sequential small-kernel temporal convolutions and memory-guided graph convolutions. Extensive experiments on real-world traffic flow datasets demonstrate that our framework outperforms state-of-the-art models under anomalous conditions.

Next Point of Interest (POI) recommendation primarily predicts future activities based on users’ past check-in data and current status, providing significant value to users and service providers. We observed that the popular check-in times for different POI categories vary. For example, coffee shops are crowded in the afternoon because people like to have coffee to refresh after meals, while bars are busy late at night. However, existing methods rarely explore the relationship between POI categories and time, which may result in the model being unable to fully learn users’ tendencies to visit certain POI categories at different times. Additionally, existing methods for modeling time information often convert it into time embeddings or calculate the time interval and incorporate it into the model, making it difficult to capture the continuity of time. Finally, during POI prediction, various weighting information is often ignored, such as the popularity of each POI, the transition relationships between POIs, and the distances between POIs, leading to suboptimal performance. To address these issues, this article proposes a novel next POI recommendation framework called Graph Disentangler with POI Weighted Module (GDPW) . This framework aims to jointly consider POI category information and multiple POI weighting factors. Specifically, the proposed GDPW learns category and time representations through the Global Category Graph and the Global Category-Time Graph. Then, we disentangle category and time information through contrastive learning. After prediction, the final POI recommendation for users is obtained by weighting the prediction results based on the transition weights and distance relationships between POIs. We conducted experiments on two real-world datasets, and the results demonstrate that the proposed GDPW outperforms other existing models, improving performance by 3% to 11%.

Dengue outbreaks pose significant public health and socio-economic risks in endemic regions. We propose SEIR-DST, a forecasting model that predicts district-level Dengue cases by integrating SEIR-based epidemiological simulation with spatio-temporal deep learning. The model combines compartmental disease dynamics, attention mechanisms, and dilated temporal convolutions to capture both temporal trends and spatial diffusion. Evaluations on real-world data from Tainan City, Taiwan, show that SEIR-DST outperforms nine baseline models in predictive accuracy, enabling earlier risk identification and more targeted interventions. Our findings highlight the value of combining epidemiological priors with graph-based deep learning for fine-grained epidemic forecasting.