Traffic prediction remains a key challenge in spatio-temporal data mining, despite progress in deep learning. Accurate forecasting is hindered by the complex influence of external factors such as traffic accidents and regulations, often overlooked by existing models due to limited data integration. To address these limitations, we present two enriched traffic datasets from Tokyo and California, incorporating traffic accident and regulation data. Leveraging these datasets, we propose ConFormer (Conditional Transformer), a novel framework that integrates graph propagation with guided normalization layer. This design dynamically adjusts spatial and temporal node relationships based on historical patterns, enhancing predictive accuracy. Our model surpasses the state-of-the-art STAEFormer in both predictive performance and efficiency, achieving lower computational costs and reduced parameter demands. Extensive evaluations demonstrate that ConFormer consistently outperforms mainstream spatio-temporal baselines across multiple metrics, underscoring its potential to advance traffic prediction research. The code is released in https://github.com/Dreamzz5/ConFormer.

Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they often fail to account for dynamic deviations between current inputs and historical patterns. These deviations contain critical signals that can significantly affect model performance. To fill this gap, we propose $\textbf{ST-SSDL}$, a $\underline{S}$patio-$\underline{T}$emporal time series forecasting framework that incorporates a $\underline{S}$elf-$\underline{S}$upervised $\underline{D}$eviation $\underline{L}$earning scheme to capture and utilize such deviations. ST-SSDL anchors each input to its historical average and discretizes the latent space using learnable prototypes that represent typical spatio-temporal patterns. Two auxiliary objectives are proposed to refine this structure: a contrastive loss that enhances inter-prototype discriminability and a deviation loss that regularizes the distance consistency between input representations and corresponding prototypes to quantify deviation. Optimized jointly with the forecasting objective, these components guide the model to organize its hidden space and improve generalization across diverse input conditions. Experiments on six benchmark datasets show that ST-SSDL consistently outperforms state-of-the-art baselines across multiple metrics. Visualizations further demonstrate its ability to adaptively respond to varying levels of deviation in complex spatio-temporal scenarios. Our code and datasets are available at https://github.com/Jimmy-7664/ST-SSDL.

Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been extensively studied and achieved, however, the effective incorporation of both spatial and temporal information into such GNN-based models remains challenging. Temporal information is extracted from users' trajectories, while spatial information is obtained from POIs. Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs. To address the challenge, we propose Mobility Graph Transformer (MobGT) that enables us to fully leverage graphs to capture both the spatial and temporal features in users' mobility patterns. MobGT combines individual spatial and temporal graph encoders to capture unique features and global user-location relations. Additionally, it incorporates a mobility encoder based on Graph Transformer to extract higher-order information between POIs. To address the long-tailed problem in spatial-temporal data, MobGT introduces a novel loss function, Tail Loss. Experimental results demonstrate that MobGT outperforms state-of-the-art models on various datasets and metrics, achieving 24% improvement on average. Our codes are available at https://github.com/Yukayo/MobGT.

Remote sensing object detection has been an important and challenging research hot spot in computer vision that is widely used in military and civilian fields. Recently, the combined detection model of convolutional neural network (CNN) and transformer has achieved good results, but the problem of poor detection performance of small objects still needs to be solved urgently. This letter proposes a deformable end-to-end object detection with transformers (DETR)-based framework for object detection in remote sensing images. First, multiscale split attention (MSSA) is designed to extract more detailed feature information by grouping. Next, we propose multiscale deformable prescreening attention (MSDPA) mechanism in decoding layer, which achieves the purpose of prescreening, so that the encoder–decoder structure can obtain attention map more efficiently. Finally, the A–D loss function is applied to the prediction layer, increasing the attention of small objects and optimizing the intersection over union (IOU) function. We conduct extensive experiments on the DOTA v1.5 dataset and the HRRSD dataset, which show that the reconstructed detection model is more suitable for remote sensing objects, especially for small objects. The average detection accuracy in DOTA dataset has improved by 4.4% (up to 75.6%), especially the accuracy of small objects has raised by 5%.

With the popularity of network utility, network pricing is becoming an emerging research hotspot. This paper studies the revenue maximization problem based on network utilization optimizing approach. In order to reduce the implemental complexity, we present a SG (Super Group) method whose time complexity is O(1) to regroup users. A precision control variable ε is introduced to control the group size. We then design a distribution related network resource reschedule scheme called RR (Resource Reschedule scheme) to optimize the network utilization. Two important factors, resource threshold and monitoring timeslot, which will affect the dynamic reschedule process were proposed and tested in the simulation experiment. After combining SG method and RR scheme, we make the pricing process faster and more practical. We also prove that our new approach can achieve the same or even more revenue gaining than original usage-based pricing scheme.