Label Distribution Learning (LDL) offers a promising solution to label ambiguity by employing Label Distributions (LDs) instead of traditional logical labels. However, acquiring LDs for real-world data is both expensive and challenging. To address this issue, Label Enhancement (LE) techniques have been proposed to derive LDs from readily available logical labels. While much of the prior work has focused on enhancing LE for better recovery performance, the ultimate objective remains improving LDL’s overall effectiveness. In this paper, we introduce a novel LE method, Topological Label Enhancement via Optimal Transport (TLEOT), which integrates Optimal Transport (OT) theory with topological space analysis. This method goes beyond improving LE, targeting the enhancement of LDL performance by aligning the feature and label distributions within a unified topological framework. Additionally, we present two innovative topological techniques designed to further improve LDL. Extensive experimental evaluations on real-world datasets demonstrate that TLEOT consistently outperforms nine state-of-the-art methods in predictive tasks. Furthermore, the proposed topological techniques significantly enhance LDL’s performance, validating their practical utility in real-world applications.

Detecting spatiotemporal anomalies is imperative for addressing critical societal and engineering challenges, including public safety assurance, environmental hazard identification, epidemic surveillance, and transportation system optimization. Existing methodologies, however, face persistent limitations due to sparse labeled datasets and the inherent complexity of dynamic spatiotemporal systems. In order to bridge this gap, we present unsupervised-semi-supervised stacking (USemiS), a novel framework that synergizes semi-supervised learning with ensemble meta-learning. USemiS introduces three core innovations: 1) unsupervised component learners that extract low-level representations of heterogeneous anomalies, 2) a consensus-based tuning mechanism that dynamically weights robust learners via stability metrics, and 3) spatiotemporal MixUp (ST-MixUp), a tailored augmentation strategy that interpolates anomalies across spatial and temporal dimensions to enhance decision boundaries. By integrating these components, USemiS effectively disentangles latent anomaly patterns while mitigating label scarcity. Evaluated on large-scale traffic anomaly and crowd fall detection datasets, USemiS achieves state-of-the-art performance, outperforming existing methods by 1.3% and 2.1% in AUC under extreme low-label regimes (0.4% and 0.8% labeled data, respectively). These results underscore USemiS's capacity to generalize across diverse spatiotemporal contexts, offering a scalable and robust solution for real-world applications where labeled anomalies are scarce yet critical.

Transportation simulation is non-trivial due to the co-existence of thousands of heterogeneous decision makers (or vehicles). Such large-scale decision making is intrinsically a complex decentralized problem, the resolution of which is at the forefront of transportation simulation. Despite many physical or mathematical models proposed to date, underlying them is usually a set of universal rules plus some random perturbations to characterize vehicular movements. Inspired by the decision-making mechanism of rational human beings (i.e., learning iteratively from experience), this study proposes a novel two-stage ensemble reinforcement learning (TERL) paradigm for large-scale decentralized decision making in transportation simulation, in order to enhance the computational efficiency and thus scalability of RL to practical applications at scale. After establishing a problem-specific Markov decision process, the first stage utilizes clustering to group heterogeneous vehicles into quasi-homogeneous clusters. A representative RL model (or agent) is employed for each cluster where the included vehicles share and jointly optimize the policy parameters. The second stage develops an ensemble control strategy based on representative RL models for vehicles isolated as noise during clustering. While vehicles are simultaneously simulated, RL models are separately trained with cluster-specific experience replay. Application of TERL to the classical multi-user dynamic route choice problem in a real-world network of the Gusu District in Suzhou, China demonstrates the effectiveness of the proposed approach in deriving desirable simulation results, compared with the classical shortest path model and the dynamic user equilibrium model.