Accurate spatio-temporal traffic prediction is essential for optimizing urban traffic management and resource allocation. To reduce the cost and complexity of cross-city deployment, recent studies have explored spatio-temporal foundation models capable of accurate zero-shot prediction. However, these models are limited to single-modal data, which restricts their capacity to capture the complexity of real-world traffic dynamics. The increasing availability of multi-modality data—such as satellite imagery and points of interest (POI)—offers a promising avenue for enhancing cross-city traffic prediction by providing richer background contexts. Despite this potential, developing foundational models for multi-modality spatio-temporal prediction presents two challenges: the availability and quality of multi-modality data vary significantly across cities, with some cities lacking certain modalities or containing noisy information; and spatial patterns are highly localized and specific to individual regions, which hinders generalization. To address these challenges, we propose MoST, a foundation model for multi-modality spatio-temporal traffic prediction. We introduce a Multi-modality Refinement Module that encodes available modality data and adaptively selects task-relevant modalities while suppressing noisy modalities. Furthermore, we design a Spatio-Temporal Prediction Module that incorporates a spatial expert selection mechanism guided by multi-modality cues. This mechanism dynamically identifies region-specific spatial patterns and assigns appropriate spatial experts to model local dependencies. Finally, we conduct extensive experiments on real-world datasets to validate the superior performance and strong generalization capability of MoST.

Accurate prediction of waterlogging depth (WD) is a critical task for urban emergency management and smart-city development. Traditional hydrological forecasting models rely heavily on high-precision weather and terrain data, and struggle to adapt to dynamic rainfall patterns. Recently, deep learning-based methods have shown promising results in capturing complex dynamic patterns. However, these data-driven models face challenges in modeling hydrological mechanisms and effectively fusing multimodal data. To address these limitations, we propose Waterlogformer, a multimodal model for WD prediction. Waterlogformer employs a dual-branch architecture to jointly model WD and rainfall time series alongside static geospatial features. It computes cumulative rainfall based on terrain to incorporate water flow accumulation mechanisms, thereby effectively capturing the impact of rainfall on WD. Furthermore, a Multimodal Fusion Prediction Module fuses multimodal data, enhanced by spatial contrastive learning to improve the model's understanding of geographic relationships. Experiment results on a real-world dataset demonstrate the superior performance of Waterlogformer. The source code is available at https://github.com/decisionintelligence/Waterlogformer.

The financial market plays a crucial role in the modern economy by influencing capital allocation, corporate valuation, and investor behavior. However, its complex dependencies and non-stationary dynamics present significant challenges for financial stock prediction. Previous predictive approaches are typically categorized into Univariate Time Series (UTS) and Multivariate Time Series (MTS) paradigms. UTS methods overlook both cross-feature and cross-stock influences, while MTS methods can only capture one of these simultaneously. Although some recent approaches claim to model 3D Multivariate Time Series (3D-MTS) dependencies, they often discard substantial information and fail to capture the dynamics of the stock market. To address these limitations, we propose FinD3, a Financial 3D model using Dual cubic state spaces and Dynamic hypergraphs. To extract the inherent complex relationships in 3D-MTS, we propose a novel Dual Cubic State Space Model (DCSSM) to capture both cross-feature and cross-stock patterns. Furthermore, to more accurately reflect the dynamics of the stock market, we present an Evolving Hypergraph Attention (EHA) module, which captures dynamic changes in financial markets and updates the hypergraph based on a priori hypergraph. Experimental results demonstrate that FinD3 achieves state-of-the-art performance in quantitative trading performance on two real-world stock market datasets, offering a promising solution to practical quantitative trading challenges. The code is available at: https://github.com/decisionintelligence/FinD3.

Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on the topological structures from single modality data, i.e., road networks, overlooking the geometric and contextual features associated with path-related images, e.g., remote sensing images. Similar to human understanding, integrating information from multiple modalities can provide a more comprehensive view, enhancing both representation accuracy and generalization. However, variations in information granularity impede the semantic alignment of road network-based paths (road paths) and image-based paths (image paths), while the heterogeneity of multi-modal data poses substantial challenges for effective fusion and utilization. In this paper, we propose a novel Multi-modal, Multi-granularity Path Representation Learning Framework (MM-Path), which can learn a generic path representation by integrating modalities from both road paths and image paths. To enhance the alignment of multi-modal data, we develop a multi-granularity alignment strategy that systematically associates nodes, road sub-paths, and road paths with their corresponding image patches, ensuring the synchronization of both detailed local information and broader global contexts. To address the heterogeneity of multi-modal data effectively, we introduce a graph-based cross-modal residual fusion component designed to comprehensively fuse information across different modalities and granularities. Finally, we conduct extensive experiments on two large-scale real-world datasets under two downstream tasks, validating the effectiveness of the proposed MM-Path.

Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability for uncertainty estimation and denoising diffusion probabilistic models (DDPMs) have achieved great success in probabilistic time series imputation tasks with its power to model complex distributions. However, current DDPM-based probabilistic time series imputation methodologies are confronted with two types of challenges: 1) The backbone modules of the denoising parts are not capable of achieving sequence modeling with low time complexity. 2) The architecture of denoising modules can not handle the dependencies in the time series data effectively. To address the first challenge, we explore the potential of state space model, namely Mamba, as the backbone denoising module for DDPMs. To tackle the second challenge, we carefully devise several SSM-based blocks for time series data modeling. Experimental results demonstrate that our approach can achieve state-of-the-art time series imputation results on multiple real-world datasets. Our datasets and code are available at https://github.com/decisionintelligence/SSD-TS/

The advancement of intelligent transportation systems has led to a growing demand for accurate path representations, which are essential for tasks such as travel time estimation, path ranking, and trajectory analysis. However, traditional path representation learning (PRL) methods often focus solely on single-modal road network data, overlooking important physical and regional factors that influence real-world traffic dynamics. To overcome this limitation, we introduce Path-LLM, a multi-modal path representation learning model that integrates large language models (LLMs) into PRL. Our approach leverages LLMs to interpret both topological and textual data, enabling robust multi-modal path representations. To effectively align and merge these modalities, we propose TPalign, a contrastive learning-based pretraining strategy that ensures alignment within the embedding space. We then present TPfusion, a multimodal fusion module that dynamically adjusts the weight of each modality before integration. To further optimize LLM training, we introduce a Two-stage Overlapping Curriculum Learning (TOCL) approach, which progressively increases the complexity of the training data. Finally, we evaluate Path-LLM on three real-world datasets across traditional PRL downstream tasks, achieving up to a 61.84% improvement in path ranking performance on the Xi'an dataset. Additionally, Path-LLM demonstrates superior performance in both few-shot and zero-shot learning scenarios. Our code is available at: https://github.com/decisionintelligence/Path-LLM.

Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation methods that are based on numerical models and physical theories are hindered by computational inefficiencies. Recently, machine learning has emerged as an appealing alternative to improve accuracy and reduce computational time. However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. Specifically, Orca enhances the limited spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal aware encoding module. By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data. Experimental results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art performance in SWH estimation.

The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, thereby capturing the inherent uncertainty of movement and enabling improved routing quality. Thus, stochastic routing has been studied extensively in the edge-centric model, where such costs are assigned to the edges in a graph representation of a road network. However, as this model still disregards important information in trajectories and fails to capture dependencies among cost distributions, a path-centric model, where costs are assigned to paths, has been proposed that captures dependencies better and provides an improved foundation for routing. Unfortunately, when applied in this model, existing routing algorithms are inefficient due to two shortcomings that we eliminate. First, when exploring candidate paths, existing algorithms only consider the costs of candidate paths from the source to intermediate vertices, while disregarding the costs of travel from the intermediate vertices to the destination, causing many noncompetitive paths to be explored. We propose two heuristics for estimating the cost from an intermediate vertex to the destination, thus improving routing efficiency. Second, the edge-centric model relies on stochastic dominance-based pruning to improve efficiency. This pruning assumes that costs are independent and is therefore inapplicable in the path-centric model that takes dependencies into account. We introduce a notion of virtual path that effectively enables stochastic dominance-based pruning in the path-based model, thus further improving efficiency. Empirical studies using two real-world trajectory sets offer insight into the properties of the proposed solution, indicating that it enables efficient stochastic routing in the path-centric model.

Points-of-interests (POIs) have been proven to be indicative for sensing urban land use in numerous studies. However, recent progress mainly relies on spatial co-occurrence patterns among POI categories, which falls short in utilizing the rich semantic information embodied in POI hierarchical categories and in sensing the spatial distribution patterns of POIs at an individual zonal scale. In this context, we present a spatial and adversarial representation learning approach (SARL) for predicting land use of urban zones with POIs. SARL deeply mines the information from POIs from both spatial and categorical perspectives. Specifically, we first utilize a convolutional neural network to sense the spatial distribution patterns of POIs in each urban zone. We then leverage an autoencoder and an adversarial learning strategy to mine the POI categorical information in all hierarchical levels, which emphasizes the prominent and definitive POIs while preserves the overall POI hierarchical structures in each zone. Finally, we fuse these information from the two perspectives via a Wide & Deep network and carry out land use prediction with the fused embeddings. We conduct comprehensive experiments to validate the effectiveness of SARL in four European cities with real-world data. The results demonstrate that SARL substantially outperforms several competitive baselines.

The prevalence of location-based services has generated a deluge of check-ins, enabling the task of human mobility understanding. Among the various types of information associated with the check-in venues, categories (e.g., Bar and Museum ) are vital to the task, as they often serve as excellent semantic characterization of the venues. Despite its significance and importance, a large portion of venues in the check-in services do not have even a single category label, such as up to 30% of venues in the Foursquare system lacking category labels. We, therefore, address the problem of semantic venue annotation, i.e., labeling the venue with a semantic category. Existing methods either fail to fully exploit the contextual information in the check-in sequences, or do not consider the semantic correlations across related categories. As such, we devise a Tree-guided Multi-task Embedding model (TME for short) to learn effective representations of venues and categories for the semantic annotation. TME jointly learns a common feature space by modeling multi-contexts of check-ins and utilizes the predefined category hierarchy to regularize the relatedness among categories. We evaluate TME over the task of semantic venue annotation on two check-in datasets. Experimental results show the superiority of TME over several state-of-the-art baselines.

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue C ategory E mbedding M odel named Hier-CEM , which generates a latent representation for each venue category by embedding the Hier archical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.