Subseasonal-to-seasonal (S2S) forecasting, which predicts climate conditions from several weeks to months in advance, represents a critical frontier for agricultural planning, energy management, and disaster preparedness. However, it remains one of the most challenging problems in atmospheric science, due to the chaotic dynamics of atmospheric systems and complex interactions across multiple scales. Current approaches often fail to explicitly model underlying physical processes and teleconnections that are crucial at S2S timescales. We introduce TelePiT, a novel deep learning architecture that enhances global S2S forecasting through integrated multi-scale physics and teleconnection awareness. Our approach consists of three key components: (1) Spherical Harmonic Embedding, which accurately encodes global atmospheric variables onto spherical geometry; (2) Multi-Scale Physics-Informed Neural ODE, which explicitly captures atmospheric physical processes across multiple learnable frequency bands; (3) Teleconnection-Aware Transformer, which models critical global climate interactions through explicitly modeling teleconnection patterns into the self-attention. Extensive experiments demonstrate that TelePiT significantly outperforms state-of-the-art data-driven baselines and operational numerical weather prediction systems across all forecast horizons, marking a significant advance toward reliable S2S forecasting.

Recent advancements in deep learning have led to the development of Foundation Models (FMs) for weather forecasting, yet their ability to predict extreme weather events remains limited. Existing approaches either focus on general weather conditions or specialize in specific-type extremes, neglecting the real-world atmospheric patterns of diversified extreme events. In this work, we identify two key characteristics of extreme events: (1) the spectral disparity against normal weather regimes, and (2) the hierarchical drivers and geographic blending of diverse extremes. Along this line, we propose UniExtreme, a universal extreme weather forecasting foundation model that integrates (1) an Adaptive Frequency Modulation (AFM) module that captures region-wise spectral differences between normal and extreme weather, through learnable Beta-distribution filters and multi-granularity spectral aggregation, and (2) an Event Prior Augmentation (EPA) module which incorporates region-specific extreme event priors to resolve hierarchical extreme diversity and composite extreme schema, via a dual-level memory fusion network. Extensive experiments demonstrate that UniExtreme outperforms state-of-the-art baselines in both extreme and general weather forecasting, showcasing superior adaptability across diverse extreme scenarios.

Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the field of natural language processing. This progress has inspired a series of innovative studies that explore the adaptation of PLMs to time series analysis, intending to create a unified foundation model that addresses various time series analytical tasks. However, these efforts predominantly focus on Regularly Sampled Time Series (RSTS), neglecting the unique challenges posed by Irregularly Sampled Time Series (ISTS), which are characterized by uneven sampling intervals and prevalent missing data. To bridge this gap, this work takes the first step in exploring the potential of PLMs for ISTS analysis. We begin by investigating the effect of various methods for representing ISTS, aiming to maximize the efficacy of PLMs in the analysis. Furthermore, we propose a unified PLM-based framework, named ISTS-PLM, to address diverse ISTS analytical tasks. It integrates novel time-aware and variable-aware PLMs tailored to tackle the intractable intra- and inter-time series modeling in ISTS. Finally, extensive experiments on a comprehensive benchmark demonstrate that the ISTS-PLM, utilizing a structured and effective series-based representation for ISTS, consistently achieves state-of-the-art performance across various analytical tasks, such as classification, interpolation, extrapolation, few-shot and zero-shot learning scenarios, spanning scientific domains like healthcare, biomechanics, and climate science.

Spatio-temporal forecasting is a critical component of various smart city applications, such as transportation optimization, energy management, and socio-economic analysis. Recently, several automated spatio-temporal forecasting methods have been proposed to automatically search the optimal neural network architecture for capturing complex spatio-temporal dependencies. However, the existing automated approaches suffer from expensive neural architecture search overhead, which hinders their practical use and the further exploration of diverse spatio-temporal operators in a finer granularity. In this paper, we propose AutoSTF, a decoupled automatic neural architecture search framework for cost-effective automated spatio-temporal forecasting. From the efficiency perspective, we first decouple the mixed search space into temporal space and spatial space and respectively devise representation compression and parameter-sharing schemes to mitigate the parameter explosion. The decoupled spatio-temporal search not only expedites the model optimization process but also leaves new room for more effective spatio-temporal dependency modeling. From the effectiveness perspective, we propose a multi-patch transfer module to jointly capture multi-granularity temporal dependencies and extend the spatial search space to enable finer-grained layer-wise spatial dependency search. Extensive experiments on eight datasets demonstrate the superiority of AutoSTF in terms of both accuracy and efficiency. Specifically, our proposed method achieves up to 13.48x speed-up compared to state-of-the-art automatic spatio-temporal forecasting methods while maintaining the best forecasting accuracy. The source code and data are available at https://github.com/usail-hkust/AutoSTF.

Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models (UFMs), this burgeoning field faces challenges such as a lack of clear definitions and systematic reviews. To this end, this paper first introduces the concept of UFMs and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes and clarifies current UFM-related works, based on urban data modalities and types. Furthermore, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at: https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.

Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional forecasting methods, however, struggle with the irregular traffic time series resulting from adaptive traffic signal controls, presenting challenges in asynchronous spatial dependency, irregular temporal dependency, and predicting variable-length sequences. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. Specifically, we first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. After that, to capture the temporal dependency within irregular traffic state sequences, a personalized time encoding is devised to embed the continuous time signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow. Additionally, a Semi-Autoregressive Prediction Network, comprising a state evolution unit and a semiautoregressive predictor, is designed to predict variable-length traffic sequences effectively and efficiently. Extensive experiments on a newly established benchmark demonstrate the superiority of ASeer compared with twelve competitive baselines across six metrics.

Spatio-Temporal Graph Neural Network (STGNN) has been used as a common workhorse for traffic forecasting. However, most of them require prohibitive quadratic computational complexity to capture long-range spatio-temporal dependencies, thus hindering their applications to long historical sequences on large-scale road networks in the real-world. To this end, in this paper, we propose BigST, a linear complexity spatio-temporal graph neural network, to efficiently exploit long-range spatio-temporal dependencies for large-scale traffic forecasting. Specifically, we first propose a scalable long sequence feature extractor to encode node-wise long-range inputs ( e.g. , thousands of time-steps in the past week) into low-dimensional representations encompassing rich temporal dynamics. The resulting representations can be pre-computed and hence significantly reduce the computational overhead for prediction. Then, we build a linearized global spatial convolution network to adaptively distill time-varying graph structures, which enables fast runtime message passing along spatial dimensions in linear complexity. We empirically evaluate our model on two large-scale real-world traffic datasets. Extensive experiments demonstrate that BigST can scale to road networks with up to one hundred thousand nodes, while significantly improving prediction accuracy and efficiency compared to state-of-the-art traffic forecasting models.

Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. However, the energy consumption of each vehicle may diverge widely due to the personalized driving behavior under varying travel contexts. To this end, this paper proposes a preference-aware meta-optimization framework Meta-Pec for personalized vehicle energy consumption estimation. Specifically, we first propose a spatiotemporal behavior learning module to capture the latent driver preference hidden in historical trips. Moreover, based on the memorization of driver preference, we devise a selection-based driving behavior prediction module to infer driver-specific driving patterns on a given route, which provides additional basis and supervision signals for VEC estimation. Besides, a driver-specific meta-optimization scheme is proposed to enable fast model adaption by learning and sharing transferable knowledge globally. Extensive experiments on two real-world datasets show the superiority of our proposed framework against ten numerical and data-driven machine learning baselines. The source code is available at https://github.com/usail-hkust/Meta-Pec.

Machine learning-based forecasting models are commonly used in Intelligent Transportation Systems (ITS) to predict traffic patterns and provide city-wide services. However, most of the existing models are susceptible to adversarial attacks, which can lead to inaccurate predictions and negative consequences such as congestion and delays. Therefore, improving the adversarial robustness of these models is crucial for ITS. In this paper, we propose a novel framework for incorporating adversarial training into spatiotemporal traffic forecasting tasks. We demonstrate that traditional adversarial training methods designated for static domains cannot be directly applied to traffic forecasting tasks, as they fail to effectively defend against dynamic adversarial attacks. Then, we propose a reinforcement learning-based method to learn the optimal node selection strategy for adversarial examples, which simultaneously strengthens the dynamic attack defense capability and reduces the model overfitting. Additionally, we introduce a self-knowledge distillation regularization module to overcome the "forgetting issue" caused by continuously changing adversarial nodes during training. We evaluate our approach on two real-world traffic datasets and demonstrate its superiority over other baselines. Our method effectively enhances the adversarial robustness of spatiotemporal traffic forecasting models. The source code for our framework is available at https://github.com/usail-hkust/RDAT.

Recent years have witnessed the rapid development of the Cooperative Vehicle Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and autonomous vehicles (AVs) can share information among each other and work collaboratively to provide safer and more comfortable transportation experience to human beings. While many efforts have been made to develop efficient and sustainable CVIS solutions, existing approaches on urban intersections heavily rely on domain knowledge and physical assumptions, preventing them from being practically applied. To this end, this paper proposes NavTL, a learning-based framework to jointly control traffic signal plans and autonomous vehicle rerouting in mixed traffic scenarios where human-driven vehicles and AVs co-exist. The objective is to improve travel efficiency and reduce total travel time by minimizing congestion at the intersections while guiding AVs to avoid the temporally congested roads. Specifically, we design a graph-enhanced multi-agent decentralized bi-directional hierarchical reinforcement learning framework by regarding TLs as manager agents and AVs as worker agents. At lower temporal resolution timesteps, each manager sets a goal for the workers within its controlled region. Simultaneously, managers learn to take the signal actions based on the observation from the environment as well as an intention information extracted from its workers. At higher temporal resolution timesteps, each worker makes rerouting decisions along its way to the destination based on its observation from the environment, an intention-enhanced manager state representation, and a goal from its present manager. Finally, extensive experiments on one synthetic and two real-world network-level datasets demonstrate the effectiveness of our proposed framework in terms of improving travel efficiency.

Electric Vehicle (EV) has become preferable choices in modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find proper spots for charging because of the limited charging infrastructures and spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve charging experience over long-term horizons. In this paper, we propose RLCharge for intelligent EV charging station recommendation by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an agent, we formulate the problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with centralized training decentralized execution. Particularly, we propose a tailor designed centralized attentive critic with the delayed access strategy to coordinate the recommendation between geo-distributed agents during centralized training. Besides, we propose the spatio-temporal heterogeneous graph convolution module to handle the partial observability problem during decentralized execution. After that, to effectively optimize multiple divergent objectives, we develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction, and propose an adaptive imitation learning scheme to further accelerate and stabilize the policy convergence. Finally, extensive experiments on two real-world datasets demonstrate that RLCHARGE achieves the best comprehensive performance compared with ten baseline approaches.

Traffic flow imputation provides a more-complete view of traffic flows, and thus is a fundamental function in building Intelligent Transportation Systems. The performance of traffic flow imputation has a big impact on a wide range of downstream applications, such as traffic forecasting and control. Therefore, in this paper, we propose a Multi-grAph Convolutional Recurrent netwOrk (MACRO) framework for supporting fine-grained lane-level traffic flow imputation, which can help to reconstruct more complete traffic flows at the lane level. Specifically, we first design a spatial dependency module to model the diversified spatial correlations within traffic flows, where multi-relation graphs are first constructed to consider correlations from various perspective, then a multi-graph convolution neural network is proposed to capture the integrated spatial dependencies of traffic flows and adequately propagate the observed traffic values to mitigate data sparsity problem from spatial domain. Also, to handle the temporally continuous data missing issue, we adopt a modified bi-directional recurrent neural network to capture traffic flows’ temporal dependencies by considering both historical and future information, and employ a temporal decay mechanism to control the irregular information transfer between adjacent time slices. Moreover, a spatio-temporal knowledge integration module is devised to comprehensively integrate multi-resolution spatiotemporal knowledge for traffic flow imputation. Finally, extensive experiments on the real-world dataset demonstrate that the performance of MACRO outperforms several state-of-the-art baselines with respect to traffic flow imputation.

Electric Vehicles (EVs) have been emerging as a promising low-carbon transport target. While a large number of public charging stations are available, the use of these stations is often imbalanced, causing many problems to Charging Station Operators (CSOs). To this end, in this paper, we propose a Multi-Agent Graph Convolutional Reinforcement Learning (MAGC) framework to enable CSOs to achieve more effective use of these stations by providing dynamic pricing for each of the continuously arising charging requests with optimizing multiple long-term commercial goals. Specifically, we first formulate this charging station request-specific dynamic pricing problem as a mixed competitive-cooperative multi-agent reinforcement learning task, where each charging station is regarded as an agent. Moreover, by modeling the whole charging market as a dynamic heterogeneous graph, we devise a multi-view heterogeneous graph attention networks to integrate complex interplay between agents induced by their diversified relationships. Then, we propose a shared meta generator to generate individual customized dynamic pricing policies for large-scale yet diverse agents based on the extracted meta characteristics. Finally, we design a contrastive heterogeneous graph pooling representation module to learn a condensed yet effective state action representation to facilitate policy learning of large-scale agents. Extensive experiments on two real-world datasets demonstrate the effectiveness of MAGC and empirically show that the overall use of stations can be improved if all the charging stations in a charging market embrace our dynamic pricing policy.

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. The effective prediction of city-wide parking availability can boost parking efficiency, improve urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for city-wide parking availability prediction because of three major challenges: 1) the non-euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and bluetooth sensor). To this end, we propose aSemi-supervisedHierarchicalRecurrent Graph Neural Network-X(SHARE-X) to predict parking availability of each parking lot within a city. Specifically, we first propose a hierarchical graph convolution module to model the non-euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a multi-resolution soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Moreover, we devise a hierarchical attentive recurrent network module to incorporate both short and long-term dynamic temporal dependencies of parking lots. Additionally, a parking availability approximation module is introduced to estimate missing real-time parking availabilities from both spatial and temporal domains. Finally, experiments on two real-world datasets demonstrate thatSHARE-Xoutperforms eight state-of-the-art baselines in parking availability prediction.

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a non-trivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to profile the real estate from multiple perspectives~(e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features.

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve the charging experience from various aspects over a long-term horizon. In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an individual agent, we formulate this problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with the centralized attentive critic to coordinate the recommendation between geo-distributed agents. Moreover, to quantify the influence of future potential charging competition, we introduce a delayed access strategy to exploit the knowledge of future charging competition during training. After that, to effectively optimize multiple learning objectives, we extend the centralized attentive critic to multi-critics and develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction. Finally, extensive experiments on two real-world datasets demonstrate that Master achieves the best comprehensive performance compared with nine baseline approaches.