Predicting traffic speed is a crucial task in intelligent transportation systems, as it helps analyze traffic congestion and improve road flow. The complex spatio-temporal interactions present in traffic data make accurate predictions challenging. In recent years, many studies have focused on extracting and learning spatio-temporal features. Deep learning methods, particularly spatio-temporal graph learning models, show promising performance and become the mainstream approach in this area of research. However, existing methods cannot exploit and unify the heterogeneous spatio-temporal interactions hidden in traffic data to achieve multi-correlation modeling. As a result, they cannot effectively model the complex evolving patterns in traffic dynamics. To this end, we propose a heterogeneous spatio-temporal traffic graph learning framework (HSTGL) to capture these diverse spatio-temporal interactions comprehensively. In terms of design, HSTGL consists of three modules: spatio-temporal heterogeneous graph construction, spatio-temporal heterogeneous graph attention learning, and heterogeneous information supplementation. The first two modules utilize similar temporal pattern clustering and heterogeneous spatio-temporal graph attention mechanisms (HSTGAT) to learn heterogeneous spatio-temporal interactions in traffic data. The latter feature fusion module (FFM) is developed to complement potential heterogeneous information in the global spatio-temporal context. Our HSTGL conducts extensive experiments on three real-world public traffic datasets: METR-LA, PEMS-BAY, and PEMSD7M. The results demonstrate that HSTGL achieves superior predictive performance compared to representative benchmark methods with average improvements of 3.3% in MAE, 1.8% in RMSE, and 5.2% in MAPE compared to the optimal baseline.

Geospatial Entity Resolution (GER) plays a central role in integrating spatial data from diverse sources.However, existing methods are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge.While recent advances in Large Language Models (LLMs) offer strong semantic reasoning and zero-shot capabilities, directly applying them to GER remains inadequate due to their limited spatial understanding and high inference cost.In this work, we present GER-LLM, a framework that integrates LLMs into the GER pipeline.To address the challenge of spatial understanding, we design a spatially informed blocking strategy based on adaptive quadtree partitioning and Area of Interest (AOI) detection, preserving both spatial proximity and functional relationships.To mitigate inference overhead, we introduce a group prompting mechanism with graph-based conflict resolution, enabling joint evaluation of diverse candidate pairs and enforcing global consistency across alignment decisions.Extensive experiments on real-world datasets demonstrate the effectiveness of our approach, yielding significant improvements over state-of-the-art methods.The data and code is available in https

Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Existing work pays limited attention to the fine-grained functional layout semantics in urban regions, limiting their ability to capture transferable knowledge across regions. Further, inadequate handling of the unique features and relationships required for different downstream tasks may also hinder effective task adaptation. In this paper, we propose a Graph-based Urban Region Pre-training and Prompting framework (GURPP) for region representation learning. Specifically, we first construct an urban region graph and develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of entity interactions. This model pre-trains knowledge-rich region embeddings using contrastive learning and multi-view learning methods. To further refine these representations, we design two graph-based prompting methods: a manually-defined prompt to incorporate explicit task knowledge and a task-learnable prompt to discover hidden knowledge, which enhances the adaptability of these embeddings to different tasks. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our framework.

Recently, city-scale microscopic simulators are developed to analyze data-driven algorithms and strategies. Since the simulators usually require intensive computing and memory resources, distributed approaches are applied to these simulators. However, distributed simulators need to frequently synchronize workers and transmit detailed vehicle information between workers. This process consumes an unacceptable amount of time. In this paper, we speed up the simulation through lowering the synchronization frequency by MetaSync. MetaSync transmits the distributions of vehicles on roads instead of the detailed vehicle information. This allows a worker to self-generate vehicles according to the distributions without frequently interacting with other workers. We also develop a simulator using MetaSync. Results show it is 50% faster than normal synchronization and provides competitive simulation accuracy. Our strategy also enables distributed simulators to run microscopic simulations on a city-scale map with dynamic route planning in an acceptable amount of time.

Perpetrators leverage the untraceable feature to conduct illegal behaviors leading security issues with respect to mixing coins. Generally, bad coins are blocked based on a common blacklist. However, the blacklist may not be updated in time, which results in that bad coins escape the blocking. Consequently, perpetrators can still conduct illicit behaviors such as money laundering. In this paper, we apply game theory under imperfect information to study how coins' quality restrain these illicit behaviors under the incomplete scenario. More specifically, we propose a strategy for participants to submit deposits if they hope to mix coins with others even if they are not in blacklist at this time. The deposits will not be refunded when participants are included in the blacklist after mixing. Therefore, no participants have incentives to mix with bad coins. At the last part of this paper, we also simulate the incomes for participants, which indicates that deposits strategy is effective to prevent illicit behaviors.

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.