Papers Traffic Prediction
“Traffic Prediction” 태그가 달린 논문 432편 · 필터 해제
AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting
Multi-modal spatio-temporal forecasting (MM-STF) supports weather nowcasting, traffic prediction, and earth-system modeling by combining heterogeneous sources such as physical fields, satellite imagery, and in-situ senso…
Semantic SimilarityWeather ForecastingTraffic PredictionA Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction
Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfect sensing conditions. Missing observations and anomalous disturbances…
Traffic PredictionAn Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks
Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access Points (APs) and proactively optimize network performance. In this co…
Traffic PredictionFederated LearningNeural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions
Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convoluti…
Neural Architecture SearchTraffic PredictionParameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks. However, prediction remains challenging because network demand is influenced by complex mobi…
Traffic PredictionPMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting
Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction. Transformer-based models have adopted patch-based strategies to capture long-range depen…
Time Series ForecastingTraffic PredictionAn Efficient and Effective Architecture for Large-Scale Traffic Prediction via Geometry-Adaptive Square Partitioning
Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making. Despite the effectiveness of mainstream neural-network based methods, their deployment in real-world settings with …
Traffic PredictionDecision MakingContinuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks
Traffic state prediction is a fundamental task in intelligent transportation systems. In practical applications, some regions suffer from limited traffic observations due to insufficient sensing infrastructure, making cr…
Traffic PredictionMoE Enhanced Federated Learning for Spatiotemporal Prediction
Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scarcity due to limited sensor deployment and uneven urban development. Cr…
Federated LearningTraffic PredictionFrom Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal inter…
Traffic PredictionEfficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth
Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems…
Traffic PredictionSelecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimation
Accurate measurement of traffic volumes and flows is vital for modern intelligent transportation. However, despite recent technological advances in sensor devices, it is still expensive to install and maintain fixed traf…
Traffic PredictionU-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation
The efficient operation of modern cellular networks hinges on the accurate analysis of spatio-temporal traffic data. Mastering these patterns is essential for core network functions, chiefly forecasting future load to pr…
Traffic PredictionEfficient Prompt Learning for Traffic Forecasting
Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administration. Spatio-temporal graph neural networks (GNNs) have achieved state-…
Computational EfficiencyTraffic PredictionCoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
In recent years, the integration of non-topological space modeling with temporal learning methods has emerged as an effective approach for capturing spatio-temporal information in non-Euclidean graphs. However, most exis…
Continual LearningTraffic PredictionFedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction
Traffic prediction plays a central role in intelligent transportation systems (ITS) by supporting real-time decision-making, congestion management, and long-term planning. However, many existing approaches face practical…
Traffic PredictionFAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typically face a trade-off between expressiveness and efficiency: Transform…
Traffic PredictionFrom Time Series to State: Situation-Aware Modeling for Air Traffic Flow Prediction
Accurate air traffic prediction in the terminal airspace (TA) is pivotal for proactive air traffic management (ATM). However, existing data-driven approaches predominantly rely on time series-based forecasting paradigms,…
Traffic PredictionDeep Learning Network-Temporal Models For Traffic Prediction
Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions. Existing statistical-based and shallow machine learning models have shown li…
Multivariate Time Series ForecastingTraffic PredictionEffective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression
Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive periods and multiple locations, naturally re…
Time Series ForecastingTime Series AnalysisWeather ForecastingTraffic Prediction