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Traffic Prediction
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Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.
<span style="color:grey; opacity: 0.6">( Image credit: BaiduTraffic )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Are Graphs and GCNs necessary for short-term metro ridership forecasting?
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
Multi-agent Traffic Prediction via Denoised Endpoint Distribution
Enhancing Traffic Prediction with Textual Data Using Large Language Models
xMTrans: Temporal Attentive Cross-Modality Fusion Transformer for Long-Term Traffic Prediction
RACH Traffic Prediction in Massive Machine Type Communications
Cellular Traffic Prediction Using Online Prediction Algorithms
TrafficGPT: Towards Multi-Scale Traffic Analysis and Generation with Spatial-Temporal Agent Framework
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey
ST-MambaSync: The Complement of Mamba and Transformers for Spatial-Temporal in Traffic Flow Prediction
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction
Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Towards Explainable Traffic Flow Prediction with Large Language Models
Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets
Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline
BjTT: A Large-scale Multimodal Dataset for Traffic Prediction
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
Lens: A Foundation Model for Network Traffic
Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Spatial-Temporal Large Language Model for Traffic Prediction
A novel hybrid time-varying graph neural network for traffic flow forecasting
RGDAN: A random graph diffusion attention network for traffic prediction
MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data
Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
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