Traffic Prediction
114 papers with code • 32 benchmarks • 18 datasets
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.
( Image credit: BaiduTraffic )
Benchmarks
These leaderboards are used to track progress in Traffic Prediction
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Libraries
Use these libraries to find Traffic Prediction models and implementations
PeMSD8
METR-LA
PeMSD7
PeMSD4
Q-Traffic
Latest papers with no code
Lens: A Foundation Model for Network Traffic in Cybersecurity
Network traffic refers to the amount of data being sent and received over the internet or any system that connects computers.
A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs
The Cy2Mixer is composed of three blocks based on MLPs: A message-passing block for encapsulating spatial information, a cycle message-passing block for enriching topological information through cyclic subgraphs, and a temporal block for capturing temporal properties.
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction
Long-term traffic prediction has always been a challenging task due to its dynamic temporal dependencies and complex spatial dependencies.
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction.
Spatial-Temporal Large Language Model for Traffic Prediction
In this paper, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction.
A novel hybrid time-varying graph neural network for traffic flow forecasting
Real-time and accurate traffic flow prediction is the foundation for ensuring the efficient operation of intelligent transportation systems. In existing traffic flow prediction methods based on graph neural networks (GNNs), pre-defined graphs were usually used to describe the spatial correlations of different traffic nodes in urban road networks.
Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
This emphasizes the importance of using the prediction rApp and distributed actors' information jointly as part of a dynamic xApp.
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
This study introduces a novel, multimodal deep learning approach to the challenge of predicting flight-level passenger traffic, yielding substantial accuracy improvements compared to traditional models.
Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction
A sequence-aware graph neural network is proposed and integrated into the Gated Recurrent Unit (GRU) to learn non-fixed graphs at different time steps and capture local temporal relationships.
TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns.
