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
Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels.
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks.
Explainable Traffic Flow Prediction with Large Language Models
This paper contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation.
Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets
A key revelation of our research is the feasibility of sampling training data for large traffic systems from simulations conducted on smaller traffic systems.
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management.
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.
