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
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e. g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is often essential to enable safety, predictability, or reliability.
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data.
ST-SSMs: Spatial-Temporal Selective State of Space Model for Traffic Forecasting
Accurate and efficient traffic prediction is crucial for planning, management, and control of intelligent transportation systems.
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.
Towards Responsible and Reliable Traffic Flow Prediction with Large Language Models
Achieving both accuracy and responsibility in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models.
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.
