Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 6, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

8.7K

Zero-shot traffic flow prediction with foundation models: a comparison with deep learning approaches.

Yue Li1, Qunshan Zhao2, Mingshu Wang3

  • 1Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, 7 Lilybank Gardens, Glasgow, G12 8RZ, UK.

Scientific Reports
|May 4, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deletion of the N-terminal 400 amino acids of DPV pUL36 attenuated virulence and conferred effective protection against virulent challenge.

Poultry science·2026
Same author

Duck plague virus US2 promotes p62-mediated autophagic degradation of RIG-I to suppress antiviral signaling.

Poultry science·2026
Same author

Host mature tRNAome as a decoding switch regulates antiviral and proviral responses.

Nature communications·2026
Same author

Targeted mutagenesis of the ICP4 transactivation domain generates a protective DIVA vaccine against duck plague.

Poultry science·2026
Same author

Avian herpesvirus-specific LORF5 is a late gene, interacts with 19 viral and 111 host proteins, critical for virulence of Duck plague virus.

Poultry science·2026
Same author

Recognition of the wobble pair: quantum mechanical calculations of tRNA U34 or C34 modifications within the ribosomal decoding center.

Nucleic acids research·2026

Foundation models like Lag-Llama and Chronos excel at zero-shot traffic flow prediction, outperforming deep learning models. These pre-trained models offer efficient, accurate traffic forecasting without extensive task-specific training.

Area of Science:

  • Artificial Intelligence
  • Transportation Engineering
  • Data Science

Background:

  • Traffic flow prediction is vital for urban mobility, congestion reduction, and road safety.
  • Deep learning models offer accuracy but demand large datasets and extensive training.
  • Foundation models demonstrate strong performance in time series prediction tasks.

Purpose of the Study:

  • To evaluate the efficacy of foundation models (Lag-Llama, Chronos) for zero-shot traffic flow prediction.
  • To compare the predictive accuracy of foundation models against traditional deep learning models.
  • To analyze the impact of model size, context length, and training data on foundation model performance.

Main Methods:

  • Application of two foundation models, Lag-Llama and Chronos, for zero-shot traffic flow prediction.
Keywords:
Deep learningFoundation modelsTime-series predictionTraffic flows

Related Experiment Videos

Last Updated: May 6, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

8.7K
  • Comparative analysis of foundation model performance against established deep learning models.
  • Investigation of factors influencing prediction accuracy, including context length and model size.

    • Main Results:

    • Foundation models significantly outperform deep learning models in traffic flow prediction.
    • Foundation models demonstrate effectiveness under both normal conditions and disruptive events.
    • Larger models and longer context lengths improve accuracy but increase inference time.

    Conclusions:

    • Foundation models offer a practical and efficient alternative for traffic flow prediction, requiring less data and training time.
    • Model selection based on comprehensive training data is crucial for optimal zero-shot performance.
    • Foundation models represent a promising advancement for real-world intelligent transportation systems.