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: Sep 16, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.5K

Application effect of short-term traffic flow prediction method based on CNNBLSTM algorithm.

Guozhu Sui1, Meixia Song1, Ke Bian1

  • 1School of Traffic and Electrical Engineering, Dalian University of Science and Technology, Dalian, China.

Plos One
|July 7, 2025
PubMed
Summary
This summary is machine-generated.

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

Antioxidant Nanozymes: From Rational Design to Biomedical Applications.

Research (Washington, D.C.)·2026
Same author

The feasibility of ovarian tissue cryopreservation in selected females after hematopoietic stem cell transplantation.

Bone marrow transplantation·2026
Same author

Nanoporous-based biomaterials in biomedical applications: from fundamentals and biosensing to drug delivery, wound healing, and tissue engineering.

Nanoscale advances·2026
Same author

Molecular mechanism of hidden matrix-bound deoxynivalenol binding to proteins in wheat.

Food chemistry·2026
Same author

Long-term assessment of nutrient-induced eutrophication in Xiamen Bay: Implications for integrated watershed-coast management.

Marine environmental research·2026
Same author

Helicase A determines the transcription program of T<sub>H</sub>17 lineage differentiation and autoimmunity.

Science advances·2026

This study introduces an improved deep learning model for accurate short-term traffic flow prediction. The enhanced algorithm significantly boosts prediction precision and convergence speed, offering a robust solution for traffic management.

Area of Science:

  • Artificial Intelligence
  • Transportation Engineering
  • Data Science

Background:

  • Traditional traffic flow prediction algorithms struggle with spatiotemporal dynamics, leading to reduced accuracy.
  • Accurate short-term traffic flow forecasting is crucial for effective traffic management and planning.

Purpose of the Study:

  • To develop an advanced short-term traffic flow prediction method.
  • To enhance prediction accuracy and model convergence speed using deep learning techniques.

Main Methods:

  • Data preprocessing involved identifying, repairing, and decomposing abnormal traffic flow data using smoothing estimation thresholds and adaptive noise integration empirical modal decomposition.
  • A hybrid deep learning model combining an improved convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) algorithm was proposed.

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K

Related Experiment Videos

Last Updated: Sep 16, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.5K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K
  • Enhanced Adam and Lookahead optimization algorithms were integrated to improve model performance.

    • Main Results:

    • The proposed method demonstrated faster convergence and significantly lower training and validation loss values.
    • Training loss decreased from 0.0250 to 0.0021, and validation loss decreased from 0.0010 to 0.0008.
    • The model achieved superior prediction accuracy compared to traditional CNN-BiLSTM models, with an average absolute percentage error of 0.233 and a root mean square error of 23.87.

    Conclusions:

    • The developed algorithm effectively and precisely forecasts short-term traffic flow.
    • The enhanced deep learning approach provides a reliable foundation for intelligent transportation systems and traffic management decisions.
    • The study highlights the potential of integrating CNN, BiLSTM, and advanced optimization techniques for complex time-series prediction tasks.