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 Videos

Clustering-neural network models for freeway work zone capacity estimation.

Xiaomo Jiang1, Hojjat Adeli

  • 1Department of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA.

International Journal of Neural Systems
|July 10, 2004
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

Dual-representation structural MRI classification of psychiatric disorders using deep learning and large language models.

Psychiatry research. Neuroimaging·2026
Same author

Data fusion of medical imaging in neurological disorders.

Reviews in the neurosciences·2025
Same author

Image-based food groups and portion prediction by using deep learning.

Journal of food science·2025
Same author

Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.

Journal of medical systems·2024
Same author

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning.

Journal of medical systems·2024
Same author

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility.

Nonlinear dynamics·2023
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
See all related articles

New neural network models accurately estimate freeway work zone capacity using subtractive clustering with RBF and BPNN. These models, achieving less than 10% error, aid traffic management plan development.

Area of Science:

  • Transportation Engineering
  • Artificial Intelligence
  • Traffic Management

Background:

  • Estimating freeway work zone capacity is crucial for effective traffic management.
  • Traditional methods may lack accuracy and objectivity.
  • Neural networks offer advanced predictive capabilities.

Purpose of the Study:

  • To develop and evaluate novel neural network models for freeway work zone capacity estimation.
  • To integrate subtractive clustering with RBFNN and BPNN models.
  • To identify key factors influencing work zone capacity.

Main Methods:

  • Development of two clustering-based neural network models: clustering-RBFNN and clustering-BPNN.
  • Integration of subtractive clustering with Radial Basis Function Network (RBFNN) and Backpropagation Neural Network (BPNN).

Related Experiment Videos

  • Estimation of work zone capacity based on seventeen influencing factors.

    • Main Results:

    • Both clustering-neural network models demonstrated high accuracy, with errors below 10%.
    • The clustering-RBFNN model exhibited excellent training stability, accuracy, and rapid convergence.
    • Parametric studies identified significant factors affecting work zone capacity.

    Conclusions:

    • Clustering-neural network models provide a robust and accurate method for estimating freeway work zone capacity.
    • These models can effectively manage traffic with limited training data.
    • Findings support data-driven development of traffic management plans for work zones.