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Related Experiment Videos

Enhancing urban traffic congestion prediction through efficientnet and optimized ensemble learning models.

Ramesh Vatambeti1, N V RajaSekhar Reddy2, Shaik Hussain Shaik Ibrahim3

  • 1Department of CSE, Tezpur University, Tezpur, Assam, 784028, India.

Scientific Reports
|November 17, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a hybrid deep learning model for accurate traffic flow prediction, enhancing urban traffic management. The novel approach improves congestion level classification and reduces traffic jams in smart cities.

Area of Science:

  • Artificial Intelligence
  • Urban Planning
  • Transportation Engineering

Background:

  • Traffic congestion poses significant challenges to urban infrastructure and quality of life.
  • Accurate traffic flow prediction is crucial for effective traffic management strategies.
  • Existing prediction models often struggle with the complexity and dynamism of urban traffic patterns.

Purpose of the Study:

  • To develop a novel hybrid deep learning model for enhanced traffic flow prediction.
  • To improve the accuracy and efficiency of real-time traffic management systems.
  • To classify traffic flow severity into distinct congestion levels.

Main Methods:

  • An ensemble of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU) models were utilized.
Keywords:
Bidirectional unitEurygasters optimization algorithmFuzzy rulesLong short-term memoryTournament-selected glowworm swarm optimizationTraffic prediction

Related Experiment Videos

  • Feature extraction was performed using EfficientNet, with hyperparameter tuning optimized by the Eurygasters Optimization Algorithm (EOA).
  • Tournament-Selected Glowworm Swarm Optimization (TSGSO) was employed to further enhance the ensemble model's performance. Fuzzy logic was used for traffic flow severity classification.

    • Main Results:

    • The proposed hybrid deep learning model demonstrated superior prediction accuracy compared to existing methods.
    • The ensemble model, optimized with TSGSO, significantly improved traffic flow prediction capabilities.
    • The fuzzy logic classification effectively categorized traffic flow into low, medium, and high congestion levels.
    • The model achieved improved accuracy while maintaining a reasonable processing time.

    Conclusions:

    • The developed hybrid deep learning approach offers a scalable and efficient solution for real-time traffic flow prediction.
    • This research contributes to the development of smarter cities by enabling proactive traffic management and reducing congestion.
    • The findings highlight the potential of advanced deep learning techniques in addressing complex urban transportation challenges.