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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism.

Pietro Casabianca1, Yu Zhang1, Miguel Martínez-García1

  • 1Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
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Predicting vehicle destinations using deep learning improves transportation safety and efficiency. Bidirectional Long Short-Term Memory (BiLSTM) networks with attention mechanisms achieved 96% accuracy, outperforming other models.

Area of Science:

  • Artificial Intelligence
  • Transportation Engineering
  • Machine Learning

Background:

  • Satellite navigation is integral to modern travel planning and tracking.
  • Vehicle destination prediction offers significant benefits for transportation efficiency and safety.
  • Current methods require robust predictive capabilities for real-world applications.

Purpose of the Study:

  • To investigate the efficacy of deep learning models for vehicle destination prediction using historical travel data.
  • To compare the performance of various deep learning architectures, including Dense Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Bidirectional LSTM (BiLSTM) networks, with and without attention mechanisms.

Main Methods:

  • Utilized satellite navigation data representing vehicle journey histories.
Keywords:
attention mechanismbidirectional long short-term memorydeep learningvehicle destination prediction

Related Experiment Videos

Last Updated: Oct 8, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.4K
  • Implemented and evaluated Dense Neural Networks (DNNs), LSTM, and BiLSTM models.
  • Incorporated attention mechanisms within LSTM and BiLSTM architectures.
  • Tested models on their ability to accurately forecast vehicle destinations.

    • Main Results:

    • The Bidirectional LSTM (BiLSTM) model with an attention mechanism demonstrated superior destination prediction performance.
    • Achieved an average accuracy of 96% on the test set, surpassing standard BiLSTM by 4%.
    • The BiLSTM with attention consistently outperformed other tested models, showing robustness and stability.

    Conclusions:

    • Deep learning, particularly BiLSTM with attention, is highly effective for vehicle destination prediction in the automotive domain.
    • This approach offers significant potential for industrial applications, enhancing transportation systems.
    • The study validates the transferability of natural language processing techniques to automotive data analysis.