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Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs.

Muhammad Aqib1, Rashid Mehmood2, Ahmed Alzahrani3

  • 1Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Kingdom of Saudi Arabia. mpervez@stu.kau.edu.sa.

Sensors (Basel, Switzerland)
|May 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for large-scale, real-time traffic prediction using big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). It leverages an extensive 11-year dataset for enhanced transportation analytics and smart city applications.

Keywords:
TensorFlowbig dataconvolution neural networksdeep learninggraphics processing units (GPUs)in-memory computingroad traffic predictionsmart citiessmart transportation

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Area of Science:

  • Transportation Science
  • Computer Science
  • Data Science

Background:

  • Road transportation is vital but incurs significant economic, health, and environmental costs.
  • Current deep learning approaches for traffic prediction are limited by small datasets and insufficient study depth.

Purpose of the Study:

  • To develop a comprehensive, large-scale, faster, and real-time traffic prediction system.
  • To address limitations in existing deep learning models for transportation analytics.
  • To integrate big data, deep learning, in-memory computing, and GPUs for advanced traffic prediction.

Main Methods:

  • Utilized over 11 years of California Department of Transportation (Caltrans) data, the largest dataset for deep learning traffic studies.
  • Investigated various deep learning model configurations and input attribute combinations.
  • Explored the application of pre-trained models for real-time traffic prediction.

Main Results:

  • Developed novel deep learning models and algorithms for traffic prediction.
  • Implemented a comprehensive analytics methodology and software tool.
  • Demonstrated the feasibility of large-scale, real-time traffic prediction using integrated technologies.

Conclusions:

  • The study presents a significant advancement in traffic prediction capabilities.
  • The integrated approach offers a robust solution for smart city initiatives and big data analytics.
  • Novel deep learning models and tools contribute to high-performance computing in transportation.