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Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data.

Nikolaos Peppes1, Theodoros Alexakis1, Evgenia Adamopoulou1

  • 1Institute of Communication and Computer Systems, Zografou, 15773 Athens, Greece.

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Summary

This study introduces an integrated platform using machine learning to analyze vehicle sensor data, classifying driving behavior for environmental improvement. The system aims to reduce CO2 emissions by promoting eco-friendly driving habits through data-driven insights.

Keywords:
data streamingdeep learning (DL)driving behaviour analysis (DBA)machine learning (ML)vehicle sensors

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Area of Science:

  • Environmental Science
  • Computer Science
  • Automotive Engineering

Background:

  • Modern vehicles generate extensive sensor data crucial for diagnostics and driver behavior analysis.
  • Increasing transportation demands and Information and Communication Technologies (ICT) are driving the automotive sector towards greater intelligence and efficiency.
  • Reducing carbon dioxide (CO2) emissions and environmental impact is a critical global priority.

Purpose of the Study:

  • To develop a holistic platform for monitoring and improving driver behavior to reduce environmental impact.
  • To leverage vehicle sensor data for classifying driving patterns as eco-friendly or not.
  • To compare the efficacy of different machine and deep learning algorithms for analyzing driver behavior.

Main Methods:

  • Integration of machine learning and deep learning algorithms with open-source tools.
  • Data gathering, storage, processing, analysis, and correlation from various vehicle data streams.
  • Utilization of clustering techniques for driver behavior classification and comparative analysis of supervised learning algorithms on labeled datasets.

Main Results:

  • The platform successfully processes and analyzes diverse vehicle data streams.
  • Clustering techniques effectively classify driver behavior into eco-friendly and non-eco-friendly categories.
  • Comparative analysis provides insights into the performance of supervised learning models for this task.

Conclusions:

  • The developed platform offers a feasible solution for monitoring and enhancing environmentally conscious driving.
  • Machine and deep learning approaches are effective in analyzing sensor data to promote sustainable transportation.
  • This integrated system contributes to reducing CO2 emissions through data-driven behavioral insights.