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BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility.

Jamal Toutouh1, Javier Arellano2, Enrique Alba3

  • 1Departamento de Lenguajes y Ciencias de la Compuitación, Universidad de Málaga, 29071 Málaga, Spain. jamal@lcc.uma.es.

Sensors (Basel, Switzerland)
|November 28, 2018
PubMed
Summary

Researchers developed a low-cost cyber-physical system for smart city mobility using intelligent techniques. Their BiPred algorithm significantly reduced data needs by over 90% while improving machine learning prediction accuracy by 15%.

Keywords:
dataset reductionevolutionary algorithmsmachine learningroad-traffic predictionsmart mobility

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Area of Science:

  • Computer Science
  • Urban Planning
  • Data Science

Background:

  • Understanding urban mobility is crucial for modern city development.
  • Smart city initiatives require efficient data processing and analysis.
  • Existing methods for analyzing mobility data can be data-intensive and computationally expensive.

Purpose of the Study:

  • To design and implement a low-cost cyber-physical system for understanding urban mobility.
  • To develop and apply a novel evolutionary algorithm (BiPred) for optimizing machine learning model training.
  • To significantly reduce the dataset size required for learning mobility patterns without sacrificing prediction accuracy.

Main Methods:

  • Development of a smart-campus initiative at the University of Malaga as a testbed.
  • Implementation of intelligent techniques, hardware, and software for data acquisition and processing.
  • Introduction and application of the BiPred evolutionary algorithm for dataset size reduction and machine learning model training.

Main Results:

  • Achieved a data reduction of over 90% for learning campus mobility patterns.
  • Improved the prediction accuracy of applied machine learning methods by up to 15%.
  • Successfully constructed a real-world system for urban mobility analysis in a city setting.

Conclusions:

  • The proposed system and BiPred algorithm offer an efficient and effective solution for smart city mobility analysis.
  • Significant data reduction is achievable, leading to more efficient machine learning processes.
  • The work provides a comprehensive approach to smart city development using sensor networks and intelligent algorithms.