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Reconstructing commuters network using machine learning and urban indicators.

Gabriel Spadon1, Andre C P L F de Carvalho2, Jose F Rodrigues-Jr2

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Machine learning models accurately reconstruct intercity commuting networks using urban indicators, improving predictions of human mobility patterns beyond traditional methods.

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Area of Science:

  • Network Science
  • Urban Studies
  • Computational Social Science

Background:

  • Human mobility significantly impacts societal structures, including infrastructure, economics, disease spread, and crime.
  • Existing physics-inspired models for quantifying intercity migration and reconstructing mobility networks have limitations in predictive accuracy.
  • Accurate modeling of human mobility is crucial for understanding and managing complex societal dynamics.

Purpose of the Study:

  • To develop and validate a novel machine learning approach for predicting human intercity travel flow.
  • To reconstruct complex intercity commuter networks with high accuracy.
  • To identify key urban indicators that drive commuting patterns.

Main Methods:

  • Utilized machine learning algorithms trained on 22 distinct urban indicators.
  • Applied the models to predict the flow of people between cities.
  • Reconstructed the intercity commuters network based on predicted flows.

Main Results:

  • The machine learning approach successfully reconstructed the commuters network with 90.4% accuracy.
  • The model explained 77.6% of the observed variance in intercity travel flow.
  • Identified Gross Domestic Product (GDP) and unemployment rate as significant drivers of commuting, alongside distance.

Conclusions:

  • Machine learning models integrating urban indicators offer a powerful alternative for modeling and predicting human mobility.
  • Urban indicators provide crucial insights into commuting patterns, complementing traditional factors like distance.
  • The findings have broad implications for network science, urban planning, economics, and social sciences.