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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Understanding individual routing behaviour.

Antonio Lima1, Rade Stanojevic2, Dina Papagiannaki2

  • 1Civil and Environmental Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA antol@mit.edu.

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Most drivers use a few preferred routes for regular journeys, challenging the assumption that people always choose the minimum cost path. This finding impacts urban planning and transportation systems.

Keywords:
city sciencecomplex systemshuman mobilitytransportation

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Area of Science:

  • Mobility studies
  • Transportation science
  • Urban planning

Background:

  • Understanding human mobility is crucial for urban infrastructure and transportation development.
  • Current route-choice models assume minimum cost path following, lacking empirical validation.

Purpose of the Study:

  • To analyze individual route selection behavior using GPS trajectory data.
  • To investigate patterns in route choice and compare them with optimization assumptions.

Main Methods:

  • Analysis of 92,419 anonymized GPS car trajectories over 18 months.
  • Grouping trips by origin-destination and identifying preferred routes.
  • Developing a spatial probability distribution to bound route selection.

Main Results:

  • Most drivers utilize a limited set of routes for routine travel, often preferring one for frequent trips.
  • A significant portion of routes are not optimal, contradicting the minimum cost path assumption.
  • Route selection space can be bounded by an ellipse, irrespective of trip scale.

Conclusions:

  • Individual routing choices deviate from pure optimization but exhibit consistent spatial bounds.
  • Findings can inform new, realistic route-choice models for transportation planning and recommendation systems.