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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Quantifying navigation complexity in transportation networks.

Zhuojun Jiang1, Lei Dong1, Lun Wu1

  • 1Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China.

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Urban navigation is complex, but people simplify it by reusing routes. This study quantifies real-world navigation difficulty using subway data, finding decision costs are lower than expected.

Keywords:
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Area of Science:

  • Urban planning
  • Network science
  • Transportation engineering

Background:

  • Urban expansion increases navigation complexity, posing transportation challenges.
  • Large-scale empirical analysis of real-world navigation is limited by data availability.
  • Information theory offers a framework to quantify navigation difficulty.

Purpose of the Study:

  • To quantify urban navigation difficulty from an information perspective using large-scale mobility data.
  • To analyze how individuals navigate complex urban networks in reality.
  • To investigate the relationship between theoretical and empirical navigation information.

Main Methods:

  • Analysis of 225 million subway trips from three major Chinese cities.
  • Quantification of navigation difficulty using information-theoretic measures.
  • Modeling of routing behaviors in growing networks.

Main Results:

  • Individuals predominantly use a small set of frequently traveled routes.
  • Navigation information within these route-based subnetworks is significantly lower than theoretical global network values.
  • Empirical and theoretical navigation information exhibit a universal linear relationship.

Conclusions:

  • Real-world navigation decision costs are lower than theoretical upper bounds.
  • Navigability can be enhanced within subnetworks despite overall network growth.
  • Large-scale data analysis provides valuable insights for transportation planning and evaluation.