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Developing urban bicycle networks requires strategic investment. Data-driven algorithms can identify critical missing links to enhance connectivity and promote sustainable urban transport.

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Area of Science:

  • Urban planning and transportation science
  • Network analysis and computational sustainability

Background:

  • Urban transportation networks are vital for city functioning and socioeconomic life.
  • Sustainable urban transport initiatives increasingly focus on developing bicycle networks.
  • Effective and comprehensive bicycle network expansion strategies are needed, especially under budget constraints.

Purpose of the Study:

  • To investigate the structural properties of global urban bicycle networks.
  • To develop and apply data-driven algorithmic strategies for bicycle network growth.
  • To identify methods for increasing bicycle network connectedness and directness with limited investment.

Main Methods:

  • Analysis of the structure of urban bicycle networks worldwide.
  • Development of two greedy algorithms for identifying critical missing links.
  • Comparison of algorithmic strategies against random and baseline investment approaches.

Main Results:

  • Urban bicycle networks often consist of numerous disconnected components.
  • Algorithmic network growth strategies significantly improve network connectedness and directness.
  • The proposed algorithms outperform random and baseline investment strategies in enhancing network connectivity.

Conclusions:

  • Data-driven algorithmic approaches offer effective pathways for expanding urban bicycle infrastructure.
  • Focused investments guided by network analysis can optimize the development of sustainable urban transport.
  • This quantitative approach supports urban planners in developing more connected and efficient bicycle networks.