City Know-hows

How neighbourhood design influences older adults’ driving patterns

Detailed visualization of public transportation density in each census block in St. Louis City and St. Louis County.

Older adults’ driving patterns reveal how the design of our neighbourhoods impacts mobility and independence in later life. This research highlights actionable insights for urban planners to design age-friendly communities by balancing transit access, land use diversity, and safe road networks.

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Target audience

Urban planners, transportation policy makers, and age-friendly community advocates.

The problem

Older adults face unique challenges in maintaining their driving independence due to cognitive and physical changes. Built environment factors like road network design and transit accessibility can either enhance or hinder their ability to drive safely and efficiently.

What we did and why

We analyzed GPS driving data from 134 older adults in St. Louis, paired with Geographic Information Systems and machine learning techniques. Our goal was to understand how neighborhood characteristics—like land use diversity, transit accessibility, and road design—impact driving distances and trip types.

Our study’s contribution

We demonstrate the strong influence of neighborhood transit access and land use on driving behavior. We highlight thresholds where built environment factors significantly impact driving patterns and provide evidence for integrating age-friendly design into urban planning.

Impacts for city policy and practice

Urban planners should consider:

  • Increasing land use diversity near older adults’ residences to support local trip-making.
  • Improving transit accessibility within a 1 km radius to reduce dependence on long car trips.
  • Ensuring safe road designs with moderate intersection density to promote mobility while minimizing risks.

Further information

Full research article:

The neighbourhood built environment affects driving behaviours of older adults: a combined geographic information systems and machine learning method by Rayeheh Hafezifar, Shahab Alizadeh, Anne Dickerson, Brenda Vrkljan, Ganesh M. Babulal & Sayeh Bayat

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