Wake Forest, NC

AARP Community Challenge Grant Year: 2025

Project Category: Walk Audits

Description: Older residents faced safety and mobility challenges when walking through town, including missing sidewalk connections and obstacles that made short trips less predictable. These gaps limited independence for older adults and others with physical challenges, especially near housing, schools and shopping areas. The Town of Wake Forest addressed the issue by conducting a series of walk audits in different parts of town, starting downtown. Older volunteers documented barriers and identified specific improvements needed to make walking safer and more continuous. Findings were compiled into a presentation for planning staff and elected officials. One audit revealed a sidewalk that stopped short of connecting affordable housing to nearby shops, forcing residents to walk in traffic. That example helped inform discussions tied to the Age-Friendly Action Plan and future decisions about sidewalk connectivity and pedestrian investment.

Project description was created using generative AI and then reviewed for accuracy.

Wake Forest, NC

AARP Community Challenge Grant Year: 2025

Project Category: Bike Audits

Description: The Town of Wake Forest examined why older adults and other residents interested in biking often avoid local streets due to disconnected and unsafe infrastructure. Through a series of bike audits involving volunteers from the Senior Center, participants documented where bike lanes end abruptly, crossings felt unsafe and routes failed to connect neighborhoods to greenways. The audits produced clear, location-specific findings and recommendations prepared for planning staff and elected officials. Volunteers noted that riders often gather downtown and then leave town to stay safe, highlighting missed opportunities for local travel. The results are positioned to inform age-friendly planning and support future investments such as safer crossings, clearer signage and expanded bike facilities.

Project description was created using generative AI and then reviewed for accuracy.