Data-Driven Discovery of Walkability: Safety Risk Factors and Responsive Behaviors of Older Adults - PROJECT SUMMARY Even though walking is a primary mode of exercise for older adults, manifesting numerous health benefits including cardiovascular fitness, muscle strength, and balance, limited awareness of age-friendly built infrastructure and consequent lack of street design guidelines create significant gaps in walkability for older adults. The larger the gaps and barriers in age-friendly infrastructure, the greater the chance of a lack of motivation for older adults to engage in walking, resulting in critical health concerns. The current roadway design guidelines are developed based on non-older adults’ pace and expectations, which overlooks older adults’ walkability and safety needs. The distinct and unique requirements of older adults walking, such as wide and even sidewalks, safe and signal-protected intersections, and well-lit and signed trails, must be incorporated in design manuals and transportation planning in our aging society. We will implement a data-driven mixed-method study, consisting of a survey, a semi-structured interview, spatial analysis, and a field experiment to examine three key risk attributes affecting older adults’ walkability, which include physical, functional, and emotional attributes that correspond to crash risk, fall risk, and perceived safety, respectively. The survey conducted with 60 older adults will focus on subjective walkability measures and actual walking activities, while interviews will provide a deeper context of risk factors. We will triangulate these perceived risk factors with street-level walkability measures through spatial analysis to understand how older adults’ perceived risk factors differ from the conventional walkability measures. This analytic model will also reveal the most influential factors affecting perceived values vs. actual walking behaviors, and the direct and indirect relationships between perceived risks and walking behaviors. Given that walking requires constant visual, cognitive, and physical decision-making to visually confirm potential safety hazards and react to the risk, we will also implement a 0.25 mile walking experiment with these 60 older adults to determine the safety level of visual and physical responses to the identified risk factors. A second-by-second eye tracker data will characterize the time taken from making visual contact, initiating coping responses, and finally completing reactive behaviors. Using advanced modeling techniques from pattern recognition, we will understand how street-level built infrastructure barriers impact the safety outcomes of older adults’ walkability. Ultimately, combining all of the perceived and observed measures, we will determine the gaps between safety risk factors of older adults’ walkability and create human response profiles representing coping behaviors on roadway safety hazards.
