Abstract

While cost-benefit analysis has been employed in highway planning processes for decades, such analytical approaches are less frequently used in trail planning due to the limited availability of data and methodologies for quantifying trail benefits. As a result, even projects that provide critical access to safe, high-quality open space with real mobility and health benefits are often considered “amenities,” rather than “needs.” Individual agencies and trail project sponsors have measured trail use and benefits along local greenways, but there currently are no generalizable methods for predicting demand or conducting impact assessment on trails. To address this gap, the Rails-to-Trails Conservancy (RTC) is developing the Trail Modeling and Assessment Platform (T-MAP), a set of trail planning data collection and analysis tools, including a trail user survey. The T-MAP survey tool was developed to allow implementing agencies to learn more about trail use and users, especially users’ physical activity. This paper describes the development of a survey instrument and sampling protocol that yielded a response rate of 55% in field testing. The survey includes questions from the Global Physical Activity Questionnaire, an internationally validated instrument developed by the World Health Organization. We report the results of testing the effect of alternative survey distribution methods (in-person interview and online post-survey) and incentives on overall response rates and sample bias across multiple population variables, including gender, age, trail use mode, overall health status, and physical activity frequency. The highest response rates (64%) were achieved through a combination of personal intercept interview with incentive, followed by online participation after receiving a card from a surveyor on the trail. When designing a survey data collection and sampling plan, decisions such as whether to use field workers, to offer incentives, and the survey administration format all impact the response rate and the representativeness of the sample, and thus the cost and quality of the data collected. These findings can help trail managers, local planners, or advocates to estimate survey administration costs and maximize returns through efficient and valid data collection to conduct trail benefit assessments based on high-quality data.

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