Purpose: To model urban trail traffic as a function of neighborhood characteristics and other factors including weather and day of week.
Methods: We used infrared monitors to measure traffic at 30 locations on five trails for periods ranging from 12 months to more than 4 y. We measured neighborhood characteristics using geographic information systems, satellite imagery, and US Census and other secondary data. We used multiple regression techniques to model daily traffic.
Results: The statistical model explains approximately 80% of the variation in trail traffic. Trail traffic correlates positively and significantly with income, neighborhood population density, education, percent of neighborhood in commercial use, vegetative health, area of land in parking, and mean length of street segments in access networks. Trail traffic correlates negatively and significantly with the percentage of neighborhood residents in age groups greater than 64 and less than 5.
Conclusions: Trail traffic is significantly correlated with neighborhood characteristics. Health officials can use these findings to influence the design and location of trails and to maximize opportunities for increases in physical activity.
