Lindsey Greg, Han Yuling, Wilson Jeffrey, Yang Jihui
J Phys Act Health. 2006 Feb;3(s1):S139-S157. doi: 10.1123/jpah.3.s1.s139.
To model urban trail traffic as a function of neighborhood characteristics and other factors including weather and day of week.
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.
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.
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.
将城市步道交通建模为邻里特征以及包括天气和星期几等其他因素的函数。
我们使用红外监测器在五条步道的30个地点测量交通情况，测量期从12个月到4年多不等。我们使用地理信息系统、卫星图像、美国人口普查数据及其他二手数据来测量邻里特征。我们使用多元回归技术对每日交通进行建模。
统计模型解释了步道交通中约80%的变化。步道交通与收入、邻里人口密度、教育程度、商业用途邻里百分比、植被健康状况、停车场土地面积以及接入网络中街道段的平均长度呈正相关且显著相关。步道交通与年龄大于64岁和小于5岁的邻里居民百分比呈负相关且显著相关。
步道交通与邻里特征显著相关。卫生官员可利用这些发现来影响步道的设计和选址，并最大限度地增加体育活动增加的机会。
