01622750

Component

https://doi.org/10.3141/2587-09

The decline in active travel to school and the concomitant rise in numbers of children being driven to school in the United States over recent decades have affected the health of school-age children and contributed to environmental problems. In response, communities throughout the country are stepping up efforts to increase active travel, including bicycling, but they have few tools available to them to assess the potential effectiveness of proposed strategies. The purpose of this study was to develop a model with aggregated school-level data of the factors associated with bicycling to elementary and junior high schools and to examine the effectiveness of this model in predicting bicycling to school. With the use of repeated observations of bike rack counts at 11 public schools in Davis, California, binomial multilevel regression models that included factors thought to influence bicycling to school were specified. The models indicated that comfortable bicycling routes, the racial and economic makeup of the student population, and various factors that represented the daily context (e.g., day of week, season, weather) all were likely to influence rates of bicycling to school. The results indicated that models based on aggregated school-level data were not sufficient to predict the amount of bicycling to a given school on a given day but were sufficient to predict mean bicycling to a given school over a period of time. Thus this method may be sufficient for policy analysis whose aim is to increase average bicycling to school.

01586671

16-5206

English

pp 68-77

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309441391

Print

Figures; References; Tables

Bicycles; Bicycling; Nonmotorized transportation; School children; Schools; Traffic counts; Travel behavior

Pedestrians and Bicyclists; Planning and Forecasting

TRIS, TRB, ATRI

Jan 19 2017 9:18AM

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