Influence of Individual Perceptions and Bicycle Infrastructure on Decision to Bike

Gulsah Akar, Kelly J. Clifton, 2009, in Transportation Research Record

doi:10.3141/2140-18

Location	College Park, MD
Population	Other (specify)
Sample size	1128
Factor analysis type	principal components, none rotation
Stepwise regression	no
Removal of insignificant variables	yes
Reviewed by	NAH

Abstract

The focus of this study is on the opportunities and challenges presented to cyclists on and around the campus of the University of Maryland, College Park. A web-based survey was conducted to understand the travel patterns and the specific issues regarding bicyclists. The survey included questions about possible bicycle infrastructure improvements, policy, and program innovations to assess the perceptions of the campus community regarding these changes. The findings of this survey, conducted to understand cyclists' travel patterns and identify their issues and concerns, are discussed. Both nonbicycle commuters and bicycle commuters agreed that bicycle lanes, trails, and paths would encourage them to ride a bike (or ride more often) to the campus. Discrete choice models are estimated to model the commuters' mode to campus. The findings of the models suggest that people are more sensitive to time for nonmotorized modes and women are less likely to ride a bicycle. Those who perceive walking and biking as a form of exercise and identify flexibility of departure time as an important factor in their mode choice are more likely to ride a bicycle. Those more likely to choose to drive an automobile to campus assume that they do not have other options to commute to campus. Policies designed to promote the use of bicycle transportation on and to the campus based on these findings are presented. The results of this study will help practitioners and campus transportation planners understand the reasons that prevent people from bicycling and evaluate the transportation improvements that may be considered to achieve bicycle-friendly campuses.

Factors

Models