Content area
Full Text
Decisions to install public bike-share programs are increasingly based on ridership estimations, but the topography's influence on ridership is rarely quantified. This research evaluated a geographic information system-based approach for estimating ridership that accounted for hills. Double weighting a slope relative to other measures produces a realistic representation of the bicycling experience.
Introduction
Because of their benefits, bike-share programs are increasingly of interest in cities and universities across the country. A bike-share program provides short-term use bicycles to the public through a system of unattended stations for their checkout and return. This affordable form of nonmotorized transportation can reduce automobile dependency, enhance transit use by providing last mile access, and provide an alternative mode to improve transportation mobility.1
More than 527 bike-share systems are operating throughout the world.12 A 2012 Institute of Transportation Engineers article listed 26 programs in the United States including programs in Washington, DC; Denver, CO; Boston, MA; and Minneapolis, MN; with more being considered.1 The decisions about whether and where to establish these early bike-share programs were typically based on professional judgment and personal knowledge of a given city. However, given the growing demand for bike-share systems and the financial constraints on transportation investment, more defensible and quantitative methods for estimating user demand are needed. Bike-share vendors have in-house methods for evaluating infrastructure demands and the associated business viability of systems, but those methods generally are proprietary and not shared with transportation agencies.
To quantify the implementation of a bike-share program in Philadelphia, PA, USA, the Delaware Valley Regional Planning Commission used geographic information system (GIS) software to analyze 10 key indicators.3 The commission selected factors such as the locations of bike facilities and the population to predict demand for a bike-share program. While this method was analytically strong, the approach did not include topography.
This article presents an approach based on the Philadelphia model, expanded to integrate topography as an indicator of bike-share success, making the approach applicable to a broader range of cities. Typical bike-share users are casual or new bike riders, and hills are a notable challenge to them.4 Our efforts to enhance the Philadelphia model involved analyzing various ways to quantitatively incorporate topography into a GIS-based bike-share demand analysis. The resulting recommended method accounts for the influence...