Predicting Bicycle Travel Speeds Along Different Facilities Using GPS Data: A Proof-of-Concept Model

Abstract

Improvements to transportation networks to facilitate travel by non-motorized modes, such as bicycling and walking, are quickly becoming a pursued strategy to maintain levels of accessibility in congested urban areas. However, transportation planners and engineers lack many of the tools to properly predict and evaluate the effects of changes to bicycle or pedestrian networks. This paper seeks to address part of this problem by developing a model to predict travel speeds by bicyclists on various types of facilities (on-street, off-street, and mixed traffic). Using real-time GPS data collected from a small sample of bicyclists traveling on various types of facilities in Minneapolis, MN, regression models are estimated with bicycle speeds as the dependent variable. Trip characteristics, gender, the presence of an off-street facility, and an individual’s comfort level with traveling in heavy traffic are shown to influence travel speeds by bicycle. The estimated speed model is seen as a potential input to measures of bicycle accessibility, as well as improving the ability to model and forecast bicycle use, including the choice of route, on existing and planned transportation networks.