Eleni Christofa

Publication Date



Bicycling as a utilitarian mode of transportation is one of the most sustainable and cost effective modes available. Despite these benefits it is also among the least utilized transportation modes in the United States. Among the many barriers preventing people from bicycling, such as weather, physical ability, cargo requirements, transport of children, social stigmas, etc., studies have found that at least 40 percent of people feel that safety is the greatest barrier preventing them from bicycling (Sanders and Cooper, 2012; Sanders, 2013). Particularly in dense urban areas where bicycling is especially poised for success it is also the most dangerous, with urban areas accounting for approximately 69 percent of the bicyclists' fatalities every year (Zegeer et al., 2015). Mitigating this also goes beyond simple midblock treatments (e.g., bike lanes) with approximately 75 percent of all bicycle-vehicle crashes occurring at intersections in Massachusetts between 2011 and 2014 (University of Massachusetts Amherst, 2017). This reflects the importance of researching both midblock treatments as well as intersection-specfic bicycle infrastructure treatments to improve safety. To date there is a substantial amount of research investigating bicyclist behavior and safety at such innovative treatments from the bicyclists' perspective. However, little research has been conducted from the drivers' perspective towards bicycle infrastructure. There is a need to investigate driver behavior at innovative and unfamiliar bicycle infrastructure treatments in order to better evaluate and design these treatments to achieve safe operations for all users.

The objective of this research is to provide an in-depth analysis of driver behavior when approaching new and unfamiliar bicycle infrastructure treatments. The focus is on cases where the driver is not directly interacting with bicyclists to determine unprovoked driving behavior near bicycle infrastructure treatments. The particular treatments investigated are sharrows, bike lanes, bike boxes, and bike merge lanes. This research utilizes a driving simulator, eye tracker, and questionnaires to determine whether any patterns, or causalities exist between infrastructure and driver behavior. The benefit of laboratory simulation allows for not only the measurement of driver behavior, but also survey of their background through questionnaires. This combined information provides insights into how driver experience as a bicyclist and exposure to bicycle infrastructure can affect driver behavior.

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