The Moving Ahead for Progress in the 21st Century Act emphasized the use of data and performance measurement to track progress toward its transportation policy and safety goals. As U.S. cities and states implement policies to eliminate traffic deaths and serious injuries, exposure data are needed to contextualize crash analyses and prioritize effective countermeasures to reduce future risk. However, comprehensive counting programs are resource intensive. Research suggests that so-called big data can supplement traditional counting programs, fill the data gap, and allow for more robust exposure modeling. This paper presents the results of an abbreviated exposure estimation process to develop ballpark pedestrian and bicycle estimates for the city of Seattle, Washington, conducted as part of a major bicycle and pedestrian safety analysis for Seattle's Vision Zero effort. This paper contributes to existing research on exposure estimation and demonstrates a case study of practice-ready bicycle and pedestrian exposure models. Because of budget and time constraints, the exposure estimates used available data sources and were based on models from earlier bicycle and pedestrian volume estimation studies. The pedestrian model (pseudo- R2 = .76) fit with published models, and the bicycle model had decent explanatory power (pseudo- R2 = .57). After Strava data were added to the bicycle model, the explanatory power rose to 62% and the model was simplified. The estimates were tested in a multivariate crash analysis and used to support countermeasure identification and project prioritization. This type of abbreviated process may be appropriate for other cities seeking to estimate exposure but without the resources for a full-scale estimation effort.

This study aimed to use robust analysis methods to identify and screen locations at risk for pedestrian crashes and injuries to help Seattle, Washington, a Vision Zero city, broaden treatment priorities beyond only high-crash locations. For this objective, data from the entire network were used to develop safety performance functions (SPFs) for two pedestrian crash types: total pedestrian crashes at intersections (a high frequency type) and a subset of intersection crashes involving through motorists striking crossing pedestrians (a high severity type). Many variables from roadway, built environment, census, and activity measures were tested. A similar but not identical set of variables, including measures of activity and intersection size and complexity, significantly contributed to crash prediction in both models. Pedestrian volume exhibited a curved relationship to crashes and demonstrated a tendency for expected crashes to begin to decline above a threshold value; however, the causes of this relationship were unknown. The SPFs were used in several ranking methods, including SPF-predicted crashes, empirical Bayes estimated crashes, and potential for safety improvement, to aid in prioritization of locations that might have been candidates for safety improvement but that had not necessarily experienced a high frequency of crashes. On the basis of this example, this approach is feasible for jurisdictions that wish to be more proactive in addressing potential crashes and injuries. Jurisdictions must, however, begin routinely collecting the data needed to implement the method efficiently.

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