Abstract
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.
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