Project Description

The proposed research aims to create a set of adjustment factors

accounting for the built environment, socio-economic, and land-use characteristics which

can be applied to crowdsourced data so that policymakers and transportation

practitioners across the Southern California region can begin to incorporate exposure

estimates more reliably and consistently into their safety, infrastructure planning, and

decision-making analysis. The proposed solution will be relatively easy to use and will

bring potentially substantial cost and resource savings to communities throughout the

country. Public agencies using crowdsourced data can benefit from our proposed

methodologies for validating exposure estimates and reproducing methodologies for

working with similar datasets. By bridging the gap between crowdsourced data and the

resources needed to reliably use that data, these factors will put exposure estimates at

the fingertips of communities that urgently need data but have not prioritized it due to

resource constraints. This research will also provide insights into bicycling patterns that

may be more broadly applicable, such as geographic and sociodemographic variables

that consistently impact bicycling volumes in certain contexts or on certain street types

regardless of context. These insights will be useful irrespective of whether a community

has crowdsourced data or an established counting program. It will also highlight aspects

of disparities in access to safe bicycling amenities that are often not well captured in

count programs conducted by local authorities. The underrepresented communities

which are often left out of planning decisions will be accounted for in the modeling

framework by means of additional data acquired from US Census Bureaus’ American

Community Survey.