A Context-Sensitive Street Classification Framework for Speed Limit Setting

Project Description

Historically, speed limit setting (SLS) procedures in many states have relied on driver-behavior-based approaches such as the 85th percentile speed. Researchers, however, have identified several shortcomings, including that drivers underestimate their speeds, issues with speed creep, and the lack of consideration for vulnerable road users. States can move towards a context-sensitive approach to SLS by developing a street classification framework that includes context. New Zealand provides a leading example with its SLS procedure that uses a street category framework based on the Movement and Place principle. We will develop a US street category framework for SLS using objective, publicly available datasets that capture functional classification (movement) and variables associated with vulnerable road user activity (place), such as land use mix, population density, job density, urban/rural designation, and transit access. We will perform a pilot study of 5 geographically diverse states to categorize the streets on their road networks based on the Movement and Place principle. The research will include a best practice literature review, GIS data preparation and linking, application of classification methods, validation of classification, and reporting.