A context-sensitive roadway classification framework for speed limit setting in the US

In the US, speed limit setting (SLS) procedures have historically relied on driver-behavior-based methods, such as the 85th percentile speed, which are considered objective and allow for consistent application. However, this approach has notable shortcomings, including drivers’ tendency to underestimate their speeds, speed creep, and insufficient consideration of vulnerable road users, which may conflict with the Safe System Approach and Vision Zero initiatives endorsed by the USDOT (US Department of Transportation). In contrast, context-sensitive approaches, which classify roads based on roadway typologies, have been developed in countries like New Zealand, Sweden, the Netherlands, and Australia. While effective, these approaches have largely been applied outside the US, leaving many US roads with speed limits that may not fit their surroundings or adequately address pedestrian and cyclist safety. Drawing on New Zealand’s One Network Framework, we developed a US-based, context-sensitive roadway classification framework for urban and suburban areas that incorporates “Place,” which captures surrounding land uses and locational contexts, and “Movement,” which relates to the road’s transport function. Using nationally available data from the Smart Location Database (SLD) and the Highway Performance Monitoring System (HPMS), we evaluated our roadway classification framework through internal reviews by our research team and external interviews with state-level practitioners, uncovering both opportunities and challenges in adopting a context-sensitive SLS approach in the US. Our findings demonstrate the feasibility of creating an objective context-sensitive roadway classification in the US and offer insights for developing new speed-limit guidance aligned with the Safe System framework.