ArcGIS GDB for CPBS Report 23UCB01 - A Context-sensitive Street Classification Framework for Speed Limit Setting [supporting dataset]

In the US, speed limit setting (SLS) has historically relied on driver-behavior-based approaches, such as using the 85th percentile speed. While these approaches are considered objective and allow for consistent application, they have significant limitations, including drivers’ tendencies to underestimate their speeds, the phenomenon of speed creep, and inadequate consideration of vulnerable road users. These issues 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 effectively implemented in countries like New Zealand, Sweden, the Netherlands, and Australia. Despite their success, such approaches have not been widely adopted in the US, resulting in many roads with speed limits that may not reflect their actual conditions or adequately ensure pedestrian and cyclist safety. Inspired by New Zealand’s One Network Framework, the authors developed a US-based context-sensitive roadway classification framework. This framework integrates “Place,” which considers surrounding land uses and locational contexts, and “Movement,” which pertains to the road’s transport function. Using data from the Smart Location Database (SLD) and the Highway Performance Monitoring System (HPMS), the authors validated their framework through internal reviews and external interviews with state-level practitioners. This process revealed both opportunities and challenges in implementing a context-sensitive SLS approach in the US. The findings demonstrate the feasibility of establishing an objective, context-sensitive roadway classification system in the US and provide valuable insights for developing new speed-limit guidance aligned with the Safe System framework.