Pedestrians, bicyclists, and other non-motorist road users accounted for a growing share of all US roadway fatalities over the last few decades. An even larger number of non-motorists are seriously injured each year in collisions involving motor vehicles. Addressing the safety of non-motorists so they have full and safe access to our transportation system requires a multifaceted, collaborative, and comprehensive approach. Foundational to this approach is a better understanding of non-motorized road user safety risks, including crash patterns or crash type and related factors. A crash type is simply a variable that captures the movements or actions of the involved parties just prior to a crash. Thus, the crash type helps to characterize the conflict type that led up to the crash. Unfortunately, crash types are frequently missing or not well-defined in existing crash databases when non-motorized road users are involved in collisions with motorists.

The PBCAT crash typing application helps users develop these missing variables. PBCAT 3 turns information on the pre-crash maneuvers into variables that capture these proximal events and conflict types. This information improves the ability to identify and target effective safety measures to preventable crash types and develop new types of safety measures. Developed from syntheses of prior studies and stakeholder input, the crash types generated within PBCAT 3 are unique to this application, and are not available in other current crash typing tools or datasets.

Crash Type and other PBCAT variables relating to non-motorist safety and the crash context can be used to help detect emerging safety issues or changing trends, identify prevalent crash conflict patterns for potential roadway improvements, and define crash scenarios and circumstances that may be useful for crash avoidance technologies research. Analysts can also use the data in crash prediction studies to improve systemic safety processes, and to inform upstream policy decisions to help prevent these types of scenarios. Thus, the data may be useful to planners, engineers, road designers, vehicle and technology designers, safety researchers, and policy-makers, as well as those who work to collect and improve crash data.

The User Guide provides a more detailed discussion on using PBCAT and uses of crash type data.