Better Crash Data That Doesn’t Victim Blame
“Blame is how we control the terror stirred up by the seeming randomness of accidental tragedy. There is nothing productive in this process.”– Jessie Singer, There are No Accidents
There is a plethora of guides, manuals, regulations and other policies that shape roadways safety and most of them — like the MUTCD — aren’t household names. As part of our federal advocacy, the League works to ensure the voices of bicyclists are included when these policies come up for comments in the Federal Register and through other means. Today’s lesser-known document is the Model Minimum Uniform Crash Criteria (MMUCC).
The MMUCC is promoted by the National Highway Traffic Safety Administration (NHTSA), the Governors Highway Safety Administration and the United States Department of Transportation (USDOT) as a best practice for documenting motor vehicle crashes. It is coming up on its sixth edition, after first being developed in 1998 and having undergone four previous updates – in 2003, 2008, 2012, and 2017.
The MMUCC aims to standardize the data collected around a motor vehicle crash. With a standard dataset across the country, the goal is to generate the information necessary to improve highway safety at the national, state, and local levels. But any conclusions drawn from the data are only as complete and accurate as the data collected at the outset.
The use of the MMUCC and conformity with it is voluntary. In most jurisdictions, crash reports are completed by law enforcement officers and are only completed when a threshold of crash severity is met. The MMUCC recommends reporting all motor vehicle traffic crashes statewide involving death, personal injury, or property damage of $1,000 or more. At the outset, bicyclist crashes that do not involve $1,000+ bikes or personal injury, are excluded.
Police crash reports form the basis for traffic safety statistics and the proposed sixth edition of the MMUCC continues an unfortunate tradition of victim blaming people outside of vehicles for crashes while excusing the actions of drivers. The same action, “improper passing”, is attributed to drivers as a “related factor” but to people biking and walking as a “contributing circumstance.” This subtly, or not, means that in official crash reports and traffic safety statistics the actions of a driver are only related to them, but the actions of a person biking or walking are actions that may have contributed to the crash. This difference is both not helpful for descriptive statistics and does not reflect the Safe System Approach promoted by USDOT, which focuses on proactive changes to our transportation system over victim blaming.
Other examples of unnecessarily victim blaming language perpetuated in the proposed MMUCC includes:
- The data attribute “Dart/Dash” to describe a person entering the roadway, even if they stumble into the roadway. “Dart/Dash” was attributed to more than 100 people aged 75 years or older in the last five years.
- The data attribute “Not Visible (Dark Clothing, No Lighting, etc.)” to describe an officer’s judgment that the person hit was not visible although other attributes more clearly capture lighting and it is not a crime to wear dark clothing.
- The data attribute “Improper Crossing of Roadway or Intersection (Jaywalking)” which unnecessarily uses the term jaywalking. The term jaywalking was popularized by automotive companies to reinforce that roads are for cars rather than people. The descriptive terms “Improper Crossing of Roadway or Intersection” fully capture the concept of this attribute. Adding “jaywalking” only serves to scold and blame the person crossing the road.
The United States can do better than victim blaming. Our crash data should inform us on systemic improvements to our roadways that follow the Safe System Approach adopted by the USDOT. The League supports strong data collection practices and our comments to NHTSA on the proposed MMUCC express our concerns that victim blaming is still baked into our data collection. We also point out potential improvements to bike facility, demographic, and vehicle attributes that we believe are likely to lead to better crash data.