Data/Surveillance

Practitioners and researchers need high-quality data to both identify problems and to evaluate countermeasure effectiveness. Three primary types of data are needed for a more complete picture of bicyclist safety:

• Safety/outcome data that describes crash events or reports surrogate measures.
• Exposure data that describes the amount of activity in a place or by a group.
• Contextual data that describes the environment in which travel occurs and can provide insight into potential risk factors associated with crashes.

This document’s focus on safety data should not minimize the importance of exposure data or contextual data. Exposure is a crucial aspect of analyzing crash risks because, all other factors being equal, greater exposure will increase the chance of a crash. High crash figures may simply reflect high bicycling activity. Alternatively, a corridor or intersection with low crash figures may be a result of actual or perceived danger that dissuades people from using the facility.

Since 2014 NHTSA’s NCSA has used the crash typing framework PBCAT to describe the events and maneuvers that preceded fatal bicyclist and pedestrian crashes (i.e., the crashes included in FARS). An updated version of  PBCAT (PBCAT3) that improves user functionality and offers crash typing logic to support coding consistency and objectivity, has been released. The new version complements the data that States currently collect. It is important to consider on-site field review of behaviors and site-specific characteristics before determining which engineering or behavioral countermeasures are appropriate (Zegeer et al., 2009). Another pedestrian and bicycle crash typing schema is the Location-Movement Classification Method, which focuses on where the person was and which direction the person was heading relative to the motor vehicle at the time of the crash (Schneider & Stefanich, 2016).

Another consideration when analyzing crash data are that bicyclist crashes (as well as pedestrian crashes) tend to be underreported. Underreporting of traffic-related crashes on road rights-of-way likely decreases as the crash severity increases because police are likely to be called to injury and fatal crashes, and the bicyclist is more likely to be transported or seek treatment at a healthcare facility. Many States may not require reporting nor collect off-road or private-road crash records. Non-roadway crashes may, however, constitute a significant portion of bicyclist-related crashes with motorists. In several studies looking at pedestrian and bicyclist crashes, parking lot and driveway-related crashes represented up to 15% to 25% or more of all reported pedestrian crashes (Stutts & Hunter, 1999; Agran, 1990).

Hospital and EMS data, such as NEMSIS, can be an important form of safety data, as not all crashes involve police response. These data are usually more accurate than police reported crash data, especially for determining crash severity outcomes. They also may include more information about the nature of an injury and crash than police reports, but rarely include detailed location data. Health-related datasets are often deidentified, which makes it challenging to link them with other datasets (i.e., police-reported crash data). Sometimes linkage is possible by working with individual States or after negotiating data agreements.

Crash and injury data is often the only available form of safety data, but in a small area (or short duration of time) there may not be enough data for proper analysis. Some have turned to using naturalistically observed traffic interactions, or “near-misses”, as proxy data to supplement crash data. Near misses are interactions that could have been crashes but were avoided. These near-misses can reveal patterns that might lead to potential risky situations. A combination of crash data and near-miss observations may be an effective means of understanding where interventions are needed (Cloutier et al., 2017). Research has linked bicycling (and walking) behavior to perceptions of safety, and if certain locations feel unsafe, there may be no bicycle traffic. Thus, measuring suppressed trips is also important for gaining a more complete understanding of safety problems (Ferenchak & Marshall, 2019). Last, collecting input from the local residents, safety practitioners, and law enforcement or public works can enhance understanding of safety concerns in a particular location.

More importantly, as mentioned above, in most areas of the country, measures of exposure are lacking. Exposure to traffic and crashes is affected by the number of trips as well as where, when, and for how long a bicyclist rides. The lack of data accounting for the percentage of people on bicycles riding in various situations means we are not able to calculate a rate of crashes for any one location at any given time. This not only hinders full understanding of how bicyclist safety is affected by the built environment, roadway infrastructure, or traffic conditions, but makes comparing safety and risk across the transportation network challenging.

Classifying Crash Types

Bicycle crashes can be classified into types based on bicyclist and motor vehicle pre-crash actions and the location of the crash. Nationally, common bicyclist crash types can be grouped into broad categories:  motorist overtaking crashes, turning crashes, and failure to yield crashes. A recent examination of bicyclist crash types also noted a higher frequency of wrong way and sidewalk crashes among young people on bicycles, suggesting that perhaps these riders were not comfortable riding in existing roadway conditions (Thomas et al., 2019). It is important to note that 23% of bicyclist fatalities in 2021 involved hit-and-run drivers; that is, for nearly one-fourth of fatalities, investigators and researchers may never know the condition and characteristics of the driver or the vehicle involved (NHTSA, 2023). Many other factors such as historical and ongoing investment in the built environment, alcohol impairment, traffic speed, larger and more powerful vehicles, etc., may increase the risk of certain crash types and the risk of injury.

Considerations for Improving Data

Improving data on bicycling transportation is a critical need. A research roadmap developed for AASHTO’s Council on Active Transportation calls “improving data on pedestrian and bicyclist fatalities” a high priority (Dill et al., 2021). While crash data is the main source of safety data, a comprehensive nonmotorized safety analysis often means being able to access and integrate a wide array of data from sources and disciplines. Key to achieving better understanding of safety is improving police reported crash data, improving exposure data, and increasing the frequency of travel surveys.

A consensus report by the Safe States Alliance provides an overview of pedestrian injury surveillance data that could supplement State level crash data or to bolster analyses of safety for bicyclists and pedestrians (Injury Surveillance Workgroup 8, 2017). Fatality and injury data and (primarily) proxy measures of exposure from a variety of sources can be tailored to local needs and used in analyses to understand crashes in greater detail and context.