How do Observed Vehicle Speeds Influence Pedestrian Exposure and Pedestrian Crash Outcomes?

Project Description

After decreasing for more than three decades, US pedestrian fatalities increased by 83% in just 13 years (4,109 in 2009 to 7,522 in 2022). Pedestrians now account for approximately 18% of all US traffic fatalities. One of the many theories that could help explain this trend is that vehicle speeds may have increased, particularly in urban areas where vehicles and pedestrians interact the most. However, the relationship between actual vehicle speeds in the roadway network and the likelihood of pedestrian crash events is not well understood. One particular challenge is that increasing vehicle speeds is likely to increase both actual and perceived risk (i.e., create a more stressful pedestrian environment), which can reduce pedestrian exposure, or activity levels.

Our study will investigate the following questions:

1) How is pedestrian activity in a roadway corridor influenced by the speed of adjacent motor vehicles? Many studies have identified relationships between pedestrian volumes and land use context and roadway design variables, but few have quantified the relationship between pedestrian volumes and observed vehicle speeds.

2) How is pedestrian crash risk related to actual motor vehicle speeds along a roadway corridor? Most previous studies of pedestrian crash risk have used surrogate measures of vehicle speed (e.g., speed limit, functional classification, or other roadway characteristics), but few have used actual vehicle speed measurements.

We will explore our research questions by developing two negative binomial models based on a sample of roadway corridor measurements in North Carolina and Wisconsin. The roadway corridors will be urban arterial roadways that are part of the National Highway System (NHS). The first model will quantify the relationship between pedestrian volumes and a set of roadway characteristics, adjacent land uses, adjacent neighborhood socioeconomic characteristics, or other variables. The second model will relate the number of fatal and severe pedestrian injury crashes with a similar set of explanatory variables.

The central explanatory variable to test in both of our models will be actual vehicle speed data from the National Performance Management Research Data Set (NPMRDS). The NPMRDS database is available for free from FHWA and contains speed and travel time data from cars and trucks equipped with mobile devices (probe vehicles). The speed data are provided for NHS segments that are typically 0.5 to 1 mile in length in urban areas and are available back to 2013. We will consider several potential speed variables in our analysis, such as overall 50th and 85th percentile speeds for each segment (based on all available speed measurements over multiple years), nighttime speeds, daytime speeds, and frequency of speed measurements that are 10 mph above the speed limit. Longitudinal measures will also be examined to compare with long-term exposure trends, where available.

Understanding these relationships will help quantify the anticipated safety impacts of reducing speed through engineering, enforcement, and education treatments.