University of Minnesota (UMN) researchers have developed new methodologies and tools for estimating bicyclist’s exposure to risk, with their work illustrating, through case studies, how these new measures can be used to assess crash risk and be incorporated into planning-level studies to improve safety.
Protecting vulnerable road users such as cyclists is a top priority for both transportation managers and the public. However, a lack of data on bicycle crash risk makes implementing effective safety measures difficult. The goal of the UMN research was to provide tools and methodologies that support a data-driven approach.
Led by principal investigator, Professor Greg Lindsey, the researchers began by conducting a literature review summarizing recent advances in demand modeling, estimating exposure to risk (defined as bicyclist demand, or traffic volumes), and assessing crash risk.
Next, they developed new bicycle demand models using a large database of afternoon peak-period bicycle counts in Minneapolis. Using this information, they then developed additional models to demonstrate how estimates of exposure correlate with the probability of crashes in the city.
Lindsey’s team used the exposure estimates to assess the need for countermeasures at 184 roadway-trail crossings in Minneapolis. They discovered most locations that potentially warrant traffic signals or pedestrian hybrid beacons already have them, but found as many as 17 crossings that warrant site-specific analyses to determine whether additional safety countermeasures may be needed.
Expanding their work beyond the Twin Cities, the researchers examined bike data in two other cities. In Duluth, they developed count-based models of bicyclist exposure and tested the correlation between measures of exposure and crashes. In Bemidji, researchers completed exploratory analysis of available count and crash data.
“The demand models correlate the count data with different road and built-environment characteristics, enabling estimation of the number of bicyclists through any given segment in a network,” explained Lindsey. “By combining these demand models with crash data, it becomes possible to predict and understand the risk of bicyclist-car crashes at various locations and times. It also becomes easier to plan where to put countermeasures.”
Michael Petesch, bicycle and pedestrian data coordinator with Minnesota DOT’s office of transit and active transportation, commented, “While MnDOT is a multimodal agency working to connect people and places, our roads and facilities are often barriers for people walking or biking to their destination. This study, along with several ongoing MnDOT efforts to characterize risk, will be useful in developing proactive approaches for planning and programming safety countermeasures on projects throughout Minnesota.”
Maren Webb, Safe Routes to School/SHIP coordinator in Grand Marais/Cook County, added, “In our community, we cannot rely on bicycle and pedestrian crash data to prioritize infrastructure investments, as our number of incidents is very small. However, anecdotal evidence and other data suggest that improvements are needed.
“This research will assist communities like ours to use locally collected bicycle and pedestrian count data to estimate and assess safety and crash risk, and to inform local ‘Safe Routes to School’ planning and other infrastructure investments.”