Researchers developed sophisticated models for high-density asphalt pavement mixtures. After calibrating the model to experimental data available from 5 percent air void asphalt mixtures, the research team conducted tests on three Minnesota mixtures to further refine the model. A Phase II study will develop multiple high-density mix designs for Minnesota applications.Continue reading High-Density Asphalt Pavement Mixtures Viable with local Aggregates
In recent years, the transportation community has introduced significant changes to improve left-turn safety at signalized intersections—and for good reason. Nationally, intersection crashes represent one-fifth of all fatal crashes, and most of these are crashes involving left turns.
In response to this serious safety problem, the FHWA has adopted a new national standard for permissive left turns: the flashing yellow arrow. This signal warns drivers that they should proceed with a left turn only after yielding to any oncoming traffic or pedestrians. Flashing yellow arrow signals can help prevent crashes, move more traffic through an intersection, and provide additional traffic management flexibility.
Many transportation agencies, including MnDOT, are interested in using the new flashing yellow arrow signals to accommodate within-day changes: protected left turns (signaled by a green arrow) could be used when needed to lower crash risk, while permitted left turns (signaled by a flashing yellow arrow) could be used to reduce delay when crash risk is low.
“Of course, this requires being able to predict how the risk of left-turn crashes changes as intersection and traffic characteristics change within the course of a day,” says Gary Davis, a professor of civil, environmental, and geo- engineering at the University of Minnesota.
To help engineers make more informed decisions about when to use flashing yellow arrows, Davis is leading the development of a model that could help predict the probability of left-turn crash risk at a given intersection at different times of day. This model—which will ultimately be available as a set of spreadsheet tools—will help traffic engineers determine when the crash risk is sufficiently low to allow for the safe use of flashing yellow arrows. The project is sponsored by MnDOT and the Minnesota Local Road Research Board.
To develop the statistical model, the researchers needed to determine how the risk for left-turn crashes varies depending on time of day, traffic flow conditions, and intersection features (such as number of opposing lanes, number of left-turn lanes, and median size). The process included developing a database containing left-turn crash information, intersection features, and traffic volumes, as well as developing a set of 24-hour traffic pattern estimates to help fill gaps where hourly traffic volume counts were not available. The resulting statistical model uses this information to determine relative crash risk for every hour of the day at a given type of intersection.
Currently, Davis and his team are using the model to develop a spreadsheet tool that will allow traffic engineers to choose their type of intersection and enter the available turning movement count. The tool will then generate a specialized graph for that intersection showing the relative crash risk by time of day. Any time the crash risk is at or below the level identified as acceptable, engineers can consider using flashing yellow arrows.
“By simulating how crash risk changes as traffic conditions change, this model could help identify conditions when permitted left-turn treatments would be a good choice and what times of day a protected left turn might be a better option,” Davis says.
Moving forward, Davis is leading an additional project related to the use of flashing yellow arrows, funded by the Roadway Safety Institute. The project will first review video data of drivers making permitted left turns to characterize left-turn gap acceptance and turning trajectories. Then, Davis will incorporate the findings into the existing statistical model. To further improve the model’s accuracy, the study will compare the crashes described by the simulation model with reconstructed real-world left-turn crashes.