Long-term & short-term measures of roadway snow and ice control performance : final report.

/pdf/long-term-short-term-measures-of-roadway-snow-and-ice-16wsllbpw6.pdf

Long-Term & Short-Term Measures of Roadway Snow and Ice Control Performance

Winter weather in Iowa is often unpredictable and can have an adverse impact on traffic flow. The Iowa Department of Transportation (Iowa DOT) attempts to lessen the impact of winter weather events on traffic speeds with various proactive maintenance operations. In order to assess the performance of these maintenance operations, it would be beneficial to develop a model for expected speed reduction based on weather variables and normal maintenance schedules. Such a model would allow the Iowa DOT to identify situations in which speed reductions were much greater than or less than would be expected for a given set of storm conditions, and make modifications to improve efficiency and effectiveness. The objective of this work was to predict speed changes relative to baseline speed under normal conditions, based on nominal maintenance schedules and winter weather covariates (snow type, temperature, and wind speed), as measured by roadside weather stations. This allows for an assessment of the impact of winter weather covariates on traffic speed changes, and estimation of the effect of regular maintenance passes. The researchers chose events from Adair County, Iowa and fit a linear model incorporating the covariates mentioned previously. A Bayesian analysis was conducted to estimate the values of the parameters of this model. Specifically, the analysis produces a distribution for the parameter value that represents the impact of maintenance on traffic speeds. The effect of maintenance is not a constant, but rather a value that the researchers have some uncertainty about and this distribution represents what they know about the effects of maintenance. Similarly, examinations of the distributions for the effects of winter weather covariates are possible. Plots of observed and expected traffic speed changes allow a visual assessment of the model fit. Future work involves expanding this model to incorporate many events at multiple locations. This would allow for assessment of the impact of winter weather maintenance across various situations, and eventually identify locations and times in which maintenance could be improved.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1037&context=intrans_techtransfer

Quantifying Uncertainty in Real Time Performance Measurement for Highway Winter Maintenance Operations – Phase 2

Traffic flow is often disrupted to varying degrees during precipitation, frost, and blowing snow events. In general, as winter weather events cause road conditions to deteriorate, traffic has a tendency to slow down. Winter maintenance activities affect road conditions and therefore impact traffic speed, so a measurement system using traffic speed has the potential to be a direct measure of the impact of maintenance activities. A prototype model has been developed to predict the average traffic speed at a given time during a winter storm event using commonly reported and forecast road weather data. The prototype model has shown much promise in the quantitative evaluation of winter maintenance by comparing the simulated traffic speed at any point in time to the actual traffic speeds observed by traffic speed sensors. Winter operations may be considered successful when observed traffic speed is found to be at or above the model prediction, and operations may be considered unsuccessful when speeds are significantly less than the model predicts. The minute-by-minute nature of the model output makes it easier to evaluate the specific series of events contributing to the microscale successes or failures over the course of an event. The prototype model is being analyzed and improved for incorporation into real-time performance analysis systems.

Matt Haubrich

Papers

Winter Performance Measurement Using Traffic Speed Modeling