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

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Date
2014-10-01
Authors
Lyon, Jillian
Zhu, Zhengyuan
Kaiser, Mark
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Institute for Transportation
InTrans administers 14 centers and programs, and several other distinct research specialties, and a variety of technology transfer and professional education initiatives. More than 100 Iowa State University faculty and staff work at InTrans, and from 200 to 250 student assistants from several ISU departments conduct research while working closely with university faculty. InTrans began in 1983 as a technical assistance program for Iowa’s rural transportation agencies.
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Abstract

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.

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The related 30-page full report, under the same title, can be found in http://lib.dr.iastate.edu/intrans_reports/

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