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

dc.contributor.author Lyon, Jillian
dc.contributor.author Zhu, Zhengyuan
dc.contributor.author Zhu, Zhengyuan
dc.contributor.author Kaiser, Mark
dc.contributor.department Institute for Transportation
dc.date 2018-02-15T06:01:09.000
dc.date.accessioned 2020-06-30T04:51:24Z
dc.date.available 2020-06-30T04:51:24Z
dc.date.embargo 2014-11-03
dc.date.issued 2014-10-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>The related 2-page tech transfer summary, under the same title, can be found in http://lib.dr.iastate.edu/intrans_techtransfer/</p>
dc.format.mimetype PDF
dc.identifier archive/lib.dr.iastate.edu/intrans_reports/71/
dc.identifier.articleid 1070
dc.identifier.contextkey 6317998
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath intrans_reports/71
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44913
dc.language.iso English
dc.relation.ispartofseries Iowa DOT SPR RB03-013; InTrans Project 13-388
dc.source.bitstream archive/lib.dr.iastate.edu/intrans_reports/71/IADOT_InTrans_RB03_013_Quantifying_Uncertainty_RT_Perf_Meas_Highway_Winter_Maint_Ops_Phase_2_2014.pdf|||Sat Jan 15 01:42:04 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.keywords Performance measurement
dc.subject.keywords Traffic flow
dc.subject.keywords Traffic speed
dc.subject.keywords Weather conditions
dc.subject.keywords Winter maintenance
dc.subject.keywords Adair County (Iowa)
dc.subject.keywords RB03-013
dc.title Quantifying Uncertainty in Real Time Performance Measurement for Highway Winter Maintenance Operations – Phase 2
dc.type article
dc.type.genre report
dspace.entity.type Publication
relation.isAuthorOfPublication 51db2a08-8f9d-4f97-bdbc-6790b3d5a608
relation.isOrgUnitOfPublication 0cffd73a-b46d-4816-85f3-0f6ab7d2beb8
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