Risk analysis of performance measure forecasts in road safety engineering

dc.contributor.authorMilligan, Craig Alexander
dc.contributor.examiningcommitteeRegehr, Jonathan (Civil Engineering) Serieux, John (Economics) Hildebrand, Eric (University of New Brunswick)en_US
dc.contributor.supervisorMontufar, Jeannette (Civil Engineering)en_US
dc.date.accessioned2015-01-15T15:35:22Z
dc.date.available2015-01-15T15:35:22Z
dc.date.issued2014en_US
dc.date.issued2014en_US
dc.degree.disciplineCivil Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractThis research contributes to improved risk analysis of performance measure forecasts in road safety engineering by designing and applying a method to characterize uncertainty associated with forecast input data in cases where input uncertainty is not known. The research applies this method to quantify uncertainty in three categories of inputs used in risk analysis of performance measure forecasts in road safety engineering: (1) estimates of pedestrian exposure to collision risk; (2) estimates of vehicular exposure to collision risk; and (3) estimates of engineering economics parameters that assign valuations to mortality risk reductions based on individual willingness to pay. The common methods used in each of these categories are repeated comparisons of input ground truth to input estimations, the use of simulation approaches (e.g. the simulation of short-term counts by sampling permanent count data), and the use of non-parametric techniques to characterize input uncertainty. Some highlights of quantified input uncertainty levels include: (1) when obtaining pedestrian risk exposure estimates at a site in Winnipeg, MB by expanding two-hour short-term counts using the National Bicycle and Pedestrian Documentation Project method, 90% of errors are between 62% and 170%; (2) when obtaining estimates of vehicle exposure to collision risk by expanding two 48-hour counts using the individual permanent counter method for Manitoba highways, 92 % of errors are between 9.5% and 10.8%; and (3) when applying an income-disaggregated transfer function to estimate value of a statistical life for road safety in developing countries, 90% of errors are between 53% and 54%. The results provide further detail on the structure of these input uncertainties. Analytic and computational capabilities in forecasting and risk analysis have advanced beyond our understanding of corresponding input uncertainty levels; this research closes some of this gap and enables better risk analysis of performance measure forecasts in road safety engineering.en_US
dc.description.noteFebruary 2015en_US
dc.identifier.citationMilligan, C., Kopp, A., Dahdah, S., Montufar, J. 2014. Value of a statistical life in road safety: A benefit-transfer function with risk-analysis guidance based on developing country data. Accident Analysis and Prevention (71), 236-247.en_US
dc.identifier.citationMilligan, C., Poapst, R., Montufar, J. 2013. Performance measures and input uncertainty for pedestrian crossing exposure estimates. Accident Analysis and Prevention (50), 490-498.en_US
dc.identifier.urihttp://hdl.handle.net/1993/30226
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.publisherElsevieren_US
dc.rightsopen accessen_US
dc.subjectroad safetyen_US
dc.subjectperformance measuresen_US
dc.subjectrisk analysisen_US
dc.subjectexposureen_US
dc.subjectAADTen_US
dc.subjectpedestrianen_US
dc.subjectvalue of a statistical lifeen_US
dc.subjecttraffic monitoringen_US
dc.subjectdeveloping countriesen_US
dc.titleRisk analysis of performance measure forecasts in road safety engineeringen_US
dc.typedoctoral thesisen_US
local.subject.manitobayesen_US
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