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A detection scheme that uses classical and spatial statistics has been developed to identify roadways with the most severe safety needs. It is based on the null hypothesis that all roadways have the same crash risk, that is, all have the same nonfatal and fatal crash rates throughout the entire study region. Fatal and nonfatal crash rates, which are assumed to be randomly distributed as Poisson processes, are modeled with a marked homogeneous Poisson process model. Since the traffic exposures are typically unknown at the crash sites, they are predicted with a geostatistical model. Locations, where the null hypothesis is rejected, are safety treatment candidates. P-value risk rankings are used to identify locations with the most severe safety needs. The results from alternative analyses--one using vehicle miles traveled and another using population as measures of traffic exposure--are conducted and compared. The state of New Hampshire is used in a case study. The effects of analyzing areas with small traffic exposures, the so-called 'small-area estimation' problem, and the aim to develop a data-driven detection scheme that meets the requirements as an objective decision-making tool are discussed. DOI: 10.1061/(ASCE)TE.1943-5436.0000048 CE Database subject headings: Traffic safety; Traffic models; Spatial analysis; Spatial data; Spatial distribution; Statistics; Stochastic processes. Author keywords: Traffic safety; Traffic models; Spatial analysis; Spatial data; Spatial distribution; Statistics; Stochastic processes.

A Bayesian statistical approach for determining the parameter uncertainty of a storm-water treatment model is reported. The storm-water treatment technologies included a sand filter and a subsurface gravel wetland. The two field systems were loaded and monitored in a side-by-side fashion over a two-year period. The loading to each system was storm-water runoff generated by ambient rainfall on a commuter parking lot. Contaminant transport is simulated by using a one-dimensional advection-dispersion model. The unknown parameters of the model are the contaminant deposition rate and the hydrodynamic dispersion. The following contaminants are considered in the study: total suspended solids, total petroleum hydrocarbons-diesel range hydrocarbons, and zinc. Parameter uncertainties are addressed by estimating the posterior probability distributions through a conventional Metropolis-Hastings algorithm. Results indicate that the posterior distributions are unimodal and, in some instances, exhibit some level of skewness. The Bayesian approach allowed the estimation of the 10th, 25th, 50th, 75th, and 95th percentiles of the posterior probability distributions. The prediction capabilities of the model were explored by performing a Monte Carlo simulation using the calculated posterior distributions and two rainfall-runoff events not considered during the calibration phase. The objective is to estimate effluent concentrations from the treatment systems under different scenarios of flow and contaminant loads. In general, estimated effluent concentrations and the total estimated mass fell within the defined uncertainty limits. [ABSTRACT FROM AUTHOR]