Paul J. Ossenbruggen

Differences in causality factors for single and multi-vehicle crashes on two-lane roads

Past research has found a non-linear relationship between traffic intensity or level of service (LOS) and highway crash rates. This paper investigates this relationship further by including the effects of site characteristics and estimating Poisson regression models for predicting single and multi-vehicle crashes separately. Analysis focuses on rural two-lane highways, with hourly LOS, traffic composition, and highway geometric characteristics as independent variables. The resulting models for single and multi-vehicle crashes have different explanatory variables. Single-vehicle crash rates decrease with increasing traffic intensity (lower LOS), shoulder width and sight distance. Multi-vehicle crash rates increase with the number of signals, the daily single-unit truck percentage, and the shoulder width, and decreased on principal arterials compared to other roadway classes. LOS does not significantly explain variation in the number of multi-vehicle crashes. Ongoing research by the authors is aimed at identifying other site factors, such as driveway density and intersection LOS, that can better explain the differing effects reported here and predict crash rates of both types better.

Assessment of a two-step nitrification model for activated sludge

The objective of developing empirical models with well determined parameters is to obtain a better understanding of the nitrification of activated sludge. The D-optimal experimental design criterion and nonlinear regression analysis were used to obtain precise parameter estimates. Our experiment consisted of a series of 14 batch runs where activated sludge was spiked with ammonium chloride and sodium nitrite individually and in combination. Specific respiration rate, the ratio of respiration rate to mixed liquor suspended solids concentration, was used as the model response variable. Empirical models for specific respiration rate were specified as piecewise, Monod type functions of ammonium and nitrite concentration. Statistical methods were used to evaluate the effects of nonlinearity and parameter correlation on the models. The effect of experimental design on model specification and on parameter estimation is discussed.

Swap: A computer package for solid waste management

Abstract SWAP is a package of computer programs that aids in the decision-making process of establishing a solid waste management plan. The core of the package is a linear programming algorithm that finds the minimum cost solution for a waste management district described as a network. The analysis weighs the cost associated with shipping and processing waste and the benefits from waste recovery of various competing alternatives. In this paper, the program and its theory and application are described.

Toward optimum control of the activated sludge process with reliability analysis

Abstract A scheme to maximize reliability and to identify upsets caused by influent and process disturbances of the activated sludge treatment system with recycle is presented. Reliability is the probability that the effluent wastewater quality meets the maximum contamination standard for BOD5. The plant is assumed to operate under steady-state conditions. Since influent flow and substrate concentration have significant variation, they are introduced into the model as random variables. thus concerns about plant stability are addressed. Reliability is expressed as a function of return sludge concentration and recycle flow. Control charts are used to identify upsets caused by influent disturbance, combinations of flow and BOD5. and process disturbance, combinations of F/M (food to microorganism ratio) and MLSS (mixed liquor suspended solids). Recommendations to maximize reliability in the overall context of plant performance are presented and discussed.

Predicting THM concentration in treated water with highly correlated data

Chlorine is an effective disinfectant in killing bacteria and viruses; however, trihalomethanes (THM) and several chlorination by-products of the drinking water treatment process are suspected carcinogens. In order to develop treatment strategies to minimize THM production, mathematical models containing appropriate raw water quality and control predictor variables are needed. Some of these predictor variables are known to be highly correlated; therefore, when collectively introduced into a regression model, questions about the validity of the model arise. Condition indexes and variance decompositions, as well as ridge regression, are used to identify the degree and causes of ill-conditioning. Path analysis is used to support the inclusion of predictor variables in the final model.

System model development with ill-conditioned data: case studies of trihalomethane formation in drinking water

Abstract Trihalomethane (THM) and other by-products are formed by the reaction of free chlorine and organic precursors. The collective effects of different raw water quality and chlorine treatment measures are used as candidate variables in the formulation and parameter estimation of models to predict THM concentration in treated water. Some of these predictor variables involving raw water quality (state variables) are highly correlated. In addition, retrospective studies often involve treatment measures (control variables) which, either because of practical exigencies or poor design, are correlated. As a result of correlations among variables, regression models that are fit with ordinary least squares are of questionable merit because model parameter estimates are generally poor. Yet, the potential value of such studies and modelling efforts should not be ignored. An approach consisting of the use of condition indexes and variance-decomposition proportions, ridge regression, and path analysis is used for...

Congestion Probability and Traffic Volatility

The primary purpose of this study is to understand better the roadway performance and the conditions that trigger congestion. Incidents of recurrent congestion were encountered at a location on New Hampshire Interstate 93 northbound, where radar measurements of speed were continuously recorded from April through November 2010. The root cause for the onset of congestion, both recurrent and nonrecurrent, is impossible to discern and explain by using exploratory data analyses alone; the data are too noisy. A time series modeling approach suggests that the magnitude of traffic flow and volatility, measured as the second (variance) and third (skewness) moments of flow residuals, can explain the triggering of congestion events for a highly variable environment that changes time by the time and day. The approach includes two types of mathematical models: generalized additive binomial probability models for roadway congestion that use functions of traffic flow and volatility as explanatory variables and functional data models that decompose and smooth traffic data to add insight to the roles that flow and volatility play in the congestion process. Most notably, the probability of congestion is shown to be a function of the short-term history of flow and volatility. The changes in these values—the first derivatives of flow and second and third moments of flow residuals derived from functional data model—are shown as well. Model selection, parameter estimation, and checking are presented.

A diffusion model to explain and forecast freeway breakdown and delay

The chance that a freeway will breakdown, transition from a free-flow to a congested state, is normally assumed to increase with an increase in traffic volume V (vehicles per unit time). In this paper, this assumption is challenged. Traffic density K (vehicles per unit length) proves to be a better predictor. Diffusion or stochastic differential equation (SDE) modeling is used to substantiate the claim. SDE modeling is especially useful in explaining the role that traffic noise (volatility) plays in breakdown. The SDE models take advantage of the unique properties of the geometric Brownian motion (gBM) and Ornstein-Uhlenbeck (OU) model structures. The breakdown probability model of π (K) and delay models provide accurate forecasts.

An approach to teaching civil engineering systems

Abstract The approaches used in offering a required course for undergraduate students, an elective course for both undergraduate and graduate students, and a Master of Science program in civil engineering systems are described. Student attitudes about systems analysis and teaching challenges are discussed. My teaching philosophy is to integrate economic and engineering principles into an unified approach for solving civil engineering design, planning and management problems. The manner in which this philosophy is incorporated into systems courses and the MS program is illustrated.