Infrastructure agencies worldwide develop and continue to refine quantitative decision support systems for performance and investment projections. In the earlier stages of the creation of infrastructure decision support systems in the United States, agencies focused on developing appropriate databases and deterministic analysis models to capture important investment and performance relationships. As decision support systems have matured, stronger emphasis has been placed on improving the quality of information for decision makers. Analyzing data and analysis model uncertainties is one logical approach for addressing the information quality of infrastructure decision support systems. This paper develops a computer simulation approach to explore the effects of data and model uncertainties on highway performance estimation, using the federal-level highway decision support system as a case in point. The paper illustrates how data-induced and model-induced influences on the expected value and variance of performance estimates may be used to track real changes in highway performance estimates while capturing changes in the performance evaluation criteria and efficiency of modeling procedures.

Traffic incidents are nonrecurrent and pseudorandom events that disrupt the normal flow of traffic and create a bottleneck in the road network. The probability of incidents is higher during peak flow rates when the systemwide effect of incidents is most severe. Model-based solutions to the incident detection problem have not produced practical, useful results primarily because the complexity of the problem does not lend itself to accurate mathematical and knowledge-based representations. A new multiparadigm intelligent system approach is presented for the solution of the problem, employing advanced signal processing, pattern recognition, and classification techniques. The methodology effectively integrates fuzzy, wavelet, and neural computing techniques to improve reliability and robustness. A wavelet-based denoising technique is employed to eliminate undesirable fluctuations in observed data from traffic sensors. Fuzzy c-mean clustering is used to extract significant information from the observed data and to reduce its dimensionality. A radial basis function neural network (RBFNN) is developed to classify the denoised and clustered observed data. The new model produced excellent incident detection rates with no false alarms when tested using both real and simulated data.

This paper presents a recursive traffic flow prediction algorithm using artificial neural networks. The system prediction model is specified based on the understanding of how disturbances in traffic flow are propagated, and the order of the model is determined by correlation analysis. The parameters of the model, on the other hand, can be obtained through nonlinear optimization. Preliminary studies show that this approach can yield reasonably accurate results.

Ordinary least-squares (OLS) regression is routinely applied by transportation analysts to forecast energy use, trip attractions, trip productions, automobile emissions, VMT growth, pavement condition, and accident occurrence to name a few examples. An important challenge when estimating OLS models is to derive an appropriate specification. Common misspecification errors include omission of important variables, inclusion of irrelevant variables, and inclusion of variables in an incorrect functional form. These errors often produce biased parameter estimates, inefficient parameter estimates, and an inability to conduct accurate hypothesis tests. Analysts typically rely on previous empirical research, a priori knowledge, and underlying theory to identify acceptable model functional forms, to determine important interactions, and to derive defensible models. In exploratory research, however, the analyst rarely knows a priori the correct form of the relationships being modeled, and previous research illuminating the “correct” relationships is scant. This paper presents an iterative modeling method that combines desirable properties of OLS with a heuristic procedure known as hierarchical tree-based regression (HTBR). This combined approach, named iteratively specified tree-based regression (ISTBR), is shown to provide insight into data structure provided by hierarchical tree-based regression, while retaining the desirable parametric properties of OLS. ISTBR equips the modeler with improved tools for exploring and identifying alternative model specifications and affords the analyst insight into systematic patterns or “structure” in data that might otherwise go undetected. An example of the ISTBR approach is provided using trip generation data from Michigan.

An optimal trip table correction function for the user equilibrium based trip table estimation problem is derived and used in place of Turnquist and Gur's heuristic trip table correction function. The derivation of this new correction function is based on a new concept—path equivalent capacity—that unifies the individual capacities of the links in a path into a single path capacity. Once a path between two zones is represented as a link with a single capacity, the travel time of the path can be computed directly for any path flow. This permits an optimal trip table correction function to be determined at each iteration of Turnquist and Gur's trip table algorithm. Test results show that the new method yields more accurate and consistent solution trip tables than the heuristic method.

This paper investigates the quality of traffic service at intersections that are nominally “priority unsignalized” but operate with partial respect for control measures. The operation of such intersections with moderate traffic volumes is characterized by the use of delay and conflict measures. A simple simulation model reflecting driver behavior and traffic stream interactions is developed as a proof of this concept. Intersection performance with similar flow levels but under other types of control is evaluated and compared with observed measures, and trade-offs are identified. It is concluded that basic models that incorporate driver learning and impatience, aggressive driver attitudes, and modes of stream interaction different from simple gap acceptance are helpful in explaining traffic performance at intersections that have incomplete enforcement of priorities. The study also indicates that, although lack of control may result in lower delays at intersections for certain levels of traffic volumes where self-organization may be possible, such intersection performance reflects aggressive driving practices and potentially occurs at the expense of safety.

This paper analyzes the impacts of different light-duty trucks (LDTs) on the capacity of signalized intersections. Data were collected at two intersections in Austin, Tex., and regression analysis generated estimates of mean headways associated with various categories of LDTs, as well as passenger cars. Using the estimated headways, passenger-car equivalents were calculated, and these suggest that the impacts of LDTs should be given special consideration when analyzing the capacity of signalized intersections. For example, a single large sport-utility vehicle in through traffic is equivalent to 1.41 passenger cars, and a van is equivalent to 1.34. Such headways reduce intersection capacity and increase urban congestion.

This report documents a number of issues relative to the Federal Highway Administration's requirement that all state transportation agencies have switched to the Traffic Noise Model by April 1, 2000. The Georgia Department of Transportation uses a version of STAMINA that utilizes vehicle noise emission data specific to the state of Georgia. That model has shown consistently close agreement with measured sound levels over several years. This study was designed to first compare measured sound levels to data generated by both the Traffic Noise Model and STAMINA at the same location. After the measurement data were analyzed, both models were used to design four noise barriers on I-475 in Macon, Ga.

The sealing and resealing of joints and cracks in pavements is assumed to be an important component of pavement maintenance. If performed effectively and timely, sealing may retard pavement deterioration and prolong pavement life. Historically, sealing and resealing has been an accepted practice by state highway agencies (SHAs) but this practice has recently been challenged by research. Additionally, studies that provide a clear quantitative defense that joint/crack sealing is cost effective are few in number and limited in scope. Millions of dollars are spent annually by SHAs to accomplish joint and crack sealing with no quantitative evidence to justify the expenditure. The sealing operations are conducted simply because the “industry” assumes the benefits of sealing outweigh the costs. The objective of this research was to investigate the cost-effectiveness of joint/crack sealing specifically in relation to pavement performance. This report represents a synthesis of practice (extensive literature review and survey of practice) used for determining whether further research was needed to determine the cost-effectiveness of crack/joint sealing in Indiana. Ultimately, it led to the development of a comprehensive field study that the Indiana Department of Transportation is sponsoring.

Most unpaved road dust measuring devices have employed one or more of the air sampling techniques used by atmospheric scientists, namely, sedimentation, filtration, and photometric techniques. The dust measurement quantifications have been for a section of unpaved road either involving the use of moving devices, or at a point(s) along a section involving the use of a stationary device. Devices based on the photometric technique have measured and quantified dust on a real-time basis without laboratory analyses of field data, while devices based on the sedimentation and filtration techniques have relied on laboratory analyses of field data for dust quantification. The Colorado State University dustometer, an experimental road dust measurement device based on the filtration technique is presented in this paper. The device and technique were developed, field tested, and used in comparative fugitive dust emission studies at Colorado State University and documented by Addo and Sanders. The device, which is basically a moving dust sampler, consists of a standard high volumetric sampler with associated filter media and accessories mounted on the rear of a pickup truck. Many dust measurements can be made in a single day using the dustometer. The dustometer has proved to be a very quantitative, reproducible, and precise measurement device for unpaved road dust measurements.