Omar Smadi

Pavement Marking Retroreflectivity: Analysis of Safety Effectiveness

Previous research has shown that greater longitudinal pavement marking retroreflectivity levels increase drivers’ visibility and detection distance. However, increased visibility may cause drivers to feel too comfortable during nighttime conditions, and drivers may then pay less attention, operate their vehicles at unsafe speeds, or both. Before-and-after studies have been conducted on a pavement marking improvement such as repainting stripes or changing to a more durable marking material. Studies have also used models to estimate the retroreflectivity on the basis of the date of installation and vehicle exposure or have assumed a linear reduction in retroreflectivity over time. Only two studies have related field-measured pavement marking retroreflectivity to safety performance (crash data). One study analyzed the relationship between 3 years of pavement marking retroreflectivity data collected by the Iowa Department of Transportation on all state primary roads and corresponding crash and traffic data. The other study developed a spatial–temporal database by using measured retroreflectivity data to account for the deterioration of pavement markings over time, along with a statewide crash database to attempt to quantify a relationship between crashes and the quality (measured by retroreflectivity) of pavement marking. Three sets of data were analyzed: the complete database, records for two-lane roads, and records with retroreflectivity values less than or equal to 200 mcd/m2/lx only. The distributions and models of the entire database and the two-lane records did not show that poor pavement marking retroreflectivity correlated to a higher crash probability. However, when only records with retroreflectivity values of 200 mcd/m2/lx or less were examined, a statistically significant, albeit weak, relationship was determined.

Use of Pavement Management Information System for Verification of Mechanistic-Empirical Pavement Design Guide Performance Predictions

The performance models used in the Mechanistic–Empirical Pavement Design Guide (MEPDG) are nationally calibrated with design inputs and performance data obtained primarily from the national Long-Term Pavement Performance database. It is necessary to verify and calibrate MEPDG performance models for local highway agencies’ implementation by taking into account local materials, traffic information, and environmental conditions. This paper discusses the existing pavement management information system (PMIS) with respect to the MEPDG and the accuracy of the nationally calibrated MEPDG prediction models for Iowa highway conditions. All the available PMIS data for Interstate and primary road systems in Iowa were retrieved from the Iowa Department of Transportation (DOT) PMIS. The retrieved databases were then compared and evaluated with respect to the input requirements and outputs for Version 1.0 of the MEPDG software. Using Iowa DOTs comprehensive PMIS database, researchers selected 16 types of pavement sections across Iowa (not used for national calibration in the NCHRP 1-37A study). A database of MEPDG inputs and the actual pavement performance measures for the selected pavement sites were prepared for verification. The accuracy of the MEPDG performance models for Iowa conditions was statistically evaluated. The verification testing showed promising results in terms of MEPDGs performance prediction accuracy for Iowa conditions. Recalibrating the MEPDG performance models for Iowa conditions is recommended to improve the accuracy of pavement performance predictions.

Using Classification Trees for Predicting National Bridge Inventory Condition Ratings

In bridge management practice, bridge condition is the fundamental information needed to allow decision makers to make well-informed decisions regarding preservation, rehabilitation, or replacement of a bridge or network of bridges. The National Bridge Inventory (NBI) condition ratings, collected since the early 1970s, and the Commonly Recognized (CoRe) element condition data, collected since the early 1990s, are two major sources of bridge condition data in the United States. General NBI condition ratings are utilized for performance assessment, performance reporting, resource allocation, and selection of bridge projects by all levels of government. Since the early 1990s, the bridge management community has been interested in an algorithm to predict the categorical NBI condition rating classes from the more quantitative and detailed CoRe element condition data. An algorithm with sufficient predictive accuracy would make only CoRe element inspections necessary and would provide time and resource savings. This paper presents a new methodology for this purpose, using classification and regression trees (CARTs). The CART analyses were conducted with the bridge condition data provided by three state transportation agencies, using data from 2006 to 2010. The statistical results point to a more accurate prediction method than the previous algorithms described in the literature.

Infrastructure Asset Management Education: Active Learning and Engagement-Based Practices

Asset management of civil infrastructure facilities and systems is gaining importance both in practice and in academia as the public demands increased accountability in government, laws are passed requiring infrastructure asset management, and growth and development accelerate the deterioration of already aged infrastructures. In response, more agencies continue to adopt and develop their asset management practices and more academic institutions and continuing education programs define courses and programs in civil infrastructure asset management. This paper focuses on an infrastructure management class offered at the graduate level mainly for civil engineering and transportation planning students. The outline of the class and a brief overview of active and engagement-based learning techniques are presented, and the way these techniques are integrated with the delivery of the asset management material is described.

A knowledge-based expert systems application in library acquisitions: Monographs

Abstract The decision-making process in any environment, including the library, is potentially subjective and arbitrary. The impetus for this research has been to eliminate the discretionary component in vendor selection by designing a quantitative decision-making model. Technically, the model is based on the artificial intelligence concept known as an expert system. The model uses rules provided by the expert, policies of the individual institution, and quantitative decision techniques to assign orders to vendors. Advantages to this knowledge-based model are the following: (1) assurance of quantitative objectivity; (2) efficient allocation of limited resources; (3) improved use of personnel time; (4) creation of a database of vendor price structuring; and (5) usefulness as a training resource.

Integrated Approach to Pavement Marking Management

Providing good pavement marking performance is an essential component of the transportation system. According to Tom Welch, state safety engineer for the Iowa Department of Transportation (DOT), “Every older driver forum has included a consistent demand for brighter and more durable pavement markings.” This paper summarizes some management tools developed for Iowa DOT as part of a pavement marking management system (PMMS). A summary of how retroreflectivity data are used statewide in managing pavement marking performance is provided as well as a discussion of how the PMMS was integrated with other infrastructure management resources for Iowa DOT.

Pavement Marking Retroreflectivity and Crash Frequency: Segmentation, Line Type, and Imputation Effects

This paper investigates the effects of segment length, line types, and imputed retroreflectivity values on pavement-marking retroreflectivity and crash frequency analyses. Road data, crash data, pavement condition data, and pavement-marking retroreflectivity data from Iowa Department of Transportation (DOT) databases were acquired and spatially integrated. Data sets for 1-, 3-, and 5-mi segments were prepared to investigate the effect of segmentation. Additional data sets with imputed and measured retroreflectivity data were prepared for comparison. A series of negative binomial regression analyses were run to estimate the expected number of crashes on varying segment lengths and data subsets based on the two retroreflectivity collection methods. The findings show that using smaller segments and data sets with measured retroreflectivity rather than imputed retroreflectivity leads to a more significant relationship between the retroreflectivity of longitudinal pavement markings and crash frequency. The findings also suggest that keeping longitudinal pavement markings in good condition has significant positive effects on safety. The results further suggested that the expected number of annual crashes significantly decreased with the increasing retroreflectivity of white-edge lines (WELs) and yellow-edge lines (YELs) for four-lane road segments. In addition, a significant relationship between pavement condition, measured with the International Roughness Index (IRI) and the expected number of crashes was found for all data sets.

Speed reduction impact of dynamic speed feedback signs on high crash curves

Lane departure crashes are a significant safety concern. The majority of lane departure crashes occur in rural areas, mostly on 2-lane roadways. A disproportionate number of these crashes occur on horizontal curves. Curve related crashes involve a number of roadway and driver causative factors. A primary driver factor is speeding and a large number of run-off-road fatal crashes on curves are speed related. The amount of speed change needed from the tangent speed to the speed required to traverse a curve has an impact on the frequency and severity of crashes on curves.

Management of Bridges Under Aging Mechanisms and Extreme Events: Risk-Based Approach

Current bridge management systems predict the condition state of bridge elements primarily on the basis of the extent of continuous structural deterioration. Although the existing systems deliver a range of capabilities for the management of bridges under normal operational conditions, these systems do not take into account the consequences of sudden extreme events in a systematic way. Considering the uncertainties involved in natural and manufactured hazards in addition to the ones associated with environmental exposure conditions, there is a critical need to develop risk-based approaches that not only take into account the site-specific aging mechanisms and extreme events at the same time but also accommodate the spatial and temporal randomness originated from them. This study introduced a risk-based, life-cycle analysis framework to be implemented in the current bridge management systems used by the transportation agencies. A set of representative bridges exposed to environmental stressors and seismic hazards was investigated to demonstrate the capabilities of this framework. The condition states of the bridges were predicted in accordance with the Markovian transition matrices that were generated for both aging mechanisms and seismic events. The outcome of this study highlights how the developed framework can contribute to improve the life-cycle performance and cost predictions, especially when the adverse effects of extreme events cannot be neglected in the management of bridges.

Practice of Rumble Strips and Rumble Stripes

This synthesis documents current practices used by states installing rumble strips and explores variations in practice in terms of design, criteria, and locations for installation, maintenance, perceived benefits, communication of benefits, and important issues. Information was acquired through a literature review, survey on current practice, and case examples. The survey also included questions specific to the state of practice for rumble stripes, which is the term used when the pavement marking lines are painted on the rumble strip in an effort to increase visibility during inclement weather conditions. The findings indicate that two-thirds of the state agencies rely on traditional methods to address the noise issue: skipping rumbles in residential areas, adjusting their depth, or not installing rumbles at all. The overwhelming majority of responding states, 83%, have developed policies or guidance to modify their rumble design practices to be sensitive to cyclists. Fewer than half the respondents have developed policies/guidance to address pavement deterioration; several agencies indicated that pavement condition is a factor on whether to install a rumble or not. The survey also showed that very few state DOTs have created public campaigns to explain that the use of rumbles improves safety so that complaints regarding noise, bicycle issues, and other are minimized.