Improving road safety through proper pavement engineering and maintenance should be one of the major objectives of pavement management systems. When pavements are evaluated in terms of safety, a number of factors related to pavement engineering properties are raised, such as pavement geometric design, paving materials and mix design, pavement surface properties, shoulder type, and pavement color and visibility. Each year there are voluminous annual reports on traffic accident statistics and discussions of such road safety issues as road safety modeling and pavement safety measurements and criteria. Although road safety may be considered a separate area, it should be incorporated into pavement management systems. The main pavement engineering relationships associated with road safety are identified, and the various aspects of road safety related to pavement management, such as pavement types, pavement surface macrotexture and microtexture, and pavement safety measurements, criteria, and evaluation methods, are discussed. A systematic approach is proposed for the coordination of pavement maintenance programs with road safety improvement and the incorporation or integration of safety management with pavement and other management systems. Finally, a list of possible remedial measures for road safety improvements associated with pavement maintenance activities is recommended.

Accurate prediction of pavement deterioration is the most important factor in the determination of pavement repair years and optimization programming of highway network maintenance. The Nonhomogeneous Markov Probabilistic Modeling Program, developed to determine pavement deterioration rates in different stages, is described. In this program the transition probability matrices (TPMs) are considered as a time-related transition process. Each element of the TPMs is determined on the basis of a reliability analysis and a Monte Carlo simulation technique. This avoids the use of the existing conventional methods, which involve taking an average subjective opinion of pavement engineers or observing a large number of multiyear pavement performance data and conducting a number of statistical calculations. As a result a series of TPMs for an individual pavement section for different stages can be determined by running the program. Furthermore, the pavement condition state in terms of a probability vector at each stage (year) is calculated. In applying the models both the predicted actual traffic (in terms of equivalent single axle loads) at each stage and the maximum traffic that the pavement can withstand at each defined pavement condition state interval are considered to be random variables. In addition, the sensitivities of pavement deterioration rates to pavement design parameters, such as traffic growth rate, subgrade strength, and material properties, are studied. Finally, an example of calculating the TPMs for a pavement section located in southeastern Ontario, Canada, is demonstrated. It shows that the sensitivities of the TPMs to traffic growth rate, subgrade deflection, and pavement thickness are significant.

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TRANSPORTATION RESEARCH RECORD 1524 203 Accurate prediction of pavement deterioration is the most important factor in the determination of pavement repair years and optimization programming of highway network maintenance. The Nonhomogeneous Markov Probabilistic Modeling Program, developed to determine pavement deterioration rates in different stages, is described. In this program the transition probability matrices (TPMs) are considered as a timerelated transition process. Each element of the TPMs is determined on the basis of a reliability analysis and a Monte Carlo simulation technique. This avoids the use of the existing conventional methods, which involve taking an average subjective opinion of pavement engineers or observing a large number of multiyear pavement performance data and conducting a number of statistical calculations. As a result a series of TPMs for an individual pavement section for different stages can be determined by running the program. Furthermore, the pavement condition state in terms of a probability vector at each stage (year) is calculated. In applying the models both the predicted actual traffic (in terms of equivalent single axle loads) at each stage and the maximum traffic that the pavement can withstand at each defined pavement condition state interval are considered to be random variables. In addition, the sensitivities of pavement deterioration rates to pavement design parameters, such as traffic growth rate, subgrade strength, and material properties, are studied. Finally, an example of calculating the TPMs for a pavement section located in southeastern Ontario, Canada, is demonstrated. It shows that the sensitivities of the TPMs to traffic growth rate, subgrade deflection, and pavement thickness are significant.The development of probabilistic models for the prediction of network deterioration has been a key technical challenge to pavement engineers. Other models used in network-level pavement management are mainly dependent on the reliability of the prediction of deterioration for each individual pavement section. In other words, pavement deterioration prediction influences the quality of many other components of pavement management, such as determination of the years that rehabilitation is needed and the corresponding treatment alternatives, improving the existing road network to a required service level, and selecting the optimal cost-effective rehabilitation and maintenance alternatives. Over the last two decades, although considerable progress has been made toward the achievement of effective management systems, there is still a need for probabilistic modeling of network pavement deterioration (1). OVERVIEW OF EXISTING PREDICTION MODELSSince the concepts of pavement management were initiated in the 1960s, many prediction models have been developed in North Amer-ica and elsewhere. Basically, the current prediction models can be divided into two categories: deterministic and probabilistic (2). These two types of prediction models have been used by many highway agencies ...

Oral codeine preparations, widely used for analgesia and cough suppression, are abused by some individuals for their mood-altering properties. The enzymatic O-demethylation of codeine is catalyzed by cytochrome P450 2D6 (CYP2D6), leading to the production of metabolites (morphine, morphine-6-glucuronide) that are pharmacologically more potent than codeine. A placebo-controlled, single-blind study was conducted to characterize the subjective effects of codeine associated with abuse liability and to determine the importance of metabolic O-demethylation to codeine abuse liability. Twelve non-drug-dependent subjects received oral administration of placebo and codeine 60, 120, and 180 mg, and a favorite dose (FD) was determined for each subject. The FD was readministered after pretreatment with placebo, 50 mg of quinidine (a specific, selective CYP2D6 inhibitor) once, or 50 mg of quinidine given four times a day for 4 days. Single-dose quinidine pretreatment significantly decreased the recovery of O-demethylated metabolites in plasma (p < 0.01) and resulted in a decrease in the positive (e.g., "high," p < 0.05) and negative (e.g., nausea, p < 0.05) subjective effects of codeine in both the FD120 and FD180 groups. Short-term quinidine pretreatment inhibited codeine O-demethylation more than did single-dose quinidine pretreatment (p < 0.01), and it decreased positive codeine effects in the FD120 group (N = 7), but unexpectedly not in the FD180 group (N = 5). These results suggest that the O-demethylated metabolites contribute substantially to the subjective effects and abuse liability of codeine.

Comparison of 7-hydroxylation of coumarin, a CYP2A6 substrate, in human and African green and cynomolgus monkey liver microsomes was made by means of an HPLC assay with UV detection. In human liver microsomes, the Km and Vmax values for the metabolic conversion were 2.1 microM and 0.79 nmol/mg/min, respectively. While African green monkey showed Km and Vmax values of 2.7 microM and 0.52 nmol/mg/min, which were similar to human, higher Km and Vmax values were found in cynomolgus monkey. Coumarin 7-hydroxylation in human and African green monkey was selectively inhibited by methoxsalen and pilocarpine (CYP2A6 inhibitors) but not by other inhibitors, i.e. alpha-naphthoflavone (CYP1A1), orphenadrine (CYP2B6), sulfaphenazole (CYP2C9), quinidine (CYP2D6) and ketoconazole (CYP3A4). Immunoinhibition results supported CYP2A6 involvement in human and its homolog in monkey in coumarin 7-hydroxylation, as only anti-CYP2A6, but not CYP2B1, CYP2C13, CYP2D6, CYP2E1 or CYP3A antibodies, inhibited this conversion. African green monkey was found to be similar to human in catalytic activity of coumarin 7-hydroxylation and response to CYP2A6 inhibitors or antibody inhibition. However, the monkey CYP2A6 is not identical to the human in that Ki values were different, and differences were observed with some CYP2A6 inhibitors, such as nicotine and methoxsalen, suggesting that, under some circumstances, studies of nicotine kinetics and drug taking behavior in monkey may not be comparable to human.

A good pavement management system should have the capacity to predict pavement structural and functional deterioration versus age or accumulated traffic loading. Basically, there are two types of performance prediction models in pavement management: deterministic and probabilistic. Although both performance models can be used to predict pavement deterioration, the inherent relationship between the two models has not been explored. An investigation was directed to find the relationship in terms of system conversion. Some of the findings related to system conversion, including the concepts and techniques applied in model conversion, the characteristics of model development, comparisons of prediction results between the two models, sensitivity analysis of the probabilistic models, and sample applications in real situations, are highlighted. The deterministic models that are to be converted to probabilistic models are the flexible pavement deterioration model used in the Ontario Pavement Analysis of Costs system and the flexible pavement design model recommended in the 1993 AASHTO design guide. The converted probabilistic models are time-related (nonhomogeneous) Markov processes, which are represented by a set of yearly transition probability matrices (TPMs). TPMs can be established for any individual pavement section in a road network.

A new approach to multiyear maintenance and rehabilitation (M&R) optimization programming for pavement network management is discussed; the approach can be used to help highway agencies make strategic decisions in choosing the optimal investment for their pavement networks. The M&R treatments are standardized in terms of costs, benefits, and performance impacts on the existing pavements. Each standardized pavement treatment strategy, ranging from minor and routine maintenance to major rehabilitation or reconstruction, is defined by its effect and improvement on the existing pavement serviceability. The optimization model is a cost-effectiveness-based integer M&R programming on a year-by-year basis. The objective of the optimization system is to select the most effective M&R projects for each programming year. The optimization system can also be used to calculate the minimum budget requirements for maintaining a prescribed level of the pavement network performance or serviceability. In such a case, sensitivity analysis can be performed to evaluate the annual budget effect on individual pavement performance. The prediction of individual pavement deterioration is modeled as a time-related (nonhomogeneous) Markov transition process. The investigation described was primarily concerned with integration of the performance prediction model, the standardized M&R treatments, and the network optimization process. The principle and methodology developed can be applied to different levels of pavement network management. Finally, a sample application of the integrated pavement optimization model is demonstrated.