Sufficient quantities of water for household use, including for drinking, food preparation and hygiene, are needed to protect public health and for well-being and prosperity.



This second edition reviews the evidence about the relationships between water quantity, water accessibility and health. The effects of water reliability, continuity and price on water use, are also covered. Updated guidance, including recommended targets, is provided on domestic water supply to ensure beneficial health outcomes.

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Domestic water quantity, service level and health

Dynamic Traffic Assignment (DTA) has evolved substantially since the pioneering work of Merchant and Nemhauser. Numerous formulations and solutions approaches have been introduced ranging from mathematical programming, to variational inequality, optimal control, and simulation-based. The aim of this special issue is to document the main existing DTA approaches for future reference. This opening paper will summarize the current understanding of DTA, review the existing literature, make the connection to the approaches presented in this special issue, and attempt to hypothesize about the future.

Foundations of dynamic traffic assignment : the past, the present and the future

Heat island phenomenon rises the temperature of cities, increases the energy demand for cooling and deteriorates comfort conditions in the urban environment. To counterbalance the impact of the phenomenon, important mitigation techniques have been proposed and developed. Pavements present a very high fraction of the urban areas and contribute highly to the development of heat island in cities. The use of cool pavements presenting substantially lower surface temperature and reduced sensible heat flux to the atmosphere, appears to be one of the most important proposed mitigation solutions. The present paper investigates and describes the actual state of the art on the field of cool pavements. The main thermal and optical parameters defining the thermal performance of pavements are analyzed. Almost all of the developed technologies, where data and results are available, are considered while emphasis is given on the presentation of reflective and permeable/water retentive pavements. The main technological achievements on both fields are reviewed while existing applications are described and performance data are given when available. The existing results clearly show that the mitigation and cooling potential of cool pavements is very significant and can highly contribute to decrease temperature on the urban environment.

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Using cool pavements as a mitigation strategy to fight urban heat island—A review of the actual developments

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1999.

Modeling drivers' acceleration and lane changing behavior

Robust design has been developed with the expectation that an insensitive design can be obtained. That is, a product designed by robust design should be insensitive to external noises or tolerances. An insensitive design has more probability to obtain a target value, although there are uncertain noises. Theories of robust design have been developed by adopting the theories of other fields. Based on the theories, robust design can be classified into three methods: 1) the Taguchi method, 2) robust optimization, and 3) robust design with the axiomatic approach. Each method is reviewed and investigated. The methods are examined from a theoretical viewpoint and are discussed from an application viewpoint. The advantages and drawbacks of each method are discussed, and future directions for development are proposed.

Robust Design: An Overview

Markovian transition probabilities have been used extensively in the field of infrastructure management to provide forecasts of facility conditions. However, existing approaches used to estimate these transition probabilities from inspection data are mostly ad hoc and suffer from important methodological limitations. In this paper, we present a rigorous econometric method for the estimation of infrastructure deterioration models and associated transition probabilities from condition rating data. This methodology, which is based on ordered probit techniques, explicitly treats facility deterioration as a latent variable, recognizes the discrete ordinal nature of condition ratings, and, as opposed to state-of-the-art methods, explicitly links deterioration to relevant explanatory variables. An empirical case study using a bridge inspection data set from Indiana demonstrates the capabilities of the proposed methodology.

Estimation of infrastructure transition probabilities from condition rating data

Sound infrastructure deterioration models are essential for accurately predicting future conditions that, in turn, are key inputs to effective maintenance and rehabilitation decision making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors’ ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Consequently, probabilistic discrete-state models are often used to characterize deterioration. Such models are based on transition probabilities that capture the nature of the evolution of condition states from one discrete time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented. A probabilistic model of the time spent in a state (referred to as duration) is developed, and the approach used for estimating its parameters is described. Furthermore, the method for determining the corresponding state transition probabilities from the estimated duration models is derived. The testing for the Markovian property is also discussed, and incorporating the effects of history dependence, if found present, directly in the developed duration model is described. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.

Computation of Infrastructure Transition Probabilities using Stochastic Duration Models

Statistical models of infrastructure facility deterioration are typically estimated using panel data sets of in-service facilities. For example, biannual ratings of bridges have been used to develop discrete models of component deterioration by a number of researchers. Unfortunately, these models have not accounted for the presence of heterogeneity in the panel data, which may lead to biased coefficient estimates. Furthermore, researchers have usually imposed a Markovian specification in the development of such models, implying that the probabilistic deterioration in a given period is independent of history. This assumption may be unrealistic for some types of facilities in which early distress initiation leads to accelerated deterioration in later stages of their lives. In this paper, we adopt a random-effects specification to control for heterogeneity in a probit model of bridge-deck deterioration and extend the model to investigate the presence of state dependence. The proposed model yields improved results in comparison with a simple probit model and provides evidence that is inconsistent with the Markovian assumption in bridge-deck deterioration. An implication of this study is that both heterogeneity and state dependence may need to be accounted for in developing probabilistic infrastructure deterioration models.

Probabilistic Infrastructure Deterioration Models with Panel Data

State-of-the-art decision-making models in the area of infrastructure maintenance and rehabilitation (which are based on the Markov Decision Process) do not take into account the uncertainty in the measurement of facility condition. This paper presents a methodology, the Latent Markov Decision Process (LMDP), which explicitly recognizes the presence of random errors in the measurement of the condition of infrastructure facilities. Two versions of the LMDP are presented. In the first version, the inspection schedule is fixed, which is the usual assumption made in state-of-the-art models. The second version of the LMDP minimizes the sum of inspection and M & R costs. An empirical comparison of the two versions of the LMDP and the traditional MDP illustrates the importance of incorporating measurement uncertainty in decision-making and of optimizing the inspection schedule.

Optimal Inspection and Repair Policies for Infrastructure Facilities

A Bayesian updating model is developed to capture the mechanism by which travelers update their travel time perceptions from one day to the next in light of information provided by Advanced Traveler Information Systems (ATIS) and their previous experience. The availability and perceived quality of traffic information are explicitly modeled within the proposed framework. The uncertainty associated with a driver's travel time estimate is modeled in a stochastic dynamic framework and is incorporated in a travel choice model. Each driver uses a disutility function of perceived travel time and perceived schedule delay to evaluate the alternative travel choices, then selects an alternative based on the utility maximization principle. The perception updating model and the choice model are integrated with a dynamic traffic simulator (DYNASMART). Empirical results from the simulation experiments and their implications are also presented.

Samer Madanat

Papers

Estimation of infrastructure transition probabilities from condition rating data

Computation of Infrastructure Transition Probabilities using Stochastic Duration Models

Probabilistic Infrastructure Deterioration Models with Panel Data

Optimal Inspection and Repair Policies for Infrastructure Facilities

Perception updating and day-to-day travel choice dynamics in traffic networks with information provision