A knowledge-based system for controlling automobile traffic

Transportation network capacity variations arising from accidents, roadway maintenance activity, and special events as well as fluctuations in commuters' travel demands complicate traffic management. Artificial intelligence concepts and expert systems can be useful in framing policies for incident detection, congestion anticipation, and optimal traffic management. This paper examines the applicability of intelligent route guidance and control as decision aids for traffic management. Basic requirements for managing traffic are reviewed, concepts for studying traffic flow are introduced, and mathematical models for modeling traffic flow are examined. Measures for quantifying transportation network performance levels are chosen, and surveillance and control strategies are evaluated. It can be concluded that automated decision support holds great promise for aiding the efficient flow of automobile traffic over limited-access roadways, bridges, and tunnels

Initial Financial Assessment of the Fraport Greece Cluster A Concession

There is a worldwide trend in the privatization of transport infrastructure and airports. Likewise, the Greek government launched an extensive privatization program that granted Fraport AG the right to operate 14 airports for the next forty years. The two separate concessions for clusters of seven airports each are named Cluster A and Cluster B. The financial assessment of privatization contracts is crucial so decision-makers can accurately assess the value of aviation enterprises. This paper applies the Economic Value Added (EVA) methodology and enterprise valuation on Cluster A by assessing the concession company\u27s balance sheets and income statements. We concluded that Cluster A has a high Debt-to-Equity (D/E) ratio but also outstanding results when examining profitability ratios. After the adverse effects of the COVID-19 pandemic, we discovered that the concession has a vast potential for development and profitability. Overall, the privatization has successfully transferred operating risk to the concessionaire while ensuring timely airport upgrading/refurbishment. Finally, a high level of services has been attained, as evidenced that during the 2022 Airport Service Quality (ASQ) Awards Thessaloniki Makedonia Airport was recognized as one of the top airports in Europe in the category of airports that handle 5-15 million passengers per year

Quantifying uncertainty in health impact assessment: a case-study example on indoor housing ventilation.

Quantitative health impact assessment (HIA) is increasingly being used to assess the health impacts attributable to an environmental policy or intervention. As a consequence, there is a need to assess uncertainties in the assessments because of the uncertainty in the HIA models. In this paper, a framework is developed to quantify the uncertainty in the health impacts of environmental interventions and is applied to evaluate the impacts of poor housing ventilation. The paper describes the development of the framework through three steps: (i) selecting the relevant exposure metric and quantifying the evidence of potential health effects of the exposure; (ii) estimating the size of the population affected by the exposure and selecting the associated outcome measure; (iii) quantifying the health impact and its uncertainty. The framework introduces a novel application for the propagation of uncertainty in HIA, based on fuzzy set theory. Fuzzy sets are used to propagate parametric uncertainty in a non-probabilistic space and are applied to calculate the uncertainty in the morbidity burdens associated with three indoor ventilation exposure scenarios: poor, fair and adequate. The case-study example demonstrates how the framework can be used in practice, to quantify the uncertainty in health impact assessment where there is insufficient information to carry out a probabilistic uncertainty analysis

The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations

In scheduling, estimations are affected by the imprecision of limited information on future events, and the reduction in the number and level of detail of activities. Overlapping of processes and activities requires the study of their continuity, along with analysis of the risks associated with imprecision. In this line, this paper proposes a fuzzy heuristic model for the Project Scheduling Problem with flows and minimal feeding, time and work Generalized Precedence Relations with a realistic approach to overlapping, in which the continuity of processes and activities is allowed in a discretionary way. This fuzzy algorithm handles the balance of process flows, and computes the optimal fragmentation of tasks, avoiding the interruption of the critical path and reverse criticality. 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Composite Criticality in Machinery Fleet Management of Construction Projects

During construction operations, fleet management aims at maximizing the uptime and efficiency of construction machinery while also minimizing the cost of ownership through lifecycle planning and management. In the deterministic approach, the theory suggests that one type of machinery is considered to be critical. However, taking into account the real circumstances under which projects are performed with issues such as machine reliability, worker performance, and/or errors in estimating the scope of work, it is evident that there are significant limitations to the existing approach. Hence, to address this issue, uncertainty in fleet productivity is modelled with fuzzy set theory. In this context, the notion of composite criticality under which the productivity of a fleet depends on more than one type of machinery because of the fluctuations of the individual productivities is introduced. To this purpose, a simple case study is presented to illustrate this concept. It is concluded that this approach leads to a better understanding of the estimation of activity duration and cost estimation which in turn means better project scheduling and financial planning