Abstract

The scope of this study was to evaluate the impact of the air traffic controller-to-pilot communication standard known as CPDLC or Data-Communication on the future air traffic operations. The impact was evaluated from the double viewpoint of airport delays and air traffic controllersâ workload. RAMS simulation software is used to perform all the runs and from its output data the values of terminal area delays and controllers workload are obtained. The New York Metroplex terminal area was used as a case study. Because of its complexity, where three major airports (i.e. JFK, Newark, and La Guardia) interact and constraint each other, this area was particularly interesting to be studied and the data analyzed gave a valuable insight on the possible future impact of Data-Communication in congested terminal areas. The results of the study, based on some previous man-in-the-loop simulations performed by the FAA in the nineties, showed that significant potential benefits could be obtained with the complete implementation of such technologies in the workload experienced by air traffic controllers. Moreover some small but not negligible benefits were obtained in the total delays accrued by each airport studied. On the other hand, the simulations of the future demand predicted by the FAA demonstrated that without a significant increment in capacity or limitation on the traffic growth intolerable delays would be recorded across the NAS in the future. For the complexity of the simulation model calibration and for the very time-consuming run time not all the scenarios described in the methodology were tested, demonstrating the weakness of RAMS as a ground simulation model. Master of Science

Abstract

The on-going H2020 project INFRALERT aims to increase rail and road infrastructure capacity in the current"br" framework of increased transportation demand by developing and deploying solutions to optimise maintenance"br" interventions planning. INFRALERT develops an ICT platform - the expert-based Infrastructure Management"br" System eIMS - which follows a modular approach including several expert-based toolkits. This paper presents"br" the architecture of the eIMS as well as the functionalities, methodologies and exemplary results of the toolkits"br" for i) nowcasting and forecasting of asset condition, ii) alert generation, iii) RAMS & LCC analysis and iv)"br" decision support. The applicability and effectiveness of the eIMS and its toolkits will be demonstrated in two"br" real-world pilot scenarios, which are described in the paper: a meshed road network in Portugal under the"br" jurisdiction of Infraestruturas de Portugal (IP) and a freight railway line in Northern Europe managed by"br" Trafikverket.

Abstract

Life-cycle cost (LCC) analysis is an assessment technique used to evaluate costs incurred during the life-cycle of"br" a system to help in long term decision making. In railway and road transport infrastructures, costs are subject to"br" numerous uncertainties associated to the operation and maintenance phase. By integrating in the LCC the"br" stochastic nature of failure using Reliability, Maintainability, Availability and Safety (RAMS) analyses,"br" maintenance costs can be more reliably estimated. This paper presents an innovative approach for a combined"br" RAMS&LCC methodology for linear transport infrastructures which has been developed under the H2020"br" project INFRALERT. Results of the application of such methodology in two real use cases are shown, one for"br" rail and another one for road. The use cases show how the approach is implemented in practice.