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NASA NTRS2014

Meningitis and Climate: From Science to Practice

Meningococcal meningitis is a climate sensitive infectious disease. The regional extent of the Meningitis Belt in Africa, where the majority of epidemics occur, was originally defined by Lapeysonnie in the 1960s. A combination of climatic and environmental conditions and biological and social factors have been associated to the spatial and temporal patterns of epidemics observed since the disease first emerged in West Africa over a century ago. However, there is still a lack of knowledge and data that would allow disentangling the relative effects of the diverse risk factors upon epidemics. The Meningitis Environmental Risk Information Technologies Initiative (MERIT), a collaborative research-to-practice consortium, seeks to inform national and regional prevention and control strategies across the African Meningitis Belt through the provision of new data and tools that better determine risk factors. In particular MERIT seeks to consolidate a body of knowledge that provides evidence of the contribution of climatic and environmental factors to seasonal and year-to-year variations in meningococcal meningitis incidence at both district and national scales. Here we review recent research and practice seeking to provide useful information for the epidemic response strategy of National Ministries of Health in the Meningitis Belt of Africa. In particular the research and derived tools described in this paper have focused at "getting science into policy and practice" by engaging with practitioner communities under the umbrella of MERIT to ensure the relevance of their work to operational decision-making. We limit our focus to that of reactive vaccination for meningococcal meningitis. Important but external to our discussion is the development and implementation of the new conjugate vaccine, which specifically targets meningococcus A

Mineral dust↗

NASA NTRS1990

Systems engineering and integration: Cost estimation and benefits analysis

Space Transportation Avionics hardware and software cost has traditionally been estimated in Phase A and B using cost techniques which predict cost as a function of various cost predictive variables such as weight, lines of code, functions to be performed, quantities of test hardware, quantities of flight hardware, design and development heritage, complexity, etc. The output of such analyses has been life cycle costs, economic benefits and related data. The major objectives of Cost Estimation and Benefits analysis are twofold: (1) to play a role in the evaluation of potential new space transportation avionics technologies, and (2) to benefit from emerging technological innovations. Both aspects of cost estimation and technology are discussed here. The role of cost analysis in the evaluation of potential technologies should be one of offering additional quantitative and qualitative information to aid decision-making. The cost analyses process needs to be fully integrated into the design process in such a way that cost trades, optimizations and sensitivities are understood. Current hardware cost models tend to primarily use weights, functional specifications, quantities, design heritage and complexity as metrics to predict cost. Software models mostly use functionality, volume of code, heritage and complexity as cost descriptive variables. Basic research needs to be initiated to develop metrics more responsive to the trades which are required for future launch vehicle avionics systems. These would include cost estimating capabilities that are sensitive to technological innovations such as improved materials and fabrication processes, computer aided design and manufacturing, self checkout and many others. In addition to basic cost estimating improvements, the process must be sensitive to the fact that no cost estimate can be quoted without also quoting a confidence associated with the estimate. In order to achieve this, better cost risk evaluation techniques are needed as well as improved usage of risk data by decision-makers. More and better ways to display and communicate cost and cost risk to management are required.

Dean, ED↗

DOE OSTI2025

Cybersecurity Considerations for Hydrogen Infrastructure in Airport Environments

This report explores key cybersecurity concerns and best practices within environments that serve as reference points for the development of hydrogen fueling infrastructure for aviation. This cybersecurity analysis leverages prior NREL studies: 1) hydrogen fueling station component validation to identify vulnerabilities and failure events documented in physical equipment, and 2) electric aircraft charging infrastructure analysis to explore primary cybersecurity vulnerabilities. It reviews the criticality of digitized technologies in sustaining hydrogen fuel production, storage, and fueling systems, noting cybersecurity concerns that are universal to power systems and industrial control systems in general. In considering cybersecurity vulnerabilities within a future landscape of hydrogen energy for aviation applications, a reference architecture was intended to reveal the points of connection between assets and the potential sensors that are vulnerable to manipulation in the event of compromised access or communication within a SCADA system. A generalized reference architecture can help stakeholders, engineers, or strategists understand connections, criticalities, and standard practices when it comes to designing and planning for new systems. There are several gaps to account for in assessing the future of hydrogen production, storage, and fueling for aviation. Engaging stakeholders, including aircraft manufacturers, electric utilities, site property owners, and local communities, will inform decision-making around site structure, operations, and resources for future hydrogen fueling infrastructure to understand operational needs and cybersecurity awareness. Cybersecurity mitigation strategy must consider physical attack vectors that emerge with the integration of hydrogen systems into existing airport security requirements. The cybersecurity risk assessment contained in this report is an entry point into potential future granular-level analyses to be conducted as part of hazard and risk assessments for safe aviation hydrogen infrastructure, determining how the scale of hydrogen fuel infrastructure for aviation impacts the volume of cyber attack vectors, and what, if any, are the vulnerabilities associated with different types of on-board hydrogen systems. In this nascent development phase, assessing how best to integrate cybersecurity practices into an evolving U.S. aviation landscape provides critical insights into building increased awareness and stakeholder engagement to support a cyber-resilient infrastructure.

08 HYDROGEN↗

NASA NTRS2006

An Experimental Global Monitoring System for Rainfall-triggered Landslides using Satellite Remote Sensing Information

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.

Hong, Yang↗

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

NASA NTRS2013

Understanding Crew Decision-Making in the Presence of Complexity: A Flight Simulation Experiment

Crew decision making and response have long been leading causal and contributing factors associated with aircraft accidents. Further, it is anticipated that future aircraft and operational environments will increase exposure to risks related to these factors if proactive steps are not taken to account for ever-increasing complexity. A flight simulation study was designed to collect data to help in understanding how complexity can, or may, be manifest. More specifically, an experimental apparatus was constructed that allowed for manipulation of information complexity and uncertainty, while also manipulating operational complexity and uncertainty. Through these manipulations, and the aid of experienced airline pilots, several issues have been discovered, related most prominently to the influence of information content, quality, and management. Flight crews were immersed in an environment that included new operational complexities suggested for the future air transportation system as well as new technological complexities (e.g. electronic flight bags, expanded data link services, synthetic and enhanced vision systems, and interval management automation). In addition, a set of off-nominal situations were emulated. These included, for example, adverse weather conditions, traffic deviations, equipment failures, poor data quality, communication errors, and unexpected clearances, or changes to flight plans. Each situation was based on one or more reference events from past accidents or incidents, or on a similar case that had been used in previous developmental tests or studies. Over the course of the study, 10 twopilot airline crews participated, completing over 230 flights. Each flight consisted of an approach beginning at 10,000 ft. Based on the recorded data and pilot and research observations, preliminary results are presented regarding decision-making issues in the presence of the operational and technological complexities encountered during the flights.

Young, Steven D.↗

DOE OSTI2026

Guideline for Characterizing and Evaluating a Candidate Project Site for Solar Thermal Applications

This document presents a structured procedure for characterizing and evaluating candidate project sites for concentrating solar power (CSP) and solar heat for industrial processes (SHIP) applications. The objective is to provide project developers, researchers, and other stakeholders with a consistent, technology-agnostic framework for early-stage site assessment, enabling informed decision-making prior to significant investment in project development. Site selection is a critical factor in project success or failure for both CSP and SHIP projects. Key factors such as solar resource availability, land characteristics, environmental and regulatory constraints, infrastructure availability, and community context are determined by the choice of project site and can materially impact project performance, cost, schedule, and overall viability. This procedure is designed to systematically evaluate these factors, identify potential fatal flaws, and prioritize the most favorable candidate sites for further development. The process begins with rapid screening-level evaluation, using publicly available data to assess solar resource, land availability and suitability, zoning and land-use compatibility, and exclusion zones such as protected lands or sensitive habitats. Sites that meet the minimum screening criteria advance to a more detailed characterization. Subsequent sections of this report provide guidance for a next-level assessment of the most important technical and environmental parameters, including: 1) Solar resource quality, variability, and uncertainty using multiyear datasets and, where appropriate, on-site measurement campaigns; 2) Meteorological conditions such as wind, temperature, extreme weather events, and soiling impacts; 3) Land characteristics including slope, shading, and geotechnical conditions; and 4) Environmental and regulatory considerations, including permitting processes, endangered species, cultural resources, and visual impacts. The procedure also addresses infrastructure and integration considerations, including: 1) Grid interconnection requirements for CSP power generation projects; 2) Electrical and operational integration for SHIP facilities; 3) Water availability, quality, and permitting constraints, which are particularly critical for CSP in arid regions; and 4) Site access, construction logistics, and availability of workforce and supporting services. Recognizing the importance of social and economic context, the procedure includes evaluation of community engagement factors, such as stakeholder sentiment, proximity to sensitive visual receptors, workforce development opportunities, and local economic incentives. The outputs of these assessments are synthesized in a cost and risk evaluation, translating site characteristics into expected impacts on capital cost, operating cost, schedule, and technical risk. This is complemented by screening-level performance modeling, including 8760 simulations and long-term projections, to quantify expected energy or thermal output, assess variability thereof, and support comparison between candidate sites. Finally, the procedure provides high-level guidance on a structured go/no-go decision framework, categorizing sites based on identified risks and constraints, and outlining a clear path forward to feasibility studies and front-end engineering design for viable projects. By standardizing the site characterization process across both CSP and SHIP applications, this guideline aims to: 1) Improve consistency and transparency in early-stage project evaluation; 2) Reduce development risk and avoid investment in nonviable project sites; 3) Support collaboration between developers, researchers, and public agencies; and 4) Accelerate successful deployment of concentrating solar technologies for both power generation and industrial process heat.

14 SOLAR ENERGY↗

DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

ReVise: A Human-AI Interface for Incremental Algorithmic Recourse

The recent adoption of artificial intelligence in socio-technical systems raises concerns about the black-box nature of the resulting decisions in fields such as hiring, finance, admissions, etc. If data subjects—such as job applicants, loan applicants, and students—receive an unfavorable outcome, they may be interested in algorithmic recourse, which involves updating certain features to yield a more favorable result when re-evaluated by algorithmic decision-making. Unfortunately, when individuals do not fully understand the incremental steps needed to change their circumstances, they risk following misguided paths that can lead to significant, long-term adverse consequences. Existing recourse approaches focus exclusively on the final recourse goal but neglect the possible incremental steps to reach the goal with real-life constraints, user preferences, and model artifacts. To address this gap, we formulate a visual analytic workflow for incremental recourse planning in collaboration with AI/ML experts and contribute an interactive visualization interface that helps data subjects efficiently navigate the recourse alternatives and make an informed decision. We also present one of the many usage scenarios, developed during exploratory feedback sessions with twelve graduate students using a real-world dataset, which demonstrates that our approach can be instrumental for data subjects in choosing a suitable recourse path.

algorithmic recourse↗

DOE OSTI2024

MSD CoP Webinar: Accounting for distributive justice in model-based decision support

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Model-based analyses play an ever-increasing role in informing policy and decision-making. However, many large-scale societal challenges unavoidably involve questions about the fair distribution of positive and negative consequences. How are the costs of the energy transition distributed over households and businesses? How are flood risks redistributed under different flood risk management plans? How are the impacts of climate change and climate mitigation distributed over different parts of the world?These kinds of questions play an important role when deliberating public policy, and the lack, in many cases, of clear answers becomes an obstacle to decision-making. At recent COPs, for example, wicked questions about loss and damages and the phase-out versus phase-down of coal became obstacles to global climate action, illustrating the misalignment of the direction of scientific research and decision-makers' information needs. In this talk, I'll explore advances that are being made that enable analysts to start providing grounded model-based answers to questions of distributive justice, as well as argue that analysts should embrace and explicate the normative nature of their work instead of hiding behind the purported neutrality of science. Presenters: Dr. Jan Kwakkel (Delft University of Technology) Moderator(s): Rebecca Saari (MSD CoP Working Group Co-Chair; Univ. of Waterloo), Matt Sparks (Univ. of Waterloo); Sarah Fletcher (MSD CoP Working Group Co-Chair; Stanford University), Juan Moreno-Cruz (Univ. of Waterloo), Patrick Reed (MSD CoP Facilitation Team Member, Moderator and Organizer) This webinar was held on: November 13, 2024 from 1 PM - 2:15 PM ET

Kwakkel, Jan H.↗

NASA NTRS1993

Life support technology investment strategies for flight programs: An application of decision analysis

Applied research and technology development (R&TD) is often characterized by uncertainty, risk, and significant delays before tangible returns are obtained. Given the increased awareness of limitations in resources, effective R&TD today needs a method for up-front assessment of competing technologies to help guide technology investment decisions. Such an assessment approach must account for uncertainties in system performance parameters, mission requirements and architectures, and internal and external events influencing a development program. The methodology known as decision analysis has the potential to address these issues. It was evaluated by performing a case study assessment of alternative carbon dioxide removal technologies for NASA's proposed First Lunar Outpost program. An approach was developed that accounts for the uncertainties in each technology's cost and performance parameters as well as programmatic uncertainties such as mission architecture. Life cycle cost savings relative to a baseline, adjusted for the cost of money, was used as a figure of merit to evaluate each of the alternative carbon dioxide removal technology candidates. The methodology was found to provide a consistent decision-making strategy for development of new life support technology. The case study results provided insight that was not possible from more traditional analysis approaches.

Schlater, Nelson J.↗

NASA NTRS1993

Life support technology investment strategies for flight programs: An application of decision analysis

Applied research and technology development (R&TD) is often characterized by uncertainty, risk, and significant delays before tangible returns are obtained. Given the increased awareness of limitations in resources, effective R&TD today needs a method for up-front assessment of competing technologies to help guide technology investment decisions. Such an assessment approach must account for uncertainties in system performance parameters, mission requirements and architectures, and internal and external events influencing a development program. The methodology known as decision analysis has the potential to address these issues. It was evaluated by performing a case study assessment of alternative carbon dioxide removal technologies for NASA"s proposed First Lunar Outpost program. An approach was developed that accounts for the uncertainties in each technology's cost and performance parameters as well as programmatic uncertainties such as mission architecture. Life cycle cost savings relative to a baseline, adjusted for the cost of money, was used as a figure of merit to evaluate each of the alternative carbon dioxide removal technology candidates. The methodology was found to provide a consistent decision-making strategy for the develpoment of new life support technology. The case study results provided insight that was not possible from more traditional analysis approaches.

Schlater, Nelson J.↗

NASA NTRS2005

Trusted Autonomy for Space Flight Systems

NASA has long supported research on intelligent control technologies that could allow space systems to operate autonomously or with reduced human supervision. Proposed uses range from automated control of entire space vehicles to mobile robots that assist or substitute for astronauts to vehicle systems such as life support that interact with other systems in complex ways and require constant vigilance. The potential for pervasive use of such technology to extend the kinds of missions that are possible in practice is well understood, as is its potential to radically improve the robustness, safety and productivity of diverse mission systems. Despite its acknowledged potential, intelligent control capabilities are rarely used in space flight systems. Perhaps the most famous example of intelligent control on a spacecraft is the Remote Agent system flown on the Deep Space One mission (1998 - 2001). However, even in this case, the role of the intelligent control element, originally intended to have full control of the spacecraft for the duration of the mission, was reduced to having partial control for a two-week non-critical period. Even this level of mission acceptance was exceptional. In most cases, mission managers consider intelligent control systems an unacceptable source of risk and elect not to fly them. Overall, the technology is not trusted. From the standpoint of those who need to decide whether to incorporate this technology, lack of trust is easy to understand. Intelligent high-level control means allowing software io make decisions that are too complex for conventional software. The decision-making behavior of these systems is often hard to understand and inspect, and thus hard to evaluate. Moreover, such software is typically designed and implemented either as a research product or custom-built for a particular mission. In the former case, software quality is unlikely to be adequate for flight qualification and the functionality provided by the system is likely driven largely by the need to publish innovative work. In the latter case, the mission represents the first use of the system, a risky proposition even for relatively simple software.

Freed, Michael↗

NASA NTRS2005

Space Technologies for Enhancing the Resilience and Sustainability of Indigenous Reindeer Husbandry in the Russian Arctic

To adapt successfully to the major changes - climate, environment, economic, social and industrial - which have taken place across the Arctic. in recent years, indigenous communities such as reindeer herders must become increasingly empowered with the best available technologies to add to their storehouse of traditional knowledge. Remotely-sensed data and observations are providing increased capabilities for monitoring, risk mapping, and surveillance of parameters critical to the characterization of pasture quality and migratory routes, such as vegetation distribution, snow cover, infrastructure development, and pasture damages due to fires. This paper describes a series of remote sensing capabilities, which are useful to reindeer husbandry, and gives the results of the first year of a project, "Reindeer Mapper", which is a remote sensing and GIs-based system to bring together space technologies with indigenous knowledge for sustainable reindeer husbandry in the Russian Arctic. In this project, reindeer herders and scientists are joining together to utilize technologies to create a system for collecting and sharing space-based and indigenous knowledge in the Russian Arctic. The "Reindeer Mapper" system will help make technologies more readily available to the herder community for observing, data collection and analysis, monitoring, sharing, communications, and dissemination of information - to be integrated with traditional, local knowledge. This paper describes some of the technologies which comprise the system including an intranet system to enable to the team members to work together and share information electronically, remote sensing data for monitoring environmental parameters important to reindeer husbandry (e.g., SAR, Landsat, AVHRR, MODIS), indigenous knowledge about important environmental parameters, acquisition of ground- based measurements, and the integration of all useful data sets for more informed decision-making.

Maynard, Nancy G.↗

NASA NTRS2007

All Source Solution Decision Support Products Created for Stennis Space Center in Response to Hurricane Katrina

In the aftermath of Hurricane Katrina and in response to the needs of SSC (Stennis Space Center), NASA required the generation of decision support products with a broad range of geospatial inputs. Applying a systems engineering approach, the NASA ARTPO (Applied Research and Technology Project Office) at SSC evaluated the Center's requirements and source data quality. ARTPO identified data and information products that had the potential to meet decision-making requirements; included were remotely sensed data ranging from high-spatial-resolution aerial images through high-temporal-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) products. Geospatial products, such as FEMA's (Federal Emergency Management Agency's) Advisory Base Flood Elevations, were also relevant. Where possible, ARTPO applied SSC calibration/validation expertise to both clarify the quality of various data source options and to validate that the inputs that were finally chosen met SSC requirements. ARTPO integrated various information sources into multiple decision support products, including two maps: Hurricane Katrina Inundation Effects at Stennis Space Center (highlighting surge risk posture) and Vegetation Change In and Around Stennis Space Center: Katrina and Beyond (highlighting fire risk posture).

Ross, Kenton W.↗

NASA NTRS2009

Distributed Trajectory Flexibility Preservation for Traffic Complexity Mitigation

The growing demand for air travel is increasing the need for mitigation of air traffic congestion and complexity problems, which are already at high levels. At the same time new information and automation technologies are enabling the distribution of tasks and decisions from the service providers to the users of the air traffic system, with potential capacity and cost benefits. This distribution of tasks and decisions raises the concern that independent user actions will decrease the predictability and increase the complexity of the traffic system, hence inhibiting and possibly reversing any potential benefits. In answer to this concern, the authors propose the introduction of decision-making metrics for preserving user trajectory flexibility. The hypothesis is that such metrics will make user actions naturally mitigate traffic complexity. In this paper, the impact of using these metrics on traffic complexity is investigated. The scenarios analyzed include aircraft in en route airspace with each aircraft meeting a required time of arrival in a one-hour time horizon while mitigating the risk of loss of separation with the other aircraft, thus preserving its trajectory flexibility. The experiments showed promising results in that the individual trajectory flexibility preservation induced self-separation and self-organization effects in the overall traffic situation. The effects were quantified using traffic complexity metrics based on Lyapunov exponents and traffic proximity.

Idris, Husni↗

NASA NTRS2009

Analysis of Trajectory Flexibility Preservation Impact on Traffic Complexity

The growing demand for air travel is increasing the need for mitigation of air traffic congestion and complexity problems, which are already at high levels. At the same time new information and automation technologies are enabling the distribution of tasks and decisions from the service providers to the users of the air traffic system, with potential capacity and cost benefits. This distribution of tasks and decisions raises the concern that independent user actions will decrease the predictability and increase the complexity of the traffic system, hence inhibiting and possibly reversing any potential benefits. In answer to this concern, the authors proposed the introduction of decision-making metrics for preserving user trajectory flexibility. The hypothesis is that such metrics will make user actions naturally mitigate traffic complexity. In this paper, the impact of using these metrics on traffic complexity is investigated. The scenarios analyzed include aircraft in en route airspace with each aircraft meeting a required time of arrival in a one-hour time horizon while mitigating the risk of loss of separation with the other aircraft, thus preserving its trajectory flexibility. The experiments showed promising results in that the individual trajectory flexibility preservation induced self-separation and self-organization effects in the overall traffic situation. The effects were quantified using traffic complexity metrics, namely dynamic density indicators, which indicated that using the flexibility metrics reduced aircraft density and the potential of loss of separation.

Idris, Husni↗

NASA NTRS2016

Climate Science for Decision-Making in the New York Metropolitan Region

New York City is one of the world's most vulnerable cities to coastal flooding, due to a high concentration of population and assets near a coastline exposed to warm-season tropical storms and cold -season Nor'easter storms. Among U.S. cities, New York City is second only to New Orleans in population living less than 4 ft above the local high tide. By the 2050s, average annual losses due to coastal flooding alone could exceed $2 billion for the combined New York City-Newark region. Perhaps the most iconic example of a vulnerable New York City asset is the financial district located at the southern tip of Manhattan, however low-lying coastal assets include the full complement of major highways, subways and tunnels, hospitals, schools, wastewater treatment plants, food distribution centers, and people's homes. Given the magnitude of the assets at risk, a compelling case can be made that long-term adaptation makes economic sense for New York City. Given New York's access to economic, human, and technological resources for resilience measures, the City may be able to achieve this resilience. The city's political environment-New York City is a place where climate science is generally not a partisan issue-and the city's experience with uncertainty and overall risk framing (e.g., financing of bond issues for multi-billion dollar infrastructure with multidecade expected lifetimes), encourage climate risk framing.

Horton, Radley↗