Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Validation of New Technology using Legacy Metrics: Examination of Surf-IA Alerting for Runway Incursion Incidents

New flight deck technology designed to mitigate runway incursions may not be effective in triggering a flight deck alert to avoid high speed surface collisions for runway incursions classified as serious by legacy metrics. This study demonstrated an innovative method of utilizing expert raters and actual high-risk incidents to identify shortcomings of using legacy metrics to measure the effectiveness of new technology designed to mitigate hazardous incidents. Expert raters were used to validate the Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF-IA) model for providing alerts to pilots to reduce the occurrence of pilot deviation type runway incursion incidents categorized as serious (Category A or B) by the FAA/ICAO Runway Incursion Severity Classification (RISC) model. This study used archival data from Aviation Safety Information Analysis and Sharing (ASIAS) incident reports and video reenactments developed by the FAA Office of Runway Safety. Two expert raters reviewed nine pilot deviation type serious runway incursion incidents. The raters applied the baseline minimally compliant implementation of the RTCA/DO 323 SURF-IA model to determine which incidents would have an alerting SURF-IA outcome. Inter-rater reliability was determined by percentage agreement and Cohen’s kappa and indicated perfect agreement between the raters who assessed six of the incidents with a SURF-IA alerting outcome and three as non-alerting. Specific aircraft states were identified in the baseline SURF-IA model that precluded an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model: (a) wrong runway departures, (b) no alert if traffic entered runway after ownship lift-off from same runway, and (c) helicopter operations. The study concluded that the SURF-IA model did not yield an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model. Even if the SURF-IA model had performed to design, the best it could have achieved would have been a 70% alerting outcome for incidents classified as serious by the legacy RISC model metric. In the qualitative analysis both raters indicated that neither the legacy RISC definition of on-runway nor the SURF-IA definition was appropriate. Hence, the raters’ recommendation was not to adopt either model’s definition, but rather develop an entirely new definition through further study. The raters were explicit about the criticality of appropriate and harmonized definitions used in the models. The different outcomes between the RISC and SURF-IA models may result in misleading information when using the reduction in serious runway incursion incidents as a metric for the benefit of SURF-IA technology. It is recommended that prior to using the ASIAS runway incursion data as a metric for the benefit of SURF-IA, the FAA develop a process for identifying and tracking ASIAS reported PD type serious runway incursion incidents which will not trigger a SURF-IA alert. Consideration should be made to improving the SURF-IA model technical capabilities to accommodate all possible aircraft states that the RISC model would classify as serious runway incursion incidents

Systems Analysis of NASA Aviation Safety Program: Final Report

A three-month study (February to April 2010) of the NASA Aviation Safety (AvSafe) program was conducted. This study comprised three components: (1) a statistical analysis of currently available civilian subsonic aircraft data from the National Transportation Safety Board (NTSB), the Federal Aviation Administration (FAA), and the Aviation Safety Information Analysis and Sharing (ASIAS) system to identify any significant or overlooked aviation safety issues; (2) a high-level qualitative identification of future safety risks, with an assessment of the potential impact of the NASA AvSafe research on the National Airspace System (NAS) based on these risks; and (3) a detailed, top-down analysis of the NASA AvSafe program using an established and peer-reviewed systems analysis methodology. The statistical analysis identified the top aviation "tall poles" based on NTSB accident and FAA incident data from 1997 to 2006. A separate examination of medical helicopter accidents in the United States was also conducted. Multiple external sources were used to develop a compilation of ten "tall poles" in future safety issues/risks. The top-down analysis of the AvSafe was conducted by using a modification of the Gibson methodology. Of the 17 challenging safety issues that were identified, 11 were directly addressed by the AvSafe program research portfolio

A Methodology to Improve the Proactive Mitigation of Helicopter Accidents Related to Loss of Tail Rotor Effectiveness

Loss of tail rotor effectiveness (LTE) has been recognized to be a major contributing factor in several helicopter accidents where pilots lost directional control. However, it has been noticed that different definitions of this phenomenon exist in the rotorcraft community. Further, the somewhat imprecise representation of LTE in some flight training simulators has led to its low awareness, placing pilots at a much higher risk for potential accidents. One significant method to specifically address those gaps and support rotorcraft safety involves the proactive mitigation of LTE via the analysis of flight data within the Helicopter Flight Data Monitoring (HFDM) program. Through this program, the pilots receive constant flight evaluation reports to promote improved LTE risk evaluations. The main method used for flight data analysis is the detection of safety metrics, i.e., predefined hazardous flight conditions. Nevertheless, a sufficiently reliable LTE safety metric still does not exist, leading to false or missed detections that degrade the quality of the overall safety analysis. The objective of this thesis is to formulate a methodology to enhance the detection capability of the proximity to LTE within the HFDM program. This promotes the awareness of LTE within the rotorcraft community while supporting the proactive mitigation of helicopter accidents related to this critical helicopter safety threat. An alternative approach is used to develop a more reliable LTE safety metric, using a combination of physics-based simulations and machine learning techniques. First, a physics-based investigation is performed to enhance the understanding of the nature of the LTE. A more comprehensive LTE definition is proposed and analyzed, including three different aspects that can lead to LTE behavior, i.e., loss of weathercock stability, running out of pedal (tail rotor collective) for trim, and tail rotor vortex ring state. The modeling of the flight dynamics of each phenomenon is individually analyzed to ensure an accurate physics-based representation of LTE. Further, the parameters that support the detection of LTE are investigated to enable the recognition and classification of each LTE phenomenon in simulation results. Ultimately, a physics-based investigation of the aircraft flight envelope is combined with the application of supervised learning techniques to develop the predictive models of the different LTE phenomena. This provides the operator with a physics-based LTE safety metric designed to detect the proximity to LTE without the need for a simulation model. The methodology is implemented using a generic nonlinear helicopter simulation model. To verify the enhanced capabilities of the final methodology, the physics-based LTE safety metric is compared against the LTE metric currently used within the HFDM program. The results confirm the improved detection of the proximity to LTE, validating the overarching hypothesis of this research and satisfying the research objective.Ph.D

A computational framework for data-driven infrastructure engineering using advanced statistical learning, prediction, and curing

Over the past few decades, in most science and engineering fields, data-driven research has been becoming a promising next-generation research paradigm due to noticeable advances in computing power and accumulation of valuable databases. Despite this valuable accomplishment, the leveraging of these databases is still in its infancy. To address this issue, this dissertation investigates the following studies that use advanced statistical methods. The first study aims to develop a computational framework for collecting and transforming data obtained from heterogeneous databases in the Federal Aviation Administration and build a flexible predictive model using a generalized additive model (GAM) to predict runway incursions for 15 years in the top major US 36 airports. Results show that GAM is a powerful method for RI prediction with a high prediction accuracy. A direct search for finding the best predictor variables appears to be superior over the variable section approach based on a principal component analysis. The prediction power of GAM turns out to be comparable to that of an artificial neural network (ANN). The second study is to build an accurate predictive model based on earthquake engineering databases. As with the previous study, GAM is adopted as a predictive model. The result shows a promising predictive power of GAM with application to existing reinforced concrete shear wall databases. The primary objective of the third study is to suggest an efficient predictor variable selection method and provide relative importance among predictor variables using field survey pavement and simulated airport pavement data. Results show that the direct search method always finds the best predictor model, but the method takes a long time depending on the size of data and the variables\u27 dimensions. The results also depict that all variables are not necessary for the best prediction and identify the relative importance of variables selected for the GAM model. The fourth study deals with the impact of fractional hot-deck imputation (FHDI) on statistical and machine learning and prediction using practical engineering databases. Multiple response rates and internal parameters (i.e., category number and donor number) are investigated regarding the behavior and impacts of FHDI on prediction models. GAM, ANN, support vector machine, and extremely randomized trees are adopted as predictive models. Results show that the FHDI holds a positive impact on the prediction for engineering-based databases. The optimal internal parameters are also suggested to achieve a better prediction accuracy. The last study aims to offer a systematic computational framework including data collection, transformation, and squashing to develop a prediction model for the structural behavior of the target bridge. Missing values in the bridge data are cured by using the FHDI method to avoid an inaccurate data analysis due to biasness and sparseness of data. Results show that the application of FHDI improves prediction performances. This dissertation is expected to provide a notable computational framework for data processing, suggest a seamless data curing method, and offer an advanced statistical predictive model based on multiple projects. This novel research approach will help researchers to investigate their databases with a better understanding and build a statistical model with high accuracy according to their knowledge about the data

Transportation System Performance Measures Using Internet of Things Data

The transportation system is undergoing a rapid change with innovative and promising technologies that provide real-time data for a variety of applications. As we transition into a technology-driven era and Internet of Things (IoT) applications, where everything is connected via a network of smart sensors and cloud computing, there will be an increasing amount of real-time data that will allow a better understanding of the transportation system. Devices emerging as a part of this connected environment can provide new and valuable data sources in a variety of transportation areas including safety, mobility, operations and intelligent transportation systems. Agencies and transportation professionals require effective performance measures and visualization tools to mine this big data to make design, operation, maintenance and investment decisions to improve the overall system performance. This dissertation discusses the development and demonstration of performance measures that leverage data from these emerging IoT devices to support analysis and guide investment decisions. Selected case studies are presented that demonstrate the impact of these new data sources on design, operation, and maintenance decisions. Performance measures such as vibration, noise levels and retroreflectivity were used to conduct a comprehensive assessment of different rumble strip configurations in the roadway and aviation environment. The results indicated that the 12 in sinusoidal wavelength satisfied the National Cooperative Highway Research Program (NCHRP) recommendations and reduced the noise exposure to adjacent homeowners. The application of low-cost rumble strips to mitigate runway incursions at general aviation airports was evaluated using the accelerations on the airframe. Although aircraft are designed for significant g-forces on landing, the results of analyzing accelerometers installed on airframes showed that long-term deployment of rumble strips is a concern for aircraft manufacturers as repeated traversal on the rumble strips may lead to excessive airframe fatigue. A suite of web dashboards and performance measures were developed to evaluate the impact of signal upgrades, signal retiming and maintenance activities on 138 arterials in the Commonwealth of Pennsylvania. For five corridors analyzed before and after an upgrade, the study found a reduction of 1.2 million veh-hours of delay, 10,000 tons of CO2 and an economic benefit of $32 million. Several billion dollars per year is expended upon security checkpoint screening at airports. Using wait time data from consumer electronic devices over a one-year period, performance dashboards identified periods of the day with high median wait times. The performance measures outlined in this study provided scalable techniques to analyze operating irregularities and identify opportunities for improving service. Reliability and median wait times were also used as performance measures to compare the standard and expedited security screening. The results found that the expedited screening was highly reliable than the standard screening and had a median wait time savings of 5.5 minutes. Bike sharing programs are an eco-friendly mode of transportation gaining immense popularity all over the world. Several performance measures are discussed which analyze the usage patterns, user behaviors and effect of weather on a bike sharing program initiated at Purdue University. Of the 1626 registered users, nearly 20% of them had at least one rental and around 6% had more than 100 rentals, with four of them being greater than 500 rentals. Bikes were rented at all hours of the day, but usage peaked between 11:00 and 19:00 on average. On a yearly basis, the rentals peaked in the fall semester, especially during September, but fell off in October and November with colder weather. Preliminary results from the study also identified some operating anomalies, which allowed the stakeholders to implement appropriate policy revisions. There are a number of outlier filtering algorithms proposed in the literature, however, their performance has never been evaluated. A curated travel time dataset was developed from real-world data, and consisted of 31,621 data points with 243 confirmed outliers. This dataset was used to evaluate the efficiency of three common outlier filtering algorithms, median absolute deviation, modified z-score and, box and whisker plots. The modified Z-score had the best performance with successful removal of 70% of the confirmed outliers and incorrect removal of only 5% of the true samples. The accuracy of vehicle to infrastructure (V2I) communication is an important metric for connected vehicle applications. Traffic signal state indication is an early development in the V2I communication that allows connected vehicles to display the current traffic signal status on the driver dashboard as the vehicle approaches an intersection. The study evaluated the accuracy of this prediction with on-field data and results showed a degraded performance during phase omits and force-offs. Performance measures such as, the probability of expected phase splits and the probability of expected green for a phase, are discussed to enhance the accuracy of the prediction algorithm. These measures account for the stochastic variations due to detectors actuations and will allow manufacturers and vendors to improve their algorithm. The application of these performance measures across three transportation modes and the transportation focus areas of safety, mobility and operations will provide a framework for agencies and transportation professionals to assess the performance of system components and support investment decisions

Aviation Trends Related to Atmospheric Environment Safety Technologies Project Technical Challenges

Current and future aviation safety trends related to the National Aeronautics and Space Administration's Atmospheric Environment Safety Technologies Project's three technical challenges (engine icing characterization and simulation capability; airframe icing simulation and engineering tool capability; and atmospheric hazard sensing and mitigation technology capability) were assessed by examining the National Transportation Safety Board (NTSB) accident database (1989 to 2008), incidents from the Federal Aviation Administration (FAA) accident/incident database (1989 to 2006), and literature from various industry and government sources. The accident and incident data were examined for events involving fixed-wing airplanes operating under Federal Aviation Regulation (FAR) Parts 121, 135, and 91 for atmospheric conditions related to airframe icing, ice-crystal engine icing, turbulence, clear air turbulence, wake vortex, lightning, and low visibility (fog, low ceiling, clouds, precipitation, and low lighting). Five future aviation safety risk areas associated with the three AEST technical challenges were identified after an exhaustive survey of a variety of sources and include: approach and landing accident reduction, icing/ice detection, loss of control in flight, super density operations, and runway safety

Aeronautics and Space Report of the President: Fiscal Year 2007 Activities

The National Aeronautics and Space Act of 1958 directed the annual Aeronautics and Space Report to include a "comprehensive description of the programmed activities and the accomplishments of all agencies of the United States in the field of aeronautics and space activities during the preceding calendar year." In recent years, the reports have been prepared on a fiscal-year basis, consistent with the budgetary period now used in programs of the Federal Government. This year's report covers activities that took place from October 1, 2006, through September 30, 2007

Developing banking intelligence in emerging markets: Systematic review and agenda

The current banking industry is heavily dependent on technological artifacts supported by intelligent systems for performance on operational and marketing parameters. However, the attributes for enabling practice between such technological interfaces with managerial adoption are been lagging creating a knowledge gap. To address this, present research surveys the prior work from 1970 to 2020 on intelligent decision support models specific to banking. Subsequently, findings are synthesized on quadrant outcomes; technology; employees, customers, and organizations for service ecosystems. In addition, the managerial perceptions of technology on work are captured through short survey. Finally, scope of advancements like big data, internet of things (IoT), virtual reality (VR) along other untapped conceptual relationships into this framework are discussed