Leveraging Aviation Risk Models to Combat Cybersecurity Threats in Vehicular Networks

The rapidly developing technology and lack of standards in the transportation industry for the proposed Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) networks, which all vehicles will operate under, drives concern about information validity and authenticity due to the risk of erroneous or malicious information being injected into a vehicular network (VN). In this paper, we apply a risk management process to a vehicular network that will identify hazards and possible controls that can lower their risk. After researching and reviewing various technologies along with several risk models, we have developed a basic framework for assessing and assigning risk through a phased method that leads to input for our developed model.

A fault tree-based approach for aviation risk analysis considering mental workload overload

Many lives and aircrafts have been lost due to human errors associated with mental workload overload (MWLOL). Human errors are successfully considered in existing Fault Tree Analysis (FTA) methods. However, MWLOL is considered through Performance Shaping Factors indirectly and its information is hidden in FT construction, which is not conducive to analyze the root causes of human errors and risks. To overcome this difficulty, we develop a risk analysis method where Multiple Resources Model (MRM) is incorporated into FTA methods. MRM analyzes mental workload by estimating the resources used during performing concurrent tasks, probably including abnormal situation handling tasks introduced by basic events in FT. Such basic events may cause MWLOL and then trigger corresponding human error events. A MWLOL gate is proposed to describe MWLOL explicitly and add these new relationships to traditional FT. This new method extends previous FTA methods and provides a more in-depth risk analysis. An accident, a helicopter crash in Maryland, is analyzed by the proposed method.

Observing the impossible – in situ observations of hail trajectories using the HailSonde

<p>From the early stages of hailstone growth to the ground-impact finale, a trajectory is taken by each hailstone through the parent hailstorm. Larger hailstones form as their trajectory takes them into regions of the storm that are more favorable for growth, while others may miss out entirely. Simulation-based studies have shown that interactions between the hailstone fall speed, aerodynamics, storm winds (which continue to change along the trajectory and with new growth) can take hailstones on a myriad of different trajectories. Despite improvements in radar technology over the last 20 years, operational hail analysis techniques have changed little, and do not consider trajectories, leaving a high degree of uncertainty when estimating ground impact.</p> <p>Case studies have demonstrated that trajectory information provides significant improvements to hail impact mapping and nowcasting services, but the lack of robust<br />observational datasets to leverage new radar technology and verify trajectories prevents the transition of this new science into operations. The follow proposal presents an innovative approach to measuring trajectories within a hailstorm using hailstone-shaped probes called “HailSondes”. Recent advances in low-energy telemetry, battery technology and electronics miniaturization are combined to make this new sensor possible, which, until recently, was the realm of fantasy for meteorologists (e.g., the 1996 Hollywood classic “Twister” imagined a similar sensors for observing tornadoes). The design challenges, simulations, prototype development and deployment of HailSondes are discussed.</p> <p>HailSonde measurements will provide critical validation for the practical application radarderived trajectories for hailstorm analysis and nowcasting, supporting the transition to future hail services and benefiting a wide range of sectors from aviation, risk management, transport and public safety. This transition from science fiction into real science signifies extraordinary potential for further remote micro-sensor applications in the future. </p>

State Aviation Risk Assessment Level Determination Using Hierarchical Fuzzy Inference System Based on Cognitive Maps

During 2018, ICAO (International Civil Aviation Organization, a specialized UN organization) made available the results of its USOAP (Universal Safety Oversight Audit Program): the ratio of compliance for each ICAO member State to 1047 aviation safety-related protocol questions, divided into eight audit areas. Numbers itself has little meaning, even for aviation personnel. Using Cognitive Mapping (CogMap), a Problem Structuring Method tool, this paper develops a framework to extract and organize information from aviation specialists, allowing define Risk Assessment Level for each State, and for each Aviation Safety Branch defined. Using Fuzzy Inference Systems (FIS), helpful supporting decision making, Big Data available from ICAO is converted to Risk Levels for each State and audit area, what may be used to make informed better Safety decisions on the World Aviation Market. Up to the moment, there’s no evidence on the literature of using CogMap to establish a FIS.

Runway Veer-Off Risk Analysis: An International Airport Case Study

Runway excursions are the main risk for runway safety: operational protection areas mitigate the effects of events classified as veer-off, overrun, and undershoot. This paper presents a methodology for the quantitative risk assessment of runway veer-off in an international airport whose name will not be revealed for privacy reasons. The proposed methodology is based on similar principles adopted in other aviation risk analyses. The Real Level of Safety (RLS) related to the veer-off accident was calculated through the implementation of a retrospective analysis that permits to define a frequency model, a location model and a consequence model. Instead, Target Level of Safety (TLS) was defined through the risk matrix and acceptability criteria present in the International Civil Aviation Organization (ICAO) Safety Management Manual. Finally, the risk of veer-off accidents in the airport under evaluation was determined by using primary data provided by the airport management body. Risk values were calculated in more than 1300 points around the runway and they were used to assess the current level of safety. The authors present a risk map that allows identifying the areas in the strip with the highest risk of a veer-off accident. The obtained results demonstrate that the developed methodology represents a useful tool to define TLS and to assess whether infrastructural and operational modification need to obtain the required level of safety.

Aviation Risk Assessment in the Context of Uncertainty

The paper analysis a subject insufficiently addressed or even misunderstood (i.e. uncertainty) in relation to describing aviation safety, financial or social risks. In the context of Covid-19 pandemic, uncertainty becomes a habit and the aviation industry has to confront and mitigate it. The study outlines the characteristics of risks under uncertainty, conditions for triggering risks and the importance of proper understanding of notions as an instrument for collaborative decision making and managerial success. After reviewing various studies, the authors differentiate the informally use of the two terms from the academic (formal) one and highlight the distinction between real and perceived risk influenced by uncertainty. Finally, a research on the limitations of different performance related indicators while dealing with unclear, imprecise or incomplete knowledge is achieved.

TRIPLE PROBABILITY DENSITY DISTRIBUTION MODEL IN THE TASK OF AVIATION RISK ASSESSMENT

The probability of an airplane deviation from pre-planned trajectory is a core of aviation safety analysis. We propose to use a mixture of three probability density distribution functions it the task of aviation risk assessment. Proposed model takes into account the effect of navigation system error, flight technical error, and occurrence of rare events. Univariate Generalized Error Distribution is used as a basic component of distribution functions, that configures the error distribution model from the normal error distribution to double exponential distribution function. Statistical fitting of training sample by proposed Triple Univariate Generalized Error Distribution (TUGED) is supported by Maximum Likelihood Method. Optimal set of parameters is estimated by sequential approximation method with defined level of accuracy. The developed density model has been used in risk assessment of airplane lateral deviation from runway centreline during take-off and landing phases of flight. The efficiency of the developed model is approved by Chi-square, Akaike’s, and Bayes information criteria. The results of TUGED fitting indicate better performance in comparison with double probability density distribution model. The risk of airplane veering off the runway is considered as the probability of a rare event occurrence and is estimated as an area under the TUGED.

Risk Assessment

The chapter analyses knowledge management paradigms for the understanding and prioritisation of risks (risk assessment), leading to decision making amongst policymakers. Studies and approaches on knowledge-based risk assessment and, in general, risk management vary depending on perceptions of risk, and these perceptions affect the knowledge scope and, ultimately, affect decisions on policy. Departing from the problems of big data in aviation, the shortcomings of the existing knowledge management paradigms and the problems of data conversion to knowledge in aviation risk management approaches are discussed. The chapter argues that there is a need for transciplinarity and interdisciplinarity for greater understanding of context deriving from the challenges in the big data era and in aviation policymaking. In order to address the challenging dynamic context in aviation, the chapter proposes a strength/knowledge-based inquiry that involves public sector and high-power organisations in order to gain holistic knowledge and to aid the decision analysis of policymakers.