This research investigates factors that influence opinion in the decision to fly on fully autonomous passenger airliners primarily from the perspective of aviation and technology professionals. Bayesian statistical inference and a two-level fractional factorial survey are used to sample passengers' views on fully autonomous airliners. Eight trust, safety, and cost factors are incorporated into a vignette set in the future. Factors include automation levels, safety records, liability guarantees, airline integrity, and service disruptions. Dependent variables exist in five post-vignette questions and essentially ask "Would you" or "Would you not" be willing to fly on a fully autonomous airliner? Sixteen versions of the vignette, each with unique trust, safety, and cost levels, present varying (unknown) degrees of influence to the survey respondents. For every demographic, the research shows a 99% statistically significant difference between the "prior" and "posterior" sampled population proportions willing to fly. The most significant positive influence involves integrity characteristics of the airline, while the most negative influence relates to life insurance liability guarantees. Research from 2003 suggested that this mode of travel would be acceptable to only 10.5% of respondents. When the 2003 research is used as a Bayesian prior probability, the resulting posterior probability for the demographics sampled can be modeled as a beta distribution, indicating 95% probability that the sampled proportion of the population willing to fly is between 33.2% and 36.4%. After adjusting for age and profession demographics to match the US population, the 95% probability bounds on the proportion willing to fly are 31.35% and 34.15%.

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Travel time reliability has emerged as an indicator of roadway performance. Estimation of travel time distribution is an important starting input for measuring travel time reliability. This study used kernel density estimation to estimate travel time distribution. The Hasofer–Lind–Rackwitz–Fiessler algorithm, widely used in the field of reliability engineering, was used in this work to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway was introduced, then network travel time reliability was developed. Given probability distributions estimated by the kernel density estimation technique and an anticipated travel time from travelers, the two equations of corridor and network travel time reliability can be used to address the question, “How reliable is my perceived travel time?” The definition of travel time reliability was in the sense of on-time performance, and this study was conducted from the perspective of travelers. The major advantages of the proposed method are as follows: ( a) it demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain and ( b) it shows its flexibility for application to levels of roadways (e.g., individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can use the computed travel time reliability information to plan their trips so that they can better manage trip time, reduce costs, and avoid frustration.

Residual stress (RS) is a major processing issue for selective laser melting (SLM) of metal alloys. Postprocessing by way of heat treatment or hot isostatic pressing is usually required for acceptable mechanical properties. In this work, laser shock peening (LSP) treatment on both SLM and cast aluminum A357 alloys are compared with regard to the development of beneficial near-surface compressive RS. Experiments are conducted using high energy nanosecond pulsed laser, together with a fast photodetector connected to a high-resolution oscilloscope and high-speed camera to identify detailed temporal and spatial laser pulse profiles to improve numerical predictions. Constitutive modeling for SLM A357 alloy is performed using finite element simulation and data obtained from X-ray diffraction (XRD) measurements. Since XRD-RS measurements are accompanied with significant machine-reported error, an effective method is introduced to quantify the material constitutive model uncertainty in terms of a joint probability mass function. Conventionally, most constitutive behavior research for LSP involves deterministic material modeling. Predicted RS using deterministic approaches fail to reflect real-world variations in the materials, laser treatment, or RS measurements. A discretized Bayesian inference is used to quantify the rate-dependent plasticity material model parameters as a joint probability function. RS are then characterized as random fields, which provides far greater insight into the practical ability to attain desired residual stresses. Moreover, for identical LSP treatments, it is determined that the material models are significantly different for the SLM and the conventional cast A357 aluminum alloys, resulting in much lower magnitude of compressive RS in the SLM alloy.