Analysis of factors that may be essential in the decision to fly on fully autonomous passenger airliners

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%. Copyright © 2015 John Wiley & Sons, Ltd.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Modelling and Analysis of Turning Motion of a Subsurface Mapping AUV with Split-Hull Design

There is much need for autonomous underwater vehicles (AUVs) for inspection and mapping purposes. Most conventional AUVs use torpedo-shaped single-rigid hull, b...     

Home-to-work commuting,urban form and potential energy savings: A local scale approach to regional statistics

The link between transport energy consumption and land use patterns has been the focus of a considerable amount of academic works over the past decades. While many empirical researches are backed up with solid statistical techniques, most of them do not fully consider the influence of scale underlying empirical quantitative investigations. Using fine-scale home-to-work commuting data for Wallonia (Belgium), this paper re-evaluates Breheny’s (1995) assertion that urban structure should hold the characteristics of major cities if substantial energy savings are to be achieved. A local scale approach highlights efficient settlements in terms of transport energy consumption not only within major towns, but also within remote rural areas. Furthermore, results suggest that influencing the urban form following local energy efficient examples rather than regional ones could also yield significant gains, without an extreme policy stance of re-urbanisation in major cities.     

Collection, spillback, and dissipation in pedestrian evacuation: A network-based method

We present a method of predicting pedestrian route choice behavior and physical congestion during the evacuation of indoor areas with internal obstacles. Under the proposed method, a network is first constructed by discretizing the space into regular hexagonal cells and giving these cells potentials before a modified cell transmission model is employed to predict the evolution of pedestrian flow in the network over time and space. Several properties of this cell transmission model are explored. The method can be used to predict the evolution of pedestrian flow over time and space in indoor areas with internal obstacles and to investigate the collection, spillback, and dissipation behavior of pedestrians passing through a bottleneck. The cell transmission model is further extended to imitate the movements of multiple flows of pedestrians with different destinations. An algorithm based on generalized cell potential is also developed to assign the pedestrian flow.     

Rapid first-stage tests of on-ship infection

Protection against on-ship infectious disease—whether due to mishap or to harmful purpose—faces special situational problems. Sometimes, when infection levels on board have reached threshold levels, emergency actions are required. Often, the most thorough strategies for responding to threat are not feasible. A rapid first-stage test (RFT) is a fast, minimally invasive procedure used to rule out from possible infection a large percentage of an infection-threatened group. Prevention and control of on-ship infection need to combine various interconnected tactics. When timely criterion tests are not possible, the medical team must adopt fast alternative measures. The methods used to summarize protection against on-ship infectious agents included a scientific literature review and a web search. The fields of the review were maritime, health, and technology sources. Special attention was paid to material dealing with risks and threats of on-ship penetration by infectious agents, on-ship infection prevalence thresholds, and rapid diagnostic screens. The Bayes rule and the law of large numbers were applied to the analysis, for large on-ship populations, of RFT indications of crossing of an infection prevalence threshold. The increasing risk of serious on-ship infection—either accidental or purposeful—calls for a multi-layered protection approach. RFTs are a key part of the outer layer of such a defense. Well-designed and well-administered RFTs provide several advantages for defense against on-ship infection: low-cost, non-invasive, fast, and focuses on a drastically smaller number of infection possibilities.     

Characteristics of premium transit services that affect mode choice

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.