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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2006

Airline Choice for Domestic Flights in Sao Paulo Metropolitan Area: An Application of the Conditional Logit Model

Using the conditional (multinomial) LOGIT model, this paper addresses airline choice in the S o Paulo Metropolitan Area. There are two airports in this region, where two, three or even four airlines compete for passengers flying to an array of domestic destinations. The airline choice is believed to be a result of the tradeoff passengers face among flight cost, flight frequency and airline performance. It was found that the lowest fare better explains airline choice than the highest fare, whereas direct flight frequencies give better explanation to airline choice than indirect (connections and stops) and total (direct plus indirect) ones. Out of 15 variables tested, the lowest fare was the variable that best explained airline choice. However, its signal was counterintuitive (positive) possibly because the cheapest airline was offering few flights, so passengers overwhelmingly failed to choose the cheapest airline. The model specification most adjusted to the data considered the lowest fare, direct flight frequency in the travel day and period (morning or afternoon peak) and airline age. Passengers departing from S o Paulo-Guarulhos International Airport (GRU) airport make their airline choice in terms of cost whereas those from Sao Paulo-Congonhas Airport (CGH) airport do not. Finally, senior passengers place more importance on airline age than junior passengers.

Moreno, Marcelo Baena↗
NASA NTRS2023

Market Analysis of the Subsonic Single Aft Engine (SUSAN) Transport Aircraft Concept

This paper presents a detailed market analysis of the U.S. domestic aviation market in support of the NASA subsonic single aft engine (SUSAN) regional aircraft concept. The current scoping of the SUSAN concept is intended to compete in the medium to large (160-180 seats) narrow body market, with range capabilities of up to 2,500 nautical miles, and expected fuel burn reduction up to 40% relative to conventional 2 engine aircraft. Recent historical trends suggest growth in aviation passenger demand will continue to be met by the narrow body fleet of aircraft; however, a comprehensive review is required to understand how these current fleet trends could evolve in the future. Moreover, estimating and forecasting the potential market size is the critical first step when developing a new aircraft concept to determine commercial viability. To assist in the trade space exploration of the SUSAN concept, a generalized traffic and fleet forecast of the U.S. aviation market is conducted. Using publicly available aviation data from the U.S. Bureau of Transportation Statistics and passenger demand forecasts from FAA, a multinomial logit model is estimated to predict the composition of the future fleet by aircraft size. These fleet forecasts are then used as inputs for a fleet evolution model to provide required operational forecasts at the aircraft specific level. Forecast scenarios with and without the SUSAN concept are compared, and a breakeven analysis is performed to evaluate the commercial viability of the SUSAN aircraft from an operating cost perspective. Results from the multinomial logit fleet forecast indicate the narrow body size category of 150+ seats dominating the market, comprising 87% of the future revenue passenger miles market share in 2050 (up from 60% in 2019). Forecast scenarios with the SUSAN concept see a maximum cumulative decline in fuel cost of 20% by 2050, while a breakeven analysis shows competitive advantage of the SUSAN aircraft, due to expected fuel burn reduction, at moderate levels of increased maintenance and capital costs.

Jacob M. Wishart↗
DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2025

Misclassification in Workers’ Telecommuting Frequency Choices Using a Generalized Extreme Value Model

Telecommuting frequency is a response variable collected in travel surveys and is, therefore, prone to errors leading to mismeasurements or misclassification. Misclassification of explanatory variables is a common risk when using statistical modeling techniques. We define “misclassification” as a response reported or recorded in the wrong category; for example, a variable is recorded as a 1 when it should be 0. Here, in this context, this study aims to develop a statistical model to analyze telecommuting data which accounts for potential misclassification errors by building on existing literature in econometrics. The empirical analysis was undertaken using the 2017 National Household Travel Survey (NHTS) and the general extreme value (GEV) models available in the literature. Specifically, the frequency of telecommuting days was analyzed using the negative binomial (NB) model recast as the multinomial logit (MNL) model. By nature—and consistent with other studies—NHTS data are prone to errors that can be classified as intentional or unintentional misinformation provided by the person being interviewed. Ignoring these errors while modeling telecommuting frequencies using standard discrete count models can result in biased parameter estimates. The misclassification parameter was calculated for both over-reporting and under-reporting scenarios. The misclassification errors can be as high as 14% over-reported and 10% under-reported, particularly for the neighboring values. Statistical fit comparison between the models shows that models that ignore misclassification have worse data fit and biased parameter estimates with significant policy implications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2003

Age affects severity of venous gas emboli on decompression from 14.7 to 4.3 psia

INTRODUCTION: Variables that define who we are, such as age, weight and fitness level influence the risk of decompression sickness (DCS) and venous gas emboli (VGE) from diving and aviation decompressions. We focus on age since astronauts that perform space walks are approximately 10 yr older than our test subjects. Our null hypothesis is that age is not statistically associated with the VGE outcomes from decompression to 4.3 psia. METHODS: Our data are from 7 different NASA tests where 188 men and 50 women performed light exercise at 4.3 psia for planned exposures no less than 4 h. Prebreathe (PB) time on 100% oxygen ranged from 150-270 min, including ascent time, with exercise of different intensity and length being performed during the PB in four of the seven tests with 150 min of PB. Subjects were monitored for VGE in the pulmonary artery using a Doppler ultrasound bubble detector for a 4-min period every 12 min. There were six design variables; the presence or absence of lower body adynamia and five PB variables; plus five concomitant variables on physical characteristics: age, weight height, body mass index, and gender that were available for logistic regression (LR). We used LR models for the probability of DCS and VGE, and multinomial logit (ML) models for the probability of Spencer VGE Grades 0-IV at exposure times of 61, 95, 131, 183 min, and for the entire exposure. RESULTS: Age was significantly associated with VGE in both the LR and ML models, so we reject the null hypothesis. Lower body adynamia was significant for all responses. CONCLUSIONS: Our selection of tests produced a wide range of the explanatory variables, but only age, lower body adynamia, height, and total PB time was helpful in various combinations to model the probability of DCS and VGE.

Clinical Trial↗
DOE OSTI2025

Capacitated p -hub approach for park-and-ride facility location problem under nested logit demand function: polyhedral approaches

By generalizing the unconstrained p-hub approach for the park-and-ride (P&R) facility location problem under the multinomial logit demand function, the capacitated p-hub approach for the problem under the nested logit demand function captures a broader range of real-world cases. To solve this problem optimally, we introduce a mixed-integer linear program and accelerate its solution by enhancing the branch-and-cut procedure. To address the problem at a large scale, we introduce two other polyhedral approaches: variable neighborhood search (VNS) and adaptive randomized rounding (ARR). Downtown areas in Seoul have a high modal share of public transportation and congested road traffic, yet P&R has not been widely implemented. Therefore, we apply the ARR procedure to solve a real-world problem using traffic and geographic data from the Seoul metropolitan area. ARR performs better than VNS and addresses real-world cases. The solutions obtained by ARR present a phased expansion plan that encourages policymakers to start installing a small number of P&Rs immediately.

Capacitated p-hub approach↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗