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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Service

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones. The Missouri City zone is 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Physiological and Subjective Responses of Pilots During Advanced Air Mobility Flight Testing With Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA's Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This presentation focuses on results of pilots' physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The data provides insights into pilots' workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this endeavor enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

air mobility↗
NASA NTRS2024

Physiological and Subjective Responses of Pilots during Advanced Air Mobility Flight Testing with Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA’s Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This paper investigates into pilots’ physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The study provides insights into pilots’ workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this study enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

Kevin J. Monk↗
DOE OSTI2021

Evaluate the System-Level Impact of Connected and Automated Vehicles Coupled with Shared Mobility: An Agent-based Simulation Approach

With the rapid growth of information and communication technologies, Connected and Automated Vehicles (CAVs) are deemed to be disruptive with the potential to significantly improve overall transportation system efficiency, however, may bring Vehicle Miles Traveled (VMT) increase or other issues. Further, shared mobility systems are another disruptive force that is reshaping our travel patterns. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance and energy efficiency, we develop a mesoscopic simulation-based framework for mobility and energy efficiency evaluation considering the disruptive transportation technologies. Under this framework, we develop novel models for energy intensity and modal activity, and evaluated a variety of energy scenarios for different combinations of CAV applications, various levels of automation, roadway characteristics, and traffic conditions, while also varying different vehicle types and fuel/powertrain technologies. Based applying this modeling suite to a calibrated BEAM simulation network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes.

42 ENGINEERING↗
NASA NTRSDate not supplied

Automated Flight & Contingency Management (AFCM) Power Slide

This single slide presentation gives a summary of the Advanced Air Mobility (AAM) Automated Flight & Contingency Management (AFCM) subproject, at a high level for a potentially non-NASA technical audience.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Initial Development and Integration of a Vertiport Automation System for Advanced Air Mobility Operations

The High Density Vertiplex Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference Vertiport Automation System (VAS) to support the management of arrival and departure operations in the terminal area of a vertiport or network of inter-connected vertiports known as a vertiplex. The VAS is potentially a key element of an integrated AAM architecture that will enable greater levels of coordination and predictability for vertiport users through its integration and sharing of information, and greater control of vertiport resources for managing operators. This paper provides an overview of the VAS and a description of an initial implementation that has been integrated into a broader reference architecture as part of ongoing research. Examples from recent simulations are presented with an outline for next steps and considerations.

vertiport↗
DOE OSTI2020

SMART Mobility. Modeling Workflow Development, Implementation, and Results Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the SMART Mobility Modeling Workflow effort. The SMART Mobility Modeling Workflow was developed to evaluate new transportation technologies such as connectivity, automation, sharing, and electrification through multi-level systems analysis that captures the dynamic interactions between technologies. By integrating multiple models across different levels of fidelity and scale, the Workflow yields insights about the influence of new mobility and vehicle technologies at the system level. For information about the other Pillars, please refer to the relevant pillar’s Capstone Report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

SMART Mobility. Advanced Fueling Infrastructure Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Advanced Fueling Infrastructure Pillar. This Pillar investigated the charging infrastructure needs of electric ride-hailing and car-sharing vehicles, automated shuttle buses, and freight-delivery truck fleets. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

NAS Automation Enabled Pilot (AEP): AEP-1 Study

This presentation describes work perform under the Advanced Air Mobility project - Automated Flight and Contingency Management subproject on developing industry representative powered lift aircraft and aircraft automation capabilities for the purpose of developing evaluation methods and Means of Compliance.

powered-lift aircraft↗
DOE OSTI2020

SMART Mobility. Mobility Decision Science Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Mobility Decision Science Pillar. The Mobility Decision Science Pillar sought to fill gaps in existing knowledge about the human role in the mobility system including travel decision-making and technology adoption in the context of future mobility. The objective was to study how underlying preferences, needs, and contextual factors might constrain or hasten future transportation system scenarios. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

SMART Mobility. Multi-Modal Freight Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Decarbonizing US passenger vehicle transport under electrification and automation uncertainty has a travel budget

Abstract The transportation sector is at the beginning of a transition represented by electrification, shared mobility, and automation, which could lead to either increases or decreases in total travel and energy use. Understanding the factors enabling deep decarbonization of the passenger vehicle sector is essential for planning the required infrastructure investments and technology adoption policies. We examine the requirements for meeting carbon reduction targets of 80% and higher for passenger vehicle transport in the United States (US) by midcentury under uncertainty. We model the changes needed in vehicle electrification, electricity carbon intensity, and travel demand. Since growth in fleet penetration of electric vehicles (EVs) is constrained by fleet stock turnover, we estimate the EV penetration rates needed to meet climate targets. We find for a base case level of passenger vehicle travel, midcentury deep decarbonization of US passenger transport is conditional on reducing the electricity generation carbon intensity to close to zero along with electrification of about 67% or 84% of vehicle travel to meet decarbonization targets of 80% or 90%, respectively. Higher electricity generation carbon intensity and degraded EV fuel economy due to automation would require higher levels of fleet electrification and/or further constrain the total vehicle travel allowable. Transportation deep decarbonization not only depends on electricity decarbonization, but also has a total travel budget, representing a maximum total vehicle travel threshold that still enables meeting a midcentury climate target. This makes encouraging ride sharing, reducing total vehicle travel, and increasing fuel economy in both human-driven and future automated vehicles increasingly important to deep decarbonization.

Alarfaj, Abdullah F. (ORCID:0000000157360662)↗
DOE OSTI2021

Grid-Integrated Electric Mobility Model (GEM) v1.0

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. The convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, but requires careful integration with rapidly evolving electricity systems. We have developed the GEM Model (Grid-Integrated Electric Mobility) to examine these interactions with a U.S.-wide simulation framework encompassing private electric vehicles (EVs); shared automated EVs (SAEVs); charging infrastructure; controlled EV charging; and a grid economic dispatch model to simulate mobility futures exclusively using EVs. We find that an SAEV fleet 9% the size of today's active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly reducing GHG emissions and decreasing solar curtailment by about one-third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Sheppard, Colin↗
DOE OSTI2020

A Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles, and mobility-as-a-service concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool can help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions. This paper presents a description of the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

2020 Transportation Research Board Workshop: Infrastructure Spatial Sensing at Intersections: The Potential to Enhance Safety and Enable Automation

The sensing technology that is enabling vehicle automation also can revolutionize traffic intersection safety, control, and efficiency. Infrastructure spatial sensing technology refers to the deployment of sensors that can detect and track all objects in the field of view - for example, mounting lidar sensors, radar, and video imaging at an intersection and fusing the data to produce a three-dimensional dynamic operational awareness. Combining such sensing capability with fail-safe vehicle communications can facilitate applications such as eco-approach and departure, optimizing efficient vehicle-signal coordination, and reducing traffic accidents through increasing safety. Furthermore, evidence is mounting suggesting that infrastructure-based sensing is needed in order to safely deploy automated public mobility alongside the normal vehicle and pedestrian traffic in our cities. Six speakers were invited to present their work from various aspects (safety, efficiency, and automation) addressing infrastructure-based sensing at the workshop on Infrastructure Spatial Sensing hosted in Washington DC as part of the Transportation Research Board Annual Meeting in January 12, 2019. A summary of the presentation are included herein.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Automated Ride Hailing - Status, Projections, and Critical Safety Review

Automated ride-hailing services, commonly known as 'robo-taxis' are beginning to proliferate and scale. Currently they are available in four major US cities with another 10 cities planning their implementation in the near future. In the US, automated ride-hailing services are dominated by a single provider, with announcements from several competitors of service offerings by end of calendar year 2025. Internationally, only three companies are currently in commercial operations, with many more planned. The scaling of robo-taxi services appears to be the leading application of automated vehicles (AVs), and provides evidence of their anticipated safety in comparison to conventional vehicles. This presentation will provide an overview of the status of deployments of fully operational AVs in ride-hailing services, projections of the number of trips, vehicles, and vehicle miles traveled for the immediate future, and a critical review of the safety data that has been reported, both in the US as well as internationally.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗