Search NASA⌕ Search

SEARCH · Search NASA

Results for “Level set methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Environmental and socio-economic Pareto-front trade-off analysis of U.S. PET packaging material in a circular economy

Various recycling technologies are emerging to implement circular economy in plasticssupply chain systems. However, the environmental and socio-economic trade-offs of in circular economy are not well understood at a systems level. Particularly, quantifying these trade-offs as a function of end-of-life (EOL) management decisions, including transition of recycling technologies, systems level metrics such as circularity, recycled content, and the need for fossil-derived plastics are not well understood. Here, the present study addressed these research gaps by applying a systems analysis modeling approach that utilizes material flow analysis, life cycle assessment, socioeconomic data, and system optimization techniques for polyethylene terephthalate (PET) packaging supply chains in the United States. Pareto-front trade-offs between conflicting environmental and socio-economic impacts as well as those between socioeconomic impacts and circularity were explored using the epsilon constraint method. The Pareto-front trade-off analysis revealed the transition of EOL management strategies for PET packaging systems, including changes in selection of recycling technologies, to aid decision making process by quantifying studied system metrics. Transitioning from environmentally optimal to socio-economically optimal systems led to increased employment (by 17%), wages (by 26%), and revenues (by 6%) but also led to increased global warming potential (GWP; by 65%), energy consumption (by 59%), and reliance on fossil PET in the system (by 78%). Finally, the results show that there is not a unique set of recycling technologies to achieve a sustainable circular economy of PET packaging system, instead it depends on the decision maker’s objectives and targeted metrics of the system.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
DOE OSTI2024

The 3D Lyman- α forest power spectrum from eBOSS DR16

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-α (Ly α) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of ~205 000 quasar spectra in the redshift range 2 ≤ z ≤ 4 at an effective redshift z = 2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp( i k ∙ r), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly α skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly α skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range $(0.02 ≤ k ≤ 0.35)$ h Mpc -1 at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly α forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
DOE OSTI2025

Beyond CCSD(T) Accuracy at Lower Scaling with Auxiliary Field Quantum Monte Carlo

We introduce a black-box auxiliary field quantum Monte Carlo (AFQMC) approach to perform highly accurate electronic structure calculations using configuration interaction singles and doubles (CISD) trial states. This method consistently provides more accurate energy estimates than coupled cluster singles and doubles with perturbative triples (CCSD(T)), often regarded as the gold standard in quantum chemistry. This level of precision is achieved at a lower asymptotic computational cost, scaling as O(N 6 ) compared to the O(N 7 ) scaling of CCSD(T). Furthermore, we provide numerical evidence supporting these findings through results for challenging main group and transition metal-containing molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2025

Scale-up study of enhancing algal growth through bioaugmentation with the indole-3-acetic acid producing bacteria Azospirillum brasilense

Algae-based wastewater treatment technologies can recover nutrients while generating algal biomass with diverse application potentials. While numerous bacteria have demonstrated the ability to enhance algal growth through symbiotic relationships in laboratory settings, scaling this benefit to larger applications remains challenging due to complex wastewater conditions and microbe-algae dynamics. This study explores the effectiveness of bioaugmentation with Azospirillum brasilense, a plant growth-promoting bacterium known for producing the phytohormone indole-3-acetic acid (IAA), in boosting algal productivity within an Algaewheel wastewater treatment system. Through a series of experiments conducted at lab-, pilot-, and full-scale levels, we evaluated the impact of bioaugmentation on algal growth and developed a bioaugmentation strategy for an Algaewheel system. The results indicate that a biweekly dosing, achieving a final bioaugmenting cell density of 0.2–4 × 10 9 cells/L, effectively doubles the biomass productivity in a residential subdivision wastewater treatment plant in Northern Illinois. The bacterial production of IAA was the primary mechanism driving this enhancement, as demonstrated by the similar growth and yield improvements observed with chemically supplemented IAA and bioaugmentation across all experimental scales. High-throughput sequencing of 18S rRNA genes revealed that the algal community in the bioaugmented tank exhibited more stable biodiversity than the control. Additionally, bioaugmentation improved nutrient removal efficiency during winter, highlighting its potential to enhance the overall performance and sustainability of algae-based wastewater systems. The developed bioaugmentation and chemical treatment methods offer further operational solutions for managing yield, optimizing biochemical profiles, and enhancing biofuel production potential.

Algae-bacterial symbiotic relationship↗
DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗