Results for “data needs” — Page 58

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 OSTI2020

Global, Regional, and National Fossil-Fuel CO2 Emissions: 1751-2017

This data product is a time series of Carbon Dioxide (CO2) emissions from fossil fuel combustion and cement manufacture. Estimates of CO2 emissions are included for the globe and by nation back to 1751, and include emissions from solid fuel consumption, liquid fuel consumption, gas fuel consumption, cement production, and gas flaring. Per capita CO2 emissions and emissions from international trade (bunker fuels) are included as well; bunker fuels are not included in country totals, but are assigned to the country in which loading took place. Estimates are generated using the United Nations Energy Statistics database and the United States Geologic Survey’s cement statistics. Datasets produced from this group at Appalachian State University are located here, and are also located at https://energy.appstate.edu/research/work-areas/cdiac-appstate. Historic CDIAC data from Oak Ridge National Laboratory are located here: https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/00001_V2017. This dataset is the foundational dataset for the annual global carbon budget and other carbon cycle analyses that need relevant fossil fuel CO2 data. Within this data package are spreadsheets (.csv) of global and national estimates of CO2 emissions as well as text files of the ranking of each country’s total CO2 emissions and per capita for that year.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Deliverable D11 – Data Sharing, Storage, Security Protocols, and a Specification of a Potential Data Sharing Portal

Pacific Northwest National Laboratory (PNNL) and Technical University of Denmark (DTU) completed this deliverable as part of Work Package 2: Data Information Catalog for Distributed Wind Research for the International Energy Agency (IEA) Wind Technology Collaboration Programme Task 41: Enabling Wind to Contribute to a Distributed Energy Future. As part of the work plan, Deliverable D11 requires the development of data sharing, storage, and security protocols for metadata to be stored on the platform, if needed. The specification of a potential data sharing portal that expands on the catalog is also required.

17 WIND ENERGY↗

DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Dynamically Reconfigurable Data Readout of Pixel Detectors for Automatic Synchronization with Data Acquisition Systems

Reconfigurable detectors with dynamically selectable sensing and readout modes are highly desirable for implementing edge computing as well as enabling advanced imaging techniques such as foveation. The concept of a camera system capable of simultaneous passive imaging and dynamic ranging in different regions of the detector is presented. Such an adaptive-autonomous detector with both spatial and temporal control requires programmable window of exposure (time frames), ability to switch between readout modes such as full-frame imaging and zero-suppressed data, modification of the number of pixel data bits and independent programmability for distinct detector regions. In this work, a method is presented for seamlessly changing time frames and readout modes without data corruption while still ensuring that the data acquisition system (DAQ) does not need to stop and resynchronize at each change of setting, thus avoiding significant dead time. Data throughput is maximized by using a minimum unique data format, rather than lengthy frame headers, to differentiate between consecutive frames. A data control and transmitter (DCT) synchronizes data transfer from the pixel to the periphery, reconfigures the data to transmit it serially off-chip, while providing optimized decision support based on a DAQ definable mode. Measurements on a test structure demonstrate that the DCT can operate at 1 GHz in a 65 nm LP CMOS process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Solar Resource Measurements in Eugene, OR: Cooperative Research and Development Final Report, CRADA Number CRD-07-00252

Site-specific, long-term, continuous, and high-resolution measurements of solar irradiance are important for developing renewable resource data. These data are used for several research and development activities consistent with the NLR mission: establish a national 3-year climatological database of measured solar irradiances; provide high quality ground-truth data for satellite remote sensing validation; support development of radiative transfer models for estimating solar irradiance from available meteorological observations; provide solar resource information needed for technology deployment and operations. Data acquired under this agreement will be available to the public through NLR's Measurement & Instrumentation Data Center – MIDC (http://www.nlr.gov/midc) Or the Renewable Resource Data Center - RReDC (http://rredc.nlr.gov). The MIDC offers a variety of standard data display, access, and analysis tools designed to address the needs of a wide user audience (e.g., industry, academia, and government interests).

14 SOLAR ENERGY↗

DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗

DOE OSTI2023

Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities

Microorganisms frequently colonize surfaces within food production facilities. Detection of Listeria monocytogenes in this setting relies on culture-dependent methods, but the complex dynamics of bacterial interactions within these environments and their impact on pathogen detection remain largely unexplored. To address this challenge, we applied both 16S rRNA and shotgun quasimetagenomic (enriched microbiome) sequencing of swab culture enrichments from five seafood and seven dairy production environments. Utilizing 16S rRNA amplicon sequencing, we observed variability between 355 samples taken from these 12 production facilities and a distinctive microbiome for each environment. With shotgun quasimetagenomic sequencing, we were able to assemble L. monocytogenes metagenome-assembled genomes (MAGs) from 28 of the 32 culture-positive samples. We compared these MAGs to their corresponding whole-genome sequencing assemblies, which resulted in two polyphyletic clades consisting of L. monocytogenes lineages I and II with 13,195 and 25,556 single-nucleotide polymorphism sites, respectively. The remaining four MAGs did not produce sufficient genome coverage. To understand and establish limits for pathogen detection and subtyping using shotgun quasimetagenomics, these same data sets were downsampled in slilico to produce a titration series of abundances of L. monocytogenes and analyzed. Pathogen detection was achieved for all downsampled data sets, even those with only 3× genome coverage. This study contributes to the understanding of microbial diversity within food production environments and presents insights into the level of genome coverage needed in a metagenome sequencing data set to detect, subtype, and source track a foodborne pathogen.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

HERMES

HERMES - High-speed Event Retrieval and Management for Enhanced Spectral imaging code. This code is meant to unpack and process neutron imaging data from the TPX3Cam made by Amsterdam Scientific Instruments. The TPX3Cam utilizes the Timepix3 chip in a single photon counting mode of image acquisition. The photon counting data streaming off the TPX3Cam needs to be process and analyzed in order to create final images. Therefore we are developing both python and cpp codes to allow for users of the TPX3Cam to efficiently analyze data and create images.

Long, Alexander↗

DOE OSTI2020

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗

DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗

DOE OSTI2024

HERO WEC 2024 Hydraulic Configuration Deployment Data

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Advancements in nuclear data evaluations in the unresolved resonance region [Slides]

Minimal updates to current capabilities (SAMMY/AMPX) and to the ENDF format are proposed to include threshold reaction for outgoing charged-particle channels in the URR formalism. If average resonance parameters are reported, probability tables for newly defined reactions channels may be needed, implemented, and tested. Nuclear data evaluation of n+ 35 Cl reactions can be considered the best case to apply the proposed updates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗