Results for “Policy optimization” — Page 50

DOE OSTI2018

Ultrathin Two-dimensional Metallic Nanocrystals for Renewable Energy Electrocatalysis [Ultrathin Two-dimensional Metallic Nanocrystals for Heterogeneous Catalysis]

Even since the discovery of exfoliated graphene, exponentially growing efforts have been devoted to the search of novel 2 dimensional (2D) nanomaterials with atomic thickness and the extension of their applications. Among the huge 2D materials family, ultrathin 2D metallic nanocrystals gain vast attentions due to the unique importance of transition metals to heterogeneous catalysis. Here, we review recent representative advances in ultrathin 2D metallic nanocrystals and their catalytic applications towards a variety of reactions, including the hydrogen generation, oxygen reduction/evolution, CO 2 activation and small molecules oxidation/hydrogenation. Special focuses are placed in currently mechanical understanding of bottom-up fabrication of ultrathin 2D metallic nanocrystals, the establishment of their structure-performance relationship as well as the implication of their real applications in devices. Here, we also shed light on further structural modification of the ultrathin 2D metallic nanocrystals to achieve the optimized catalytic performance. At end of this review, we discuss the remaining challenges and future opportunities in this burgeoning research area.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science

Despite their wide and successful applications, deep learning (DL) models are prone to overfitting for small training datasets, produce a poor predictive performance for uncertain data, and provide point estimations without any indication of the accuracy and credibility. These limitations of the deterministic DL models hinder their effective application in Earth system science where the labelled data are sparse, noisy and incomplete with large uncertainty and where the predictive uncertainty quantification is needed for scientific understanding and policy decision making. Integration of Bayesian inference into DL models adds an estimate of uncertainty and regularization in the predictions. However, traditional Bayesian methods are computationally unaffordable and inflexible for high-dimensional problems, which limits their application to DL systems that typically have millions of model parameters. In this effort, we propose an efficient and general-purpose Bayesian inference method to advance DL model optimization and uncertainty quantification, so as to facilitate the adoption of DL in Earth sciences. In a demonstration, we integrate the proposed Bayesian method with a feedforward neural network (NN) to build a fast-to-evaluate surrogate of the complex Energy Exascale Earth System Land Model for efficient modeling. The formulated Bayesian NN, using a small number of training data, produces an accurate prediction with high credibility, whereas with the same small training size, the deterministic NN cannot yield a reasonable estimation and does not provide confidence information. The proposed Bayesian method is computationally efficient and flexible, capable of integration with diverse network variants such as convolutional NNs and recurrent NNs to advance the application of DL in Earth sciences.

Lu, Dan↗

DOE OSTI2016

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to power use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2014

Water Security Toolkit User Manual Version 1.2.

The Water Security Toolkit (WST) is a suite of open source software tools that can be used by water utilities to create response strategies to reduce the impact of contamination in a water distribution network . WST includes hydraulic and water quality modeling software , optimizati on methodologies , and visualization tools to identify: (1) sensor locations to detect contamination, (2) locations in the network in which the contamination was introduced, (3) hydrants to remove contaminated water from the distribution system, (4) locations in the network to inject decontamination agents to inactivate, remove, or destroy contaminants, (5) locations in the network to take grab sample s to help identify the source of contamination and (6) valves to close in order to isolate contaminate d areas of the network. This user manual describes the different components of WST , along w ith examples and case studies. License Notice The Water Security Toolkit (WST) v.1.2 Copyright c 2012 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive license for use of this work by or on behalf of the U.S. government. This software is distributed under the Revised BSD License (see below). In addition, WST leverages a variety of third-party software packages, which have separate licensing policies: Acro Revised BSD License argparse Python Software Foundation License Boost Boost Software License Coopr Revised BSD License Coverage BSD License Distribute Python Software Foundation License / Zope Public License EPANET Public Domain EPANET-ERD Revised BSD License EPANET-MSX GNU Lesser General Public License (LGPL) v.3 gcovr Revised BSD License GRASP AT&T Commercial License for noncommercial use; includes randomsample and sideconstraints executable files LZMA SDK Public Domain nose GNU Lesser General Public License (LGPL) v.2.1 ordereddict MIT License pip MIT License PLY BSD License PyEPANET Revised BSD License Pyro MIT License PyUtilib Revised BSD License PyYAML MIT License runpy2 Python Software Foundation License setuptools Python Software Foundation License / Zope Public License six MIT License TinyXML zlib License unittest2 BSD License Utilib Revised BSD License virtualenv MIT License Vol Common Public License vpykit Revised BSD License Additionally, some precompiled WST binary distributions might bundle other third-party executables files: Coliny Revised BSD License (part of Acro project) Dakota GNU Lesser General Public License (LGPL) v.2.1 PICO Revised BSD License (part of Acro project) i Revised BSD License Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Sandia National Laboratories nor Sandia Corporation nor the names of its con- tributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IM- PLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUD- ING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ii Acknowledgements This work was supported by the U.S. Environmental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The material in this document has been subject to technical and policy review by the U.S. EPA, and approved for publication. The views expressed by individual authors, however, are their own, and do not necessarily reflect those of the U.S. Environmental Protection Agency. Mention of trade names, products, or services does not convey official U.S. EPA approval, endorsement, or recommendation. The Water Security Toolkit is an extension of the Threat Ensemble Vulnerability Assessment-Sensor Place- ment Optimization Tool (TEVA-SPOT), which was also developed with funding from the U.S. Environ- mental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The authors acknowledge the following individuals for their contributions to the devel- opment of TEVA-SPOT: Jonathan Berry (Sandia National Laboratories), Erik Boman (Sandia National Laboratories), Lee Ann Riesen (Sandia National Laboratories), James Uber (University of Cincinnati), and Jean-Paul Watson (Sandia National Laboratories). iii Acronyms ATUS American Time-Use Survey BLAS Basic linear algebra sub-routines CFU Colony-forming unit CVAR Conditional value at risk CWS Contamination warning system EA Evolutionary algorithm EDS Event detection system EPA U.S. Environmental Protection Agency EC Extent of Contamination ERD EPANET results database file GLPK GNU Linear Programming Kit GRASP Greedy randomized adaptive sampling process HEX Hexadecimal HTML HyperText markup language INP EPANET input file LP Linear program MC Mass consumed MILP Mixed integer linear program MIP Mixed integer program MSX Multi-species extension for EPANET NFD Number of failed detections NS Number of sensors NZD Non-zero demand PD Population dosed PE Population exposed PK Population killed TAI Threat assessment input file TCE Tailed-conditioned expectation TD Time to detection TEC Timed extent of contamination TEVA Threat ensemble vulnerability assessment TSB Tryptic soy broth TSG Threat scenario generation file TSI Threat simulation input file VAR Value at risk VC Volume consumed WST Water Security Toolkit YML YAML configuration file format for WST iv Symbols Notation Definition Example { , } set brackets { 1,2,3 } means a set containing the values 1,2, and 3. [?] is an element of s [?] S means that s is an element of the set S . [?] for all s = 1 [?] s [?] S means that the statement s = 1 is true for all s in set S . P summation P n i =1 s i means s 1 + s 2 + * * * + s n . \ set minus S \ T means the set that contains all those elements of S that are not in set T . %7C given %7C is used to define conditional probability. P ( s %7C t ) means the prob- ability of s occurring given that t occurs. %7C ... %7C cardinality Cardinality of a set is the number of elements of the set. If set S = { 2,4,6 } , then %7C S %7C = 3. v

Klise, Katherine A.↗

DOE OSTI2018

Light-Duty Vehicle Attribute Projections (Years 2015-2030)

This report describes the approach and results associated with projecting vehicle attributes for future light-duty vehicle classes and powertrains, from year 2015 to 2030. The focus is the California light-duty vehicle market and the projected attributes include fuel economy, acceleration, driving range (total and all-electric), and manufacturer suggested retail price from year 2015 to year 2030. Attributes are weighted by California vehicle sales, which are projected using a historically validated consumer choice model - the Automotive Deployment Option Projection Tool (ADOPT) - integrated with a modeling framework that simulates vehicle fuel economy, cost, and acceleration performance while optimizing vehicle components - the Future Automotive Systems Technology Simulator model (FASTSim). Both models were developed at the National Renewable Energy Laboratory and have been adapted to represent the future California light-duty vehicle market for the present report. The analysis includes several scenarios pertaining to electricity and, essentially, electric vehicle demand in California, as established by the Energy Commission. Results suggest that implementation of certain policies, such as the Corporate Average Fuel Economy standards, impacts vehicle attribute projections. The results also show that standards and policy targets are not exclusively met by changes in vehicle attributes, but also through shifts in market demand and sales for certain vehicle powertrains.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Evaluating Utility Costs Savings for EV Charging Infrastructure [Slides]

This analysis was conducted under the first round of the Solar Energy Innovation Network (SEIN), a program led by the National Renewable Energy Laboratory (NREL) that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. As part of the Solar Energy Innovation Network, NREL used a tool called REopt to evaluate the impact on utility costs of light duty electric vehicle (EV) charging stations in Minnesota. The analysis models EV chargers at medium offices in Minneapolis, Minnesota. Specifically, the analysis explores three questions: (a) How can photovoltaics (PV) and stationary storage be co-deployed with EV charging infrastructure to lower the cost of purchasing grid electricity? (b) What are the potential savings of co-locating EV charging infrastructure with (behind the meter of) a commercial building? and (c) What savings can be gained from optimizing the times at which the EVs are charged to decrease lifecycle costs? Note that this presentation is not indented to be presented widely, but is just to provide ReOpt outputs for the partner's use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Analysis as a Key Guiding Tool for Waste Carbon Utilization

Over a billion metric tons of waste and biomass are projected to be available in a future mature market in the United States. These resources represent an opportunity to decouple chemical and polymer production from conventional fossil fuel feedstocks, but such a broad solution space can also make for challenging decision-making. This talk will provide researchers with an introduction to key analysis techniques such as techno-economic analysis, life cycle assessment, and material flow analysis: how they are conducted and how they can be used to benchmark the costs, environmental impacts, and circularity of new innovations as well as to identify opportunities for prioritization and optimization. Using a series of examples related to plastic recycling and chemical manufacturing, we will explore how analysis can guide where and how to leverage waste carbon in supply chains towards a future circular economy.

chemical↗

DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗