Results for “vehicle data collection” — Page 2

NASA NTRSDate not supplied

Continued Environmental Microbiology Monitoring of The International Space Station (ISS) Veggie Unit Used for In-Flight, Crop-Based Food Systems

The International Space Station is a closed environment where rotating sets of Crewmembers live and work. This environment is monitored to ensure occupants’ health and safety during their spaceflight residency by routine Environmental Health System (EHS) collection of microbial samples including air, surface, and water. The microbial samples are collected, enumerated, and analyzed quarterly to monitor on-board system contamination and potential risks to crew health. Quarterly monitoring of the microorganisms in the ISS environment supports crew safety and contributes to a large set of microbial concentration and diversity data. The current in-inflight microbial requirements were developed using this historical data collected by the routine environmental monitoring. These in-flight microbial requirements have been established to maintain the health and safety of the spacecraft environment. This study leverages quarterly operational EHS sampling by collecting additional microbial samples from the surface of the Veggie plant production system on ISS. These samples will yield microbial concentration and diversity that can be compared and analyzed with nominal surface samples from the vehicle. The data collected in this study will aid in the development of requirements for spaceflight-based food production systems. Continued surface sampling of the internal and external surfaces of the Veggie system, along with collaboration from both Johnson Space Center (JSC) & Kennedy Space Center (KSC) scientists studying the microbiome of the veggie-crop systems, will be implemented as part of the future development of crop-based food system requirements for the ISS and beyond.

Christian Mena↗

NASA NTRS1988

Aircraft and ground vehicle friction correlation test results obtained under winter runway conditions during joint FAA/NASA Runway Friction Program

Aircraft and ground vehicle friction data collected during the Joint FAA/NASA Runway Friction Program under winter runway conditions are discussed and test results are summarized. The relationship between the different ground vehicle friction measurements obtained on compacted snow- and ice-covered conditions is defined together with the correlation to aircraft tire friction performance under similar runway conditions.

Yager, Thomas J.↗

DOE OSTI2026

2013 California Vehicle Survey

The 2013 California Vehicle Survey (CVS) collected data on household and commercial vehicle usage, and on future vehicle purchases. ICF International conducted the survey on behalf of the California Energy Commission. Approximately 8,000 respondents, from California households and businesses, completed the survey. The household component of the CVS included a selection of households from the 2010-2012 California Household Travel Survey (CHTS), who had stated their intention to purchase a vehicle in the near future. Both household surveys used the same survey ID numbers enabling the integration of responses. The commercial vehicle component of the CVS—a stand-alone survey of commercial fleet owners in California—asked vehicle owners questions pertaining to economic and demographic attributes, current fleets, and preferences about planned vehicle purchases.

1Hz data↗

DOE OSTI2022

Federal Automotive Statistical Tool: FY 2022 Data Call Status & Review Tools [Slides]

This presentation presents an overview of current federal vehicle fleet current data collection efforts covering required information submissions about the motor vehicles, fueling centers, and electric vehicle supply equipment (EVSE) inventory through the Federal Automotive Statistical Tool (FAST). The presentation also provides an overview of the capabilities within FAST to assist federal agency users with reviewing and improving the quality of their fleet data submissions. This presentation is intended for delivery via WebEx at the November 9, 2022 meeting of the DOE-sponsored INTERFUEL working group. FAST is a web-based information management tool developed by INL and funded by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

NASA Langley Aerothermodynamics Laboratory: Hypersonic Testing Capabilities

A description of the NASA Langley Research Center’s Langley Aerothermodynamics Laboratory (LAL) will be presented in the paper, along with descriptions and details of the facility test techniques and recent upgrades. The LAL consists of three hypersonic blow-down wind tunnels covering Mach numbers of 6 and 10 and unit Reynolds number ranges of 0.5 to 8.3 million per foot as well as a 60-ft Vacuum Sphere Test Chamber. LAL facilities are used to study and define the aerodynamic performance and aeroheating characteristics of flight vehicle concepts. Data collected in the facilities have been used for design and optimization, anchoring computational predictions, generation of aerodynamic databases and design of Thermal Protection Systems. Over the years modifications and enhancements have been made to the facility hardware and instrumentation to increase efficiency, data quality, capabilities and reliability to better meet the programmatic requirements. Recent utilization information illustrates the need for the capabilities associated with these facilities. Recent test programs include the Space Shuttle Program, Crew Exploration Vehicle/Orion/Multi-Purpose Crew Vehicle, Hypersonic International Flight Research Experimentation (HIFiRE), Mars Science Laboratory, Hypersonic Inflatable Aerodynamic Decelerator System (HIADS) and X-51 among others and usage has been split between NASA, Commercial Crew, Department of Defense and private company programs. Plans for future improvements to the facility infrastructure and instrumentation will also be presented.

Karen Berger↗

DOE OSTI2022

Route Optimization for Energy Efficient Airport Shuttle Operations - A Case Study from Dallas Fort Worth International Airport

Air travel and requisite surface traffic supporting passenger arrival/departure constitutes a significant portion of travel and emissions in cities with large airports. An airport trip can segment into three parts namely: i) travel from a location in the city to the airport; ii) travel from a parking lot or rental car center to the terminal (i.e., within the airport premises), and iii) travel inside the terminal. Depending on the airport access mode all or a part of these legs comprise a traveler’s journey to the airport. The priority of airport ground transport management teams is to provide passengers with a seamless travel experience within the airport, so it is understandable that within airport shuttle routes might not be optimized for minimizing energy consumption. Solutions that meet the dual objective of reducing energy consumption from airport shuttle operations without compromising on passenger travel experience are key to improving system efficiency. There is currently a dearth of research and tools that can inform airports in making such decisions. Addressing this need, this research effort puts forth an optimization model that generates optimal shuttle routes for a given set of constraints, and a discrete-event simulator that evaluates the optimal solutions in a stochastic environment to understand the tradeoffs between passenger wait times, and within airport shuttle energy consumption. The proposed set of tools are tested in the context of optimizing airport shuttles routes within the Dallas Fort Worth International Airport (DFW). In addition to shuttle spatial positioning, and passenger demand information, high-fidelity vehicle data was collected using data loggers installed on DFW shuttles. Results show that 20% energy reduction in shuttle operations is possible with a modest two-minute increase in average passenger wait times. The tools developed in this research effort are designed to be generalizable and can help optimize shuttle operations planning at any major airport.

air travel↗

DOE OSTI2023

Quantifying and Understanding the Access Time to Dockless Micromobility: A Case Study in Washington, D.C.

Micromobility has been widely deployed in many cities. Similar as how access time/distance affects the travel demand to use public transit and informs transit system design, access time/distance to micromobility service measures its service efficiency and also serves as an equity indicator to inform city agencies from a regulation perspective. Though there is an increasing need to understand it, access to micromobility has not been sufficiently studied. This paper developed a framework to quantity the access time to dockless micromobility service (i.e., the minimum time needed to walk to reach the closest dockless micromobility vehicles). Based on the real-time vehicle location data collected from Washington, DC, this research quantified the access time to dockless micromobility, analyzed its spatial and temporal variation patterns and investigated its relationship with socio-demographic variables (i.e., population density, employment density and low-income population). The results revealed that the access time to dockless micromobility ranges between 0 to 4 minutes with the most frequently observed range of 0.5 to 1 minutes, and the city center area tends to have shorter access time than the outskirts areas. Results also indicate a quite stable access time level in DC with access time standard deviation of 0.2 to 0.5 minutes. After correlating the access time at census-block-group level with socio-demographic data, it was discovered that shorter access time usually aligns with larger population and employment density, and the proportion of low-income population was found not helpful with explaining the access time variation, which indicating a relatively equitable micromobility program.

access time↗

DOE OSTI2025

Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development

While the Adaptive Cruise Control (ACC) system in automated vehicles (AVs) is expected to impact transportation energy significantly, existing AV energy consumption models only directly adopt those developed with Human-driven Vehicle (HV) data without even slight adaptation or calibration to accommodate unique AV energy consumption features. This study will investigate how accurately HV data-based models can predict the energy consumption of AVs. Empirical trajectory data and corresponding instantaneous energy consumption rates from both AVs and HVs were collected. We adopted two classical HV data-based models to fit these data. The calibration results indicated that these models yield around 20 30% prediction errors for AVs. To further improve the prediction accuracy, this study designed an AV-Micro model by incorporating components of multiple classic energy consumption models that better capture ACC energy consumption features, including piecewise driving behavior. With this, the AV-Micro model achieves lower than 10% prediction errors. The AV-Micro model’s high consistency across different test runs was verified with statistical significance tests, demonstrating its adaptability in different driving profiles. To confirm the discrepancies between the energy consumption features of AVs and HVs, more statistical significance tests were conducted to show that the AV-Micro model cannot be directly applied to HV data. The findings by calibrated AV-Micro models revealed that AVs consume approximately 80.5–146.4 J more energy than HVs for each meter traveled. Furthermore, the frequency analysis of energy consumption indicates that there is still some room for AVs to improve energy efficiency, particularly given their larger amplitude high-frequency fluctuations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Energy Impact of Connected and Automated Vehicle Technologies. Final report

The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles; (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data; (3) An open-source SUMO model of Ann Arbor was developed; (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential; (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time; (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles; and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.

02 PETROLEUM↗

DOE OSTI2026

Cadillac CT6 Super Cruise On-road Data

This dataset encompasses about 60 individual drives of a 2019 Cadillac CT6 with Super Cruise in its relevant operational domain covering more than 1,000 miles. Adhering to the limited operational design domain of the investigated version of the Supercruise system, the majority of the data was collected during highway driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![cadillac-ct6 image](ct6.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Tesla Model 3 Autopilot On-road Data

This dataset encompasses about 60 individual drives of a 2020 Tesla Model 3 with Autopilot in its relevant operational domain covering more than 1,000 miles. The majority of the data was collected during highway and suburban driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![tesla m3 image](tesla-m3.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Measurements from mobile surface vehicles during the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE)

Abstract. Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

GEMINI Case Study

Sandia's GEMINI-Scout Mine Rescue Robot is an unmanned ground vehicle designed to enter potentially hazardous environments to explore, assess, and evaluate dangerous situations first responders may face when conducting a rescue mission. GEMINI is approximately four feet long and two feet tall, which enables the robot to maneuver through small locations on rough terrains caused by earthquakes, fires, or radiological incidents. GEMINI uses track propulsion to climb stairs, travel through gravel and sand pits, pivot in place, and traverse 45-degree climbs with few problems. Furthermore, the vehicle's dual tracked-chassis design allows it to operate in hostile, dark, muddy, high-temperature, and explosive debris-strewn environments, while maintaining efficient ground mobility. The mobility and modularity of the vehicle allow for easy integration of sensors to conduct gas and temperature sensing and offers pan/tilt, zoom color, and thermal camera video streaming capabilities. The vehicle is also able to carry a payload of about 50 pounds of batteries and can handle an additional 200 pounds of payload, whether for additional diagnostics, supplies, or clothing for those trapped in an effected area. GEMINI is remotely operated through a wireless connection and an onboard computer running a customized embedded control application, which directly communicates to all onboard components except for the audio and video systems. When line of sight is not possible, operators use a shockresistant fiber optic cable to ensure continuous functionality of the vehicle. This allows for direct local control of the vehicle, which streams collected data back to the operator for enhanced situational awareness. In addition, the vehicle incorporates safety features such as explosion proof housing to ensure safe electronic operations in hazardous gas or flooded environments; a four-channel video link and two-way audio to ensure located survivors can communicate with operators; and an MSHA-approved multi-gas sensor to monitor air quality.

42 ENGINEERING↗

DOE OSTI2020

Gasoline Engine and Fuels Offering Reduced Fuel Consumption and Emissions: Vehicle Modeling Final Report

The Gasoline Engine and Fuels Offering Reduced Fuel Consumption and Emissions (GEFORCE) project was proposed in response to the U.S. Department of Energy’s Funding Opportunity Announcement 0991 by a team made up of the members of the Coordinating Research Council (CRC) and the research staff at Oak Ridge National Laboratory (ORNL.) The project focused on investigating the potential benefits that might be attained through synergistic use of specific engine technology together with fuels formulated to represent potential directions that high-octane fuels of the future might progress. A stated objective in the DOE FOA was to demonstrate a 25% reduction in petroleum consumption through optimization of the engine technologies together with a suitable fuel. An advanced engine was constructed and used with a matrix of research fuels to investigate potential avenues for efficiency improvement. The engine incorporated technologies expected to become mainstream for boosted engines in the next 10 to 20 years. These included increased compression ratio, a two-stage turbocharger, and cooled external exhaust gas recirculation (EGR). The fuel matrix was designed to investigate impacts from research octane number (RON), volumetric ethanol content, and the final boiling point of the fuel. The engine calibration was optimized for each fuel individually and data collected to enable vehicle system modelling that projected energy consumption, fuel economy, tailpipe CO₂ emissions, and impact on petroleum consumption for an industry-average mid-size sedan. The engine calibration and data collection were carried out at IAV in Michigan and is the subject of a separate report. IAV provided the engine data to Oak Ridge National Laboratory to support the vehicle modelling portion of the project. The vehicle modelling results show the following trends: Ethanol content does have a consistently strong influence on the fuel economy results for all cycles and all fuels. Among the fuels of a nominal RON level, increasing ethanol content consistently lowers fuel economy, with the 30% ethanol fuels always providing the lowest fuel economy for a given RON level. However, in some cases the energy consumption improvement allows the 30% ethanol fuels to match the fuel economy of the ethanol-free fuel P. These observations underscore the importance of both engine efficiency and fuel volumetric energy content on vehicle fuel economy.; There was no consistent trend in the projected energy consumption results for differences in fuel T90 for all fuels and cycles. Fuel economy projections did show a consistent trend, with the higher T90 fuel providing slightly greater fuel economy when compared to the low T90 fuel of the same ethanol content. The observed trends were consistent with differences in the heating value of the fuels.; The 102-RON fuels provided reduced energy consumption and greater fuel economy for the advanced engine on all drive cycles. The engine compression ratio of 11.5 was higher than would typically be used in a turbocharged engine when 92-RON fuel use is expected. Hence, the engine experiences more efficiency degradation from knock avoidance when using the 92-RON fuels. This degradation causes the fuel economy results for the 92-RON fuels to be lower than those for the 102-RON fuels.; Fuels E and F (92-RON, 30% ethanol) are projected to achieve 10% or greater reduction in petroleum consumption, with fuels K (102-RON, 30% ethanol) and O (97 RON, 30% ethanol) achieving greater than a 20% reduction. Fuel L (102-RON, 30% ethanol) achieves greater than 25% reduction, meeting the petroleum reduction target of the project. All of the fuels that achieve 10% or greater reduction in petroleum consumption are 30% ethanol blends.; Increasing final boiling point increased fuel economy at fixed ethanol content when the 102-RON fuels were used. This trend is a result of differences in the volumetric energy content of the fuels and the projected energy consumption values for the fuels. In the case of the 102-RON fuels, increasing final boiling point also resulted in an increase in the energy content of the fuel. There was not a consistent trend between the energy content and final boiling point for the 92-RON fuels at fixed ethanol content.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗