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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2020

Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data

Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure, including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third-party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan, U.S. Ten signalized corridors in Columbus, Ohio, were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in 2-h intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18%–32%), average delay (9.4–20.5 s), and level of travel time reliability (1.23–2.73) were calculated for each corridor direction. Intersection-level metrics were also introduced, which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
NASA NTRS1991

Results of the space shuttle vehicle ascent air data system probe calibration test using a 0.07-scale external tank forebody model (68T) in the AEDC 16-foot transonic wind tunnel (IA-310), volume 1

A recalibration of the Space Shuttle Vehicle Ascent Air Data System probe was conducted in the Arnold Engineering Development Center (AEDC) transonic wind tunnel. The purpose was to improve on the accuracy of the previous calibration in order to reduce the existing uncertainties in the system. A probe tip attached to a 0.07-scale External Tank Forebody model was tested at angles of attack of -8 to +4 degrees and sideslip angles of -4 to +4 degrees. High precision instrumentation was used to acquire pressure data at discrete Mach numbers ranging from 0.6 to 1.55. Pressure coefficient uncertainties were estimated at less than 0.0020. Data is given in graphical and tabular form.

Collette, J. G. R.↗
NASA NTRS1991

Results of the space shuttle vehicle ascent air data system probe calibration test using a 0.07-scale external tank forebody model (68T) in the AEDC 16-foot transonic wind tunnel (IA-310), volume 2

A recalibration of the Space Shuttle Vehicle Ascent Air Data System probe was conducted in the Arnold Engineering and Development Center (AEDC) transonic wind tunnel. The purpose was to improve on the accuracy of the previous calibration in order to reduce the existing uncertainties in the system. A probe tip attached to a 0.07-scale External Tank Forebody model was tested at angles of attack of -8 to +4 degrees and sideslip angles of -4 to +4 degrees. High precision instrumentation was used to acquire pressure data at discrete Mach numbers ranging from 0.6 to 1.55. Pressure coefficient uncertainties were estimated at less than 0.0020. Additional information is given in tabular form.

Collette, J. G. R.↗
DOE OSTI2019

Ubiquitous Traffic Volume Estimation through Machine Learning Procedure

High-quality traffic volume data is critical for transportation planning, operations, and travel-energy calculations. However, vehicle count data from roadside sensors is very sparse owing to high capital cost of installing, and on-going maintenance of electronic sensor equipment. This research effort aims to bring to market a first of its kind traffic volume data product that provides accurate estimates of traffic volumes across the entire road network for all times (24x7) and all locations (100% coverage). This product greatly improves the coverage and quality of traffic volume information by combining commercial probe traffic data with traditional traffic measurement using state-of-the-art machine learning. Commercial probe traffic data are derived from vehicles that self-report their position and speed as well as crowd sourced data from smartphone applications.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure

Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. Quality volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume on freeways are typically collected through continuous count stations installed by state DOTs, while there is lack of traffic volume observability on off-freeway roads. The National Renewable Energy Laboratory (NREL), in Collaboration with the I-95 Corridor Coalition and the University of Maryland, extended its research into estimating volumes anywhere anytime from industry probe based data for off-freeway roads. NREL combined vehicle probe count data with several other data sets (speed, whether, roadway geometry, time-of-day, day-of-week, etc.) to estimate hourly volumes as well as AADTs. The research validated and demonstrated the machine learning model, namely XGBoost, using data collected from Pennsylvania, North Carolina, and Tennessee.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Spatial Transferability of Machine Learning Based Volume Estimation Models

High-quality traffic volume data is essential for efficient transportation planning and operations. However, such high-quality data is expensive to collect, owing primarily to the high capital cost of installing and maintaining continuous counting stations (CCSs). Recent availability of probe-based vehicle data offers a cost-effective solution for increasing the observability of traffic volumes. However, having ample ground truth traffic data is a prerequisite for developing robust volume estimation models. Though this might not be a big issue in many states, states with scarce CCS data might be able to benefit from robust volume estimation models developed in (adjacent) data-rich states. While there is a reasonable amount of spatial transferability research in the transportation domain, there is a dearth of knowledge on the spatial transferability of probe-based volume estimation models. To address this gap, this paper explores spatial transferability of volume estimation models developed from data in three states (Colorado, North Carolina, and Pennsylvania). Results indicate that it is extremely important to maintain temporal consistency when attempting spatial transferability of volume estimation models. It was also found that models trained on regions with lower peak traffic volumes will limit the performance of models transferred to states with higher peak hourly traffic volumes. Corroborating findings from existing spatial transferability research on other topics, it was found that a meta-model (developed using data from multiple states) performs better than volume estimation models developed within any one of the states.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Mobility Energy Productivity Evaluation of Prediction-Based Vehicle Powertrain Control Combined with Optimal Traffic Management

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used. MEP specifically measures the connectedness of a system while accounting for time and energy externalities of modes that provide mobility in a given location. A SUMO model is developed to reflect real traffic patterns in Fort Collins, Colorado and data is collected by a probe SUMO vehicle which is validated against data collected on a real vehicle driving the same routes through the city. Individual vehicle and system level efficiencies are calculated using SUMO outputs for scenarios which integrate POEMS and ITS independently as well as jointly. Results from application of POEMS and ITS show improvement in energy consumption and travel times respectively when compared to the respective baseline scenarios. Our conclusion is that there are promising synergistic benefits to travel time and energy efficiency when POEMS and ITS are combined.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1995

European Mobile Satellite Services (EMSS): A regional system for Europe

The European Space Agency is presently procuring two L-band payloads in order to promote a regional system for the provision of European Mobile Satellite Services (EMSS). These are the EMS payload on the Italsat I-F2 satellite and the LLM payload on the ARTEMIS satellite. Telecommunication system studies have been concentrating on mobile applications where full European geographical coverage is required. Potential applications include high priority Private Mobile Radio networks requiring national or European coverage, such as civil security, fire brigades, police and health services, as well as a dedicated system for provision of Air Traffic Services to the civil aviation community. A typical application is an intelligent road traffic management system combining a geographically selective traffic data collection service based on probe vehicles with a geographically selective traffic information broadcast service. Network architectures and bearer services have been developed both for data only and voice/data services. Vehicle mounted mobile transceivers using CDMA access techniques have been developed. The EMSS operational phase will start with the EMS payload in orbit in 1996 and continue with the LLM payload in 1997.

Loisy, C.↗
NASA NTRS1976

Results of an investigation to determine local flow characteristics at the air data probe locations using an 0.030-scale model (45-0) of the space shuttle vehicle orbiter configuration 140A/B (modified) in the NASA Ames Research Center unitary plan wind tunnel (OA161, A, B, C), volume 1

Results are presented of wind tunnel test 0A161 of a 0.030-scale model 45-0 of the configuration 140A/B (modified) space shuttle vehicle orbiter in the NASA Ames Research Center Unitary Plan Wind Tunnel facilities. The purpose of this test was to determine local total and static pressure environments for the air data probe locations and relative effectiveness of alternate flight-test probe configurations. Testing was done in the Mach number range from 0.30 to 3.5. Angle of attack was varied from -8 to 25 degrees while sideslip varied between -8 and 8 degrees.

Nichols, M. E.↗
NASA NTRS1972

Prediction of electron concentration reductions in re-entry flow fields due to electrophilic liquid and water injection.

Discussion of an analytical model which leads to predictions of reductions in electron concentrations in reentry flow fields due to the injection of electrophilic liquids and water. The processes incorporated into the model are: penetration and breakup of the liquid jet, droplet acceleration and vaporization, expansion of the liquid spray due to droplet vaporization, electrophilic vapor diffusion, heterogeneous and homogeneous charged species recombination kinetics and homogeneous electron attachment kinetics. Spray boundary calculations are shown to be in good agreement with photographic observations of water and Freon E-3 sprays in wind tunnel tests of a scale model RAM C-III flight vehicle. Fixed-bias electrostatic probe data taken during the RAM C-III flight are interpreted in terms of effective jet penetration distances - which are shown to be consistent with calculations using the present model.

Pergament, H. S.↗
NASA NTRS1960

Ionospheric Measurements Using Environmental Sampling Techniques

Two rockets were flown to peak altitudes of 220 km in September 1959 to test various methods planned for future measurements of ionization parameters in the ionosphere, exosphere, and interplanetary plasma. The experiments used techniques which sample the ambient environment in the immediate vicinity of the research vehicle. Direct methods were chosen since indirect propagation techniques do not provide the temperatures of charged particles, are insensitive to ion densities, and cannot measure local electron densities under all conditions. Very encouraging results have been obtained from a preliminary analysis of data provided by one of the two flights. A new rf probe technique was successfully used to determine the electron density profile. This was indicated by its agreement with the results of a companion cw propagation experiment, particularly when the probe data were corrected for the effects of the ion sheath which surrounds the vehicle. The characteristics of this sheath were determined directly in flight by an electric field meter which provided the sheath field, and by a Langmuir probe which measured the total potential across the sheath. The electron temperatures deduced from the Langmuir probe data are greater than the neutral gas temperatures previously measured for the same location and season, but these measurements possibly were taken under different atmospheric conditions. Ion densities were calculated from the ion trap data for several altitudes ranging from 130 to 210 km and were found to be within 20 percent of the measured electron densities.

Bourdeau, R. E.↗
NASA NTRS2016

Cubesat Application for Planetary Entry (CAPE) Missions: Micro-Return Capsule (MIRCA)

The Cubesat Application for Planetary Entry Missions (CAPE) concept describes a high-performing Cubesat system which includes a propulsion module and miniaturized technologies capable of surviving atmospheric entry heating, while reliably transmitting scientific and engineering data. The Micro Return Capsule (MIRCA) is CAPE's first planetary entry probe flight prototype. Within this context, this paper briefly describes CAPE's configuration and typical operational scenario, and summarizes ongoing work on the design and basic aerodynamic characteristics of the prototype MIRCA vehicle. CAPE not only opens the door to new planetary mission capabilities, it also offers relatively low-cost opportunities especially suitable to university participation. In broad terms, CAPE consists of two main functional components: the "service module" (SM), and "CAPE's entry probe" (CEP). The SM contains the subsystems necessary to support vehicle targeting (propulsion, ACS, computer, power) and the communications capability to relay data from the CEP probe to an orbiting "mother-ship". The CEP itself carries the scientific instrumentation capable of measuring atmospheric properties (such as density, temperature, composition), and embedded engineering sensors for Entry, Descent, and Landing (EDL). The first flight of MIRCA was successfully completed on 10 October 2015 as a "piggy-back" payload onboard a NASA stratospheric balloon launched from Ft. Sumner, NM.

Cubesat↗