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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
NASA NTRS1989

Evaluation of the ride quality of a light twin engine airplane using a ride quality meter

A ride quality meter was used to establish the baseline ride quality of a light twin-engine airplane planned for use as a test bed for an experimental gust alleviation system. The ride quality meter provides estimates of passenger ride discomfort as a function of cabin noise and vibration (acceleration) in five axes (yaw axis omitted). According to the ride quality meter, in smooth air the cabin noise was the dominant source of passenger discomfort, but the total discomfort was approximately the same as that for the smooth-air condition. The researcher's subjective opinion, however, is that the total ride discomfort was much worse in the moderate turbulence than it was in the smooth air. The discrepancy is explained by the lack of measurement of the low-frequency accelerations by the ride quality meter.

Stewart, Eric C.↗
NASA NTRS1977

Validation of the Passenger Ride Quality Apparatus (PRQA) for simulation of aircraft motions for ride-quality research

The NASA passenger ride quality apparatus (PRQA), a ground based motion simulator, was compared to the total in flight simulator (TIFS). Tests were made on PRQA with varying stimuli: motions only; motions and noise; motions, noise, and visual; and motions and visual. Regression equations for the tests were obtained and subsequent t-testing of the slopes indicated that ground based simulator tests produced comfort change rates similar to actual flight data. It was recommended that PRQA be used in the ride quality program for aircraft and that it be validated for other transportation modes.

Bigler, W. B., II↗
NASA NTRS1980

A design tool for estimating passenger ride discomfort within complex ride environments

A series of experimental studies utilizing approximately 2200 test subjects has led to the development of a general empirical model for the prediction of passenger ride discomfort in the presence of complex noise and vibration inputs. The ranges of vibration and noise stimuli used to derive the model included the amplitudes and frequencies that are known to most influence passenger comfort. The ride quality model accounts for the effects of combined axis vibrations (up to three axes simultaneously) and includes corrections for the effect of vibration duration and interior noise. Output of the model consists of an estimate of the passenger discomfort produced by a given noise and/or vibration environment. The discomfort estimate is measured along a continuous scale that spans the range from below discomfort threshold to values of discomfort that are far above discomfort threshold

Leatherwood, J. D.↗
NASA NTRS1975

Application of ride quality technology to predict ride satisfaction for commuter-type aircraft

A method was developed to predict passenger satisfaction with the ride environment of a transportation vehicle. This method, a general approach, was applied to a commuter-type aircraft for illustrative purposes. The effect of terrain, altitude and seat location were examined. The method predicts the variation in passengers satisfied for any set of flight conditions. In addition several noncommuter aircraft were analyzed for comparison and other uses of the model described. The method has advantages for design, evaluation, and operating decisions.

Jacobson, I. D.↗
DOE OSTI2021

Ride-hailing and taxi versus walking: Long term forecasts and implications from large-scale behavioral data

Introduction: Although ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers. Methods: Based on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking. Results: The results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity. Conclusions: The study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1975

Automobile ride quality experiments correlated to iso-weighted criteria

As part of an overall study to evaluate the usefulness of ride quality criteria for the design of improved ground transportation systems an experiment was conducted involving subjective and objective measurement of ride vibrations found in an automobile riding over roadways of various roughness. Correlation of the results led to some very significant relationships between passenger rating and ride accelerations. The latter were collapsed using a frequency-weighted root mean square measure of the random vibration. The results suggest the form of a design criterion giving the relationship between ride vibration and acceptable automobile ride quality. Further the ride criterion is expressed in terms that relate to rides with which most people are familiar. The design of the experiment, the ride vibration data acquisition, the concept of frequency weighting and the correlations found between subjective and objective measurements are presented.

Healey, A. J.↗
DOE OSTI2020

Factors Influencing Willingness to Pool in Ride-Hailing Trips

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Furthermore, multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

The impact of ride hailing on parking (and vice versa)

Researching emerging transportation services is critical to forecasting mode choice and providing appropriate infrastructure. One such infrastructure is parking, as parking demand may shift with the availability of ride-hailing services. This study uses ethnographic methods - complemented with passenger surveys collected when driving for Uber and Lyft in the Denver, Colorado, region - to gather quantitative and qualitative data on ride-hailing and analyze the impacts of ride-hailing on parking, including changes in parking demand and parking as a reason to deter driving. The study also examines relationships between parking time and cost. This includes building a classification tree-based model to predict the replaced driving trips as a function of car ownership, destination land type, parking stress, and demographics. The results suggest that: i) ride-hailing is replacing driving trips and could reduce parking demand, particularly at land uses such as airports, event venues, restaurants, and bars; ii) parking stress is a key reason respondents chose not to drive; and iii) respondents are generally willing to pay more for reduced parking time and distance. Conversely, parking supply, time, and cost can all influence travel behavior and ride-hailing use. This work provides insight into potential benefits and disadvantages of ride-hailing as related to parking.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1972

Ride evaluation in aerospace and surface vehicles

The vibration environment in a wide range of aerospace and surface vehicles is examined, and definitions related to ride evaluation are reviewed. Three provinces of research and application of ride data are recognized: (1) ride affecting passenger and operator comfort; (2) ride affecting human efficiency; and (3) ride affecting the health and safety of occupants occupationally or repeatedly exposed. Specific reference is made to the proposed ISO guide on human exposure to whole-body vibration. The applications as well as the advantages and limitations of this guide for evaluating vehicle ride are discussed. The derivation of the limits is reviewed with regard to the supporting data and the compromises necessary for wide applicability. Special discussions are included of the frequency and time dependence of these limits and approaches in progress for adjusting them according to different criteria of application.

Broderson, A. B.↗
NASA NTRS1975

The 1975 Ride Quality Symposium

A compilation is presented of papers reported at the 1975 Ride Quality Symposium held in Williamsburg, Virginia, August 11-12, 1975. The symposium, jointly sponsored by NASA and the United States Department of Transportation, was held to provide a forum for determining the current state of the art relative to the technology base of ride quality information applicable to current and proposed transportation systems. Emphasis focused on passenger reactions to ride environment and on implications of these reactions to the design and operation of air, land, and water transportation systems acceptable to the traveling public. Papers are grouped in the following five categories: needs and uses for ride quality technology, vehicle environments and dynamics, investigative approaches and testing procedures, experimental ride quality studies, and ride quality modeling and criteria.

Source record↗
NASA NTRS1977

Effects of control laws and relaxed static stability on vertical ride quality of flexible aircraft

State variable techniques are utilized to generate the RMS vertical load factors for the B-52H and B-1 bombers at low level, mission critical, cruise conditions. A ride quality index is proposed to provide meaningful comparisons between different controls or conditions. Ride quality is shown to be relatively invariant under various popular control laws. Handling quality variations are shown to be major contributors to ride quality variations on both vehicles. Relaxed static stability is artificially implemented on the study vehicles to investigate its effects on ride quality. The B-52H ride quality is generally degraded when handling characteristics are automatically restored by a feedback control to the original values from relaxed stability conditions. The B-1 airplane shows little ride quality sensitivity to the same analysis due to the small rigid body contribution to load factors at the flight condition investigated.

Roberts, P. A.↗
DOE OSTI2022

Provably-Stable Overload Ride-Through Control for Grid-Forming Inverters Using System-Wide Lyapunov Function Analysis

A key challenge associated with a grid-forming (GFM) inverter based resource (IBR) is its behavior during severe grid disturbances: since a GFM inverter regulates voltage in the fast timescale instead of current or power, it may experience a transient overload of current, power and/or energy during a severe grid disturbance. While many promising control strategies for overload ride-through have been proposed over the past two decades, transient stability of the system during and after the transition to an overload ride-through control mode remains difficult to guarantee. In this work, a novel overload ride-through control strategy is proposed for a system of grid-forming inverters that takes both self-protection and system-wide transient stability into account. A proposed system-level supervisory control uses slow communication to pre-emptively assign a set of local ride through control parameters to individual GFM IBR, including a current-limiting virtual reactance, that guarantees that synchronism is still preserved for any set of anticipated grid disturbances. At the core of the supervisory control lies a Lyapunov-function-based routine capable of establishing a strong, albeit conservative, transient stability guarantee for the system. Here, the proposed overload ride-through control strategy is validated via numerical integration of a reduced-order model, as well as through detailed electromagnetic transient (EMT) simulation.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Data-Driven Multi-Step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 min. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

Range anxiety remains one of the key concerns for ride-sourcing drivers to adopt battery electric vehicles (BEVs). To investigate the feasibility of using BEVs for ride-sourcing services, we propose an optimization-based methodology to estimate the daily driving trip patterns of ride-sourcing vehicles based on widely available non-identifiable trip data. Furthermore, we investigate the charging needs of electrified ride-sourcing vehicles using agent-based simulation. The methodologies are illustrated through a case study in the city of Chicago. Through sensitivity analysis on driver working hours and initial charging status, we quantify the range of daily average vehicle miles traveled (VMT) per car and identify the hot spots of current public charging demand and potential unsatisfied charging demand. This study can be used to determine the priorities of future charging infrastructure investment to further mitigate range anxiety and promote the adoption of electrified ride-sourcing services

42 ENGINEERING↗
DOE OSTI2022

Estimating Fast Charging Infrastructure Requirements to Fully Electrify Ride-Hailing Fleets Across the United States

Ride-hailing electrification is made challenging by a variety of factors, including limited driver access to overnight charging and a nascent network of public direct-current fast charging (DCFC). This article projects the DCFC infrastructure needed to electrify ride-hailing across 384 cities in the United States. Research leveraging a novel infrastructure projection tool, Electric Vehicle Infrastructure Tool for On-Demand Mobility Services model (EVI-OnDemand), estimates that 25,400 DCFC ports capable of charging vehicles at a mean power of 150 kW are required to electrify 1.45 million ride-hailing vehicles in the baseline scenario. Results show that ride-hailing vehicles require 17.5 DCFC ports per 1000 vehicles, three times that of personal electric vehicles (EVs). Infrastructure demand is also shown to vary across cities; 63% more DCFC ports are needed to support 1000 battery EVs in Chicago versus Miami. The analysis also includes sensitivity studies that explore variation in charging demand across input assumptions, finding that charging power, vehicle class, overnight charging access rates, and port utilization influence the network size needed. Projections vary from 18,600 to 41,300 DCFC ports across sensitivity scenarios, indicating that ride-hailing electrification will require significant expansion of access to fast charging even in the most optimistic scenarios, and investments into supporting assets can reduce the scale of the public charging expansion needed.

33 ADVANCED PROPULSION SYSTEMS↗