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DOE OSTI2016

WE-H-BRC-05: Catastrophic Error Metrics for Radiation Therapy

Purpose: Intuitive evaluation of complex radiotherapy treatments is impractical, while data transfer anomalies create the potential for catastrophic treatment delivery errors. Contrary to prevailing wisdom, logical scrutiny can be applied to patient-specific machine settings. Such tests can be automated, applied at the point of treatment delivery and can be dissociated from prior states of the treatment plan, potentially revealing errors introduced early in the process. Methods: Analytical metrics were formulated for conventional and intensity modulated RT (IMRT) treatments. These were designed to assess consistency between monitor unit settings, wedge values, prescription dose and leaf positioning (IMRT). Institutional metric averages for 218 clinical plans were stratified over multiple anatomical sites. Treatment delivery errors were simulated using a commercial treatment planning system and metric behavior assessed via receiver-operator-characteristic (ROC) analysis. A positive result was returned if the erred plan metric value exceeded a given number of standard deviations, e.g. 2. The finding was declared true positive if the dosimetric impact exceeded 25%. ROC curves were generated over a range of metric standard deviations. Results: Data for the conventional treatment metric indicated standard deviations of 3%, 12%, 11%, 8%, and 5 % for brain, pelvis, abdomen, lung and breast sites, respectively. Optimum error declaration thresholds yielded true positive rates (TPR) between 0.7 and 1, and false positive rates (FPR) between 0 and 0.2. Two proposed IMRT metrics possessed standard deviations of 23% and 37%. The superior metric returned TPR and FPR of 0.7 and 0.2, respectively, when both leaf position and MUs were modelled. Isolation to only leaf position errors yielded TPR and FPR values of 0.9 and 0.1. Conclusion: Logical tests can reveal treatment delivery errors and prevent large, catastrophic errors. Analytical metrics are able to identify errors in monitor units, wedging and leaf positions with favorable sensitivity and specificity. In part by Varian.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy

Traditional metrics measuring transportation and energy outcomes can be augmented to better represent impacts on people's lives and systems-level performance. In this context, this study introduces two novel metrics: road capacity (as number of people traveling and accessing services) and energy intensity (as energy use for people traveling and accessing services). Current national-level distributions of available data in the United States for factors contributing to the two new integrated metrics are used as context to evaluate potential outcomes. These factors include vehicle occupancy, mode share, fuel economy, and trip distance. Variations in input values provide insights on how these factors shape efficiencies in road capacity and energy intensity. Parametric sensitivity analysis indicates that the impact of each input depends upon the metric being evaluated. For the human-centered road capacity mobility metric, increasing vehicle occupancy has the largest effect – twice that of increasing mode share for bike, walk, and transit. For the energy intensity mobility metric, the effect of improving fuel economy is the largest. However, when the focus is on accessibility (instead of mobility), for both metrics the effect of lowering average trip distance is the largest. Additionally, a novel interactive tool to visualize the results for various parameter combinations makes the metrics practitioner ready. The findings suggest that the diffusion of new human-centric metrics that benchmark outcomes associated with road capacity and energy may be significant in motivating new sustainable transportation investments and efficient utilization of infrastructure, mobility assets, and services.

ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Review of Noise Metric Sensitivities for Analysis of Quiet Supersonic Overflight

Six different noise metrics (PL, ASEL, BSEL, DSEL, ESEL, and ISBAP) are currently used to quantify sonic boom levels from overflight of supersonic aircraft. A previous meta-analysis using laboratory subjective data identified these metrics, each of which correlated well with human perception of low-boom sounds both indoors and outdoors. Because the analysis did not identify a single metric that was significantly superior, no internationally agreed-upon metric has been chosen for the quiet supersonic aircraft noise certification procedures currently under development. Other analyses of metric sensitivities using existing empirical and simulation datasets, however, have shown significant differences between metrics. Variability of metrics due to macro-atmospheric effects and atmospheric turbulence perturbations have been explored, as well as variability in measurements due to microphone setup configurations, array layouts, and ambient noise effects. This paper reviews these prior studies, summarizes the current understanding of metric sensitivities, and discusses how they may impact future downselection of metrics for certification procedures.

sonic boom↗

DOE OSTI2014

Software Quality Metrics for Geant4: An Initial Assessment - Paper 122

In the context of critical applications, such as shielding and radiation protection, ensuring the quality of simulation software they depend on is of utmost importance. The assessment of simulation software quality is crucial both to determine its adoption in experimental applications and to guarantee reproducibility of outcome over time. Geant4 is a simulation system used in a wide range of experimental applications, some of which, such as radiation protection and biomedical studies, may involve critical use cases and would especially profit from an objective assessment of the software quality. In this study, we present the first results from an ongoing analysis of Geant4 code based on established software metrics from the perspective of software engineering. The analysis evaluates the current status of the code to quantify its characteristics with respect to documented quality standards; further appraisals concern evolutions over a series of release distributions. The quantitative assessment of the software is initially focused on a subset of Geant4 packages, which play a key role in scientific applications and are representative of different software development processes. We describe the chosen software metrics that map the entities of a software system to numeric values through mathematical definitions, quantifying software attributes ranging from functionality to maintainability. To measure such metrics, we exploited a set of software metrics tools, i.e. programs that implement a set of software metrics definitions. They allow software engineers to assess a software system by extracting the required attributes from the software and providing the corresponding metric values. To correlate software metrics values in a well-defined way, we used software quality standard models, such as the ISO/IEC 9126 standard, which aggregate numerical values to aid quality analysis and assessment. With this work we aim to create a baseline for evaluating correlations between software quality embedded in the Geant4 development process and simulation observables produced by Geant4-based applications. Furthermore, we intend to highlight what metrics are most effective at evaluating radiation transport software quality. We are confident that the result will provide constructive guidance both to improve key software tools, such as Geant4, and to estimate their contribution in risk analysis concerning sensitive applications. The remainder of this paper is structured as follows. We first discuss the approach adopted to fulfil our assessment. Then, we present the setup of our experiments and the way we collected data. We provide an initial interpretation of a subset of data resulting from measurements made on the selected Geant4 packages. As the analysis is still ongoing at the time of submitting this abstract, some results will be documented in the final paper and in the conference presentation. With this work we aim to create a proper Geant4 data set for evaluating its software characteristics and identifying both strengths and weaknesses. In this initial study, we considered the geometry and processes packages, which play a major role in most simulation applications. The data analysis is in progress at the time of writing; its complexity is related to the differences among software metrics tools, to the large amount of data to be evaluated and to the pioneering character of this project. We are confident that the results will provide valuable indications for the Geant4 user community and constructive guidance to improve key Geant4 packages. Other Monte Carlo codes could profit from the methodology developed in this pilot project for similar assessments of their software quality. At the end of this study, we intend to create a baseline for evaluating correlations between software quality embedded in the Geant4 development process and simulation observables produced by Geant4-based applications. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2017

MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis

The Madden-Julian Oscillation (MJO) simulation diagnostics developed by MJO Working Group and the process-oriented MJO simulation diagnostics developed by MJO Task Force are applied to 37 Coupled Model Intercomparison Project phase 5 (CMIP5) models in order to assess model skill in representing amplitude, period, and coherent eastward propagation of the MJO, and to establish a link between MJO simulation skill and parameterized physical processes. Process-oriented diagnostics include the Relative Humidity Composite based on Precipitation (RHCP), Normalized Gross Moist Stability (NGMS), and the Greenhouse Enhancement Factor (GEF). Numerous scalar metrics are developed to quantify the results. Most CMIP5 models underestimate MJO amplitude, especially when outgoing longwave radiation (OLR) is used in the evaluation, and exhibit too fast phase speed while lacking coherence between eastward propagation of precipitation/convection and the wind field. The RHCP-metric, indicative of the sensitivity of simulated convection to low-level environmental moisture, and the NGMS-metric, indicative of the efficiency of a convective atmosphere for exporting moist static energy out of the column, show robust correlations with a large number of MJO skill metrics. The GEF-metric, indicative of the strength of the column-integrated longwave radiative heating due to cloud-radiation interaction, is also correlated with the MJO skill metrics, but shows relatively lower correlations compared to the RHCP- and NGMS-metrics. Our results suggest that modifications to processes associated with moisture-convection coupling and the gross moist stability might be the most fruitful for improving simulations of the MJO. Though the GEF-metric exhibits lower correlations with the MJO skill metrics, the longwave radiation feedback is highly relevant for simulating the weak precipitation anomaly regime that may be important for the establishment of shallow convection and the transition to deep convection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Optimization of the hop-byte metric for effective topology aware mapping

Suitable mapping of processes to the nodes of a massively parallel machine can substantially improve communication performance by reducing network congestion. The hop-byte metric has been used as a measure of the quality of such a mapping by several recent works. Optimizing this metric is NP hard, and thus heuristics are applied. However, the heuristics proposed so far do not directly try to optimize this metric. Rather, they use some intuitive methods for reducing congestion and use the metric just to evaluate the quality of the mapping. In fact, heuristics intending to optimize other metrics too don't directly optimize for them, but, rather, use the metric to evaluate the results of the heuristic. In contrast, we pose the mapping problem with the hop-byte metric as a quadratic assignment problem and use a heuristic to directly optimize for this metric. We evaluate our approach on realistic node allocations obtained on the Kraken system at NICS. Our approach yields values for the metric that are up to 75% lower than the default mapping and 66 % lower than existing heuristics. However, the time taken to produce the mapping can be substantially more, which makes this suitable for somewhat static, though possibly irregular, communication patterns. We introduce new heuristics that reduce the time taken to be comparable to that of existing fast heuristics, while still producing mappings of higher quality than existing ones. We also use theoretical lower bounds to suggest that our mapping may be close to optimal, at least for medium sized problems. Consequently, our work can also provide insight into the tradeoff between mapping quality and time taken by other mapping heuristics.

Sudheer, C. D.↗

DOE OSTI2018

Definition of Market Performance Metrics to Enable Design and Valuation of Retail Transactive Energy System Markets

This paper defines market performance metrics that will be useful for the comparison of alternative retail transactive energy system (TES) designs. Our key focus is to define an appropriate set of market performance metrics such that the metrics are free of bias and should be applicable to all TES designs. First, we categorize market performance into five categories — market efficiency, market power, market volatility, market fairness, and market reliability. Next, metrics that can quantify TES market performance were developed for each market performance category. To ease implementation, each metric is documented with a uniform structure: metric overview, formulation, significance/interpretation, inputs-outputs, and implementation. The paper is focused on comprehensive discussion on theoretical reasoning and applicability of these metrics to retail TES markets. Since TES theory is in its early stages, these market performance metrics will form a base set for market performance evaluation of TES designs. In addition, these metrics will provide not only insights about the effectiveness of alternative TES designs, but also guidance to further improve retail TES market designs.

Transactive Energy System, Valuation, Retail Elect↗

DOE OSTI2014

MO-A-16A-01: QA Procedures and Metrics: In Search of QA Usability

Radiation therapy has undergone considerable changes in the past two decades with a surge of new technology and treatment delivery methods. The complexity of radiation therapy treatments has increased and there has been increased awareness and publicity about the associated risks. In response, there has been proliferation of guidelines for medical physicists to adopt to ensure that treatments are delivered safely. Task Group recommendations are copious, and clinical physicists' hours are longer, stretched to various degrees between site planning and management, IT support, physics QA, and treatment planning responsibilities.Radiation oncology has many quality control practices in place to ensure the delivery of high-quality, safe treatments. Incident reporting systems have been developed to collect statistics about near miss events at many radiation oncology centers. However, tools are lacking to assess the impact of these various control measures. A recent effort to address this shortcoming is the work of Ford et al (2012) who recently published a methodology enumerating quality control quantification for measuring the effectiveness of safety barriers. Over 4000 near-miss incidents reported from 2 academic radiation oncology clinics were analyzed using quality control quantification, and a profile of the most effective quality control measures (metrics) was identified.There is a critical need to identify a QA metric to help the busy clinical physicists to focus their limited time and resources most effectively in order to minimize or eliminate errors in the radiation treatment delivery processes. In this symposium the usefulness of workflows and QA metrics to assure safe and high quality patient care will be explored.Two presentations will be given:Quality Metrics and Risk Management with High Risk Radiation Oncology ProceduresStrategies and metrics for quality management in the TG-100 Era Learning Objectives: Provide an overview and the need for QA usability metrics: Different cultures/practices affecting the effectiveness of methods and metrics. Show examples of quality assurance workflows, Statistical process control, that monitor the treatment planning and delivery process to identify errors. To learn to identify and prioritize risks and QA procedures in radiation oncology. Try to answer the question: Can a quality assurance program aided by quality assurance metrics help minimize errors and ensure safe treatment delivery. Should such metrics be institution specific.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Competitiveness Metrics for Electricity System Technologies

The relative economic competitiveness of power generation technologies is a topic of much interest to diverse electric industry participants. However, assessing competitiveness can be challenging as it requires considering both total costs and total system value of each technology, which are complicated by the (1) numerous and diverse grid services needed to operate a reliable power system; (2) variations in the economic value of the grid services with system state and location, and over multiple timescales, due to the challenges of transporting and storing electricity; and (3) the unique characteristics of different electric system assets. Ideally, metrics designed or used to convey technology competitiveness must consider these complexities, but existing metrics often fall short. For example, the levelized cost of energy does not consider the system economic value of the various technologies nor does it consider services beyond electricity production. Various other metrics have been designed with the purpose of more-accurately communicating the economic viability of electric system technologies. In this report, we summarize the primary sources and components of costs and value and review the known competitiveness metrics by presenting their definitions, applications, advantages, and disadvantages. We also introduce a new set of competitiveness metrics, which we refer to as System Profitability metrics, that more-directly applies the economic principles of return-on-investment to electric system technologies. We use conceptual examples to show how the System Profitability metrics better reflect economic viability and relative technology competitiveness compared with existing metrics. We also describe how competitiveness metrics can be quantified using optimization-based models and demonstrate this capability using a U.S. electric sector capacity expansion model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Metric Type in the Target-matrix Mesh Optimization Paradigm

The Target Matrix Optimization Paradigm (TMOP) is a method for improving the accuracy, efficiency, and robustness of numerical solutions to partial differential equations by improving the geometric quality of the computational mesh, primarily through node movement. TMOP has been successfully applied to a number applications even though the paradigm was not fully understood at the time. With this work, TMOP can be seen to be a tightly woven fabric of interconnecting ideas and concepts that provides a powerful approach to mesh optimization. The central unifying concepts in TMOP are the concept of a Target Matrix and the concept of Metric Type. Target matrices are motivated by the desire to make mesh quality improvement application-specific and, when needed, solution-adaptive. Metric type plays an essential role because it provides, through the use of typed metrics, the bridge between application-specific quality and target construction. It is shown that there are eight theoretical metric types, including the shape and shape+size types used informally in the past. It is shown further that there exist well-posed metrics corresponding to six of the eight metric types. A well-posed metric is a metric that is typed and convex, polyconvex, or invex, and further, it is a metric that simplifies target construction.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

Semantic Metrics for Object Oriented Design

The purpose of this proposal is to research a new suite of object-oriented (OO) software metrics, called semantic metrics, that have the potential to help software engineers identify fragile, low quality code sections much earlier in the development cycle than is possible with traditional OO metrics. With earlier and better Fault detection, software maintenance will be less time consuming and expensive, and software reusability will be improved. Because it is less costly to correct faults found earlier than to correct faults found later in the software lifecycle, the overall cost of software development will be reduced. Semantic metrics can be derived from the knowledge base of a program understanding system. A program understanding system is designed to understand a software module. Once understanding is complete, the knowledge-base contains digested information about the software module. Various semantic metrics can be collected on the knowledge base. This new kind of metric measures domain complexity, or the relationship of the software to its application domain, rather than implementation complexity, which is what traditional software metrics measure. A semantic metric will thus map much more closely to qualities humans are interested in, such as cohesion and maintainability, than is possible using traditional metrics, that are calculated using only syntactic aspects of software.

Etzkorn, Lethe↗

NASA NTRS2019

NASA EOSDIS Data Usage Metrics- Insight and Assessment

NASA's Earth Science Data and Information System (ESDIS) Project collects Earth science data usage metrics on a daily basis through the ESDIS Metrics System (EMS). This includes metrics on distribution of data products, users, data volumes, and number of files, which are key parameters in evaluating system-level performance of any of the Distributed Active Archive Centers (DAACs) encompassed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS). EOSDIS data usage metrics illustrate the benefits of making NASA data openly available to the public and show a rapid growth in data distribution to a worldwide user community. In fact, each year since 2014 the EOSDIS has distributed over one billion data files of products from EOS satellite, airborne, and in situ observations. An assessment of the long-term trends of data usage metrics and user characterization provides insights into data usability.This study will focus on describing the EMS as a metrics collection tool and will provide a comprehensive analysis of EOSDIS data usage metrics over the last 10 years. This study will also characterize the product distribution metrics by various tools and services, such as Giovanni, the Open-source Project for a Network Data Access Protocol (OPeNDAP), and subsets, to address how these tools/services have extended the usage of data in the EOSDIS collection. Data usage patterns based on discipline and study area will further assist in understanding how EOSDIS data user needs have evolved over time. Results from this study will provide useful information for the DAACs that can help them improve the functionality of their tools and services as well as more efficiently allocate the resources necessary for enhanced access and availability of their data products. Knowledge of these metrics may also benefit user discovery of data in the EOSDIS collection, promote research collaboration, and stimulate new ideas from work and research conducted using specific datasets and data collections.

Kafle, Durga N.↗

NASA NTRS2019

Integrated Analysis of Multiple User Metrics - A “Sequel”; and Introducing the Google Analytic

For decades, the Goddard Earth Sciences Data and Information Services Center (GES DISC) has archived and distributed enormous volumes of NASA Earth science data (accompanied with many developed tools and services) to various research/applications communities and the general public. Being “immersed” in the Big Data era, we have inevitably faced the challenges of our continually increasing archived data in both volume and variety, as well as enhanced user needs and demands. In recent years, we have actively analyzed different types of user metrics, such as operational distribution metrics (recording numbers of distinct users and downloaded data files, size of distributed data volume): user publication metrics (mining info from our Giovanni users’ publications): and Bugzilla metrics (collecting info from user questions or feedback from user assistance tickets). Such metrics have helped us achieve a better understanding of user needs, demands, characteristics, and behaviors, which has then helped us improve our user services. Now we will present a “Sequel” of integrated analysis of multiple metrics at the GES DISC by introducing and adding one new kind of metrics acquired via utilizing our recently implemented Google Analytic 360 suite. Several “newer” reports, e.g., “What web site features and links are the most popular (and least)?” and “What are the top 25 dataset Keyword searches?” retrieved from this new metrics set will be presented, along with the aforementioned “traditional” metrics results.

Shie, Chung-Lin↗

DOE OSTI2016

A New CE TSUNAMI-3D Capability for Calculating Undersampling Metrics and Biases

This study investigates the viability of several statistical metrics for predicting the undersampling biases in continuous-energy (CE) Monte Carlo transport simulation tally estimates. Undersampling is a phenomenon in which a Monte Carlo simulation fails to use enough particles per generation to adequately sample fission points in all regions of a problem, resulting in biases in tally estimates that are much larger than the statistical uncertainties, potentially leading to erroneous conclusions regarding system performance and safety. Previous work found that biases due to undersampling could be as large as several hundred per cent mille (pcm) for eigenvalue estimates and up to tens or hundreds of percent for flux tally estimates. This paper documents the encapsulation of undersampling metrics within CE TSUNAMI-3D in the SCALE code system and examines the correlation between the various undersampling metric scores and the magnitude of various undersampling biases. Of the metrics examined, the RHW metric, the Tally Entropy metric, and the uncertainty in the scores for these metrics produce scores that correlated well with the fraction of the undersampling biases in various eigenvalue, flux, and energy-integrated reaction rate tallies in an infinitely reflected model of a PWR fuel assembly. The average number of nonzero scores per generation encountered by a tally was also found to show promise at predicting the magnitude of undersampling biases, and it also found use in detecting and filtering extremely undersampled tallies in the application of the other metrics. Future work will strengthen these conclusions by improving the degree of convergence for the examined tally and undersampling bias estimates, apply these metrics to a more broad variety of systems, and investigate variance reduction approaches for mitigating undersampling in Monte Carlo tally estimates. (authors)

42 ENGINEERING↗

DOE OSTI2022

A biology-informed similarity metric for simulated patches of human cell membrane

Complex scientific inquiries rely increasingly upon large and autonomous multiscale simulation campaigns, which fundamentally require similarity metrics to quantify ‘sufficient’ changes among data and/or configurations. However, subject matter experts are often unable to articulate similarity precisely or in terms of well-formulated definitions, especially when new hypotheses are to be explored, making it challenging to design a meaningful metric. Furthermore, the key to practical usefulness of such metrics to enable autonomous simulations lies in in situ inference, which requires generalization to possibly substantial distributional shifts in unseen, future data. Here, we address these challenges in a cancer biology application and develop a meaningful similarity metric for ‘patches’—regions of simulated human cell membrane that express interactions between certain proteins of interest and relevant lipids. In the absence of well-defined conditions for similarity, we leverage several biology-informed notions about data and the underlying simulations to impose inductive biases on our metric learning framework, resulting in a suitable similarity metric that also generalizes well to significant distributional shifts encountered during the deployment. We combine these intuitions to organize the learned embedding space in a multiscale manner, which makes the metric robust to incomplete and even contradictory intuitions. Our approach delivers a metric that not only performs well on the conditions used for its development and other relevant criteria, but also learns key spatiotemporal relationships without ever being exposed to any such information during training.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Metrics as tools for bridging climate science and applications

In climate science and applications, the term “metric” is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are “fit for purpose.” Metrics can also provide a valuable reference point for co-production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. Here, this paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision-making.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

A Metric Space with LCOE Isolines for Research Guidance in wind and hydrokinetic energy systems

This paper introduces a new Metric Space to guide the design of advanced wind energy systems and hydrokinetic energy converters such as tidal, ocean current and riverine turbines. The Metric Space can analyse farms that combine different or identical turbines and stand-alone turbines. The first metric (M1) of the space considers the efficiency of the turbines in the farm, which is also proportional to the specific power per swept area at a given wind/water velocity (W/m 2 ). The second metric (M2) describes the specific rotor area per unit of mass of the turbines (m 2 /kg). Both metrics depend on the primary design characteristics of the turbines, such as swept area, system size and mass, materials and efficiency, and are independent at first from external characteristics, such as atmospheric and ocean site conditions, cost of materials and economic factors. Combining both metrics, and for a given set of external characteristics, the resulting Metric Space M2/M1 displays the Levelized Cost of Energy (LCOE) standards as isolines. This graphical representation provides a quick understanding of the cost and state of the technology. It also offers a practical guidance to choose the research tasks and strategy to design advanced wind and hydrokinetic energy systems. The paper applies the new Metric Space to several case studies, including large and small onshore wind turbines, floating and bottom-fixed offshore wind turbines, downwind rotors, multi-rotor and hybrid systems, airborne wind energy systems, wind farms and tidal energy converters.

17 WIND ENERGY↗

DOE OSTI2019

Measuring Fundamental Improvements in Sustainable Urban Mobility: The Mobility-Energy Productivity Metric

Recent technological advancements in mobility are creating many options for connecting citizens with employment, goods, and services, particularly in urban areas where modes such as bike and car shares, electric scooters, ridesourcing, and ridesharing are proliferating at a rapid pace. Analysis and tools for overall transportation planning are dominated by urban regional travel demand models whose roots in highway operations poorly reflect the system dynamics in denser areas where parking costs, convenience, and availability - not to mention sustainability concerns and quality of life - are driving people to an ever-greater spectrum of mobility services. In this paper, we present a new paradigm for evaluating mobility options within an urban area. First developed for the U.S. Department of Energy's Energy Efficient Mobility System research program, this metric is termed the mobility-energy productivity (MEP) metric. At its heart, the MEP metric measures accessibility and appropriately weights it with travel time, cost, and energy of modes that provide access to opportunities in any given location. The proposed metric is versatile in that it can be computed from readily available data sources or derived from outputs of regional travel demand models. End times associated with parking, curb access, cost, and reliability and frequency of service need to be carefully considered to obtain an appropriate and accurate perspective when computing the metric. Ultimately, the MEP metric can be used to reflect the impacts of new mobility technologies (transportation network companies, electric scooters), business models (car shares and bike shares), and land-use practices (such as transit-oriented development) on sustainable urban mobility. This paper lays out the need, requirements, and framework for this new metric, and offers it, in collaboration with the American Society for Civil Engineers (ASCE), as a foundational metric for smart city assessment.

30 DIRECT ENERGY CONVERSION↗