Shakouri, Mahmoud; Lee, Hyun Woo. (2016). Mean-Variance Portfolio Analysis Data For Optimizing Community-Based Photovoltaic Investment. Data In Brief, 6, 840 – 842.
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Abstract
The amount of electricity generated by Photovoltaic (PV) systems is affected by factors such as shading, building orientation and roof slope. To increase electricity generation and reduce volatility in generation of PV systems, a portfolio of PV systems can be made which takes advantages of the potential synergy among neighboring buildings. This paper contains data supporting the research article entitled: PACPIM: new decision-support model of optimized portfolio analysis for community-based photovoltaic investment [1]. We present a set of data relating to physical properties of 24 houses in Oregon, USA, along with simulated hourly electricity data for the installed PV systems. The developed Matlab code to construct optimized portfolios is also provided in Supplementary materials. The application of these files can be generalized to variety of communities interested in investing on PV systems. (C) 2016 The Authors. Published by Elsevier Inc.
Keywords
Community Solar; Photovoltaic System; Portfolio Theory; Energy Optimization; Electricity Volatility
Zhang, Jinlei; Chen, Feng; Shen, Qing. (2019). Cluster-based LSTM Network for Short-term Passenger Flow Forecasting in Urban Rail Transit. Ieee Access, 7, 147653 – 147671.
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Abstract
Short-term passenger flow forecasting is an essential component for the operation of urban rail transit (URT). Therefore, it is necessary to obtain a higher prediction precision with the development of URT. As artificial intelligence becomes increasingly prevalent, many prediction methods including the long short-term memory network (LSTM) in the deep learning field have been applied in road transportation systems, which can give critical insights for URT. First, we propose a novel two-step K-Means clustering model to capture not only the passenger flow variation trends but also the ridership volume characteristics. Then, a predictability assessment model is developed to recommend a reasonable time granularity interval to aggregate passenger flows. Based on the clustering results and the recommended time granularity interval, the LSTM model, which is called CB-LSTM model, is proposed to conduct short-term passenger flow forecasting. Results show that the prediction based on subway station clusters can not only avoid the complication of developing numerous models for each of the hundreds of stations, but also improve the prediction performance, which make it possible to predict short-term passenger flow on a network scale using limited dataset. The results provide critical insights for subway operators and transportation policymakers.
Keywords
Traffic Flow; Neural-network; Prediction; Ridership; Models; Volume; Lstm; Short-term Passenger Flow Forecasting; Urban Rail Transit; K-means Clustering; Deep Learning
Bogus, Susan M.; Migliaccio, Giovanni C.; Cordova, Arturo A. (2010). Assessment of Data Quality for Evaluations of Manual Pavement Distress. Transportation Research Record, 2170, 1 – 8.
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Abstract
Assessment of the conditions of current assets is a task of major relevance in a transportation agency asset management program It not only provides information on the current condition of the asset but also helps the agency make decisions on future maintenance and rehabilitation activities Although low volume roadways represent a large proportion of the total road network in the United States little research on the management of these assets has been done Two major data collection techniques are used for roadway condition assessment manual and automated Although automated techniques have been found to be safer and quicker manual condition surveys have been proven to offer preciseness and cost effectiveness In the case of low volume roadway assessment for which the funds available to asset managers are limited manual condition surveys are often preferred Nevertheless manual condition surveys must address the potential subjectivity of the results Therefore agencies could benefit from a system for ensuring quality on manual condition surveys This paper proposes a framework for assessment of data quality and presents a case study of its implementation in the Northern New Mexico Pavement Evaluation Program The proposed framework is easily implementable and able to identify potential and actual data collection issues The framework can be used as part of an asset management program and could be particularly beneficial in the case of low volume roads
Keywords
Interrater Reliability; Agreement; Ratings
Stewart, Orion T.; Carlos, Heather A.; Lee, Chanam; Berke, Ethan M.; Hurvitz, Philip M.; Li, Li; Moudon, Anne Vernez; Doescher, Mark P. (2016). Secondary GIS Built Environment Data for Health Research: Guidance for Data Development. Journal Of Transport & Health, 3(4), 529 – 539.
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Abstract
Built environment (BE) data in geographic information system (GIS) format are increasingly available from public agencies and private providers. These data can provide objective, low-cost BE data over large regions and are often used in public health research and surveillance. Yet challenges exist in repurposing GIS data for health research. The GIS data do not always capture desired constructs; the data can be of varying quality and completeness; and the data definitions, structures, and spatial representations are often inconsistent across sources. Using the Small Town Walkability study as an illustration, we describe (a) the range of BE characteristics measurable in a GIS that may be associated with active living, (b) the availability of these data across nine U.S. small towns, (c) inconsistencies in the GIS BE data that were available, and (d) strategies for developing accurate, complete, and consistent GIS BE data appropriate for research. Based on a conceptual framework and existing literature, objectively measurable characteristics of the BE potentially related to active living were classified under nine domains: generalized land uses, morphology, density, destinations, transportation system, traffic conditions, neighborhood behavioral conditions, economic environment, and regional location. At least some secondary GIS data were available across all nine towns for seven of the 9 BE domains. Data representing high-resolution or behavioral aspects of the BE were often not available. Available GIS BE data - especially tax parcel data often contained varying attributes and levels of detail across sources. When GIS BE data were available from multiple sources, the accuracy, completeness, and consistency of the data could be reasonable ensured for use in research. But this required careful attention to the definition and spatial representation of the BE characteristic of interest. Manipulation of the secondary source data was often required, which was facilitated through protocols. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords
Geographic Information-systems; Physical-activity; Land-use; Walking; Neighborhood; Associations; Density; Design; Adults; Travel; Active Travel; Pedestrian; Urban Design; Community Health; Rural
Kim, Jinyhup; Bae, Chang-Hee Christine. (2020). Do Home Buyers Value the New Urbanist Neighborhood? The Case of Issaquah Highlands, WA. Journal Of Urbanism, 13(3), 303 – 324.
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Abstract
This study compares Issaquah Highlands’ home prices with those of traditional suburban single-family homes in the city of Issaquah. Issaquah Highlands is a community that was developed using New Urbanism principles. The null hypothesis is that the sale prices of houses in Issaquah Highlands are not different from the conventional suburban neighborhood in the city of Issaquah. The principal database consists of US Census Washington State Geospatial Data Archive, and the King County Tax Assessments. The final dataset contains 1,780 single family homes over the seven-year period from 2012 to 2018 based on sale records throughout the city of Issaquah. This study uses the hedonic pricing technique to assess the impact of New Urbanism on the value of single-family residences. The findings suggest that people are willing to pay a $92,700–96,800 premium (approximately 7.1–12.0 percent of the sales prices) for houses in Issaquah Highlands.
Keywords
New Urbanism; Home Prices; Real Property; Sustainable Development; Spatial Analysis (statistics); Hedonic Pricing Model; Property Value; Smart Growth; Spatial Autocorrelation; Neighborhoods; Databases; Taxation; Spatial Data; Suburban Areas; Census; Prices; Housing Prices; Urbanism; Houses; Willingness To Pay; Residential Areas; Null Hypothesis; Cities; Buyers; Hedonism; Sales; Highlands; Tax Assessments
Hsieh, Shang-hsien; Lin, Hsien-tang; Chi, Nai-wen; Chou, Kuang-wu; Lin, Ken-yu. (2011). Enabling The Development Of Base Domain Ontology Through Extraction Of Knowledge From Engineering Domain Handbooks. Advanced Engineering Informatics, 25(2), 288 – 296.
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Abstract
Domain ontology, encompassing both concepts and instances, along with their relations and properties, is a new medium for the storage and propagation of domain specific knowledge. A significant problem remains the effort which must be expended during ontology construction. This involves collecting the domain-related vocabularies, developing the domain concept hierarchy, and defining the properties of each concept and the relationships between concepts. Recently several engineering handbooks have described detailed domain knowledge by organizing the knowledge into categories, sections, and chapters with indices in the appendix. This paper proposes the extraction of concepts, instances, and relationships from a handbook of a specific domain to quickly construct base domain ontology as a good starting point for expediting the development process of more comprehensive domain ontology. The extracted information can also be reorganized and converted into web ontology language format to represent the base domain ontology. The generation of a base domain ontology from an Earthquake Engineering Handbook is used to illustrate the proposed approach. In addition, quality evaluation of the extracted base ontology is performed and discussed. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords
Ontology; Earthquake Engineering; World Wide Web; Theory Of Knowledge; Vocabulary; Programming Languages; Domain Handbook; Domain Ontology; Owl; Web Ontology Language; Knowledge Representation Languages; Ontologies (artificial Intelligence); Base Domain Ontology; Knowledge Extraction; Engineering Domain Handbooks; Domain Specific Knowledge Storage; Domain Specific Knowledge Propagation; Domain-related Vocabularies; Domain Concept Hierarchy; Development Process; Web Ontology Language Format; Earthquake Engineering Handbook; Semantic Web; Management; Design
Neff, Gina; Tanweer, Anissa; Fiore-Gartland, Brittany; Osburn, Laura. (2017). Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies And Data Science. Big Data, 5(2), 85 – 97.
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Abstract
What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the sociomaterial arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, data for good projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Using ethnographic vignettes from two large multiresearcher field sites, we develop a set of concepts for analyzing and advancing the practice of data science and improving critical data studies, including (1) communication is central to the data science endeavor; (2) making sense of data is a collective process; (3) data are starting, not end points, and (4) data are sets of stories. We conclude with two calls to action for researchers and practitioners in data science and critical data studies alike. First, creating opportunities for bringing social scientific and humanistic expertise into data science practice simultaneously will advance both data science and critical data studies. Second, practitioners should leverage the insights from critical data studies to build new kinds of organizational arrangements, which we argue will help advance a more ethical data science. Engaging the insights of critical data studies will improve data science. Careful attention to the practices of data science will improve scholarly critiques. Genuine collaborative conversations between these different communities will help push for more ethical, and better, ways of knowing in increasingly datum-saturated societies.
Keywords
Big; Communication; Politics; Critical Data Studies; Data For Good; Data Science; Ethics; Qualitative Methods; Theory
Lee, Wonil; Migliaccio, Giovanni C.; Lin, Ken-Yu; Seto, Edmund Y. W. (2020). Workforce Development: Understanding Task-Level Job Demands-Resources, Burnout, and Performance in Unskilled Construction Workers. Safety Science, 123.
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Abstract
This study examines how task demands and personal resources affect unskilled construction worker productivity and safety performance. It extends the job demands-resources (JD-R) burnout model to show how job characteristics interact with burnout to influence performance. A modified model was designed to measure burnout, with exhaustion and disengagement among unskilled construction workers taken into consideration. An observational study was conducted in a laboratory environment to test the research hypotheses and assess the prediction accuracies of outcome constructs. Twenty-two subjects participated in multiple experiments designed to expose them to varying levels of task-demands and to record their personal resources as they performed common construction material-handling tasks. Specifically, both surveys and physiological measurements using wearable sensors were used to operationalize the model constructs. Moreover, partial least squares structural equation modeling was applied to analyze data collected at the task and individual levels. Exhaustion and disengagement exhibited different relationships with productivity and safety performance outcomes as measured by unit rate productivity and ergonomic behavior, respectively. Subjects with high burnout and high engagement showed high productivity but low safety performance. Thus, exhausted workers stand a greater chance of failing to comply with safety. As the sample and the task performed in the experiment do not cover the experience and trade of all construction workers, our findings are limited in their application to entry-level and unskilled workers, whose work is mainly manual material-handling tasks.
Keywords
Construction Workers; Structural Equation Modeling; Job Descriptions; Labor Productivity; Labor Supply; Burnout; Job Demand-resources Model; Partial Least Squares Structural Equation Modeling; Productivity; Safety; Wearable Sensors; Biomechanics; Construction Industry; Ergonomics; Occupational Health; Occupational Safety; Occupational Stress; Personnel; Statistical Analysis; Workforce Development; Understanding Task-level Job Demands-resources; Unskilled Construction Workers; Task Demands; Personal Resources; Unskilled Construction Worker Productivity; Job Demands-resources Burnout Model; Job Characteristics Interact; Exhaustion; Disengagement; Outcome Constructs; Varying Levels; Task-demands; Common Construction Material-handling Tasks; Physiological Measurements; Model Constructs; Individual Levels; Unit Rate Productivity; High Burnout; Low Safety Performance; Exhausted Workers; Entry-level; Unskilled Workers; Manual Material-handling Tasks; Heart-rate-variability; Labor Productivity Trends; Physiological Demands; Emotional Exhaustion; Safety Climate; Role Stress; Engagement; Fatigue; Workload; Task Analysis; Workforce; Level (quantity); Construction Materials; Personnel Management; Materials Handling; Multivariate Statistical Analysis
Launching the Inspire Fund: An early step for CBE’s Office of Research “For a small college, CBE has a broad range of research paradigms, from history and arts, to social science and engineering.” — Carrie Sturts Dossick, Associate Dean of Research Upon taking on the role of Associate Dean of Research, Carrie Sturts Dossick, professor in the Department of Construction Management, undertook listening sessions to learn about the research needs of faculty, staff and students across the College of Built…
I am interested in developing analysis methods and metrics for accurate daylight analysis. More concretely, I would like to work on developing color accurate sky models through analyzing HDR photographs, and to integrate it to annual daylight simulation method. Additionally, I am also interested in integration of daylight simulation in environmental design.