A Practical Approach to Evaluating the Economic and Technical Feasibility of LED Luminaires

LED roadway luminaires are currently under consideration for widespread implementation with departments of transportation, facilities managers, and city planners. This research focuses on a case study in Missouri and presents relevant research findings calculated by the authors as part of a project funded by the Missouri Department of Transportation. Although high-pressure sodium (HPS) luminaires have been the standard product for roadway illumination, advances in LED technologies have led many departments of transportation to consider them as viable options along state routes. For this case study, pilot sites were developed across the state of Missouri in sites assessed as moderately busy, medium pedestrian conflict zones. These zones were along roadways with an R3 pavement classification. This case study details the economic feasibility findings from the study; a life cycle cost approach was used. In addition, a technical feasibility analysis was conducted to determine fit with Illumination Engineering Society (IES) standards for the traffic pattern and pavement classification at study sites. Key findings reveal that LED roadway luminaires fail to outperform HPS in their current design, but may become technically and economically feasible in the future

A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions

The Application of Fuzzy Analytic Hierarchy Process in Sustainable Project Selection

The project selection process is a crucial step in sustainable development. Effective sustainable development depends on the ability to select the appropriate sustainable project to implement to ensure that the desired goals are met. Some of the most common characteristics or criteria used in evaluating sustainable projects include novelty, uncertainty, skill and experience, technology information transfer, and project cost. Prioritizing these criteria based on relative importance helps project managers and decision makers identify elements that require additional attention, better allocate resources, as well as improve the selection process when evaluating different sustainable project alternatives. The aim of this research is to use the fuzzy analytic hierarchy process (FAHP) methodology in which fuzzy numbers are utilized to realistically represent human judgment to rank the different project criteria based on relative importance and impact on sustainable projects. The results from the FAHP show that the most important criterion to consider in sustainable project selection is project cost, followed by novelty and uncertainty as the second and third most important criteria, respectively. The two least important criteria out of the total of five examined in this research were the skill and experience and technology information transfer, respectively. These results will help project managers and decision makers identify selection criteria with higher weights of importance. Given that the selection criteria chosen for this research are not limited to the evaluation of a specific type of sustainable projects or a specific location, they can be used to evaluate different types of sustainable projects in different environments and locations

A Mixed Method Study of Infrastructure Resilience Education and Instruction

As the frequency and severity of natural and man-made disasters increases, the importance of improving the resilience of complex infrastructure systems in an uncertain environment is increasingly critical. Proper training and education are key components to addressing this issue, but it is unclear how and where modeling under uncertainty, infrastructure systems management, and resilient systems are integrated into the standard undergraduate and graduate engineering management curriculum. This research uses a mixed method to determine whether and at what level engineering managers receive instruction regarding the implementation of tools and techniques to improve infrastructure resilience. A review of current courses and content informs a systems-thinking approach to resilience and investigates how the topic of infrastructure resilience is being taught. The results of the study identify gaps in existing engineering management curriculum with respect to the topic of resilience. The findings from these results can be used to by the engineering management educator to provide coursework and training that can be used to lead teams that design, build, analyze the resiliency of current infrastructure systems, or restore damaged infrastructure systems to their original state

Flood Prediction and Uncertainty Estimation using Deep Learning

Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning model was more accurate than the physical and statistical models currently in use while providing information in 15 minute increments rather than six hour increments. It was also found that the use of data sub-selection for regularization in deep learning is preferred to dropout. These results make it possible to provide more accurate and timely flood prediction for a wide variety of applications, including transportation systems

Flood Management Deep Learning Model Inputs: A Review of Necessary Data and Predictive Tools

Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural test site are presented subject to available data and input from key stakeholders. The transportation safety or disaster planner can use these results to begin integrating deep learning methods in their planning strategies based on region-specific geospatial data and information

Agent based Modeling for Flood Inundation Mapping and Rerouting

Natural disasters like earthquakes and floods can have a serious impact on road networks, which are critical to supply chain infrastructure and to provide connectivity. These extreme events can result in isolating people in the affected area from hospitals and emergency response. This paper presents an agent-based model for understanding flood propagation and developing inundation mapping. The results from the mapping are used to identify the roads prone to floods based on elevation data and flood simulation. A simulation environment was set up in SUMO, and the costs associated with the traffic disruption are evaluated. This paper discusses the integration of various techniques for a comprehensive flood prediction and rerouting system

Opportunities and Challenges for Rural Broadband Infrastructure Investment

Insufficient internet access is holding back local economies, reducing educational outcomes, and creating health disparities in rural areas of the U.S. At present, federal and state funding is available for rural broadband infrastructure deployment, but existing efforts have not invested in analytical work to maximize efficiency and minimize cost. In this study, we use a state-of-the-art matrix (SAM) to identify key challenges and opportunities facing rural broadband infrastructure from previous research and government reports. We focus on six themes: (1) technology, (2) hardware costs, (3) financing, (4) adoption, (5) regulatory/legal, and (6) management. We highlight key issues to be addressed by both private and public decision-makers to effectively manage broadband investment as well as engage stakeholders to improve access and adoption. Much of the challenge for rural broadband infrastructure is related to a low return on investment due to high capital costs and low population densities. However, there are many innovative approaches to overcoming this barrier from technical, policy, and social perspectives. Unfortunately, adoption and management are understudied and would benefit from additional research to design effective decision-making tools and programs. From a systems perspective, solutions that leverage tools from a diverse set of perspectives, rather than purely focusing on technology deployment, are more likely to be sustainable in the long-term. We outline an agenda for future work based on the needs of rural communities as well as local and state governments

Identifying Geographical Interdependency in Critical Infrastructure Systems Using Open Source Geospatial Data in Order to Model Restoration Strategies in the Aftermath of a Large-Scale Disaster

In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium- to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium- to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the ‘geographical interdependency’) of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center’s supply chain in the wake of an extreme event

What New Faculty Need to Know, But Don\u27t Know to Ask

A smooth transition to life at an academic institution and the surrounding community is essential to the professional careers of new faculty members. The transition begins during the hiring process and startup package negotiations. Once at an institution, aspects of academia including teaching, proposal writing, and the tenure process inevitably generate issues and concerns for new faculty members. Research has shown that mentoring new faculty members early in their academic career can have significant impact on professional success. This is especially true at a research-based institution where the demands of funded scholarship add an extra level of complexity. A survey was conducted of faculty members at Missouri University of Science & Technology (Missouri S&T) in their first three years of a tenure track appointment to determine areas of concern for new faculty members. This paper presents the survey results, discusses the issues raised by the survey, and makes recommendations for effective mentoring relationships. Specific questions for new faculty members discussed in this paper include: What to look for in a mentor? What to consider in selecting where to submit papers? When to say yes and when to say no to service? Where to begin the hunt for research funding? What are quality resources for teaching? The paper also provides insight to mentors relative to junior faculty members\u27 concerns. This paper evaluates issues that are critical to forming effective mentoring relationships. Guidance offered provides value to mentors in understanding which areas are of greatest concern to new faculty. It provides information to proteges as well in determining key characteristics of an effective mentor