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Articles 1 - 30 of 396

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Exploring The Relationship Between Sustainable Projects And Institutional Isomorphisms: A Project Typology, Rakan Alyamani, Suzanna Long, Mohammad Nurunnabi May 2020

Exploring The Relationship Between Sustainable Projects And Institutional Isomorphisms: A Project Typology, Rakan Alyamani, Suzanna Long, Mohammad Nurunnabi

Engineering Management and Systems Engineering Faculty Research & Creative Works

With the increase in awareness about the wide range of issues and adverse effects associated with the use of conventional energy sources came an increase in project management research related to sustainability and sustainable development. Part of that research is devoted to the development of sustainable project typologies that classify projects based on a variety of external factors that can significantly impact these projects. This research focuses on developing a sustainable project typology that classifies sustainable projects based on the external institutional influences. The typology explores the influence of the coercive, normative, and mimetic institutional isomorphisms on the expected level ...


Flood Prediction And Uncertainty Estimation Using Deep Learning, Vinayaka Gude, Steven Corns, Suzanna Long Mar 2020

Flood Prediction And Uncertainty Estimation Using Deep Learning, Vinayaka Gude, Steven Corns, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

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 ...


Computational Model For Neural Architecture Search, Ram Deepak Gottapu Jan 2020

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats ...


Microgrid Design, Control, And Performance Evaluation For Sustainable Energy Management In Manufacturing, Md. Monirul Islam Jan 2020

Microgrid Design, Control, And Performance Evaluation For Sustainable Energy Management In Manufacturing, Md. Monirul Islam

Doctoral Dissertations

"This research studies the capacity sizing, control strategies, and performance evaluation of the microgrids with hybrid renewable sources for manufacturing end use customers towards a distributed sustainable energy system paradigm. Microgrid technology has been widely investigated and applied in commercial and residential sector, while for manufacturers, it has been less explored and utilized. To fill the gap, the dissertation first proposes a cost-effective sizing model to identify the capacities as well as control strategies of the components in microgrids considering a commonly used energy tariff, i.e., Time of Use (TOU). Then, the sizing model is extended by integrating control ...


Correction To: Better Beware: Comparing Metacognition For Phishing And Legitimate Emails (Metacognition And Learning, (2019), 14, 3, (343-362), 10.1007/S11409-019-09197-5), Casey I. Canfield, Baruch Fischhoff, Alex Davis Dec 2019

Correction To: Better Beware: Comparing Metacognition For Phishing And Legitimate Emails (Metacognition And Learning, (2019), 14, 3, (343-362), 10.1007/S11409-019-09197-5), Casey I. Canfield, Baruch Fischhoff, Alex Davis

Engineering Management and Systems Engineering Faculty Research & Creative Works

The article "Better beware: comparing metacognition for phishing and legitimate emails", written by Casey Inez Canfield, Baruch Fischhoff and Alex Davis, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 20 July 2019 without open access.


Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke Dec 2019

Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication ...


Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke Dec 2019

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P ...


Better Beware: Comparing Metacognition For Phishing And Legitimate Emails, Casey I. Canfield, Baruch Fischhoff, Alex Davis Dec 2019

Better Beware: Comparing Metacognition For Phishing And Legitimate Emails, Casey I. Canfield, Baruch Fischhoff, Alex Davis

Engineering Management and Systems Engineering Faculty Research & Creative Works

Every electronic message poses some threat of being a phishing attack. If recipients underestimate that threat, they expose themselves, and those connected to them, to identity theft, ransom, malware, or worse. If recipients overestimate that threat, then they incur needless costs, perhaps reducing their willingness and ability to respond over time. In two experiments, we examined the appropriateness of individuals' confidence in their judgments of whether email messages were legitimate or phishing, using calibration and resolution as metacognition metrics. Both experiments found that participants had reasonable calibration but poor resolution, reflecting a weak correlation between their confidence and knowledge. These ...


Preface, Cihan H. Dagli, Gursel A. Suer Aug 2019

Preface, Cihan H. Dagli, Gursel A. Suer

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.


Joint Manufacturing And Onsite Microgrid System Control Using Markov Decision Process And Neural Network Integrated Reinforcement Learning, Wenqing Hu, Zeyi Sun, Y. Zhang, Y. Li Aug 2019

Joint Manufacturing And Onsite Microgrid System Control Using Markov Decision Process And Neural Network Integrated Reinforcement Learning, Wenqing Hu, Zeyi Sun, Y. Zhang, Y. Li

Mathematics and Statistics Faculty Research & Creative Works

Onsite microgrid generation systems with renewable sources are considered a promising complementary energy supply system for manufacturing plant, especially when outage occurs during which the energy supplied from the grid is not available. Compared to the widely recognized benefits in terms of the resilience improvement when it is used as a backup energy system, the operation along with the electricity grid to support the manufacturing operations in non-emergent mode has been less investigated. In this paper, we propose a joint dynamic decision-making model for the optimal control for both manufacturing system and onsite generation system. Markov Decision Process (MDP) is ...


A Framework Of Integrating Manufacturing Plants In Smart Grid Operation: Manufacturing Flexible Load Identification, Md. Monirul Islam, Zeyi Sun, Wenqing Hu, Cihan H. Dagli Aug 2019

A Framework Of Integrating Manufacturing Plants In Smart Grid Operation: Manufacturing Flexible Load Identification, Md. Monirul Islam, Zeyi Sun, Wenqing Hu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation ...


System Of Systems (Sos) Architecture For Digital Manufacturing Cybersecurity, Lirim Ashiku, Cihan H. Dagli Aug 2019

System Of Systems (Sos) Architecture For Digital Manufacturing Cybersecurity, Lirim Ashiku, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Technology advancements of real time connectivity and computing powers has evolved the way people manage activities triggering heavy reliance on smart devices. This has reshaped the ability to memorize crucial information, instead accumulate the information into devices allowing real-time fingertip access when needed. Inability to access such information when needed is routinely assumed with device malfunctioning bypassing the probability of compromise, but what if the information is now being accessed by adversaries depriving the data-owner access to crucial information? Cyber manufacturing systems are not immune from these issues. It is possible to approach this problem as generating SoS meta-architecture. In ...


Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin Aug 2019

Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin

Computer Science Faculty Research & Creative Works

Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers' behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize ...


Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo May 2019

Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo

Engineering Management and Systems Engineering Faculty Research & Creative Works

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans' actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators' actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on ...


System Architecting Approach For Designing Deep Learning Models, Ram Deepak Gottapu, Cihan H. Dagli Apr 2019

System Architecting Approach For Designing Deep Learning Models, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Deep Learning (DL) models have proven to be very effective in solving many challenging problems, especially, those related to computer vision, text, and speech. However, the design of such models is challenging because of the vast search space and computational complexity that needs to be explored. Our goal in this paper is to reduce the human effort required to design architectures by using a system architecture development process that allows the exploration of large design space by automating certain model construction, alternative generation, and assessment. The proposed framework is generic and targeted at all deep learning architectures that can be ...


Supply Chain Infrastructure Restoration Calculator Software Tool -- Developer Guide And User Manual, Akhilesh Ojha, Bhanu Kanwar, Suzanna Long, Thomas G. Shoberg, Steven Corns Jan 2019

Supply Chain Infrastructure Restoration Calculator Software Tool -- Developer Guide And User Manual, Akhilesh Ojha, Bhanu Kanwar, Suzanna Long, Thomas G. Shoberg, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This report describes a software tool that calculates costs associated with the reconstruction of supply chain interdependent critical infrastructure in the advent of a catastrophic failure by either outside forces (extreme events) or internal forces (fatigue). This tool fills a gap between search and recover strategies of the Federal Emergency Management Agency (or FEMA) and construction techniques under full recovery. In addition to overall construction costs, the tool calculates reconstruction needs in terms of personnel and their required support. From these estimates, total costs (or the cost of each element to be restored) can be calculated. Estimates are based upon ...


Quantifying Restoration Costs In The Aftermath Of An Extreme Event Using System Dynamics And Dynamic Mathematical Modeling Approaches, Akhilesh Ojha Jan 2019

Quantifying Restoration Costs In The Aftermath Of An Extreme Event Using System Dynamics And Dynamic Mathematical Modeling Approaches, Akhilesh Ojha

Doctoral Dissertations

"Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The ...


Freeform Extrusion Fabrication Of Advanced Ceramics And Ceramic-Based Composites, Wenbin Li Jan 2019

Freeform Extrusion Fabrication Of Advanced Ceramics And Ceramic-Based Composites, Wenbin Li

Doctoral Dissertations

"Ceramic On-Demand Extrusion (CODE) is a recently developed freeform extrusion fabrication process for producing dense ceramic components from single and multiple constituents. In this process, aqueous paste of ceramic particles with a very low binder content ( < 1 vol%) is extruded through a moving nozzle to print each layer sequentially. Once one layer is printed, it is surrounded by oil to prevent undesirable water evaporation from the perimeters of the part. The oil level is regulated just below the topmost layer of the part being fabricated. Infrared radiation is then applied to uniformly and partially dry the top layer so that the yield stress of the paste increases to avoid part deformation. By repeating the above steps, the part is printed in a layer-wise fashion, followed by post-processing. Paste extrusion precision of different extrusion mechanisms was compared and analyzed, with an auger extruder determined to be the most suitable paste extruder for the CODE system. A novel fabrication system was developed based on a motion gantry, auger extruders, and peripheral devices. Sample specimens were then produced from 3 mol% yttria stabilized zirconia using this fabrication system, and their properties, including density, flexural strength, Young's modulus, Weibull modulus, fracture toughness, and hardness were measured. The results indicated that superior mechanical properties were achieved by the CODE process among all the additive manufacturing processes. Further development was made on the CODE process to fabricate ceramic components that have external/internal features such as overhangs by using fugitive support material. Finally, ceramic composites with functionally graded materials (FGMs) were fabricated by the CODE process using a dynamic mixing device"--Abstract, page iv.


The Tabu Ant Colony Optimizer And Its Application In An Energy Market, David Donald Haynes Jan 2019

The Tabu Ant Colony Optimizer And Its Application In An Energy Market, David Donald Haynes

Doctoral Dissertations

"A new ant colony optimizer, the 'tabu ant colony optimizer' (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the ...


Biofuel Supply Chain Restructuring -- An Economic Viability And Environmental Sustainability Investigation For Enhancing Second Generation Biofuel Adoption, Rajkamal Kesharwani Jan 2019

Biofuel Supply Chain Restructuring -- An Economic Viability And Environmental Sustainability Investigation For Enhancing Second Generation Biofuel Adoption, Rajkamal Kesharwani

Doctoral Dissertations

"Biofuel is a promising clean alternative to fossil fuels. Currently, first generation biofuels are commercially produced by using corn grain as biomass feedstock. However, the use of edible matter of crops, may lead to a competition between food and fuel. Therefore, there is a significant push in both industry and academia to commercialize second generation biofuel manufacturing technology, which uses non-edible matter from crops. Most research focuses on individual manufacturing processes for producing second generation biofuel, but the economic and environmental impacts of a large-scale adoption of second generation biofuel manufacturing have been less widely reported.

This work investigates the ...


Application Of Computational Intelligence To Explore And Analyze System Architecture And Design Alternatives, Gene Lesinski Jan 2019

Application Of Computational Intelligence To Explore And Analyze System Architecture And Design Alternatives, Gene Lesinski

Doctoral Dissertations

"Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research ...


Routing Algorithm For The Ground Team In Transmission Line Inspection Using Unmanned Aerial Vehicle, Yu Li Jan 2019

Routing Algorithm For The Ground Team In Transmission Line Inspection Using Unmanned Aerial Vehicle, Yu Li

Masters Theses

"With the rapid development of robotics technology, robots are increasingly used to conduct various tasks by utility companies. An unmanned aerial vehicle (UAV) is an efficient robot that can be used to inspect high-voltage transmission lines. UAVs need to stay within a data transmission range from the ground station and periodically land to replace the battery in order to ensure that the power system can support its operation. A routing algorithm must be used in order to guide the motion and deployment of the ground station while using UAV in transmission line inspection. Most existing routing algorithms are dedicated to ...


Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns Nov 2018

Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify ...


System Of Systems Architecting Problems: Definitions, Formulations, And Analysis, Hadi Farhangi, Dincer Konur Nov 2018

System Of Systems Architecting Problems: Definitions, Formulations, And Analysis, Hadi Farhangi, Dincer Konur

Engineering Management and Systems Engineering Faculty Research & Creative Works

The system of systems architecting has many applications in transportation, healthcare, and defense systems design. This study first presents a short review of system of systems definitions. We then focus on capability-based system of systems architecting. In particular, capability-based system of systems architecting problems with various settings, including system flexibility, fund allocation, operational restrictions, and system structures, are presented as Multi-Objective Nonlinear Integer Programming problems. Relevant solution methods to analyze these problems are also discussed.


Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli Nov 2018

Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis ...


Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli Nov 2018

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet) can be substantially more accurate with less number of parameters if each layer in the network is connected with every other layer in a feed forward fashion. We embrace this idea and generate new architecture that can assign each pixel/voxel in an MR image of ...


Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li Nov 2018

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li

Engineering Management and Systems Engineering Faculty Research & Creative Works

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential ...


Study Of Cost Overrun And Delays Of Department Of Defense (Dod)'S Space Acquisition Program, Nazareen Sikkandar Basha, Benjamin J. Kwasa, Christina Bloebaum Oct 2018

Study Of Cost Overrun And Delays Of Department Of Defense (Dod)'S Space Acquisition Program, Nazareen Sikkandar Basha, Benjamin J. Kwasa, Christina Bloebaum

Engineering Management and Systems Engineering Faculty Research & Creative Works

Defense and Aerospace Systems Acquisition projects, just like any other Large-Scale Complex Engineered Systems (LSCES) experience delays and cost overrun during the acquisition process. Cost overrun and delays in LSCES are due, in part, to high complexity, size of the project, involvement of various stakeholders, organizations, political disruptions, changes in requirements and scope. These uncertainties, due to the exogenous factors, have cost the federal government billions of dollars and delays in completion of the programs. Cost estimation of federal programs is usually based on previous generations of systems produced and almost all the time the costs are underestimated. Underestimation of ...


A Retention Model For Community College Stem Students, Jennifer Snyder, Elizabeth A. Cudney Jun 2018

A Retention Model For Community College Stem Students, Jennifer Snyder, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

The number of students attending community colleges that take advantage of transfer pathways to universities continues to rise. Therefore, there is a need to engage in academic research on these students and their attrition in order to identify areas to improve retention. Community colleges have a very diverse population and provide entry into science, technology, engineering, and math (STEM) programs, regardless of student high school preparedness. It is essential for these students to successfully transfer to universities and finish their STEM degrees to meet the global workforce demands. This research develops a predictive model for community college students for degree ...


Data Driven Decision Making Tools For Transportation Work Zone Planning, Samareh Moradpour Jan 2018

Data Driven Decision Making Tools For Transportation Work Zone Planning, Samareh Moradpour

Doctoral Dissertations

"This research provides tools and methods for integrating stakeholder input and crash data analytics to better guide transportation engineers in effective work zone design and management. Three key contributions are presented: the importance of stakeholder input in traffic management strategies, application of data mining and pattern recognition to identify high-risk drivers in work zones, and the use of multinomial logistic regression (MLR) as a tool to understand key findings from historic crash data. Work zone signage is mandated by the Manual on Uniform Traffic Control Devices (MUTCD), but the current configurations are often criticized by the driving public and state ...