Transportation, Logistics, & Finance Doctoral Work

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Identification, investigation, and spatial analysis of various contributing factors to crash and injury severity in different crash types
(North Dakota State University, 2024) Khan, Ihsan
This dissertation had three main objectives related to improving road safety by investigating factors that contribute to injury severity in different types of single-vehicle crashes. The first objective was to develop a generalized ordered logit model to examine factors affecting injury severity of occupants in single-vehicle rollover crashes using 5 years of U.S. crash data from 2012-2016. Results showed likelihood of serious/fatal injuries increased in rollovers with occupant ejection, speeding, higher speed limits, roadside/median rollovers, undulating terrain, blacktop surfaces, rural roads, evenings, weekdays, older drivers, lack of occupant protection, previous driver crashes, distracted/aggressive driving, and passenger cars. Airbag deployment reduced serious/fatal injury risk. Regional variations also impacted injury severity. The second objective identified high-risk areas for lane departure crashes on rural North Dakota roads using techniques like Global/Local Moran's I, network kernel density estimation (NetKDE), and emerging hotspot analysis. While Global Moran's I indicated clustering, Local Moran's I revealed specific hot/cold spots. NetKDE quantified and prioritized crash clusters by density along roadways. Emerging hotspot analysis evaluated temporal patterns of hot/cold spots. This approach can guide deployments of education, enforcement, and infrastructure countermeasures. The third objective used a mixed logit model to analyze factors contributing to injury severity in single-vehicle run-off-road (ROR) crashes for passenger cars, SUVs, and pickups. Common factors increasing injury risk were older driver age, impaired driving, no seatbelt, no airbag, high speeds, and older vehicles. However, driver age impacts were most pronounced for pickups. Seatbelts substantially mitigated injury severity across all vehicle classes. Passenger cars had a higher injury risk than SUVs/pickups, especially over 75 mph. Future research should examine additional factors stratified by vehicle class using larger datasets.
Risk Assessment in the Upstream Crude Oil Supply Chain: Leveraging Analytic Hierarchy Process
(North Dakota State University, 2010) Briggs, Charles Awoala
For an organization to be successful, an effective strategy is required, and if implemented appropriately the strategy will result in a sustainable competitive advantage. The importance of decision making in the oil industry is reflected in the magnitude and nature of the industry. Specific features of the oil industry supply chain, such as its longer chain, the complexity of its transportation system, its complex production and storage processes, etc., pose challenges to its effective management. Hence, understanding the risks, the risk sources, and their potential impacts on the oil industry's operations will be helpful in proposing a risk management model for the upstream oil supply chain. The risk-based model in this research uses a three-level analytic hierarchy process (AHP), a multiple-attribute decision-making technique, to underline the importance of risk analysis and risk management in the upstream crude oil supply chain. Level 1 represents the overall goal of risk management; Level 2 is comprised of the various risk factors; and Level 3 represents the alternative criteria of the decision maker as indicated on the hierarchical structure of the crude oil supply chain. Several risk management experts from different oil companies around the world were surveyed, and six major types of supply chain risks were identified: 1) exploration and production, 2) environmental and regulatory compliance, 3) transportation, 4) availability of oil, 5) geopolitical, and 6) reputational. Also identified are the preferred methods of managing risks which include; 1) accept and control the risks, 2) avoid the risk by stopping the activity, or 3) transfer or share the risks to other companies or insurers. The results from the survey indicate that the most important risk to manage is transportation risk with a priority of .263, followed by exploration/production with priority of .198, with an overall inconsistency of .03. With respect to major objectives the most preferred risk management policy option based on the result of the composite score is accept and control risk with a priority of .446, followed by transfer or share risk with a priority of .303. The least likely option is to terminate or forgo activity with a priority of .251.
Use Agreements and Economic Performance of U.S. Airports
(North Dakota State University, 2020) Karanki, Fecri
In the U.S., airport use agreements are developed based on three common rate-setting approaches: the residual, compensatory, and hybrid methods. Under a residual agreement, the financial risk of the host airport is borne by the signatory airlines, and in return, the signatory airlines pay reduced user fees. Under a compensatory agreement, however, the airport bears its own financial risks and offers no reduced user fees to airlines. A hybrid agreement combines the features of residual and compensatory agreements. Under a hybrid agreement, the airport usually bears its own financial risks in terminal operations while the signatory airlines take over the financial risks in airfield operations. This dissertation aims to contribute to air transportation literature concerning the implication of use agreements on airport economic performance and rate differentials. Using the data of 59 U.S. hub airports from years 2009 to 2016, I studied the effects of use agreements on airport operational efficiency (in Chapter 2) and on cost efficiency (in Chapter 3), as well as the sources of aeronautical charge differentials between use agreements (in Chapter 4). The major findings of this dissertation are (1) airports with residual-type agreements tend to have lower operational efficiency compared to their peers adopting either the compensatory or hybrid agreement; (2) airports adopting the residual rate-setting method is less cost-efficient than the airports adopting either the hybrid or compensatory method; (3) compensatory airports have the highest average aeronautical and non-aeronautical charges; (4) non-aeronautical charges are a significant determinant of compensatory airports’ aeronautical charges; (5) airports adopting the hybrid method have lower aeronautical charges than the airports adopting the other two methods due to differences in the average cost level. The first two results imply that under a residual agreement, increased airport inefficiency may undercut any potential benefits of signatory airlines, and this result may indicate the presence of a moral hazard problem in the contractual relationship between the airlines and airports as a result of unequal risk-sharing and information symmetry.
Logistics of North Dakota State Local Food System: An Empirical Study to Measure Local Food Supply and Demand for Regional Food Hub Feasibility Study
(North Dakota State University, 2021) AlQublan, Hamad
Because North Dakota (ND) is one of only four states that do not have a food hub, the purpose of the regional food hub feasibility study was to empirically evaluate whether the ND local food system needs a food hub from a supply and demand perspective. The 21st century has shifted American agriculture and rural life. The number of small and mid-sized farms decreases while the number of more significant farms increases. The food hubs concept has become an increasingly popular response to this agricultural problem. Food hubs cover the gap between farmers and markets and add value to the food supply chain infrastructure. The food hub concept is widely distributed among US states as a response to solving local food issues. Unfortunately, ND is one of the four states that do not have a food hub. Hence, this study's main exploratory research question was Does the ND food system need a regional food hub? Furthermore, the author of this research found no comprehensive literature review concentrating solely on the local food system in ND. For that, a regional food hub feasibility study was conducted. The ND regional food hub feasibility was divided into two independent cross-sectional surveys. The ND food hub feasibility was divided part-A (the supply-side) and part-B (the demand-side). Each survey had 51 questions, including qualitative and quantitative factors. Investigating both sides of the ND local food system led to a better comprehension of the research area and provided a complete understanding of the ND food system. Both surveys were statistically analyzed descriptively and inferentially. Our findings indicated that ND food producers and customers defined local food as all food produced or grown in ND. Additionally, we found that the regional ND food hub project was feasible. The ND regional food hub's suggested best model had a cooperative legal structure and a hybrid business structure that can work for-profit at both the state and national levels.
Sustainable Transportation Planning During an Era of Technological Evolution
(North Dakota State University, 2023) Hungness, Derek John
“Sustainable Transportation Planning During an Era of Technological Evolution” provides an in-depth analysis spanning three pivotal chapters, each focusing on the nexus of climate change, transportation, and metropolitan planning. Chapter 2 delves into climate change's role in transportation plans. The literature reviews transportation’s effects on climate change and the significance of Metropolitan Planning Organization (MPO) transportation planning. After identifying research gaps and ethical considerations, the chapter outlines a methodology employing Latent Dirichlet Allocation (LDA) model construction, text preprocessing, and evaluation metrics. Python’s Natural Language Processing (NLP) toolbox is utilized to analyze the contents of 42 long-range transportation plans. Semantic interpretations emphasize word frequencies, climate keywords, transportation plan similarities, and data visualizations, culminating in a discussion of the findings and research conclusions. Chapter 3 shifts to the modeling of autonomous vehicles (AV) concerning sustainable development. It reviews the implications of technological advancements on mobility, long-term planning, and urban expansion. The methodology presents land use forecasts, transportation modeling, and AV simulation parameters. Various scenarios, such as increased auto availability and decreased parking costs, are explored. A detailed discussion synthesizes these findings, leading to a research conclusion. Chapter 4 targets current MPO transportation planning activities, accentuating climate action. Data is collected via a PDF survey completed and returned by 13 of Wisconsin’s 14 MPOs. A description of the survey methodology is followed by an examination of the findings, offering key insights and latent thematic comparisons to the findings documented in Chapter 2 concerning climate action currently being taken by Wisconsin’s MPOs via their long-range transportation planning activities.
Three Essays on Railroad Safety Analysis Using Non-Parametric Statistical Methods
(North Dakota State University, 2022) Dhingra, Neeraj
he FRA mandated railroad companies to install a new monitoring system known as Positive Train Control (PTC). This system overlays sensors, signals, and transponders over existing track and other wayside infrastructure. Technologists designed the system to prevent accidents mainly caused by human negligence and communications. However, PTC will not address track-related defects, which is the second dominant cause of accidents. A new track monitoring system called Railway Autonomous Inspection Localization System (RAILS) was proposed to address track-related accidents. RAILS is based on low-cost sensor technology that identifies defect symptoms, ranks their severity, classifies defect types, and localizes their positions. So, RAILS technology can augment the PTC by identifying track-related issues. The main objectives of this dissertation are: (1) To compare the potential performance of RAILS with traditional inspection methods based on its fundamental theory of operation; (2) To identify factors contributing to railroad accidents; and (3) To determine and rank factors responsible for severe financial damages caused by railroad accidents.The first two objectives will help compare the proposed technology and identify the major factors responsible for causing train accidents. The final objective will help to categorize accidents based on the potential financial damage severity. Categorizing such incidents would help to create a database that prioritizes issues and suggest possible countermeasure based on the problems. The study's key findings are as follows: (1) RAILS is more efficient in conducting continuous inspection and identifying potential defects than traditional systems by 33%, with only two trains per day and a 50% first-pass detection probability; (2) Nonparametric methods provide implicit information about rail accidents and function better than parametric methods by highlighting factors that are responsible for causing accidents rather than identifying the cause-and-effect relationship; (3) The most significant reasons for causing the financial damages are the number of derailed freight cars and the absence of territory signalization; and (4) Nonparametric methods automatically categorize rail accidents and, using text narratives, highlight causative factors responsible for a train derailment.
Three Essays on Renewable Jet Fuel Supply Chain Network Design and Traffic Safety
(North Dakota State University, 2022) Ebrahimi, Sajad
Because of rising energy consumption, climate change, and environmental concerns about fossil fuels, finding alternative renewable energy sources is becoming increasingly crucial. With the non-advanced share of the U.S. Renewable Fuel Standard having been mainly met by corn ethanol, many states are considering cellulosic or non-edible oilseed crops as the next source of biofuels. This study seeks to design a supply chain to produce renewable jet fuel (RJF) within the Midwest region and southeastern U.S. This is accomplished through the use of optimization models (mixed-integer linear programming). Furthermore, because RJF manufacturing incurs higher expenses than conventional jet fuel, the use of various monetary incentives is being studied to establish their usefulness in commercializing the supply chain. The findings of this study can be used by energy policymakers, RJF producers, and investors to operate in a competitive market while safeguarding the environment. In another study, we evaluate speeding crash risk in North Dakota counties. In the United States, one of the most common contributing factors to car crashes is speeding. Speeding impairs a driver's ability to control and steer properly, as well as respond to a dangerous situation in a timely manner. Speeding crashes account for one-third of fatal crashes in the United States and are one of the risks for drivers on U.S. highways. Speeding crash risk can vary among regions. When it comes to allocating road safety expenditures to regions in order to reduce speeding crashes, it's vital for road management to understand which areas are at higher risk and should be prioritized for safety measures. This study uses a failure mode effect analysis method to evaluate the speeding crash risk.
Contributing Factors of DUI Recidivism Among First-Time Offenders in North Dakota
(North Dakota State University, 2022) Zhou, Yun
This study explored utilizing tree-based machine learning models to identify associations in a range of 107 factors and DUI recidivism among first-time DUI offenders. Three tree-based machine learning models, Decision Tree, Random Forest, and Gradient Boosting were performed on 12,879 first-time DUI offenders during 2013-2017 using a three-year following period, to classify repeat DUI offenders. Study cohorts include 11,651 drivers without recidivism and 1,228 drivers with recidivism. The models tested 107 variables/predictors, including the driver’s demographic factors, drinking behaviors, traffic violations, crash histories, DUI-related violations, social-economic factors, and health and safety factors based on the driver’s residence. oversampling technique was used to balance two classes in the training data in all three models. The top 15-20 predictors were selected from the feature impact analyses of these predictions. Lastly, multiple logistic regression analyses were performed to quantify the effects of selected factors/predictors on the outcome. Among the three models, Gradient Boosting achieved the best predictions on both the original and oversampled datasets. Oversample techniques did improve prediction performances by roughly 10% on the F1 score for Gradient Boosting. Results coalesced around two findings. First, male drivers with higher BAC values, younger age at first DUI citation, whose first DUI citation took place during the weekday, had at least one low-risk citation within three years before first DUI citation, and lived in counties with lower income inequality ratio and higher violent crime rate were more likely to commit a subsequent DUI offense. Second, male drivers who complied with a BAC test upon arrest, whose first DUI citation took place on a weekday, had at least one low-risk citation within three years before the first DUI citation, lived in a county with a lower income inequality ratio, and higher violent crime rate were more likely to commit a subsequent DUI offense. Findings can be used by stakeholders in implementing and improving DUI prevention strategies. The study is limited to a single state, but the comparison of techniques and their shared findings suggest that a multitude and variety of approaches may be appropriate in future impaired driving prevention research.
Optimal Resource Allocation to Minimize Last Mile Delivery Costs
(North Dakota State University, 2022) Srinivasan, Raghavan
This study focusses on a decision-making tool to assist an organization in planning for capacity needed for the Last Mile Delivery (LMD) services which is the most expensive part of the entire supply chain. Considering the use of Crowdsourcing for Logistics (CSL), the decision-making tool’s objective is to provide an optimal combination of fulltime, seasonal and CSL resources that lead to minimum operational LMD costs and meet the variable demand. To achieve this, a three phased approach is used, where in the first analytical phase an expected cost model is numerically validated. In the second stochastic program phase, the capacity and cost of the CSL resources are varied. Finally, in the third simulation phase, the approach is further extended to consider the daily employee attrition rate and unsatisfied demand being carried over to the next day. Lastly, the use of automation or newer technologies, such as robots, for LMD services is introduced in this simulation phase to show the benefits in terms of the operations costs. The results from the analytical model described the optimal values of fulltime and seasonal considering the utilization of CSL and experienced some penalty costs. In this case, the parameters being fixed, does not capture the differences due to the variability of CSL availability or costs, which is addressed in the stochastic program phase. Though the output from the stochastic model is higher, it does consider the variability in the CSL capacity and cost, which is practically observed. The simulation section gives a further refined optimal combination of fulltime, seasonal and CSL that meets the demand considering the attrition rate of fulltime and seasonal, and rollover the units by one day. Within this simulation, the consideration of automated delivery systems like using a robot for LMD services leads to further cost savings opportunity. Here, the fulltime delivery cost is benefited, with low utilization of seasonal and CSL limited for optimizing delivery strategy. In conclusion a tool is provided for aggregate delivery capacity planning that would consider an optimal combination of fulltime, seasonal and CSL resources lowering the LMD costs and meeting the variable demand.
Evaluating the Performance of Emergency Medical System in the US
(North Dakota State University, 2022) Ebrahimi, Zhila Dehdari
New and exciting opportunities are emerging for operational researchers to create and use models that provide managers with solutions to enhance the quality of their services as the importance of the service sector grows in industrialized countries. The key to this process is the creation of time-dependent models that analyze complicated service systems and produce efficient staff schedules, allowing organizations to strike a balance between delivering high-quality services and avoiding unnecessary personnel costs. There is a need, particularly in the healthcare sector, to encourage effective management of an EMS, where the likelihood of survival is strongly correlated with the response time.Motivated by case studies investigating the operation of the Emergency Medical System (EMS), this dissertation aims to examine how operations research (OR) techniques can be developed to determine staff scheduling and maximize the ambulance to decrease service system delays. A capacity planning tool is developed that integrates a combination of queueing theory and optimization techniques to reduce the delay in the service system and maximize ambulance coverage. The research presented in this dissertation is novel in several ways. Primarily, the first section considers the Markovian models with sinusoidal arrival rates and state-dependency of service rate and uses a numerical method known as Stationary Independent Period by Period (SIPP) to determine the staff requirement of the service system. The final section considers the time dependency in locating an ambulance station across the network and allocating the ambulance to the patients to cover more 911 calls.
Modeling Pavement Performance and Preservation
(North Dakota State University, 2011) Lu, Pan
The number of highway lane-miles in the United States increased by 7% from 1980 to 2007, while vehicle-miles of travel almost doubled. During the same period, the Federal Highway Trust Fund (the major source of funding for highways) grew by only 40% in constant 1980 dollars. With growth in trade and commerce, truck traffic levels are expected to increase significantly in the future. Highway agencies throughout the United States are facing complex decisions about maintaining, repairing, and renewing existing pavements in the most cost-effective ways. Decision makers need to learn: to what degrees different pavement preservation treatments will improve a pavement condition; how pavement conditions will change over time; when to apply which treatment to what section; and what budget level will be needed to maintain and improve pavement conditions. The objectives of this dissertation are to 1) estimate the effectiveness of appropriate different levels of pavement preservation treatments, 2) evaluate pre-treatment and posttreatment pavement performances, and 3) use the uniformed results (of the first two objectives) to develop a decision making tool for integrated pavement management systems. The dissertation will utilize data from the Long Term Pavement Performance (LTPP) program. LTPP data will be used to estimate statistical models of the benefit effectiveness of preservation-related treatments and pavement performance, including models of performance jump--i.e., the instantaneous improvement in the performance or condition of a pavement due to a maintenance treatment. The forecast values from the statistical models will be used as inputs to optimization models that will allow for the simultaneous solution of several objectives or constraints. The results will benefit pavement management systems and improve pavement preservation planning in the United States.
Safety Management System for Highway-Rail Grade Crossings
(North Dakota State University, 2021) Keramati, Amin
As a result of the considerable differences in mass between vehicles and trains, accidences at highway-rail grade crossings (HRGCs) may result in severe injuries and fatalities. Therefore, HRGCs safety is considered one of the crucial transportation safety issues. Transportation decision makers and agencies need an efficient safety decision-making framework which is bale to consider crash occurrence and severity likelihood simultaneously. This study proposed a novel methodology and a statistical approach for HRGC crash analysis. The proposed method is competing risk model and the approach is Cox proportional hazard regression. This predictive method was well established in bioscience area but never been utilized in transportation area. Competing Risk Model (CRM) is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest, in transportation safety analysis the competing multiple outcomes are accident severity levels while the event of interest is accident occurrence. Transportation decision makers need a prioritization system to categorize crossings’ risk level based on their expected crash frequency and crash severity simultaneously. Therefore, with a hazard-ranking approach which considers crossings’ crash severity and frequency output, transportation decision makers are able to ensure that federal and state funds for grade crossing improvement projects are spent at the crossings that are considered the most in need of improvement. In this study two hazard-ranking models are proposed, the first one is based on the crash likelihood resulted by the proposed CRM output, and the second one is a hybrid accident prediction model/hazard index based on crash severity likelihoods estimated by the same CRM. Finally, to integrate the results of both hazard-ranking approaches, and classify grade crossings and crossings’ location based on their crash frequency and severity likelihood simultaneously, the risk analysis is conducted by using the risk matrix and spatial risk analysis.
Outpatient Appointment Scheduling Study: Utilization Projection, No-Show Prediction, and Capacity Allocation
(North Dakota State University, 2021) Yuan, Fangzheng
Long waiting times could result in many negative effects, such as low capacity utilization, high patient no-show rates, and loss of social benefits, which will also lead to a waste of public resources. Therefore, to better utilize healthcare resources and serve the community, my dissertation will focus on three objectives: To study the relationship between appointment utilization and indirect waiting time (IWT); to predict the patient’s no-shows without profiling them; to develop an optimization model for appointment capacity allocation. To achieve these objectives, multiple models and approaches have been developed in this dissertation. For the first model, two mixture distribution models, including a beta geometric (BG) and a discrete Weibull (BdW) model were carried out to project the appointment utilization over IWT. The results indicated that appointment utilization is positively related to the IWT but tends to fluctuate after the first couple of weeks. Two mixture distribution models were also proved to be more accurate for projecting the appointment utilization when compared with commonly used curve-fitting models. For the second objective, a conditional inference tree model was applied to predict the patient’s no-show probability and classified the no-show probability without profiling patients. This model was also compared with the general linear model and typically used logistic model, the result showed that using the conditional inference tree model with classified data will lead to a more accurate prediction and higher R-squared value. For the final objective, three optimization methods and two scheduling strategies were examined. The proposed solution of capacity allocation provided a more robust, flexible, and efficient allocation plan for outpatient appointments, which significantly improved the average daily profit and capacity utilization rate. By completing those three objectives, this dissertation did not only provide a more accurate way to monitor and predict outpatient appointments but also proposed a more practical and efficient appointment capacity allocation strategy. This will help our society save healthcare resources, reduce unnecessary costs for the healthcare providers, and provide better healthcare services to the community.
Three Essays on Urban Public Transit Systems in the U.S.
(North Dakota State University, 2020) Malalgoda, Narendra Dhananjaya Kumara
Public transportation is a critical component of urban communities and plays an important role in facilitating mobility which is integral to economic development and the quality of life of urban residents. In recent years, urban transportation has evolved rapidly with the emergence of transportation network companies (TNCs) and e-commerce that drastically transformed urban living. The availability of TNCs has given consumers more transportation options. However, the implications of TNCs on public transit ridership are unknown. In addition, the rising online shopping trend has drastically reduced the businesses of brick-and-mortar retailers, but does the shift in consumer shopping behavior reduce the demand for public transit? The objective of this dissertation is to address the following three research questions: (1) How is U.S. public transit ridership impacted by the rise of TNCs? (2) How have transit subcontracting (or purchased transportation) and TNC partnership affected transit productivity in recent years? (3) Has increased online shopping reduced the demand for public transit service? The key findings of my study are: (1) transit effectiveness of both bus and rail transits declined over the study period; (2) TNC availability increased rail transit ridership in 2015; (3) transit effectiveness was highly significant for public transit, and when examining its effect year-by-year, rail transit effectiveness trumped TNC availability; (4) TNCs are neither a complement nor a substitute of bus transit; (5) for bus transit agencies, outsourcing or purchased transportation is associated with negative efficiency and productivity changes; (6) although purchased transportation has a positive effect on technological change for bus transit, the effect is not significant; (7) TNC partnerships also have a negative effect on efficiency and productivity changes in bus transit; (8) there is a positive significant relationship between shopping mall visits and public transit use; (9) however, the effect of mall visits on transit use is small relative to the effects of car ownership; (10) taken together, the marginal effect of car ownership is 9 times larger than the effect of mall visits on transit use.
Limiting Financial Risk from Catastrophic Events in Project Management
(North Dakota State University, 2020) Simonson, Peter Douglas
This dissertation develops a mixed integer linear program to establish the upper and lower bounds of the Alphorn of Uncertainty. For a project manager, planning for uncertainty is a staple of their jobs and education. But the uncertainty associated with a catastrophic event presents difficulties not easily controlled with traditional methods of risk management. This dissertation brings and modifies the concept of a project schedule as a bounded “Alphorn of Uncertainty” to the problem of how to reduce the risk of a catastrophic event wreaking havoc on a project and, by extension, the company participating in that project. The dissertation presents new mathematical models underpinning the methods proposed to reduce risk as well as simulations to demonstrate the accuracy of those models. The dissertation further assesses the complexity of the models and thus their practical application. Finally, the dissertation presents strategies to reduce the risk to a project of a catastrophic event using the upper bound of the Alphorn as the measure of risk.
Three Essays on Shared Micromobility
(North Dakota State University, 2020) Rahim-Taleqani, Ali
Shared micromobility defines as the shared use of light and low-speed vehicles such as bike and scooter in which users have short-term access on an as-needed basis. As shared micromobility, as one of the most viable and sustainable modes of transportation, has emerged in the U.S. over the last decade., understanding different aspects of these modes of transportation help decision-makers and stakeholders to have better insights into the problems related to these transportation options. Designing efficient and effective shared micromobility programs improves overall system performance, enhances accessibility, and is essential to increase ridership and benefit commuters. This dissertation aims to address three vital aspects of emerging shared micromobility transportation options with three essays that each contribute to the practice and literature of sustainable transportation. Chapter one of this dissertation investigates public opinion towards dockless bikes sharing using a mix of statistical and natural language processing methods. This study finds the underlying topics and the corresponding polarity in public discussion by analyzing tweets to give better insight into the emerging phenomenon across the U.S. Chapter two of this dissertation proposes a new framework for the micromobility network to improve accessibility and reduce operator costs. The framework focuses on highly centralized clubs (known as k-club) as virtual docking hubs. The study suggests an integer programming model and a heuristic approach as well as a cost-benefit analysis of the proposed model. Chapter three of this dissertation address the risk perception of bicycle and scooter riders’ risky behaviors. This study investigates twenty dangerous maneuvers and their corresponding frequency and severity from U.S. resident’s perspective. The resultant risk matrix and regression model provides a clear picture of the public risk perception associated with these two micromobility options. Overall, the research outcomes will provide decision-makers and stakeholders with scientific information, practical implications, and necessary tools that will enable them to offer better and sustainable micromobility services to their residents.
Impact of Public Transit and Walkability on Quality of Life and Equity Analysis in Terms of Access to Non-Work Amenities in the United States
(North Dakota State University, 2020) Khan, Muhammad Asif
The past literature suggest that transportation can impact quality of life (QOL) both directly and indirectly. The first part of this dissertation attempted to comprehensively evaluate the impact of transportation (specifically public transit, and walkability) along with physical built environment, and sociodemographic indicators on community QOL, and overall life satisfaction (OLS) of an individual living in his community. The study used an advanced technique of structural equation modeling (SEM) to evaluate the impact of these factors on community QOL and individual’s OLS. The study results revealed that physical built environment, public transit need for a community, perceived public transit importance for a community, quality of public transit services, quality of walkability conditions, ease of travel in a community (mobility indicator), and sociodemographic indicators significantly impact community QOL, and also individual’s OLS either directly or indirectly through community QOL mediating variable. The literature review suggests that accessibility to important non-work amenities improve people’s QOL. So, it is important to examine social equity in terms of individual’s ability to access non-work amenities that are important for their daily life interests. The second part of dissertation focused on equity analysis in terms of people’s ability to access non-work amenities through public transit, and walk in the US. The non-work amenities considered in this study are: 1) grocery store or supermarket, 2) personal services, 3) other retail shopping, (4) recreation and entertainment, and (5) health care facility. It is concluded that equity in terms of public transit access to non-work amenities is regressive for the older age people, people without driving license, individuals who are covered under Medicare/Medicaid program (elderly, low income, people with disabilities), and non-metro area residents disadvantaged groups. In terms of walk access to non-work amenities, it is concluded that older age people, people without driving license, physically disable people, unemployed and students, people living in non-metro areas, and females face injustice. These groups are already disadvantaged in society because of their financial, and physical health constraints and should be having sufficient and easy public transit and walk access to their daily needs.
A Market Incentives Analysis of Sustainable Biomass Bioethanol Supply Chains with Carbon Policies
(North Dakota State University, 2020) Haji Esmaeili, Seyed Ali
Given the increasing demand for energy, climate change, and environmental concern of fossil fuels, it is becoming increasingly significant to find alternative renewable energy sources. Bioethanol as one sort of cellulosic biofuel produced from lignocellulosic biomass feedstocks has shown great potential as a renewable resource. Delivering a competitive, sustainable biofuel product requires comprehensive supply chain planning and design. Developing economically and environmentally optimal supply chain models is necessary in this context. Also, designing biomass bioethanol supply chain (BBSC) models addressing social issues requires using second-generation biomass which is not a source of food for humans. Currently, corn as a first-generation feedstock is the primary source of bioethanol in the United States which has given growth to new social issues such as the food versus fuel debate. Considering incentives for first-generation bioethanol producers to switch to second-generation biomass and associated production technologies will help to address such social issues. The scope of this study focuses on analyzing economic and environmental market incentives for second-generation bioethanol producers while considering different carbon policies as penalties and restrictions for emissions coming from BBSC activities. First, we develop an integrated life cycle emission and energy optimization model for analyzing an entire second-generation bioethanol supply chain using switchgrass as the source of biomass while finding the most appropriate potential locations for building new cellulosic biorefineries in North Dakota. Second, we propose a supply chain model by comparing a first-generation (corn) and a second-generation (corn stover) bioethanol supply chain to analyze how policymakers can incentivize first-generation bioethanol producers to switch their technology and biomass supply from first-generation to second-generation biomass. Third, we develop the model further by investigating the impact of four different carbon policies including the carbon tax, carbon cap, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. This research will help to design robust BBSCs focused on sustainability in order to optimally utilize second-generation biomass resources in the future. The findings can be utilized by renewable energy policy decision makers, bioethanol producers, and investors to operate in a competitive market while protecting the environment.
Corporate Social Responsibility and Traffic Congestion: A Mixed Methods Study
(North Dakota State University, 2020) Bakare, Bukola
Traffic congestion (TC) is a complex issue having an adverse impact on the environment, business operations and health. Many cities are taking action to curb it. Corporations have increasingly engaged in corporate social responsibility (CSR) actions. Using corporations headquartered in the top-rated traffic congested cities in the United States, this study examines the relationship between TC and CSR. The quantitative research employed a general linear model with two datasets, traffic speed data and CSRHub ratings. The speed data was used to calculate travel time index (TTI), a measure of TC. Using Atlanta BeltLine Inc. as a case study, a phenomenological thematic approach was utilized to assess stakeholders’ viewpoints of congestion mitigation efforts in Atlanta, GA. This study adds to research on CSR by examining the effects that CSR actions have on a specific local event, e.g., TC. In addition, research reflecting on the impact of CSR on TC has not been conducted. This study aims to fill this gap. Of the four areas of CSR studied in the quantitative phase, the community, environment, and governance ratings are significantly related to TTI, with community and environment having an inverse relationship to TTI. The qualitative study showed that stakeholders struggle with TC, and that the relationship between CSR and TC is not obvious to them. This quantitative study was conducted on eighteen top-rated congested cities. Further study on other major congested cities may shed more light on CSR and TC. A future qualitative analysis can explore the viewpoint of city government. Findings in this study are expected to be a leverage point for public-private TC mitigation and to inform policies that incorporate TC reduction as a CSR indicator. Although the quantitative analysis showed that a relationship exists between CSR and TC, the literature and DOT reports revealed increased and continuous congestion in these cities. The case study of the ABI project in the qualitative research indicated that TC is an area where CSR can have a major local impact. Some corporate respondents acknowledged that TC has a business cost, however no serious steps are taken to tackle TC.
The Ship of Change: A Model for Organizational Diagnosis and Change Management
(North Dakota State University, 2019) Swearingen, Robert
Grounded in developmental theory, the Ship of Change provides a renewed look at diagnostic relationships between organizational elements, and their interactions through the lens of a metaphorical ship analogy. Elements are identified and arranged based on empirical studies from the field with causal considerations emphasized by Burke-Litwin. The model uses a two-tiered visual perspective to depict multi-dimensionality that links core organizational elements to work unit activities through the interplay of culture, communication and climate. The model is intended for both the conveyance of principles related to open systems theory, and the practical application of diagnosing organizations for planning and implementing change. The model was tested in a case study with a transportation company using multiple methods data collection including a communication satisfaction survey, workplace observations, and employee interviews. The model was used to categorize and interpret data and to inform recommendations for change.

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