Transportation, Logistics, & Finance

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Research from the Department of Transportation, Logistics, & Finance. The department information can be found on the College of Business website https://www.ndsu.edu/business/

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Analyzing Supply Chain Networks for Blood Products
(North Dakota State University, 2019) Xu, Yuan
The blood supply chain, starting from the donor until the blood is used to meet transfusion demands of patients, is a multi-echelon and complex system. The perishable and lifesaving characteristics of blood products, such as red blood cells and platelets, as well as uncertainties in both supply and demand make it difficult to maintain a balance between shortage and wastage due to expiry. An effective blood supply chain should be able to meet the demand while at the same time reducing wastage and total operational cost. In order to be cost effective, the related organizations have to decide how much blood should be collected from donors, how much blood products should be produced at the blood center, and how much blood products should be distributed to hospitals or transshipped between hospitals. The objective of this dissertation is to provide these tactical and operational decisions to guide those who work in healthcare supply chain management and explore new opportunities on performance improvement for an integrated blood supply chain by optimization with aim of minimizing total cost, consideration of transshipment between hospitals, and application of a coordinated multi-product model. This dissertation presents three multi-stage stochastic models for an integrated blood supply chain to minimize total cost incurred in the collection, production, inventory, and distribution echelons under centralized control. The scope of this study focuses on modeling a supply chain of blood products in one regional blood center, several hospitals and blood collection facilities. First, we develop an integrated model for the platelet supply chain that accounts for demand uncertainty and blood age information, then we develop this model further by investigating the impact of transshipment between hospitals on cost savings, and then we propose a multi-product model that accounts for red blood cells and platelets at the same time and compare it with an uncoordinated model where the red blood cell and platelet supply chains are considered separately.
Application of Data Mining Techniques in Transportation Safety Study
(North Dakota State University, 2018) Zheng, Zijian
Most of current studies are based on Generalized Linear Models (GLMs), which require several assumptions. Those assumptions limit GLMs with the nature of data, and jeopardize models’ performance when handling data with complex and nonlinear patterns, high missing values, and large number of input variables with various data types. Data mining models are famous for strong capability of extracting valuable information and detecting complex patterns from large noisy data. However, they are not popular in transportation safety research, because they are criticized to be unable to provide interpretable and practical outputs. In this study, data mining models are tested in transportation safety research to prove their feasibility to be served as alternative models in safety study. Influential variable importance, contributor variable marginal effect analysis, and model predicting accuracy are further conducted to identify complex and nonlinear patterns in study dataset, and to respond to the criticism that data mining models do not provide practical outputs. Due to the high fatality rate, two types of crashes are selected as research areas: 1) predicting crashes at Highway Rail Grade Crossings (HRGCs); and 2) commercial truck involved crash injury severity. In the HRGC crash likelihood study, three data mining models, Decision Tree (DT), Gradient Boosting (GB), and Neural Network (NN), are tested, and demonstrated to be solid in Highway Rail Grade Crossing (HRGC) crash likelihood study. In the commercial truck involved crash injury severity study, the GB model identifies 11 out of 25 studied variables to be responsible for more than 80% of injury severity level forecasting, and their nonlinear impact on the severity level. Several factors such as trucking company attributes (e.g., company size), safety inspection values, trucking company commerce status (e.g., interstate or intrastate), and registration condition are found to be significantly associated with crash injury severity. Even though most of the identified contributing factors are significant for all four levels of crash severity, their relative importance and marginal effect are all different. Findings in this study can be helpful for transportation agencies to reduce injury severity level, and develop efficient strategies to improve safety.
Building a Predictive Model on State of Good Repair by Machine Learning Algorithm on Public Transportation Rolling Stock
(North Dakota State University, 2018) Mistry, Dilip
Achieving and maintaining public transportation rolling stocks in a state of good repair is very crucial to provide safe and reliable services to riders. Besides, transit agencies who seek federal grants must keep their transit assets in a state of good repair. Therefore, transit agencies need an intelligent predictive model for analyzing their transportation rolling stocks, finding out the current condition, and predicting when they need to be replaced or rehabilitated. Since many transit agencies do not have good analytical tools for predicting the service life of vehicles, this simple predictive model would be a valuable resource for their state of good repair needs and their prioritization of capital needs for replacement and rehabilitation. The ability to accurately predict the service life of revenue vehicles is crucial achieving the state of good repair. In this dissertation, three unique tree-based ensemble learning methods have been applied to build three predictive models. The machine learning methods used in this dissertation are random forest regression, gradient boosting regression, and decision tree regression. After evaluation and comparison of the performance results amongst all models, the gradient boosting regression model with the top 30 most important features was found to be the best fit for predicting the service life of transit vehicles. This model can be used to predict the projected retired year for all nationwide vehicles in operation, the single transit agency’s transit vehicle, and any single vehicle. The revenue vehicle inventory data from National Transit Database (NTD) has been used to build the machine learning predictive model. Before feeding the data into the model, a variety of new features were created, missing data were fixed, and extreme values or outliers were handled for the machine learning algorithm.
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.
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.
Derived Demand for Grain Freight Transportation, Rail-Truck Competition, and Mode Choice and Allocative Efficiency
(North Dakota State University, 2016) Ndembe, Elvis Mokake
The demand for grain freight transportation is a derived demand; consequently changes in the grain supply chain in production and handling, and those in the transportation domain will affect the demand for grain transportation. The U.S. transportation industry (e.g. railroad and trucking), and the grain supply chain in general have witnessed structural changes over the years that have potential long-run implications for demand, intermodal competition, and grain shippers mode choices both nationally and regionally. Deregulation of the railroad and trucking industries initiated innovations (e.g. shuttle trains) that have revolutionized the way grain is marketed. These and other related trends in agriculture including bioenergy suggest a dynamic environment surrounding grain transportation and the need to revisit agricultural transportation demand and evaluate changes over time. A majority of freight demand studies are based on aggregate data (e.g. regional) due to lack of disaggregate data. Aggregation of shippers over large geographic regions leads to loss of information with potential erroneous elasticity estimates. This study develops a method to estimate transportation rates at the grain elevator level to estimate a shipper link specific cost function for barley, corn, durum, hard red spring wheat, and soybeans shippers. The aim of this study is to assess and characterize the nature of rail-truck competition for the transportation of five commodities over distance and time as well as to assess whether North Dakota grain shippers’ mode choices reflect an allocatively efficient mix assuming the choice of mode is based on shipping rates. Our findings indicate that in general, rail dominates most of the grain traffic, however, the degree of dominance is variable by commodity. Additional findings suggest that grain shippers utilize more rail than they would if they chose modes based on rates. This may suggest unmeasured service quality advantages of rail in comparison to truck.
Developing Input to “Best-Value” Vehicle Procurement Practice: An Analysis of Supplier Evaluation and Selection in the U.S. Public Transportation Industry
(North Dakota State University, 2011) Scott, Marc Angus
Collectively, US public transportation systems operated 137,047 vehicles per peak period in 2008 (American Public Transportation Association 2010). Buses accounted for the largest segment among these vehicles, and the passenger van segment was second. Together, they accounted for 78% of the vehicles operated per peak period (American Public Transportation Association 2010). Due to their pervasive use in the public transportation industry, buses and vans have been the focus in various academic research studies. However, very few studies have focused on vehicle procurement. Further, none have focused on the specific vehicle procurement function of supplier evaluation and selection. The over-arching objective of this research is to gain a deeper understanding of the relative importance of vehicle supplier attributes in reference to the Federal Transit Administration's (FTA) “best-value” procurement initiative and the influence of these attributes on the evaluation and selction of bus and van suppliers. This research studies vehicle procurement decision-makers at public transportation agencies to determine which supplier attributes they perceive to be the most important when evaluating vehicle suppliers. Results indicate that the top five supplier attributes were quality, reliability, after-sales support, warranties and claims, and integrity. The order of these top five attributes changed according to the type of supplier being evaluated, i.e., conventional fuel vehicle supplier versus alternative fuel vehicle supplier. The reason for this change was explained as being due to the increased engineering and technological expertise required of alternative fuel vehicle suppliers. Utilizing Analysis of Covariance (ANCOVA), the research showed that the variation in the perception of the importance of particular supplier attributes was not generally influenced by an agency's urban classification, its vehicle fleet size, its capital expenditure level, its decision-makers' education level, or their years of experience. However, FTA region was determined to have an influence on two attributes. Utilizing a conditional logit discrete choice model, the research also found that in practice price and not quality had the highest parameter estimate and was therefore deemed most important. It was followed by quality, after-sales support, technical capability, and delivery. Further, to garner a deeper understanding of attributes' relative importance, participants in the research identified 41 attribute components and provided metrics by which to measure these components and, by extension, the attributes. This research contributes in four areas. These are government procurement initiatives, agency “best-value” procurement practice, vehicle supplier marketing, and academic research in supplier evaluation and selection in the public transportation industry.
Dynamics of Deprivation Cost in Last Mile Distribution The Integrated Resource Allocation and Vehicle Routing Problem
(North Dakota State University, 2014) Itani, Maher
One of the most critical tasks after a natural disaster is to organize and execute humanitarian relief operations effectively and efficiently while reaching an equitable outcome. However, due to limited resources in the initial stage of response, it becomes challenging for logistics planning authorities to target needed individuals. The concerns would be with providing an unbiased platform to make decisions about equitable distribution schedules. Therefore, developing an effective and efficient disaster relief plan that tries to treat individuals as equitable as possible was the main motivation in this research. For this purpose, this dissertation studied a novel last mile distribution plan in the initial response phase where the key focus is the preservation of lives. An integrated vehicle routing and resource allocation problem was investigated and formulated in an routing-allocation model (RAP). The theoretical foundation of RAP is formulated as an egalitarian model where resources are to be distributed so as to maximize the welfare of those in greatest need. The strategic goal is to alleviate human deprivation and suffering by minimizing the response time in regard to each beneficiary’s needs fulfillment and delivery delay on the route. Equity is quantified with a min-max objective on a deprivation cost, which is a non-linear function of deprivation time. The objective function is set to minimize the maximum deprivation cost of the deliveries so that supplies arrive in a cyclical manner while all demand sites are treated equitably.
Economic Modeling of Agricultural Production in North Dakota Using Transportation Analysis and Forecasting
(North Dakota State University, 2018) Dharmadhikari, Nimish Laxmikant
Agricultural industry is crucial for the economy; agricultural transportation is an integrated part of that industry. Optimization of the transportation and logistics costs is an important part of the transportation economics. This study focuses on the minimization of the total cost of transportation logistics. Sugar-beet is one of the important crops in the state of North Dakota and there has been sporadic research in the sugar-beet transportation economic modeling. Therefore, this research focuses on the transportation economic modeling of the sugar-beet including yield forecasting to reduce the uncertainty in this process. This study begins with developing a yield forecasting model which is presented as a way to sustain the agricultural transportation under stochastic environments. The stochastic environment includes variation in weather conditions, precipitation, soil type, and randomness of natural disasters. The yield forecasting model developed uses Normalized Difference Vegetation Index (NDVI), Geographical Information System (GIS), and statistical analysis. The second part of this study focuses on economic model to calculate the total cost associated with the sugar-beet transportation. This model utilizes the GIS analysis to calculate the distances travelled from member coop farms during harvest and transport to processing facilities in various locations. This model sheds light on the critical cost factors associated with the total economic analysis of sugar-beet harvest, transportation, and production. Since the sugar-beet yield varies significantly based on different factors, it provides for a variable optimal harvesting time based on the plant maturity and sugar content. Sub-optimized pilers location result in the high transportation and utilization costs. The third part of this research focuses on minimizing the sum of transportation costs to and from pilers and the piler utilization cost. A two-step algorithm, based on the GIS with global optimization method, is used to solve this problem. In conclusion, this research will provide a primary stepping stone for farmers, planners, and engineers to develop a data driven analytical tool which will help to minimize the total logistics cost of the sugar-beet crop while at the same time keeping the sugar content intact and predict the sugar yield and truck volume.
Essays on Biomass Supply Chain Network Design
(North Dakota State University, 2018) Mohamed Abdul Ghani, N. Muhammad
This dissertation is about the biomass supply chain network design considering the incentives as a financial support for entities in the supply chain such as the growers (farm) and biorefinery (plant) to produce energy (bioethanol) from the corn stover as a renewable energy feedstock. This dissertation consists of two journal papers that I have worked on during the past years of my Ph.D. studies where one of them has been published in Energy Policy journal. In the first paper, we presented a linear program (LP) model for the biomass supply chain network design in bioethanol production using corn stover. The distribution of the corn stover from farm to storage and plant, and the bioethanol from the plant to customer is modeled with the consideration of financial incentives. We explore the dollar value paid to the farmers to encourage them to convert the corn stover into bioethanol rather than burn it in the farm. Results show that only 37% of corn stover can be converted to bioethanol due to plant capacity limitation. In the second paper found in Chapter 3 in this dissertation, we presented a mixed integer linear program (MILP) model to overcome the plant capacity problem in the previous model. To make sure 100% corn stover converted to bioethanol, the MILP model will decide whether to expand the existing plant or build new plant based on existing plant configuration (EP) and combination of existing and new plant configuration (ENP). Results indicated that 100% corn stover converted to bioethanol can be achieved by expanding all existing plant and build a few new plants. It is also indicated that some farms are making losses in the EP configuration. Finally, we analyze the interaction of the farm and plant on the corn stover price and transportation cost to increase the profitability of the affected farms that are not making profit in the EP configuration.
Estimation of Increased Traffic on Highways in Montana and North Dakota due to Oil Development and Production
(North Dakota State University, 2012) Dybing, Alan Gabriel
Advances in oil extraction technology such as hydraulic fracturing have improved capabilities to extract and produce oil in the Bakken and Three Forks shale formations located in North Dakota, Montana, Manitoba, and Saskatchewan. From 2004 to the present, there has been a significant increase in oil rigs and new oil wells in these areas, resulting in increased impacts to the local, county, state, and federal roadway network. Traditional methods of rural traffic forecasting using an established growth rate are not sufficient under the changing traffic levels. The goal of this research is to develop a traffic model that will improve segment specific traffic forecasts for use in highway design and planning. The traffic model will consist of five main components: 1) a Geographic Information Systems (GIS) network model of local, county, state and federal roads, 2) a truck costing model for use in estimating segment specific user costs, 3) a spatial oil location model to estimate future oil development areas, 4) a series of mathematical programming models to optimize a multi-region oil development area for nine individual input/output movements, and 5) an aggregation of multiple routings to segment specific traffic levels in a GIS network model.
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.
Game Theory Approach to the Vertical Relationships for U.S. Containerized Imports
(North Dakota State University, 2013) Liu, Qing
Multi-player interactions and vertical relationships in the U.S. containerized-import shipment market are investigated using game theory approaches. Bi-level programming problems (BLPP) are built to capture the hierarchy structure of the container shipping industry, whereas the ocean carriers (OC) are considered as the market leader. For a case study with five players from several levels of the shipment chain, 16 BLPPs are built to analyze the 32 coalition possibilities. Two routes are compared: The West Coast route (WCR) includes one terminal (P1) and one railroad (R); the East Coast route (ECR) includes a second terminal (P2) and the Panama Canal (PC). The impact of Panama Canal expansion is investigated by comparing scenarios with different assumptions of vessel size. Capacity constraints at port terminals are also analyzed by assuming different capacity levels. The grand coalition of the five players is found to be very unstable because of the unavoidable competition within the coalition; hence, following games are further created, supposing the grand coalition could not form. Model results indicate the OC prefers to form an East Coast Coalition (ECC) with East Coast players if the grand coalition could not form. Sensitivity analyses on some parameter values for the grand coalition and for the ECC bring some interesting findings. With higher cargo values, the WCR becomes more appealing because of its quicker delivery time and lower inventory costs compared with the ECR. The Panama Canal expansion will improve market power and profit shares for the East Coast players if the canal operator could increase its competitive price more than the increase of costs. Generally, a player will gain more market power if its cost could be reduced. A player's upper bound rate is a reflection of its relative market power. But in a complicated market characterized with various cooperation-competition strategies and an ambiguous definition of partners and competitors, the impact of a player's upper bound rate on the market power structure could not be easily explained. For future research, the challenge mainly lies on the large number of BLPPs that need to be constructed and solved in order to study more players.
Green Supply Chain Management Practices and Determinant Factors: A Quantitative Study on Small and Medium Enterprises Using Structural Equation Modeling
(North Dakota State University, 2017) Zahid, Sardar Muhammad
Considering the prominence of green supply chain management (GrSCM) research has developed expressively in this field. However, there is a dearth of studies from emerging economies comprised of modelling and empirical testing of hypotheses. Moreover, the literature is lacking the empirical evidence on the determinants of GrSCM practices by small and medium enterprises (SMEs) especially in the case of Pakistan. The literature has yet to determine what green practices are being adopted by SMEs in Pakistan, an elucidation why GrSCM practices are adhered, what construct is appropriate to evaluate adoption of GrSCM practices by SMEs in Pakistan, and whether mediation of internal factors exits between the relationship of GrSCM practices and external pressure. This dissertation uses Structural Equation Modelling (SEM) to investigate GrSCM practices adoption, the appropriate construct for evaluating green practices, and examining three potentially important determinants in Pakistani SMEs. With the data collected in two stages from the SMEs sector of Pakistan, exploratory factor analysis (EFA) revealed a three-dimension structure for measuring the GrSCM practices. Subsequently, the confirmatory factor analysis (CFA) was carried out on two measurement models (i.e. first and second order) of GrSCM adoption based on EFA. The empirically outcomes advocates that both models for GrSCM adoption are valid and reliable, however the second order model has better fit indices. The SEM testing shows significant results for mediation of internal factors in the hypothesized relationship among the GrSCM practices and external pressures. For academicians and supply chain mangers these results yield several exciting theoretical and practical implications.
Heavy Vehicle Impact on Rural Two Lane Highway Segments Operating Under Various Levels of Service Conditions
(North Dakota State University, 2014) Zheng, Zijian
Oil boom in Western North Dakota State brings increasing number of oil trucks. The distinct characteristics of heavy vehicles such as oil trucks: low speed, large size, and slow accelerate and decelerate results in inaccuracy in traffic capacity forecasting and safety analysis. In this research, to calculate passenger car equivalent (PCE) factor of heavy vehicles, such as oil trucks, on two-lane rural highway, an improved analytical method based on headway and delay is introduced. It considers several elements that have effect on PCE factor: vehicle speed, safety passing time, headway distribution, level of service (LOS), and delay to downstream traffic. The new set of PCE factor values are classified into three groups corresponding to different LOS.
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.
The Impact of Automated Requisitioning Systems on the Effectiveness of Emergency Supply Chains
(North Dakota State University, 2014) Shatzkin, Matthew Patterson
This research examines the relevance of an automated requisitioning system on an emergency supply chain's performance. In this context, "automated requisitioning" refers to the ability to transmit requisitions through an automated method that can be viewed and acted upon by multiple members of the supply chain. Automated requisitioning suggests some sophistication compared to manual methods which include phone calls, email and text messaging. These manual methods carry an implied higher probability of error and also have a limited capacity to process higher volumes of requisitions. Emergency supply chains are characterized by some demand that can be anticipated and other demand that must be addressed through a requisitioning procedure. Two subcategories of emergency supply chains are military expeditions and nongovernmental organizations. While military and disaster relief supply chains each provide supplies to different customers, they are similar in their need to both push and pull required commodities. Although military supply chains support soldiers while disaster relief supply chains provide relief to people in need, both supply chains involve pushing supplies while requesting specific needs based on the particular situation, overall addressing a demand that is largely unknown. This research examines the role automated requisitioning plays in the midst of these push and pull systems by simulating automation in a military expedition, then generalizing the results to suggest conclusions regarding a disaster relief supply chain.
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.
Impact of the Panama Canal Expansion in Global Supply Chain: Optimization Model for U.S. Container Shipment
(North Dakota State University, 2015) Park, Ju Dong
The transportation of containerized shipments will continue to be a topic of interest in the world because it is the primary method for shipping cargo globally. The primary objective of this study is to analyze the impact of the Panama Canal Expansion (PCE) on the trade flows of containerized shipments between the United States and its trade partners for US exports and imports. The results show that the Panama Canal Expansion would affect the trade flows of US imports and exports significantly. The major findings are as follows: (1) the PCE affects not only US domestic trade flows, but also international trade flows since inland transportation and ocean transportation are interactive, (2) delay cost and toll rate at the Panama Canal do not have a significant impact on trade volume and flows of US containerized shipments after the Panama Canal Expansion mainly because delay cost and toll rate at the canal account for a small portion of the total transportation costs after the PCE, (3) West Coast ports would experience negative effects and East Coast ports would experience positive effects from the PCE, while Gulf ports would experience no effects from the PCE, and (4) an optimal toll rate is inconclusive in this study because changes in toll rate at the canal account for a small portion of the total transportation costs and the PNC competes with shipments to/from Asia shipping to the US West.
Improving the Methodology to Estimate Joint Logistics Over-the-Shore Operational Throughput and Duration
(North Dakota State University, 2019) Froberg, Robert Bryan
Joint Logistics Over-the-Shore (JLOTS) is the method the United States (US) Army and Navy use to discharge cargo from large seafaring vessels onto a bare beach when an enemy force has denied access to a deep-water port or the ports have been damaged by natural disasters, terrorist actions, sabotaged by military forces, etc. The last large scale, published analytic study on JLOTS was conducted in 1993 during the Ocean Venture 93 exercise at Camp Lejeune, NC; since that time, nearly the entire US Army inventory of wheeled vehicles have been replaced and tracked systems have increased in size and weight with the additions of reactive armor tiles and urban survival kits. The current estimation method for determining how long a JLOTS operation will take relies on the median duration values in order to determine total operational length. This research shows that the JLOTS activity duration medians published in current military doctrine are no longer representative of the current inventory of US Army vehicles. New planning factors are defined based on JLOTS subject matter expert opinions as well as a new method of JLOTS duration estimation is described through the use of discrete-event simulation. The results of the proposed duration estimation method were compared to both the existing methodology using both the published planning factors and the new planning factors defined through subject matter expert opinion. In both comparisons the current estimation method was found to consistently overestimate operational throughput while underestimating duration since it fails to capture the queuing actions that occur in a resource constrained environment such as JLOTS. It is the recommendation of this research that a time and motion study be conducted on JLOTS operations in order to more accurately define the probability distributions associated with JLOTS activities. These distributions would replace the triangular distributions defined by subject matter experts in this research in order to generate a more accurate estimate of JLOTS duration and throughput. More accurate estimates for JLOTS operations will enable cost savings by providing maritime transportation providers with greater fidelity on scheduling while reducing the time these ships are vulnerable to enemy actions.

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