Transportation, Logistics, & Finance Doctoral Work

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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.
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.
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.
Organizing Transit in Small Urban and Rural Communities
(North Dakota State University, 2012) Ripplinger, David
The justification of government support of rural transit on the basis of the presence of increasing returns to scale and the most efficient regional organization of transit is investigated. Returns to density, size, and scope at most levels of output were found. Cost subadditivity, where a monopoly firm can provide service at a lower cost than two firms, was found for many, but not all observations. The presence of natural monopoly in rural transit in a strict sense is rejected. The findings and implications are directly applicable to rural transit in North Dakota and should be helpful in informing future federal policy as well as rural transit policy, service design, and operation in other states.
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.
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.
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.
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.
Structural Changes in North American Fertilizer Logistics
(North Dakota State University, 2014) Shakya, Sumadhur
Nitrogen-based fertilizer industry in United States is undergoing major changes the demand for which is primarily driven by agriculture. Traditionally, this industry sources anhydrous ammonia through imports from Canada and U.S.-Gulf, the latter comprises bulk of imports, or produces domestically to be supplied as is or converted into urea or UAN variations of nitrogen-based fertilizer with various combinations with other minerals. With change in composition of crops and increasing acreage of crops that are fertilizer intensive, there is an increased demand for nitrogen-based fertilizer in order to promote foliar growth as a standalone form, for example Urea, or in combination, for example Di-ammonium phosphate (DAP). Second compelling reason for change in industry is reduction in prices of natural-gas, in part due to oil exploration, that makes it cheaper to produce anhydrous ammonia domestically. Anhydrous ammonia is perquisite for making other types of nitrogen-based fertilizer and highly energy intensive. Thus, lower natural-gas prices provide incentive for domestic firms to either expand existing fertilizer plants or opens up the possibility of new entrants. Many companies/firms have recently announced their plans to expand existing plants or open new units, exerting competitive pressure on an industry that already has lot of surplus capacity but highly competitive in terms of production costs and technology used. It is to be noted that natural-gas prices are volatile; therefore, any commitment to expand or open new plant is subject to volatility in demand, natural-gas prices, and import price of fertilizers. The purpose of this dissertation is to analyze spatial competition among U.S. nitrogen-based fertilizer plants and their respective market boundaries. This dissertation also derives the structure of the supply chain for nitrogen-based fertilizer in the United States (at macro level); and the stochastic spatial-optimization model to account for risk in random variables. Locational information is used to account for spatial nature of problem, and linear and mixed-integer based optimization techniques are applied to arrive at current and most likely future cases. Combination of linear optimization, and mixed-integer, and geographical information systems helps in determining regional areas where competition is expected to be ruinous and most intense; and provide insights on viability of newly announced fertilizer plants that are most likely to be successful and significantly impact the structure of overall supply chain.
Pavement Performance Evaluation Using Connected Vehicles
(North Dakota State University, 2015) Bridgelall, Raj
Roads deteriorate at different rates from weathering and use. Hence, transportation agencies must assess the ride quality of a facility regularly to determine its maintenance needs. Existing models to characterize ride quality produce the International Roughness Index (IRI), the prevailing summary of roughness. Nearly all state agencies use Inertial Profilers to produce the IRI. Such heavily instrumented vehicles require trained personnel for their operation and data interpretation. Resource constraints prevent the scaling of these existing methods beyond 4% of the network. This dissertation developed an alternative method to characterize ride quality that uses regular passenger vehicles. Smartphones or connected vehicles provide the onboard sensor data needed to enable the new technique. The new method provides a single index summary of ride quality for all paved and unpaved roads. The new index is directly proportional to the IRI. A new transform integrates sensor data streams from connected vehicles to produce a linear energy density representation of roughness. The ensemble average of indices from different speed ranges converges to a repeatable characterization of roughness. The currently used IRI is undefined at speeds other than 80 km/h. This constraint mischaracterizes roughness experienced at other speeds. The newly proposed transform integrates the average roughness indices from all speed ranges to produce a speed-independent characterization of ride quality. This property avoids spatial wavelength bias, which is a critical deficiency of the IRI. The new method leverages the emergence of connected vehicles to provide continuous characterizations of ride quality for the entire roadway network. This dissertation derived precision bounds of deterioration forecasting for models that could utilize the new index. The results demonstrated continuous performance improvements with additional vehicle participation. With practical traversal volumes, the achievable precision of forecast is within a few days. This work also quantified capabilities of the new transform to localize roadway anomalies that could pose travel hazards. The methods included derivations of the best sensor settings to achieve the desired performances. Several case studies validated the findings. These new techniques have the potential to save agencies millions of dollars annually by enabling predictive maintenance practices for all roadways, worldwide.
Transportation Sustainability on Economic and Environmental Aspects in the United States: Statistical and Quantitative Approaches
(North Dakota State University, 2015) Choi, Jaesung
The dissertation consists of three essays: 1) Productivity growth in the transportation industries in the United States: An application of the DEA Malmquist productivity index; 2) how does a carbon dioxide emissions change affect transportation productivity? A case study of the U.S. transportation sector from 2002 to 2011; and 3) forecast of CO2 emissions from the U.S. transportation sector: Estimation from a double exponential smoothing model. The first essay reviews productivity growth in the five major transportation industries in the United States (airline, truck, rail, pipeline, and water) and the pooled transportation industry from 2004 to 2011. The major findings are that the U.S. transportation industry shows strong and positive productivity growth except in the years of the global financial crisis in 2007, 2008, and 2010, and among the five transportation industries, the rail and water sectors show the highest productivity growth in 2011. The second essay examines the effects of a carbon dioxide (CO2) emissions change on actual productivity in the U.S. transportation sector. This study finds that a CO2 emissions increase from 2002 to 2007 had a negative effect on actual productivity in the U.S. transportation sector, but the CO2 emissions reduction for 2008–2011 increases actual productivity. States mainly showing sustainable growth patterns (decrease in CO2 emissions concurrent with increasing actual productivity) experience higher technological innovation increase than an efficiency decrease. This finding suggests that fuel-efficient and carbon reduction technologies as well as alternative transportation energy sources may be essential factors to both grow transportation and slow global warming. The third essay reviews whether the decreasing trend in U.S. CO2 emissions from the transportation sector since the end of the 2000s is consistent across all states in the nation for 2012‒2021. A double exponential smoothing model is used to forecast CO2 emissions for the transportation sector in the 50 states and the U.S., and its findings are supported by pseudo out-of-sample forecasts validity testing. This study concludes that the decreasing trend in transportation CO2 emissions in the U.S. will continue in most states in the future.
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.
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.
Modeling Petroleum Supply Chain: Multimodal Transportation, Disruptions and Mitigation Strategies
(North Dakota State University, 2016) Kazemi, Yasaman
The petroleum industry has one of the most complex supply chains in the world. A unique characteristic of Petroleum Supply Chain (PSC) is the high degree of uncertainty which propagates through the network. Therefore, it is necessary to develop quantitative models aiming at optimizing the network and managing logistics operations. This work proposes a deterministic Mixed Integer Linear Program (MILP) model for downstream PSC to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. Three products are considered in this study: gasoline, diesel, and jet fuel. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. Furthermore, this work proposes a two stage Stochastic Mixed Integer Linear Program (SMILP) models with recourse for PSC under the risk of random disruptions, and a two stage Stochastic Linear Program (SLP) model with recourse under the risk of anticipated disruptions, namely hurricanes. Two separate types of mitigation strategies – proactive and reactive – are proposed in each model based on the type of disruption. The SMILP model determines optimal DC locations and capacities in the first stage and utilizes multimode transportation as the reactive mitigation strategy in the second stage to allocate transfer volumes. The SLP model uses proactive mitigation strategies in the first stage and employs multimode transportation as the reactive mitigation strategy. The goal of both stochastic models is to minimize the expected total supply chain costs under uncertainty. The proposed models are tested with real data from two sections of the U.S. petroleum industry, PADD 3 and PADD 1, and transportation networks within Geographic Information System (GIS). It involves supply at the existing refineries, proposed DCs and demand nodes. GIS is used to analyze spatial data and to map refineries, DCs and demand nodes to visualize the process. Sensitivity analysis is conducted to asses supply chain performance in response to changes in key parameters of proposed models to provide insights on PSC decisions, and to demonstrate the impact of key parameters on PSC decisions and total cost.
Municipal Solid Waste Collection Route Optimization Using Geospatial Techniques: A Case Study of Two Metropolitan Cities of Pakistan
(North Dakota State University, 2016) Hina, Syeda
The population growth in many urban cities and its activities in developing countries have resulted in an increased solid waste generation rate and waste management has become a global environmental issue. Routing of solid waste collection vehicles in developing countries like Pakistan poses a challenging task. In the process of solid waste management, collection and transportation play a leading role in waste collection and disposal, in which collection activities contributed the most to total cost for solid waste collection activities. Therefore, this study describes an attempt to design and develop an appropriate collection, transportation and disposal plan for the twin cities of Pakistan by using Geographic Information System (GIS) and Remote Sensing (RS) techniques to determine the minimum cost/distance/time efficient collection paths for the transportation of the solid wastes to the landfill sites. In addition to this, identification of solid waste disposal sites and appropriately managing them is a challenging task to many developing countries and Pakistan is no exception to that. The existing landfill sites for the twin cities are not technically viable and environmentally acceptable and are thus damaging to the environment due to their location and the type of waste dumped. Therefore, the second aim of our study was to find out the suitable landfill sites for the twin cities and the study employed Multi-Criteria Evaluation (MCE) methods to combine necessary factors considered for landfill site selection for the twin cities. Hence, our present study has proved that GIS is a tool that can be used in integration with other techniques such as MCE for a identifying new landfill sites and it can help decision makers deal with real-world developmental and management issues. Finally, the study has developed a Wed-Based Decision Support System (DSS) via Application Programming Interface (API) which will help decision-makers to search for cost-effective alternatives and it can be operated by people who don’t have knowledge of GIS. The proposed study can be used as a decision support tool by the municipalities of the twin cities for efficient management and transportation of solid wastes to landfill sites, managing work schedules for workers, etc.
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.
Innovative Approach to Estimating Demand for Intercity Bus Services in a Rural Environment
(North Dakota State University, 2017) Mattson, Jeremy
Because existing models have their limitations, there is a significant need for a model to estimate demand for intercity bus services, especially in rural areas. The general objective of this research was to develop an intercity mode choice model that can be incorporated into a statewide travel demand model to estimate demand for rural intercity bus services. Four intercity transportation modes were considered in the study: automobile, bus, rail, and air. A stated preference survey was conducted of individuals across the state of North Dakota, and a mixed logit model was developed to estimate a mode choice model. Results from the mode choice model showed the significant impacts of individual, trip, and mode characteristics on choice of mode. Gender, age, income, disability, trip purpose, party size, travel time, travel cost, and access distance were all found to have significant impacts on mode choice, and traveler attitudes were also found to be important. The study demonstrated how the mode choice model can be incorporated into a statewide travel demand model, and intercity bus mode shares were estimated for origin-destination pairs within the state. Alternative scenarios were analyzed to show how mode shares would change under different conditions or service characteristics. This study was conducted in the largely rural state of North Dakota, but results could be transferable to other areas with similar geographic characteristics.
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.
Three Essays on Sustainability of Transportation and Supply Chain
(North Dakota State University, 2018) Park, Yong Shin
Climate change has emerged as one of the most problematic issues and key global threats to mankind, and sustainability has become an important issue for any organizations. Therefore, managing supply chains in a more sustainable way has become an increasing concern for many businesses across a wide range of companies around the world. Designing efficient and effective supply chains improves overall environmental performance in business operations and is essential to not only mitigate climate change, but also to benefit human life and environment. The objective of this dissertation is to address issues in sustainability of transportation and supply chains with three essays focusing on three aspects - measure, manage, and mitigate - that contribute to the practice and literature of sustainable transportation and supply chain. Chapter Two of this dissertation utilizes slack-based date envelopment analysis to form an environmental efficiency index comprising various sustainability indicators in transportation sector. This index may function as a decision-making tool for transportation planners and practitioners to compare sustainability performance of U.S. states, to benchmark sustainability performance, and also to develop carbon emission reduction strategies. Chapter Three of this dissertation adopts multimodal transportation to formulate the cost-effective strategies for managing a switchgrass-based biofuel supply chain. This study captures the benefit of a modal shift in designing the biofuel supply chain network; thus, practitioners who want to plan for multimodal transportation of biofuel can learn its practical relevance. Chapter Four of this dissertation address greening the biomass supply chain for animal manure by formulating mathematical model for design and management of biomass to a biogas supply chain, including anaerobic digestion as a source of renewable energy production. This study handled waste management issues by incorporating carbon policy into the biomass supply chain, with due consideration accorded to both monetary and environmental factors. Overall, the research outcome will provide practitioners and researchers with scientific information and tools that will enable them to become better stewards by virtue of healthy and sustainable development and practices in transportation and supply chain.
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.

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