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.
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.
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.
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.
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 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.
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.
An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
(North Dakota State University, 2018) Gebremikael, Fesseha
This study investigates the use of social media platforms (SMPs) for acquiring supply chain intelligence (SCI) to improve supply chain performance. Given the growth of social media use, there is an urgency for increased understanding of the effectiveness of emerging SMPs. In today's competitive global environment, supply chain managers need to have a precise understanding about the SMPs that have become one of the premier sources of gaining SCI and in turn foster supply chain performance. Organizations need a methodology for selecting SMPs to remain proactive ahead of their competitors. The evolution of SMPs has caused a paradigm shift in how organizations obtain SCI to increase their revenues, profitability and reputation. The aim of this study is to apply a multi-criteria analysis using the analytic hierarchy process (AHP) to select SMPs. Stage 1 represents the primary goal, the decision maker wishes to gain in executing SMPs; Stage 2 consists of decision criteria; Stage 3 is composed of sub-criteria; and finally Stage 4 represents the SMP alternatives reported in the organizational hierarchy structure. The objective of this model is to rank the SMPs. The model includes key supply chain performance factors in the organization. The hierarchical models are used to breakdown the complex notion of supply chain performance into its constituent parts. The second phase of the hierarchical model consists of the performance indicators of which supply chain performance is composed. Hence, the modeled value is the supply chain performance in the organization. Our results indicate that the top three supply chain performance indicators are quality, assurance of supply and delivery. Meanwhile the top three types of supply chain intelligence are logistics intelligence, product/process intelligence and supply chain visibility intelligence. The top three SMP alternatives are, LinkedIn, Facebook and Twitter.
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.
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.
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.
Optimizing Transportation Infrastructure and Global Supply Chain Integration for Nicaragua’s Autonomous Caribbean Regions through Network Modernization
(North Dakota State University, 2019) Leiman, James
The autonomous regions of the Nicaraguan Caribbean Coast are resource rich, yet they are among the poorest regions of Latin America. To realize economic growth and potential, this research examined Nicaragua’s primary-sector economic activities and developed a transportation network that would enable the creation of a functional logistics network, therefore enabling integration into the global supply chain for timber, beef, seafood, and light manufactured goods. The main goal of this research is to determine the minimum cost of developing a multimodal transportation network in the region by using roads, rail, intracoastal waterways, and Caribbean Sea transport. In addition to the initial construction costs, a 50-year horizon was evaluated, including operation and maintenance expenses for all possible modes as well as the cost to move all goods from point to point within the network using various options per Ton-kilometer. Several sensitivities were also run using Excel Solver in order to determine what triggers would alter the network’s construction and operation plan for each transportation arc. In the aggregate, the least-expensive option, to include deployment of rail, road, and intracoastal waterway use, costs $861,419,624.87 over a 50-year period. This cost captured the initial construction expenses, operation and maintenance estimates, and the rate to move goods across the network; the best-case scenario enabled construction over a 5-year period. More expensive options for the network’s construction and operation/movement of goods are more likely given the region’s inefficiencies. This research will be given to the Nicaraguan Department of Transportation with the hope that the findings may be used to orchestrate economic and community development in the region.
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.
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.
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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