Modeling Economic Impacts of the Inland Waterway Transportation System

The inland waterway transportation system of the United States (U.S.) handles 11.7 billion tons of freight annually and connects the heartland of the U.S. with the rest of the world by providing a fuel-efficient and environmentally friendly mode of transportation. This dissertation aims to create decision support tools for maritime stakeholders to measure the economic impacts of the inland waterway transportation systems under real world scenarios including disruptions, demand changes, port expansion decisions, and channel deepening investments. Monte Carlo simulation, system dynamics, discrete-event simulation, agent-based modeling, and multiregional input-output modeling techniques are utilized to analyze the complex relationships between inland waterway transportation system components and regional economic impact factors. The first research contribution illustrates that the expected duration of a disruption determines whether decision makers are better off waiting for the waterway system to reopen or switching to an alternative mode of transportation. Moreover, total disruption cost can be reduced by increasing estimation accuracy of disruption duration. The second research contribution shows that without future investment in inland waterway infrastructure, a sustainable system and associate economic impacts cannot be generated in the long-term. The third research contribution illustrates that investing in bottleneck system components results in higher economic impact than investing in non-bottleneck components. The developed models can be adapted to any inland waterway transportation system in the U.S. by utilizing data obtained by publically available sources to measure the economic impacts under various scenarios to inform capital investment decisions and support an economically sustainable inland waterway transportation system

ROBUST DECISION-MAKING AND DYNAMIC RESILIENCE ESTIMATION FOR INTERDEPENDENT RISK ANALYSIS

When systems and subsystems are put under external shocks and duress, they suffer physical and economic collapse. The ability of the system components to recover and operate at new stable production levels characterizes resilience. This research addresses the problem of estimating, quantifying and planning for resilience in interdependent systems, where interconnectedness adds to problem complexity. Interdependence drives the behavior of sectors before and after disruptions. Among other approaches this study concentrates on economic interdependence because it provides insights into other levels of interdependence. For sectors the normalized losses in economic outputs and demands are suitable metrics for measuring interdependent risk. As such the inoperability input-output model enterprise is employed and expanded in this study to provide a useful tool for measuring the cascading effects of disruptions across large-scale interdependent infrastructure systems. This research defines economic resilience for interdependent infrastructures as an "ability exhibited by such systems that allows them to recover productivity after a disruptive event in a desired time and/or with an acceptable cost". Through the dynamic interdependent risk model resilience for a disrupted infrastructure is quantified in terms of its average system functionality, maximum loss in functionality and the time to recovery, which make up a resilience estimation decision-space. Estimating such a decision-space through the dynamic model depends upon the estimation of the rate parameter in the model. This research proposes a new approach, based on dynamic data assimilation methods, for estimating the rate parameter and strengthening post-disaster resilience of economic systems. The solution to the data assimilation problem generates estimates for the rate of resilient recovery that reflects planning considerations interpreted as commodity substitutions, inventory management and incorporating redundancies. The research also presents a robust optimization based risk management approach for strengthening interdependent static resilience estimation. There is a paucity of research dealing with quantification and assessment of uncertainties in interdependency models. The focus here is more on the extreme bounds of event and data uncertainties. The deterministic optimization becomes a robust optimization problem when extremes of uncertainties are considered. Computationally tractable robust counterparts to nominal problems are presented here. Also presented in this research is a discrete event simulation based queuing model for studying multi-modal transportation systems with particular focus on inland waterway ports. Such models are used for impact analysis studies of inland port disruptions. They can be integrated with the resilience planning methodologies to develop a framework for large-scale interdependent risk and recovery analysis

Economic contribution and impact analysis of the 2019 flood's disruption of the Oklahoma-McClellan-Kerr Navigation System

Record flooding in spring 2019 caused Oklahoma’s inland navigable waterways to close. Closure disrupted the supply chains of agricultural and manufacturing industries for months, causing economic loss in other industries of the state’s economy. Anecdotal accounts estimated direct losses of 2 million dollars per day. This research uses a multi-regional input-output model to estimate the short term direct, indirect and induced economic impacts of the Oklahoma portion of the McClellan-Kerr Arkansas River Navigation System’s disruption from the spring 2019 flood. First the contribution of the water transportation industry to Oklahoma, Arkansas, Colorado, and Kansas’ economies is estimated, and the losses in economic output, employment, and value added caused by various length of flood disruption periods. We also estimate the effects the disruption had on the economies of Oklahoma’s metropolitan and non-metropolitan regions. This study finds losses in employment, output, and value added for each of the congressional districts in Oklahoma, Arkansas, Colorado, and Kansas. Indirect and induced losses were disproportionately experienced in Oklahoma metropolitan and non-metropolitan agricultural and manufacturing industries. Benefits the public received were diminished due to disruption of the waterway as a result of the spring flooding

BAYESIAN KERNEL METHODS FOR THE RISK ANALYSIS AND RESILIENCE MODELING OF CRITICAL INFRASTRUCTURE SYSTEMS

The protection of critical infrastructures has recently garnered attention with an emphasis on analyzing the risk and improving the resilience of such systems. With the abundance of data, risk managers should be able to better inform preparedness and recovery decision making under uncertainty. It is important, however, to develop and utilize the necessary methodologies that bridge between data and decisions. The goal of this dissertation is to (i) predict the likelihood of risk, (ii) assess the consequences of a disruption, and (iii) inform preparedness and recovery decision making. This research presents a data-driven analysis of the risk and resilience of critical infrastructure systems. First, a new Bayesian kernel model is developed to predict the frequency of failures and a Beta Bayesian kernel model is deployed to model resilience-based importance measures. Bayesian kernel models were developed for Gaussian distributions and later extended to other continuous probability distributions. This research develops a Poisson Bayesian kernel model to accommodate count data. Second, interdependency models are integrated with decision analysis and resilience quantification techniques to assess the multi-industry economic impact of critical infrastructure resilience and inform preparedness and recovery decision making under uncertainty. Examples of critical infrastructure systems are inland waterways, which are critical elements in the nation’s civil infrastructure and the world’s supply chain. They allow for a cost-effective flow of approximately $150 billion worth of commodities annually across industries and geographic locations, which is why they are called “inland marine highways.” Aging components (i.e., locks and dams) combined with adverse weather conditions, affect the reliability and resilience of inland waterways. Frequent disruptions and lengthy recovery times threaten regional commodity flows, and more broadly, multiple industries that rely on those commodities. While policymakers understand the increasing need for inland waterway rehabilitation and preparedness investment, resources are limited and select projects are funded each year to improve only certain components of the network. As a result, a number of research questions arise. What is the impact of infrastructure systems disruptions, and how to predict them? What metrics should be used to identify critical components and determine the system’s resilience? What are the best risk management strategies in terms of preparedness investment and recovery prioritization? A Poisson Bayesian kernel model is developed and deployed to predict the frequency of locks and dams closures. Economic dynamic interdependency models along with stochastic inoperability multiobjective decision trees and resilience metrics are used to assess the broader impact of a disruption resulting in the closure of a port or a link of the river and impacting multiple interdependent industries. Stochastic resilience-based measures are analyzed to determine the critical waterway components, more specifically locks and dams, that contribute to the overall waterway system resilience. A data-driven case study illustrates these methods to describe commodity flows along the various components of the U.S. Mississippi River Navigation System and employs them to motivate preparedness and recovery strategies

Criticality of infrastructure networks under consideration of resilience-based maintenance strategies using the example of inland waterways

Transportation infrastructures as backbone of modern, globalized, and networked societies ensure flows of people and goods and thus sustain social and economic prosperity. Concurrently, more and more infrastructure construction assets are facing the problem of systematic obsolescence due to deficient structural conditions, maintenance backlogs, and a lack of or misallocation of resources for the construction and maintenance of infrastructure buildings. This problem construct necessitates a resilience-based maintenance strategy for the asset portfolio. In particular, inland navigation as a mode of transport features large transport volumes and few redundancies. Combined with its increasing importance due to its comparatively high environmental friendliness, a predestined, yet in the literature underrepresented research subject results. This dissertation aims to investigate essential factors of infrastructure management and thereby identify the potential for improvement in the complex construct of maintenance management and related areas. The emphasis is on enhancing the resilience of inland waterways as a complex System-of-Systems with all its interdependencies. Thus, a holistic risk and resilience assessment is essential and is underlined with the aspects infrastructure availability and business decisions (Study A, B, C and D) and stakeholder communication and risk analysis (Study E, F, G) which are addressed by seven studies published as companion articles. Study A deals with assessing the reliability of transport infrastructure networks as part of supply chains, highlighting the importance of available and thus maintained infrastructure assets for functioning supply chains. Study B aims to identify critical warning times before closures of transport infrastructure networks and therefore suggests a mixed-methods approach, making it possible to derive and evaluate critical thresholds. Study C examines the corresponding company decisions, i.e., decisions as reaction towards neglected maintenance of public transport infrastructure, which comprises risk coping strategies, examined by empirical investigations. Study D extends this problem observation by showing that companies could see incentives for outsourcing if they face a lack of access to available transport infrastructure. Hence, the study analyzes facility relocation problems in dependence on infrastructure availability. Study E heads toward stakeholder communication and risk analysis and examines the processes across stakeholders, using an approach of collaborative serious gaming, which simultaneously enhances situation awareness and communication among stakeholders. Study F provides the implementation of a systemic approach and its visualization as a GIS-based risk dashboard, shedding light on interdependencies among critical infrastructures and cascading effects. Study G closes with an examination of the evaluation of the potential of infrastructure funds. For this purpose, the study conducts an online survey to determine investors’ willingness to pay for various fund mechanisms, integrating the option of private coverage. Despite the geographic focus of the case studies on Germany, valuable insights can be gained for infrastructure management that can also apply to other countries. In addition to the case study findings, general recommendations for infrastructure owners are derived. As a result, it can be stated that it is essential that maintenance strategies have to be more resilience-based than traditional strategies, which are mainly based on fixed time intervals for maintenance. Moreover, the application of both serious gaming and GIS visualization can help to enhance situation awareness and thus the resilience of infrastructure systems. An essential finding for which this dissertation provides methodological approaches is that considering the local area’s attractiveness for business locations should receive more attention regarding investment decisions. Thereby a focus should be set on the realistic threat of relocations as response to deteriorating infrastructure conditions. Eventually, public debates should strengthen the knowledge about infrastructure and its funding, while deficits in alongside mechanisms in infrastructure funding must be encountered. Consequently, this dissertation provides insights into the potential of infrastructure management. Mainly, it offers the potential to improve the resilience of the waterway transportation system and address stakeholders accordingly