Bayesian methods for the data-driven recovery of networks: measuring impact and building resilience in infrastructures and communities
Funding agency: National Science Foundation
The objective of this work is to develop a new data-driven optimization framework to improve (i) the ability to model the performance of infrastructure networks, and (ii) the ability to plan for the recovery of these networks after a disruption, with an emphasis on community resilience and economic productivity.
The research approach is composed of three components. The first component develops a new statistical technique, the hierarchical Bayesian kernel method, which integrates the Bayesian property of improving predictive accuracy as data are dynamically obtained, the kernel function that adds specificity to the model and can make nonlinear data more manageable, and the hierarchical property of borrowing information from different sources in sparse and diverse data situations which are common in disruptive events scenarios. The second component develops an infrastructure network recovery optimization formulation that minimizes the larger impact of infrastructure network performance with data-driven (and dynamically updated) hierarchical Bayesian kernel parameters of infrastructure recovery, along with solution techniques that account for the size and dynamic nature of model parameters. The application of the first two integrated components to electric power networks (where impact is measured on the safety and resilience of the community) and inland waterways (where impact is measured on economic productivity across multiple industries), constitutes the third component, offering two application perspectives on the impact of infrastructure network resilience and recovery.
Multi-scale modeling and observations of landscape dynamics, mass balance, and network connectivity for a sustainable Ganges-Brahmaputra delta
Funding agency: National Science Foundation
River deltas around the world are in a state of modest to severe decline, primarily in response to anthropogenic activities. These settings are also among the world’s most physically dynamic, being impacted by sea-level rise and subsidence, river flooding, channel erosion, and storms. Such vulnerabilities are further magnified in highly populated delta systems, notably the large mega-deltas that rim Asian coasts in politically sensitive regions from Pakistan to China. These environments suffer not only from having more humans, infrastructure, and livelihoods in peril, but also from the anthropogenic strain that large populations place on physical and ecological support systems. In Bangladesh and West Bengal, India, the Ganges-Brahmaputra-Meghna delta (GBMD) may well be the prime example as the world’s largest and most densely populated delta system, hosting 150 million people in an area the size of Louisiana. In this Coastal SEES project, a diverse group of scholars with expertise across several earth-science and engineering disciplines are brought together to answer questions about the fate and future sustainability of the GBMD and its human population.
Specifically, our group wants to understand how are human activities affecting this channel-network system, and what are the subsequent repercussions on the infrastructure network and the economy. We are developing network models integrated with Bayesian approaches to assess the risk and resilience of the inland transportation network governed by the GBMD. Interdependency economic models will help understand the regional and national impact of changes in river deltas.
Cyber-physical applications for freight transportation systems
Funding agency: Tennessee Department of Transportation
Freight transportation systems constitute key factors in the productivity, the environment, and the energy consumption in Tennessee, as well as beyond the state’s borders. To achieve more efficient, safe, secure and sustainable transportation, the freight transportation industry is relying heavily on the use of cyber-physical (CP) applications. This involves deploying computing software/hardware to control or monitor physical components in real-time (e.g., automation, sensors, mobile technologies, GPS). CP technologies present opportunities for freight management and operations in both the public (e.g., ports, traffic operations, incident management) and private (e.g., shippers, carriers, warehouse/distribution operators) sectors. Concerns have been expressed, however, as to potential limitations to CP adoption due to issues involving information fidelity, application scalability, and acquisition/operating costs. Moreover, excessive dependency on CP systems can introduce vulnerability to accidental and intentional security breaches, a growing concern as many freight operators are shying away from investing in backup systems.
The objective of this research is to perform a comprehensive review of existing and anticipated CP technologies and applications, with a critical eye on their role in improving freight transportation management and operations. These technologies will be evaluated according to their performance in achieving system efficiency, safety, security, and sustainability. The goal is to perform a quantitative cost-benefit analysis to understand the improvement to freight management and operations as well as increased exposure to vulnerabilities.
Mississippi river supply chain management
Funding agency: U.S. Department of Housing and Urban Development through the Tennessee Department of Economic and Community Development
The project focus is primarily on the port development project in Lake County, where significant investments have been made to create an attractive port and economic development infrastructure. The Cates Landing Port is Tennessee’s newest port and sits in the center of a multistate agricultural and industrial development region, located not just in a protected harbor along the Mississippi River but near the Canadian national mainline between Chicago and New Orleans, the intersection of several interstate highways and adjacent to the designated I-69 “North American Corridor.”
The objective of this project is to help stakeholders in the affected region understand how their actions and behaviors can impact the resilience of the prospective waterborne commerce that might be attracted to this region and the new port developments that have been implemented in recent years. Extreme weather impacts the overall economic vitality of the target region and the resulting competitiveness in attracting development that can take advantage of the natural locational advantages.
A cradle to grave approach to supply chain modeling will establish the barriers to shippers locating proximate to the port footprint. A mode choice analysis will focus particularly on the inland maritime service options that are available as a result of the infrastructure buildout at Cates Landing. And finally, an all hazards analysis including use of Hazus developed by FEMA that can look particularly at how vulnerabilities in the region may affect the relative attractiveness of development and what opportunities for mitigation may be possible.
Maritime Transportation Research and Education Center (MarTREC)
Funding agency: U.S. Department of Transportation
The Maritime Transportation Reseach and Education Center (MarTREC) is a consortium led by the University of Arkansas in which Vanderbilt is a member. MarTREC is a USDOT Tier 1 University Transportation Center.
Vanderbilt University, specifically the Vanderbilt Center for Transportation and Operational Resiliency (VECTOR), is involved in research areas focused on (i) improving understanding of the impacts to inland marine transportation due to the shift in domestic energy production, (ii) the potential impacts of climate change and extreme weather on the inland marine transportation systems and operations, and (iii) improving decision support using advanced data analytics including agent-based modeling, river information services, simulations, among other methodologies.
Vanderbilt Initiative for Smart-City Operations Research (VISOR)
Funding agency: Trans-Institutional Programs, Vanderbilt University
The rapidly emerging smart city concept aims to enhance the quality and performance of urban services, reduce costs and resource consumption, and provide an infrastructure to engage citizens more effectively. Successful smart city initiatives require coordinated technological, industrial, educational, and policy advances. The goal of this initiative is to work with Metro Nashville governmental agencies to develop a technical and policy platform that enables living city labs in which to study, research, and develop solutions to the challenges and problems faced by the city and by extension other cities around the world. In particular, the proposed center will conduct research that combines the development of computational infrastructures at scale for collecting and analyzing heterogeneous data across the city, with a focus on social and policy impact. Real-time and predictive data analytics and mining techniques applied to this data will yield decision support systems that (i) support city planners and city residents and (ii) address challenges related to improving mobility, increasing safety, and supporting sustainable and affordable housing developments. At the core of the proposed effort, three cross-cutting research initiatives are undertaken, (i) designing a software platform that integrates networked sensors and computational elements, (ii) creating a data analytics toolbox to serve multiple smart city applications, and (ii) developing decision support systems that make our technology innovations readily available to city residents and visitors.
As part of this initiative, our group is building analytical models that are founded in statistical learning and systems optimization to detect and predict delays in transit systems and propose an optimized schedule that minimizes such delays when the city’s transportation system wants to accommodate severe weather, road construction, or increased traffic due to special events in music and sports.
Vanderbilt Initiative for Intelligent Resilient Infrastructure Systems (IRIS)
Funding agency: Trans-Institutional Programs. Vanderbilt University
A perfect storm is brewing involving civil infrastructure protection. On one hand, infrastructure protection systems are exposed to more natural disturbances (e.g., hurricanes, tsunamis, drought-flood cycles) with ever increasing severity, as a consequence of climate change. On the other, the nation’s civil infrastructure is aging and in poor health, with increasingly restricted budgets allocated to maintain, repair and restore them. The consequences of inaction are severe as demonstrated very recently during Superstorm Sandy in 2012 and Hurricane Katrina in 2005, and across the nation and around the world. The future of civil infrastructure is in building intelligent and resilient systems that organically interact (i.e., inform as well as adapt to demands) with local communities and decision makers in order to function efficiently and effectively. Through this initiative, the core team, which includes faculty from A&S and Engineering with complementary and cross-disciplinary expertise is establishing interrelationships between (i) the physical system with diagnostics and prognostics capabilities for infrastructure resilience, and (ii) the decision support system and the associated science and modeling tools that informs the state of the infrastructure system and possible scenarios to the decision makers and other key stakeholders, including the community at large.
Our group is focused on data analytics and network analysis to model the interdependent relationships across infrastructure systems as they respond to flood events.
Multi-Modal Freight Transportation System Capacity and Diversion Assessment
Funding agency: Tennessee Department of Transportation
Tennessee’s economic health relies heavily on the timely and efficient flow of commodities which is governed by a multi-modal freight transportation system. There is growing concern regarding the ability of such an interconnected system to withstand disruptive events and maintain the necessary level of commodity flow to support the economy and mitigate cascading impacts. Ideally, multi-modal transportation systems should provide redundancy in the network of freight movement to ensure effective diversion in the event of a disruption to one or more modes. Added complexity is introduced due to emerging interests in expanding commuter rail service by utilizing portions of the freight rail network. A case in point is the mobility challenge facing middle Tennessee, where the passenger rail option may be constrained by the nearly maximized capacity of CSX freight lines. These circumstances beg the following questions: (i) To what extent is the freight system in Tennessee operating at or near full capacity? (ii) How does that impact the performance of freight transportation and opportunities for passenger rail service? (iii) How capable is the system for handling freight diversion in the case of disruptive events?
The research will focus on a data-driven, comprehensive analysis of multi-modal freight system capacity to understand the system’s capability to (i) serve the anticipated demand for freight (and possibly passenger) traffic and (ii) be able to accommodate additional commodity flow in the event that diversion is needed.
Geospatial investigation of future climate conditions: the impact on supply chain and USACE water resource infrastructure using enhanced simulation and spatial visualization capability
Funding agency: U.S. Army Corps of Engineers
Our nation’s inland waterway system is a vital part of the supply chains for domestic and international shippers. This system has proven to be particularly vulnerable to climate-induced disruptions and future climate change will likely further degrade infrastructure assets, delay shipments of goods, and increase costs for the management agencies involved, shippers, barge operators and the general public. To prepare for anticipated effects of climate change in the face of an aging infrastructure and competing demands placed on the water resources that make up the system, actions are needed to guide cost-effective navigation system planning and management. A compelling aspect of this concern is the waterway freight transport supply chain.
The research will be performed in three phases: 1) understanding how respective decisions are made by individuals and groups (i.e., agents) managing and operating on the waterways when faced with climate-induced events, 2) utilizing available data and GIS tools to identify climate “hot spots” from a supply chain perspective, and 3) performing a “deep dive” at select “hot spots” to perform system-level analysis of the impacts of extreme weather on a broader supply chain. The result of these activities will make it possible for the USACE to recognize where climate adaptation strategies on the inland system will best serve the navigational needs of the waterborne freight community. As the overall technical approach incorporates both physical and social components involving a complex network, a combination of three interrelated analysis techniques will be utilized: 1) agent-based modeling, 2) participatory simulation, and 3) geographic information systems (GIS) and other visualization tools.