Search for: All records

Recent disruptions in transportation systems resulting from natural disasters, cyber accidents, and other factors clearly show the fragility of the airports and underscore the need for building resilience. This study introduces a comprehensive framework for evaluating the resilience of airport infrastructure, integrating critical functions and performance indicators in the context of specific missions that the airport needs to achieve. By focusing on the Dallas-Fort Worth International Airport (DFW) as a case study, the paper outlines a multi-criteria decision analysis (MCDA) methodology for identifying and assessing the critical functions of airports as well as their ability to recover and adapt under different threat scenarios including threat-agnostic situation. The methodology and its application to the DFW case study offer insights into the resilience of airport operations, highlighting key areas for improvement and the potential for policy intervention. This study provides a robust tool for airport administrators and policymakers to enhance infrastructure resilience through a detailed analysis and visualization of airport performance indicators, thereby contributing to the broader discourse on transportation system sustainability and disaster preparedness. 

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.