Benchmark analysis for robustness of multi-scale urban road networks under global disruptions

To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful - if partial - representation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multi-scale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI's ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs

Cost Optimization Modeling for Airport Capacity Expansion Problems in Metropolitan Areas

The purpose of this research was to develop a cost optimization model to identify an optimal solution to expand airport capacity in metropolitan areas in consideration of demand uncertainties. The study first analyzed four airport capacity expansion cases from different regions of the world to identify possible solutions to expand airport capacity and key cost functions which are highly related to airport capacity problems. Using mixedinteger nonlinear programming (MINLP), a deterministic optimization model was developed with the inclusion of six cost functions: capital cost, operation cost, delay cost, noise cost, operation readiness, and airport transfer (ORAT) cost, and passenger access cost. These six cost functions can be used to consider a possible trade-off between airport capacity and congestion and address multiple stakeholders’ cost concerns. This deterministic model was validated using an example case of the Sydney metropolitan area in Australia, which presented an optimal solution of a dual airport system along with scalable outcomes for a 50-year timeline. The study also tested alternative input values to the discount rate, operation cost, and passenger access costs to review the reliability of the deterministic model. Six additional experimental models were tested, and all models successfully yielded optimal solutions. The moderating effects of financial discount rate, airport operation cost, and passenger access costs on the optimal solution were quantitatively the same in presence of a deterministic demand profile. This deterministic model was then transformed into a stochastic optimization model to address concerns with the uncertainty of future traffic demand, which was further reviewed with three what-if demand scenarios of the Sydney Model: random and positive growth of traffic demand, normal distribution of traffic demand changes based on the historical traffic record of the Sydney region, and reflection of the current COVID- 19 pandemic situation. This study used a Monte Carlo simulation to address the uncertainty of future traffic demand as an uncontrollable input. The Sydney Model and three What-if Models successfully presented objective model outcomes and identified the optimal solutions to expand airport capacity while minimizing overall costs. The results of this work indicated that the moderating effect of traffic uncertainties can make a difference with an optimal solution. Therefore, airport decision-makers and airport planners should carefully consider the uncertainty factors that would influence the airport capacity expansion solution. This research demonstrated the effectiveness of combining MINLP and the Monte Carlo simulation to support a long-term strategic decision for airport capacity problems in metropolitan areas at the early stages of the planning process while addressing future traffic demand uncertainty. Other uncertainty factors, such as political events, new technologies, alternative modes of transport, financial crisis, technological innovation, and demographic changes might also be treated as uncontrollable variables to augment this optimization model

Development of a Framework for the Analysis and Assessment of Daily Airport Operations

Tremendous progress has been made over the last two decades towards modernizing the National Airspace System (NAS) by way of technological advancements, and the introduction of procedures and policies that have maintained the safety of the United States airspace. However, as with any other system, there is a need to continuously address evolving challenges pertaining to the sustainment and resiliency of the NAS. One of these challenges involves efficiently analyzing and assessing daily airport operations for the identification of trends and patterns to inform better decision making so as to improve the efficiency and safety of airport operations. Efforts have been undertaken by stakeholders in the aviation industry to categorize airports as a means to facilitate the analysis of their operations. However, a comprehensive, repeatable, and robust approach for this very purpose is lacking. In addition, these efforts have not provided a means for stakeholders to assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations. Furthermore, an efficient and secure framework for extracting, processing, and storing the data needed for the analysis and assessment of daily airport operations is needed, as the current process employed by FAA analysts is manual, time-consuming, and prone to human error. Consequently, this dissertation addresses these gaps through a set of methodologies that 1) leverage unsupervised Machine Learning algorithms to categorize daily airport operations, 2) leverage a supervised Machine Learning algorithm to determine the category that subsequent daily airport operations belong to, 3) facilitate the comparison of similar and different daily airport operations for the identification of trends and patterns, 4) enable stakeholders to analyze and assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations, and 5) develop a framework to facilitate the efficient and secure extraction, processing and storage of data needed for the analysis and assessment of daily airport operations. The developed framework automates the flow of data from extraction through storage, and enables users to track the flow of data in real time. It also facilitates data provenance by logging the history of all processes and is equipped with the capability to log errors and their causes, and to notify analysts via email whenever they occur. In addition, it has the capacity to automatically extract, process, and store the data needed for the analysis and assessment of the daily operations of all airports in the NAS. Indeed, this framework will be one of the first of its kind to be deployed into the FAA's Enterprise Information Management platform and will serve as a template for leveraging cloud-based services and technologies to improve operations in the NAS. Finally, this framework will enable FAA analysts to analyze and assess daily airport operations in an efficient manner to facilitate the identification of trends and patterns for better decision making, which will lead to improved airport operational performance.Ph.D