Tatsuya Kotegawa

Network-Theoretic Approach for Analyzing Connectivity in Air Transportation Networks

Research reported in this paper is motivated by the need to better understand the structure of connectivity in air transportation networks.Theaim istoinvestigateanalysismodelsandtechniquesfrom modernnetworktheoryas a framework to provide both characterization of network structure and a useful systems analysis approach to derive implications from both local and global topology characteristics. Recent developments in network theory establish meanstoquantifytopological structureinamannerthatmayindicateexpectedperformanceandrobustness.Inthis paper, a mathematical footing from network theory is introduced for examining transport networks in the U.S. domestic air transportation system. Using data for the 2004 travel year, the structure of the transport network (service routes between airports) and several subnetworks is exposed in terms of degree distribution, and the importance of airportsis assessed through several networkmeasures. Useful implications are drawn from measures andfurther analysis that directly maps these measures to system performance is presented. The general approachis found to merit further investigation as part of a larger, more comprehensive, and design-oriented systems analysis framework for air transportation.

Establishment of a Network-based Simulation of Future Air Transportation Concepts

*† † This paper describes key elements involved in applying a system-of-systems approach to modeling future air transportation architectures. Overall, this approach links together not only the technical aspects of the National Transportation System (NTS) and National Airspace System (NAS), but also to incorporate the political, socio-economic, operational and alternative transportation systems into a single architecture. Since the potential scope is huge, representations of the individual systems are simplistic, focusing on uncovering highlevel patterns in the structure of networks and operational rules that lead to scalability in transportation goals. In particular, this paper describes the use of transportation data for instantiation of the simulation model built for the task. Analysis of simulation results exhibit trends in the time scales of action in the capacity network that may lead to the formulation of future policies and regulations.

Complexity enabled design space exploration

The proposed paper presents a framework for complexity enabled design space exploration. A circuit problem is introduced where different designs are generated from a component library to create a low pass filter circuit. Performance and complexity metrics are defined to measure the fitness of the design. A scheme for complexity enabled design space exploration is introduced and the results obtained are compared to the traditional performance based exploration. It is found that the complexity enabled exploration results in identification of regions within the performance-complexity trade space which are difficult to obtain using a performance based exploration. Characteristic features of designs that result in high performance but low complexity are also identified. Later a scheme for local design space exploration is proposed which investigates the designs in the nearest neighborhood of a particular design. This provides insights about the flexibility and adaptability of the design.

Utilization of Network Theory for the Enhancement of ATO Air Route Forecast

Current air traffic forecast methods employed at the FAA function under the assumption that the network of flight routes will not change, that is, no new flight routes will be added nor existing flight routes removed. However, the competitive nature of the airline industry is such that new routes are routinely added between cities possessing significant passenger demand and other city-pairs are removed. This paper investigates models for forecasting network reconfiguration that exploit knowledge of network topology structure in the Air Transportation System (ATS). Algorithms for new route prediction are combined with the FAA’s existing method for an initial exploration of improving the quality of overall forecasts that drive policy and infrastructure enhancement decision-making.

Evolution of Service Provider Behaviors via Network-based Analysis

Developing solutions for future air transportation architectures requires an understanding of the behaviors and dynamics that drive the system. When viewed in a network modeling context, this understanding is crystallized in the ability to project topology changes of individual service providers. Such changes are the result of complicated decision-making processes. However, the research reported in this p aper seeks to determine whether network topology measures studied over time provide clues t o the particulars of air transportation network evolution. Data describing the service netw ork evolution from 1990-2005 of four major US airlines is studied. Measures of network c ohesiveness (clustering coefficient) and nodal importance (eigenvector centrality) are found to correlate with the character of network evolution. Further investigations needs to focus on the interrelation between these measures as well as other externalities. Ultimately , this line of research seeks to exploit the aggregate structure uncovered by network topology analysis to better forecast dynamics in future air transportation networks.