Xiaoqian Sun

Intelligent Multicriteria Decision Support System for Systems Design

It is challenging to assess new technology in complex, interdisciplinary integrated systems, such as air transportation systems. Conflicting disciplines and technologies are always involved in aerospace systems design processes. Multicriteria Decision Analysis techniques can help decision makers to effectively deal with such situations and make wise design decisions. There is a variety of existing methods; thus, selection of the most appropriate method is critical because the use of inappropriate methods is often the cause of misleading design decisions. In this paper, an appropriateness index is used to quantify the goodness of a method for solving the problem under consideration. This method selection approach is implemented and an intelligent knowledge-based system is developed consisting of a multicriteria decision analysis library storing widely used decision analysis methods and a knowledge base providing the information required for the method selection process. Furthermore, a new approach for unce...

Computationally efficient attack design for robustness analysis of air transportation networks

Maintaining robustness is a key challenge for present and future air transportation. The analysis of network robustness is a time-demanding task, whose complexity increases with the size of networks. Accordingly, network attacks are often built on network metrics, for instance, attacking the nodes in decreasing order of their degree or betweenness. Albeit the results can be insightful, there is no guarantee regarding the quality or optimality of these attacks. In this paper, we propose a new exploration/exploitation search technique for a computationally efficient attacking model, adapted from general game playing. We propose an incremental solution for the efficient computation of robustness measures, by exploiting the network similarity before and after executing an attack, and thus, avoiding redundant computations. We define four tasks in the attacking model: Static attack, interactive attack, dynamic attack, and finding the best attack. The analysis of real-world air transportation networks reveals that commonly used network metric-based attacking strategies are already suboptimal for short attacks of length two. Our computationally efficient attacking model contributes to scalable analysis of robustness, not only for air transportation, but also for networks in general.

Efficient Compression of 4D-Trajectory Data in Air Traffic Management

Air traffic management (ATM) is facing a tremendous increase in the amount of available flight data, particularly four-dimensional (4D) trajectories. Computational requirements for analysis and storage of such bulk of data are steeply increasing. Compression is one key technology to address this challenge. In this paper we propose two techniques for compressing air traffic 4D trajectories. Our first technique analyzes a set of samples and computes a prediction for the most likely picked successor coordinate by a random walker. The second technique, i.e., referential compression, compresses a 4D trajectory as a collection of subtrajectory pointers into a reference trajectory. We evaluate our algorithms on trajectory data from the Demand Data Repository provided by EUROCONTROL. We show that a combination of our referential and statistical compression techniques compresses 4D trajectories of all air traffic over Europe in the year 2013 from 60 GB down to 0.78 GB, achieving a compression ratio of more than 75 : 1. The compression ratio for our techniques increases with the number of to-be-compressed flights, whereas standard compression techniques achieve a fixed compressed ratio for any number of flights. Our work contributes toward efficient handling of the increasing amount of traffic data in ATM.

Air Transport versus High-Speed Rail: An Overview and Research Agenda

The development of high-speed rail (HSR) services throughout the last decades has gradually blurred the concept of competition and cooperation with air transportation. There is a wide range of studies on this subject, with a particular focus on single lines or smaller regions. This article synthesizes and discusses recently published studies in this area, while aiming to identify commonalities and deviations among different regions throughout the world, covering services from Europe, Asia, and North America. Our meta-analysis reveals that the literature is highly controversial and the results vary substantially from one region to another, and a generalization is difficult, given route-specific characteristics, such as demand distribution, network structure, and evolution of transportation modes. As a major contribution, we propose a list of five challenges as a future research agenda on HSR/air transport competition and cooperation. Among others, we see a need for the construction of an open-source dataset for large-scale multimodal transport systems, the comprehensive assessment of new emerging transport modes, and also taking into account the resilience of multimodal transport systems under disruption.

QRE: Quick Robustness Estimation for large complex networks

Abstract Robustness estimation is critical for the design and maintenance of resilient networks. Existing studies on network robustness usually exploit a single network metric to generate attack strategies, which simulate intentional attacks on a network, and compute a metric-induced robustness estimation, called R . While some metrics are easy to compute, e.g. degree, others require considerable computation efforts, e.g. betweenness centrality. We propose Quick Robustness Estimation (QRE), a new framework and implementation for estimating the robustness of a network in sub-quadratic time, i.e., significantly faster than betweenness centrality, based on the combination of cheap-to-compute network metrics. Experiments on twelve real-world networks show that QRE estimates the robustness better than betweenness centrality-based computation, while being at least one order of magnitude faster for larger networks. Our work contributes towards scalable, yet accurate robustness estimation for large complex networks.

Evolution of domestic airport networks: a review and comparative analysis

ABSTRACT Previous studies on airport networks are strongly bounded time-wise or only conducted for single networks at distinct levels of abstraction and for distinct topological features. Here, we review and compare the evolution of domestic airport networks (DANs) for Australia, Brazil, Canada, China, India, Russia, US and Europe during the period 2002–2013. This is the first study on a consistent global dataset and allows for direct comparisons of network features. The air passenger traffic is tremendously increasing in all eight networks, with the largest number of passengers in US, followed by Europe and China. Degree distributions can often be best fitted with a truncated power-law (e.g. Brazil and US) or log-normal (e.g. Australia and Canada). While all eight networks clearly exhibit small-world properties, the average shortest path length is between 2.1 (China/Russia) and 4.0 (Canada). Our study sets a baseline for understanding the topology and evolution of DANs.

Worldwide Railway Skeleton Network: Extraction Methodology and Preliminary Analysis

Understanding and improving global mobility has gained increased interest during the last decades. However, studies on the railway network are spatially limited so far, mostly investigating the domestic network of a country. Data availability is a major limiting factor for the analysis of these networks. Despite the increased open data movement, network operators are often reluctant to publish their infrastructure and passenger data. Existing large-scale studies usually make use of hand-collected data, for instance, based on historical cartographies. In this paper, we develop and implement a methodology to extract the worldwide railway skeleton network from the open data repository OpenStreetMap, where nodes are stations/waypoints and links are weights with information such as spatial distance, gauge, and maximum speed. We describe how we solved several data cleansing and scalability issues and developed network simplification techniques, in order to obtain an adequate representation of the network. We show that the network breaks down into few large and many small components. Furthermore, we show that this public data set can be used for efficient minimum travel time estimation between stations or cities. This paper leads to the development of a new research data set and contributes toward the ability of analyzing global mobility patterns, particularly regarding multimodality and cross-country transportation.

Worldwide air transportation networks: a matter of scale and fractality?

ABSTRACT In this study, we take a new view on air transportation networks, inspired by the physical concept of fractality. While other studies analyze networks individually, we aim to provide a unified understanding of the transitions among network layers. As a case study, we investigate the worldwide air transportation networks for the year 2015. We derive aggregated network instances at six different levels: airports, cities, spatial distance 100u2009km, spatial distance 200u2009km, regional network, and country network. While few nodes are important at all levels of aggregation, others only become important for few aggregation levels. Fractality analysis highlights that, as one moves from finer granularity to more coarse aggregation level, the network becomes denser but with fluctuating assortativity patterns; and that the modularity and the number of communities both decrease slightly. Networks at higher aggregation levels are more robust than the fine-grained counterparts, airport and city networks.

Finding -Hub Median Locations: An Empirical Study on Problems and Solution Techniques

Hub location problems have been studied by many researchers for almost 30 years, and, accordingly, various solution methods have been proposed. In this paper, we implement and evaluate several widely used methods for solving five standard hub location problems. To assess the scalability and solution qualities of these methods, three well-known datasets are used as case studies: Turkish Postal System, Australia Post, and Civil Aeronautics Board. Classical problems in small networks can be solved efficiently using CPLEX because of their low complexity. Genetic algorithms perform well for solving three types of single allocation problems, since the problem formulations can be neatly encoded with chromosomes of reasonable size. Lagrangian relaxation is the only technique that solves reliable multiple allocation problems in large networks. We believe that our work helps other researchers to get an overview on the best solution techniques for the problems investigated in our study and also stipulates further interest on cross-comparing solution techniques for more expressive problem formulations.

Network repair based on community structure

Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.