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Analysis of air transportation using complex networks "D3"."UIB - IFISC" Literature Review

Document information

PhD Project Analysis of air transportation using complex networks

Name of the Network ComplexWorld

Deliverable Name Literature Review

Deliverable ID D3

PhD student Pablo Fleurquin

Professor Jose Javier Ramasco & Maxi San Miguel

University Universitat Illes Balears, IFISC

Edition

Task contributors

IFISC

Abstract

This is a report that summarizes the results in complex network and ATM literature that fall within the scope of our project. It seeks to synthesize the information and organize it within a coherent framework in order to be used as a source of reference in future works as well as for the bibliography of coming papers and of the PhD thesis.

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Authoring & Approval

Prepared By

Name & organisation Position / Title Date

Pablo Fleurquin / UIB - IFISC PhD Student / Eng. Apr 01 2011

Reviewed By

Name & organisation Position / Title Date

José J. Ramasco / UIB - IFISC RyC researcher / Dr Apr 01 2011

Approved By

Name & organisation Position / Title Date

José J. Ramasco / UIB - IFISC RyC researcher / Dr Apr 01 2011

Document History

Edition Date Status Author Justification

V0 Apr 01 2011 Pablo Fleurquin

V1 Apr 01 2011 Jose Javier Ramasco

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TABLE OF CONTENTS

1. Introduction .......................................................................................................................... 4

1.1 Structure of the Document .......................................................................................... 4

2. Progress and originality of the research ............................................................................ 4

3. Summary of the cited work ................................................................................................. 4

4. Description of the work included. ....................................................................................... 6

A. Basic concepts in network theory. ............................................................................... 6

B. Statistics and structural analysis applied to air-transportation networks. ................... 8

C. Processes on networks. ................................................................................................ 9

D. Dynamics of group evolution. ................................................................................... 10

E. ATM concepts and related work on flight delay. ...................................................... 11

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1. Introduction

This document is the Literature Review Report (deliverable D3) of WP-E PhD Analysis of air transportation using complex networks. Its content is divided in the following sections.

1.1 Structure of the Document

Section 2 describes what has been done in the reporting period. After it, Section 3 lists the articles and books that in Section 4 are detailed. Worth mentioning is the fact that in the last section, references are organized into different categories that provides a clearer description of the general framework.

2. Progress and originality of the research

Although delay propagation has a major impact in airport and airline performance, the research done to get a better understanding of it has been scarce so far.

We think that complex network provides a suitable framework to address this problem. The main lines of this project are not only to understand the processes behind delay propagation but to characterize and simulate this phenomenon in order to help devise more efficient strategies for delay management from the point of view of ATM and of airline planning.

In the first place, we are interested in understanding the topology and structure of the air-traffic network, and apply this to investigate the community structures of underperformed or delayed airports within the network. This will lead us to develop dynamical models that may replicate the behavior of this group of airports, in order to comprehend what is happening throughout the network.

During these last three months we have worked on the characterization of data referring to delays in an airport network. We have introduced and tested a set of quantifiers for the delays in the routes, in the airports and delay propagation in the network that will be described in a coming paper. We have also advanced in the modelization of the system, reaching the point where it is possible to simulate the evolution of the air traffic. We are now testing the model whose results are to be compared with the data that we have in the following months.

3. Summary of the cited work

The following section lists the articles and books that have been useful for pursuing the scope mentioned in section 2.

A. Basic concepts in network theory and reviews.

1. DJ Watts, SH Strogatz, Collective dynamics of ‘small-world’ networks, Nature 393, 440 (1998).

2. AL Barabasi, R Albert, Emergence of Scaling in Random Network, Science 286, 509 (1999).

3. R. Albert, A.L. Barabasi, Statistical Mechanics of complex networks, Rev. Mod. Phys. 74, 47 (2002).

4. M.E.J. Newman, The structure and function of complex networks, SIAM Review 45, 167 (2003).

5. S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Complex Networks: Structure and dynamics, Physics Reports 424, 175 (2006).

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6. S. Fortunato, Community detection in graphs, Physics Reports 486, 75 (2010).

7. M.E.J Newman, Networks: An Introduction, Oxford Univ. Press (2010).

B. Statistics and structural analysis applied to air-transportation networks.

8. A Barrat, R Pastor-Satorras, A Vespignani, The architecture of complex weighted networks, Proc. Natl. Acad. Sci. USA 101, 3747 (2004)

9. R Guimera, S Mossa, M Sales-Pardo, LAN Amaral, The worldwide air transportation network: Anomalous centrality, community structure and cities global roles, Proc. Natl. Acad. Sci. USA 102, 7794 (2005).

10. M Sales-Pardo, R Guimera, AA Moreira, LAN Amaral, Extracting the hierarchical organization of complex systems, Proc. Natl. Acad. Sci. USA 104, 15224 (2007).

11. A Gautreau, A Barrat, M Barthelemy, Microdynamics in stationary complex networks, PNAS 106, 8847 (2009).

C. Processes on networks.

12. V Colizza, A Barrat, M Barthelemy, A Vespignani, The role of airline transportation network in the prediction and predictability of global epidemics, PNAS 103, 2015 (2005).

13. D Balcan, V Colizza, B Goncalves, H Hu, JJ Ramasco, A Vespignani, Multiscale mobility networks and the spatial spreading of infectious diseases, Proc. Natl. Acad. Sci. USA 106, 21484 (2009).

14. D Balcan, V Colizza, B Goncalves, H Hu, JJ Ramasco, A Vespignani, P Bajardi, C Paolotti, N Perra, M Tizzione and W Van den Brocke, Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility, BMC Medicine 7, 45 (2009).

15. L Lacasa, M Cea, M Zanin, Jamming transition in air transportation networks, Physica A: Statitstical Mechanics and its Applications 388, 3948-3954 (2009)

16. H. Jo, R.K. Pan, K. Kaski,Time-Varying Priority Queuing Models for Human Dynamics (2011) [ available at: http://arxiv.org/abs/1109.5990v2]

D. Dynamics of group evolution.

17. J Hopcroft, O Khan, B Kulis, B Selman,Tracking evolving communities in large linked networks, PNAS 101, 5249 (2004).

18. G Palla, A Barabasi, T Vicsek, Quantifying social group evolution, Nature 446, 664 (2007).

19. S. Asur, S. Parthasarathy, D. Ucar, An Event-based Framework for Characterizing the Evolutionary Behavior of Interaction Graphs, TKDD 3, 913 (2009).

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E. ATM concepts and related work on flight delay.

20. P Bonnefoy, RJ Hansman, Emergence of secondary airports and dynamics of regional airport system in the United States, Report ICAT 2005-02 (2005)

21. P Bonnefoy, R Weibel, Air Transportation System Architecture Analysis, (2006)

22. N G. Rupp, Further Investigations into the Causes of Flight Delays (2007) [available at: http://www.ecu.edu/cs-educ/econ/wp2007.cfm]

23. P Belobaba, A Odoni, C Barnhart, The Global Airline Industry, John Wiley & Sons (2009).

24. M. Jetzki, PhD Thesis, The propagation of air transport delays in Europe. Department of Airport and Air Transportation Research RWTH Aachen University (2009).

25. R Beatty, R Hsu, L Berry, J Rome, 2nd USA/Europe air traffic management R&D seminar, Preliminary evaluation of flight delay propagation through an airline schedule, [available at: http://www.atmseminar.org/seminarContent/seminar2/papers/p_038_APMMA.pdf]

4 Description of the work included.

A. Basic concepts in network theory.

This framework contains the seminal works of Barabasi and Strogatz and the most important reviews on the field. DJ Watts, SH Strogatz, Collective dynamics of ‘small-world’ networks, Nature 393, 440 (1998) In this article the authors introduced a simplified network model to show how the so-called “small-world” effect can appear in a network. Through a rewiring procedure, the network passes from a lattice in 1D to a random network. The average path length between any pair of nodes in the network is drastically reduced from the 1D expectations as soon as a small fraction of shortcuts is introduced. This work is important to understand the small-world effect that appears in most real-world networks, including airport networks, and as introduction to a set of theoretical tools used to quantify the structural properties of a graph. Some examples are the clustering coefficient and the characteristic path length, which are suitable to explore the basic features of many networks. AL Barabasi, R Albert, Emergence of Scaling in Random Network, Science 286, 509 (1999) Barabasi and Albert showed that many systems found in nature are best described by graphs in which the vertex connectivity is highly heterogeneous. These are called free-scale networks and show a quite complex topology. They explain this phenomena by a self-organizing mechanism in which the network grows in time (by addition of new vertices) and each new vertex seeks to connect to vertices that are already well connected (a rich get richer phenomenon). After this work became popular, it was known that actually this multiplicative mechanism has been also proposed in other areas by Yule or Simon in context of social sciences and economy. This study shows thus the relevance of the degree distribution as a basic features to explore in a network.

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R. Albert, A.L. Barabasi, Statistical Mechanics of complex networks, Rev. Mod. Phys. 74, 47 (2002). This is the foremost review on complex network theory. Basically, it focus on topology description and explains the first network growth models. It is a good introductory review when describing the network structure. M.E.J. Newman, The structure and function of complex networks, SIAM Review 45, 167 (2003). In this review the author introduces the reader in the main ideas about network topology but focus principally on propagation models on networks. This review is helpful to characterize the propagation models that we are developing and to compare their features with the dynamics of the basic epidemic models on network. S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Complex Networks: Structure and dynamics, Physics Reports 424, 175 (2006). This is the most recent review on network theory and includes graph topology as well as propagation on networks. S. Fortunato, Community detection in graphs, Physics Reports 486, 75 (2010). A thoroughly review on structure detection on networks. Specifically we are interested in the applications for real data and the main concepts stated under community dynamics. M.E.J Newman, Networks: An Introduction, Oxford Univ. Press (2010).

It is an introductory textbook in the field of networks and a desk-side reference when approaching problems from a complex network perspective. It covers the fundamentals of graph theory, from random to scale-free networks; methods for analyzing network data from different fields such as physics, sociology, statistics and mathematics, and the dynamics taking place on networks. Some of the chapters that have been found relevant are:

Chapter 6. Mathematics of networks. Includes the basic concepts of network analysis and the introductory ideas to the basic structures of network representation. Some of the key ideas explained in these chapters are: adjacency matrix, adjacency list, weighted and directed networks, degree, path, components, etc.

Chapter 7. Measures and metrics. Some of the tools that referred throughout this deliverable, being applied to different cases, are summarized in this chapter: degree centrality, eigenvector centrality, hubs, betweenness centrality, homophily and assortative mixing.

Chapter 8. The large-scale structure of networks. This chapter deepens in the understanding of some topological measures as clustering coefficient and degree distributions.

Chapters 12 to 15. Explain and characterize the basic network models, from random networks to scale-free networks.

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Chapter 17. Epidemics on networks. Describes the basic spreading models as the SI,SIR,SIS and SIRS and their time dependent properties. Where S stands for Susceptible, I for Infected and R for Recovered. These models assumes that the probability of catching a disease from an infected person is proportional to the probability of coming into contact with that person, therefore the network or web of contacts plays an important role in the dynamics of the spreading disease.

B. Statistics and structural analysis applied to air-transportation networks.

Former studies in air-transportation networks are included in this section of the document. These works focus on statistical and topological features and are based mainly in the worldwide air transportation network or the US air-transportation network (USAN). A Barrat, R Pastor-Satorras, A Vespignani, The architecture of complex weighted networks, Proc. Natl. Acad. Sci. USA 101, 3747 (2004) Air-transportation network is represented as a weighted network where each edge is proportional to the number of available seats in flights between two vertices (airports). The authors introduce topological measures but applied to weighted network. They quantify node strength distributions, weighted clustering coefficient and weighted average nearest-neighbors degree, in order to exemplify respectively, the cohesiveness found on local triplets and the affinity to connect with hubs or non-hubs neighbors (this shows assortative or disassortative behavior). The article introduces useful concepts when dealing with weighted networks as the worldwide air-transportation network. R Guimera, S Mossa, M Sales-Pardo, LAN Amaral, The worldwide air transportation network: Anomalous centrality, community structure and cities global roles, Proc. Natl. Acad. Sci. USA 102, 7794 (2005). The authors study the topology and communities of the worldwide air transportation network. They demonstrate that the structure of the graph is a scale-free small-world network by quantifying degree distributions. Furthermore, measures of centrality have been done, showing that hubs are not necessarily the most central nodes. Some of the tools applied in this work will be of high relevance for our own work once the European network of delays is built. M Sales-Pardo, R Guimera, AA Moreira, LAN Amaral, Extracting the hierarchical organization of complex systems, Proc. Natl. Acad. Sci. USA 104, 15224 (2007). In this work a method for extracting the hierarchical organization of a complex network is proposed and validated. By this method they explore the hierarchical organization of the air-transportation network. The method identifies the different levels in the hierarchy and the composition and number of modules at each level. Modules consist on groups of nodes that are more densely connected between them that they are with other groups. Implementing the method for the air-transportation network shows that it is strongly modular and very hierarchical. We intend to explore if this type of network organization influence congestion, bottleneck formation and therefore, delay propagation.

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A Gautreau, A Barrat, M Barthelemy, Microdynamics in stationary complex networks, PNAS 106, 8847 (2009).

In this work the authors consider the dynamic of the network topology, advancing from the static point of view used so far and show that there can be an intense activity occurring at different time scales. They apply the new tools and concepts proposed to analyze the US airport network. In this network, they introduce methods to measure link dynamics and passenger dynamics and how the seasonality can play an important role in the structure an evolution of the network. These tools are of great relevance for us since they can be applied to study the dynamics of a network generated with flight delays.

C. Processes on networks.

Once we built the air-traffic networks based on real traffic data and analyze their main structural features (see the previous section) we intend to simulate delay propagation models over it. The concepts and methods that we will use are based on those introduced in the following papers.

V Colizza, A Barrat, M Barthelemy, A Vespignani, The role of airline transportation network in the prediction and predictability of global epidemics, PNAS 103, 2015 (2005).

The authors analyze the relationship between the connectivity patterns present in worldwide airport networks and the propagation of infectious diseases. The role of the particular topology of the airport network and its influence on the propagation is explored. D Balcan, V Colizza, B Goncalves, H Hu, JJ Ramasco, A Vespignani, Multiscale mobility networks and the spatial spreading of infectious diseases, Proc. Natl. Acad. Sci. USA 106, 21484 (2009).

This work presents a computational model to understand the spatial spreading of infectious diseases where human mobility is the key ingredient. The spatiotemporal pattern that arises from the spreading is due to airline traffic flow (long-range) and commuting flows (short-scale) between the affected populations. The framework introduced by this metapopulation model is analog to what we intend to recreate in delay propagation models. D Balcan, V Colizza, B Goncalves, H Hu, JJ Ramasco, A Vespignani, P Bajardi, C Paolotti, N Perra, M Tizzione and W Van den Brocke, Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility, BMC Medicine 7, 45 (2009).

In this case the metapopulation analysis is applied to the pandemic spreading influenza A (H1N1) that happened in 2009. This work identifies different scenarios for the future behavior of the pandemic. Related to our research we intend that our model could help not only to understand the causes of delay propagation but, as in this case, anticipate what-if scenarios and help to develop policies in each case.

L Lacasa, M Cea, M Zanin, Jamming transition in air transportation networks, Physica A: Statitstical Mechanics and its Applications 388, 3948-3954 (2009)

In this article a model to analyze the diffusion of a given number of aircrafts over an air transportation network is proposed and checked taking into account the European air-transportation network. The graph is constructed with nodes representing a given airport and edges being flight routes between

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each pair of nodes. In this work, the authors use a weighted graph with nodes and edges, weighted, respectively, by its load capacity and the Euclidean distance that separate each pair of vertices. A random network topology was selected to construct the graph and local rules describe the interaction of nodes with the surrounding flow. Numerical simulations for this model demonstrate a jamming transition between an efficient and congested phase with queue and bottlenecks formation. The research we are conducting takes into consideration queuing models with finite or limited capacity as the model introduced in this article. H Jo, R Pan, K Kaski,Time-Varying Priority Queuing Models for Human Dynamics (2011) [ available at: http://arxiv.org/abs/1109.5990v2]

An application of queuing models based on agents performing tasks with different priority. An individual carry out two tasks: one that its priority may vary in time and another one, with random priority, that disrupts the first task. The authors show that this kind of task execution produce bimodal and unimodal waiting time distribution for the task completion. This could be applied to explore how priority queuing protocols, unlike first-in first-served protocols (used in airport operations), could lead to different results when referring to delay propagation.

D. Dynamics of group evolution.

Another way of understanding the processes involved in delay propagation is to track the time evolution of the different groups of airports with similar performance characteristics. To do so, we could base our analysis in previous work in the related field of community evolution. J Hopcroft, O Khan, B Kulis, B Selman,Tracking evolving communities in large linked networks, PNAS 101, 5249 (2004).

This article provides a framework for analyzing the dynamic of community evolution. This type of analysis is still in its infancy but the concepts used in it are applicable to study the formation and evolution of groups of underperformed airports due to flight delay. G Palla, A Barabasi, T Vicsek, Quantifying social group evolution, Nature 446, 664 (2007). The authors investigate the changes in community structure when evolving in time. They found different behavior depending on the group size. Concepts, as the auto-correlation function and time evolution representation used in the article, are fruitful for characterizing the cluster dynamics of underperformed airports. S. Asur, S. Parthasarathy, D. Ucar, An Event-based Framework for Characterizing the Evolutionary Behavior of Interaction Graphs, TKDD 3, 913 (2009).

In this article the authors define a series of measures, ‘critical events’, to describe time evolution patterns of communities within a graph. The approach used is helpful to identify the evolving groups in the network and characterize the type of change. Applicable quantities to measure the group dynamics in air-traffic network are: k-merge, continue, form, etc.

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E. ATM concepts and related work on flight delay.

The works included in this section are studies that cover the field of flight delay or connected themes from an air traffic management perspective. The book by Beloababa, Odoni and Barnhart is a suitable introduction to ATM. Furthermore, the thesis by M Jetzki is a thorough work on delay propagation over the European air traffic network. Other articles in this section are linked with the main topic of study or with complementary subjects related to it. P Bonnefoy, RJ Hansman, Emergence of secondary airports and dynamics of regional airport system in the United States, Report ICAT 2005-02 (2005)

In this work the authors analyze the use of secondary airports in the USAN network as a solution for increasing demand. This research is useful to take into account future modification in air-transport networks. P Bonnefoy, R Weibel, Air Transportation System Architecture Analysis, (2006) A detailed explanation of the structure of the USAN and analyzed by each component (layer). This work is useful to understand the main characteristics and properties of the USAN and it is a source of comparison with the European air-transportation network. N G. Rupp, Further Investigations into the Causes of Flight Delays (2007) [available at: http://www.ecu.edu/cs-educ/econ/wp2007.cfm] This paper provides some useful quantities concerning flight delays and approaches the subject from the passengers and airline perspective. Furthermore, it explores some of the causes of flight delays that provides the BTS. P Belobaba, A Odoni, C Barnhart, The Global Airline Industry, John Wiley & Sons (2009).

It is a valuable introduction to the air transport system and a roadmap to the ATM field. It is based mainly in US research in the field. This book brings together topics such as air transportation economics, operations, planning, industrial relations and human resources. Related to our research project, particular interest is dedicated to chapters that deal with planning and operations. To the purpose of the investigation the most important chapters are:

Chapter 5. Airline Operating Costs and Measures of Productivity This project is expected to help devise more efficient strategies for delay management therefore it is expected to enhance productivity. Taking this into account, it is important to have an idea how this expected improvement will affect airline operating costs. Other points covered in this chapter that are appropriate to appreciate the context of our project in the ATM field are: comparison of operating expenses between legacy and low-cost airline, common measures of aircraft and employee productivity and different types of commercial aircraft.

Chapter 6. The Airline Planning Process. This chapter is of particular interest because it provides a snapshot of decisions related to scheduling and route planning. Because of the profound connection that the latter have with flight delays, to understand the fundamental decisions behind mid-term planning is of major importance.

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Chapter 7. Airline Schedule Optimization. More insight is given in this chapter to topics covered in the previous one. It provides an overview of fleet planning, airline scheduling development and reviews some optimization models to develop of flight schedules.

Chapter 8. Airline Flight Operations. This chapter examines how flight operations, including maintenance, ground service regulations and scheduling of flight crews, are conducted in the current ATM system.

Chapter 9. Irregular Operations: Schedule Recovery and Robustness.

An optimized schedule rarely is fulfilled without deviations. In this chapter the author investigates two substantial sources of disruption: airport and airspace capacity shortages and airline resource shortages.

M. Jetzki, PhD Thesis, The propagation of air transport delays in Europe. Department of Airport and Air Transportation Research RWTH Aachen University (2009). In this thesis, the author focuses in the analysis of delay propagation through the European air-traffic network. This study is one of the first that includes reactionary delays as part of the problem. Jetzki, also examine the problem taking into account the different airline business models. This thesis is appropriate to our project because it thoroughly describe the European data sources; main characteristics, limitations and errors. Furthermore, it explains the data process used to clean it from errors. It introduces important ATM concepts and explains the key performance indicators (KPI) used to quantify and describe the propagation of delays. R Beatty, R Hsu, L Berry, J Rome, 2nd USA/Europe air traffic management R&D seminar, Preliminary evaluation of flight delay propagation through an airline schedule, [available at: http://www.atmseminar.org/seminarContent/seminar2/papers/p_038_APMMA.pdf]

The analysis of flight delay causes is based on airline schedules and it shows how the flight delay propagates through different flight legs due to crew and aircraft connectivity. It also, comes up with the concept of Delay Multiplier (DM), useful for the analysis of reactionary delay in our research project.