Dynamics of Air Transport Networks: A Review from a Complex Systems Perspective
📝 Abstract
Air transport systems are highly dynamic at temporal scales from minutes to years. This dynamic behavior not only characterizes the evolution of the system but also affect the system’s functioning. Understanding the evolutionary mechanisms is thus fundamental in order to better design optimal air transport networks that benefits companies, passengers and the environment. In this review, we briefly present and discuss the state-of-art on time-evolving air transport networks. We distinguish the structural analysis of sequences of network snapshots, ideal for long-term network evolution (e.g. annual evolution), and temporal paths, preferred for short-term dynamics (e.g. hourly evolution). We emphasize that most previous research focused on the first modeling approach (i.e. long-term) whereas only a few studies look at high-resolution temporal paths. We conclude the review highlighting that much research remains to be done, both to apply already available methods and to develop new measures for temporal paths on air transport networks. In particular, we identify that the study of delays, network resilience and optimization of resources (aircraft and crew) are critical topics that can benefit of temporal network analysis.
💡 Analysis
Air transport systems are highly dynamic at temporal scales from minutes to years. This dynamic behavior not only characterizes the evolution of the system but also affect the system’s functioning. Understanding the evolutionary mechanisms is thus fundamental in order to better design optimal air transport networks that benefits companies, passengers and the environment. In this review, we briefly present and discuss the state-of-art on time-evolving air transport networks. We distinguish the structural analysis of sequences of network snapshots, ideal for long-term network evolution (e.g. annual evolution), and temporal paths, preferred for short-term dynamics (e.g. hourly evolution). We emphasize that most previous research focused on the first modeling approach (i.e. long-term) whereas only a few studies look at high-resolution temporal paths. We conclude the review highlighting that much research remains to be done, both to apply already available methods and to develop new measures for temporal paths on air transport networks. In particular, we identify that the study of delays, network resilience and optimization of resources (aircraft and crew) are critical topics that can benefit of temporal network analysis.
📄 Content
Dynamics of Air Transport Networks: A Review from a Complex Systems Perspective Luis E C ROCHA1,2 1Department of Mathematics, Université de Namur Rue Rempart de la Vierge 8 B5000 Namur, Belgium 2Department of Public Health Sciences, Karolinska Institutet Tomtebodavägen 18A S17177 Stockholm, Sweden luis.rocha@ki.se May 17, 2016
Abstract
Air transport systems are highly dynamic at temporal scales from minutes to years. This dynamic behavior not only characterizes the evolution of the system but also affect the system’s functioning. Understanding the evolutionary mechanisms is thus fundamental in order to better design optimal air transport networks that benefits companies, passengers and the environment. In this review, we briefly present and discuss the state-of-art on time-evolving air transport networks. We distinguish the structural analysis of sequences of network snapshots, ideal for long-term network evolution (e.g. annual evolution), and temporal paths, preferred for short-term dynamics (e.g. hourly evolution). We emphasize that most previous research focused on the first modeling approach (i.e. long-term) whereas only a few studies look at high-resolution temporal paths. We conclude the review highlighting that much research remains to be done, both to apply already available methods and to develop new measures for temporal paths on air transport networks. In particular, we identify that the study of delays, network resilience and optimization of resources (aircraft and crew) are critical topics that can benefit of temporal network analysis.
Keywords: Air Transport; Complex Network; Airport Network; Temporal Network; Dynamic Network
- Introduction
Air transport has been increasingly important as means of transportation in both developed and undeveloped countries [1,2]. Although associated to relatively high costs, air transport is generally safer and faster in comparison to other means of transportation [3,4] particularly to connect isolated rural areas and islands with urbanized areas, or to connect mutually distant locations such as cities in different continents. Unfortunately, air transport also contributes for the efficient spread of infectious diseases over large spatial regions [5,6]. Similarly to other modes of transport, airplanes follow pre-defined airways according to regulations of the aerial space of a given country. The collection of source-destination of flights however fundamentally characterizes the air transport network irrespective of the routes taken by aircrafts.
Altogether, pairs of cities (or airports) form a complex network of flights in which nodes represent the locations and links represent the fact that at least one flight occurred between the two locations during some interval of time [7-9]. This network perspective helps to understand the inter-connections and inter-dependencies between the multiple parts of the air transport system. On the other hand, the network framework also decreases the own complexity of air transport by reducing the model to pair-wise interactions without taking directly into account particularities of the system, as for example, impact of the weather, official regulations or types of aircrafts. This information however can be added in a sophisticated dynamic network model. Such simplifying approach is not exclusive of air transport networks but has been used in various disciplines to study the most diverse natural and man-made systems [8,9]. It helps to identify the most relevant mechanisms driving the evolution and functioning of the system. The goal is to reduce the complexity of the problem by focusing on the structure of connections between its parts. There are a number of studies focusing on the structural properties of air transport network [7,8,10]. Such studies have been increasingly appreciated by scholars studying air transport using standard methods [11]. By using network science, one is able to identify the centrality or importance of certain airports at a global or regional scale. In other words, one is able to identify bottlenecks or clusters of airports with global relevance beyond the trivial measures of accumulated traffic or size of an airport. Due to the architecture of air transport system, sometimes, medium-size airports are more strategic to connect different parts of the network than larger hub-airports [12]. These central airports may not only indicate fragile parts of the network [12], i.e. failure or attack of these airports may severely disrupt a large portion of the network, but also indicate strategic airports to implement screening and infection control in order to avoid worldwide pandemics.
One important feature of air transport networks is their dynamic structure. The timings of departure and arrival of flights vary considerable within a day, during the week or at different seasons. Given these intrinsically dynamic characteristics, t
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