Source-linked AI summary
Evolution of Chinese airport network
Jun Zhang, Xian-Bin Cao, Wen-Bo Du, Kai-Quan Cai
TL;DR
The paper asks how the Chinese Airport Network evolves as aviation infrastructure and traffic develop alongside economic growth. Using complex-network analysis of CAN topology and traffic, it finds stable aggregate topology with internal switching, exponentially growing seasonal traffic, and city-dependent passenger–cargo relationships.
Problem
The paper investigates how an economically important airport network evolves in topology, traffic, and airport or airline relevance.
Method
The study applies complex network theory to CAN, analyzing topology data, traffic flows, and their relationship with airport degree.
Results
CAN’s main topological indicators remain quite stationary despite dynamic switching, while traffic grows exponentially with seasonal fluctuations and airport throughput correlates nonlinearly with degree.
Takeaways & Limitations
Passenger and cargo traffic are positively related, but their ratios differ across city types, providing insight into national airport-network evolution.
Takeaways & Limitations
Topology analysis uses scheduled domestic timetable data, excludes international airlines, aggregates multiple airports in one city, and may differ from real flights because of weather or emergencies.
Abstract
from arXiv · showhide
With the rapid development of economy and the accelerated globalization process, the aviation industry plays more and more critical role in today's world, in both developed and developing countries. As the infrastructure of aviation industry, the airport network is one of the most important indicators of economic growth. In this paper, we investigate the evolution of Chinese airport network (CAN) via complex network theory. It is found that although the topology of CAN remains steady during the past several years, there are many dynamic switchings inside the network, which changes the relative relevance of airports and airlines. Moreover, we investigate the evolution of traffic flow (passengers and cargoes) on CAN. It is found that the traffic keeps growing in an exponential form and it has evident seasonal fluctuations. We also found that cargo traffic and passenger traffic are positively related but the correlations are quite different for different kinds of cities.
1 Introduction
The paper frames airport systems as complex networks and investigates how the Chinese Airport Network evolves, building on evidence that airport-network structure can remain statistically stable while changing internally.
- Complex network theory represents airports as vertices and flights as edges, enabling analysis of aviation infrastructure as a network.
- Prior studies found that worldwide airport networks combine scale-free and small-world properties, while highly connected airports are not necessarily the most central.
- Studies of US and Brazilian airport networks reported stationary aggregate indicators alongside microscopic link changes and shifting airport and airline relevance.
- This paper examines CAN from 1950 to 2008, using detailed traffic data from 1991 to 2008 and detailed topology data from 2002 to 2009.
- The study analyzes CAN data, topology, traffic-flow evolution, and the relationship between network structure and traffic.
2 Development of CAN with Chinese GDP
Chinese GDP, airports, airlines, and aviation traffic do not evolve identically: airport and airline counts show constrained or episodic growth, while traffic grows with GDP and was nearly unchanged between 2007 and 2008.
- Figure 1 tracks Chinese GDP, airline counts, and airport counts from 1950 to 2008, while Figure 2 tracks passenger and cargo development and their relations with GDP.
- 1950–2008 airport growth occurred mainly during 1950–1975, 1987–1995, and 2005–2008, with stable counts between these periods.The first two increases connected large and medium prefecture-level cities, respectively.
- Since 1995, the number of airlines remained constant until rising in 2007–2008, largely because mature networks favor few hubs and avoid extra operating costs.New airports in 2007–2008 were accompanied by naturally launched airlines.
- Aviation traffic grows almost linearly with GDP despite constrained infrastructure growth, with 1 million RMB of GDP supporting about 7 passengers and 153 kg cargoes.
- 2008 traffic was almost the same as 2007, consistent with reported declines in important operating indicators during the global financial crisis.
3 Topological properties of CAN
CAN has stable aggregate topology but exhibits dynamic internal switching, while its first-half-2009 structure shows heterogeneous connectivity, clustering, reciprocity, and transit-airport importance.
- Degree structure: A two-regime power-law degree distribution characterizes CAN, with exponents λ1 = −0.49 and λ2 = −2.63.The directed in-degree and out-degree distributions are nearly identical to the undirected degree distribution.
- Airport importance: Betweenness generally increases exponentially with degree, but Urumqi, Xi′an, and Kunming have exceptionally high betweenness.These western cities act as transit bridges linking western airports with eastern political and economic centers.
- Evolution: CAN topology remains statistically stable from 2002 to 2009 despite ongoing internal network changes.The changes include airport additions and removals and airline switching.
- Comparison with BAN: CAN has average shortest path length d around 2.25, slightly smaller diameter than BAN, higher clustering, and greater reciprocity.About 10% of first-half-2009 paths are direct, and over 98% use no more than two flights.
- Network structure: CAN is an asymmetric small-world network with high clustering, short paths, negative degree-degree and clustering-degree correlations, and exponential betweenness-degree correlation.These properties summarize the reported first-half-2009 topology.
- Dynamic switching: Airport fluctuations usually range from 5 to 15 and changed airlines are usually below 20%, with stronger switching in late 2007 and early 2008.During that period, airport additions and removals made aON and dOR the majority of changes.
4 The traffic of CAN
CAN traffic grows exponentially after seasonal fluctuations are averaged out, while passenger and cargo traffic remain positively and linearly related but vary by city type.
- CAN traffic, including passengers and cargoes, shows evident seasonal fluctuations and increases exponentially after those fluctuations are averaged out.
- Average CAN traffic per link and per node increased about 200% during 17 years, compared with 20%–35% passenger growth in the U.S. over 10 years.
- The 2003 SARS outbreak caused a sudden drop in passenger traffic, whereas cargo traffic was not knocked by SARS.
- Airport passenger and cargo throughputs span broad distributions of five and seven orders of magnitude, respectively, with nonlinear throughput–degree relationships.
- Cargo and passenger traffic are strongly linearly correlated, but their ratios differ: the national slope is 0.045, lower for Beijing and Shanghai and higher for Chengdu and Kunming.
5 Conclusion
The paper finds that CAN’s main topological indicators remain stationary despite dynamic switching among airports and airlines, while traffic grows exponentially with seasonal fluctuations and city-dependent cargo–passenger ratios.
- Although CAN’s main topological indicators are quite stationary, airports and airlines are continuously added and removed through an underlying dynamic switching process.
- Traffic flow on CAN grows exponentially with seasonal fluctuations, and an airport’s throughput has a nonlinear correlation with its degree.
- Cargo and passenger traffic are positively related, but their ratios differ across kinds of cities.