The Structure and Periodicity of the Chinese Air Passenger Network

Hongqi Li, Haotian Wang, Ming Bai and Bin Duan
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Hongqi Li: School of Transportation Science and Engineering, Beihang University. No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Haotian Wang: School of Transportation Science and Engineering, Beihang University. No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Ming Bai: School of Transportation Science and Engineering, Beihang University. No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Bin Duan: School of Transportation Science and Engineering, Beihang University. No. 37 Xueyuan Road, Haidian District, Beijing 100191, China

Sustainability, 2018, vol. 11, issue 1, 1-16

Abstract: China’s air transportation system is evolving with its own unique mechanism. In particular, the structural features of the Chinese air passenger network (CAPN) are of interest. This paper aims to analyze the CAPN from holistic and microcosmic perspectives. Considering that the topological structure and the capacity (i.e., available passenger-seats) flow are important to the air network’s performance, the CAPN structure features from non-weighted and weighted perspectives are analyzed. Subnets extracted by time-scale constraints of one day or every two-hours are used to find the temporal features. This paper provides some valuable conclusions about the structural characteristics and temporal features of the CAPN. The results indicate that the CAPN has a small-world and scale-free structure. The cumulative degree distribution of the CAPN follows a two-regime power-law distribution. The CAPN tends to be disassortative. Some important airports, including national air-hubs and local air-hubs, remarkably affect the CAPN. About 90% of large capacities exist between airports with large degrees. The properties of CAPN subnets extracted by taking two hours as the time-scale interval shed light on the air network performance and the changing rule more accurately and microcosmically. The method of the spectral destiny estimation is used to find the implicit periodicity mathematically. For most indicators, a one-day cycle, two-day cycle, and/or three-day cycle can be found.

Keywords: air passenger network; complex network; structural property; temporal feature; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
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