The Structure and Periodicity of the Chinese Air Passenger Network

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.Sustainability 2019, 11, 54 2 of 16 to have attracted adequate research interest [5,6]. Analyzing the complex network model of airports considering all flights among destination airports throughout a country or the world has been the subject of many complex network studies. For air transportation, the structure and properties of the air network play a vital role. Complex network theory is naturally a useful tool for practitioners and researchers to grasp the overall air network features and flow patterns, to identify the importance of individual airports, and so on [4].(i) The literature on complex network models of airline networks.In complex network models of airline networks, the air network is modelled as a graph that comprises airports as vertices linked by flights. In the literature, the air network structure has been investigated from world-wide or national angles [1,4,[7][8][9][10][11][12][13][14][15][16][17][18][19].The world-wide airline network (WAN) has been analyzed by its topology, as well as by the perspective of its dynamics. The majority of published studies have reached the consensus that the WAN exhibits scale-free and small-world properties [7][8][9][10][11][12]. The small-world property implies that the average topological distances between vertices increase very slowly (logarithmically or even more slowly), along with the number of vertices increasing. Research on the correlation between weight and topology indicated that the correlation follows a power-law [8,9]. To the centrality of airports, the most connected vertices on the WAN ar...

Методи Рішення Задач З Організації Пасажирських Перевезень Автомобільним Транспортом

“…Авторами [37] досліджено визначення параметрів пасажиропотоку при міжміських пасажирських автобусних перевезеннях методом кластеризації траєкторії характерних точок для оцінки пасажиропотоку. Під час моделювання згенеровані траєкторії використано формулу (24) для обчислення точок ознак напрямок руху між сусідніми кадрами:…”

“…24) де: Т = {(xl, y1), (x2, y2),…,(xn, yn)} є координатна послідовність траєкторії; θi є кутом напрямок руху в i-му кадрі.Траєкторії, створені одним і тим же пасажиром, мають дуже схожі тимчасові та просторові властивості. У[37] використано різницю в часі початку для вимірювання тимчасової подібності між траєкторіями.…”