TY - JOUR
T1 - Reshaping China's urban networks and their determinants
T2 - High-speed rail vs. air networks
AU - Yang, Haoran
AU - Du, Delin
AU - Wang, Jiaoe
AU - Wang, Xiaomeng
AU - Zhang, Fan
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/11
Y1 - 2023/11
N2 - As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks.
AB - As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks.
KW - Airline
KW - Dynamic evolution
KW - High-speed railway
KW - Machine learning
KW - Urban network
UR - https://www.scopus.com/pages/publications/85171748827
U2 - 10.1016/j.tranpol.2023.09.007
DO - 10.1016/j.tranpol.2023.09.007
M3 - 文章
AN - SCOPUS:85171748827
SN - 0967-070X
VL - 143
SP - 83
EP - 92
JO - Transport Policy
JF - Transport Policy
ER -