China Communications and Transportation Association

Beijing, China

China Communications and Transportation Association

Beijing, China

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Wang J.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Mo H.,China Communications and Transportation Association | Wang F.,Louisiana State University | Wang F.,Yunnan University of Finance and Economics
Journal of Transport Geography | Year: 2014

This paper analyzes the evolution process of the air transport network of China (ATNC) since 1930. Based on the network analysis results, the ATNC has significantly improved in connectivity based on (1) rising alpha, beta and gamma indices, (2) declining diameter and centre index and (3) decreasing average path length and increasing clustering coefficient. The network centralization index reveals a fluctuation phase before 1952, a pre-1980 centralization phase before the economic reform era, a centralization phase after the mid-1990s deregulation, and a decentralization phase between. The k-core decomposition method helps identify the evolution of core network and hierarchy of the ATNC over time. The spatial development model characterizes its structure change in six stages: (1) scattered development, (2) trunk line connection, (3) circular linkage, (4) hub formation, (5) a complex network structure, and (6) emerging multi-airport systems. © 2014 Elsevier Ltd.


Wang J.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Mo H.,China Communications and Transportation Association | Wang F.,Yunnan University of Finance and Economics
Journal of Transport Geography | Year: 2014

This paper analyzes the evolution process of the air transport network of China (ATNC) since 1930. Based on the network analysis results, the ATNC has significantly improved in connectivity based on (1) rising alpha, beta and gamma indices, (2) declining diameter and centre index and (3) decreasing average path length and increasing clustering coefficient. The network centralization index reveals a fluctuation phase before 1952, a pre-1980 centralization phase before the economic reform era, a centralization phase after the mid-1990s deregulation, and a decentralization phase between. The k-core decomposition method helps identify the evolution of core network and hierarchy of the ATNC over time. The spatial development model characterizes its structure change in six stages: (1) scattered development, (2) trunk line connection, (3) circular linkage, (4) hub formation, (5) a complex network structure, and (6) emerging multi-airport systems. © 2014 Elsevier Ltd. All rights reserved.


Wang J.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Wang J.,University of Oxford | Cheng Y.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Cheng Y.,Beijing Normal University | Mo H.,China Communications and Transportation Association
Sustainability (Switzerland) | Year: 2014

Border ports play a substantial role in socio-economic exchanges, which reflect the diplomatic relations between neighboring countries. This paper maps and analyzes the evolution process of border ports in China since the 1930s, in terms of the spatial distribution, transport modes, cargo and flows of people. Four development modes of border ports and cities are summarized based on the functions and development level of border ports and their proximity to urban core areas. The four modes include: (1) Port-Port mode; (2) City-Port-Port-City mode; (3) City (Port)-Port-City mode; (4) City (Port)-City (Port) mode, which also reflect the spatio-temporal evolution process of certain border ports and cities. The results show that the development of border ports is closely related to the bilateral relations with neighboring countries and their complementarities of natural resources and economic development, national foreign policies, as well as the physical, historical and cultural context. The findings of this study are helpful to promote the sustainable development of the border port system which is crucial for win-win reciprocity between China and its neighboring countries. © 2014 by the authors.


Wang H.-R.,China Communications and Transportation Association | Wang H.-R.,China University of Mining and Technology | Gao Y.-E.,Harbin Institute of Technology | Nie B.-S.,China University of Mining and Technology | Sun Z.-G.,China Communications and Transportation Association
Chang'an Daxue Xuebao (Ziran Kexue Ban)/Journal of Chang'an University (Natural Science Edition) | Year: 2013

In order to determine speed limits of second-class arterial highway, parameter such as traffic flow, roadway and environment characteristics were collected at segments which met reasonable speed limit assumptions. In view of significant differences among speed samples of various roadway segment types, dummy variables were established to reflect their influences on speed limit value. Multi-linear regression speed limit model and panel data speed limit model of second-class arterial highway were established respectively. On comparing these two models in aspects of statistical indicators and speed limit influencing factors, it could be concluded that multi-linear regression method could obtain better effect than panel data regression method. The results show that operating speed, terrain, subgrade width, the density of entrances and exits, large vehicle ratio, and roadway segments with small radius and large slope are main influencing factors of second-class arterial highway speed limit for speed zone, and operating speed is the decisive factor. The results quantify the effect of influencing factors on second-class arterial highway speed zone, which provides a new attempt for solving speed limit problem.


Wang J.-E.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Mo H.-H.,China Communications and Transportation Association
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | Year: 2014

Graph index and complex network methods were used to evaluate the evolution process of China's air transport network (ATNC) during 1952-2008. The allometric growth was explored in the development history of ATNC, with the fluctuating growth of nodes (cities) and edges (airlines or city-pairs). The average path length in ATNC was reduced from 5.74 in 1952 to 2.24 in 2008, which showed spatiotemporal convergence and increasing efficiency. In contrast, the clustering coefficient rose from 0 to 0.69. Both the average path length and the clustering coefficient indicated a developing trajectory of small-world network. A hierarchical structure was shaped in the upper airport system with degree over 14. Degree distribution showed the long-tail characteristics from 1952 and 1962, and then turned to a scale-free network. The degree-degree correlation shows as an inverse-U pattern, which is affected by complicated factors such as distance, technology, and economic elements. In summary, the paper gives an analysis on the evolution process of ATNC, which supplement the shortage of the complex network theory model and its application in air transport network, and provides an experimental base for establishing theoretical evolution models.

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