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Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
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Department of Geography, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
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Engineering Research Center for Spatiotemporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan 430079, China
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National & Local Joint Engineering Research Center of Geo-spatial Information Technology, Fuzhou University, Fuzhou 350002, China
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Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
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School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4197; https://doi.org/10.3390/s19194197
Received: 17 August 2019 / Revised: 21 September 2019 / Accepted: 25 September 2019 / Published: 27 September 2019
(This article belongs to the Section Intelligent Sensors)
Automatic Identification System (AIS) data could support ship movement analysis, and maritime network construction and dynamic analysis. This study examines the global maritime network dynamics from multi-layers (bulk, container, and tanker) and multidimensional (e.g., point, link, and network) structure perspectives. A spatial-temporal framework is introduced to construct and analyze the global maritime transportation network dynamics by means of big trajectory data. Transport capacity and stability are exploited to infer spatial-temporal dynamics of system nodes and links. Maritime network structure changes and traffic flow dynamics grouping are then possible to extract. This enables the global maritime network between 2013 and 2016 to be investigated, and the differences between the countries along the 21st-century Maritime Silk Road and other countries, as well as the differences between before and after included by 21st-century Maritime Silk Road to be revealed. Study results indicate that certain countries, such as China, Singapore, Republic of Korea, Australia, and United Arab Emirates, build new corresponding shipping relationships with some ports of countries along the Silk Road and these new linkages carry significant traffic flow. The shipping dynamics exhibit interesting geographical and spatial variations. This study is meaningful to policy formulation, such as cooperation and reorientation among international ports, evaluating the adaptability of a changing traffic flow and navigation environment, and integration of the maritime economy and transportation systems. View Full-Text
Keywords: maritime network; multi-layer dynamics; traffic flow maritime network; multi-layer dynamics; traffic flow
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MDPI and ACS Style

Yu, H.; Fang, Z.; Lu, F.; Murray, A.T.; Zhao, Z.; Xu, Y.; Yang, X. Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road. Sensors 2019, 19, 4197. https://doi.org/10.3390/s19194197

AMA Style

Yu H, Fang Z, Lu F, Murray AT, Zhao Z, Xu Y, Yang X. Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road. Sensors. 2019; 19(19):4197. https://doi.org/10.3390/s19194197

Chicago/Turabian Style

Yu, Hongchu, Zhixiang Fang, Feng Lu, Alan T. Murray, Zhiyuan Zhao, Yang Xu, and Xiping Yang. 2019. "Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road" Sensors 19, no. 19: 4197. https://doi.org/10.3390/s19194197

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