abbr. SJ GMU
ISSN 2657-5841 (printed)
ISSN 2657-6988 (online)
Clustering analysis and identification of marine traffic congested zones at Wusongkou, Shanghai
Shanghai, with its natural, cultural and historical wealth, is not only one of China’s most beautiful cities, but it is also one of the most exciting cities in the world. However, there are enormous challenges for navigation in the Shanghai Strait due to its geographical, geopolitical and oceanographic structure. One of the challenges is the marine traffic which crosses from one side to other of the strait. In this study, an attempt is made to identify of vessel traffic zones based on DBSCAN in the Wusongkou. It is located along the north end of Huangpu river which flows from South-West of Shanghai to the North-East and flows into Yangtze river. Ship’s domain is introduced into the DBSCAN algorithm, a particle suitable clustering algorithm is improved for clustering the real-time ship’s dynamic data and detecting potential traffic congested areas at sea, and define three neighborhood models. In addition, fuzzy evaluation model is applied to identify traffic congestion degree. At the end of study, combining the improved DBSCAN algorithm and fuzzy evaluation model for traffic congestion degree, using three neighborhood models with different size to analyses the AIS data from the vessels nearby Wusongkou in Shanghai, and build the corresponding figure of traffic condition visualisation, used to visualise the evaluation result. The result indicate that the neighborhood three model (length is seventeen times of ship’s length, width is six point four times of ship’s length plus ship’s width) can identify the traffic congested zones better.
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