College of Transport&Communications, Shanghai Maritime University, Shanghai 200000, China
By extracting and processing the AIS data, the berthing and berthing records of a certain port are obtained for a period of time, and the time required for different ships to enter and leave the port under the congestion of such ports is calculated, and the grading is used as an evaluation index of the congestion degree of the port. The classification of the category boundary is completed by the clustering method, wherein the Gaussian fitting curve can determine the maximum and minimum values of the index, and finally determine the level limit by optimizing the maximum and minimum point points, and the ship data is obtained. Divided into different congestion states. It provides research ideas for the analysis of ship congestion, and also provides data support for mitigating port congestion.
Port congestion, Clustering, Gaussian density clustering
Lingyi Li. Port Congestion Evaluation Based on Gaussian Density Clustering——Taking Qingdao Port as an Example. Academic Journal of Humanities & Social Sciences (2019) Vol. 2, Issue 5: 16-30. https://doi.org/10.25236/AJHSS.020503.
 Reasons for the congestion of container terminal yard and countermeasures [J]. Wang Weibin. China Water Transport. 2019 (04)
 Xu Jianhua.Exploration of the Causes of Congestion in Shanghai Port Container Terminal and Suggestions [J]. Chinese ship inspection, 2017 (07): 16-18+108-109.
 Wang Furong, Ding Yi, Lin Guolong. Optimization of Coastal Shipping Network Considering Port Congestion Factors [J]. Computer Engineering and Applications, 2017, 53 (10): 218-224.
 Lin Guolong, Ma Libo, Zhang Chenyan, He Hongdi. Multifractal Study on Congestion Index of Ports in China and Brazil [J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2016, 35 (06): 148-152.
 Zhijia Tan, Wan Li, Xiaoning Zhang, Hai Yang. Service charge and capacity selection of an inland river port with location-dependent shipping cost and service congestion [J]. Transportation Research Part E, 2015, 76.
 Matteo Balliauw, Peter M. Kort, Hilde Meersman, Eddy Van de Voorde, Thierry Vanelslander. The case of public and private ports with two actors: capacity investment decisions under congestion and uncertainty [J]. Case Studies on Transport Policy, 2019.
 Wan, Zhang, Li. Port competition with accessibility and congestion: a theoretical framework and literature review on empirical studies [J]. Maritime Policy & Management, 2018, 45 (2).
 Lin Tianyi, Lu Chunxia.A Model of Hub-Ship Maritime Network Hub Port Selection Based on Congestion Control [J]. Journal of Shanghai Maritime University, 2013, 34 (04): 59-66.
 Gao Weibo, Du Taili, Zhang Yong, Huang Lianzhong.Application of Clustering Algorithm in Ship Energy Efficiency Data Mining [J]. Journal of Wuhan University of Technology (Transportation Science and Engineering Edition), 2019, 43 (02): 286-290.
 Zhang Yonglai, Zhou Yaojian. A Survey of Clustering Algorithms [J/OL]. Computer application: 1-14 [2019-05-22]. http://kns.cnki.net/kcms/detail/ 51.1307.TP.20190415.1412.004.html.
 Li Zhifeng, Zhang Wei. Analysis and Evaluation of Clustering Analysis Algorithm [J]. Electronic Technology and Software Engineering, 2019 (07): 157.
 Huang Bingsen, Chen Yuzhong, Guo Kun. A Community Discovery Algorithm Based on Improved Density Peak Clustering [J]. Microcomputer system, 2019, 40 (04): 782-786.
 Wang Feiyu, Hu Zhixiang, Huang Wei. Modal Parameter Identification Based on Density Peak Clustering Algorithm [J]. Vibration and shock, 2019, 38 (02): 172-178.
 Sun Zhijun, Xue Lei, Xu Yangming, Wang Zheng. A Review of Deep Learning Research [J]. Application Research of Computers, 2012, 29 (08): 2806-2810.
 Chen Shuwen. Port congestion problems endanger the global shipping system [J]. China Ocean Shipping, 2007 (03): 24-25.