Academic Journal of Humanities & Social Sciences, 2019, 2(5); doi: 10.25236/AJHSS.020503.

## Port Congestion Evaluation Based on Gaussian Density Clustering——Taking Qingdao Port as an Example

Author(s)

Lingyi Li

Corresponding Author:
Lingyi Li
Affiliation(s)

College of Transport&Communications, Shanghai Maritime University, Shanghai 200000, China

### Abstract

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.

### Keywords

Port congestion, Clustering, Gaussian density clustering

### Cite This Paper

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.

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