地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (1): 153-164.doi: 10.12082/dqxxkx.2022.210144

• 地理空间分析综合应用 • 上一篇    下一篇

AIS数据在集装箱港口服务效率的应用研究

陈伟杰1(), 赵楠2, 张婕姝2, 宋炳良1,*()   

  1. 1.上海海事大学 经济管理学院,上海 201306
    2.上海海事大学 上海国际航运研究中心,上海 201306
  • 收稿日期:2021-03-23 修回日期:2021-05-23 出版日期:2022-01-25 发布日期:2022-03-25
  • 通讯作者: * 宋炳良(1958— ),男,浙江平湖人,博导,教授,主要从事港口经济、交通运输规划与管理研究。 E-mail: blsong@shmtu.edu.cn
    * 宋炳良(1958— ),男,浙江平湖人,博导,教授,主要从事港口经济、交通运输规划与管理研究。 E-mail: blsong@shmtu.edu.cn
  • 作者简介:陈伟杰(1991— ),男,河南信阳人,博士,助理研究员,主要从事港口发展、航运中心建设研究。E-mail: 18817338520@163.com
  • 基金资助:
    国家社会科学基金一般项目(20BJY177)

Research on Service Efficiency of Container Ships at Port based on AIS Data

CHEN Weijie1(), ZHAO Nan2, ZHANG Jieshu2, SONG Bingliang1,*()   

  1. 1. School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
    2. Shanghai International Shipping Institute, Shanghai Maritime University, Shanghai 201306, China
  • Received:2021-03-23 Revised:2021-05-23 Online:2022-01-25 Published:2022-03-25
  • Supported by:
    National Social Science Fund of China(20BJY177)

摘要:

港口是物流供应链中的核心环节,港口服务效率会决定整个物流供应链的效率。本文提出了一种基于海量船舶AIS(Automatic Identification System)轨迹数据的港口服务效率计算框架,利用集装箱船舶AIS轨迹、港口地理信息等海事大数据,采用滑动窗口算法等数据挖掘方法判断船舶在港内的状态,估算出反映港口服务效率的AWT/AST指标,从时间维度对港口服务效率评价,为港口管理运营部门和航运公司决策提供参考。并以上海港、宁波港、深圳港、釜山港为例,采用2018年全年全球5600余艘集装箱船舶的AIS轨迹数据,量化评价4个亚洲集装箱港口的服务效率。结果显示:① 船舶抵港泊位作业时间近似正太分布,正太分布均值在14~18 h之间,船舶泊位作业时间集中在10~30 h;② 船舶泊位作业时间与船舶船型大小成正相关,船型越大则泊位作业时间越长;③ 32%的船舶抵达上海港会发生等待时间,体现上海港集装箱码头整体处于供不应求的状态。宁波港整体服务效率较高,船舶发生等待事件较少。作为区域性枢纽港,釜山港近洋区域性运输频繁使得釜山港抵港船舶频率较高。④ 洋山四期码头为自动化码头,其港口装卸工艺与其他码头不同,但其码头作业效率并未高很多。

关键词: 水路运输, 数据挖掘, AIS船舶数据, 港口效率, 相关性分析, 泊位作业时间, 锚地等待时间, 滑动窗口算法

Abstract:

As the key node of logistics supply chain, port service plays a very important role in the flow of cargos. In this paper, we introduce a computation framework of port service efficiency based on maritime big data. We use ship AIS trajectories and port geographic information to detect the status of the ship based on the sliding window algorithm and estimate the service efficiency of container ships in port. The service efficiency of the container ports, including Shanghai, Ningbo, Shenzhen, and Busan, is evaluated based on the statistical analysis. The results of this paper show that: (1) The ship's arrival berthing time is approximately normally distributed. The average value of the normal distribution is between 14~18 h, and the ship's berthing time ranges between 10~30 h; (2) Ship berthing time is positively correlated with ship size, the berth operation time increases with larger ship sizes; (3) In Shanghai Port, nearly 32% of the ships suffered from port congestion because of waiting for berth. The service efficiency of Ningbo Port is relatively high, and there are fewer waiting incidents for ships. Busan Port also shows a higher frequency of ship arrivals; (4) The Phase IV of Yangshan Deepwater Port is an automated container terminal, having a different port handling technology from other terminals. However, its terminal service efficiency doesn't increase.

Key words: water transport, data mining, AIS data, port service efficiency, correlation analysis, berthing time, waiting time, sliding window algorithm