基于船舶大数据的港口装卸效率值计算方法

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (2): 217-223.

基于船舶大数据的港口装卸效率值计算方法

廖诗管^1a，杨冬^*2，白茜文³，翁金贤^{1a, 1b}

1. 上海海事大学，a. 海洋科学与工程学院，b. 交通运输学院，上海 201306；2. 香港理工大学，物流及航运学系，香港 999077；3. 清华大学，工业工程系，北京 100084

收稿日期:2020-12-11 修回日期:2021-01-16 出版日期:2021-04-25 发布日期:2021-04-25
作者简介:廖诗管(1994- )，男，福建福鼎人，博士生。
基金资助:
国家自然科学基金/National Natural Science Foundation of China(71971185，52072237)。

Estimation Method of Port Handling Efficiency Value Based on Ship Big Data

LIAO Shi-guan^1a , YANG Dong^*2 , BAI Xi-wen³, WENG Jin-xian^{1a, 1b}

1a. College of Ocean Science and Engineering, 1b. College of Transport and Communication, Shanghai Maritime University, Shanghai 201306, China; 2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong 999077, China; 3. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China

Received:2020-12-11 Revised:2021-01-16 Online:2021-04-25 Published:2021-04-25

摘要/Abstract

摘要：

集装箱港口的装卸效率是衡量港口竞争力和吸引船公司前来挂靠的关键指标之一。为准确估计港口的装卸效率值，基于船舶自动识别系统(AIS)数据，利用 Greatmaps(GMap)可视化技术，提出一种计算港口装卸效率值的方法。利用该方法估算上海港、新加坡港、深圳港和宁波-舟山港的月度装卸效率值，4 个港口 2017 年上半年的装卸效率月度均值分别为 2.85、1.87、2.17 和 2.10。基于上半年估计的装卸效率值，对4个港口下半年的月度吞吐量进行估计，估算误差均值分别为2.77%、2.06%、2.93%和2.46%。结果表明，该方法能够较为准确地反映港口的装卸效率，可应用于推断和实时监控港口的吞吐量，为港口提高绩效和船公司选择港口策略提供理论参考，提升港口数字化管理水平。

关键词: 水路运输, 港口装卸效率, GMap可视化技术, 集装箱港口, AIS数据

Abstract:

The handling efficiency of container port is one of the key indicators that reflects the port's competitiveness and attracts shipping companies to call. To accurately estimate the port's handling efficiency value, this paper proposes a method with Greatmaps (GMap) visualization technology to calculate the port's handling efficiency value based on the data of the Automatic Identification System (AIS). Empirically, this method was applied to estimate the monthly handling efficiency values of Shanghai Port, Singapore Port, Shenzhen Port and Ningbo-Zhoushan Port, the average monthly handling efficiency values of the four ports in the first half of 2017 were respectively 2.85, 1.87, 2.17 and 2.10. Based on the obtained values in the first half of the year, the study managed to estimate the monthly throughput for the above four ports in the second half of the year, with the average estimation error being respectively 2.77% , 2.06%, 2.93% and 2.46%. The results show that the method can generate the ports' handling efficiency value with good accuracy and can be used to infer and monitor the port's throughput in real time. Further, results calculated by the method could provide a theoretical reference for the port to improve the performance and help the shipping company to choose the port strategy, and ultimately improve the port's digital management level.

Key words: waterway transportation, port handling efficiency, GMap visualization technology, container port, AIS data

中图分类号:

U653.5

廖诗管，杨冬，白茜文，翁金贤. 基于船舶大数据的港口装卸效率值计算方法[J]. 交通运输系统工程与信息, 2021, 21(2): 217-223.

LIAO Shi-guan,YANG Dong, BAI Xi-wen,WENG Jin-xian. Estimation Method of Port Handling Efficiency Value Based on Ship Big Data[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 217-223.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract30232.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I2/217

参考文献

[1] 于旭会, 唐国磊, 郭子坚, 等. 基于多智能体仿真的集装箱港口作业效率研究[J]. 水运工程, 2017(9): 83-87, 93. [YU X H, TANG G L, GUO Z J, et al. Container terminal operational performance based on multi-agent system simulation[J]. Port and Waterway Engineering, 2017(9): 83-87, 93.]

[2] CHARNES A, COOPER W W, RHODES E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1978, 2(6): 429-444.

[3] 李丹, 栾维新, 片峰. 航运企业投资对码头运营效率的影响研究[J]. 交通运输系统工程与信息, 2014, 15(1): 43- 48. [LI D, LUAN W X, PIAN F, Assessing the influence of shipping company investment for efficiency of terminal[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 15(1): 43-48.]

[4] CHEON S H, DOWALL D E, SONG D W. Evaluating impacts of institutional reforms on port efficiency changes: Ownership, corporate structure, and total factor productivity changes of world container ports[J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46(4): 546-561.

[5] WU Y C J, GOH M. Container port efficiency in emerging and more advanced markets[J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46 (6): 1030-1042.

[6] 陈龙彪, 张大庆, 李石坚, 等. 基于海事大数据的港口感知计算[J]. 地球信息科学学报, 2016, 18(11): 1485- 1493. [CHEN L B, ZHANG D Q, LI S J, et al. Port sensing computation based on maritime big data[J]. Journalof Geo-information Science, 2016, 18(11): 1485- 1493.]

[7] ZHONG H, LIN Y, YIP T L, et al. A novel oil port risk and efficiency performance measured by using AIS data and maritime open data: The case of Guangzhou, China [J]. Ocean Engineering, 2020, 216: 107859.

[8] 虞哲人. 集装箱岸桥新型组合武吊具上架的技术与应用介绍 [J]. 港口科技, 2014(5): 26-30. [YU Z R, Technology and application of combined headlock of container crane[J]. Port Science and Technology, 2014 (5): 26-30.]

[9] YANG D, WU L X, WANG S A, et al. How big data enriches maritime research: A critical review of automatic identification system (AIS) data applications [J]. Transport Reviews, 2019, 39(6): 755-773.

[10] 中交水运规划设计院有限公司, 中交第一航务工程勘察设计院有限公司.《海港总体设计规范》[S]. 北京. 人民交通出版社, 2014. [China Communications Construction Company Water Transportation Planning and Design Institute Co Ltd, China Communications Construction Company First Harbor Consultants Co, Ltd. Design code of general layout for sea ports[S]. Beijing: China Communication Press, 2014.]

[11] FENG M X, SHAW S L, PENG G J, et al. Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data[J]. Journal of Transport Geography, 2020, 86: 102741.

[12] INTERNATIONAL TRANSPORT FORUM. ITF transport outlook 2015[J/OL]. International Transport Forum and OECD, (2015-01-27) [2020-12-11]. https:// doi.org/10.1787/9789282107782-en.

[1]	廖诗管, 翁金贤, 胡甚平. 液化天然气船通航模式下的航道通过能力评价方法[J]. 交通运输系统工程与信息, 2022, 22(2): 290-297.
[2]	刘文, 汪文博. 基于秩最小化矩阵去噪的船舶轨迹重构方法[J]. 交通运输系统工程与信息, 2022, 22(1): 106-114.
[3]	靳志宏, 焉红, 王小寒, 邢磊, 徐奇. 滚装甩挂运输牵引车调度优化及作业模式类比分析[J]. 交通运输系统工程与信息, 2022, 22(1): 142-151.
[4]	李德昌, 杨华龙, 赵帅奇, 郑建风. 排放控制区下集装箱班轮运输船期设计与燃油补给联合优化[J]. 交通运输系统工程与信息, 2022, 22(1): 273-281.
[5]	邵斐, 张永锋, 真虹. 中国进口铁矿石海运网络抗毁性仿真[J]. 交通运输系统工程与信息, 2022, 22(1): 311-321.
[6]	彭子烜, 于滨. 内外贸同船运输下长江经济带穿梭巴士网络优化[J]. 交通运输系统工程与信息, 2022, 22(1): 322-331.
[7]	杨华龙, 赵帅奇, 方旭, 段静茹. 考虑海上意外时间和合作协议的班轮运输船期设计鲁棒优化[J]. 交通运输系统工程与信息, 2021, 21(6): 210-216.
[8]	严新平, 李晨, 刘佳仑, 游旭, 王树武, 马枫. 新一代航运系统体系架构与关键技术研究[J]. 交通运输系统工程与信息, 2021, 21(5): 22-29.
[9]	葛颖恩, 温馨. 环境可持续集装箱班轮运输管理研究综述[J]. 交通运输系统工程与信息, 2021, 21(4): 6-22.
[10]	楚金华, 李俊鹤, 王春娟, 陈超. 排放控制区下集装箱班轮航线路径规划与航速调度集成决策[J]. 交通运输系统工程与信息, 2021, 21(4): 230-238.
[11]	戈佳威, 袁克镖, 殷明, 王学锋. 传播动力学视角下集装箱海运网络关键港口节点识别[J]. 交通运输系统工程与信息, 2021, 21(4): 256-262.
[12]	戈艳艳，王姗姗. 考虑碳排放的港口全要素生产率及影响因素分析[J]. 交通运输系统工程与信息, 2021, 21(2): 22-29.
[13]	高天航，徐力，靳廉洁，葛彪. 考虑航艏向与数据变化差异的船舶轨迹预测[J]. 交通运输系统工程与信息, 2021, 21(1): 90-94.
[14]	房斯明，刘正江，封室丞，王新建. 船舶倾斜对不同行人流疏散效率影响的数值分析[J]. 交通运输系统工程与信息, 2021, 21(1): 201-206.
[15]	邵丽花，李宁海，唐清，宋丽英，王新昌. 制造业离岸和回流对我国港口发展的影响[J]. 交通运输系统工程与信息, 2020, 20(6): 47-56.