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基于船舶大数据的港口装卸效率值计算方法
引用本文: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. DOI: 10.16097/j.cnki.1009-6744.2021.02.031
作者姓名:廖诗管  杨冬  白茜文  翁金贤
作者单位: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
基金项目:国家自然科学基金/National Natural Science Foundation of China(71971185,52072237)。
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...

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
收稿时间:2020-12-11

Estimation Method of Port Handling Efficiency Value Based on Ship Big Data
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. DOI: 10.16097/j.cnki.1009-6744.2021.02.031
Authors:LIAO Shi-guan  YANG Dong  BAI Xi-wen  WENG Jin-xian
Affiliation:1.a. 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
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.
Keywords:waterway transportation  port handling efficiency  GMap visualization technology  container port  AIS data  
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