Build up vessel dynamic information platform by combining automatic identification system (AIS) and liner shipping schedule

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簡歷

基本資料

Project title

Code/計畫編號

MOST109-2622-E019-009

Translated Name/計畫中文名

結合船舶自動識別系統(AIS)與定期航運船期建置船舶動態資訊整合平台

Project Coordinator/計畫主持人

Feng-Ming Tsai

Funding Organization/主管機關

National Science and Technology Council

Co-Investigator(s)/共同執行人

李選士

Department/Unit

Department of Shipping and Transportation Management

Website

https://www.grb.gov.tw/search/planDetail?id=13609510

Year

2020

Start date/計畫起

01-11-2020

Expected Completion/計畫迄

31-10-2021

Bugetid/研究經費

660千元

ResearchField/研究領域

交通運輸

隨者大數據資料在海運業應用越來越廣泛，航商與託運人紛紛投入資源建構智慧海運資訊系統，以提升企業營運的效率及開創新的商業模式。目前雖有許多海運平台，但資訊太過凌亂，讓使用者花大量的時間蒐集及整合資訊，因此需要建置一套良好的船舶即時動態資訊平台。本產學合作計畫主要目的在協助產學合作廠商結合船舶自動識別系統(AIS)的大數據資料庫及定期貨櫃航商航期，建立一套適合海運產業在定期貨櫃航線船舶之準點率及艙位供給的評選機制，提供給航商、船務代理人、承攬業者、託運人、收貨人等，船舶即時動態資訊轉換為高度可視化的資訊，以提升海運運輸整體效率，藉由建立船舶及船隊準點率評分系統，作為調整船隊的營運部署及艙位預定的參考依據，進而提高整體貨物運輸效率，提升企業整體國際競爭力。本研究利用類神經網路(Artificial Neural Network)方法預測船對中每一艘船舶的準點率，將預測的結果與AIS的即時位置資訊比對，以作為校估預測模型之參考標準，進而得以提高預測船隊準點率之情形，透過預測結果調整船隊，以提供航商作為船隊規劃管理之依據。The application of big data in the maritime industry becomes more and more extensive, liners and forwarders have invested resources in integrating intelligent maritime information systems to improve the efficiency of corporate operations and create new business models. Although there are some existing information platform, but it is too messy to let users costly collect and integrate information. Therefore, it is necessary to build a good ship real-time information platform. The main purpose of this study is to assist industries to combine the database of Automatic Identification System (AIS) and the schedules of liner service routes to establish a punctuality rates and space supply selection mechanism suitable for shipping companies, shipping agents, contractors, shippers, consignees, etc. The real-time dynamic information of ships is converted into highly visualized information to improve the overall efficiency of maritime transportation. A scoring system for the punctuality rate of ships and fleets is established as a reference basis for adjusting the operational deployment and space reservation of the fleet. This study applies artificial neural network (ANN) to predict the punctuality rate of each ship, and compares the results with the real-time position information of AIS to calibrate the prediction model. In order to ensure the punctuality rate of the forecast is improved, the fleet is adjusted through the forecast results to provide the carrier as the basis for fleet planning and management.

Keyword(s)

船舶自動識別系統
即時動態資訊平台
類神經網路
定期貨櫃航線
Automatic Identification System (AIS)
real-time information platform
Artificial Neural Network(ANN)
liner service routes

DSpace CRIS

Build up vessel dynamic information platform by combining automatic identification system (AIS) and liner shipping schedule

基本資料

Description