Title: Estimating vessel payloads in bulk shipping using AIS data

Authors: Haiying Jia; Vishnu Prakash; Tristan Smith

Addresses: Centre for Applied Research, Norwegian School of Economics, Helleveien 30, 5045 Bergen, Norway ' UCL Energy Institute, University College of London, Central House, 14 Upper Woburn Place, London WC1H 0NN, UK ' UCL Energy Institute, University College of London, Central House, 14 Upper Woburn Place, London WC1H 0NN, UK

Abstract: The cargo payload of a merchant vessel is a crucial variable in calculating revenue for a particular voyage and estimating global trade flows for key commodities. However, due to the opaque nature of the industry, payload information is usually not publicly available. This research utilises, for the first time, vessel draught information reported by the automatic identification system (AIS) to estimate vessel payloads. The applicability and reliability of draught measurements from AIS captured via satellites and terrestrial receivers are addressed in the process of identifying the most efficacious way to estimate vessel payloads. The performance of estimating vessel payloads using AIS draught data is compared to two models that rely on principles from physics and naval architecture, and the results show similarity and consistency. Being able to reliably estimate a vessel's payload is essential in assessing vessel utilisation, fleet productivity, and subsequently the supply and demand conditions in shipping markets.

Keywords: automatic identification system; AIS; cargo payload; draught; capacity utilisation; maritime big data; bulk shipping; line-up reports; trade flow; commodity; satellite; naval architecture.

DOI: 10.1504/IJSTL.2019.096864

International Journal of Shipping and Transport Logistics, 2019 Vol.11 No.1, pp.25 - 40

Received: 09 Mar 2017
Accepted: 26 Nov 2017
Published online: 12 Dec 2018 *