A data fusion approach to predict shipping efficiency for bulk carriers

Maritime waterways are critical transportation systems that connect economies and manufacturing centers. Growing demand for freight movement, along with industry commitment to minimize its environmental impact, has increased emphasis on port vessel efficiency. Yet, few objective performance measures exist inform decision making system improvements. There is an existing gap in quantifiable metrics maritime transport which motivated this work investigate waterway efficiencies through big data analytics. Availability of affords practitioners researchers the opportunity develop new performance-based improve logistics. This study focused short sea shipping logistics iron ore Great Lakes makes three fundamental contributions. Principally, we propose a efficiency (MTE) metric attained fusion from Automatic Identification System (AIS) navigation lock integrates travel time payload. We present linear model predict capacity based water surface elevation will enable better adapt seasonal changes dredging needs specific Lakes. Additionally, statistics bulk carriers observed historical AIS extends body knowledge earlier works establishes reference performance. Techniques presented here effective capturing non-linear interconnected system. data-driven approach offers insights planning optimization direct applications inland systems. These fleet allow querying simulation cost impact investment strategies aimed or maximize value operational expenses.