Data-Driven Vessel Service Time Forecasting using Long Short-Term Memory Recurrent Neural Networks

In this paper, we provide a proof of concept on how to model and forecast average Vessel Service Time $\left( {\overline {VST} } \right)$ using Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs). The proposed model is learned from the Automatic Identification System (AIS) data by using machine learning. Geohash area (GeoArea) with a certain precision, convex hull area (ConvArea), and average vessel proximity (Δ) are mined for the port of Singapore every hour. These three metrics are used to calculate port spatial complexity (SpComplexity) and port spatial density (SpDensity) indicators. In addition, we propose an algorithm to mine the $\overline {VST} $ and associate that with the mined GeoArea, ConvArea, and Δ and the calculated indicators (i.e., SpDensity and SpComplexity). Then, an LSTM model is trained and subsequently tested to forecast future $\overline {VST} $, as Port Authorities are increasingly relying on data-driven insights for decision-making purposes. We trained and tested several LSTM models with four different time aggregation granularities (2, 4, 6, and 8 hours) and provided performance comparisons between them in terms of Mean Square Error (MSE). The experiments emphasized the feasibility of the proposed LSTM model to forecast $\overline {VST} $.