| dc.description.abstract | The emerging availability of data and the development of real-time tracking systems, also
known as AIS, have engaged a new field of study within the shipping segment. AIS data
has a pivotal role in enhancing safety at sea. Moreover, the accessibility of real-time
data over the majority of merchant vessels around the world has instigated researchers to
investigate how to adopt this information to create further value in the decision-making
process.
Together with machine learning methodologies and data processing capability, this thesis
aspires to contribute to further investigate the deployment of AIS-derived data. More
specifically, we will examine the predictive ability of AIS data on a route-specific freight
rate. In addition to AIS variables, we have included other data expected to influence
freight rate, and the results from a series of machine learning models have been thoroughly
examined. Our results indicate that AIS-derived data offer some additional value when
predicting the freight rate. However, in this exact case, the additional contributory value
is negligible. | en_US |
