Arrival Time Prediction Model to a Pier for Public Transportation Boats
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Abstract
This paper proposed a time prediction model of public transportation boats arrival to a pier using navigational data from the Internet of Things (IoT) devices attached to public boats from 27 November 2018 to 31 May 2019 in order to analyze the relationship of public transportation boats data used in creating a prediction model. Six algorithms which are Linear Regression, Random Forest, Gradient Boost, eXtreme Gradient Boost, Light Gradient Boost, and CatBoost, were created into models. After that, the performance of the models were tested by Root Mean Square Error in order to find the optimal model and evaluated by testing the predicted arrival time models then compared to the arrival time from IoT devices at transportation boats. The results show that the model created by CatBoost algorithm performed the optimal of Root Mean Square Error values at 88.25 seconds of the travelling time from the origin to the destination pier and at 90.06 seconds of the return trip compared to other algorithms.
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References
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