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http://dx.doi.org/10.5394/KINPR.2022.46.1.33

Prediction of Traffic Speed in a Container Terminal Using Yard Tractor Operation Data  

Kim, Taekwang (Graduate School of Pusan National University)
Heo, Gyoungyoung (Graduate School of Pusan National University)
Lee, Hoon (Research Institute, Total Soft Bank Ltd.)
Ryu, Kwang Ryel (School of Computer Science and Engineering, Pusan National University)
Publication Information
Journal of Navigation and Port Research / v.46, no.1, 2022 , pp. 33-41 More about this Journal
Abstract
An important operational goal of a container terminal is to maximize the efficiency of the operation of quay cranes (QCs) that load and/or unload containers onto and from vessels. While the maximization of the efficiency of the QC operation requires minimizing the delay of yard tractors (YT) that transport containers between the storage yard and QCs, the delay is often inevitable because of traffic congestion. In this paper, we propose a method for learning a model that predicts traffic speed in a terminal using only YT operation data, even though the YT traffic is mixed with that of external trucks. Without any information on external truck traffic, we could still make a reasonable traffic forecast because the YT operation data contains information on the YT routes in the near future. The results of simulation experiments showed that the model learned by the proposed method could predict traffic speed with significant accuracy.
Keywords
container terminal; YT operation; traffic prediction; machine learning; vehicle routing;
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