Kernel-Based Machine Learning Models for Predicting Daily Truck Volume at Seaport Terminals

Abstract

The heavy truck traffic generated by major seaports can have huge impacts on local and regional transportation networks. Both transportation agencies and port authorities have a need to know in advance the amount of truck traffic in order to accommodate them accordingly. Several previous studies have developed models for predicting the daily truck traffic at seaport terminals using terminal operation data. In this study, two kernel-based supervised machine learning methods are introduced for the same purpose: Gaussian processes (GPs) and