Kernel-Based Machine Learning Methods for Modeling Daily Truck Volume at Seaport Terminals

The heavy truck traffic generated by major seaports can have huge impacts on local and regional transportation networks. Transportation agencies, port authorities, and terminal operators have a need to know in advance the 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 operations data. In this study, two kernel-based supervised machine learning methods are introduced for the same purpose: Gaussian Processes (GP) and ε-Support Vector Machines (ε-SVMs). They are compared against the Multilayer Feed-forward Neural Networks (MLFNNN) model, which was used in past studies, to provide a comparison of their relative performance. The model development is done using the data from Bayport and Barbours Cut (BCT) container terminals at the Port of Houston. Truck trips generated by import and export activities at the two terminals are investigated separately, generating four sets of data for model testing and comparison. For all test datasets, the GP and ε-SVMs models perform equally well and their prediction performance compares favorably to that of the MLFNN model. On a practical note, the GP and ε-SVMs models require less effort in model fitting compared to the MLFNN model. The strong performance of the GP and ε-SVMs models relative to the commonly used MLFNN model suggest that they can be considered as alternative approaches to the MLFNN in other predictive applications.


Issue Date:
2010-03
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/207231
Total Pages:
19




 Record created 2017-04-01, last modified 2017-08-28

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