Title

Development of a Java applet for generating truck trips from freight data

Authors

Authors

J. Klodzinski; A. Al-Daralseh; M. Georgiopoulos; H. M. Al-Deek;Trb

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Keywords

Engineering, Civil; Transportation Science & Technology

Abstract

As freight transportation becomes a more significant concern, the ability to estimate accurately truck trips generated by freight activity at an intermodal facility is important for transportation engineering and planning. Truck trip generation models that used vessel freight data were developed by the University of Central Florida Transportation Systems Institute and have been statistically proved to determine accurately the number of trucks generated at a seaport. To apply these previously developed models more efficiently, a Java applet was developed to execute a selected artificial neural network (ANN) port model and a trainable ANN port model. This applet provides the user with an easy-to-understand interface for entering data and executing models. The two models developed were the Port Everglades ANN model and a hybrid of the Port Everglades model that allowed the user to retrain the ANN model before execution for desired data. Although the trainable model requires additional retraining with new data, the added complexity comes with the benefit of producing a network with a higher degree of accuracy. This applet equips the user with two models and therefore has expanded the capabilities of the previously limited Port Everglades ANN truck trip generation model. The developed Java applet can be expanded to include more ANN models and thus more flexibility, depending on the type of freight data available. This successful adaptation of the ANN model into a Java applet sets the foundation for further applications.

Journal Title

Data and Information Technology

Issue/Number

1870

Publication Date

1-1-2004

Document Type

Article

Language

English

First Page

10

Last Page

17

WOS Identifier

WOS:000227333000002

ISSN

0361-1981; 0-309-09464-X

Share

COinS