Use of vessel freight data to forecast heavy truck movements at seaports

    Authors

    H. M. Al-Deek; Trb;Trb

    Comments

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    Keywords

    Computer Science, Artificial Intelligence; Computer Science, Information; Systems; Computer Science, Interdisciplinary Applications; Engineering, ; Civil; Transportation Science & Technology

    Abstract

    Ports are primary generators of truck traffic in the United States. Seaport operations will require operational and infrastructure changes to maintain the growth of international cargo operations. Truck trip generation models will provide transportation planners and public agencies with valuable information necessary for prioritizing funds for roadway upgrade projects and port infrastructure modifications. A new methodology is presented that combines backpropagation neural networks (BPN) and time series to forecast inbound and outbound heavy truck movements at seaports. The new method uses vessel freight data to identify which parameters are relevant for use as model input in predicting truck traffic at seaports. The method is successfully applied to five ports in Florida-Miami, Tampa, Palm Beach, Jacksonville, and Ever glades-thus demonstrating its transferability. Details are provided for the Port of Everglades. The commodities at this port are classified into tons, barrels, and containers. It was found that the primary factors affecting truck traffic are imported containers, imported tonnage, imported barrels, exported containers, exported tonnage, and the particular weekday of operation. Separate BPN models were developed for inbound and outbound truck traffic at the ports. The new method forecasts that the Port of Everglades will have a 33% increase in average daily inbound heavy trucks and a 30% increase in average daily outbound heavy trucks by 2005 (2000 is the base year). The accuracy of the inbound and the outbound truck models is 93% and 92%, respectively.

    Journal Title

    Transportation Data and Information Technology Research: Planning and Administration

    Issue/Number

    1804

    Publication Date

    1-1-2002

    Document Type

    Article

    Language

    English

    First Page

    217

    Last Page

    224

    WOS Identifier

    WOS:000180589200029

    ISSN

    0361-1981; 0-309-07730-3

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