Methodology for modeling a road network with high truck volumes generated by vessel freight activity from an intermodal facility

    Authors

    J. Klodzinski; H. M. Al-Deek;Trb

    Comments

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    Keywords

    Engineering, Civil; Transportation Science & Technology

    Abstract

    An innovative methodology has been developed for analyzing freight movement on local road networks by merging previously developed truck trip generation models using artificial neural networks (ANNs) and a microscopic network simulation model. Through computer simulation, this methodology comprehensively analyzes a seaport considered a special generator of heavy truck traffic and an adjacent road network that includes identified intermodal routes that connect to a seaport. Truck traffic from the seaport is initially modeled with ANNs using vessel freight activity, at the seaport. These ANN models have been incorporated into the methodology to provide accurate truck and total traffic volumes for modeling the networks. This methodology was successfully tested with two network microscopic simulation models. Transferability was successfully tested with two seaports with different characteristics. Three months of field data from each port and selected locations on the networks were used in calibration and validation. Both models were successfully validated and showed no statistically significant difference between the field and model output data. This methodology can be used to evaluate local port networks to manage traffic efficiently during heavy congestion or investigate forecasted port growth. These networks can also be used for information technology applications such as incident management or alternative route choice. The ability to develop a network that includes significant truck volumes generated by freight activity is a useful tool especially for engineers and planners involved in intermodal transportation analysis.

    Journal Title

    Intermodal Freight Transportation; Freight Transportation Planning

    Issue/Number

    1873

    Publication Date

    1-1-2004

    Document Type

    Article

    Language

    English

    First Page

    35

    Last Page

    44

    WOS Identifier

    WOS:000227334200005

    ISSN

    0361-1981; 0-309-09467-4

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