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    Econometric techniques for freight demand modeling and management

    Author
    Campbell, Shama J.
    View/Open
    180574_Campbell_rpi_0185E_11457.pdf (2.859Mb)
    Other Contributors
    Holguín-Veras, José; Wang, Xiaokun (Cara); Reilly, Jack; He, Xiaozheng (Sean); Simons, Kenneth L.;
    Date Issued
    2019-05
    Subject
    Transportation engineering
    Degree
    DEng;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/2692
    Abstract
    Econometric modeling can serve as a useful tool for transportation planning as it is able to estimate a variety of demand metrics which can be used to assess and support the implementation of demand management strategies. In addition, econometrics could be used to gain insight into the behavioral determinants of the demand for transportation. Demand models are essential to transportation planning processes strategically, tactically and operationally. These models are useful in numerous ways. They provide public agencies with estimates of the transportation needs for the movement of both people and cargo. Using this information, public agencies are able to provide the necessary infrastructure, human resources and policies to accommodate this movement. The private sector may use the forecasts from transportation demand models to predict needs such as funding, labor, and equipment requirements. The insights gained from econometric behavioral models can support the design of freight demand management strategies to address negative externalities produced by truck traffic. These strategies facilitate modification of the underlying demand as opposed to modifying the logistical activities or the vehicle traffic.; This research contributes to transportation planning and modeling practices in a number of ways. First, it estimates econometric models of freight production and service trips, which could be used to quantify freight and service activity using secondary data. Secondly, it estimates freight mode choice models which allow for the prediction of the market share split between freight truck and rail modes. Lastly, it uses behavioral models to gain insight into how best to foster off-hour deliveries in congested cities.;
    Description
    May 2019; School of Engineering
    Department
    Dept. of Civil and Environmental Engineering;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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    • RPI Theses Online (Complete)

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