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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorHolguín-Veras, José
dc.contributorWang, Xiaokun (Cara)
dc.contributorReilly, Jack
dc.contributorHe, Xiaozheng (Sean)
dc.contributorSimons, Kenneth L.
dc.contributor.authorCampbell, Shama J.
dc.date.accessioned2021-11-03T09:25:28Z
dc.date.available2021-11-03T09:25:28Z
dc.date.created2021-07-08T15:42:41Z
dc.date.issued2019-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2692
dc.descriptionMay 2019
dc.descriptionSchool of Engineering
dc.description.abstractEconometric 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.
dc.description.abstractThis 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.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectTransportation engineering
dc.titleEconometric techniques for freight demand modeling and management
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid180573
dc.digitool.pid180574
dc.digitool.pid180575
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeDEng
dc.relation.departmentDept. of Civil and Environmental Engineering


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