Graduation Year


Document Type




Degree Granting Department

Civil Engineering

Major Professor

Ram M. Pendyala, Ph.D.

Committee Member

Steven E. Polzin, Ph.D., P.E.

Committee Member

Jian J. Lu, Ph.D., P.E.


commodity groups, population, employment, ADF-WLS estimation, sensitivity analysis, data requirements


The modeling of freight travel demand has gained increasing attention in the recent past due to the importance of efficient and safe freight transportation to regional economic growth. Despite the attention paid to the modeling of freight travel demand, advances in modeling methods and the development of practical tools for forecasting freight flows have been limited. The development of freight demand models that incorporate the behavioral aspects of freight demand face significant hurdles, partially due to the data requirements, which are a consequence of the inherent complexity of the mechanisms driving freight demand. This research attempts to make a contribution in this context by proposing a relatively data simple, but behaviorally robust statewide modeling framework for the state of Florida, in the spirit of an aggregate level four-step planning process.

The modeling framework that is developed in this research can be applied to the modeling of freight travel demand using data contained in readily available commercial databases such as the Reebie TRANSEARCH database and the InfoUSA employer database. The modeling methodology consists of a structural equations modeling framework that can accommodate multiple dependent variables simultaneously. This framework predicts freight flows on various modes between two zipcodes based on the socio-economic characteristics and the modal level of service characteristics. Separate models have been developed for various commodity groups.

The estimated models for various commodity groups are found to offer statistically valid indications and plausible interpretations suggesting that these models may be suitable for application in freight transportation demand forecasting applications. The sensitivity analysis conducted on these models clearly added evidence to the fact that employment is the key factor influencing freight flows between two regions.