Commercial vehicle movement within an urban area is an integral part of a region’s economic growth and has significant impact on the quality of life. Commercial traffic grows with economic activity and population growth. However, in regional models commercial traffic is not described as well as person travel. Modeling commercial vehicles is complex because of the involvement of multiple decision agents including shippers, carriers, and receivers and their interactions. The proprietary nature of truck data often limits development of behavioral econometric models that have superior predictive and policy analysis abilities. The efficient movement of goods is a very important component to urban civilization and economic development and therefore, understanding truck movement behavior is an important area of interest for transportation policy planning. The objective of this dissertation is to contribute to apply advanced choice modeling methods to analyze commercial vehicle travel behavior within an urban area. This research collects disaggregate level truck generation data from the business establishments located in a sample urban region and uses the collected data to evaluate factors that affect truck trip generation patterns using linear regression and ordered logit model structures. The results of the study show that employment size, business industrial class, truck ownership, land-use class, and land-value affect trip generation behavior. This research also analyzed three different multiple discrete-continuous (MDC) choice situations encountered by commercial vehicles on a daily basis. These choices are 1) the choice of tour chain(s) and the number of trips in each tour chain, 2) the time (s) of day choice to perform daily activities and the corresponding vehicle-miles traveled; and 3) the choice of destination location(s) among alternative destination zones and the number of stops at each destination zone. The study find that commercial vehicle characteristics, shipment characteristics, transportation network attributes, base location and intermediate stop location features affect the first two choice situations while the level of service and zonal attributes affect the destination choice behavior of commercial vehicle daily travel.