This thesis evaluates truck route choice set generation algorithms and derives guidance on using the algorithms for effective generation of choice sets for modeling truck route choice. Specifically, route choice sets generated from a breadth first search link elimination (BFS-LE) algorithm are evaluated against observed truck routes derived from large streams of GPS traces of a sizeable truck fleet in the Tampa Bay region of Florida. A systematic evaluation approach is presented to arrive at an appropriate combination of spatial aggregation and minimum number of trips to be observed between each origin-destination (OD) location for evaluating algorithm-generated choice sets. The evaluation is based on both the ability to generate relevant routes that are typically considered by the travelers and the generation of irrelevant (or extraneous) routes that are seldom chosen. Based on this evaluation, the thesis offers guidance on effectively using the BFS-LE approach to maximize the generation of relevant routes. It is found that carefully chosen spatial aggregation can reduce the need to generate large number of routes for each trip. Further, estimation of route choice models and their subsequent application on validation datasets revealed that the benefits of spatial aggregation might be harnessed better if irrelevant routes are eliminated from the choice sets. Lastly, a comparison of route attributes of the relevant and irrelevant routes shed light on presence of systematic differences in route characteristics of the relevant and irrelevant routes.