This research was modeled after a consumer market-segmentation technique (SEGMENT) successfully used in Europe, for its usefulness to transportation demand management (TDM) campaigns in the United States. The SEGMENT project examined how consumer market-segmentation techniques can influence travel behavior choices in favor of more energy-sustainable modes of travel. Data were collected from 1,900 individuals in Florida, Oregon, and Virginia. The data contain approximately 200 fields with information about respondents’ demographics and attitudes toward different modes of transportation, such as car, train, bike, and walking. Clustering analysis was applied to divide the sample into segments so that members of the same group share similar travel attitudes. Next, a classification model was built to predict group membership. Dividing the sample into seven segments, three non-driver and four driver, was found to be the most stable and distinctive segmentation. Seventeen questions, referred to as “golden questions,” were found to separate segments most significantly and predict group membership with 84% accuracy. Significant differences in age and household distribution between segments were observed. Mean responses to each question were used to create an attitudinal profile for each group. Major contributions are the validation of an existing segmentation technique for applicability in the United States, which could improve the effectiveness of TDM campaigns on changing travel behavior. Golden questions can be added to existing surveys to gather information about the proportion of individuals that belong to segments in an area. Additionally, limited resources can be better allocated to target those segments most susceptible to behavior change.
