In Arizona, the Grand Canyon National Park’s south rim experiences severe traffic and parking congestion during the summer. To alleviate the issues, a free pilot shuttle route was operated between the gateway community of Tusayan and the park’s information plaza during the 2008 summer season. During the pilot program, a traveler information system consisting of a portable dynamic message sign and highway advisory radio was implemented along Route 64 with the intent to increase shuttle ridership. The objective of this study is to evaluate the impact of the intelligent transportation system on shuttle ridership. Data were collected during the pilot program, before and after the implementation of this system. Different analysis methods including multiple linear regression, backpropagation neural network, and support vector regression were applied for analysis. The results found that the support vector regression method performed best in modeling shuttle ridership and associated explanatory variables (traffic volume and system deployment). The results from support vector regression suggested that the traveler information system increased shuttle ridership by 30%, which was consistent with the results from visitor surveys that were completed at the same time as this study. The study showed that the system was effective in shifting visitors from passenger cars to free shuttle buses.