ABSTRACT
A B S T R A C T Transit agencies are under constant pressure to increase ridership. Many system changes and new technologies, such as making real-time information available, have the potential to increase ridership. However, measuring traveller response to system changes is notoriously difficult. The objective of this chapter is to develop a new method to quantify changes in the number of transit trips due to system changes, and the method is applied to real-time information as an example. The method combines smart card data with survey responses to study the behavior of individual riders before and after the availability of real-time information. First, three conditions are imposed on the joint survey/smart card dataset to assess if each record accurately reflects an individual’s travel behavior. The first condition necessitates that the respondent began using real-time information in the appropriate timeframe and had the smart card sufficiently long for the before-after analysis. The second condition tests if one smart card accurately represents one traveller, and the third condition verifies that the smart card record corresponds to the respondent’s stated travel behavior. Then, difference of means tests and regression analysis are used to assess changes in monthly transit trips between real-time information users and non-users. In this case, the results suggest that real-time information did not have a significant effect on the number of transit trips in the study; however, the final sample size was small. The primary contribution of this research is the method, which could be repeated to evaluate other transit system changes or technologies.
