The pattern of activities by transit passengers and the factors affecting this pattern are recently addressed in the literature. Also, the pattern of trips and the factors affecting trips are the subject of many studies. However, little is known about the correlation between the activity and trip of passengers in the public transit network. This study investigates how similar are activities of passengers if they have similar trips (or vice versa)? And what factors impact the similarity of the activity and trip of passengers? Answering these questions is useful in understanding the mobilization patterns of passengers and developing group-based transit services. Also, smart card data have provided an opportunity, which was not available before, to analyze the activity and trip of the passengers in a large scale network. In this paper, the correlation between activity similarity and trip similarity of public transit passengers is investigated. The correlation between the activity and trip of the passengers is analyzed using histograms, Pearson correlation coefficient, conditional probabilities, and hexagonal binning technique. In addition, the impact of trip length and duration on the activity and trip similarity are examined using histograms and hexagonal binning diagrams. The proposed methodology is implemented for two-day smart card data in Brisbane, Australia. Results show that it is more likely to have the activity similarity when there is a trip similarity than having the trip similarity when there is activity similarity. Also, there is a nonlinear correlation between the activity and trip similarity with the trip length and duration.
