AbstractWith the rapid development of information and telecommunication technology, joint activity–travel constitutes an ever-increasing share of an individual’s daily activity–travel pattern. In recent years, joint activity–travel pattern (JATP) scheduling models have been developed to investigate individuals’ independent and joint activity–travel choice behavior. The additional benefit resulting from joint activity–travel, related to the length of the joint episode, is identified as a significant concern in individuals’ JATP scheduling. In previous JATP scheduling models, joint activity–travel benefit generally is modeled with simulated parameters. As a pioneering endeavor, this study quantified the relationship between joint activity–travel benefit and JATP utility, considering the joint episode’s length. A rule-based method is used to infer individuals’ joint activity–travel behaviors. A two-stage framework is proposed to estimate joint activity–travel benefit in the JATP scheduling model. The joint activity–travel benefit is estimated in the first stage. In the second stage, the Kalman filter is used to reduce the influence of deviation of network flow on the accuracy of estimating joint activity–travel benefit. The proposed method was examined with the metro smart card data collected in Suzhou, China. The results showed that the proposed method effectively estimates joint activity–travel benefit for the JATP scheduling model.