In this paper we introduce a mesoscopic Train Following Model which accurately captures train interactions and predicts delays based on spacing between consecutive trains. The Train Following Model is applied recursively block by block estimating train trajectories given initial conditions (i.e. the trajectory of an initial train and dispatching headways of following trains from the terminal station). We validate the proposed model using data from the Red Line of the Massachusetts Bay Transportation Authority (MBTA). The results indicate that it accurately represents train operations under both normal and disrupted conditions. Based on the model developed, the impacts of factors such as service frequency, headway variations, passenger demand, and initial train delays on line performance (i.e. line throughput and train knock-on delays) are explored. The proposed Train Following Model is generic and can be developed based on readily available historical train tracking data. It is not as resource intensive as micro simulation models, while it can efficiently address the drawbacks of macro-scale analytical models and complex discrete algebraic models. The proposed model can be used to predict system performance either off-line or in real-time.