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.

在本文中，我们介绍了一种细观列车跟随模型，它可以准确捕捉列车相互作用并根据连续列车之间的间距预测延误。在给定初始条件（即初始列车的轨迹和从终点站出发的后续列车的调度车头时距）的情况下，逐块递归地应用列车跟随模型来估计列车轨迹。我们使用来自马萨诸塞湾交通管理局 (MBTA) 红线的数据验证了所提出的模型。结果表明，它准确地代表了正常和中断条件下的列车运行。基于所开发的模型，探讨了服务频率、车头时距变化、乘客需求和初始列车延误等因素对线路性能（即线路吞吐量和列车连锁延误）的影响。拟议的列车跟随模型是通用的，可以根据现成的历史列车跟踪数据进行开发。它不像微观仿真模型那样占用大量资源，同时可以有效解决宏观分析模型和复杂离散代数模型的缺点。所提出的模型可用于离线或实时预测系统性能。