Predicting on-time performance in scheduled railroad operations: methodology and application to train scheduling

In previous work, the authors developed an analytical line delay model for analyzing rail line-haul operations, and validated the model as a predictive tool. This paper examines the application of the model as a prescriptive tool for the generation of train schedules. A unique feature of the model is that it incorporates dynamic meet/pass priorities in order to approximate an optimal meet/pass planning process. Extensive Monte Carlo simulations are conducted to examine the application of the model for adjusting real-world schedules to improve on-time performance and reduce delay. This empirical work represents the first attempt to investigate the impacts of the scheduling methodology on on-time line-haul performance. The problems are based on historical data from a major North American railroad.