Date of Award
Doctor of Philosophy in Civil Engineering (PhD)
College, School or Department Name
Department of Civil and Environmental Engineering
There are two general approaches to improve the capacity in a rail corridor, either by applying new capital infrastructure investment or by improving the operation of the rail services. Techniques to evaluate the railway operation include modeling and optimization through the use of commercial timetable management and rail simulation tools. However, only a few of the existing tools include complete features of timetable management techniques (e.g. timetable compression) are equipped with an optimization model for rescheduling and timetable improvement and this is especially true when it comes to the U.S. rail environment that prevalently uses unstructured operation practices.
This dissertation explores an application of timetable (TT) management techniques (e.g. rescheduling and timetable compression techniques) in the U.S. rail environment and their effect on capacity utilization and level of service (LOS) parameters. There are many tools and simulation packages used for capacity analysis, by both European and the U.S. rail industry, but due to the differences in the operating philosophy and network characteristics of these two rail systems, European studies tend to use timetable-based simulation tools (e.g. RailSys, OpenTrack) while the non-timetable based tools (e.g. RTC) are commonly used in the U.S. (Chapter 1). This research study investigated potential benefits of using a “Hybrid Simulation” approach that would combine the advantages of both the U.S. and European tools. Two case studies (a single track and a multiple-track case study) were developed to test the hybrid simulation approach, and it was concluded that applying timetable management techniques (e.g. timetable compression technique) is promising when implemented in a single track corridor (Chapter 2), but it is only applicable for the multiple track corridors under directional operation pattern (Chapter 3). To address this, a new heuristic rescheduling and rerouting technique was developed as part of the research to convert a multiple track case study from non-directional operation pattern to a fully directional operation pattern (Chapter 4).
The knowledge and skills of existing software, obtained during the development and testing of “Hybrid Simulation”, was used to develop an analytical rescheduling/optimization model called “Hybrid Optimization of Train Schedules” (HOTS) (Chapter 5). While the results of the “Hybrid simulation approach” are promising, the method was also time consuming and challenging, as all respective details and database of the given corridors had to be replicated in both simulation tools. The “HOTS Model” could provide the same functions and features of train rescheduling, but with much less efforts and challenges as in the hybrid simulation. The HOTS model works in conjunction with any commercial rail simulation software and it can reschedule an initial timetable (with or without conflict) to provide a “Conflict-Free” timetable based on user-defined criteria. The model is applicable to various types of rail operations, including single, double and multiple-track corridors, under both directional and nondirectional operation patterns. The capabilities of the HOTS model were tested for the two case studies developed in the research, and its outcomes were compared to those obtained from the commercial software. It was concluded that the HOTS model performed satisfactorily in each of the test scenarios and the model results either improved or maintained the initial timetable characteristics. The results are promising for the future development of the model, but limitations in the current model structure, such as station capacity limits, should be addressed to improve the potential of applying the model for industrial applications.
Pouryousef, Hamed, "TIMETABLE MANAGEMENT TECHNIQUE IN RAILWAY CAPACITY ANALYSIS: DEVELOPMENT OF THE HYBRID OPTIMIZATION OF TRAIN SCHEDULES (HOTS) MODEL", Dissertation, Michigan Technological University, 2015.