This paper concentrates on organising, planning and managing the train movement in a network. The three classic management levels for rail planning, i.e., strategic, tactical and operational, are introduced followed by decision support systems for rail traffic control. In addition, included in this paper are discussions on train operating forms, railway traffic control and train dispatching problems, rail yard technical schemes and performance of terminals, as well as timetable design. A description of analytical methods, simulation techniques and specific computer packages for analysing and evaluating the behaviour of rail systems and networks is also provided. © 2012 Elsevier Ltd

Uncertainty affects the overall performance of railway systems. It can be captured tracking the successive departure and arrival events of trains at stations in the scheduled railways. Markov chains has shown to be an effective means to model variabilities in the departure and arrival delays of trains. The empirical data of actual operations are used to develop the Markov matrices. Transitions between delay states comprise the internal dynamics of running and dwelling processes in probabilistic terms, presenting particular patterns of deterioration, recovery and state keeping in the prevailing timetable setting. All these features conform to the practice of railway operations as well as train dispatchers and drivers. Hence, delay-based Markov chains becomes a simple and effective tool to predict delays in the following events of trains and distribution of delays over a longer period of time using simple performance measures. We have developed the homogeneous and non-homogeneous Markov models and tested their performances using the actual data collected in the Netherlands railway network. The analysis and evaluation show that the railway corridor considered in this study performs well and the corresponding Markov chain models can be used effectively to predict train delays and to evaluate timetable robustness. The outcomes of the homogeneous and non-homogeneous model are evaluated with respect to the observed data resulting that the non-homogeneous model performs better than the homogeneous model.

A rescheduling algorithm for trains on a single-track railway was developed in case of disturbances that would cause conflicts between trains. This algorithm is based on mixed integer linear programming (MILP) with speed-up routines. The model considers station capacities explicitly (i.e., the number of available tracks for meeting and overtaking operations). Because the model is too hard for the solvers (CPLEX in this study) to tackle, three speed-up routines were devised when rescheduling trains. These routines are a greedy heuristic to reduce the solution space, using the lazy constraint attribute of the solver and a multiobjective approach to find a good initial feasible solution that conforms to actual railway operation. The algorithm was tested on a hypothetical rail line for different sizes of timetable instances with disturbed trains in a maximum two-hour time horizon. It managed to solve the hardest instances within a three-minute time limit thus minimizing the total weighted delay of rescheduled trains. The optimality gap metric is used to show the effectiveness and efficiencies of the speed-up heuristics developed.