Application and validation of a timetabling algorithm to a large Italian network

The timetable represents a key element for the competitiveness of railways, since it allows exploiting the existing infrastructures at their maximum. To support timetable planners in meeting the variety of requirements given by operators and customers, since the early 90s a rich literature has appeared on the Train Timetabling Problem (TTP), that is the problem of determining, for each train, the arrival and departure time at each of the stations the train visits (see, e.g., the recent survey [1]). For the TTP it is possible to distinguish two main classes of models: the ones based on the solution of the Periodic Event Scheduling Problem [2,3] and the ones based on the multicommodity flow models [4,5]. For the above models both exact and heuristic solution approaches have been proposed. In particular, heuristic solutions are implemented when the rail network considered is of medium-large size. Despite of the wide literature, few applications of the TTP under real conditions can be found. This work presents a large-scale application of the heuristic solution approach to a multicommodity flow model. The model is tested on a real network and the results are compared to the timetables created by timetable planners using conventional methods in order to assess its concrete applicability. The model is based on a mesoscopic infrastructure, which allows a significantly higher accuracy compared to the macroscopic models used in most scientific work. The mesoscopic model allows a realistic estimation of the headway times and of the conflicts on lines and stations as well as a calculation of running times and time-losses performed with the same detail enabled by simulation models. In order to maximize the accuracy in the definition of the timetable, various parameters can be defined for each train, including the buffer times, the priority and the allowances. The model is applied to the rail network of the North-East of Italy. It is tested under different realistic demand conditions, for example considering an increase of the demand for freight slots or a different structure of regional services. Moreover, it is used to obtain a rough estimate of the maximum capacity for freight trains combined to fixed passenger services and the effects of infrastructure improvements. In addition, the limits of the mathematical modeling of a complex system and the consequent difficulties of introducing decision support system in the real word operations are discussed.