Optimisation and simulation tools are vital for planning and managing rail systems, providing performance analysis and evaluation of interactions without any kind of disturbance to the service. We may distinguish between combinatorial optimisation and simulation models which can be also classified into macroscopic and microscopic models. The former models describe the network and the timetable in a simple way by means of a simplified graph. The latter models consist of the specification of all technical characteristics related to infrastructure, rolling stock and signalling system as well as timetable data. Macroscopic models are useful during the planning process when the design of service frequencies and capacity to satisfy demand are carried out. The major benefit of this approach is the possibility to consider jointly several features of the rail system obtaining reliable results. By contrast, microscopic models reproduce the network as closely as possible to the 'real world'; they allow evaluating the interactions among trains and the performance of the network precisely. The aim of this paper is to propose a new approach for planning and managing the rail system combining both approaches macroscopic and microscopic. In particular, an optimisation model, based on a macroscopic approach, represents the kernel of the procedure and it is used as a first step to study any kind of scenario. The microscopic simulation model, by contrast, generates detailed (and precise) data, such as headways or running times, to overcome the approximations of the macroscopic model. Above all, in case of disruptions, the combination of the two models provides reliable results taking advantage of the benefits of the two approaches. Numerical applications have been applied in a realistic case taken from a real metro network located in the south of Italy; the preliminary results show the effectiveness of the proposed approach.
Placido, A., Cadarso, L., & D’Acierno, L. (2014). Benefits of a combined micro-macro approach for managing rail systems in case of disruptions. In Transportation Research Procedia (Vol. 3, pp. 195–204). Elsevier. https://doi.org/10.1016/j.trpro.2014.10.105