A passenger-oriented framework for supporting energy-saving strategies in the case of rail/metro systems

Rail and metro systems are characterised by high-performing and environmentally-friendly features which make them a crucial factor for driving modal split towards public transport modes, thus reducing private car use and related externalities (such as air and noise pollution, traffic congestion and accidents). Within this framework, the adoption of suitable energy-saving policies allowing to reduce energy consumption, without prejudicing service stability and preserving passenger satisfaction, turns out to be imperative. In particular, the proposed methodology is conceived as a Decision Support System (DSS) for the implementation of eco-driving strategies in rail/metro contexts. Such strategies can follow different approaches; however, whatever the implemented method, they produce an increase in train running times which provides two main implications. First of all, the adoption of these measures is feasible only if there is an extra-time availability to be exploited for compensating the increase in running times without prejudicing timetable stability. These time resources have to be adequately designed in the timetable planning phase and, therefore, a proper tool for allowing a reliable estimation of them has been developed. In particular, our proposal is to rely on the reserve time which is represented by the sum of two aliquots, namely buffer time and layover time. The former is used to compensate for possible delays and the latter is a time spent by the convoy at the terminus waiting for the subsequent trip, in order to comply with timetable requirements. The second implication concerns service quality and passenger satisfaction. Indeed, an increase in train running time is equivalent to an increase in user travel times, with a consequent degradation in system performance. Hence, the aim is to provide a suitable tool for evaluating the trade-off between the reduction in energy consumption and the increase in passenger discomfort. For this two-fold purpose, proper simulation models and travel demand estimation techniques have been applied. Moreover, the interaction between rail service and passenger flows has been modelled by means of a microscopic approach. Finally, a bi-level constrained optimisation problem has been formulated and solved in the case of real rail networks, so as to show the effectiveness of the proposed methodology.