The research work conducted presents the innovative RailEvo system, a new non-stop on-demand (24/7) rail transportation model consisting of small driverless electric trains and a double-track rail network with dedicated trackless stations and nodes. The primary objective of this project is to create a totally autonomous, flexible and dynamic transportation system that can be adapted to the needs of users (taxi-like) with a personalized service on demand. In fact, each passenger will have the possibility of deciding the start time of his/her ride (Departure) by reserving, among the fleet of trains arranged in the parking area of the multiple stations (High Availability), the vehicle useful for the completion of the route selected upstream by the customer: in this way, the possibility of generation of cascading delays will almost border on 0% chance.

For this purpose, the present work verified the technical feasibility and overall goodness-of-fit of RailEvo from RAMS analyses of each ground/ on - board (ex novo) component and subsystem, performed through a multidisciplinary approach that integrates different physical-mathematical methods, numerical analysis techniques for vehicle study and computational methodologies/models for RailEvo line capacity assessment.

Moreover, the results achieved demonstrate the executive feasibility, reliability and robustness of the new technological proposals and mathematical models personally designed, confirming the expected degree of optimization compared to today's lines in terms of vehicle stability and energy efficiency, reduction of operating costs and of the risk of vehicle overturning or derailment, and increase of real capacity up to 700 trains/h.

In conclusion, the study carried out confirmed RailEvo as the most effective and affordable solution, significantly superior to the current transportation market proposals, to meet future sustainable mobility needs free of highly polluting vehicles.

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