In order to increase its attractiveness with respect to other transportation systems, passenger trains have to travel faster, with improved safety and comfort conditions. For short and medium distances, modern high speed trains are able to compete with air transportation, having the advantage of presenting better energy efficiency and causing less pollution.

For rolling-stock manufacturers becomes a daily challenge to respect the client specifications for reductions in the overall operational costs and to comply with the international regulations. They put particular attention to the railway vehicles maintenance costs and to the aggressiveness of rolling stock on the infrastructures. A very sensitive issue for railway operators and infrastructure managers is the impact of traffic on the infrastructure and, conversely, the damages on vehicles provoked by the track conditions. In fact, there is a growing tendency to define the track access charges, i.e. the prices billed by the infrastructure managers to the railway operators, according to the predicted damage that the trainsets cause to the infrastructure. The uncertainties associated to the maintenance intervals and the costs involved in such procedures also raise the urgency of acquiring a better understanding on how the vehicle characteristics, the track features and the service conditions influence the life cycle costs of railway equipment.

Due to their multidisciplinary, all the issues involving railway systems are complex. Furthermore, the requirements for higher operational speeds, greater loads per axle and the quest for interoperability of different trainsets in existing and projected networks put an extra level of demand on the ability to study such problems. Therefore, the use of accurate, reliable and efficient computational tools that represent the state of the art and that are able to characterize the modern designs and to predict the vehicles’ performance, by using validated mathematical models, is essential.

The development of computational tools that are able to model with detail the vehicle, the infrastructure, including the track and the overhead power line (catenary) and the interaction among them, is presented here. Instead of using the traditional approach, in which these subsystems are handled independently, here they are integrated in common, reliable and user friendly modules that allow studying the vehicle and track performance and to access their long-term behaviour in order to predict their maintenance intervals and life cycle costs. The methodologies are validated in close collaboration with the railway industry using real data. These procedures can be used to support the development of solutions with technological relevance giving answer to the industry’s most recent needs and contributing to improve the competitiveness of the railway transport.