Abstract

This paper proposes a new, two-stage methodology to estimate the relative marginal cost of different types of vehicles running on the rail infrastructure. This information is important particularly where the infrastructure managers wish to differentiate the track access charges by vehicle type for the purpose of incentivizing the development and use of more track-friendly vehicles. EU legislation requires that the European infrastructure managers set the access charges based on the incremental (marginal) cost of the running trains on their networks. The novelty of the approach derives from the combination of: (1) engineering simulation methods that estimate the track damage caused by the rail vehicles; and (2) econometric methods that estimate the relationship between the actual maintenance costs and the different damage mechanisms. This two-stage approach fills an important gap in the literature, given the limitations of the existing “single-stage” engineering or econometric approaches in obtaining the relative marginal costs for different types of damage. The authors demonstrate the feasibility of the method using 45 track sections from Sweden, for which the data on maintenance costs are available together with relevant track and vehicle data for 2012 (supplied by the Swedish Transport Administration). The authors demonstrate the feasibility of producing summary, section-level damage measures for the three damage mechanisms (wear, rolling contact fatigue, and track settlement), which can be taken forward to the second stage. The econometric results of the second stage indicate that it is possible to obtain sensible relationships between cost and the different damage types, and thus produce relative marginal costs by the damage mechanism and in turn the vehicle type. Based on this feasibility study, tracksettlement has been found to be the most expensive (in terms of maintenance cost) of the three mechanisms, followed by the rolling contact fatigue and then the wear. Future applications should focus on larger datasets in order to produce the required degree of precision on the estimation of the marginal cost.