Estimating the relative cost of track damage mechanisms: combining economic and engineering approaches

This paper proposes a new, two-stage methodology to estimate the relative marginal cost of different vehicle types running on rail infrastructure. This information is important particularly where infrastructure managers wish to differentiate track access charges by vehicle type for the purpose of incentivising the development and use of more track friendly vehicles. EU legislation requires that European infrastructure managers set access charges based on the incremental (marginal) cost of 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 rail vehicles; and (2) econometric methods that estimate the relationship between actual maintenance costs and the different damage mechanisms. This two-stage approach fills an important gap in the literature, given the limitations of existing “single-stage” engineering or econometric approaches in obtaining relative marginal costs for different types of damage.

We demonstrate the feasibility of the method using 45 track sections from Sweden, for which we have data on maintenance costs together with relevant track and vehicle data for 2012
(supplied by the Swedish Transport Administration). We demonstrate the feasibility of producing summary, section-level damage measures for three damage mechanisms (wear, rolling contact fatigue and settlement) which can be taken forward into the second stage. The second stage econometric results indicate that it is possible to obtain sensible relationships between cost and the different damage types – and thus produce relative marginal costs by damage mechanism and in turn vehicle type. Based on this feasibility study, settlement is found to be the most costly (in terms of maintenance cost) of the three mechanisms, followed by rolling contact fatigue and then wear. Future applications should focus on larger datasets in order to produce the required degree of precision on the marginal cost estimates.