Economic theory advocates marginal cost pricing for efficient utilisation of transport infrastructure. A growing body of literature has emerged on the issue of rail marginal infrastructure wear and tear costs, but the majority of the work is focused on costs for infrastructure maintenance. Railway track renewals are a substantial part of an infrastructure manager's budget, but in disaggregated statistical analyses they cause problems for traditional regression models since there is a piling up of values of the dependent variable at zero. Previous econometric work has sought to circumvent the problem by aggregation in some way. In this paper we instead apply corner solution models to disaggregate (tracksection) data, including the zero observations. We derive track renewal cost elasticities with respect to traffic volumes and in turn marginal renewal costs using Swedish railway renewal data over the period 1999 to 2009. This paper is the first attempt in the literature to apply corner solution models, and in particular the two-part model, to disaggregate renewal cost data in railways. It is also the first paper that we are aware of to report usage elasticities specifically for renewal costs and therefore adds important new evidence to the previous literature where there is a paucity of studies on renewals and considerable uncertainty over the effects of rail traffic on renewal costs. In the Swedish context, we find that the inclusion of marginal track renewal costs in the track access pricing regime, which currently only reflects marginal maintenance costs, would add substantially to the existing track access charge. This change would also increase the cost recovery ratio of the Swedish infrastructure manager, thus meeting a policy objective of government.

The presence of outliers in the data has implications for stochastic frontier analysis, and indeed any performance analysis methodology, because they may lead to imprecise parameter estimates and, crucially, lead to an exaggerated spread of efficiency predictions. In this paper we replace the normal distribution for the noise term in the standard stochastic frontier model with a Student's t distribution, which generalises the normal distribution by adding a shape parameter governing the degree of kurtosis. This has the advantages of introducing flexibility in the heaviness of the tails, which can be determined by the data, as well as containing the normal distribution as a limiting case, and we outline how to test against the standard model. Monte Carlo simulation results for the maximum simulated likelihood estimator confirm that the model recovers appropriate frontier and distributional parameter estimates under various values of the true shape parameter. The simulation results also indicate the influence of a phenomenon we term 'wrong kurtosis' in the case of small samples, which is analogous to the issue of 'wrong skewness' previously identified in the literature. We apply a Student's t-half normal cost frontier to data for highways authorities in England, and this formulation is found to be preferred by statistical testing to the comparator normal-half normal cost frontier model. The model yields a significantly narrower range of efficiency predictions, which are non-monotonic at the tails of the residual distribution.

In Stochastic Frontier Analysis the presence of outliers in the data, which can often be safely ignored in other forms of linear modelling, has potentially serious consequences in that it may lead to implausibly large variation in efficiency predictions when based on the conditional mean. This motivates the development of alternative stochastic frontier specifications which are appropriate when the two-sided error has heavy tails. Several existing proposals to this effect have proceeded by specifying thick tailed distributions for both error components in order to arrive at a closed form loglikelihood. In contrast, we use simulation-based methods to pair the canonical inefficiency distributions (in this example half-normal) with a logistically distributed noise term. We apply this model to estimate cost frontiers for highways authorities in England, and compare results obtained from the conventional normal-half normal stochastic frontier model. We show that the conditional mean yields less extreme inefficiency predictions for large residuals relative to the use of the normal distribution for noise.

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 ''singlestage'' 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.

This article is concerned with the role of innovation in cost reduction and the mechanisms for bringing it about. In the first section, it investigates the efficiency of UK's railways through the medium of cost benchmarking of both UK and continental European costs. It finds that Britain's rail infrastructure manager faces an efficiency gap of 40 per cent against European best practice and that train operating costs have also risen substantially, both because of rising factor prices (wages and fuel) and because of deteriorating productivity. It then explores the situation surrounding incentives for shaping technological innovation through a series of semistructured interviews with senior managers representing a wide range of railway interests. This section highlights the presence and successful functioning of the commercial mechanism for technology development in the industry both through natural commercial factors and through mechanisms such as track access charges. Finally, it studies the feasibility of modelling systems subject to technological change, with the aim of creating a methodology to assess, at an early stage in the development cycle, the physical impact innovation might have on the existing system. It finds that the objective data needed to construct such models can be extracted from existing technical standards and that systems engineering techniques provide a suitable framework for structuring and linking that data.