Railway markets in Europe have been reorganized to allow competition between different operators. Thus, European railways have been vertically separated, separating infrastructure management from provisions of train services. This allows several train operators to compete for passengers and freight services. Different ways have emerged for vertical separation, capacity allocation and track access charges. This paper reviews, compares and discusses important deregulation aspects, using examples from a number of European countries to show different possible solutions. The study describes how competition has been introduced and regulated, with a particular focus on describing the different ways capacity is allocated and how conflicting requests by different train operators are resolved. It also reviews the related issue of how access charges are constructed and applied. Although guided by the same European legislation, we conclude that the studied railways have different deregulation outcomes, e.g., market organization, capacity allocation. Besides, few countries have so far managed to create efficient and transparent processes for allocating capacity between competing train operators. Although allowed by the legislation, market-based allocation is absent or never used. In order to foster more competition which can yield substantial social benefits, the survey indicates that most European railways still need to develop and experiment with more efficient and transparent capacity allocation procedures.

On deregulated railway markets, efficient capacity allocation is important. We study the case where commercial trains and publicly controlled traffic (“commuter trains”) use the same railway infrastructure and hence compete for capacity. We develop a method that can be used by an infrastructure manager trying to allocate capacity in a socially efficient way. The method calculates the loss of societal benefits incurred by changing the commuter train timetable to accommodate a commercial train path request, and based on this calculates a reservation price for the train path request. If the commercial operator’s willingness-to-pay for the train path exceeds the loss of societal benefits, its request is approved. The calculation of these benefits takes into account changes in commuter train passengers’ travel times, waiting times, transfers and crowding, and changes in operating costs for the commuter train operator(s). The method is implemented in a microscopic simulation program, which makes it possible to test the robustness and feasibility of timetable alternatives. We show that the method is possible to apply in practice by demonstrating it in a case study from Stockholm, illustrating the magnitudes of the resulting commercial train path prices. We conclude that marginal societal costs of railway capacity in Stockholm are considerably higher than the current track access charges.

Social cost-benefit analysis is often used to analyse transport investments, and can also be used for transport operation planning and capacity allocation. If it is to be used for resolving capacity conflicts, however, it is important to know whether transit agencies' timetable requests are consistent with the cost-benefit framework, which is based on passenger preferences. We show how a public transport agency's implicit valuations of waiting time and crowding can be estimated by analysing timetables, apply the method to commuter train timetables in Stockholm, and compare the implicit valuations to the corresponding passenger valuations in the official Swedish cost-benefit analysis guidelines. The results suggest that the agency puts a slightly lower value on waiting time and crowding than the passenger valuations codified in the official guidelines. We discuss possible reasons for this and implications for using cost-benefit analysis for capacity allocation. We also find that optimal frequencies are more sensitive to the waiting time valuation than to that of crowding.

The train timetabling problem (TTP) consists of finding a feasible timetable for a number of trains which minimises some objective function, e.g., sum of running times or deviations from ideal departure times. One solution approach is to solve the dual problem of the TTP using so-called bundle methods. This paper presents a new bundle method that uses disaggregate data, as opposed to the standard bundle method which in a certain sense relies on aggregate data. We compare the disaggregate and aggregate methods on realistic train timetabling scenarios from the Iron Ore line in Northern Sweden. Numerical results indicate that the proposed disaggregate method reaches better solutions faster than the standard aggregate approach.

Passenger origin–destination data is an important input for public transport planning. In recent years, new data sources have become increasingly common through the use of the automatic collection of entry counts, exit counts and link flows. However, collecting such data can be sometimes costly. The value of additional data collection hence has to be weighed against its costs. We study the value of additional data for estimating time-dependent origin–destination matrices, using a case study from the London Piccadilly underground line. Our focus is on how the precision of the estimated matrix increases when additional data on link flow, destination count and/or average travel distance is added, starting from origin counts only. We concentrate on the precision of the most policy-relevant estimation outputs, namely, link flows and station exit flows. Our results suggest that link flows are harder to estimate than exit flows, and only using entry and exit data is far from enough to estimate link flows with any precision. Information about the average trip distance adds greatly to the estimation precision. The marginal value of additional destination counts decreases only slowly, so a relatively large number of exit station measurement points seem warranted. Link flow data for a subset of links hardly add to the precision, especially if other data have already been added.