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.

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.

Methods for economic assessment are often used in the rail sector to evaluate large infrastructure investments such as new high-speed lines. With larger networks and ageing infrastructure, these methods can also be used for planning maintenance. In this paper, we focus on the newly introduced concept of maintenance windows in Sweden. These are pre-allocated slots in the train timetable dedicated to performing, among others, periodic/frequent maintenance activities. To justify the pre-allocation of such windows, this study presents a method to find minimal utilization rates depending on window designs and traffic situations. Using a cost-benefit approach, the windows are assessed using a total social cost including work costs, loss in traffic production and reliability gains in future traffic. Based on a case study from the Southern main line in Sweden, we study the minimal utilization rate in different test scenarios, i.e., night or day shifts, asset degradation functions and designs of maintenance windows. The results show that lower utilization rates (4-42%) can be accepted during low-volume traffic or for partial closures, while higher utilization rates (47-83%) are required for full closures during high-volume traffic. Whether the rates are measured as the share of used window time or the share of utilized windows is less important, especially when higher utilization is required. Sensitivity analyses of asset knowledge indicate that parameters such as asset degradation function and minimum asset quality (and to a lesser extent traffic volume, discount rate and failure likelihood) can have a substantial effect on the minimum required utilization rates.

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.

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.

Faced with increasing challenges, railways around Europe have recently undergone major reforms aiming to improve the efficiency and competitiveness of the railway sector. New market structures such as vertical separation, deregulation and open access can allow for reduced public expenditures, increased market competition, and more efficient railway systems.

However, these structures have introduced new challenges for managing infrastructure and operations. Railway capacity allocation, previously internally performed within monopolistic national companies, are now conferred to an infrastructure manager. The manager is responsible for transparent and efficient allocation of available capacity to the different (often competing) licensed railway undertakings.

This thesis aims at developing a number of methods that can help allocate capacity in a deregulated (vertically separated) railway market. It focuses on efficiency in terms of social welfare, and transparency in terms of clarity and fairness. The work is concerned with successive allocation of capacity for publicly controlled and commercial traffic within a segmented railway market.

The contributions include cost benefit analysis methods that allow public transport authorities to assess the social welfare of their traffic, and create efficient schedules. The thesis also describes a market-based transparent capacity allocation where infrastructure managers price commercial train paths to solve capacity conflicts with publicly controlled traffic. Additionally, solution methods are developed to help estimate passenger demand, which is a necessary input both for resolving conflicts, and for creating efficient timetables.

Future capacity allocation in deregulated markets may include solution methods from this thesis. However, further experimentations are still required to address concerns such as data, legislation and acceptability. Moreover, future works can include prototyping and pilot projects on the proposed solutions, and investigating legal and digitalisation strategies to facilitate the implementation of such solutions.

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.

The deregulation of railway markets has brought new challenges to the capacity allocation process. In this context, we present a new hybrid methodology for allocating railway capacity to commercial train operators. Commercial freight or passenger train operators compete with each other as well as with subsidised local commuter trains. In the method presented in this article, minimal track access charges are computed using a social cost-benefit analysis of alternative commuter train timetables. These minimal charges are used as a starting or reservation price in an auction-based method for capacity allocation between the commercial operators. The aim of this study is to assess the use of such a welfare-based track access charging system in a real case scenario. On a congested line in the region of Stockholm, we evaluate the welfare-based access charges of commercial operators. We show that the new methodology can be used to allocate capacity between subsidized and commercial train operators. Moreover, we provide an estimate of the marginal minimal access charge per train path that the commercial operators request.

Many railway networks suffer from high capacity utilisation. For scheduling all services, adjustments to the desired slots are often needed. Such adjustments might lead to longer travel times, crowded trains, longer waiting times for boarding and for transfers. All of this has an important socio-economic impact on both travellers and train operators. This raises the question of the socio-economic assessment of changes in commuter train timetables including transfers. Thus, the aim of this study is to evaluate the effects of adjustments of commuter train timetables on the traveller (i.e. consumer costs) and the train operator (i.e. producer costs). These costs are estimated based on all train trips and operations in the network. In a case study, the effect of changes in departure times (resulting in non-regular interval timetables) is analysed. Further, the price of cancelling a two-way service during different times of the day is compared. The results show that changing departure times can both decrease and increase the total costs, and that regularity for parallel services might not be as important as expected if it is kept for each separate service. For the second study, waiting times for transfers were indicated to have a (too) large impact which can lead to misleading results and might be adjusted in future work. The model is adequate for such kind of questions but needs some more adjustments. For railway networks with dense and heterogeneous traffic (as is the case in Sweden), the contributions of this model are useful for making the challenging timetabling process easier and commuter train services less costly.

On highly used railway lines with heterogeneous traffic, timetabling is challenging. In particular, the limited existing capacity means that to guarantee an acceptable level of quality, the infrastructure manager must cancel some train services on the expense of others. In this article, we study the conflict between commercial long-distance trains and subsidized commuter trains with a socio-economic perspective (i.e. travelers and train operators). The study attempts to answer the following question: What is the socio-economic effect of modifying the timetable of a commuter service?

The case study treats the commuter train services in Stockholm. Trip data was collected from the local commuter train operator. An entropy maximization-based model was implemented to estimate the dynamic network Origin-Destination (OD) matrix. This dynamic matrix, of one full working day, was then used to estimate the number of travelers per train, and further converted for use in the microscopic simulation tool RailSys. Travel and waiting time are estimated for each OD pair and with that the generalized costs for the travelers and operators. The effect of crowding in the trains is included in the estimation. The article can be considered as an initiation to a novel method to calculate effects of changes in commuter train timetables. This novel approach enables to price commercial train slots in the capacity allocation process such as in an auction. It provides a new way to estimate the local train operator´s valuation of the different parameters (i.e. waiting, travel time and interchanges). Using RailSys for the estimation of times makes it possible to include capacity aspects that normally are difficult to reveal.