Different levels of detail can be used to model a railway network when optimizing the timetable. While microscopic models are required to accurately represent the train operations, they are often quite complex and require long computation times. Therefore, macroscopic models are often used instead. Microscopic simulation is then used in a later stage to determine if the timetable is conflict-free. In this work, we examine the impact of the level of detail that is used to model the network on the optimization of the timetable, more specifically on improving the timetable robustness. This way, we obtain more insight into which level of detail is most useful in practice in terms of the quality of the solution and the computation time. To do this, four different network representations with an increasing level of detail are considered. This includes a macroscopic, two mesoscopic and a microscopic representation. For each of these representations, we formulate a mathematical model to optimize the robustness of the timetable. These models are applied to a line on the Swedish network. The results show that including more details in the optimization leads to better solutions compared to optimizing with less details and solving the conflicts in a later stage. Surprisingly, for our experiments, including more details actually led to a significant decrease in the computation times. 

Changing trains is a crucial part of many rail journeys, and it is important that arrival and departing trains are allocated close to each other for a swift interchange. Today the platform allocation of trains is often based on local traditions, where trains of the same type to the same destination depart from the same track. In this paper we address the problem of using the platform tracks in the best way, balancing crossing train paths with easy interchanges at the same platform. We use a RailSys model of the station in Norrköping, Sweden, to assess three platform allocation strategies with respect to crossing train paths and train changes at the same platform. Moreover, the capacity utilisation is calculated by a timetable compression-based method. The results show that a platform allocation maximising the changes at the same platform leads to more crossing train paths and higher capacity utilisation. Future work includes more accurate modelling of train connections, assessment of delays through simulation, and a cost-benefit analysis to find the best balance between easy interchanges and conflicting train paths.

Changing trains is a crucial part of many rail journeys, and it is important that arrival and departing trains are allocated close to each other for a swift interchange. Today the platform allocation of trains is often based on local traditions, where trains of the same type to the same destination depart from the same track. In this paper we address the problem of using the platform tracks in the best way, balancing crossing train paths with easy interchanges at the same platform. We use a RailSys model of the station in Norrköping, Sweden, to assess three platform allocation strategies with respect to crossing train paths and train changes at the same platform. Moreover, the capacity utilisation is calculated by a timetable compression-based method. The results show that a platform allocation maximising the changes at the same platform leads to more crossing train paths and higher capacity utilisation. Future work includes more accurate modelling of train connections, assessment of delays through simulation, and a cost-benefit analysis to find the best balance between easy interchanges and conflicting train paths.

In this study, we consider rescheduling freight trains to reduce the effects of major interruptions. We assume that the interruption is an unexpected marshalling-yard closure, and we develop a macroscopic Mixed-Integer-Linear-Programming (MILP) model to reschedule the timetable. Furthermore, we design a rescheduling strategy of letting trains wait on the way when the destination yard has a closure. We consider stopping restrictions and the capacity of each segment and station in the model. The order of the trains affected by the interruption is not fixed. We present experimental results for three different cases of various sizes.

Tågförseningar tillsammans med ökande efterfrågan leder till betydande sociala kostnader. Ett sätt att minska dessa kostnader är genom effektiv kommunikation av olika typer av trafikinformation till passagerare. Beroende på vilken/när information tas emot minskar passagerarnas beteendeanpassningar deras resekostnader, särskilt vid förseningar.Detta projekt syftar till att beräkna de samhällsekonomiska nyttor som sådan trafikinformation ger. Detta åstadkoms genom att utveckla en kvantitativ modell för att kvantifiera värdet av att kommunicera information till resenärerna vid tågförseningar.Genom att studera olika störningsscenarier i ett studiefall från Stockholmspendeltåg, ger studien insikter till infrastrukturförvaltaren om samhällsnytta av sådana informationssystem som en del av de stora infrastrukturinvesteringarna. Dessa insikter kan också hjälpa till att utforma effektivare informationsstrategier för att minska samhällsekonomiska kostnader för tågstörningar. 

Train delays with increasing demand result in substantial social costs. One way to reduce these costs is through efficient communication of various types of traffic information to passengers. Depending on which/when information is received, passengers’ behavioral adaptations reduce their travel costs especially in the event of delays.This project aims at calculating the social benefits of such traffic information. The purpose is to develop a quantitative model to quantify the value of communicating information to passengers in cases of train delays.Using different disruption scenarios in a case study from Stockholm commuter train services, the study provides insights to the infrastructure manager on the social value of such information systems as part of the large infrastructure investments. These insights can also help design more effective information strategies to reduce social costs of train disruptions.

This study is about rescheduling freight trains to reduce the eff ects of major interruptions. In this paper, we consider that the interruption is an unexpected marshallingyard closure. We develop a macroscopic Mixed-Integer Linear Programming (MILP)model to reschedule railway timetables. One important principle is that we simultaneously reschedule several trains, instead of one-by-one. Furthermore, we consider arescheduling strategy of letting trains wait on the way when the destination yard havea closure. The model considers stopping restrictions and the capacity of each segmentand station. The order of the trains aff ected by the interruption is not fi xed. We presentexperimental results of three diff erent cases, which are all based on artifi cial data.

This study is about rescheduling freight trains to reduce the effects of major interruptions. In this paper, we consider that the interruption is an unexpected marshalling-yard closure. We develop a macroscopic Mixed-Integer Linear Programming (MILP) model to reschedule railway timetables. One important principle is that we simultaneously reschedule several trains, instead of one-by-one. Furthermore, we consider a rescheduling strategy of letting trains wait on the way when the destination yard have a closure. The model considers stopping restrictions and the capacity of each segment and station. The order of the trains affected by the interruption is not fixed. We present experimental results of three different cases, which are all based on artificial data.

When several trains are planned to use the same infrastructure resource, there is always a risk for spreading of delays, which can be hard to recover from. It is a challenge for the Infrastructure Manager to make timetables that accommodate as much traffic as possible, without causing bad on-time performance. Timetable planners are in need of quantitative indicators to assess timetable robustness and accurate methods for how to make the timetable more robust.

In this paper we assess the robustness for single-track lines with non-periodic timetables. At single-track lines, trains use the line for running in both directions and the trains can only pass or overtake each other at passing loops. This makes the system more sensitive for delays. In this paper we present a robustness indicator which captures the dependencies between trains at a single-track line. The indicator can be used to illustrate weaknesses in a timetable and also to indicate where and how to insert more robustness. In a simulation study, we show that it is possible to improve the performance by making small timetable adjustments according the indicator, without increasing runtimes or capacity utilization.

Interchange stations are essential for a high-quality public transport system. Many passengers pass through a station during the course of a day and the time spent at a station has a large effect on their experience of the whole journey. In this study, we aim to improve the passenger experience at a bus terminal by minimizing the walking distances for all passengers. To this end, an integer linear optimization model which allocates bus lines to the stops of a bus terminal is presented. The model is tested in a numerical experiment using synthetic passenger data. Two alternative approaches, either randomly allocated or based on the number of non-transferring passengers, are used for comparisons. The average improvement in relation to the random allocation strategy is 13%, which shows that the allocation approach has potential. It is thus of interest to collect data from a real bus terminal to further explore the model and the potential benefits it can provide.