European infrastructure managers (IMs) create annual timetables for trains that will run during a year. Freight trains in Sweden often deviate from this by being added , canceled , delayed or early , resulting in increased costs for IMs and railway undertakings (RUs). We investigate the frequency of and causes for these deviations, using one year of operational data for 48,000 trains, and 15 stakeholder interviews. We find that about 20% of freight trains are added once the timetable has been created, and that cancelations occur for about 35% of freight trains, mostly at the RUs’ initiative. Delays are common: some 40% of departures, 30% of runtimes, and 20% of dwell times are delayed. Running early is even more common: 80% are ready to depart early, and 60% do so, while 40% of runtimes and 75% of dwell times are shorter than scheduled. We find links and feedback loops between the root causes for these deviations and suggest that IMs reserve more of the capacity that is needed for freight trains and instead distribute it throughout the year. This could lead to more appropriate, attractive, and reliable timetables for freight trains, whilst greatly reducing the amount of planning effort.

Punctuality is regarded as an important measure of the performance of a railway system, and is the one most commonly used and discussed measure both in the industry and among travelers. In many countries, the punctuality of trains, and thus the performance of the railway system, is deemed as lacking. The aim of this article is to study and quantify how several weather-, timetable, operational and infrastructure-related variables influence punctuality in passenger train traffic. This can contribute to better understanding of the performance of railway systems, and help identify possible improvements. The study is based on a dataset containing detailed timetables and records of all 32.4 million train movements for all trains in Sweden during the year of 2015, over 1.1 million departures. Supporting this is a comprehensive register of over 80 000 infrastructure elements, and almost 87 million weather observations. We consider the size and allocation of margins, the existence of negative margins, two measures for traffic volume, the journey time and distance, how often different vehicle individuals are used, the number of line and station interactions between trains, the amount of precipitation, the temperature, wind speed, snow depth, and eight types of infrastructure elements. We show how these variables affect punctuality, and estimate how much of the variation in punctuality can be explained by them. The findings can be used to design timetables, change operational parameters and modify infrastructure design so that punctuality improves. They can also help identify areas which should be prioritized in planning, maintenance and research.

This study investigates the relationship between passenger volumes and the frequency and size of dwell times for commuter trains. We use data collected by automatic passenger count systems in Stockholm and show that dwell times often take longer than scheduled. To understand the effect of passenger volumes on delays, we use the frequencies of delays for different passenger volumes and delay sizes. We find that these frequencies differ significantly from a frequency distribution independent of passenger volumes, indicating that passenger volumes have an effect on the frequency of dwell time delays. Neglecting passenger volumes underestimates the frequency of delays in most cases, especially for smaller delays. Although the frequency of dwell time delays increases as passenger volumes increase, the same is not necessarily true for their size. The relationship between passenger volumes and delay sizes is thus non-linear, and it depends on scheduled dwell times. We conclude that small increases in dwell times can result in sharp increases in on-time performance but that only increasing dwell times is not sufficient. Measures to speed up the boarding and alighting process are also necessary.

Punctuality is an important aspect of train operations, highly valued by passengers. Both Swedish and Norwegian railways have introduced frameworks to systematically improve punctuality in their systems, inspired by an extensive literature on Total Quality Management. After about a decade with these frameworks, we can see that punctuality has risen by about 2–3 percentage points. However, this pace of improvements is slower than desired. We propose that there is a gap between what most individual improvement efforts deliver, and what can be detected by directly monitoring punctuality. This gap stifles the desired culture of constant improvements. We instead propose a methodology for how to monitor punctuality improvements, by focusing on the constituents of a train trip. Using 20 years of data from commuter trains in three metropolitan regions (Stockholm, Gothenburg & Malmö), we show the frequency of runtime and dwell time delays is directly related to punctuality. These delay frequencies are also easy to measure and target, and more easily capture the intended effects of specific improvement efforts. Our hope is that this framework and measures such as these will better enable systematic efforts to improve railway punctuality.