This paper attempts to provide a better understanding of the service quality risks that are associated with freight train operations in mixed traffic. Adequate operational quality requires a certain level of robustness to delays and disruptions, but on segments with confined capacity, robustness becomes more fragile with increased traffic demand and speed heterogeneity. This trade-off between capacity utilization and service robustness predominantly manifests itself by compromising the reliability of rail freight services for two main reasons. Firstly, passenger trains are often prioritized in dispatching over freight trains. Secondly, many freight trains operate over longer distances and they accumulate more delays. Decreasing speed heterogeneity in mixed traffic may become pivotal in the interplay between robustness and capacity utilization. In this paper, we investigate possible improvements in capacity utilization and in railway service quality when introducing faster freight trains in mixed-traffic operations. The analysis is carried out on the Swedish Southern Main Line, which forms part of the Scandinavian-Mediterranean freight corridor. Microscopic simulation is used to explore performance indicators, such as simulated running times with respect to scheduled running times, capacity utilization, and punctuality, by implementing and comparing scenarios with different maximum speeds for freight trains. The results suggest that increasing the top speed of freight trains might seem as a promising approach for reduced utilization of capacity in the planning stage, but the stochasticity of operations in mixed traffic may become more challenging for delay recovery.

Aim and research questions 

This paper aims to investigate railway punctuality in the three Swedish metropolitan regions of Stockholm, Gothenburg, and Malmö through macroscopic simulation. The results will contribute to a more detailed knowledge of delays and punctuality in the three biggest metropolitan regions in Sweden, which gives support to finding actions to decrease delays and improve punctuality. Moreover, the need for further research concerning how to decrease the amount of primary delays and increase punctuality will be identified. This paper seeks to answer the following research questions: 

• How do the primary and secondary delay distributions, respectively, differ between the three metropolitan regions?
• How much would the primary delays have to decrease to achieve 95% punctuality? 

As part of the research, we have formed three hypotheses which will be proved or disproved. The hypotheses are: 

H1: The more and denser the traffic in a region, the more secondary delays will occur. Thus, we expect the Stockholm region, which is the biggest, to have the largest amount of secondary delays followed by the Gothenburg region and, lastly, the Malmö region. 

H2: Entry delays, run-time delays, and dwell-time delays do not contribute equally to the total amount of delays. 

H3: The different types of delays vary in share between the regions, e.g. more dwell time delays in the Stockholm region and more run-time delays in the Gothenburg region. This is in line with findings from (Palmqvist and Kristoffersson, 2022). 

State of the art 

The Swedish railway has many challenges. There is a high demand for travel and transport and wishes to further increase the number of trains operated to meet this demand, but at the same time, there are problems with a lack of capacity and insufficient punctuality, where the goal of 95% of the trains being punctual at their final destination is not reached even with the current traffic volume. To run even more trains leads to even more secondary delays, and to expand the traffic with acceptable quality the amount of primary delays has to decrease. 

Previous studies addressing various aspects of punctuality and delays through simulation have been performed, e.g. simulation of the punctuality in Skåne with a current timetable and with a 2025 timetable with increased traffic (Johansson et al., 2022b; Palmqvist et al., 2023). (Johansson et al., 2022a) performed a case study on the Southern Main Line in Sweden comparing the total punctuality for the case when the freight trains were allowed to depart before schedule to the case when they were not allowed early departures. 

Method 

The railway operations are simulated with the simulation tool PROTON (Sipilä, 2023), which is a macroscopic tool, i.e. not modelling all details of the infrastructure, vehicles, and timetable. The advantage of macroscopic simulation is that larger networks can be simulated without significantly increasing the computational time. The macroscopic simulation will use different scaling factors for 

the respective entry, run-time, and dwell-time delays. Turning trains will be handled for better modelling of secondary delays during the full day, compared to previous simulation studies with PROTON. The timetable from 2019 will be used since it is relatively new and not affected by operational changes due to the COVID-19 pandemic. The metropolitan regions will be simulated separately, meaning that delays in one region will not affect the simulation outcome in the other regions. 

The “Design of experiments” (DOE) tool of Circumscribed Central Composite Design is used to find the 20 relevant delay distribution scenarios to study. Thereafter, these scenarios are simulated, and a regression model is solved to find the scenarios where the shares of run time, dwell, and entry delays most likely are primary delays. 

Results and analysis 

The results are expected to be useful in prioritising efforts to improve punctuality in the three metropolitan regions. In addition, the results will give a deeper knowledge of the similarities and differences between types of delays occurring per region, and the interaction effects between the different types of delays; entry, run-time, and dwell-time delays. 

References 

Johansson, I., Palmqvist, C.-W., Sipilä, H., Warg, J., Bohlin, M., 2022a. Microscopic and macroscopic simulation of early freight train departures. J. Rail Transp. Plan. Manag. 21. https://doi.org/10.1016/j.jrtpm.2022.100295 

Johansson, I., Sipilä, H., Palmqvist, C.-W., 2022b. Simulating the Punctuality Impacts of Early Freight Train Departures, in: Proceedings of 13th World Congress on Railway Research (WCRR). Birmingham, UK. 

Palmqvist, C.-W., Johansson, I., Sipilä, H., 2023. A method to separate primary and secondary train delays in past and future timetables using macroscopic simulation. Transp. Res. Interdiscip. Perspect. 17, 100747. https://doi.org/10.1016/j.trip.2022.100747 

Palmqvist, C.W., Kristoffersson, I., 2022. A Methodology for Monitoring Rail Punctuality Improvements. IEEE Open J. Intell. Transp. Syst. 3, 388–396. https://doi.org/10.1109/OJITS.2022.3172509 

Sipilä, H., 2023. Simulations with PROTON and RailSys: Use of a macroscopic and microscopic railway simulation tool in Swedish applications (No. TRITA–ABE–RPT–2323). 

Punctuality is a key factor in railway operations and is affected by both primary and secondary delays to differing degrees. Being able to separate these two types of delays is very important when simulating operations, and when conducting punctuality improvement efforts. However, it is not easy to estimate the relative proportions of primary versus secondary delays using historical data. In this paper, we demonstrate a method that uses repeated runs of a macroscopic simulation tool to estimate what share of delays has been primary or secondary. Using the Swedish region of Skåne as a case study, we estimate that about 36% of delays in 2019 were primary, leaving 64% as secondary. We further show that in order for operations to reach the targeted level of punctuality, 95% instead of the observed 87%, primary delays would have had to be cut by half. Using a draft timetable for 2025, we also simulate what the punctuality would be given different assumptions of primary delays. Assuming the same level of primary delays in 2025 as in 2019, we estimate that the punctuality would drop by a further 5 percentage points due to increased density of operations. In order to reach the punctuality target of 95% in 2025, primary delays would instead need to be reduced by two-thirds. At the request of the infrastructure manager, we also show the predicted geographical distribution of secondary delays in this future timetable. Our results highlight the need for drastic delay reduction measures to reach set targets.

Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by R2 of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively.

Projektet har genomförts inom branschprogram KAJT (Kapacitet i Järnvägstrafiken). Projektets huvudsyfte är att utveckla ett verktyg (en modell) som kan användas som stöd för att göra headwayberäkningar i ETCS level 2 (ETCS L2). Verktyget ska modellera broms- och ingripandekurvor enligt de specifikationer som gäller för Baseline 3.6.0 och beskrivs i ERA Subset-26-3 [1]. När dessa beräknats för en viss bansträcka och antagna fordonstyper kan teknisk headway beräknas för respektive signalsträcka. Teknisk headway är den minsta tid som måste finnas mellan två på varandra följande tåg vid respektive signalpunkt (signalsträcka). Tiden beror på en kombination av signalpunkternas placeringar, signalsystemsparametrar, bankarakteristik (hastigheter, lutningar) och fordonsparametrar.

Idén med verktyget är att flera parametrar enkelt ska kunna varieras och framför allt att påverkan av signalpunkternas placeringar relativt enkelt ska kunna undersökas systematiskt. Beroende på hur många olika uppsättningar av placeringar som läggs in kan headway beräknas för flera fall samtidigt och jämföras mot varandra. Ett typiskt fall att undersöka, givet en grunduppsättning av signalpunktsplaceringar, är om teknisk headway kan minskas på vissa platser och med hur mycket om placeringen ändras för en eller flera signalpunkter och/eller om en eller flera nya punkter läggs in. En grunduppsättning kan vara föreslagna signalpunktsplaceringar i ett förprojekteringsskede och exempelvis vara i stort sett samma som nuvarande signalplaceringar i ATC (1:1 principen) eller vara ett första placeringsförslag på en framtida bana. Alternativt skapas en helt fiktiv bana på vilken headwayberäkningar görs.

Liknande analyser kan göras i exempelvis RailSys men hanteringen med att variera olika parametrar är betydligt mer omständlig och tar tid. RailSys är inte utformat för att enkelt och relativt snabbt kunna skapa och beräkna olika kombinationer av signalpunktsplaceringar.

Most railways use fixed block technology, which could be replaced with moving block technology with associated high cost. It is therefore interesting to gradually upgrade the signalling system exploiting hybrid technologies. This paper aims to investigate the impact on capacity of various signalling systems (including fixed block technology and hybrid technology) using a microscopic simulation tool under scheduled (static) conditions without considering probability functions. To perform comparative analysis between European Train Control System (ETCS) Hybrid Level 3, ETCS Level 2, and the Swedish ATC2 legacy system, three signalling system scenarios are designed and capacity consumption is considered as a performance indicator. The study was performed on the central section of Stockholm’s commuter train network with peak hour conditions from the 2020 timetable. The results show that ETCS L2 delivers lower capacity consumption in total compared to the ATC2 legacy system. ETCS Hybrid Level 3 with existing trackside train detection and partially shortened block sections delivers lower capacity consumption compared to ETCS L2 and ATC2. The implementation of hybrid solutions such as ETCS Hybrid Level 3 in addition to allowing for gradual upgrading of signalling systems to the next generation (moving block system) can improve capacity of high-density commuter lines.

In Sweden and other countries it is not an uncommon practice that freight trains depart more or less on-demand instead of strictly following a pre-planned timetable. However, the systematic effects of freight trains departing late or (in particular) early has long been a contested issue. Although some microscopic simulation tools currently have the capability to evaluate the effect of freight trains departing before schedule, it has yet not been established how macroscopic simulation tools, capable of fast simulation of nation-wide networks, can manage such tasks. This paper uses a case study on a line between two large freight yards in Sweden to investigate how the results of microscopic and macroscopic simulation, represented by two modern simulation tools, differ when it comes to this particular problem. The main findings are that both the microscopic and the macroscopic tools replicated the empirical punctuality fairly well. Furthermore, allowing early departures of freight trains increased overall freight train punctuality while the passenger train punctuality decreased slightly, as determined by both tools. The results are encouraging, but further studies are needed to determine if macroscopic simulation is on-par with microscopic simulation.

Aim and research questions

The aim of this paper is to use simulation to investigate the relationship between primary delays and the resulting punctuality. Furthermore, by utilising a fast, macroscopic railway traffic simulation tool for the study, the potential for macroscopic simulation to cover larger networks and/or several cases is also highlighted. As a case study, we use the region of Scania in Southern Sweden, using the real timetable and operational data for 2019, as well as a draft timetable for 2025. In particular, this paper seeks to answer the following research questions:

• What proportions of delays are primary and secondary?
• By how much would primary delays have to be reduced to meet the Swedish railway industry target of 95% punctuality, today?
• By how much would primary delays have to be reduced to meet the target of 95% punctuality, using a draft timetable for 2025?
• What would the punctuality be in 2025, given the current level of primary delays?

State of the art

Punctuality is known to be a key factor for railway customers. While passengers can plan for longer travel times that are known in advance, it is difficult to plan for unreliable services. Passengers must compensate by adding margins of an unknown size, and thus value delay time much higher than regular travel time. Extensive research has been done, and continues to be done, on creating timetables that are robust to delays. Högdahl et al. (2019), Lee et al. (2017), Sels et al. (2016), and Bešinović et al.(2016) are only some examples of a vast literature. There is also a literature that more broadly investigates the concept of timetable quality (i.e. Watson 2008, Gestrelius et al. 2020, Palmqvist et al. 2019, Lenze & Nießen 2021 to name a few). An increasing amount of research has also been done into factors that influence punctuality (e.g. Økland & Olsson 2020, Olsson & Haugland 2004, Palmqvist et al. 2017, Palmqvist et al. 2022, van der Kooij et al. 2017, Veiseth et al. 2007, Xia et al. 2013). Simulation is a common tool in railway research for evaluating punctuality and robustness of timetables and disturbance scenarios. Often, microscopic simulation is used, characterised by a high level of detail for the infrastructure as well as the vehicles and the timetable. Examples of such simulators are RailSys, OpenTrack, LUKS, and Trenissimo. Another approach is to use macroscopic simulation, modelling the railway network with a given number of tracks along the lines and the stations as nodes, not accounting for the exact track layout or what happens during operations aside from station arrival and departure times. PROTON is a macroscopic simulation tool under development by DB Analytics enabling computationally efficient and fast simulation on a network scale (Zinser et al. 2019).

Method

The simulated network covers most of the railway lines in the southernmost part of Sweden. It consists of parts of two double-tracked main lines, Southern Main Line and West Coast Line, as well as the track network in the Malmö area and multiple single track lines. Especially on the main lines, the traffic is highly mixed with long-distance, regional, and local passenger trains as well as freight trains. The single track lines are to varying degrees operated by regional and local passenger trains, and freight trains. Empirical data from weekdays during 2019 within the simulated area were used as inputs into the model. All in all, this contains about 4.9 million observed train movements between adjacent timing points. These data were then separated by train type (local, regional, and long-distance passenger trains, as well as freight trains) and direction (North- or Southbound) to generate distributions for departure (at the entrance to the simulation area), run time, and dwell time delays. Each of these distributions contain both positive and negative delays, expressed in 1-minute intervals in the pm60 minute range. Larger deviations than these were not included, because they are extremely rare, with 99.4% of run and dwell time deviations in the ±10-minute range, 92.7% in the ±3-minute range, a full 75% in the ±1 range. To calibrate the simulation, we ran the timetable for the three selected dates in 2019 with a set of 21 different scaling levels of primary delays. These ranged from 0% to 100%, in 5% intervals. The logic is that a proportion of empirically observed delays were primary, with the rest being secondary, but that this proportion is not explicitly observed. By testing a range of scenarios, we can find a level of input delays that yields an output in terms of punctuality that is close to what was observed empirically. We found that the lines for the simulated and observed punctuality intersect at about the level of 36%, and the closest scenario we simulated was 35%.

Analysis and results

We find that that 36% of delays in the empirical data from 2019 were primary, corresponding to a punctuality level of 87%. Thus, every one part of primary delays on average caused about two parts of secondary delays. This is in line with earlier simulations we have done for similar case studies. The same level of primary delays in the timetable of 2025 would result in a punctuality level of about 82%. With the same distribution of primary delays as in 2019, punctuality would thus be expected to fall by about 5 percentage points in 2025, due to an increase in the number of trains run. A scale factor of 19% in 2019 would have resulted in the desired level of punctuality. This implies that about half of the primary delays would have to be removed to reach a punctuality of 95%, given the same infrastructure and timetable. In other words, there were about twice as many delays as would have been required to reach the target punctuality. To reach the desired punctuality in 2025, a scale factor of 13% would have been required. This would imply a reduction in primary delays by about two thirds from the observed level in 2019. This is a tall order and highlights the need for drastic delay reduction measures.

References

Bešinović, N., Goverde, R., Quaglietta, E., Roberti, R., 2016. An integrated micro–macro approach to robust railway timetabling. Transportation Research Part B: Methodological 87, 14–32. doi:10.1016/j.trb.2016.02.004.

Gestrelius, S., Peterson, A., Aronsson, M., 2020. Timetable quality from the perspective of a railway infrastructure manager in a deregulated market: An interview study with Swedish practitioners. Journal of Rail Transport Planning & Management 15. doi:10.1016/j.jrtpm.2020.100202.

Högdahl, J., Bohlin, M., Fröidh, O., 2019. A combined simulation optimization approach for minimizing travel time and delays in railway timetables. Transportation Research Part B: Methodological 126, 192–382. doi:https://doi.org/10.1016/j.trb.2019.04.003.

van der Kooij, R.B., Landmark, A.D., Seim, A.A., Olsson, N., 2017. The effect of temporary speed restrictions, analyzed by using real train traffic data. Transportation research procedia 22, 580–587.

Lee, Y., Lu, L.S., Wu, M.L., Lin, D.Y., 2017. Balance of efficiency and robustness in passenger railway timetables. Transportation Research Part B: Methodological 97, 142–156.

Lenze, W., Nießen, N., 2021. Quality in rail timetabling - A detailed review of stakeholders’ interests compared with current practices in Germany. Journal of Rail Transport Planning & Management 20.

Olsson, N., Haugland, H., 2004. Influencing factors on train punctuality Results from some Norwegian studies. Transport Policy 11, 387-397.

Palmqvist, C., Lind, A., Ahlqvist, V., 2022. How and Why Freight Trains Deviate From the Timetable: Evidence From Sweden. IEEE Open Journal of Intelligent Transportation Systems, 210–221.

Palmqvist, C., Olsson, N., Hiselius, L., 2017. Some influencing factors for passenger train punctuality in Sweden. International Journal of Prognostics and Health Management.

Palmqvist, C., Olsson, N., Hiselius, L., 2019. Punctuality problems from the perspective of timetable planners in Sweden, in: Proceedings of 20th EURO Working Group on Transportation Meeting (EWGT) 2019, Budapest, Hungary.

Sels, P., Dewilde, T., Cattrysse, D., Vansteenwegen, P., 2016. Reducing the passenger travel time in practice by the automated construction of a robust railway timetable. Transportation Research Part B: Methodological 84, 124–156.

Veiseth, M., Hegglund, P., Wien, I., O., N.O.E., Stokland, Ø., 2007. Infrastructure’s influence on rail punctuality, in: Brebbia, C. (Ed.), Urban Transport XIII Urban Transport and the Environment in the 21st Century. Wessex Institute of Technology, UK, Southampton, UK. volume 96, pp. 481–490.

Watson, R., 2008. Train Planning In A Fragmented Railway-A British Perspective. Doctoral thesis. Loughborough University.

Xia, Y., Van Ommeren, J.N., Rietveld, P., Verhagen, W., 2013. Railway infrastructure disturbances and train operator performance: The role of weather. Transportation research part D: transport and environment 18, 97–102.

Zinser, M., Betz, T., Becker, M., Geilke, M., Terschlüsen, C., Kaluza, A., Johansson, I., Warg, J., 2019. PRISM: A Macroscopic Monte Carlo Railway Simulation, in: Proceedings of 12th World Congress on Railway Research (WCRR) 2019, Tokyo, Japan.

Økland, A., Olsson, N., 2020. Punctuality development and delay explanation factors on Norwegian railways in the period 2005 – 2014. Public Transport.