Combining Operative Train Simulation with Logistics Simulation in SUMO




operative train simulation, logistics simulation, combined simulation model, SUMO, transportation chain, container trajectories


Rail freight logistics is usually planned and analyzed using a macroscopic aggregated view on railway networks and train operations. As a result, disjoint tools have developed for simulating train operations which requires a detailed representation of track assets as well as the signaling architecture and supply chain networks in logistics analyzing the flow of goods where mode-specific capacity and traffic situations are incorporated in an aggregated manner. However, integrating the two areas could help evaluating railway-specific operative implications (such as conflicts and consequent delays) on the level of transport chains and thus single transport units instead of trains or network areas. The simulation tool SUMO is identified to meet criteria from both disciplines. It is shown how a respective methodology can be realized in SUMO to create such a simulation model. A use case of northwestern Germany shows by the means of exemplary container trajectories that the two simulative approaches can be merged.


Download data is not yet available.


Methodology for GHG Efficiency of Transport Modes. Final Report, Fraunhofer-Institute for Systems and Innovation Research ISI, CE Delft 2020

Rail Freight Forward coalition: 30 by 2030. Rail Freight strategy to boost modal shift., Accessed on: 24.02.2022

Parbo, J., Nielsen, O. A. u. Prato, C. G.: Passenger Perspectives in Railway Timetabling: A Literature Review. Transport Reviews 36 (2016) 4, S. 500–526. doi:

Lopez, P. A., Wiessner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P.,Hilbrich, R., Lücken, L., Rummel, J. u. Wagner, P.: Microscopic Traffic Simulation using SUMO. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE 2018, S. 2575–2582. doi:

Wenzel, S.: Simulation logistischer Systeme. In: Tempelmeier, H. (Hrsg.): Modellierung logistischer Systeme. Berlin, Heidelberg: Springer Berlin Heidelberg 2018, S. 1–34. doi:

Gong, W., Zhou, L. u. Ye, F.: Multi-Agent GIS Simulation for Railway Logistics Optimization. 2019 4th International Conference on Intelligent Transportation Engineering (ICITE). IEEE 2019, S. 64–68. doi:

Oliveira, J. B., Lima, R. S. u. Montevechi, J. A. B.: Perspectives and relationships in Supply Chain Simulation: A systematic literature review. Simulation Modelling Practice and Theory 62 (2016), S. 166–191. doi:

Clausen, U., Brueggenolte, M., Kirberg, M., Besenfelder, C., Poeting, M. u. Gueller, M.: Agent Based Simulation in Logistics and Supply Chain Research: Literature Review and Analysis. In: Clausen, U., Langkau, S. u. Kreuz, F. (Hrsg.): Advances in Production, Logistics and Traffic. Lecture Notes in Logistics. Cham: Springer International Publishing 2019, S. 45–59. doi:

Massow, S. von u. Afify, A.: Materialflusssimulation für die Prozessindustrie. Zeitschrift für wirtschaftlichen Fabrikbetrieb 105 (2010) 3, S. 216–221. doi:

Dragović, B., Tzannatos, E. u. Park, N. K.: Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool. Flexible Services and Manufacturing Journal 29 (2017) 1, S. 4–34. doi:

Johansson, I., Palmqvist, C.-W., Sipilä, H., Warg, J. u. Bohlin, M.: Microscopic and macroscopic simulation of early freight train departures. Journal of Rail Transport Planning & Management 21 (2022), S. 100295. doi:

Radtke, A. u. Hauptmann, D.: Automated planning of timetables in large railway networks using a microscopic data basis and railway simulation techniques. WIT Transactions on The Built Environment 74 (2004)

Nash, A. u. Hürlimann, D.: Railroad simulation using OpenTrack. WIT Transactions on The Built Environment 2004 (74)

Salido, M. A., Barber, F. u. Ingolotti, L.: Robustness for a single railway line: Analytical and simulation methods. Expert Systems with Applications 39 (2012) 18, S. 13305–13327. doi:

Abril, M., Barber, F., Ingolotti, L., Salido, M. A., Tormos, P. u. Lova, A.: An assessment of railway capacity. Transportation Research Part E: Logistics and Transportation Review 44 (2008) 5, S. 774–806. doi:

Kianinejadoshah, A. u. Ricci, S.: Comparative Application of Analytical and Simulation Methods for the Combined Railway Nodes-Lines Capacity Assessment. Transportation Research Procedia 55 (2021), S. 103–109. doi:

Borndörfer, R., Klug, T., Schlechte, T., Fügenschuh, A., Schang, T. u. Schülldorf, H.: The Freight Train Routing Problem for Congested Railway Networks with Mixed Traffic. Transportation Science 50 (2016) 2, S. 408–423. doi:

Cacchiani, V., Caprara, A. u. Toth, P.: Scheduling extra freight trains on railway networks. Transportation Research Part B: Methodological 44 (2010) 2, S. 215–231. doi:

Weik, N., Hemminki, E. u. Nießen, N.: The Effective Residual Capacity in Railway Networks with Predefined Train Services. In: Neufeld, J. S., Buscher, U., Lasch, R., Möst, D. u. Schönberger, J. (Hrsg.): Operations Research Proceedings 2019. Operations Research Proceedings. Cham: Springer International Publishing 2020, S. 725–731. doi:

Caimi, G., Kroon, L. u. Liebchen, C.: Models for railway timetable optimization: Applicability and applications in practice. Journal of Rail Transport Planning & Management 6 (2017) 4, S. 285–312. doi:

Hu, Q., Wiegmans, B., Corman, F. u. Lodewijks, G.: Integration of inter-terminal transport and hinterland rail transport. Flexible Services and Manufacturing Journal 31 (2019) 3, S. 807–831. doi:

Caballini, C., Pasquale, C., Sacone, S. u. Siri, S.: An Event-Triggered Receding-Horizon Scheme for Planning Rail Operations in Maritime Terminals. IEEE Transactions on Intelligent Transportation Systems 15 (2014) 1, S. 365–375. doi:

Mancera, A., Bruckman, D. u. Weidmann, U.: Single Wagonload Production Schemes Improvements Using GüterSim (Agent-based Simulation Tool). Transportation Research Procedia 10 (2015), S. 615–624. doi:

Noyer, U., Rudolph, F., Rummel, J. u. Weber, M.: Digitaler Hafen: Moderne Hafenregelung durch Simulation., Accessed on: 22.02.2022

Merz, G.: AI-based Disposition using a Reinforcement Learning Approach. SUMO User Conference 2020. 2020

Shankar, S., Schubert, L. A., Patil, A. J. u. Erdmann, J.: The use of big data and the SUMO transportation simulation in Rail2X. SIGNAL + DRAHT (2020) 10, S. 49–58

Janecek, David, Weymann, F. u. Schaer, T.: LUKS-integriertes Werkzeug zur Leistungsuntersuchung von Eisenbahnknoten und-strecken. ETR Eisenbahntechnische Rundschau (2010) 1-2, S. 25–32

Bundesministerium für Digitales und Verkehr: Deutschlandtakt., Accessed on: 23.02.2022

Liebchen, C.: Periodic Timetable Optimization in Public Transport. In: Waldmann, K.-H. u. Stocker, U. M. (Hrsg.): Operations research proceedings 2006. Selected papers of the annual international conference of the German Operations Research Society (GOR), jointly organized with the Austrian Society of Operations Research (ÖGOR) and the Swiss Society of Operations Research (SVOR) : Karlesruhe, September 6-8, 2006. Operations Research Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg 2007, S. 29–36. doi:

Bundesministerium für Digitales und Verkehr: Infrastruktur für einen Deutschland-Takt im Schienenverkehr., Accessed on: 28.02.2022




How to Cite

Geischberger, J., & Weik, N. (2022). Combining Operative Train Simulation with Logistics Simulation in SUMO. SUMO Conference Proceedings, 3, 145–157.

Conference Proceedings Volume


Conference papers