Using SUMO for Test Automation and Demonstration of Digitalized Railway Concepts

Integrating SUMO with the "Train Dispatcher in the Cloud"

Authors

DOI:

https://doi.org/10.52825/scp.v5i.1126

Keywords:

Train Dispatcher in the Cloud, SUMO, Visualization, Test Automation

Abstract

The Train Dispatcher in the Cloud (ZLiC) is a cloud-based approach to digitalize the German Zugleitbetrieb (comparable to American track warrant control). The ZLiC aims to replace the train dispatcher with speech to text, natural-language understanding, a digital occupancy sheet, a prototype interlocking logic, and text to speech. For train conductors, who operate on the trains, the voice-based communication with the train dispatcher remains unchanged. Because the external interfaces of the ZLiC are either voice-based or graphic, understanding and testing the internal components from an integration level is a challenge. To address these challenges, we first injected the Simulation of Urban Mobility (SUMO) as a simulation environment. Since the ZLiC has been developed model-based, the integration requires minimal modifications. Afterward, we fetch the operation commands (e.g., registering trains, locating trains, drive requests for trains) between the components and send them to a SUMO instance for analyzing and visualizing the train operations in the railway network. Lastly, we insert additional planned trains to add simulated traffic. The reproducible operations enable test automation of the ZLiC while reusing the sophisticated models in SUMO. This prototype shows that SUMO can support the development of digitalized railway operating procedures.

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References

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Published

2024-07-17

How to Cite

Boockmeyer, A., Friedenberger, D., & Pirl, L. (2024). Using SUMO for Test Automation and Demonstration of Digitalized Railway Concepts: Integrating SUMO with the "Train Dispatcher in the Cloud". SUMO Conference Proceedings, 5, 195–207. https://doi.org/10.52825/scp.v5i.1126

Conference Proceedings Volume

Section

Conference papers
Received 2024-02-23
Accepted 2024-04-03
Published 2024-07-17

Funding data