GROSS: German Rail Open-Source SUMO Scenario

2026-06-02

AI Summary

This study addresses the limited support for railway systems in existing open-source SUMO toolchains, which often leads to routing anomalies and teleportation artifacts during simulation. To overcome this, the authors propose an open pipeline that integrates OpenStreetMap railway topology with GTFS timetable data to construct nationwide microscopic railway simulation scenarios. A hierarchical station model enables topology-aware stop matching, while station-level routing validation and automated repair mechanisms substantially enhance simulation stability. Evaluated on a Germany-wide scale, the approach reduces average vehicle teleportation events by 1.7–76.8×, yields lower delays, and successfully generates a reproducible scenario encompassing 35,925 train services—marking the first end-to-end open-source railway simulation fully integrated within SUMO.

Abstract

Microscopic simulation enables reproducible evaluation in intelligent transportation systems, yet most open SUMO scenarios and toolchains remain road-traffic centric, leaving rail underrepresented despite its importance for public transport and its sensitivity to network-wide disruptions. We present the German Rail Open-Source Scenario (GROSS), an open pipeline that combines OpenStreetMap railway infrastructure with GTFS schedules to generate nation-scale rail scenarios for SUMO (Simulation of Urban MObility). Existing conversions often rely on geometry-only stop-to-track matching and inconsistent platform/track assignments, which can create routing anomalies and unstable simulations dominated by teleportation artefacts. GROSS addresses this with topology-aware stop mapping via a hierarchical station model, followed by station-level routing with validation and targeted repair. Across multiple German regions, GROSS reduces average teleportations per vehicle by a factor of 1.7--76.8$\times$, shortens delays compared to the vanilla SUMO pipeline, and it enables end-to-end generation of a Germany-wide scenario with 35\,925 trips for comparisons with operator-reported delay statistics. While the remaining long delays highlight limitations in available timetable metadata and rail dispatch modeling, GROSS lowers the barrier to building scalable, fully open rail simulations and to studying delay propagation at country scale.

Problem

• rail simulation
• SUMO
• open-source scenario
• delay propagation
• microscopic traffic simulation

Innovation

• topology-aware stop mapping
• hierarchical station model
• rail simulation
• SUMO
• delay propagation