Calibration of a Microscopic Traffic Simulation in an Urban Scenario Using Loop Detector Data
DOI: 10.52825/scp.v4i.223
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Summary
This study addresses the challenge of estimating travel demand for microscopic traffic simulations using induction loop detector data, specifically within the context of the Digital Twin Munich project. Accurate travel demand estimation is critical for realistic traffic modeling but typically requires extensive data collection. The authors investigate whether two tools provided by the Simulation of Urban MObility (SUMO) software—`flowrouter` and `routeSampler`—can effectively generate plausible traffic flows and routes based solely on loop detector counts in an urban environment. The researchers developed a detailed network model of Munich’s inner city using OpenStreetMap data, refined manually with official site plans, aerial images, and site visits. The model included geometry corrections, removal of implausible turnarounds, and the placement of induction loop detectors. Traffic signal programs were implemented by reverse-engineering fixed-cycle plans from city-provided signal status data or using original signal plans where available. A subset of this network, focusing on Sonnenstraße and eight signalized intersections, was used to test the tools during the evening peak hour (4:00–5:00 pm). The input data consisted of vehicle counts from induction loop detectors collected on July 25, 2022. Due to data quality issues, many detectors were excluded, leaving limited coverage, particularly on the main arterial road. The results demonstrated that neither `flowrouter` nor `routeSampler` produced plausible traffic estimations. `flowrouter` generated unrealistic traffic patterns, including excessive turnarounds where vehicles originated and terminated in the same direction, leading to artificial congestion. Comparisons with historic traffic counts revealed significant discrepancies in turning ratios; for instance, at intersection LSA 29, the simulation overestimated northbound turnarounds by 39% compared to historic data. Similarly, `routeSampler` caused severe congestion and simulation breakdowns within 15 minutes due to overestimated flows on minor streets and unrealistic turnaround behaviors. The authors attribute these failures primarily to the insufficient density of detector data and the complexity of the network, rather than inherent flaws in the tools. The study concludes that while the Munich network is theoretically suitable for such evaluations, the current lack of comprehensive detector data prevents a valid assessment of `flowrouter` and `routeSampler`. The authors recommend future studies utilize more extensive datasets or different study areas with better detector coverage. They also highlight the need to investigate how these tools handle scenarios where measurements are predominantly available only at intersection inflows, a limitation that significantly impacted the accuracy of the current results.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: validation psychometrics