Abstract:METANET is a widely used second-order macroscopic traffic flow model for freeway networks, supporting applications across traffic simulation, ramp metering, and variable speed limit control. The predictive accuracy of any traffic model, however, hinges on careful calibration to real-world conditions. Despite its widespread use, there have not been open-source tools for calibrating METANET's parameters. Without open-source calibration, results cannot be easily reproduced or extended to other networks. This work provides an open-source METANET calibration, simulation, and data visualization tool. The calibration is formulated as a nonlinear program (NLP) solved via the interior-point method (IPOPT), with joint ramp flow estimation. We validate our calibration on real-world freeway data from two widely used traffic monitoring systems: Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION), one of the largest open-road trajectory instruments in the country, and loop detector data from the Caltrans Performance Measurement System (PeMS), which spans nearly 40,000 detectors across California freeways and serves as a standard benchmark in traffic research. Models calibrated using our method are able to reproduce these datasets' observed traffic patterns across diverse network geometries and traffic conditions including complex stop-and-go congestion waves. As large-scale traffic monitoring infrastructure continues to expand, open-source calibration tools are essential for translating growing volumes of sensor data into validated models that can support real-world traffic control. The complete code is publicly available at this https URL to support reproducible research in freeway traffic modeling and control.
| Comments: | Accepted at the IEEE Intelligent Transportation Systems Conference (ITSC) 2026 |
| Subjects: | Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.23042 [eess.SY] |
| (or arXiv:2605.23042v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23042 arXiv-issued DOI via DataCite |
From: Monica Chan [view email]
[v1]
Thu, 21 May 2026 21:16:15 UTC (3,289 KB)
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