Hi all, I hope you had a pleasant Christmas holiday.
As said in previous posts,
I am working on a calibrated SUMO model where traffic demand and speeds are derived from real measurements.
My workflow is the following:
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Traffic counts are matched using flowrouter.py with hourly counts (interval = 60 min).
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Route feasibility is enforced using implausibleRoutes.py, and flowrouter.py is run again with the resulting restrictions.
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Speeds are calibrated using speed-only calibrators, with one speed profile per edge and per hour in the traffic measurement points (e1 detectors).
What I observe is a strong difference depending on how the simulation is run:
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If I run independent simulations of 1 hour each, the model behaves well and congestion levels look realistic.
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If I run a single continuous 24-hour simulation (0–86400 s) using exactly the same routes, flows and calibrators, the network progressively builds up heavy congestion and ends with many vehicles still running or waiting.
In particular, I would like to understand:
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Whether flowrouter.py tends to insert vehicles in a concentrated way at the beginning of each interval when using hourly counts.
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If there is a recommended way to smooth or distribute vehicle insertion over time when working with coarse (hourly) demand. I was thinking about programming a smoothing function.
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Whether the combination of route restrictions and speed-only calibrators can unintentionally reduce effective capacity over time, leading to queue accumulation in long runs.
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More generally, whether this behavior is expected in SUMO and how such effects are usually mitigated in full-day simulations.
Any insight or pointers to relevant SUMO options or best practices would be very helpful.
Thanks in advance.
Gabriel