You can lower the simulation density by increasing the values of 'tau' and 'minGap'. Note that it might be useful to draw these values from a distribution rather than having the same value for the whole fleet ( https://sumo.dlr.de/docs/Tools/Misc.html#createvehtypedistributionpy)
However, you also need to make sure that you are picking the correct data from the simulation as this might also be responsible for a mismatch between the curves. Here are a few comments: - What exactly are these plots showing? - Your y axis only mentions "vehicle count" which is neither a unit for density (due to lack of spatial component) nor for flow (due to lack of time component). - Likewise, the speed values requires some explanation. Is this averaged at a cross section (time mean speed) or over an area (space mean speed) - the question applies both to the real world data as well as the simulation data - How was the real world dataset derived and what values (output files) are you taking from the simulation? - The fit of the speeds already suggests some systematic error (possibly in the distribution of desired speeds) but also, the shapes of the speed curves are so similar as to suggest some form of calibration. Whatever you have been doing with the speeds is probably relevant to understanding the density mismatch. Am Fr., 2. Juni 2023 um 14:03 Uhr schrieb Mayank Singh < mayanksingh.dai...@gmail.com>: > Respected Sir, > In my scenario, traffic flow matches a given dataset. > But the traffic density does not match correctly. > So, what are the parameters that we should consider or tune while doing > the simulation? So that traffic density also gets aligned. > I have attached my graphs of traffic flow, traffic density, and traffic > speed for your reference. > > > Regards, > Mayank > _______________________________________________ > sumo-user mailing list > sumo-user@eclipse.org > To unsubscribe from this list, visit > https://www.eclipse.org/mailman/listinfo/sumo-user >
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