1) Yes. Using a lower step-length will work (at the price of increased
computational load). An alternative would be to generated input with
rounded departure times and extrapolated departPosition (a manual solution
to https://github.com/eclipse/sumo/issues/4277).

2) Tau is not rounded. If it is lower than action step length, vehicle
safety is compromised.

Am Mo., 9. Sept. 2019 um 15:32 Uhr schrieb Mohsen Rahmati <
[email protected]>:

> Thank you for your help.
>
> Actually, I'm looking for proper parameters that have the most effect on
> my results, in order to use them in the calibration process. my problem is
> not a single purpose, so when I try to fit headway with real data, I also
> should take care of other indicators such as average speed and etc. this is
> why I change SUMO parameters with a reasonable step to see their effects on
> my indicators. this is a sample of my simulation scenario:
> https://filebin.net/ur65tc7o7yxvgftg/SUMO.zip?t=t90azruf
>
> 1) how should I configure SUMO, in order to be able to use sub-second
> departure time? setting step-length to 0.1s could solve the problem?
> 2) if I use tau smaller than action-step-length, is tau parameters rounded
> to the nearest multiple of action-step-length or not?
>
>
> With Best Regards,
> Mohsen
>
> On Mon, Sep 9, 2019 at 5:10 PM Jakob Erdmann <[email protected]>
> wrote:
>
>> I'm not quite sure how you compute your "absolute magnitude of mean
>> difference". Possibly it is invariant to a constant headway change by all
>> vehicles. In this case you should use a tau distribution instead of setting
>> the same tau for all vehicles. See
>> https://sumo.dlr.de/wiki/Tools/Misc#createVehTypeDistributions.py
>>
>> 1) the departTime is rounded down (e.g. depart 1.7 actually departs at
>> time 1)
>> 2) the speed adapts to the safe one. If there is no safe speed, insertion
>> is delayed.
>> 3)
>> https://sumo.dlr.de/wiki/Simulation/VehicleInsertion#Insertion_.28Departure.29
>>
>> regards,
>> Jakob
>>
>> Am Mo., 9. Sept. 2019 um 14:01 Uhr schrieb Mohsen Rahmati <
>> [email protected]>:
>>
>>> Thanks, Jakob.
>>> the plots were I mentioned before:
>>> https://filebin.net/bhvv8wow4cx0e9x8/Headway.zip?t=h3x55hiu
>>> as you can see in the plots, tau was not effective to the mean headway.
>>> (all other parameters left at their SUMO default values)
>>>
>>>
>>> I have real data aggregated with a time resolution of 5 minutes, so the
>>> exact time pass of vehicles in every 5 minutes is unknown to me.
>>> in order to generate vehicles in the simulation scenario, I use
>>> exponential distribution for the departure time of each vehicle. (I've
>>> tried some distributions and exponential seems to work relatively better
>>> than others)
>>>
>>> now my questions are:
>>>
>>> 1) by leaving step-length, action-step-length and tau at their default
>>> values, what would happen if I use a subsecond time for the departure value
>>> of vehicles (for e.g, the first vehicle depart at 1.2s and the second one
>>> depart at 1.7s)? dose SUMO take care of that?
>>>
>>> 2) I use departLane="best" and departSpeed="max", what would happen if
>>> the departure time isn't safe? the departure time is delayed or the speed
>>> adapts to the safe one?
>>>
>>> 3) also, what is a safe situation in vehicles generation? the distance
>>> greater than the minGap and the headway greater than the tau or you mean
>>> something else by stating safe situation?
>>>
>>> With Best Regards,
>>> Mohsen
>>>
>>>
>>> On Mon, Sep 9, 2019 at 1:47 PM Jakob Erdmann <[email protected]>
>>> wrote:
>>>
>>>> Your plot attachments are missing (possibly blocked by the mailing
>>>> list).
>>>> Mean headway is mostly influenced by the 'tau' parameter.
>>>> However, it also depends on the desired speeds (speedFactor, speedDev)
>>>> and traffic density:
>>>> - if all vehicles have the exact same desired speed (speedDev= 0) then
>>>> they will never close the gap to their leader and the headways are fully
>>>> determined by the insertion procedure.
>>>> - if the desired speeds vary and the density is high, then most
>>>> vehicles will be in a queue following a slower leader with headways
>>>> determined by tau
>>>> - if the desired speeds vary and the density is low, then the headways
>>>> are determined by insertion as well as the width of the speed distribution
>>>> (and of course tau)
>>>>
>>>> Am Sa., 7. Sept. 2019 um 20:12 Uhr schrieb Mohsen Rahmati <
>>>> [email protected]>:
>>>>
>>>>> Hello,
>>>>> I'm working on calibrating a highway section based on real data
>>>>> gathered from detectors.
>>>>> I've changed 12 parameters* in car-following and lane-changing model
>>>>> to decrease the "Absolute Magnitude of Mead Headway Difference" but it
>>>>> seems none of them works. Plots were attached.
>>>>> I wonder if you could introduce me the parameter that has the most
>>>>> effect on mean headway?
>>>>>
>>>>> by the way:
>>>>> SUMO version: 1.3.1
>>>>> car-following model: KraussPS
>>>>> lane-changing model: LC2013
>>>>>
>>>>> Note*: the effect of simultaneous change of parameters was not
>>>>> considered. so by changing a single parameter, all other parameters were
>>>>> left at their default values.
>>>>>
>>>>>
>>>>> With Best Regards,
>>>>> Mohsen
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