The default 'accel' parameter is the same for all models (and this is
reflected in the documentation).
However, the driving dynamics still differ between the models:
- Krauss makes full use of it's default accel and decel values in almost
all traffic situations
- ACC/CACC sometimes accelerate with
Thanks, Jakob for the explanation.
To check my understanding, the default acceleration of the Krauss model is
higher than that of ACC and CACC? Is there documentation listing this?
Regards,
Royal
On Mon, Jan 31, 2022 at 4:46 AM Jakob Erdmann wrote:
> The outputs are probably "valid" for the
Junction flow is new to 1.12, and upon trying this, it was reported that a TAZ
would be needed.
Well first time examining TAZ. This seems very suitable for my needs, but still
I'm stuck.
The screenshot shows that the TAZ has edges associated with it, as expected.
Ideal to let a flow run from on
Hello,
I am trying to re-create a scenario which uses oneshot assignment. I export the
final routes by using --vehroute-output option and provide that as an input to
my simulation again
I get a totally different traffic situation a lot of teleports.
As Recommanded here https://github.com/ecli
Polyconvert can work without without a net-xml file and generates
geo-coded shapes which can be put into any geo-referenced network.
One approach might be to use those imported polygons to generate additional
netconvert input that sets the walkingarea shapes:
https://sumo.dlr.de/docs/Networks/Plai
Thanks four comment!
Having more open source code to chose from would be good.
I do think that your problem of OD estimation is a bit different from that
one that the outlined routeSampler approach solves.
The above approach takes the OD-matrix as ground truth (either es
edge-to-edge or zone-to-z
It is exactly that case, closed polygons are defined as waling areas. I
tried polyconvert but it did just generate an empty xml file. I am not sure
if a sumo network is required as an input to make it work...
On Thu, Jan 27, 2022 at 7:27 AM Jakob Erdmann wrote:
> It might be because the walking
Dear Jakob and Joel,
I am working on a similar problem so wanted to jump into this discussion. But I
am using a different optimizer than the routesampler.py.
This problem pertains to the class of problems popularly known as (Indirect) OD
demand estimation. So basically I am using another opti
Hi all,
I have some trouble understanding the notion of the weight-adaption and
weight-expand. I have a scenario where the aggregation period is 3600 and
the weight-period is 900s. Demand is in one scenario, an OD matrix for
3600s and in the other one, two OD matrices for each 3600s (similar to
ht
The outputs are probably "valid" for the particular models but whether the
ACC/ CACC models conform to the behavior of any particular real life fleet
cannot be answered without prior calibration. Please also refer to my
previous answer (https://www.eclipse.org/lists/sumo-user/msg11040.html).
Obviou
Please be aware that 'ACC' and 'CACC' are just one possible implementation
of (cooperative) automated cruise control.
They are not the "definite" models for these things.
If the models behave differently from your expectations, this may reflect
the very specific situations they were calibrated for
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