Hello,
I won't claim that the default model is able to capture the real world
variations in driver behavior accurately (because that hasn't yet been
validated to my knowledge). However, there are plenty of options for
varying your drivers to avoid over-fitting your GA solution
- sigma > 0
- speedDev > 0
- or you could use a vTypeDistribution and vary all other parameters
(accel, decel, tau ...) among the drivers
(of course then you should do multiple runs with different values for
--seed)
- what we also did is to generate a vType with individual maxSpeed and tau
for every individual driver based on some real world dataset

regards,
Jakob





2015-02-03 13:27 GMT+01:00 <vb...@bath.ac.uk>:

> Hi All,
>
> I wondered if anyone has looked at ways to represent the variability
> of vehicle behaviour due to driver personalities/human factors?
>
> I modified some vehicle parameters around reaction times,
> over-braking, etc, to represent a more 'imperfect' driver, as I wanted
> to see congestion waves appearing on motorways. I enforce a speed
> limit on a small number of vehicles to see if its can 'clear up' the
> congestion. I was thinking of running a GA to explore how many cars to
> apply the speed restriction to, for how long, and to what value, but,
> that might (probably will) just tune for the specific features of that
> driver performance.
>
> For example I read Seele et al (2012), Cognitive Agents for
> Microscopic Traffic Simulations in Virtual Environments, which
> suggests using a 'Five Factor Model' from psychology, which I could
> imagine helping, but, probably with quite a lot of implementation
> effort needed to map that into specific driving behaviours.
>
> So, I was hoping it might have been something a user of SUMO somewhere
> might have looked at already, to mitigate the effect of otherwise
> 'perfect' drivers? Or tackled some other way maybe?
>
> Thanks
>
> Vincent
>
>
>
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