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free and the blocked lane to measure the emergent traffic behavior. The flow on the
blocked lane was low for PB agents since polite drivers would weigh the disadvantage
for oncoming agents higher than their own advantage. As a result they get stuck be-
hind the obstacle until a polite agent on the free lane allows the waiting agent to pass.
EB agents achieved higher flows on the blocked lane indicating that they will not wait
until allowed to pass, but force their way around the obstacle if they have been wait-
ing too long. This effectively models drivers whose patience is limited, just as is the
case in real life. The resulting shorter waiting times are more plausible to an observer.
Additionally, the model is able to emulate behavior that is frequently observable in
real traffic: If a queue had formed behind the waiting agent, several enqueued agents
will tailgate the first agent as soon as it starts to pass the obstacle.
4
Conclusions and Future Work
We proposed a model that uses personality profiles to generate consistent behavior
patterns for individual agents, improving their credibility. A proof of concept was
evaluated in a specific traffic scenario. Static personality-based drivers produced im-
plausible behavior in certain situations (e.g., unrealistic waiting times). Adding an
emotion model for adaptive behavior did not only improve plausibility of directly
observable agent behavior, but also improved traffic flow, resulting in more plausi-
bility on a macroscopic scale.
In future work it needs to be investigated whether the proposed agent model is able
to generate plausible behavior for other (general) scenarios. Additionally, it remains
to be evaluated whether players really perceive the proposed agents as more realistic.
This could be evaluated by integrating them into an existing application, like the
FIVIS project (vc.h-brs.de/fivis), for a detailed user study.
Acknowledgements
. The presented work received funding by the DGUV (grant
FP307) and the BMBF (grant 17028X11).
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