Decentralized Approaches to Adaptive Traffic Control
Traffic systems are highly complex
multi-component systems suffering from instabilities and
non-linear dynamics, including chaos. This is caused by the
non-linearity of interactions, delays, and fluctuations, which can
trigger phenomena such as stop-and-go waves, noise-induced
breakdowns, or slower-is-faster effects. The recently upcoming
information and communication technologies (ICT), including cheap
optical, radar, video, or infrared sensors and
mobile communication technologies promise new solutions leading
from the classical, centralized control to decentralized
approaches in the sense of collective (swarm) intelligence and ad
hoc networks. Such concepts reduce the problem of data flooding by
restricting to the locally relevant information only and reach
more adaptiveness, flexibility, resilience and robustness with
respect to local requirements and temporary failures.
One focus of my talk will be adaptive cruise
control (ACC) systems, which do not only increase the comfort and
safety of car passengers, but also enhance the stability of
traffic flows and the capacity of the road. We call this ``traffic
assistance''. Specifically, I will present an automated driving
strategy that adapts the operation mode of an ACC system to the
autonomously detected, local traffic situation. The
impact on the traffic dynamics is investigated by means of a
multi-lane microscopic traffic simulation.
In the future, vehicles will become automatic
traffic state detection, data management, and communication
centers when forming ad hoc networks
through inter-vehicle communication (IVC).
The applicability of short-range inter-vehicle communication
for the detection of dynamic congestion fronts on freeways will be
shortly discussed. Adaptive, self-organized traffic control in urban
road networks is another interesting application field for
decentralized traffic optimization strategies. Therefore, this
talk will also discuss control principles that allow one to reach
a self-organized synchronization of traffic lights. It turns out
that a local optimization fails to reach high performance, and
that a stabilizing strategy is not successful as well. A suitable
combination of the two, however, can perform superior to
centralized, cycle-based approaches.
The focus of this talk will be adjusted to the interest of the audience.