7.7 Cars with Collective Intelligence
139
communicating with neighboring cars through wireless car-to-car communication,
5
the vehicles can assess the situation they are in (such as the surrounding traffic state),
take autonomous decisions (e.g. adjust driving parameters such as speed), and give
advice to drivers (e.g. warn of a traffic jam behind the next curve). One could say,
such vehicles acquire “social” abilities in that they can autonomously coordinate
their movements with other vehicles.
7.8 Self-Organizing Traffic Lights
Let’s have a look at another interesting example: the coordination of traffic lights.
In comparison to the flow of traffic on freeways, urban traffic poses additional challenges. Roads are connected into complex networks with many junctions, and the
main problem is how to coordinate the traffic at all these intersections. When I began
to study this difficult problem, my goal was to find an approach that would work
not only when conditions are ideal, but also when they are complicated or problematic. Irregular road networks, accidents or building sites are examples of the types of
problems, which are often encountered. Given that the flow of traffic in urban areas
greatly varies over the course of days and seasons, I argue that the best approach is
one that flexibly adapts to the prevailing local travel demand, rather than one which
is pre-planned for “typical” traffic situations at a certain time and weekday. Rather
than controlling vehicle flows by switching traffic lights in a top-down way, as it is
done by traffic control centers today, I propose that it would be better if the actual
local traffic conditions determined the traffic lights in a bottom-up way.
But how can self-organizing traffic lights, based on the principle of distributed
control, perform better than the top-down control of a traffic center? Is this possible
at all? Yes, indeed. Let us explore this now. Our decentralized approach to traffic light
control was inspired by the discovery of oscillatory pedestrian flows. Specifically,
Peter Molnar and I observed alternating pedestrian flows at bottlenecks such as
doors.
6 There, the crowd surges through the constriction in one direction. After
some time, however, the flow direction turns. As a consequence, pedestrians surge
through the bottleneck in the opposite direction, and so on. While one might think
that such oscillatory flows are caused by a pedestrian traffic light, the turning of the
flow direction rather results from the build-up and relief of “pressure” in the crowd.
Could one use this pressure-based principle underlying such oscillatory flows
to define a self-organizing traffic light control?
7 In fact, a road intersection can be
understood as a bottleneck too, but one with flows in several directions. Based on
this principle, could traffic flows control the traffic lights in a bottom-up way rather
than letting the traffic lights control the vehicle flows in a top-down way, as we
have it today? Just when I was asking myself this question, a student named Stefan
5 Kesting et al. [5].
6 Helbing and Molnár [6].
7 Helbing et al. [7].
139
communicating with neighboring cars through wireless car-to-car communication,
5
the vehicles can assess the situation they are in (such as the surrounding traffic state),
take autonomous decisions (e.g. adjust driving parameters such as speed), and give
advice to drivers (e.g. warn of a traffic jam behind the next curve). One could say,
such vehicles acquire “social” abilities in that they can autonomously coordinate
their movements with other vehicles.
7.8 Self-Organizing Traffic Lights
Let’s have a look at another interesting example: the coordination of traffic lights.
In comparison to the flow of traffic on freeways, urban traffic poses additional challenges. Roads are connected into complex networks with many junctions, and the
main problem is how to coordinate the traffic at all these intersections. When I began
to study this difficult problem, my goal was to find an approach that would work
not only when conditions are ideal, but also when they are complicated or problematic. Irregular road networks, accidents or building sites are examples of the types of
problems, which are often encountered. Given that the flow of traffic in urban areas
greatly varies over the course of days and seasons, I argue that the best approach is
one that flexibly adapts to the prevailing local travel demand, rather than one which
is pre-planned for “typical” traffic situations at a certain time and weekday. Rather
than controlling vehicle flows by switching traffic lights in a top-down way, as it is
done by traffic control centers today, I propose that it would be better if the actual
local traffic conditions determined the traffic lights in a bottom-up way.
But how can self-organizing traffic lights, based on the principle of distributed
control, perform better than the top-down control of a traffic center? Is this possible
at all? Yes, indeed. Let us explore this now. Our decentralized approach to traffic light
control was inspired by the discovery of oscillatory pedestrian flows. Specifically,
Peter Molnar and I observed alternating pedestrian flows at bottlenecks such as
doors.
6 There, the crowd surges through the constriction in one direction. After
some time, however, the flow direction turns. As a consequence, pedestrians surge
through the bottleneck in the opposite direction, and so on. While one might think
that such oscillatory flows are caused by a pedestrian traffic light, the turning of the
flow direction rather results from the build-up and relief of “pressure” in the crowd.
Could one use this pressure-based principle underlying such oscillatory flows
to define a self-organizing traffic light control?
7 In fact, a road intersection can be
understood as a bottleneck too, but one with flows in several directions. Based on
this principle, could traffic flows control the traffic lights in a bottom-up way rather
than letting the traffic lights control the vehicle flows in a top-down way, as we
have it today? Just when I was asking myself this question, a student named Stefan
5 Kesting et al. [5].
6 Helbing and Molnár [6].
7 Helbing et al. [7].