7.9 How to Outsmart Centralized Control
143
The above graph shows a further noteworthy effect: the combination of two bad
strategies can be the best one! In fact, clearing the longest queue (see the grey dashdotted line in Fig. 7.6) always performs worse than top-down optimization (dashed
red line). When the capacity utilization of the intersection is high, strict travel time
minimization also produces longer queues (see the dotted violet line). Therefore, if
the two strategies (clearing long queues and minimizing travel times) are applied in
isolation, they are not performing well at all. However, contrary to what one might
expect, the combination of these two under-performing strategies, as it is applied in
the other-regarding kind of self-organization, produces the best results (see the solid
blue curve).
This is, because the other-regarding self-organization of traffic lights flexibly
takes advantage of gaps that randomly appear in the traffic flow to ease congestion
elsewhere. In this way, non-periodic sequences of green lights may result, which
outperform the conventional periodic service of traffic lights. Furthermore, the otherregarding self-organization creates a flow-based coordination of traffic lights among
neighboring intersections. This coordination spreads over large parts of the city in a
self-organized way through a favorable cascading effect.
7.10 A Pilot Study
After our promising simulation study, Stefan Lämmer approached the public transport authority in Dresden, Germany, to collaborate with them on traffic light control.
So far, the traffic center applied a state-of-the-art adaptive control scheme producing
“green waves”. But although it was the best system on the market, they weren’t
entirely happy with it. Around a busy railway station in the city center they could
either produce “green waves” of motorized traffic on the main arterials or prioritize public transport, but not both. The particular challenge was to prioritize public
transport while so many different tram tracks and bus lanes cut through Dresden’s
highly irregular road network. If public transport (buses and trams) would be given a
green light whenever they approached an intersection, this would destroy the green
wave system needed to keep the motorized traffic flowing. Inevitably, the resulting
congestion would spread quickly, causing massive disruption over a huge area of the
city.
When we simulated the expected outcomes of the other-regarding selforganization of traffic lights and compared it with the state-of-the art control they
used, we got amazing results.
13 The waiting times were reduced for all modes of
transport, dramatically for public transport and pedestrians, but also somewhat for
motorized traffic. Overall, the roads were less congested, trams and buses could
be prioritized, and travel times became more predictable, too. In other words, the
to a cascading failure. This outcome can be viewed as a traffic-related “tragedy of the commons”,
as the overall capacity of the intersections is not used in an efficient way.
13 Lämmer and Helbing [16], Lämmer et al. [17].
143
The above graph shows a further noteworthy effect: the combination of two bad
strategies can be the best one! In fact, clearing the longest queue (see the grey dashdotted line in Fig. 7.6) always performs worse than top-down optimization (dashed
red line). When the capacity utilization of the intersection is high, strict travel time
minimization also produces longer queues (see the dotted violet line). Therefore, if
the two strategies (clearing long queues and minimizing travel times) are applied in
isolation, they are not performing well at all. However, contrary to what one might
expect, the combination of these two under-performing strategies, as it is applied in
the other-regarding kind of self-organization, produces the best results (see the solid
blue curve).
This is, because the other-regarding self-organization of traffic lights flexibly
takes advantage of gaps that randomly appear in the traffic flow to ease congestion
elsewhere. In this way, non-periodic sequences of green lights may result, which
outperform the conventional periodic service of traffic lights. Furthermore, the otherregarding self-organization creates a flow-based coordination of traffic lights among
neighboring intersections. This coordination spreads over large parts of the city in a
self-organized way through a favorable cascading effect.
7.10 A Pilot Study
After our promising simulation study, Stefan Lämmer approached the public transport authority in Dresden, Germany, to collaborate with them on traffic light control.
So far, the traffic center applied a state-of-the-art adaptive control scheme producing
“green waves”. But although it was the best system on the market, they weren’t
entirely happy with it. Around a busy railway station in the city center they could
either produce “green waves” of motorized traffic on the main arterials or prioritize public transport, but not both. The particular challenge was to prioritize public
transport while so many different tram tracks and bus lanes cut through Dresden’s
highly irregular road network. If public transport (buses and trams) would be given a
green light whenever they approached an intersection, this would destroy the green
wave system needed to keep the motorized traffic flowing. Inevitably, the resulting
congestion would spread quickly, causing massive disruption over a huge area of the
city.
When we simulated the expected outcomes of the other-regarding selforganization of traffic lights and compared it with the state-of-the art control they
used, we got amazing results.
13 The waiting times were reduced for all modes of
transport, dramatically for public transport and pedestrians, but also somewhat for
motorized traffic. Overall, the roads were less congested, trams and buses could
be prioritized, and travel times became more predictable, too. In other words, the
to a cascading failure. This outcome can be viewed as a traffic-related “tragedy of the commons”,
as the overall capacity of the intersections is not used in an efficient way.
13 Lämmer and Helbing [16], Lämmer et al. [17].