140
7 Digitally Assisted Self-Organization
Lämmer knocked at my door and wanted to write a Ph.D. thesis.
8 This is where our
investigations began.
7.9 How to Outsmart Centralized Control
Let us first discuss how traffic lights are controlled today. Typically, there is a traffic
control center that collects information about the traffic situation all over the city.
Based on this information, (super)computers try to identify the optimal traffic light
control, which is then implemented as if the traffic center were a “benevolent dictator”. However, when trying to find a traffic light control that optimizes the vehicle
flows, there are many parameters that can be varied: the order in which green lights
are given to the different vehicle flows, the green time periods, and the time delays
between the green lights at neighboring intersections (the so-called “phase shift”).
If one would systematically vary all these parameters for all traffic lights in the city,
there would be so many parameter combinations to assess that the optimization could
not be done in real time. The optimization problem is just too demanding.
Therefore, a typical approach is to operate each intersection in a periodic way and
to synchronize these cycles as much as possible, in order to create a “green wave”.
This approach significantly constrains the search space of considered solutions, but
the optimization task may still not be solvable in real time. Due to these computational
constraints, traffic-light control schemes are usually optimized offline for “typical”
traffic flows, and subsequently applied during the corresponding time periods (for
example, on Monday mornings between 10am and 11am, or on Friday afternoons
between 3 pm and 4 pm, or after a soccer game). In the best case, these schemes
are subsequently adapted to match the actual traffic situation at any given time, by
extending or shortening the green phases. But the order in which the roads at any
intersection get a green light (i.e. the switching sequence) usually remains the same.
Unfortunately, the efficiency of even the most sophisticated top-down optimization schemes is limited. This is because real-world traffic conditions vary to such a
large extent that the typical (i.e. average) traffic flow at a particular weekday, hour,
and place is not representative of the actual traffic situation at any particular place
and time. For example, if we look at the number of cars behind a red light, or the
proportion of vehicles turning right, the degree to which these factors vary in space
and time is approximately as large as their average value.
So how close to optimal is the pre-planned traffic light control scheme really?
Traditional top-down optimization attempts based on a traffic control center produce
an average vehicle queue which increases almost linearly with the “capacity utilization” of the intersection, i.e. with the traffic volume. Let us compare this approach
with two alternative ways of controlling traffic lights based on the concept of selforganization (see Fig. 7.4).
9 In the first approach, termed “selfish self-organization”,
8 Lämmer [8].
9 Lämmer and Helbing [9], Lämmer et al. [10]; Helbing et al. [11]; Helbing and Lämmer [12].
7 Digitally Assisted Self-Organization
Lämmer knocked at my door and wanted to write a Ph.D. thesis.
8 This is where our
investigations began.
7.9 How to Outsmart Centralized Control
Let us first discuss how traffic lights are controlled today. Typically, there is a traffic
control center that collects information about the traffic situation all over the city.
Based on this information, (super)computers try to identify the optimal traffic light
control, which is then implemented as if the traffic center were a “benevolent dictator”. However, when trying to find a traffic light control that optimizes the vehicle
flows, there are many parameters that can be varied: the order in which green lights
are given to the different vehicle flows, the green time periods, and the time delays
between the green lights at neighboring intersections (the so-called “phase shift”).
If one would systematically vary all these parameters for all traffic lights in the city,
there would be so many parameter combinations to assess that the optimization could
not be done in real time. The optimization problem is just too demanding.
Therefore, a typical approach is to operate each intersection in a periodic way and
to synchronize these cycles as much as possible, in order to create a “green wave”.
This approach significantly constrains the search space of considered solutions, but
the optimization task may still not be solvable in real time. Due to these computational
constraints, traffic-light control schemes are usually optimized offline for “typical”
traffic flows, and subsequently applied during the corresponding time periods (for
example, on Monday mornings between 10am and 11am, or on Friday afternoons
between 3 pm and 4 pm, or after a soccer game). In the best case, these schemes
are subsequently adapted to match the actual traffic situation at any given time, by
extending or shortening the green phases. But the order in which the roads at any
intersection get a green light (i.e. the switching sequence) usually remains the same.
Unfortunately, the efficiency of even the most sophisticated top-down optimization schemes is limited. This is because real-world traffic conditions vary to such a
large extent that the typical (i.e. average) traffic flow at a particular weekday, hour,
and place is not representative of the actual traffic situation at any particular place
and time. For example, if we look at the number of cars behind a red light, or the
proportion of vehicles turning right, the degree to which these factors vary in space
and time is approximately as large as their average value.
So how close to optimal is the pre-planned traffic light control scheme really?
Traditional top-down optimization attempts based on a traffic control center produce
an average vehicle queue which increases almost linearly with the “capacity utilization” of the intersection, i.e. with the traffic volume. Let us compare this approach
with two alternative ways of controlling traffic lights based on the concept of selforganization (see Fig. 7.4).
9 In the first approach, termed “selfish self-organization”,
8 Lämmer [8].
9 Lämmer and Helbing [9], Lämmer et al. [10]; Helbing et al. [11]; Helbing and Lämmer [12].