Intelligent Traffic Light Control

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29 Οκτ 2013 (πριν από 4 χρόνια και 6 μήνες)

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Intelligent Traffic Light Control

by Marco Wiering

Growing numbers of road users and the limited resources provided by current infrastructures lead
to ever increasing traveling times. The Intelligent Traffic Light Control project pursued at Utrecht
rsity aims at diminishing waiting times before red traffic lights in a city.

Traffic in a city is very much affected by traffic light controllers. When waiting for a traffic light, the driver
looses time and the car uses fuel. Hence, reducing waiting times

before traffic lights can save our European
society billions of Euros annually. To make traffic light controllers more intelligent, we exploit the emergence
of novel technologies such as communication networks and sensor networks, as well as the use of mo
sophisticated algorithms for setting traffic lights. Intelligent traffic light control does not only mean that traffic
lights are set in order to minimize waiting times of road users, but also that road users receive information
about how to drive throu
gh a city in order to minimize their waiting times. This means that we are coping with
a complex multi
agent system, where communication and coordination play essential roles. Our research
has led to a novel system in which traffic light controllers and th
e behaviour of car drivers are optimized
using machine
learning methods.

Our idea of setting a traffic light is as follows. Suppose there are a number of cars with their destination
address standing before a crossing. All cars communicate to the traffic li
ght their specific place in the queue
and their destination address. Now the traffic light has to decide which option (ie, which lanes are to be put
on green) is optimal to minimize the long
term average waiting time until all cars have arrived at their
stination address. The learning traffic light controllers solve this problem by estimating how long it would
take for a car to arrive at its destination address (for which the car may need to pass many different traffic
lights) when currently the light wou
ld be put on green, and how long it would take if the light would be put on
red. The difference between the waiting time for red and the waiting time for green is the gain for the car.
Now the traffic light controllers set the lights in such a way to maxim
ize the average gain of all cars standing
before the crossing. To estimate the waiting times, we use 'reinforcement learning' which keeps track of the
waiting times of individual cars and uses a smart way to compute the long term average waiting times usin
dynamic programming algorithms. One nice feature is that the system is very fair; it never lets one car wait
for a very long time, since then its gain of setting its own light to green becomes very large, and the optimal
decision of the traffic light wil
l set his light to green. Furthermore, since we estimate waiting times before
traffic lights until the destination of the road user has been reached, the road user can use this information to
choose to which next traffic light to go, thereby improving its
driving behaviour through a city. Note that we
solve the traffic light control problem by using a distributed multi
agent system, where cooperation and
coordination are done by communication, learning, and voting mechanisms. To allow for green waves during

extremely busy situations, we combine our algorithm with a special bucket algorithm which propagates gains
from one traffic light to the next one, inducing stronger voting on the next traffic controller option.

Figure 1: Optimal control of traffic lights.

Figure 2:

The simulator showing the infrastructure, road users, and plots of average waiting times.

We have implemented the 'Gre
en Light District', a traffic simulator in Java in which infrastructures can be
edited easily by using the mouse, and different levels of road usage can be simulated. A large number of
fixed and learning traffic light controllers have already been tested i
n the simulator and the resulting average
waiting times of cars have been plotted and compared. The results indicate that the learning controllers can
reduce average waiting times with at least 10% in semi
busy traffic situations, and even much more when
igh congestion of the traffic occurs.

We are currently studying the behaviour of the learning traffic light controllers on many different
infrastructures in our simulator. We are also planning to cooperate with other institutes and companies in
The Netherl
ands to apply our system to real world traffic situations. For this, modern technologies such as
communicating networks can be brought to use on a very large scale, making the necessary communication
between road users and traffic lights possible.

Please c

Marco Wiering, Utrecht University, The Netherlands

Tel: +31 30 253 9209