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Author:López Vidal, Alejandro
Title:Traffic flow simulation and optimization using evolutionary strategies
Publication type:Master's thesis
Publication year:2011
Pages:xii + 80 s. + liitt. 8      Language:   eng
Department/School:Tietotekniikan laitos
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Honkela, Timo ; Sáez Achaerandio, Yago
Instructor:Honkela, Timo
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto     | Archive
Keywords:traffic flow
traffic light
social simulation
evolutionary algorithm
neuroevolution
biological evolution
cultural evolution
language emergence
cellular automata
learning algorithms
artificial neural network
Abstract (eng): Different studies, such as the survey that IBM yearly conducts about commuting to work, verify the importance of a well-known problem: the traffic congestion in big cities.
Solving this problem has concerned professionals from many scientific and technological disciplines, including Artificial Intelligence (AI).
However, we found that the use of Artificial Neural Networks (ANN)-an important tool in this field due to their great features-to control city lights has never been fully seized by any of the past researches, in our opinion, as a consequence of the adaptation process adopted in these investigations.

In this thesis, we study the effect of different neuroevolutionary methods in adapting ANNs to efficiently control traffic semaphores.
These methods include biological, cultural and linguistic evolution.
Furthermore, the performance of our methods is compared with previous approaches using a microscopic traffic simulator enlarged to include different realistic scenarios in a square shaped city.
The model has been implemented using a combination of Java language, Netlogo social simulation environment and Matlab.

The results of this work illustrate the potential of our concept, which opens the door to further research in the topic and possible expansions to other research areas.
ED:2011-08-16
INSSI record number: 42647
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