  Traffic congestion is an increasing problem in most cities around the world.
It impacts businesses as well as commuters, small cities and large ones in
developing as well as developed economies. One approach to decrease urban
traffic congestion is to optimize the traffic signal behaviour in order to be
adaptive to changes in the traffic conditions. From the perspective of
intelligent transportation systems, this optimization problem is called the
traffic signal control problem and is considered a large combinatorial problem
with high complexity and uncertainty. A novel approach to the traffic signal
control problem is proposed in this thesis. The approach includes a new
mechanism for Genetic Programming inspired by Epigenetics. Epigenetic
mechanisms play an important role in biological processes such as phenotype
differentiation, memory consolidation within generations and environmentally
induced epigenetic modification of behaviour. These properties lead us to
consider the implementation of epigenetic mechanisms as a way to improve the
performance of Evolutionary Algorithms in solution to real-world problems with
dynamic environmental changes, such as the traffic control signal problem. The
epigenetic mechanism proposed was evaluated in four traffic scenarios with
different properties and traffic conditions using two microscopic simulators.
The results of these experiments indicate that Genetic Programming was able to
generate competitive actuated traffic signal controllers for all the scenarios
tested. Furthermore, the use of the epigenetic mechanism improved the
performance of Genetic Programming in all the scenarios. The evolved
controllers adapt to modifications in the traffic density and require less
monitoring and less human interaction than other solutions because they
dynamically adjust the signal behaviour depending on the local traffic
conditions at each intersection.
