- author = "Esteban {Ricalde Gonzalez}",
- title = "A Genetic Programming System with an Epigenetic Mechanism for Traffic Signal Control",
- school = "Department of Computer Science, Memorial University",
- year = "2019",
- address = "St. John's, Newfoundland, Canada",
- month = feb,
- keywords = "genetic algorithms, genetic programming, Dynamic Environment, Epigenetic Mechanism, Traffic Signal Control, Traffic Simulation, Control systems-Computer simulation, Traffic congestion-Computer simulation",
- eprint = "1903.03854",
- timestamp = "Sun, 31 Mar 2019 00:00:00 +0100",
-
biburl = "
https://dblp.org/rec/journals/corr/abs-1903-03854.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "http://arxiv.org/abs/1903.03854",
-
URL = "https://research.library.mun.ca/13780/",
-
URL = "
https://research.library.mun.ca/13780/1/thesis.pdf",
- size = "183 pages",
-
abstract = "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.
A microscopic traffic simulator and an open-source modular generic framework to evaluate traffic controllers were developed as part of the research project. The pro-posed framework is the first open-source configurable framework to test machine learning methods on the traffic signal control problem.",
-
notes = "Small differences between arXiv and Mun.
item ID: 13780
Supervisor: Wolfgang Banzhaf",
