Grammar-Guided Genetic Programming for UAV-Based Mitigation of Urban Disaster Traffic Congestion
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- @InProceedings{Wlodarczyk:2025:CCNC,
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author = "Damian Wlodarczyk and Takfarinas Saber",
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title = "Grammar-Guided Genetic Programming for {UAV-Based}
Mitigation of Urban Disaster Traffic Congestion",
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booktitle = "2025 IEEE 22nd Consumer Communications \& Networking
Conference (CCNC)",
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year = "2025",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Disasters,
Roads, Urban areas, Cost function, Autonomous aerial
vehicles, Time measurement, Road traffic, Time factors,
Traffic congestion, Urban Disaster, Unmanned Aerial
Vehicles, Simulation of Urban MObility, Grammar-Guided
Genetic Programming, G3P",
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ISSN = "2331-9860",
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DOI = "
doi:10.1109/CCNC54725.2025.10975833",
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abstract = "As the global population grows and more people migrate
to urban areas, road traffic becomes an increasingly
critical problem. Minimizing and mitigating the effects
of disaster traffic are crucial for reducing emissions,
economic losses, and saving lives by enabling faster
emergency response times. Existing works aim to
optimise traffic and rerouting in disaster-struck
scenarios, but different cost functions are effective
under different circumstances and information levels.
No single method consistently produces an optimal
rerouting cost function regardless of the road segment
affected or the information provided. This study aims
to develop a method to generate a cost function that
consistently produces optimal solutions and outperforms
existing functions, regardless of the road segment
affected or the amount of information available. We
simulated five different disaster scenarios in Dublin
City Centre, with each scenario involving a
disaster-struck road segment. We considered the use of
Unmanned Aerial Vehicles to capture data on road
conditions, dividing it into five information levels,
with each level adding data about roads farther from
the disaster. Furthermore, we used Grammar-Guided
Genetic Programming (G3P) to evolve efficient cost
functions, which were then tested in the SUMO simulator
using Dijkstra's algorithm to reroute traffic. The
performance was measured by the Average Travel Time of
vehicles. Our approach successfully generated efficient
cost functions for each scenario and information level.
In all but one case, the G3P-generated functions
outperformed existing methods. In some scenarios, the
G3P-generated functions achieved better average arrival
times than those in non-disaster conditions. We
observed a maximum improvement of 44.23percent in the
Average Arrival Time compared to the no rerouting
scenario and 14.99percent compared to the non-disaster
scenario. The findings suggest that our proposed
G3P-based rerouting approach could be beneficial even
in regular non-disaster situations.",
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notes = "Also known as \cite{10975833}",
- }
Genetic Programming entries for
Damian Wlodarczyk
Takfarinas Saber
Citations