Hyper-Heuristic Algorithm for Urban Traffic Flow Optimization
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InProceedings{Hu:2023:ICACI,
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author = "Xiao-Min Hu and Yu-Hui Duan and Min Li and Ying Zeng",
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booktitle = "2023 15th International Conference on Advanced
Computational Intelligence (ICACI)",
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title = "Hyper-Heuristic Algorithm for Urban Traffic Flow
Optimization",
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year = "2023",
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abstract = "Traffic flow assignment optimisation is a core issue
in the field of intelligent transportation. The goal of
this problem is to find suitable routes for all travel
needs and improve the overall efficiency of the
transportation network. This paper proposes a city
traffic flow optimisation method based on
hyper-heuristic. This method uses terminal sets and
function sets designed according to the characteristics
of urban road networks to construct hyper-heuristic
strategies and simulate them on small-scale road
networks to test the optimisation effects. The
hyper-heuristic strategy formulates the current optimal
route for each vehicle on the road network and uses
Genetic Programming (GP) for iterative training. The
average traveling time at the end of each simulation
serves as the evaluation value for GP, and finally
iteratively outputs the best strategy for simulation
and test on larger-scale urban road networks. Tests on
different sizes and regions of road networks show that
using GP iterative training can improve the traffic
efficiency of urban road networks with hyper-heuristic
strategies.",
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keywords = "genetic algorithms, genetic programming, Training,
Roads, Heuristic algorithms, Urban areas,
Transportation, Optimisation methods, Traffic flow
assignment, hyper heuristic, intelligent
transportation",
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DOI = "doi:10.1109/ICACI58115.2023.10146154",
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month = may,
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notes = "Also known as \cite{10146154}",
- }
Genetic Programming entries for
Xiao-Min Hu
Yu-Hui Duan
Min Li
Ying Zeng
Citations