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14 February 2024 Reinforcement learning-based genetic algorithm for solving low carbon multimodal transportation path planning problem
Wenying Zhu, Yuxian Li
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Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130181K (2024) https://doi.org/10.1117/12.3024045
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China
Abstract
To address the low-carbon multimodal transportation path planning problem, this paper firstly constructs a model of multimodal transportation path planning considering carbon trading, which minimizes total transportation cost as the objective function. A reinforcement learning-based genetic algorithm is designed to solve the model, and the Q-learning algorithm is implemented to adjust the crossover probability and mutation probability in the genetic algorithm to optimize the ability to avoid getting trapped in the local optima of the algorithm. Finally, arithmetic experiment verifies that the reinforcement learning-based genetic algorithm has a superior performance and the method can effectively reduce the transportation cost of multimodal transportation, improve transportation efficiency and reduce carbon emission.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Wenying Zhu and Yuxian Li "Reinforcement learning-based genetic algorithm for solving low carbon multimodal transportation path planning problem", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130181K (14 February 2024); https://doi.org/10.1117/12.3024045
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KEYWORDS
Transportation
Genetic algorithms
Carbon
Mathematical optimization
Evolutionary optimization
Modeling
Roads
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