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Machine Learning Theory and Practice, 2022, 3(4); doi: 10.38007/ML.2022.030402.

Multi-objective Optimization Algorithm in Machine Learning for Traffic Planning


Xianmin Ma

Corresponding Author:
Xianmin Ma

Department of Information Engineering, Heilongjiang International University, Harbin 150025, China


Nowadays, urban rail transportation is booming. As a high-capacity transportation mode with strong public service nature, the operational energy consumption, punctuality and travel comfort of urban rail transit are crucial and have high requirements in operation. In order to address the shortcomings of existing transportation planning studies, this paper briefly discusses the project parameters input and project profile for the traffic line optimization system proposed in this paper, based on the discussion of urban rail transit planning and design, multi-objective genetic algorithm optimization and constraints. And the design of the traffic line optimization system is discussed, and finally the proposed multi-objective genetic algorithm optimization is tested experimentally for the optimization of train running time. The experimental data show that the average time of the multi-objective genetic algorithm optimization for five train runs is less than 23.44 s, while the lowest runtime after HE-NSGA-II and MOPSO optimization both reach 112.4 s. Therefore, the proposed multi-objective genetic algorithm optimization for traffic planning has certain superiority.


Machine Learning, Multi-objective Optimization, Traffic Planning, Genetic Algorithm

Cite This Paper

Xianmin Ma. Multi-objective Optimization Algorithm in Machine Learning for Traffic Planning. Machine Learning Theory and Practice (2022), Vol. 3, Issue 4: 9-17. https://doi.org/10.38007/ML.2022.030402.


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