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.

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.

[1] Tzanakaki A , Anastasopoulos M P , Simeonidou D . Converged optical, wireless, and data center network infrastructures for 5G services. Optical Communications and Networking, IEEE/OSA Journal of, 2019, 11(2):111-122. https://doi.org/10.1364/JOCN.11.00A111

[2] Phan C , Golenbock D , Diaz R , et al. A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments. Industrial Management & Data Systems, 2022, 122(7):1707-1737. https://doi.org/10.1108/IMDS-02-2022-0076

[3] Vidal P , Olivera A . Management of urban traffic flow based on traffic lights scheduling optimization. Latin America transactions, 2019, 17(01):102-110. https://doi.org/10.1109/TLA.2019.8826701

[4] Weimin M A , Lin N , Chen X , et al. A robust optimization approach to public transit mobile real-time information. Promet Traffic & Transportation, 2018, 30(5):501-512. https://doi.org/10.7307/ptt.v30i5.2609

[5] Bortas, Ivan, Brnjac, et al. Transport routes optimization model through application of fuzzy logic. Promet-traffic & transportation: Scientific journal on traffic and transportation research, 2018, 30(1):121-129. https://doi.org/10.7307/ptt.v30i1.2326

[6] Loprencipe G , Moretti L , Cantisani G , et al. Prioritization methodology for roadside and guardrail improvement:Quantitative calculation of safety level and optimization of resources allocation. Journal of Traffic & Transportation Engineering, 2018, 5(05):20-32. https://doi.org/10.1016/j.jtte.2018.03.004

[7] Аndrii Prokhorchenko a, Lp A , Ak A , et al. Improvement of the technology of accelerated passage of low-capacity car traffic on the basis of scheduling of grouped trains of operational purpose. Procedia Computer Science, 2019, 149(C):86-94. https://doi.org/10.1016/j.procs.2019.01.111

[8] Sd A , It B . Interpretable machine learning approach in estimating traffic volume on low-volume roadways - ScienceDirect. International Journal of Transportation Science and Technology, 2020, 9(1):76-88. https://doi.org/10.1016/j.ijtst.2019.09.004

[9] Shetty C , Sowmya B J , Seema S , et al. Air pollution control model using machine learning and IoT techniques - ScienceDirect. Advances in Computers, 2020, 117(1):187-218. https://doi.org/10.1016/bs.adcom.2019.10.006

[10] Sombolestan S M , Rasooli A , Khodaygan S . Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning. Journal of ambient intelligence and humanized computing, 2019, 10(5):1841-1850. https://doi.org/10.1007/s12652-018-0777-4

[11] Chittora D . How al and machine learning helps in up shilling to better career opportunities. Pc Quest, 2019, 32(3):20-21.

[12] Baumhauer, Judith, Mitten, et al. Using PROs and machine learning to identify "at risk" patients for musculoskeletal injury. Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation, 2018, 27(Suppl.1):S9-S9.

[13] Paiva F D , Cardoso R N , Hanaoka G P , et al. Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Application, 2019, 115(JAN.):635-655. https://doi.org/10.1016/j.eswa.2018.08.003

[14] Boulanouar' K , Hadjali A , Lagha M . Trends summarization of times series: a multi-objective genetic algorithm-based model. Journal of Smart Environments and Green Computing, 2022, 2(1):19-33. https://doi.org/10.20517/jsegc.2021.25

[15] Wade B M . A multi-objective optimization of ballistic and cruise missile fire plans based on damage calculations from missile impacts on an airfield defended by an air defense artillery network. Journal of Defense Modeling & Simulatio, 2019, 16(2):103-117. https://doi.org/10.1177/1548512918788503

[16] Hosseinian A H , Baradaran V . A multi-objective multi-agent optimization algorithm for the multi-skill resource-constrained project scheduling problem with transfer times. RAIRO - Operations Research, 2021, 55(4):2093-2128. https://doi.org/10.1051/ro/2021087

[17] Harry, Humfrey, Hongjian, et al. Dynamic charging of electric vehicles integrating renewable energy: a multi-objective optimisation problem. IET Smart Grid, 2019, 2(2):250-259. https://doi.org/10.1049/iet-stg.2018.0066

[18] Pankajakshan A , Waldron C , Quaglio M , et al. A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms. Engineering, 2019, 5(6):1049-1059. https://doi.org/10.1016/j.eng.2019.10.003