Journal of South China University of Technology(Natural Science) >
Optimization of Metro Feeder Bus Routes Based on Surrogate-Assisted NSGA-Ⅱ Algorithm
Received date: 2023-09-22
Online published: 2024-05-10
Supported by
the National Natural Science Foundation of China(52172310);the Key R&D Program of Hunan Province(2023GK2014)
The connection between urban rail transit and bus transit is the key to meet the various urban travel demand and to promote the development of urban public transportation system. Existing studies lack the consideration of some important micro-indicators, such as the ratio of waiting for multiple buses during peak hours and the level of congestion inside the bus, when constructing the optimization model. Additionally, there is a lack of consideration for the stochastic and heterogeneous requirements in route operation, which results in poor performance in practical applications. To address these issues, this study firstly established an optimization model based on the service process of the bus transit, with the objective of minimizing the travel cost of passengers and the cost of enterprises. The model considers the influencing factors such as operating speed, vehicle type, departure frequency, route fare, vehicle crowdedness, and route line type and it is solved by the non-dominated sorting genetic algorithm (NSGA-Ⅱ), in which the genetic operation part is improved. Furthermore, a microscopic simulation algorithm was designed to evaluate the solution in order to improve the accuracy of the model solution. Accordingly, a Kriging surrogate model was used to assist the calculation to improve the solution efficiency of the algorithm. Finally, taking the connection between metro and bus system in Shenzhen city as an example, the proposed algorithm was validated with the IC card data collected in metro and bus system. The sensitivity analysis was conducted for the factors of route fare, operating speed, operating mode and passenger volume, and the operating improvement was proposed based on the analysis results. The results demonstrate that the algorithm produces superior route solutions compared to the conventional NSGA-Ⅱ, with the same solving time. There is a 35.49% reduction in total cost and a notable 26.94% increase in the iteration speed. The optimization method for connecting between metro and bus transit proposed in this study has practical significance in improving connecting efficiency and operational level.
TANG Jinjun , REN Maoxin , LI Zhitao , GAO Yifan . Optimization of Metro Feeder Bus Routes Based on Surrogate-Assisted NSGA-Ⅱ Algorithm[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(11) : 95 -105 . DOI: 10.12141/j.issn.1000-565X.230595
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