Abstract
To improve the train line plan quality and meet more transportation requirements, a model is presented to solve the train stops setting problem. We analyze the factors on the train setting problem and define the passenger transport efficiency. Then, an optimization model to improve the transport efficiency is constructed. The quantum particle swarm optimization algorithm is hired to solve the problem. Computing case based on Shanghai–Hangzhou high-speed railway proved the rationality of the model and the high performance of the algorithm. It is a new approach to design train stop plans which also offers constructive support for the managers of the railway bureau.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (Grant 61263027), the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2015K004), Beijing Jiaotong University, Natural Science Foundation of Gansu Province (Grant 1310RJZA068).
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© 2016 Springer-Verlag Berlin Heidelberg
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Meng, X., Jia, L., Qin, Y., Xu, J. (2016). Train Stops Setting Based on a Quantum-Inspired Particle Swarm Algorithm. In: Qin, Y., Jia, L., Feng, J., An, M., Diao, L. (eds) Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Lecture Notes in Electrical Engineering, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49370-0_47
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DOI: https://doi.org/10.1007/978-3-662-49370-0_47
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