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Issue title: Special section: Intelligent data analysis and applications & smart vehicular technology, communications and applications
Guest editors: Valentina Emilia Balas and Lakhmi C. Jain
Article type: Research Article
Authors: Chen, Taoa; b; c | Lv, Hongxiaa; b; c; * | Sun, Yichena; b; c | Wang, Xiaoyid
Affiliations: [a] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China | [b] National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China | [c] National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China | [d] LCD Operates Branch Company, China Ningbo Rail Transit Group Co., Ningbo, Zhejiang, China
Correspondence: [*] Corresponding author. Hongxia Lv, Tel.: +86 028 87600706; E-mail: [email protected].
Abstract: As one kind of highest hierarchy node on the network, the transfer scheme of high-speed railway hub timetable should be studied at priority. After defining the problem that optimizing transfer scheme of timetable at high-speed railway hub, this paper proposes time adjusting strategy and platform adjusting strategy to optimize the problem, of which the first strategy introduces a FUZZY set of reasonable time range to reduce the possible train conflicts at adjacent stations on the network, and the second strategy helps to use different transfer time to match the arrival and departure of trains. Then, an optimization model of timetable based on passenger transfer is established with the minimized invalid transfer waiting time for passenger and train conflicts at adjacent stations as the objective function. The model is solved by the above two strategies in MATLAB software. Finally, the rationality and effectiveness of this model are verified, taking Shanghai Hongqiao Station as an example.
Keywords: High-speed railway, hub, transfer, timetable optimization
DOI: 10.3233/JIFS-179662
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5743-5752, 2020
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