• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 30 Issue 2
Apr.  2017
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Article Contents
HUANG Minghua, QU Hezhou, LIU Xiaobo, TANG Youhua. Transfer-Oriented Dispatching Optimization of Rail Transit Network[J]. Journal of Southwest Jiaotong University, 2017, 30(2): 326-333. doi: 10.3969/j.issn.0258-2724.2017.02.016
Citation: HUANG Minghua, QU Hezhou, LIU Xiaobo, TANG Youhua. Transfer-Oriented Dispatching Optimization of Rail Transit Network[J]. Journal of Southwest Jiaotong University, 2017, 30(2): 326-333. doi: 10.3969/j.issn.0258-2724.2017.02.016

Transfer-Oriented Dispatching Optimization of Rail Transit Network

doi: 10.3969/j.issn.0258-2724.2017.02.016
  • Received Date: 30 Jun 2015
  • Publish Date: 25 Apr 2017
  • As bottlenecks of rail transit network due to its high volume of transfer passengers during peak periods in big cities, transfer stations are facing problems of increasing safety risk, operation inefficiency, etc. To solve these problems, a mathematical model is developed using the nonlinear programming method to minimize the total transfer time in the rail transit network. Taking passengers' walking time in transfer into consideration, this model selects the adjustment amount of train dispatching time as the decision variable to optimize the transfer coordination time, by adjusting the departure time of trains at the start terminal and their timetables. Then, optimal solutions of train departure time and timetables are obtained using a simulated annealing algorithm. As a case study, the proposed methodology was applied to optimization of a real-life metro network with five lines intersecting at thirteen stations. The results show that compared to the existing operation plan, the total transfer waiting time of the optimized schedule in the morning peak hour is reduced by 689 h, and the system transfer efficiency is increased by 22%.

     

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