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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2012  >  12(4): 59-66
HU Hui, CHENG Ling, XUAN Deng-dian, HU Da-wei, ZHAO Li-ping. Comprehensive passenger hub layout model of combined selection for capacity limitation and transportation mode[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 59-66. doi: 10.19818/j.cnki.1671-1637.2012.04.008
Citation: HU Hui, CHENG Ling, XUAN Deng-dian, HU Da-wei, ZHAO Li-ping. Comprehensive passenger hub layout model of combined selection for capacity limitation and transportation mode[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 59-66. doi: 10.19818/j.cnki.1671-1637.2012.04.008
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Comprehensive passenger hub layout model of combined selection for capacity limitation and transportation mode

doi: 10.19818/j.cnki.1671-1637.2012.04.008
  • 1.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Department of Transport of Inner Mongolia, Hohhot 010020, Inner Mongolia, China

Funds:

National Key Technology R&D in the 11th Five-year Plan of China 2009BAG13A04

Transportation Technology Project of Inner Mongolia NJ2003-004

Special Fund for Basic Scientific Research of Central Colleges CHD2011JC002

More Information
  • Author Bio:

    HU Hui(1979-), Female, Jinzhou, Liaoning, Lecturer of Chang'an University, PhD, Research on Transportation Planning and Logistics Optimization, +86-29-82334426, huhui@chd.edu.cn

  • Received Date: 2012-02-06
  • Publish Date: 2012-08-25
    Abstract
  • The traditional optimization model of comprehensive passenger hub layout was analyzed, and the constraints of transportation mode and transfer capability were considered simultaneously. The improved optimization model of comprehensive passenger hub layout was proposed, and the improved genetic algorithm was introduced to solve the model. LINGO software was used to test the effectiveness, Solomon standard test data with 8 and 50 nodes were calculated respectively, and the improved model was compared with the classical algorithm. Calculation result shows that while calculating Solomon standard test data with 8 nodes, the average running time is 5 043 s and the optimal cost is 1 952 418 yuan by using LINGO software, the average running time is 62 s and the optimal cost is 1 955 900 yuan by using genetic algorithm and MATLAB software. While calculating Solomon standard test data with 50 nodes, the average running time is 574 s and the optimal cost is 8 500 600 yuan by using genetic algorithm and MATLAB software. While calculating AP data set with 25 nodes and hub node number is 3, the average running time is 612 s and the optimal cost is 155 148 yuan, the optimal cost decreases 108 yuancompared with the classical algorithm. So the improved model is effective. 6 tabs, 6 figs, 20 refs.

     

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