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随机OD需求下的多目标离散交通网络设计模型与算法

杨明,  苏标,  孙志杰,  许怡

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文章导航 > 西南交通大学学报  > 2014 >  27(1): 119-125.
杨明, 苏标, 孙志杰, 许怡. 随机OD需求下的多目标离散交通网络设计模型与算法[J]. 西南交通大学学报, 2014, 27(1): 119-125. doi: 10.3969/j.issn.0258-2724.2014.01.019
引用本文: 杨明, 苏标, 孙志杰, 许怡. 随机OD需求下的多目标离散交通网络设计模型与算法[J]. 西南交通大学学报, 2014, 27(1): 119-125. doi: 10.3969/j.issn.0258-2724.2014.01.019
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YANG Ming, SU Biao, SUN Zhijie, XU Yi. Model and Algorithm of Multi-objective Discrete Transportation Network Design under Stochastic Demand among OD Pairs[J]. Journal of Southwest Jiaotong University, 2014, 27(1): 119-125. doi: 10.3969/j.issn.0258-2724.2014.01.019
Citation: YANG Ming, SU Biao, SUN Zhijie, XU Yi. Model and Algorithm of Multi-objective Discrete Transportation Network Design under Stochastic Demand among OD Pairs[J]. Journal of Southwest Jiaotong University, 2014, 27(1): 119-125. doi: 10.3969/j.issn.0258-2724.2014.01.019
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随机OD需求下的多目标离散交通网络设计模型与算法

doi: 10.3969/j.issn.0258-2724.2014.01.019
基金项目: 

交通运输部联合科技攻关项目（2008353363430）

Model and Algorithm of Multi-objective Discrete Transportation Network Design under Stochastic Demand among OD Pairs

    摘要
  • 摘要: 为解决实际OD对之间交通需求不确定性及优化目标多样性问题，采用机会约束模型及多目标优化理想点模型构建上层规划模型，采用固定需求下的用户平衡配流模型构建下层规划模型，建立了同时优化交通管理目标、环境保护目标、投资费用目标和用户出行目标的随机多目标离散交通网络设计双层规划模型.为保证所构建模型的求解精度，设计了基于Frank-Wolfe算法、Monte Carlo模拟和自适应小生境淘汰技术的遗传算法求解模型，并在Matlab平台上开发了相应的算法程序.采用Nguyen-Dupuis网络测试了模型和算法的有效性，结果表明：模型可以反映实际路网规划目标和约束，算法具有良好的全局收敛性，可为路网规划提供指导.

     

    Abstract: In order to solve the multi-objective optimization problem under uncertain traffic demand among practical OD pairs, a bi-level programming model was proposed to optimize the traffic management, environment protection, investment cost, and user behavior for stochastic multi-objective discrete transportation network design. The upper-level programming model was constructed using the chance constrained model and the ideal point model for multi-objective optimization, and the lower-level programming model was constructed using the user equilibrium assignment model under a fixed traffic demand. To ensure the solution accuracy of the proposed model, a genetic algorithm based on Frank-Wolfe algorithm, Monte-Carlo simulation, and adaptive niche technology was designed, and its corresponding program was developed using Matlab. In addition, the model and algorithm were tested in the Nguyen-Dupuis network. The result indicates that the model can reflect the objectives and constrains of practical network planning, and the algorithm is global convergent, hence providing a reference for the practical transportation planning.

     

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出版历程
  • 收稿日期:  2013-04-05
  • 刊出日期:  2014-01-25

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