中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (10): 174-182.doi: 10.16265/j.cnki.issn1003-3033.2024.10.0520

• 公共安全 • 上一篇    下一篇

突发大客流下地铁客流控制优化模型

米根锁(), 张园香   

  1. 兰州交通大学 自动化与电气工程学院,甘肃 兰州 730070
  • 收稿日期:2024-04-19 修回日期:2024-07-19 出版日期:2024-12-05
  • 作者简介:

    米根锁 (1966—),男,内蒙古卓资人,硕士,教授,主要从事轨道交通运营管理方面的研究。E-mail:

  • 基金资助:
    国家自然科学基金资助(61763025)

Optimization model of subway passenger flow control under sudden large passenger flow

MI Gensuo(), ZHANG Yuanxiang   

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China
  • Received:2024-04-19 Revised:2024-07-19 Published:2024-12-05

摘要:

为及时响应和缓解地铁线路突发大客流,避免出现过度拥挤现象,提出地铁客流控制优化模型。首先以最小化乘客总等待时间与最大化区间通过客流量为目标,以允许进站客流量为决策变量,考虑供给侧、需求侧与客流控制强度等约束,构建精细化的确定型模型(RDM)。在此基础上,分析客流需求的波动性,结合鲁棒优化理论建立鲁棒模型(RM)。其次,利用鲁棒对等转换理论,线性化处理RM中的非线性约束,并借助Lingo优化求解器进行求解。最后,以某地铁线路为例进行分析验证。结果表明: 在RDM模型中,利用运力平衡系数来决策允许进站客流量,可以有效缓解客流拥挤压力,提高区间运输效率;在应对不确定客流需求时,通过RM模型引入鲁棒系数来调节客流需求的波动区间,从而降低客流聚集风险,提高客流控制方案的可靠性。

关键词: 突发大客流, 地铁, 客流控制, 优化模型, 精细化的确定型模型(RDM), 鲁棒模型(RM)

Abstract:

To respond and alleviate the sudden large passenger flow of metro lines in time, a subway passenger flow control optimization model was proposed. Firstly, with the goals of minimizing the total waiting time of passengers and maximizing passenger flow through the interval, permitted inbound passenger flow was used as a decision-making variable to propose a RDM considering constraints such as the supply side, demand side, and passenger flow control intensity. Moreover, the volatility of passenger flow demand was analyzed, and a RM was developed by combining robust optimization theory. The volatility of passenger flow demand was analyzed, and an RM was developed combined with robust optimization theory. Secondly, the robust equivalent transformation theory was used to linearize the nonlinear constraints in RM and solved by the Lingo optimization solver. Finally, a metro line was taken as an example for analysis and verification. The results showed that the RDM model using capacity balance coefficients to decide the permissible inbound passenger flow effectively alleviated the pressure of passenger congestion and improved the efficiency of interval transport. When dealing with uncertain passenger demand, robustness coefficients were introduced in the RM model to adjust fluctuations range of passenger flow demand, thereby reducing the aggregation passenger flow risk and improving the reliability of the passenger flow control scheme.

Key words: sudden large passenger flow, metro systems, passenger flow control, optimization model, refined deterministic model (RDM), robust model (RM)

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