交通运输系统工程与信息

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (3): 156-162.

• 系统工程理论与方法 • 上一篇    下一篇

考虑跳停策略的城轨列车运行图与车站限流协同优化研究

孟凡婷a，杨立兴*a，卢亚菡a，郭戎格b   

  1. 北京交通大学，a. 轨道交通控制与安全国家重点实验室； b. 综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044
  • 收稿日期:2021-01-06 修回日期:2021-02-19 出版日期:2021-06-25 发布日期:2021-06-25
  • 作者简介:孟凡婷(1990- )，女，山东滨州人，博士生。
  • 基金资助:

    国家自然科学基金/ National Natural Science Foundation of China (71825004, 71621001)。

Collaborative Optimization of Urban Rail Transit Operation and Passenger Flow Control at Stations Using Skip-stop Pattern Strategy

MENG Fan-tinga , YANG Li-xing*a, LU Ya-hana , GUO Rong-geb   

  1. a. State Key Laboratory of Rail Traffic Control and Safety; b. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
  • Received:2021-01-06 Revised:2021-02-19 Online:2021-06-25 Published:2021-06-25

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摘要：

为应对日趋严重的地铁系统拥堵问题及客流过饱和情况，从系统优化角度出发，将服务供给侧与需求侧综合为一个整体进行研究。考虑乘客的持续性到达特征，提出考虑跳停策略的城轨列车运行图与车站限流协同优化方法。首先，引入列车运行图与车站限流相关决策变量，以提高列车运行效率、减少客流乘车延误人数为优化目标，建立轨道交通列车运行与车站限流协同优化双目标整数非线性规划模型。其次，为便于模型求解，引入0-1变量，使用时间重构和大M方法将模型中的非线性约束线性化处理，将模型重构为整数线性规划模型，利用CPLEX软件求解。算例结果表明，双目标优化方法与传统单目标优化方法相比，相较于仅考虑列车服务时间，本文模型可使客流乘车延误人数显著减少；相较于仅考虑客流乘车延误人数，本文方法可使列车服务时间降低2%~3%。

关键词: 城市交通, 协同优化, 双目标优化, 列车运行图, 跳停, 车站限流

Abstract:

To relieve severe traffic congestion and the over- saturation of rail transit system, this study integrates the service and demand from the perspective of system optimization, and considers the continuous arrival characteristics of passenger flow in the analysis. A collaborative optimization method is developed for the train operation schedule and passenger flow control at stations using the skip-stop pattern strategy. By introducing the train schedule and passenger flow control decision variables, a bi-objective integer nonlinear collaborative optimization model is formulated to improve the train operation efficiency and to reduce the number of delayed passengers. Then, the nonlinear constraints are linearized by time reconstruction and big-M method with 0-1 variables to solve the proposed model. The model is reconstructed into an integer linear programming model, which can be easily solved by the CPLEX solver. The numerical examples are executed to verify the effectiveness of the proposed model. The results show that compared to the single objective optimization method and only with the train service time，the proposed model significantly reduces the number of delayed passengers. Compared to only considering number of delayed passengers, the train running time is reduced by 2% to 3%.

Key words: urban traffic, collaborative optimization, bi-objective planning, train schedule, skip-stop pattern, passenger flow control at stations

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