Abstract:

This paper extends the applications of the controllability theory in the field of urban rail passenger volume control. The relationship of incoming, outgoing and the cross-sectional passenger flow were analyzed and the urban rail transit network was proven to be a linear time-invariant system, which demonstrated the applicability of the complex network controllability on urban rail transit network. Based on the exact controllability framework and the subnet generation strategy the paper proposed the method to identify the stations to apply passenger volume control measures. The effectiveness of the proposed method was verified by drawing on relevant evaluation metrics in machine learning. The results show that the controllability is 0.043 in Beijing rail transit network during off-peak hours, which indicates the network is stable and the control scheme is unnecessary. During the peak periods, the control schemes recommended passenger volume control on central stations of the network from the generation to dissipation of the congestion. The control schemes generated by the proposed method can match the actual control scheme to the degree of 70%. When the number of control stations are the same for both schemes, the scheme generated by the proposed method recommended controlling the stations in the western and central part of the city

Key words: urban traffic, passenger volume control, complex networks, exact controllability, passenger volume control stations

摘要：

为进一步拓展可控性理论在城市轨道交通客流控制领域的应用，首先根据相邻车站间进出站客流和断面客流的关系，论证城市轨道交通客流网络为线性时不变系统，证明可控性理论在城市轨道交通网络上的适用性。基于严格可控性框架和滞留人数为核心的子网生成策略，得到客流控制车站的识别方法。进一步地，引入机器学习领域的相关评价指标评估该方法的效果。研究结果表明：平峰时段北京市城市轨道交通网络的可控性为0.043，意味着该时段的网络状态较为稳定，无需采取客流控制措施；高峰时段，识别方案在拥堵生成到消散的过程中，更加侧重于对线网中心车站的控制。通过识别方法得到的客流控制方案与实际客流控制方案的吻合度最高可达70%。当两种方案控制车站的数量相同时，识别方法得到的客流控制方案更加侧重于对城市西部和中心区域的站点进行控制。

关键词: 城市交通, 客流控制, 复杂网络, 严格可控性, 限流车站