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基于AnyLogic的地铁站台客流引导方式优化

Optimization of subway platform's passenger flow guidance methodbased on AnyLogic

  • 摘要: 为避免地铁车厢客流过度集中,引导站台乘客分散乘车,文章以地铁各车厢客流均衡为目标,以均衡度标准差为指标,综合考虑车厢现有人数、候车人数及预计下车人数,优化地铁站台客流引导方式。通过AnyLogic仿真软件,模拟广州地铁珠江新城5号线站台条件,对比了客流量范围在12万人/日~60万人/日条件下,不同引导方式对车厢乘客分布不均衡度的影响。结果表明,地铁站客流量达到24万人/日时,考虑预计下车人数的站台客流引导方式下的车厢客流均衡度标准差明显提升;与不考虑该站下车人数的引导方式相比,客流均衡度标准差提升幅度最大达到25.6%,相比人工引导方式的提升幅度达53.6%,有效提升了地铁精细化服务能力。

     

    Abstract: In order to avoid excessive concentration of passenger flow in subway carriages and guide passengers on the platform to disperse and board trains, this paper aimed to balance the passenger flow in each carriage of the subway, used the standard deviation of balance degree as an indicator, comprehensively considered the existing number of passengers in the carriage, the number of waiting passengers, and the expected number of alighting passengers, to optimize the subway platform passenger flow guidance method. Through AnyLogic simulation software, the paper simulated the platform conditions of the Pearl River New Town Line 5 of Guangzhou Metro, and compared the influence of different guidance modes on the uneven distribution of passengers in the carriage under the conditions of passenger flow ranging from 120 000 people/day to 600 000 people/day. The results show that when the passenger flow of the subway station reaches 240 000 people/day, the standard deviation of the passenger flow balance of the carriages under the platform passenger flow guidance method considering the expected number of passengers getting off significantly improves. Compared with the guidance method that does not consider the number of passengers getting off at the station, the standard deviation of passenger flow balance has increased by a maximum of 25.6%, and compared to the manual guidance method, the improvement has reached 53.6%. The optimized guidance method effectively improves the refined service capacity of the subway.

     

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