综合客运枢纽疏散流线设计优化方法

李兴华; 王天佐; 张晓光; 赵军舰; 成诚

doi:10.3963/j.jssn.1674-4861.2023.01.014

综合客运枢纽疏散流线设计优化方法

doi: 10.3963/j.jssn.1674-4861.2023.01.014

李兴华^{1, 2,},
王天佐^{1, 3},
张晓光⁴,
赵军舰⁴,
成诚^{2, 3, ,}

1.
同济大学城市交通研究院上海 201804
2.
同济大学道路与交通工程教育部重点实验室上海 201804
3.
同济大学中国交通研究院上海 200092
4.
中交远洲交通科技集团有限公司石家庄 050035

基金项目:

上海市社会发展科技攻关项目 21DZ1205102

中交集团科技研发专项 2019-ZJKJ-ZDZX02

湖南机场股份有限公司咨询课题项目 DKT3-咨-2019-007

详细信息

作者简介:
李兴华（1967—），博士，教授. 研究方向：综合交通规划、交通政策与管理. E-mail：xinghuali@tongji.edu.cn

通讯作者:
成诚（1989—），博士，副研究员. 研究方向：交通仿真优化、智能交通管控. E-mail：18608@tongji.edu.cn

中图分类号: U491
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- 文章访问数: 464
- HTML全文浏览量: 158
- PDF下载量: 26
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-06
- 网络出版日期: 2023-05-13

A Method for Optimizing the Design of Evacuation Streamline for Multimodal Passenger Transportation Hubs

LI Xinghua^{1, 2
,},
WANG Tianzuo^{1, 3},
ZHANG Xiaoguang⁴,
ZHAO Junjian⁴,
CHENG Cheng^{2, 3
, ,}

1.
Urban Mobility Institute, Tongji University, Shanghai 201804, China
2.
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
3.
China Transportation Institute at Tongji, Shanghai 200092, China
4.
Chinaroads Communications Science & Technology Group CO., Ltd, Shijiazhuang 050035, China

摘要

摘要: 综合客运枢纽内部人员密集、通道网络复杂，且进出口数量较多，客流疏散效率不易提高。针对这个问题，不同于现有研究大多考虑改造物理设施，本文提出了通过控制通道开闭状态及通行流向，充分发挥既有设施通行能力的疏散流线设计优化方法。设计了由系统输入、疏散仿真及疏散流线优化模块组成的疏散流线设计仿真优化框架。其中，系统输入模块包含疏散需求、疏散网络、疏散行为参数；疏散仿真模块用于给定疏散流线方案下疏散效率的模拟测算；疏散流线优化模块则基于疏散仿真模拟结果迭代优化疏散流线方案。疏散仿真方面，考虑到行人在疏散途中可能动态修改疏散路线的特点，基于Logit模型构建了行人疏散择路行为模型。疏散流线优化方面，为提高疏散效率，避免局部通道过于拥挤，设计了以整体疏散时长、所有个体总疏散时间和通道最大饱和度最低为目标的疏散流线优化模型，并应用基于NSGA-Ⅲ的疏散流线多目标优化算法进行求解。以虹桥火车站到达层疏散场景为例开展模型验证，结果表明：相比于常规情况无特殊流线设计的疏散方案，优化方案的整体疏散时长、所有个体总疏散时间和通道最大饱和度分别降低36.2%、16.6%、51.6%。该方法对建设安全高效的综合客运枢纽内部行人疏散系统具有较好的参考及应用价值。
- 交通工程 /
- 综合客运枢纽 /
- 疏散流线设计 /
- 仿真优化 /
- 多目标优化
Abstract: Multimodal passenger transportation hubs are typically crowded by pedestrians, and are composed of complex corridor networks, multiple entrances, and exits. It has been challenging to improve the evacuation efficiency of multimodal passenger transportation hubs. To address this challenge, most of previous studies focus on the layout redesign of facilities, while this study proposes a method to fully utilize the existing facility capacity by controlling the opening/closing state and walking direction of corridors. A simulation-based optimization framework for designing the evacuation streamline is proposed, including system input, simulation for the evacuation process, and optimization modules for the evacuation streamline. The input module of the system requires to specify the following three parameters: evacuation demand, evacuation network, and evacuation behavior. The evacuation simulation module is used to evaluate the efficiency of passenger evacuation under a specific plan of evacuation streamline. The optimization of evacuation streamline plan is conducted by the optimization module based on the results of evacuation simulation runs. Regarding the evacuation simulation, as pedestrians may dynamically modify their evacuation routes, a dynamic model for simulating route choice behavior is developed based on the Logit modeling framework. An optimization model of evacuation streamline is designed to minimize overall evacuation time, total evacuation duration of all individuals, and the level of maximum corridor saturation. An optimization algorithm for multi-objective evacuation streamline is developed based on the NSGA-Ⅲ. The proposed method is validated based on an evacuation scenario of the arrival level of Hongqiao Railway Station. Study results indicate that, compared to the conventional evacuation scenarios without an optimized design of evacuation streamline, the overall evacuation time, total evacuation duration and corridor maximum saturation of the optimized scenario are reduced by 36.2%, 16.6%, and 51.6%, respectively. In general, the proposed method should be beneficial for developing a safe and efficient pedestrian evacuation plan for the multimodal passenger transportation hubs.
- traffic engineering /
- integrated passenger transportation hub /
- evacuation streamline design /
- simulation-based optimization /
- multi-objective optimization

HTML全文

图 1 基于仿真优化的枢纽疏散流线设计框架

Figure 1. Hub evacuation streamline generation framework via simulation-based optimization

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图 2 疏散系统建模

Figure 2. Evacuation system modeling

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图 3 行人疏散仿真流程图

Figure 3. Pedestrian evacuation simulation flowchart

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图 4 NSGA-Ⅲ算法流程图

Figure 4. NSGA-Ⅲ algorithm flowchart

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图 5 上海虹桥火车站到达层平面布局图

Figure 5. Layout of the Shanghai Hongqiao railway station arrival floor

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图 6 到达层网络模型及疏散需求分布热力图

注：核密度分析半径取10m。

Figure 6. Network model of the arrival floor and the heatmap of initial evacuation distribution

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图 7 入口需求生成模型

Figure 7. Entrance demand generation model

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图 8 NSGA-Ⅲ迭代过程

Figure 8. NSGA-Ⅲ iteration process

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图 9 疏散流线设计方案A₂

Figure 9. Evacuation plan A₂

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表 1 需求生成函数参数取值

Table 1. Values of demand generation function parameters

入口类型	入口位置	max/(人/s)	t₁/s	t₂/s	总需求量/人
铁路到达入口	西	5	45	15	1 908
	中	6	45	15
	东	7	45	15
地铁到达入口	西	3	45	15	318
扶梯入口	西	2	45	15	530
扶梯入口	东	3	45	15	530

下载: 导出CSV

表 2 疏散流线方案对比

Table 2. Comparison of evacuation streamline plans

评价指标	流线优化方案A₂	全双向通行方案A₀ (A₂相对于A₀的优化比例/%)	全单向通行方案A₁ (A₂相对于A₁的优化比例/%)
整体疏散时长/s	146	229 （-36.2）	175 （-16.6）
所有个体总疏散时间/s	478 138	573 365 （-16.6）	485 005 （-1.4）
通道最大饱和度	0.95	1.96 （-51.6）	1.41 （-32.8）

下载: 导出CSV

参考文献(25)

[1]	李明华, 袁振洲, 许琰, 等. 基于那个人点排队模型的多出口疏散优化模型研究[J]. 交通运输系统工程与信息, 2015, 15(4): 166-172. doi: 10.3969/j.issn.1009-6744.2015.04.026 LI M H, YUAN Z Z, XU Y, et al. An optimization model of multi-exit evacuation based on point queue model[J]. Journal of Transporation Systems Engineering & Information Technology, 2015, 15(4): 166-172. (in Chinese) doi: 10.3969/j.issn.1009-6744.2015.04.026
[2]	GALáN S F. Fast Evacuation method: using an effective dynamic floor field based on efficient pedestrian assignment[J]. Safety Science, 2019(120): 79-88.
[3]	LIU H, XU B, LU D J, et al. A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm[J]. Applied Soft Computing, 2018, (68): 360-376.
[4]	FENG L, MILLER-HOOKS E. A network optimization-based approach for crowd management in large public gatherings[J]. Transportation Research Part C: Emerging Technologies, 2014, (42): 182-199.
[5]	TANEJA L, BOLIA N B. Network redesign for efficient crowd flow and evacuation[J]. Applied Mathematical Modelling, 2018, (53): 251-266.
[6]	郭海湘, 曾杨, 陈卫明. 基于社会力模型的多出口室内应急疏散仿真研究[J]. 系统仿真学报, 2021, 33(3): 721-731. GUO H X, ZENG Y, CHEN W M. Research on indoor emergency evacuation simulation of multi-exit based on social force model[J]. Journal of System Simulation, 2021, 33(3): 721-731. (in Chinese)
[7]	张蜇. 城市轨道交通车站客流集散优化控制研究[D]. 北京: 北京交通大学, 2017. ZHANG Z. Study on optimization-based control of passenger flow distribution in subway stations[D]. Beijing: Beijing Jiaotong University, 2017. (in Chinese)
[8]	JIN C J, JIANG R, WONG S C, et al. Observational characteristics of pedestrian flows under high-density conditions based on controlled experiments[J]. Transportation Research Part C: Emerging Technologies, 2019, (109): 137-54.
[9]	叶锐. 通道中单向与相向行人流运动特征的实验研究[D]. 合肥: 中国科学技术大学, 2020. YE R. Experimental study on uni-and bidirectional pedestrian flows in corridors[D]. Hefei: University of Science and Technology of China, 2020. (in Chinese)
[10]	李贺楠, 严建伟. 高铁枢纽应急疏散流线和空间设计方法研究[J]. 天津大学学报(社会科学版), 2013, 15(2): 135-137. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXS201302006.htm LI H N, YAN J W. Research on the emergency evacuation streamline and space design of the high-speed railway station[J]. Journal of Tianjin University (Social Sciences), 2013, 15(2): 135-137. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXS201302006.htm
[11]	综合交通运输标准化技术委员会. 综合客运枢纽术语: JT/ T 1065-2016[S]. 北京: 中国标准出版社, 2016. Comprehensive Transportation Standardization Technical Committee. The terminology of integrated passenger transportation hub: JT/T 1065-2016[S]. Beijing: Standards Press of China, 2016. (in Chinese)
[12]	徐丽敏. 基于旅客集散行为的大型客运枢纽旅客流线优化研究[D]. 重庆: 重庆交通大学, 2014. XU L M. Study on the passenger flow of large transportation hub based on the behavior of distribution passenger[D]. Chongqing: Chongqing Jiaotong University, 2014. (in Chinese)
[13]	GUO K, ZHANG L M. Simulation-based passenger evacuation optimization in metro stations considering multi-objectives[J]. Automation in Construction, 2022(133): 104010.
[14]	SHIN Y, KIM S, MOON I. Simultaneous evacuation and entrance planning in complex building based on dynamic network flows[J]. Applied Mathematical Modelling, 2019, (73): 545-562.
[15]	李东, 汪定伟. 基于仿真的优化方法综述[J]. 控制工程, 2008, 15(6): 672-677, 702. doi: 10.3969/j.issn.1671-7848.2008.06.015 LI D, WANG D W. A survey on simulation-based optimization[J]. Control Engineering of China, 2008, 15(6): 672-677, 702. (in Chinese) doi: 10.3969/j.issn.1671-7848.2008.06.015
[16]	ANH-TUAN N, REITER S, RIGO P. A review on simulation-based optimization methods applied to building performance analysis[J]. Applied Energy, 2014(113): 1043-1058.
[17]	汪瑞琪, 张缨. 城市轨道交通车站内客流集散瓶颈识别及排序方法[J]. 交通信息与安全, 2017, 35(1): 71-79, 91. doi: 10.3963/j.issn.1674-4861.2017.01.009 WANG R Q, ZHANG Y. Identification and ranking approaches of bottleneck for gathering and distributing capacity in stations of urban rail transit[J]. Journal of Transport Information and Safety, 2017, 35(1): 71-79, 91. (in Chinese) doi: 10.3963/j.issn.1674-4861.2017.01.009
[18]	王志刚, 石嵘, 高伟君. 上海轨道交通车站乘客走行时间函数的分析[J]. 城市轨道交通研究, 2010, 13(12): 57-60. WANG Z G, SHI R, GAO W J. Analysis of passengers' walking time function at Shanghai urban railway station[J]. Urban Mass Transit, 2010, 13(12): 57-60. (in Chinese)
[19]	马剑, 许素梅, 范文博, 等. 大规模人群网络流瓶颈识别方法研究[J]. 系统工程理论与实践, 2016, 36(1): 164-173. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201601017.htm MA J, XU S M, FAN W B, et al. Method of bottleneck identification for massive crowd circulating network[J]. Systems Engineering-Theory & Practice, 2016, 36(1): 164-173. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201601017.htm
[20]	董崇杰, 刘毅, 彭勇. 改进布谷鸟算法在人群疏散多目标优化中的应用[J]. 系统仿真学报, 2016, 28(5): 1063-1069. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201605009.htm DONG C J, LIU Y, PENG Y. Improved cuckoo search algorithm applied to multi-objective optimization of crowd evacuation[J]. Journal of System Simulation, 2016, 28(5): 1063-1069. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201605009.htm
[21]	孙迪, 周进, 高学英, 等. 地铁站内行人疏散路径选择仿真研究[J]. 系统仿真学报, 2019, 31(9): 1819-1826. SUN D, ZHOU J, GAO X Y, et al. Simulation Research on pedestrian evacuation path selection in the metro station[J]. Journal of System Simulation, 2019, 31(9): 1819-1826. (in Chinese)
[22]	MEURER A, SMITH CP, PAPROCKI M, et al. SymPy: symbolic computing in Python[J]. PeerJ Computer Science, 2017, 3(3): 1-27.
[23]	DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part Ⅰ: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.
[24]	全国公共安全基础标准化技术委员会(SAC/TC 351). 地铁安全疏散规范: GB/T 33668-2017[S]. 北京: 中国标准出版社, 2017. China National Public Safety Fundamental Standardization Technical Committee(SAC/TC 351). Code for safety evacuation of metro: GB/T 33668-2017[S]. Beijing: Standards Press of China, 2017. (in Chinese)
[25]	赵启凡, 李多, 戴一正. 基于疏散模拟的铁路客站换乘站厅商业空间优化策略: 以上海虹桥铁路客站为例[J]. 新建筑, 2021(2): 104-109. ZHAO Q F, LI D, DAI Y Z. Research on commercial space of transfer station hall based on pathfinder: the case of Shanghai Hongqiao Railway Passenger Station[J]. New Architecture, 2021(2): 104-109. (in Chinese)