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摘要: 基于近年来民用机场乘客登机流程优化研究，从面向乘客、面向飞机、面向研究方法和面向新冠肺炎疫情方面分析了当前研究现状和研究成果，探讨了民用机场乘客登机流程优化的方法及措施，根据现有研究的不足展望了未来的研究方向。研究结果表明：面向乘客的优化研究通常将群体乘客作为主要考虑因素，以最小登机时间及登机干扰作为目标函数建立相关模型，并针对不同优先级乘客和迟到乘客进行分组考虑；WilMA和RP是综合性能较优的2种登机策略，Side-Slip新型座椅对登机时间影响最显著；乘客登机优化的求解方法包括模型法和仿真法，其中模型主要包括统计物理模型和数学模型，仿真包括元胞自动机和智能体；面向新冠肺炎疫情的登机研究更多地考虑了乘客的健康问题，并将登机时间和健康风险作为评估登机优劣的两大主要指标；未来研究需综合不同登机策略的优点以弥补单个策略的不足；自主性强的智能体仿真和未突出个体性差异的元胞自动机仿真需要相互结合；优化模型应考虑更多因素，寻找求解质量更高的启发式算法；需更加注重社交距离、戴口罩乘客人数及群体登机人数等对疫情环境下登机流程的影响研究；疫情防控常态化情况下如何最大化乘客安全和登机效率也是重要的研究方向。Abstract: On the basis of the recent research on the optimization of passenger boarding process at civil airports, the research status and achievements in this area was analyzed from the aspects of passenger, aircraft, methodology, and COVID-19 pandemic, the methods and measures to optimize the passenger boarding process at civil airports were discussed, and the directions of future studies were explored according to the deficiency in current research. Research results show that in the passenger-oriented optimization studies, the group passengers are considered a significant factor. The minimum boarding time and boarding interference are used as the objective functions to build relevant models, and the passengers with different priorities and latecomers are often considered in groups. WilMA and RP are two boarding strategies with excellent comprehensive performance, and the new Side-Slip seat has the most significant influence on boarding time. The solution of passenger boarding optimization include model methods and simulation methods, in which models mainly include statistical physical model and mathematical model, and simulations include cellular automata and agents. In terms of the COVID-19 pandemic-oriented boarding studies, the health of passengers is the main consideration, where boarding time and health risks are the two primary indicators to evaluate the quality of passenger boarding. In future studies, the advantages of different boarding strategies should be combined to overcome the shortcomings of different individual strategies. The agent-based simulation with strong independence should be combined with the cellular automata simulation that does not highlight the individual heterogeneity. More factors should be considered in optimization models, and better heuristic algorithms should be explored to solve optimization models. Moreover, the impacts of the factors such as the safe social distance, the number of passengers wearing masks, and the number of group boarding passengers on the process of boarding should be attached more importance. It is also an important research direction to explore how to maximize passenger safety and boarding efficiency under the regular COVID-19 epidemic control.
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Key words:
- air transport /
- boarding process optimization /
- review /
- boarding strategy /
- passenger /
- aircraft /
- study methodology /
- COVID-19 pandemic
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表 1 登机策略概述
Table 1. Overview of boarding strategies
登机策略 描述 优点 缺点 代表航空公司 Random 仅一个登机组；登机没有给定的顺序，但是每位乘客都分配到一个特定的座位 易于理解；结伴乘客可一起登机；速度较快；迟到的乘客不会影响效率 鲁棒性差 南方航空、春秋航空、首都航空、东方航空、吉祥航空、山东航空、EasyJet、Tarom、WizzAir、Northwest、Air France BF 从飞机后部到前部按乘客组顺序依次登机 容易实现；结伴乘客可以一起登机 拥堵会集中在飞机的一小块区域；速度较慢 中国国航、Eva Air、Air Canada、British Airways、American Airlines、Japan Airlines、Korean Air、Virgin Atlantic、Spirit OI/WilMA 乘客被分为三组，靠窗座位的乘客组首先登机，然后是中间座位的乘客组，最后是过道座位乘客组 速度较快，没有座位干扰 结伴乘客可能因此会分开登机 United Airlines RP BF和OI的混合模式；乘客按照对角线方案登机，首先是飞机后排靠窗座位乘客组登机，最后是前排过道座位乘客组登机 登机速度快 登机相当复杂；结伴乘客可能因此会分开登机 America West、JetBlue By Block 连续几排的乘客一起登机；BF或Front-to- Back的一般化登机形式；可以跳过一些排或组，稍后再让这些乘客一起登机 结伴乘客可以一起登机 拥堵集中在飞机的小区域；超过一组，登机速度慢于Random；组数越多，登机速度越慢 American Airlines By Row 同一排乘客一起登机 结伴乘客可以一起登机 登机速度慢，登机组数量大 Continental Airlines、Delta Airlines Steffen 首个登机的乘客在最后一排靠窗位置就座，第2个乘客在相隔两排的靠窗位置就座(20A、18A、16A、…)，两侧靠窗位置坐满以后，以相同的方式继续让乘客坐在中间和过道的座位上 非常快；没有座位干扰，少量过道干扰 相当复杂；结伴乘客可能因此会分开登机 Open Seating 没有为乘客指定座位；乘客在登机时可以选择任意空闲的座位 结伴乘客可以一起登机；乘客可以通过选择座位来避免干扰 大部分乘客认为有压力；先到先服务原则的缺点为最后一批群体登机的乘客可能没有相邻的座位 Southwest Airlines 
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表 2 群体乘客登机文献概述
Table 2. Literature overview of group passenger boarding
文献 评估指标 登机场景 模型 [25] 登机时间 单门单过道飞机A321；3人组团、2人组团、单独出行乘客比例分别为7%、38%、55%；携带0、1、2件行李的乘客比例分别为45%、40%、15% 基于MATLAB的仿真模型 [26] 登机时间、座位干扰 单门单过道飞机(30×6=180座位)；设置2种载客率；设置2种群体出行比例；设置2种步行速度的乘客比例；乘客携带1件行李； 基于智能体仿真模型 [27] 登机时间 单门单过道飞机(31×6=186座位)；群体出行乘客设置为3人；乘客最多可携带2件行李 考虑乘客期望的数学模型(SA求解) [24] 登机时间、检票延误 单门单过道飞机(25×6=150座位)；群体出行和单独出行乘客随机分布；乘客最多可携带2件行李 考虑乘客群体特征和行李的常微分方程模型 [28] 登机时间、座位干扰 单门单过道飞机(25×6=150座位)；群体出行和单独出行乘客随机分布；群体出行乘客优先选择座位 [29] 登机时间 单门单过道飞机(25×6=150座位)；群体出行和单独出行乘客随机分布；携带0、1、2件行李的乘客比例分别为20%、60%、20% [31] 登机时间 双门单过道飞机(30×6=180座位)；双摆渡车；群体出行和单独出行乘客按比例分布；设置7种行李分配比例 混合整数规划模型 [32]、[33] 登机时间、过道座位风险持续时间、靠窗座位风险持续时间 双门单过道飞机(29×4=116座位); 4人乘客结伴登机并就座；社交距离为1.6 m 混合整数规划模型 [34] 过道座位风险持续时间、靠窗座位风险持续时间 单门单过道飞机(20×6=120座位)；不同人数群体乘客出行的组别；设置不同的社交距离 混合整数规划模型 [35] 登机时间、座位干扰、过道干扰 单门单过道飞机(25×6=150座位)；3人组团、2人组团、单独出行乘客比例分别为7%、38%、55%；机舱座椅为Side-Slip座椅 基于元胞自动机仿真
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表 3 飞机座椅文献概述
Table 3. Literature overview of aircraft seats
文献 评估指标 登机场景 模型/仿真 [43] 登机时间 3种窄体飞机分别为136座位的B737-300、144座位的A320、184座位的B757 蒙特卡罗模拟 [45]、[46] A320(29×6=174座位)；机舱座椅为Side-Slip座椅 基于ASEP的仿真，进化算法求解 [47] 单门单过道飞机(25×6=150座位)；机舱座椅为Side-Slip座椅 基于元胞自动机的仿真 [44] 3种机舱类型分别为每排9、6、4个座位；设置7种行李分配比例 MATLAB编程仿真 [35] 登机时间、座位干扰、过道干扰 单门单过道飞机(25×6=150座位)；3人组团、2人组团、单独出行乘客比例分别为7%、38%、55%；机舱座椅为Side-Slip座椅 基于元胞自动机仿真
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表 4 飞机过道文献概述
Table 4. Literature overview of aircraft aisle
文献 评估指标 登机场景 模型/仿真 [48]、[49] 登机时间、座位干扰、过道干扰 单门单过道飞机(23×6=138座位)；设置不同比例的载客率；携带0、1、2件行李的乘客比例分别为60%、30%、10% 基于元胞自动机的MATLAB仿真 [51] 登机时间 2种类型飞机分别为180座位的单过道飞机、300座位的双过道飞机；机舱座椅为Side-Slip座椅 基于智能体的仿真 [52] 双过道飞机A320；携带0、1、2件行李的乘客比例分别为60%、30%、10%；2种机舱座椅分别为Side-Slip座椅和普通座椅 ASEP粒子动力学模型 [50] 登机时间 B777和B380两种飞机机型；6种不同的群体乘客类型 基于智能体的仿真
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表 6 基于统计物理模型的登机优化研究
Table 6. Studies on boarding optimization based on statistical physics models
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表 7 基于数学模型的登机优化研究
Table 7. Studies on boarding optimization based on mathematical models
模型 文献 评估指标 登机场景 混合整数规划模型 [16]、[68] 登机时间、座位干扰、过道干扰 A320单门单过道(23×6=138座位) [69] 登机时间 单门单过道飞机(23×6=138座位)；携带0、1、2件行李的乘客比例分别为60%、30%、10% [70] A320单门单过道(23×6=138座位)；设置不同组别的乘客登机 [12] 单门单过道飞机(20×6=120座位)；3种行李分配比例 [7] 单门单过道飞机(20×6=120座位)；乘客最多可携带2件行李 [38] 单门单过道飞机(20×6=120座位)；乘客最多携带2件行李；设置不同比例的优先级乘客 0-1线性规划模型 [75] 登机时间 单门单过道窄体飞机 常微分方程模型 [72] 登机时间 A320单门单过道(23×6=138座位) [28] 登机时间、座位干扰 单门单过道飞机(25×6=150座位)；群体出行和单独出行乘客随机分布；群体出行乘客优先选择座位 [24] 登机时间、延误 单门单过道飞机(25×6=150座位)；群体出行和单独出行乘客随机分布；乘客最多可携带2件行李 [29] 登机时间 单门单过道飞机(25×6=150座位)；群体出行和单独出行乘客随机分布；携带0、1、2件行李的乘客比例分别为20%、60%、20% 指数高斯分布模型 [76] 登机时间、乘客感知时间 单门单过道飞机 离散事件模型 [71] 登机时间 A320单门单过道飞机(23×6=138座位) [77] A320单门单过道飞机(25×6=150座位) 行人流跟驰模型 [30]、[73] 登机时间、座位干扰、过道干扰 单门单过道飞机(25×6=150座位) 基于任务网络的多参数分析模型 [78] 登机时间 单门单过道飞机(24×6=144座位) 基于马尔科夫链蒙特卡罗优化模型 [14] 登机时间 单门单过道飞机(20×6=120座位)；随机分配行李数 结构方程模型 [20] 登机时间 单门单过道飞机 正态分布、韦布尔分布、泊松分布模型 [79] 登机时间 训练数据为北京首都机场2013年运营数据；测试数据为北京首都机场2014年1~8月运营数据 LSTM模型 [80] 登机时间 单门单过道飞机 多格子元胞自动机模型 [81] 登机时间、过道干扰(速度干扰、入座干扰) A320单门单过道飞机(23×6=138座位)；设置不同比例的载客率；携带0、1、2件行李的乘客比例分别为60%、30%、10% 具有凹边界条件的1+1多核增长模型 [82] 登机时间 A320单门单过道飞机(29×6=174座位) 二维洛仑兹几何渐进模型 [83]~[85] 登机时间 单门单过道飞机
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表 8 智能体仿真平台比较
Table 8. Comparison of different agent-based modelling platforms
分组 智能体仿真平台 StarLogo Swarm NetLogo Repast Mason 开发年份 1990 1996 1999 2000 2003 开发单位 MIT Santa Fe Institute Northwestern University University of Chicago George Mason University 官方网站 http://education.mit.edu/starlogo http://www.swarm.org/mailman/listinfo http://ccl.northwestern.edu/netlogo http://repast.sourceforge.net http://cs.gmu.edu/~eclab/projects/mason 操作系统 Windows, UNIX, Linux Windows, UNIX, Linux, Mac OSX Windows, UNIX, Linux, Mac OSX Windows, UNIX, Linux, Mac OSX Windows, UNIX, Linux, Mac 安装难度 非常容易 较难 非常容易 容易 容易 需要的编程技术 基本 专业 基本 专业 专业 集成GIS功能 × √ √ √ × 集成显卡 √ √ √ √ × 可视化环境 Interface Observer swarm Interface None ModelwithUI 实际系统环境 Observer Model swarm Observer Model Model 空间环境 Spaceland Space World Space Field Agent状态探测 Monitor Probe display Monitor Probe Inspector Agent执行事件 Procedure Action Procedure Action Steppable 运行速度 适中 适中 适中 快 非常快
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表 9 仿真试验中乘客携带行李分配
Table 9. Allocation of passengers' luggage in simulation experiment
行李分配情况 携带行李乘客的百分比/% 0 1小包 2小包 1大包 1大包和1小包 S1 10 10 0 10 70 S2 15 20 5 10 50 S3 25 20 10 15 30 S4 35 25 10 15 15 S5 60 10 10 10 10 S6 80 5 5 5 5 S7 100 0 0 0 0
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表 10 基于元胞自动机的仿真文献概述
Table 10. Literature overview of simulation based on cellular automata
文献 考虑乘客 考虑机型 考虑新型座椅 考虑行李 登机策略 载客率/% 结伴 迟到 窄体飞机 宽体飞机 Free Random BF FB RP OI RZ By Row By Seat Flying Carpet By Block Steffen [107] 100 √ √ √ √ √ √ √ [112] 100 √ √ √ √ √ √ √ [49] 100 √ √ √ √ √ √ √ √ √ √ [108] 100 [116] 100 √ √ √ √ √ √ √ √ √ [40] 100 √ √ √ √ √ √ √ √ [77] 100 √ √ √ √ √ √ [81] 30~100 √ √ [109] 100 √ √ √ √ √ [113] 100 √ √ √ √ [114] 100 √ √ [47] 100 √ √ √ √ √ √ √ √ [111] 100 √ √ √ √ √ √ [41] 100 √ √ √ √ √ √ √ √ [115] 60~100 √ √ √ √ √ √ √ √ [35] 100 √ √ √ √ √ √ √ √ √
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表 11 新冠肺炎疫情下乘客登机文献概述
Table 11. Literature overview of passenger boarding during COVID-19 pandemic
文献 评估指标 登机环境 登机策略 模型/仿真/方法 [126] 登机时间、3类座位干扰次数、放置行李总时间、过道座位风险持续时间、靠窗座位风险持续时间 单门单过道飞机(30×4=120座位)；设置7种携带行李乘客比例；设置3种社交距离分别为1.0、1.5、2.0 m Free、Random、WilMA、BF By Group、BF By Row、RP 基于智能体仿真 [127] 登机时间、3类座位干扰数量、过道座位风险持续时间、靠窗座位风险持续时间 单门单过道飞机(30×4=120座位)；设置7种携带行李乘客比例；不同社交距离 Random、BF 混合整数规划模型 [128] 登机时间、3类座位干扰数量、过道座位风险持续时间、靠窗座位风险持续时间 单门单过道飞机(30×4=120座位)；乘客分3组进行登机；携带0、1小件、1大件、2小件行李的乘客比例分别为10%、10%、10%、70%；社交距离为1.0 m RP 全网格搜索算法、局部搜索优化、基于智能体仿真 [129] BF 灰色聚类方法、基于智能体仿真 [130] 登机时间、3类座位干扰数量、过道座位风险持续时间、靠窗座位风险持续时间 单门单过道飞机(30×4=120座位)；乘客分组登机；设置7种携带行李乘客比例；设置2种社交距离分别为1.0、2.0 m RP-Spread、RP-Steep 基于智能体仿真 [131] 双门单过道飞机(30×4=120座位)；10辆机场摆渡车；乘客分组登机；设置7种携带行李乘客比例；设置2种社交距离分别为1.0、2.0 m BF、WilMA、RP [132] 单门单过道飞机(30×4=120座位)；设置7种携带行李乘客比例；设置2种社交距离分别为1.0、2.0 m BF、RP、WilMA [133] RP [32] 登机时间、过道座位风险持续时间、靠窗座位风险持续时间 双门单过道飞机(29×4=116座位)；4人乘客结伴登机并就座；社交距离为1.6 m Random、BF、By Block、OI、RP 混合整数规划模型、元胞自动机仿真
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