考虑旅客到达准时性的城市值机移动站点动态分布模型

张铭霞; 周航; 胡小兵

doi:10.3963/j.jssn.1674-4861.2023.05.017

考虑旅客到达准时性的城市值机移动站点动态分布模型

doi: 10.3963/j.jssn.1674-4861.2023.05.017

张铭霞^{1, 2,},
周航^{1, 2, ,},
胡小兵^{1, 3}

1.
中国民航大学体系安全和智能决策实验室天津 300300
2.
中国民航大学中欧航空工程师学院天津 300300
3.
中国民航大学安全科学与工程学院天津 300300

基金项目:

中央高校基本科研业务费中国民航大学专项 2000530441

详细信息

作者简介:
张铭霞（1998—），硕士研究生. 研究方向：空管智能决策. E-mail：Zhang_mx0502@163.com

通讯作者:
周航（1990—），博士，讲师. 研究方向：计算智能，空管智能决策，计算电磁学. E-mail：h-zhou@cauc.edu.cn

中图分类号: U121
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出版历程
- 收稿日期: 2023-03-12
- 网络出版日期: 2024-01-18

A Dynamic Distribution Model of Urban Mobile Stations Considering Passengers' Arrival Punctuality

ZHANG Mingxia^{1, 2
,},
ZHOU Hang^{1, 2
, ,},
HU Xiaobing^{1, 3}

1.
Laboratory of Complex System Safety and Intelligent Decisions, Civil Aviation University of China, Tianjin 300300, China
2.
Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China
3.
College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 现有城市值机移动服务站点设施分布模型在优化中未考虑旅客到达服务站点的时间不确定性，其优化结果通常与实际情况存在差异，导致无法对提前或延误到达的旅客进行服务。为解决时间不确定性对优化求解造成的不利影响，研究基于旅客准时性概率函数的动态设施分布模型。针对城市值机移动服务站点布局优化问题，构建完整的数学模型，并提出动态设施分布的优化评价指标。采用正态分布型旅客准时性概率函数，用以预估旅客实际到站时间与申报到站时间的差异。基于不同服务时段客源点的位置分布，采用涟漪扩散算法和遗传算法优化服务站点位置并计算所有旅客与站点间的最优路径。基于天津市路网和旅客分布的真实数据，对旅客准时到站和考虑旅客到站时间不确定2种场景进行仿真对比实验。结果表明：旅客到站时间概率模型优化结果优于旅客准时到站模型，动态设施分布评价指标提升4.31%。其中，旅客到达站点的平均路径长度减少0.35%，旅客可接受距离总超出量减少6.26%，站点服务容量总超出量减少4.13%。旅客到站时间概率模型能够充分考虑到站时间不确定性，并基于旅客实际到站时间更好地优化设施布局。基于旅客准时性概率函数的城市值机移动服务站点动态分布模型具有可移植性，可应用于物流服务的动态选址等问题。
- 智能交通 /
- 城市候机楼 /
- 城市值机移动服务站点 /
- 涟漪扩散算法 /
- 遗传算法 /
- 正态分布 /
- 到站时间概率
Abstract: The existing distribution model for urban mobile stations (UMS) has not considered the uncertainty in the arrival times of passengers to the service stations, resulting in discrepancy between the optimization outcomes and practical scenarios. This discrepancy can cause the inability to provide services for early-arrival and delayed passengers. To address the detrimental impact of the time uncertainty on optimization solutions, A dynamic facility-distribution model based on the probability function of passengers' arrival punctuality is proposed. In response to the layout optimization problem of UMS, a comprehensive mathematical model and evaluation indexes for the optimization of dynamic facility distribution are proposed. A punctuality probability function with a normal distribution form is introduced to estimate the difference between passengers' actual and declared arrival times. Based on the location distribution of passengers during different service periods, the ripple-spreading algorithm and genetic algorithm are adopted to optimize the positions of service stations and to compute the optimal paths between passengers and stations. Finally, based on empirical data on the road network and passengers' distribution in Tianjin, simulation experiments are conducted to compare the dynamic facility distribution models considering passengers' punctual arrival and passengers' arrival with probabilities. The results indicate that the optimization model considering the probability of passengers' arrival time outperform those of the model considering the passengers' punctual arrival, with a 4.31% enhancement in the evaluation indexes of the dynamic facility distribution model. Specifically, the average path length of passengers to arrival stations decreases by 0.35%, the total excess distance beyond acceptable distances for passengers decreases by 6.26%, and the total excess capacity beyond stations' service capacity decreases by 4.13%. Therefore, the proposed model effectively considers the uncertainty in arrival times and optimizes facility layout based on passengers' actual arrival times more efficiently. In practice, the proposed model has the feature of high portability and can be applied to many other dynamic problems, such as dynamic location choice of logistics services.
- intelligent transportation /
- city air terminal /
- urban mobile stations /
- ripple-spreading algorithm /
- genetic algorithm /
- normal distribution /
- probability of arrival time

HTML全文

图 1 城市值机移动服务站点（UMS）示意图

Figure 1. Schematic diagram of urban mobile station mode(UMS)

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图 2 UMS系统运行流程图（以07：00—09：00为例）

Figure 2. UMS system operation flow chart(take 07:00—09:00 as a case study)

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图 3 基于正态分布的旅客到站时间概率密度函数

Figure 3. Probability density function of passenger arrival time based on normal distribution

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图 4 4组旅客到站时间概率密度分布示意图

Figure 4. Schematic diagram of probability density distribution of arrival time of four groups of passengers

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图 5 旅客准时到站模型3个时段路网权重示意图

Figure 5. Schematic diagram of road network weight in three periods of passenger punctual arrival model

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图 6 旅客到站时间概率模型3个时段路网权重示意图

Figure 6. Schematic diagram of road network weight in three periods of passenger arrival time probability model

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图 7 RSA的路径优化过程

Figure 7. Path Optimization Process of RSA

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图 8 旅客准时到站模型05：00—11：00等3个时段位置信息图

Figure 8. Location information diagram of 05:00—11:00 position information diagram for three time periods

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图 9 旅客到站时间概率模型05：00—11：00共3个时段位置信息图

Figure 9. Passenger arrival time probability model 05:00—11:00 position information diagram for three time periods

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表 1 旅客准时到站模型100次测试结果

Table 1. 100 test results of passenger punctual arrival model

时段	G	G₁/km	G₂/km	G₃/（人·次）
05：00—07：00	20.42	3.32	11.7	5.4
07：00—09：00	130	3.16	20.67	106.17
09：00—11：00	104.31	3.04	10.4	90.87
11：00—13：00	80.42	3.22	12.6	64.6
13：00—15：00	47.33	3.14	7.26	36.93
15：00—17：00	51.96	3.19	12.67	36.1
17：00—19：00	66.93	2.73	9.77	54.43
19：00—21：00	6.05	2.92	3.13	0
平均	63.43	3.09	11.02	49.31

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表 2 旅客到站时间概率模型100次测试结果

Table 2. 100 test results of passenger arrival time probability model

时段	G	G₁/km	G₂/km	G₃/（人·次）
05：00—07：00	17.51	3.28	9.53	4.7
07：00—09：00	120.67	3.13	17.27	100.27
09：00—11：00	103.14	3.07	12.07	88
11：00—13：00	78.26	3.19	12.6	62.47
13：00—15：00	48.39	3.09	6.77	38.53
15：00—17：00	50.86	3.03	11.9	35.93
17：00—19：00	60.7	2.84	9.63	48.23
19：00—21：00	5.91	3.01	2.9	0
平均	60.69	3.08	10.33	47.27

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表 3 动态设施分布模型评价指标有优化率

Table 3. Optimization rate of evaluation index of dynamic facility distribution model

类型	优化率/%
类型	G	G₁	G₂	G₃
动态设施分布模型评价指标	4.31	0.35	6.26	4.13

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参考文献(22)

[1]	林智杰. 城市候机楼. 机场"圈地运动"进行时[J]. 空运商务, 2013(1): 46-48. doi: 10.3969/j.issn.1671-3095.2013.01.012 LIN Z J. City terminal: when the"enclosure campaign"is in progress at the airport[J]. Air Transport Business, 2013(1): 46-48. (in Chinese) doi: 10.3969/j.issn.1671-3095.2013.01.012
[2]	个人图书馆. 中国旅客最少的10座机场, 每天不足10人乘坐飞机[OL]. (2019-11-12). [2023-02-23]. http://www.360doc.com/content/19/1112/12/37369996_872603516.shtml Personal Library. There are 10 airports with the least passengers in China, and less than 10 people take the plane every day[OL] (2019-11-12). [2023-02-23]. http://www.360doc.com/content/19/1112/12/37369996_872603516.shtml
[3]	ZHOU H, HU X B, ZHOU J, et al. A new city air terminal service mode: urban mobile station for luggage check-in service and evolutionary approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 1-17.
[4]	TAO Y, HUANG M H, CHEN Y B, et al. Overview of research on optimal layout of electric vehicle charging facilities[J] Journal of Central South University, 2021, 28(10): 3268-3278. doi: 10.1007/s11771-021-4842-3
[5]	路尧. 大型机场旅客城市空间分布预测与交通方式选择行为研究[D]. 北京: 北京工业大学, 2019. LU Y. Prediction of spatial distribution of large airport passenger cities and research on traffic mode choice behavior[D]. Beijing: Beijing University of Technology, 2019. (in Chinese)
[6]	赵阳. 早高峰共享单车需求预测与车辆资源优化配置研究[D]. 西安: 长安大学, 2021. ZHAO Y. Research on demand forecast and optimal allocation of vehicle resources for sharing bicycles in the morning peak[D]. Xi'an: Chang'an University, 2021. (in Chinese)
[7]	周秘. 需求信息未知情况下的共享单车资源动态分配策略研究[D]. 广州: 暨南大学, 2019. ZHOU M. Research on dynamic allocation strategy of shared bicycle resources with unknown demand information[D]. Guangzhou: Jinan University, 2019. (in Chinese)
[8]	刘一麟. 基于合作覆盖的物流服务设施动态选址优化研究[D]. 南昌: 江西财经大学, 2021. LIU Y L Research on dynamic location optimization of logistics service facilities based on cooperative coverage[D]. Nanchang: Jiangxi University of Finance and Economics, 2021. (in Chinese)
[9]	周鑫鑫. 城市服务设施空间动态配置模型与量子优化方法[D]. 南京: 南京师范大学, 2021. ZHOU X X. Spatial dynamic allocation model and quantum optimization method of urban service facilities[D]. Nanjing: Nanjing Normal University, 2021. (in Chinese)
[10]	李晓晨. 考虑动态客流的地铁车站售检票设施系统优化配置[D]. 成都: 西南交通大学, 2018. LI X C. Optimal configuration of fare collection facilities system in metro stations considering dynamic passenger flow[D]. Chengdu: Southwest Jiaotong University, 2018. (in Chinese)
[11]	LI S Y, HUANG Y X, MASON S J. A multi-period optimization model for the deployment of public electric vehicle charging stations on network[J]. Transportation Research Part C: Emerging Technologies, 2016, 65(4): 128-143.
[12]	BRIMBERG J, HANSEN P, MLADENOVIC N. et al. Improvements and comparison of heuristics for solving the uncapacitated multisource weber problem[J]. Operations Research. 2000, 48(3): 444-460. doi: 10.1287/opre.48.3.444.12431
[13]	任祎程. 考虑信号优先控制的快速公交行车调度研究[D]. 上海: 上海理工大学, 2019. REN Y C. Research on BRT traffic scheduling considering signal priority control[D]. Shanghai: Shanghai University of Technology, 2019. (in Chinese)
[14]	ABDUL S, MOGANRAJ S, MOHD M. Punctuality of intercity trains and passengers' perspective towards arrival time delay[J]. Research Journal of Applied Sciences Engineering & Technology, 2013, 5(9): 1998-2002.
[15]	陆奇志. 公共交通运行时间可靠性评价方法研究[D]. 乌鲁木齐: 新疆农业大学, 2006. LU Q Z. Research on evaluation method of public transport running time reliability[D]. Urumqi: Xinjiang Agricultural University, 2006. (in Chinese)
[16]	胡小兵, 张雪梅, 周航, 等. 市区行李值机服务移动站点优化方法[J]. 交通信息与安全, 2022, 40(3): 136-145. doi: 10.3963/j.jssn.1674-4861.2022.03.014 HU X B, ZHANG X M, ZHOU H, et al. A method for improved air luggage check-in service based on optimized urban mobile stations[J]. Journal of Transport Information and Safety, 2022, 40(3): 136-145. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.03.014
[17]	SNIEDOVICH M. Dijkstra's algorithm revisited: the dynamic programming connexion[J]. Control and Cybernetics, 2006, 35(3): 599-620.
[18]	VERBAS Ö, AULD J, LEY H, et al. Time-dependent intermodal A* algorithm: methodology and implementation on a large-scale network[J]. Transportation Research Record, 2018, 2672(47): 219-230. doi: 10.1177/0361198118796402
[19]	HU X B, WANG M, LEESON M S, et al. Deterministic agent-based path optimization method by mimicking the spreading of ripples[J]. Evolutionary Computation, 2016, 24 (2): 319-346. doi: 10.1162/EVCO_a_00156
[20]	胡小兵, 陈树念, 张盈斐, 等. 求解多目标路径优化问题的涟漪扩散算法[J]. 计算机工程与应用, 2021, 57(23): 81-90. HU X B, CHEN S N, ZHANG Y F, et al. Ripple diffusion algorithm for multi-objective path optimization[J]. Computer Engineering and Application, 2021, 57(23): 81-90. (in Chinese)
[21]	杨涵晟. 基于自适应遗传算法的综合能源系统多目标优化研究[D]. 北京: 华北电力大学, 2020. YANG H S. Research on multi-objective optimization of integrated energy system based on adaptive genetic algorithm[D]. Beijing: North China Electric Power University, 2020. (in Chinese)
[22]	杨剑峰. 蚁群算法及其应用研究[D]. 杭州: 浙江大学, 2007. YANG J F. Ant colony algorithm and its application research[D]. Hangzhou: Zhejiang University, 2007. (in Chinese)