[1]毛珩懿,王雨雪,张 珣,等.多目标约束下的数字标牌优化选址——以北京市六环路以内区域为例[J].南京师大学报(自然科学版),2024,(04):11-20.[doi:10.3969/j.issn.1001-4616.2024.04.002]
　Mao Hengyi,Wang Yuxue,Zhang Xun,et al.Optimal Location of Digital Signage Under Multi-Objective Constraints: a Case Study of the Area Within the Sixth Ring Road of Beijing[J].Journal of Nanjing Normal University(Natural Science Edition),2024,(04):11-20.[doi:10.3969/j.issn.1001-4616.2024.04.002]
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多目标约束下的数字标牌优化选址——以北京市六环路以内区域为例()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
期数:
2024年04期
页码:
11-20
栏目:
空间数据智能研究
出版日期:
2024-12-15

文章信息/Info

Title:
Optimal Location of Digital Signage Under Multi-Objective Constraints: a Case Study of the Area Within the Sixth Ring Road of Beijing
文章编号:
1001-4616(2024)04-0011-10
作者:
毛珩懿1; 王雨雪2; 张 珣1; 3; 张 鑫1
(1.北京工商大学计算机与人工智能学院,食品安全大数据技术北京市重点实验室,北京 100048)
(2.中国农业大学土地科学与技术学院,北京 100193)
(3.新疆和田学院,新疆 和田 848000)
Author(s):
Mao Hengyi1; Wang Yuxue2; Zhang Xun1; 3; Zhang Xin1
(1.School of Computer and Artificial Intelligence,Beijing Technology and Business University,Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing 100048,China)
(2.College of Land Science and Technology,China Agricultural University,Beijing 100193,China)
(3.Xinjiang Hetian College,Hotan 848000,China)
关键词:
数字标牌; 广告投放; 最大覆盖模型; 优化选址; 遗传算法
Keywords:
digital signage; advertising placement; maximum coverage model; optimal location; genetic algorithm
分类号:
TP301
DOI:
10.3969/j.issn.1001-4616.2024.04.002
文献标志码:
A
摘要:
针对数字标牌优化选址研究欠缺考虑人文地理环境、布设成本和用户需求等约束,将数字标牌影响因子数据多尺度空间化,统一多源数据的空间基准,构造数字标牌优化选址的多源影响因子和多尺度建模因子序列. 在此基础上构建数字标牌多重约束最大覆盖模型,并采用改进的遗传算法求解模型,提出优化选址方案. 以北京市六环路以内区域为例,对选址结果空间分布进行分析与总结. 以覆盖情况和运行时间作为评价指标,由所提方法得到的最大受众覆盖率约为0.93、最大覆盖受众数量为185 051. 结果表明该模型能有效耦合多源异构数据优化选址,算法效果在多个指标下优于传统遗传算法和Gurobi软件,有效提升了数字标牌投放的精准性及效率,为实际公共基础设施布局优化提供了参考.
Abstract:
The research on the optimal location of digital signage lacks consideration of constraints related to human geography,deployment costs,and user needs. This paper spatializes the factors influencing digital signage across multiple scales,unifies spatial references of multi-source data,and constructs a sequence of multi-source influencing factors and multi-scale modeling factors for the optimal location of digital signage. Based on this,a multi-constraint maximum coverage model for digital signage is constructed,and an improved genetic algorithm is used to solve the model and to propose optimal location schemes. Taking the area within the sixth ring road of Beijing as an example,the spatial distribution of optimal location is analyzed and summarized. Coverage and runtime are used as evaluation indicators,the maximum audience coverage rate is approximately 0.93 and the maximum coverage number is 185 051 by the proposed method. The results indicate that the model effectively couples multi-source heterogeneous data to optimal location,the algorithm outperforms traditional genetic algorithms and Gurobi software across multiple indicators,and the accuracy and efficiency of digital signage deployment are effectively improved,which provides a reference for the optimal layout of practical public infrastructure.

参考文献/References:

[1]HOSSAIN M A,ISLAM A,LE N T,et al. Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication[J]. International journal of distributed sensor networks,2016,12(7):1-14.
[2]DAVIES N,CLINCH S,ALT F. Pervasive displays:understanding the future of digital signage[J]. Synthesis lectures on mobile & pervasive computing,2014,8(1):1-128.
[3]XIE X,ZHANG X,FU J,et al. Location recommendation of digital signage based on multi-source information fusion[J]. Sustainability,2018,10(7):2357.
[4]BAUER C,GARAUS M,STRAUSS C,et al. Research directions for digital signage systems in retail[J]. Procedia computer science,2018,141:503-506.
[5]WILLIS K,AURIGI A. Digital and smart cities[M]. London:Routledge,2017:64-85.
[6]郑杰. A公司数字标牌产品市场营销策略研究[D]. 昆明:云南大学,2014.
[7]IJAZ M F,TAO W,RHEE J,et al. Efficient digital signage-based online store layout:an experimental study[J]. Sustainability,2016,8(6):511.
[8]SHEPPARD D,FELKER N,SCHMALZEL J. Development of voice commands in digital signage for improved indoor navigation using Google Assistant SDK[C]//2019 IEEE Sensors Applications Symposium(SAS). Sophia Antipolis,France:IEEE,2019:1-5.
[9]ZHANG X,MA G,JIANG L,et al. Analysis of spatial characteristics of digital signage in Beijing with multi-source data[J]. ISPRS international journal of geo-information,2019,8(5):207.
[10]蒲金芳,刘沙沙,温良友,等. 江南丘陵区农村居民点多功能空间特征与分区优化[J]. 北京大学学报(自然科学版),2023,59(2):271-280.
[11]王中,曹凯. 耦合改进图注意力网络与深度强化学习的公共服务设施智能化选址方法[J]. 地球信息科学学报,2024,26(11):2452-2464.
[12]周林锦,杨君子,孙朝云,等. 基于深度学习的宏观风资源评估与风电场选址[J]. 农业与技术,2024,44(6):86-90.
[13]MINH D,WANG H X,Li Y F,et al. Explainable artificial intelligence:a comprehensive review[J]. Artificial intelligence review,2022,55(5):3503-3568.
[14]SHAN Y,ZHANG R. Study on the allocation of a rescue base in the Arctic[J]. Symmetry,2019,11(9):1073.
[15]王万良,徐昶,赵燕伟,等. 基于超启发式算法的选址-路径问题研究[J]. 浙江工业大学学报,2019,47(6):604-610.
[16]栾兆亮,涂伟,沈少青,等. 基于空间启发式的城市地铁线路选址优化方法[J]. 地理与地理信息科学,2019,35(4):64-69.
[17]WANG X,LIU Z,Li X. Optimal delivery route planning for a fleet of heterogeneous drones:a rescheduling-based genetic algorithm approach[J]. Computers & industrial engineering,2023,179:109179.
[18]ALHIJAWI B,AWAJAN A. Genetic algorithms:theory,genetic operators,solutions,and applications[J]. Evolutionary intelligence,2024,17:1245-1256.
[19]GEN M,LIN L. Genetic algorithms and their applications[M]//Springer Handbook of Engineering Statistics. London:Springer,2023:635-674.
[20]ZHANG X,YAO J,SILA-NOWICKA K,et al. Geographic concentration of industries in Jiangsu,China:a spatial point pattern analysis using micro-geographic data[J]. The annals of regional science,2021,66(2):439-461.
[21]CHE L,WANG H Q,FEI T,et al. Kriging interpolation method optimized by multi-scale least squares support vector machine[J]. Geo-information science,2017,19(8):1001-1010.
[22]KWAN M. The uncertain geographic context problem[J]. Annals of the association of American geographers,2012,102(5):958-968.
[23]左岍,周勇,李晴,等. 基于最优尺度的鄂西南山区景观生态风险时空变化特征[J]. 生态学杂志,2023,42(5):1186-1196.
[24]HOUSTON D. Implications of the modifiable areal unit problem for assessing built environment correlates of moderate and vigorous physical activity[J]. Applied geography,2014,50:40-47.
[25]陈得心. 考虑可塑性面积单元问题的出租车出行需求影响因素研究[D]. 成都:西南交通大学,2021.
[26]CROSS P C,CAILLAUD D,HEISEY D M. Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale:infectious disease as an example[J]. Landscape ecology,2013,28(2):247-257.
[27]SIVAKUMAR A,BHAT C R. Comprehensive,unified framework for analyzing spatial location choice[J]. Transportation research record,2007,2003(1):103-111.
[28]YU B,WANG Z,MU H,et al. Identification of urban functional regions based on floating car track data and POI data[J]. Sustainability,2019,11(23):6541.
[29]QIAN J,LIU Z,DU Y,et al. Quantify city-level dynamic functions across China using social media and POIs data[J]. Computers,environment and urban systems,2021,85:101552.
[30]北京市统计局,北京市第七次全国人口普查领导小组办公室. 北京市第七次全国人口普查公报(第三号)[N]. 北京日报,2021-05-20(004).
[31]LONDE M A,PESSOA L S,ANDRADE C E,et al. Biased random-key genetic algorithms:a review[J]. European journal of operational research,2025,321(1):1-22.

备注/Memo

备注/Memo:
收稿日期:2024-05-27.
基金项目:国家自然科学基金项目(42101470)、北京市社会科学基金项目(24YTC038).
通讯作者:张珣,博士,教授,博士生导师,研究方向:地理人工智能. E-mail:zhangxun@btbu.edu.cn
更新日期/Last Update: 2024-12-15