他们何时到来?了解并预测比赛日抵达巴塞罗那足球俱乐部球场的时间安排

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Feliu Serra-Burriel, Pedro Delicado, Fernando M. Cucchietti, Eduardo Graells-Garrido, Alex Gil, Imanol Eguskiza
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引用次数: 0

摘要

巴塞罗那足球俱乐部运营着欧洲最大的体育场(可容纳近十万人),并管理着经常性的体育赛事。这些赛事受到多种条件(时间、星期、天气、对手)的影响,并对城市动态产生影响,例如对公共交通和商店等相关服务的高峰需求。我们对体育场的观众入口进行细粒度研究,按观众类型和入口进行分类,以深入了解并预测未来比赛的到达行为,这对企业的组织绩效和生产率有直接影响。我们可以在开球前 72 小时预测入场观众的到达时间,通过预测潜在的聚集和观众行为来促进运营和组织决策。此外,我们还能识别不同类型游客的模式,了解相关因素对他们的影响。这些发现会直接影响商业和企业利益,并能改变运营物流、场地管理和安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days

When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days

Futbol Club Barcelona operates the largest stadium in Europe (with a seating capacity of almost one hundred thousand people) and manages recurring sports events. These are influenced by multiple conditions (time and day of the week, weather, adversary) and affect city dynamics—e.g., peak demand for related services like public transport and stores. We study fine grain audience entrances at the stadium segregated by visitor type and gate to gain insights and predict the arrival behavior of future games, with a direct impact on the organizational performance and productivity of the business. We can forecast the timeline of arrivals at gate level 72 h prior to kickoff, facilitating operational and organizational decision-making by anticipating potential agglomerations and audience behavior. Furthermore, we can identify patterns for different types of visitors and understand how relevant factors affect them. These findings directly impact commercial and business interests and can alter operational logistics, venue management, and safety.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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