Forecasting daily foot traffic in recreational trails using machine learning

IF 3.6 3区 管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Kyle Madden , Goda Lukoseviciute , Elaine Ramsey , Thomas Panagopoulos , Joan Condell
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引用次数: 0

Abstract

This paper discusses weather factors that may affect the level of visitation at recreational walking trails and provides insights into how specific factors (wind, rain etc.) can influence visitation. The quantity of visitors received affects trail management strategies, as there are often damaging effects attributed to the excessive visitation of natural areas. Therefore, accurate forecasting can inform trail management plans. Trail partners have expressed a demand for a system that can deliver qualitative insights to inform trail management while also providing accurate visitor forecasts. This study applied the approach, utilising Machine Learning and historic footfall data from electronic people-counting sensors alongside weather data; our model is a first in the introduction of Tourism Climate Indexes into forecasting models. Factors influencing visitation levels at three walking trails across the Atlantic Area of Europe were discussed. The results highlight that the model predicts trail use with satisfactory accuracy to inform adaptive management frameworks measuring visitor experience indicators.

Management implications

  • Environmental monitoring can gather insights into the situational factors that affect visitation levels on their trails, or if there are other contributing factors aside from weather data that could be investigated.

  • Trail-related recreation operators can formulate and develop strategies and plans to prevent the occurrence of tourist crowding or congestion in periods of high demand and increase trail visitor arrivals in low demand.

  • Trail managers can develop new service that will attract visitors under different weather conditions such as shelters, indoor museums, tents that hosts visitors during rainy or sunny days.

  • Trail managers can prepare for a lower trail visitation demand through marketing and offering alternative recreational activities.

使用机器学习预测休闲步道的每日行人流量
本文讨论了可能影响休闲步道参观水平的天气因素,并深入了解了特定因素(风、雨等)如何影响参观。接待的游客数量影响了步道管理策略,因为对自然区域的过度访问往往会产生破坏性影响。因此,准确的预测可以为跟踪管理计划提供信息。步道合作伙伴表达了对一个系统的需求,该系统可以提供定性见解,为步道管理提供信息,同时提供准确的游客预测。这项研究应用了这种方法,利用了机器学习和电子人数传感器的历史客流量数据以及天气数据;我们的模型是首次将旅游气候指数引入预测模型。讨论了影响欧洲大西洋地区三条步行道访问量的因素。结果强调,该模型以令人满意的准确性预测了步道使用情况,为衡量游客体验指标的自适应管理框架提供了信息。管理影响•环境监测可以深入了解影响其步道访问水平的情境因素,或者除了天气数据之外,是否还有其他因素可以调查。•与步道相关的娱乐运营商可以制定和制定策略和计划,以防止游客在高需求时期出现拥挤或拥堵,并在低需求时期增加步道游客人数。•步道管理人员可以开发新的服务,在不同的天气条件下吸引游客,如避难所、室内博物馆、雨天或晴天接待游客的帐篷。•步道管理员可以通过营销和提供替代娱乐活动来为较低的步道参观需求做好准备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.70
自引率
5.30%
发文量
84
期刊介绍: Journal of Outdoor Recreation and Tourism offers a dedicated outlet for research relevant to social sciences and natural resources. The journal publishes peer reviewed original research on all aspects of outdoor recreation planning and management, covering the entire spectrum of settings from wilderness to urban outdoor recreation opportunities. It also focuses on new products and findings in nature based tourism and park management. JORT is an interdisciplinary and transdisciplinary journal, articles may focus on any aspect of theory, method, or concept of outdoor recreation research, planning or management, and interdisciplinary work is especially welcome, and may be of a theoretical and/or a case study nature. Depending on the topic of investigation, articles may be positioned within one academic discipline, or draw from several disciplines in an integrative manner, with overarching relevance to social sciences and natural resources. JORT is international in scope and attracts scholars from all reaches of the world to facilitate the exchange of ideas. As such, the journal enhances understanding of scientific knowledge, empirical results, and practitioners'' needs. Therefore in JORT each article is accompanied by an executive summary, written by the editors or authors, highlighting the planning and management relevant aspects of the article.
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