[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127930-en":3,"doc-seo-127930-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127930,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Forecasting Daily Foot Traffic in Recreational Trails Using Machine Learning - Accepted Manuscript Abstract","This paper examines how weather conditions shape visitation levels on recreational walking trails and explains how specific factors such as wind and rain can drive changes in use. Because visitor volume strongly affects trail management—often through environmental degradation—reliable forecasting supports planning and mitigation. Using machine learning with historic footfall from electronic people-counting sensors and weather data, the study introduces an approach linking Tourism Climate Indexes to forecasting, and assesses three Atlantic-area trails.","Forecasting daily foot traffic in recreational trails using machine learning  \nMadden, K. , Lukoseviciute, G. , Ramsey, E. , Panagopoulos, T. , & Condell, J. (2023) . Forecasting daily foot traffic in recreational trails using machine learning. Journal of Outdoor Recreation and Tourism, 44(B), 1-14. Article 100701. [https://doi.org/10.1016/j.jort.2023.100701](https://doi.org/10.1016/j.jort.2023.100701)  \nLink to publication record in Ulster University Research Portal  \nPublished in:  \nJournal of Outdoor Recreation and Tourism  \nPublication Status:  \nPublished (in print/issue): 15/12/2023  \nDOI:  \n10.1016/j.jort.2023.100701  \nDocument Version  \nAuthor Accepted version  \nDocument Licence:  \nCC BY-NC-ND  \nFor Author Accepted Manuscripts (AAM) published under Ulster University's Rights Retention Policy for Scholarly Works (RRPSW)  \nWhen citing an AAM published under Ulster University's RRPSW please use the following citation structure:  \nAuthor, A. A. (Year) . Title of article. Journal Name,[Accepted Author Manuscript] . PURE Portal URL. Licensed under CC BY 4.0.  \nGeneral rights  \nThe copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence.  \nUnless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) .  \nIf the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) .  \nTake down policy  \nThe Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk)  \nDownload date: 05/08/2026  \nForecasting daily foot traffic in recreational trails using Machine Learning  \nKyle Madden 1, Goda Lukoseviciute2, Elaine Ramsey 1, Thomas Panagopoulos2, Joan Condell3  \n1. Dept. of Global Business and Enterprise, Ulster University, Magee Campus, Northland Rd, Londonderry, BT48 7JL, UK  \n2. Research Centre for Tourism, Sustainability and Well-Being (CinTurs), Universidade Do Algarve, Faro, Portugal  \n3. School of Computing, Engineering & Intel. Sys, Ulster University, Northland Rd, Londonderry, BT48 7JL, UK  \nAbstract  \nThis 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.  \nKeywords: random forest; BORUTA; recreational trails; visitor forecast; TCI; trail mana","cbCailfdsPN8DnC9","https://ap.wps.com/l/cbCailfdsPN8DnC9","pdf",823457,2,1,34,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data Sources\n## Machine Learning Approach\n# Results\n# Discussion\n# Conclusion","[{\"question\":\"Which factors does the study identify as influencing recreational trail visitation?\",\"answer\":\"The study focuses on weather factors, emphasizing how elements such as wind and rain affect visitation levels.\"},{\"question\":\"How is machine learning used to forecast daily foot traffic?\",\"answer\":\"The approach combines historic footfall data from electronic people-counting sensors with weather data to train a forecasting model.\"},{\"question\":\"Why are accurate forecasts important for trail management?\",\"answer\":\"Visitor quantities affect trail management because excessive visitation can cause environmental damage and reduce visitor experience quality, so forecasts help inform adaptive management plans.\"}]","Forecasting Daily Foot Traffic in Recreational Trails Using Machine Learning - Accepted Manuscript Abstract | PDF",1785943069,86,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"forecasting-daily-foot-traffic-in-recreational-trails-using-machine-learning-accepted-manuscript-abstract","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/forecasting-daily-foot-traffic-in-recreational-trails-using-machine-learning-accepted-manuscript-abstract/127930/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which factors does the study identify as influencing recreational trail visitation?","Question",{"text":76,"@type":77},"The study focuses on weather factors, emphasizing how elements such as wind and rain affect visitation levels.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is machine learning used to forecast daily foot traffic?",{"text":81,"@type":77},"The approach combines historic footfall data from electronic people-counting sensors with weather data to train a forecasting model.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are accurate forecasts important for trail management?",{"text":85,"@type":77},"Visitor quantities affect trail management because excessive visitation can cause environmental damage and reduce visitor experience quality, so forecasts help inform adaptive management plans.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]