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公共公园游憩模式的创新研究.pdf

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1、Innovative Modeling of Recreation Visits to Public Parklands Session C2:Big Data for Travel AnalysisPresented by:Mark BradleyJune 5,2023Innovative Modeling of Recreation Trips to Public ParklandsPresentation TopicsGLACIER NATIONAL PARKMONTANA2Project Motivation3 Project Challenges4Model Data5-6Model

2、s Described7-13Model Application14Reflections 15Project MotivationIn some areas of the United States,visits to national,state,and regional parklands generate a great deal of local traffic and vehicle miles traveled(VMT).While some regional and statewide travel forecasting models include a“recreation

3、”purpose,travel for outdoor recreation to and in major parklands has unique characteristics that are not included in existing models.This project provides a new model framework and initial models for potential use by state and regional planning agencies(as well as park planning agencies).Project Cha

4、llengesDifferent Variable NeedsThe type of variables in recreational travel models are different from traditional long-distance models.The variables are about the type of land characteristics:elevation,climate,coastal(or not),and park-specific attributes.Key ChallengesThe types of people attracted d

5、iffer from urban recreation tripsa range of incomes due to the ability to camp,and a range of group types(e.g.,families,retired,digital nomads)A range of trip typesday trips or overnight trips from nearby residents,and longer vacation trips from across the country(and beyond)Creating a generic/gener

6、alized model that can be applied to any major parkland destination in the country4Project Modeling:Data SourcesSome data exist in the form of visitor surveys and counts,but no single source of data exists that is comprehensive and consistent across various types of outdoor destinations.Passively col

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本文介绍了由Mark Bradley在2023年6月5日提出的关于公共公园土地休闲访问的创新建模研究。研究动机在于美国某些区域的公园访问产生了大量地方交通和车辆行驶里程(VMT),现行的区域和州级旅行预测模型未能充分反映户外休闲访问的独特特性。项目挑战包括创建适合不同土地特征和吸引不同类型访客的模型。数据来源主要依赖2019年对多种选择的公园土地在选定时间内的访问数据,以及人口普查、就业、气候、地形等辅助数据。模型结构包括访问生成模型、访问方式和机场模型、访客家庭位置模型等。研究发现,例如,大型国家公园能吸引更远距离的游客,高收入区域产生更多访问量。项目通过使用被动收集的大数据,如位置基础服务(LBS)数据,来估计模型,尽管数据质量在公园地区和访问期间可能存在问题。模型应用允许预测访问时间和持续时间,以及当地行程和车辆行驶里程,已有一些与游客调查数据对比的验证。该研究为州和区域规划机构提供了新的模型框架和初步模型。
"模型如何预测公园访问量?" "大数据如何影响公园旅游业?" "公园访问与地区经济有何关联?"
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