{"title":"利用有序对数模型分析影响休闲活动时间的因素","authors":"","doi":"10.1080/19427867.2023.2266189","DOIUrl":null,"url":null,"abstract":"<div><div>The objective of the current study is to analyze the time spent on leisure activities in Budapest, considering five influencing factors. Data were collected from Google Popular Time (GPT) via location services using Python, resulting in a dataset of 1336 entries from July 2022. The analysis utilized the Ordered Logit Model (OLM). According to the outcomes, about 17% of visitors allocate significant time to leisure, while half spend relatively less. Leisure time is positively influenced by ratings and location but negatively affected by security levels. This study demonstrates the utility of GPT data for understanding individual behavior, offering valuable insights for decision-makers, tourism managers, and planners. Additionally, it sheds light on leisure-related traffic patterns, aiding in the identification of popular locations and peak time periods for leisure activities. This information can indirectly impact traffic flow in specific areas.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 9","pages":"Pages 1081-1090"},"PeriodicalIF":3.3000,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the factors affecting the time spent on leisure activities by using an ordered logit model\",\"authors\":\"\",\"doi\":\"10.1080/19427867.2023.2266189\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The objective of the current study is to analyze the time spent on leisure activities in Budapest, considering five influencing factors. Data were collected from Google Popular Time (GPT) via location services using Python, resulting in a dataset of 1336 entries from July 2022. The analysis utilized the Ordered Logit Model (OLM). According to the outcomes, about 17% of visitors allocate significant time to leisure, while half spend relatively less. Leisure time is positively influenced by ratings and location but negatively affected by security levels. This study demonstrates the utility of GPT data for understanding individual behavior, offering valuable insights for decision-makers, tourism managers, and planners. Additionally, it sheds light on leisure-related traffic patterns, aiding in the identification of popular locations and peak time periods for leisure activities. This information can indirectly impact traffic flow in specific areas.</div></div>\",\"PeriodicalId\":48974,\"journal\":{\"name\":\"Transportation Letters-The International Journal of Transportation Research\",\"volume\":\"16 9\",\"pages\":\"Pages 1081-1090\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Letters-The International Journal of Transportation Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/org/science/article/pii/S1942786723002461\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Letters-The International Journal of Transportation Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1942786723002461","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Analysis of the factors affecting the time spent on leisure activities by using an ordered logit model
The objective of the current study is to analyze the time spent on leisure activities in Budapest, considering five influencing factors. Data were collected from Google Popular Time (GPT) via location services using Python, resulting in a dataset of 1336 entries from July 2022. The analysis utilized the Ordered Logit Model (OLM). According to the outcomes, about 17% of visitors allocate significant time to leisure, while half spend relatively less. Leisure time is positively influenced by ratings and location but negatively affected by security levels. This study demonstrates the utility of GPT data for understanding individual behavior, offering valuable insights for decision-makers, tourism managers, and planners. Additionally, it sheds light on leisure-related traffic patterns, aiding in the identification of popular locations and peak time periods for leisure activities. This information can indirectly impact traffic flow in specific areas.
期刊介绍：
Transportation Letters: The International Journal of Transportation Research is a quarterly journal that publishes high-quality peer-reviewed and mini-review papers as well as technical notes and book reviews on the state-of-the-art in transportation research.
The focus of Transportation Letters is on analytical and empirical findings, methodological papers, and theoretical and conceptual insights across all areas of research. Review resource papers that merge descriptions of the state-of-the-art with innovative and new methodological, theoretical, and conceptual insights spanning all areas of transportation research are invited and of particular interest.
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