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基于潜在类别条件Logit模型的机舱座位选择行为 被引量:2

Behavior of Cabin Seat Selection Based on Latent Class Conditional Logit Model
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摘要 为向旅客推送精细化座位推荐服务,通过潜在类别条件Logit模型探究了旅客座位偏好及座位效用差异。首先,运用解释结构模型筛选出影响旅客选座的关键因素;其次,对旅客及其选择偏好进行行为-意向调查(revealed preference-stated preference,RP-SP),运用潜在类别条件Logit模型划分旅客类别,进一步探究不同因素对旅客类别的影响程度;最后构建不同类别旅客机舱座位效用函数,根据效用最大化原则判断旅客选择行为。结果表明,旅客在机舱座位选择行为上存在异质性,旅客被划分为3个类别:类别1占比38.8%,倾向于选择第一排或紧急通道排的过道位置;类别2占比39.8%,倾向于选择靠窗座位;类别3占比21.4%,倾向于过道位置。研究将为航空公司进行座位管理及制订座位附加服务费提供理论及决策支持。 In order to push refined seat recommendation service to passengers,the latent class conditional Logit model was used to investigate the difference in passenger seat preference and seat utility.Firstly,interpretative structural modeling was used to screen out the key factors affecting passenger seat selection.Secondly,a revealed preference-stated preference(RP-SP)survey was carried out on passengers and their preferences,latent class conditional Logit model was used to classify the passenger categories,and the influence of different factors on passenger categories was further explored.Finally,the cabin seats utility functions for different class passenger were constructed to judge the passenger's seat selection behavior according to the utility-maximizing rule.The results show that passengers have heterogeneity in their cabin seat selection behaviors.Passengers are divided into three categories:class 1 accounting for 38.8%tend to choose the aisle seats of the first row or emergency aisle row,class 2 accounting for 39.8%tend to choose window seats,class 3 accounting for 21.4%tend to choose aisle seats.The research will provide theoretical and decision support for airlines'seat management and the formulation of additional seat service fees.
作者 任新惠 潘娜 REN Xin-hui;PAN Na(College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China)
出处 《科学技术与工程》 北大核心 2021年第29期12772-12780,共9页 Science Technology and Engineering
基金 国家自然科学基金(U1433111)。
关键词 航空运输 座位选择 潜在类别条件Logit 座位效用 解释结构模型 air transportation seat selection latent class conditional Logit seat utility interpretative structural modeling
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  • 1贾洪飞,龚勃文,宗芳.交通方式选择的非集计模型及其应用[J].吉林大学学报(工学版),2007,37(6):1288-1293. 被引量:70
  • 2Lee B K, Lee W N. The Effect of Information Overload on Consumer Choice Quality in an Online Environment [J]. Psychology & Marketing, 2004,21 (3): 159-183.
  • 3Park Y J, Chang K N. Individual and Group Behavior-Based Consumer Profile Model for Personalized Product Recommendation[J]. Expert Systems with Applications, 2009, 36(2):1932N1939.
  • 4Pazzani M J, Billsus D. Content-Based Recommendation Systems[M]. New York: Springer Berlin Heidelberg Press, 2007:325-341.
  • 5Schafer J B, Frankowski D, Herlocker J, et al. Collaborative Filtering Recommender Systems[M]. New York: Springer Berlin Heidelberg Press, 2007:291-324.
  • 6Burke R. Knowledge-Based Recommender Systems[J]. Encyclopedia of Library and Information Systems, 2000, 69 (32): 175-186.
  • 7Sarwar B, Karypis G, Konstan J, et al. hem-based Collaborative Filtering Recommendation Algorithms[C]. Proceedings of the 10th In- ternational Conference on World Wide Web, Hong Kong, 2001. New York: ACM, 2001:285-295.
  • 8Mooney R J, Roy L. Content-Based Book Recommending Using Learning for Text Categorization[C].Proceedings of the fifth ACM Con- ference on Digital Libraries, San Antonio, 2000. New York: ACM, 2000:195-204.
  • 9Pazzani M J. A Framework for Collaborative, Content-Based and Demographic Filtering[J]. Artificial Intelligence Review,1999,13 (5- 6): 393-408.
  • 10郭晓昊.多家航空公司开收“伸腿费”你掏吗[EB/OL].(2016—01—03)[2015—10—17]http://gzdafly.dayoo.com/html/2015-lO/17/content_3030816.htm.

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