Early warning model for passenger disturbance due to flight delays
Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, manageria...
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| Veröffentlicht in: | PloS one 2020-09, Vol.15 (9), p.e0239141-e0239141 |
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| creator | Gu, Yunyan Yang, Jianhua Wang, Conghui Xie, Guo |
| description | Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers. |
| doi_str_mv | 10.1371/journal.pone.0239141 |
| format | Article |
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This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0239141</identifier><identifier>PMID: 32956383</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Accuracy ; Aircraft safety ; Airline passengers ; Airline scheduling ; Airport terminals ; Airports ; Anger ; Automation ; Aviation ; Back propagation networks ; Behavior ; Biology and Life Sciences ; Civil aviation ; Computer and Information Sciences ; Data collection ; Decision making ; Disturbances ; Engineering and Technology ; Environmental management ; Flight ; Flight behavior ; Methods ; Neural networks ; Passenger satisfaction ; Passengers ; Physical Sciences ; Predictions ; Regression analysis ; Regression models ; Research and Analysis Methods ; Social aspects ; Social Sciences ; Transportation terminals ; Weight analysis</subject><ispartof>PloS one, 2020-09, Vol.15 (9), p.e0239141-e0239141</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Gu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Gu et al 2020 Gu et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c618t-2bf65275893d2f66df690407b4fd3fc1e5842d957968bf276d0df8bf18d084263</cites><orcidid>0000-0001-9584-879X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505425/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505425/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53770,53772,79347,79348</link.rule.ids></links><search><contributor>Zeng, Qiang</contributor><creatorcontrib>Gu, Yunyan</creatorcontrib><creatorcontrib>Yang, Jianhua</creatorcontrib><creatorcontrib>Wang, Conghui</creatorcontrib><creatorcontrib>Xie, Guo</creatorcontrib><title>Early warning model for passenger disturbance due to flight delays</title><title>PloS one</title><description>Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers.</description><subject>Accuracy</subject><subject>Aircraft safety</subject><subject>Airline passengers</subject><subject>Airline scheduling</subject><subject>Airport terminals</subject><subject>Airports</subject><subject>Anger</subject><subject>Automation</subject><subject>Aviation</subject><subject>Back propagation networks</subject><subject>Behavior</subject><subject>Biology and Life Sciences</subject><subject>Civil aviation</subject><subject>Computer and Information Sciences</subject><subject>Data collection</subject><subject>Decision making</subject><subject>Disturbances</subject><subject>Engineering and Technology</subject><subject>Environmental management</subject><subject>Flight</subject><subject>Flight behavior</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Passenger satisfaction</subject><subject>Passengers</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Research and Analysis Methods</subject><subject>Social aspects</subject><subject>Social Sciences</subject><subject>Transportation terminals</subject><subject>Weight 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gu, Yunyan</au><au>Yang, Jianhua</au><au>Wang, Conghui</au><au>Xie, Guo</au><au>Zeng, Qiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Early warning model for passenger disturbance due to flight delays</atitle><jtitle>PloS one</jtitle><date>2020-09-21</date><risdate>2020</risdate><volume>15</volume><issue>9</issue><spage>e0239141</spage><epage>e0239141</epage><pages>e0239141-e0239141</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32956383</pmid><doi>10.1371/journal.pone.0239141</doi><tpages>e0239141</tpages><orcidid>https://orcid.org/0000-0001-9584-879X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Aircraft safety Airline passengers Airline scheduling Airport terminals Airports Anger Automation Aviation Back propagation networks Behavior Biology and Life Sciences Civil aviation Computer and Information Sciences Data collection Decision making Disturbances Engineering and Technology Environmental management Flight Flight behavior Methods Neural networks Passenger satisfaction Passengers Physical Sciences Predictions Regression analysis Regression models Research and Analysis Methods Social aspects Social Sciences Transportation terminals Weight analysis |
| title | Early warning model for passenger disturbance due to flight delays |
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