Abstract
Research related to creating new and improved airplane boarding methods has seen continuous advancement, in recent years, while most of the airline companies have remained committed to the traditional boarding methods. Among the most-used boarding methods, around the world, are back-to-front and random boarding with and without assigned seats. While the other boarding methods used in practice possess strict rules for passengers’ behavior, random without assigned seats is dependent on the passengers own way of choosing the “best” seats. The aim of this paper is to meticulously model the passengers’ behavior, especially, in random boarding without assigned seats and to test its efficiency in terms of boarding time and interferences, in comparison with the other commonly-adopted methods (random boarding with assigned seats, window-middle-aisle (WilMA), back-to-front, reverse pyramid, etc.). One of the main challenges in our endeavor was the identification of the real human passengers’ way of reasoning, when selecting their seats, and creating a model in which the agents possess preferences and make decisions, as close to those decisions made by the human passengers, as possible. We model their choices based on completed questionnaires from three hundred and eighty-seven human subjects. This paper describes the resulting agent-based model and results from the simulations.
Highlights
Effective air traffic operations depend on the performance of all the actors involved in this process, such as the airport, the airline, the network manager, and the air navigation service provider [1]
Ferrari and Nagel (2005) asserted that the efficiency of the boarding strategies may depend on the aircraft occupancy and that different strategies may produce better results than others, on the same aircraft model, depending on the occupancy percentage [8]
There are a series of factors which should be taken into account when selecting a boarding strategy
Summary
Effective air traffic operations depend on the performance of all the actors involved in this process, such as the airport, the airline, the network manager, and the air navigation service provider [1]. Eurocontrol (2018) indicates an average delay of 12.4 min per flight, in Europe, in 2017, an increase of 1.1 min per flight, as compared to 2016. Considering the standard delay codes defined by the Airport Handling Manual [2], the departure delays can be segregated into seven major categories, depending on their cause—airline (starting with delays due to passenger and luggage and ending with flight operations and crewing, codes 11–69), airport Sustainability 2018, 10, 4623 SSuussttaaiinnaabbiilliittyy 22001188,, 1100,, xx FFOORR PPEEEERR RREEVVIIEEWW of 28 22 ooff 2299 arreetssttthrriieccttaiiooirnnpssoaartttttohhfee daaiierrpppaoorrrtttuoorffed,deecppodaarrettsuurr8ee3,,–cc8oo9dd)e,esse88n33-r––o88u99)t),e, ee(nna--irrroouturttaeeffi((aaciirrflttorraawffffiimcc ffallnoowwagmmemaannenaaggt eedmmueeenntttoddeuunee-ttroooueennte-drrooeuumtteeandddee/mmcaaannpdda//ccciaatypp/aacscitittayyff//s/stteaaqffffu//eeipqqmuuiieppnmmt,eenncott,,dcceoosdd8ees1s 88a11naadnndd8288)2,2))g,, oggvooevvreenrrmnnmmeneenntattlaall(s((sseeecccuuurrritiiyttyyaaannnddd iiimmmmmmiiigggrrraaatttiiiooonnn,,, cccooodddeeesss 888555 aaannnddd 888666))),,, wwweeeaaattthhheeerrr (((wwweeeaaattthhheeerrr ooottthhheeerrr ttthhhaaannn aaaiiirrr tttrrraaaffffffiiiccc fflfllooowww mmmaaannnaaagggeeemmmeeennnttt aaannnddd aaaiiirrr tttrrraaaffffffiiiccc fflfllooowww mmmaaannnaaagggeeemmmeeennnttt ddduuueeetttooowwweeeaaatthhtheeerrrooffottfhhteehdedeedssettiisnntiaanttiiaootnnio,,ncc,ooddcoeedss e77s11––777177–7aan7nddan884d4)),,8mm4)ii,sscmceeilllslaacnneleelooauunsseo((ccuoosdd(eeco9988d––e999998)),,–aa9nn9dd), arreenaadccttriieooanncaatirroyyn((allraayttee(laaarrterriivavraarllivooafflaaoiifrrccarriaarcffttr,,acfctrr,eecwwre,,wpp,aapssasseesnsneggneegrrsseroosrrorlloolaaodda,d, ,ccoocodddeee999111–––999666)).)..AAAnnnaaalllyyyzzziiinnnggg ttthhheee aaavvveeerrraaagggeee vvvaaallluuueeesssooofffeaeecaahcchhof oothffettshheeecssaeeteccgaaottreeiggesoo,rrwiieeess,,owbwseeeroovbbessteehrravvteethttehhgaartteatthhteeestggcrroeenaatttreeissbttutcciooonnnttirrsiibbbuurottiiuoognnhtiissbybbrrthooeuuggrehhattctbbioyynatthhryee drreeeaalaccyttiisoonn(5aa.r4ryy mddeeinllaapyysesr((fl55..i44g66htmminiinn20pp1ee7rr, wfflliiiggthhhtt0.ii3nn3 22m00i11n77,,pwewriifltthhig00h..t333m3 ommreiinnthppaeenrr tffhlliiaggthhintt mm20oo1rr6ee), ttfhhoaallnnowtthheaadttbiiynn a22i00rl11i6n6))e,, dffooelllllaooywwsee(dd3.b3b4yy maaiiirrnlliinnpeeerddfleelliaagyyhsst,((w33..33it44hmmaniinninppceerrreaffllsiiegghhottf,, 0ww.2iit1thhmaainnniipnneccrrreeflaaissgeehoot,ffc00o..22m11pmmariienndppteeorr 2ffl0lii1gg6hh)tt,,,accsooimmllpupasatrrreeaddtettdoo i22n0011F66ig)),,uaarsseii1llll.uussttrraatteedd iinn FFiigguurree ..
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