http://iet.metastore.ingenta.com
1887

How to drive passenger airport experience: a decision support system based on user profile

How to drive passenger airport experience: a decision support system based on user profile

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This work presents a decision support system for providing information and suggestions to airport users. The aim of the study is to design a system both to improve passengers' experience by reducing time-spent queuing and waiting and to raise airport revenues by increasing the time passengers spend in discretionary activities. Passengers' behaviour is modelled with an activity-choice model to be calibrated with their mobile phone traces. The model allows predicting activity sequences for passengers with given socio-demographic characteristics. To predict queue length at check-in desks and security control and congestion inside commercial areas, passengers' movements are simulated with a microscopic simulation tool. A system to generate suggestion has been designed: passengers are advised to perform mandatory activities when the predicted queue length is reasonable and specific discretionary activities according to time available, user profiles, location distance, location congestion and airport management preferences. A proof-of-concept case study has been developed: passengers' behaviour in both cases of receiving and not receiving suggestion has been simulated. In the first case, passengers experienced less queuing and waiting time; the time saved was spent in discretionary activities, improving passengers' airport experience and increasing airport revenues.

References

    1. 1)
      • 1. https://www.eurocontrol.int/publications/challenges-growth-2013-reports, accessed November 2017.
    2. 2)
      • 2. Guizzi, G., Murino, T., Romano, E.: ‘A discrete event simulation to model passenger flow in the airport terminal’. Proc. 11th WSEAS Int. Conf. on Mathematical Methods and Computational Techniques in Electrical Engineering, 2009, pp. 427434.
    3. 3)
      • 3. https://www.icao.int/Meetings/wrdss2011/Documents/JointWorkshop2005/ATAG_SocialBenefitsAirTransport.pdf, accessed November 2017.
    4. 4)
      • 4. http://www.intervistas.com/downloads/reports/Economic%20Impact%20of%20European%20Airports%20-%20January%202015.pdf, accessed November 2017.
    5. 5)
      • 5. Lin, Y.H., Chen, C.F.: ‘Passengers’ shopping motivations and commercial activities at airports – the moderating effects of time pressure and impulse buying tendency’, Tour. Manag., 2013, 36, pp. 426434.
    6. 6)
      • 6. Popovic, V., Kraal, B.J., Kirk, P.J.: ‘Towards airport passenger experience models’. 7th Int. Conf. on Design & Emotion, 2010, pp. 111.
    7. 7)
      • 7. Chung, Y.S., Wu, C.L., Chiang, W.E.: ‘Air passengers’ shopping motivation and information seeking behaviour’, J. Air Transp. Manag., 2013, 27, pp. 2528.
    8. 8)
      • 8. Roanes-Lozano, E., Laita, L.M., Roanes-Macías, E.: ‘An accelerated-time simulation of departing passengers’ flow in airport terminals’, Math. Comput. Simul., 2004, 67, (1–2), pp. 163172.
    9. 9)
      • 9. Schultz, M., Fricke, H.: ‘Managing passenger handling at airport terminals individual-based approach for modeling the stochastic passenger behavior’. Proc. 9th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2011, 2011, pp. 438447.
    10. 10)
      • 10. ‘Airport of the Future Project (AFP)’, available at http://www.airportsofthefuture.qut.edu.au/, accessed July 2017.
    11. 11)
      • 11. ‘Proactive Passenger Flow Management for Airports with an Advanced Forecasting System (AERFOR)’, available at http://cordis.europa.eu/project/rcn/194777_en.html, accessed July 2017.
    12. 12)
      • 12. ‘Door to Door Information for Airports and Airlines (DORA)’, available at https://dora-project.eu/, accessed July 2017.
    13. 13)
      • 13. Bierlaire, M., Robin, T.: ‘Pedestrians choices’, in ‘Pedestrian behavior’ (Harry Timmermans, Bingley, 2009), p. 1.
    14. 14)
      • 14. Hoogendoorn, S.P., Bovy, P.H.L.: ‘Pedestrian route-choice and activity scheduling theory and models’, Transp. Res. B, Methodol., 2004, 38, (2), pp. 169190.
    15. 15)
      • 15. Hoogendoorn, S.P., Bovy, P.H.L.: ‘Normative pedestrian behaviour theory and modelling’. Transportation and Traffic Theory in the 21st Centrury, 2002, pp. 219245.
    16. 16)
      • 16. Liu, X., Usher, J.M., Strawderman, L.: ‘An analysis of activity scheduling behavior of airport travelers’, Comput. Ind. Eng., 2014, 74, (1), pp. 208218.
    17. 17)
      • 17. Kalakou, S., Moura, F.: ‘Modelling passengers’ activity choice in airport terminal before the security checkpoint: the case of Portela airport in Lisbon’, Transp. Res. Proc., 2015, 10, pp. 881890.
    18. 18)
      • 18. Ma, W.: ‘Agent-based model of passenger flows in airport terminals’, PhD thesis, Queensland University of Technology, 2013.
    19. 19)
      • 19. Danalet, A.: ‘Activity choice modeling for pedestrian facilities’. PhD thesis, EPFL, 2015.
    20. 20)
      • 20. Ben-akiva, M., Bowman, J.L., Gopinath, D.: ‘Travel demand model system for the information era’, Transportation, 1996, 23, pp. 241266.
    21. 21)
      • 21. Shiftan, Y.: ‘Practical approach to model trip chaining’, Transp. Res. Rec., 1998, 1645, (1), pp. 1723.
    22. 22)
      • 22. Bhat, C.R., Singh, S.K.: ‘A comprehensive daily activity-travel generation model system for workers’, Transp. Res. A, Policy Pract., 2000, 34, (1), pp. 122.
    23. 23)
      • 23. Bowman, J.L., Ben-Akiva, M.E.: ‘Activity-based disaggregate travel demand model system with activity schedules’, Transp. Res. A, Policy Pract., 2000, 35, (1), pp. 128.
    24. 24)
      • 24. Miller, E.J., Roorda, M.J., Carrasco, J.A.: ‘A tour-based model of travel mode choice’, Transportation, 2005, 32, (4), pp. 399422.
    25. 25)
      • 25. Shiftan, Y.: ‘The use of activity-based modeling to analyze the effect of land-use policies on travel behavior’, Ann. Reg. Sci., 2008, 42, (1), pp. 7997.
    26. 26)
      • 26. Abou-Zeid, M., Ben-Akiva, M.: ‘Well-being and activity-based models’, Transportation, 2012, 39, (6), pp. 11891207.
    27. 27)
      • 27. Shiftan, Y., Ben-Akiva, M.: ‘A practical policy-sensitive, activity-based, travel-demand model’, Ann. Reg. Sci., 2011, 47, pp. 517541.
    28. 28)
      • 28. Flötteröd, G., Bierlaire, M.: ‘Metropolis-Hastings sampling of paths’, Transp. Res. B, Methodol., 2013, 48, pp. 5366.
    29. 29)
      • 29. Danalet, A., Bierlaire, M.: ‘Importance sampling for activity path choice’, 15th Swiss Transport Research Conf. (STRC), Monte Verità, Ascona, Switzerland, April 2015.
    30. 30)
      • 30. Ettema, D., Bastin, F., Polak, J., et al: ‘Modelling the joint choice of activity timing and duration’, Transp. Res. A, Policy Pract., 2007, 41, (9), pp. 827841.
    31. 31)
      • 31. Aschenbruck, N., Munjal, A., Camp, T.: ‘Trace-based mobility modeling for multi-hop wireless networks’, Comput. Commun., 2011, 34, (6), pp. 704714.
    32. 32)
      • 32. Danalet, A., Farooq, B., Bierlaire, M.: ‘A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures’, Transp. Res. C, Emerg. Technol., 2014, 44, pp. 146170.
    33. 33)
      • 33. Hastings, W.K.: ‘Monte Carlo sampling methods using Markov chains and their applications’, Biometrika, 1970, 57, (1), pp. 97109.
    34. 34)
      • 34. Reinhardt, L.B., Pisinger, D.: ‘Multi-objective and multi-constrained non-additive shortest path problems’, Comput. Oper. Res., 2011, 38, (3), pp. 605616.
    35. 35)
      • 35. Shahabi, M., Unnikrishnan, A., Boyles, S.D.: ‘An algorithm for non-additive shortest path problem’. 93th Annual Meeting of the Transportation Research Board, 2014.
    36. 36)
      • 36. Chun, H.W., Mak, R.W.T.: ‘Intelligent resource simulation for an airport check-in counter allocation system’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 1999, 29, (3), pp. 325335.
    37. 37)
      • 37. Hoogendoorn, S., Bovy, P.H.L.: ‘Simulation of pedestrian flows by optimal control and differential games’, Optim. Control Appl. Methods, 2003, 24, (3), pp. 153172.
    38. 38)
      • 38. Campanella, M.C.: ‘Microscopic modelling of waliking behaviour’, PhD thesis, Technische Universiteit Delft, 2016.
    39. 39)
      • 39. Campanella, M.C., Daamen, W., Hoogendoorn, S.: ‘User manual of the microscopic pedestrian simulation model Nomad’ (Technische Universiteit Delft, Delft, 2015).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0210
Loading

Related content

content/journals/10.1049/iet-its.2017.0210
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address