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A Data-Driven Agent-Based Simulator for Air Ticket Sales

  • Yao Wu
  • Jian CaoEmail author
  • Yudong Tan
  • Quanwu Xiao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

In order to better design sales strategy, air companies or travel agents would predict sales of air tickets. In this paper, we propose an agent-based ticket sales simulator based on data analysis. The features of air tickets and passengers are extracted by analyzing real data. A Long Short-Term Memory recurrent neural network is used to forecast the daily customer search volume. Then a purchase decision tree is designed and embedded into the customer agent to simulate the decision process when a customer tries to find and buy an air ticket. Experimental results show that our prediction model achieves better prediction accuracy than three compared approaches. Moreover, through the simulation experiment on the historical real data, we obtain good simulation results, and verify the validity and practicability of our ticket sales simulator.

Keywords

Simulator Agent-based Big data Deep learning Air ticket sales 

References

  1. Greasley, A., Owen, C.: Modelling people’s behaviour using discrete-event simulation: a review. Int. J. Oper. Prod. Manag. 38(5), 1228–1244 (2018)CrossRefGoogle Scholar
  2. Box, G.E., Jenkins, G.M., Rrinsel, G.C.: Time Series Analysis: Forecasting and Control, vol. 734. Wiley, Hoboken (2011)Google Scholar
  3. Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. Kim, B.S., Kang, B.G., Choi, S.H., Kim, T.G.: Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system. Simul. Trans. Soc. Model. Simul. Int. 93(7), 580–594 (2017)Google Scholar
  5. Changa, M., Cheungb, W., Laib, V.: Literature derived reference models for the adoption of online shopping. Inf. Manag. 42, 543–559 (2004)CrossRefGoogle Scholar
  6. Choua, P.H., Lib, P.H., Chenc, K.K., Wua, M.J.: Integrating web mining and neural network for personalized e-commerce automatic service. Expert Syst. Appl. 37(4), 2898–2910 (2010)CrossRefGoogle Scholar
  7. Ctrip Flight. http://flights.ctrip.com/. Accessed 10 May 2018
  8. Bell, D., Mgbemena, C.: Data-driven agent-based exploration of customer behavior. Simul. Trans. Soc. Model. Simul. Int. 94(3), 196–212 (2017)Google Scholar
  9. Yang, F., Cao, J., Milosevic, D.: An evolutionary algorithm for column family schema optimization in HBase. In: IEEE International Conference on Big Data Computing Service and Applications (Big Data Service), pp. 439–445 (2015)Google Scholar
  10. Forsythe, S., Liu, C., Shannon, D., Gardner, L.: Development of a scale to measure the perceived benefits and risks of online shopping. J. Interact. Mark. 20, 55–75 (2006)CrossRefGoogle Scholar
  11. Gilbert, N., Troitzsch, G.: Simulation for the Social Scientist. Open University Press McGraw-Hill Education, London (2005)Google Scholar
  12. Godes, D., Mayzlin, D.: Using online conversations to study word of mouth communication. J. Mark. Sci. Arch. 23, 545–560 (2004)CrossRefGoogle Scholar
  13. Ferreira, H.S., Azevedo, J.: Framework for multi-agent simulation of user behaviour in E-commerce sites. Faculdade de Engenharia da Universidade do Porto (2016)Google Scholar
  14. Duarte, D., Ferreira, H.S., Dias, J.P., Kokkinogenis, Z.: Towards a framework for agent-based simulation of user behaviour in E-commerce context. In: De la Prieta, F., et al. (eds.) PAAMS 2017. AISC, vol. 619, pp. 30–38. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-61578-3_3CrossRefGoogle Scholar
  15. Hummel, A., Kern, H., Kuhne, S., Dohler, A.: An agent-based simulation of viral marketing effects in social networks. In: European Simulation and Modelling Conference (2012)Google Scholar
  16. Janssen, M., Jager, W.: An integrated approach to simulating behavioural processes: a case study of the lock-in of consumption patterns. J. Artif. Soc. Soc. Simul. 2, 2 (1999)Google Scholar
  17. Wilson, J.L.: The Value of Revenue Management Innovation in a Competitive Airline Industry. Cornell University, New York (1993)Google Scholar
  18. Kalekar, P.S.: Time series forecasting using holt-winters exponential smoothing, pp. 1–13. Kanwal Rekhi School of Information Technology (2004)Google Scholar
  19. Kim, D., Ferrin, D., Raghav Rao, H.: A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44(04), 544–564 (2007)Google Scholar
  20. Liu, X., Tang, Z., Yu, J., Lu, N.: An agent based model for simulation of price war in B2C online retailers. Adv. Inf. Sci. Serv. Sci. 5, 1193–1202 (2013)Google Scholar
  21. Moe, W.: Buying: differentiating between online shoppers using in-store navigational clickstream. J. Consum. Psychol. 13, 29–39 (2003)CrossRefGoogle Scholar
  22. Alotaibi, M.B.: Adaptable and adaptive E-commerce interfaces: an empirical investigation of user acceptance. J. Comput. 8(8), 1923–1933 (2013)CrossRefGoogle Scholar
  23. North, M., et al.: Multiscale agent-based consumer market modelling. Complexity 15(5), 37–47 (2010)Google Scholar
  24. Okada, I., Yamamoto, H.: Effect of online word-of-mouth communication on buying behavior in agent-based simulation. In: 6th Conference of the European Social Simulation Association (2009)Google Scholar
  25. Said, B., Drogoul, A.: Multi-agent based simulation of consumer behavior: towards a new marketing approach. In: International Congress on Modelling and Simulation, MODSIM (2001)Google Scholar
  26. Sava, C., Aleksandar, M.: Agent-based modelling and simulation in the analysis of customer behaviour on B2C ecommerce sites. J. Simul. 11(04), 335–345 (2017)CrossRefGoogle Scholar
  27. Lee, S.: Seoul National University, Seoul, South Kerean (2000)Google Scholar
  28. Understanding LSTM Networks. http://colah.github.io/posts/2015-08-Understanding-LSTMS/. Accessed 24 May 2017
  29. Chen, Y., Cao, J., Feng, S., Tan, Y.: An ensemble learning based approach for building airfare forecast service. In: International Conference on Big Data. IEEE (2015)Google Scholar
  30. Zhang, T., Zhang, D.: Agent-based simulation of consumer purchase decision-making and the decoy effect. J. Bus. Res. 60, 912–922 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computor Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Air Ticket Business Ctrip.com International Ltd ShanghaiShanghaiChina

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