GreenCommute: An Influence-Aware Persuasive Recommendation Approach for Public-Friendly Commute Options

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Abstract

Negative impacts produced by transportation sector have increased in parallel with the increase of urban mobility. In this paper, we introduce GreenCommute, a novel recommendation system which can facilitate commuters to take public friendly commute options, while provide support to alleviate the external cost in society, such as traffic pollution, congestion and accidents. In the meanwhile, a rewarding mechanism for persuading commuters is embedded in the proposed approach for balancing the conflict between personal needs and social aims. The allocation of reward values also takes users’ influential degrees in the social network into consideration. Experimental results show that the GreenCommute can promote public friendly commute options more effectively in comparison to the traditional recommendation system.

Keywords

Recommendation system agent-based modelling social influence reward public transport 

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Notes

Acknowledgements

The authors are thankful to two anonymous reviewers for their constructive and helpful comments which helped to improve the presentation of this paper considerably.

References

  1. [1]
    Axsen, J., Orlebar, C. & Skippon, S. (2013). Social influence and consumer preference formation for pro-environmental technology: The case of a UK workplace electric-vehicle study. Ecological Economics, 95: 96–107.CrossRefGoogle Scholar
  2. [2]
    Bothos, E., Apostolou, D. & Mentzas, G. (2012). Recommending eco-friendly route plans. In: Cunningham P, Hurley N, Guy I, Anand S.S (eds.), 6th ACM Conference on Recommender Systems: 12-17, Dublin September, 09-13, 2012, ACM.Google Scholar
  3. [3]
    Burgoon, M., Dillard, J. P., Doran, N. E. & Miller, M. D. (1982). Cultural and situational influences on the process of persuasive strategy selection. International Journal of Intercultural Relations, 6(1): 85–100.CrossRefGoogle Scholar
  4. [4]
    Gkika, S. & Lekakos, G. (2014). The persuasive role of Explanations in Recommender Systems. In: 2nd Intl. Workshop on Behavior Change Support Systems (BCSS 2014), 1153: 59–68, CEUR.Google Scholar
  5. [5]
    Häubl, G. & Murray, K. B. (2003). Preference construction and persistence in digital marketplaces: The role of electronic recommendation agents. Journal of Consumer Psychology, 13(1-2): 75–91.CrossRefGoogle Scholar
  6. [6]
    Homans, G. C. (1974). Social Behavior: Its Elementary Forms. Harcourt Brace Jovanovich, Oxford.Google Scholar
  7. [7]
    Li, W., Bai, Q., Jiang, C. & Zhang, M. (2016). Stigmergy-based influence maximization in social networks. In Numao M, Theeramunkong T, Supnithi T, Ketcham M, Hnoohom N & Pramkeaw P (eds.), Pacific Rim International Conference on Artificial Intelligence: 750-762, Phuket, August, 22-26, 2016, Springer.Google Scholar
  8. [8]
    Lin, X., Shang, T. & Liu, J. (2014). An estimation method for relationship strength in weighted social network graphs. Journal of Computer and Communications, 2(04): 82–89.CrossRefGoogle Scholar
  9. [9]
    Lu, J., Wu, D., Mao, M., Wang, W. & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74: 12–32.CrossRefGoogle Scholar
  10. [10]
    Maibach, M., Schreyer, C., Sutter, D., van Essen, H., Boon, B., & Smokers, R. et al. (2008). Handbook on Estimation of External Costs in the Transport Sector, CE Delft.Google Scholar
  11. [11]
    Nuzzolo, A., Crisalli, U., Comi, A. & Rosati, L. (2014). An advanced traveller advisory tool based on individual preferences. Procedia-Social and Behavioral Sciences, 160: 539–547.CrossRefGoogle Scholar
  12. [12]
    Oinas-Kukkonen, H. & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1): 485–500.Google Scholar
  13. [13]
    Pettifor, H., Wilson, C., Axsen, J., Abrahamse, W., & Anable, J. (2017). Social influence in the global diffusion of alternative fuel vehicles–A meta-analysis. Journal of Transport Geography, 62: 247–261.CrossRefGoogle Scholar
  14. [14]
    Sengvong, S. & Bai, Q. (2017). Persuasive public-friendly route recommendation with flexible rewards. In: Liang L (ed.), 2017 IEEE International Conference on Agents (ICA): 109-114, Beijing, July, 6-9, 2017, IEEE.Google Scholar
  15. [15]
    Su, H., Zheng, K., Huang, J., Jeung, H., Chen, L. & Zhou, X. (2014). Crowdplanner: A crowd-based route recommendation system. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE): 1144-1155, Chicago, March 31-April 4, 2014, IEEE.Google Scholar
  16. [16]
    Tumas, G. & Ricci, F. (2009). Personalized mobile city transport advisory system. In: Höpken W, Gretzel U & Law R (eds.), Information and Communication Technologies in Tourism 2009: 173-183, Amsterdam, 2009, Springer.Google Scholar
  17. [17]
    Zhang, K. & Batterman, S. (2013). Air pollution and health risks due to vehicle traffic. Science of the Total Environment, 450: 307–316.CrossRefGoogle Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Engineering, Computer and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

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