Suitable Route Recommendation Inspired by Cognition

  • Hui Wang
  • Jiajin Huang
  • Erzhong Zhou
  • Zhisheng Huang
  • Ning ZhongEmail author
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)


With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. ACT-R (Adaptive Control of Thought-Rational) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this chapter, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route recommendation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to recommend the suitable routes inspired by cognition.


Mobile Phone Transition Network Human Memory Information Organization Mobile Phone Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is partially supported by the National Science Foundation of China (No. 61272345), the International Science & Technology Cooperation Program of China (2013DFA32180), and the CAS/SAFEA International Partnership Program for Creative Research Teams.


  1. 1.
    J.R. Anderson, D. Bothell, M.D. Byrne, S. Douglass, C. Lebiere, Y.L. Qin, An integrated theory of the mind. Psychol. Rev. 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  2. 2.
    W.-T. Fu, P. Pirolli, A cognitive model of user navigation on the World Wide Web. Human-Comput. Interact. 22(4), 355–412 (2007)Google Scholar
  3. 3.
    N. Caceres, J.P. Wideberg, F.G. Benitez, Deriving origin destination data from a mobile phone network. IET Intell. Transp. Syst. 1(1), 15–26 (2007)CrossRefGoogle Scholar
  4. 4.
    F. Calabrese, G.D. Lorenzo, L. Liu, C. Ratti, Estimating origin-destination flows using mobile phone location data. IEEE Pervas. Comput. 10(4), 36–44 (2011)CrossRefGoogle Scholar
  5. 5.
    F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, C. Ratti, Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011)CrossRefGoogle Scholar
  6. 6.
    J.J.-C. Ying, E.H.-C. Lu, W.-C. Lee, Mining user similarity from semantic trajectories, in Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks (ACM, 2010), pp. 19–26Google Scholar
  7. 7.
    J.J.-C. Ying, W.-C. Lee, T.-C. Weng, Semantic trajectory mining for location prediction, in Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2011), pp. 34–43Google Scholar
  8. 8.
    E.H.-C. Lu, V.S. Tseng, P.S. Yu, Mining cluster-based temporal mobile sequential patterns in location-based service environments. IEEE Trans. Knowl. Data Eng. 23(6), 914–927 (2011)CrossRefGoogle Scholar
  9. 9.
    F. Liu, D. Janssens, G. Wets, M. Cools, Annotating mobile phone location data with activity purposes using machine learning algorithms. Expert Syst. Appl. 40(8), 3299–3311 (2013)CrossRefGoogle Scholar
  10. 10.
    J. Yuan, Y. Zheng, X. Xie, G.Z. Sun, T-Drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2011)CrossRefGoogle Scholar
  11. 11.
    L.-Y. Wei, Y. Zheng, W.-C. Peng, Constructing popular routes from uncertain trajectories, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2012), pp. 195–203Google Scholar
  12. 12.
    Z.B. Chen, H.T. Shen, X.F. Zhou, Discovering popular routes from trajectories, in Proceedings of the 2011 IEEE 27th International Conference on Data Engineering (IEEE Computer Society, 2011), pp. 900–911Google Scholar
  13. 13.
    N. Zhong, J.H. Ma, J.H. Huang, J.M. Liu, Y.Y. Yao, Y.X. Zhang, J.H. Chen, Research challenges and perspectives on Wisdom Web of Things (W2T). J. Supercomput. 64(3), 862–882 (2013)CrossRefGoogle Scholar
  14. 14.
    L.A. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90(2), 111–127 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    A. Rajaraman, J. Ullman, Ming of Masssive Datasets (Cambridge University Press, Cambridge, England, 2011), pp. 305–338CrossRefGoogle Scholar
  16. 16.
    D. Manning, P. Raghavan, H. Schtze, Introduction to Information Retrieval (Cambridge University Press, Cambridge, England, 2008), pp. 158–164CrossRefGoogle Scholar
  17. 17.
    G. Antoniou, F. von Harmelen, A Semantic Web Primer (The MIT Press, Cambridge, Massachusetts London, 2003), pp. 63–111Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hui Wang
    • 1
  • Jiajin Huang
    • 1
  • Erzhong Zhou
    • 1
  • Zhisheng Huang
    • 1
    • 4
  • Ning Zhong
    • 2
    • 3
    Email author
  1. 1.International WIC Institute, Beijing University of TechnologyBeijingChina
  2. 2.Beijing Advanced Innovation Center for Future Internet Technology, The International WIC InstituteBeijing University of TechnologyBeijingChina
  3. 3.Department of Life Science and Informatics, Maebashi Institute of TechnologyMaebashiJapan
  4. 4.Department of Computer Science, Vrije University of AmsterdamAmsterdamThe Netherlands

Personalised recommendations