Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Cognition-inspired route evaluation using mobile phone data

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

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. Adaptive control of thought-rational (ACT-R) 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 paper, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route evaluation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and then recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to evaluate the suitability of the routes inspired by cognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin YL (2004) An integrated theory of the mind. Psychol Rev 111(4):1036–1060

    Article  Google Scholar 

  • Andrzej S, Jaroslaw S (2001) Information granules: towards foundations of granular computing. Int J Intell Syst 16(1):57–85

    Article  MATH  Google Scholar 

  • Antoniou G, Harmelen FV (2003) A semantic web primer. MIT, Cambridge

    Google Scholar 

  • Caceres N, Wideberg JP, Benitez FG (2007) Deriving origin destination data from a mobile phone network. IET Intell Transp Syst 1(1):15–26

    Article  Google Scholar 

  • Calabrese F, Colonna M, Lovisolo P, Parata D, Ratti C (2011a) Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans Intell Transp Syst 12(1):141–151

  • Calabrese F, Lorenzo GD, Liu L, Ratti C (2011b) Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput 10(4):36–44

  • Chen ZB, Shen HT, Zhou XF (2011) Discovering popular routes from trajectories. In: Proceedings of the 2011 IEEE 27th international conference on data engineering. IEEE Computer Society, Los Alamitos, pp 900–911

  • Dominik S, Piotr S, Arkadiusz W, Jakub W (2013) Two database related interpretations of rough approximations: data organization and query execution. Fundam Inf 127(1–4):445–459

    Google Scholar 

  • Fu W-T, Pirolli P (2007) A cognitive model of user navigation on the world wide web. Hum Comput Interact 22(4):355–412

    Google Scholar 

  • Lin TY (1999) Data mining: granular computing approach. Methodol Knowl Discov Data Min 1574:24–33

    Article  Google Scholar 

  • Liu F, Janssens D, Wets G, Cools M (2013) Annotating mobile phone location data with activity purposes using machine learning algorithms. Expert Syst Appl 40(8):3299–3311

    Article  Google Scholar 

  • Lu EH-C, Tseng VS, Yu PS (2011) Mining cluster-based temporal mobile sequential patterns in location-based service environments. IEEE Trans Knowl Data Eng 23(6):914–927

    Article  Google Scholar 

  • Manning D, Raghavan P, Schtze H (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Rajaraman A, Ullman J (2011) Ming of masssive datasets. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Wei L-Y, Zheng Y, Peng W-C (2012) Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 195–203

  • Yao Y (2007) The art of granular computing. In: Proceeding of the international conference on rough sets and emerging intelligent systems paradigms. Springer, Berlin, pp 101–112

  • Yao Y (2008) Granular computing: past, present, and future. Rough sets and knowledge technology, 5009. Springer, Berlin, pp 27–28

  • Ying JJC, Lu EHC, Lee WC (2010) Mining user similarity from semantic trajectories. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks. ACM, New York, pp 19–26

  • Ying JJC, Lee W-C, Weng T-C (2011) Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, New York, pp 34–43

  • Yuan J, Zheng Y, Xie X, Sun GZ (2011) T-Drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans Knowl Data Eng 25(1):220–232

    Article  Google Scholar 

  • Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MATH  MathSciNet  Google Scholar 

  • Zeng Y, Zhong N, Wang Y, Qin Y, Huang Z, Zhou H, Yao Y, Harmelen F (2011) User-centric query refinement and processing using granularity-based strategies. Knowl Inf Syst 27(3):419–450

    Article  Google Scholar 

  • Zhong N, Yao Y, Qin Y, Lu S, Hu J, Zhou H (2008) Towards granular reasoning on the web: scalable, tolerant and dynamic, workshop on new forms of reasoning for the semantic web

  • Zhong N, Ma JH, Huang RH, Liu JM, Yao YY, Zhang YX, Chen JH (2013) Research challenges and perspectives on wisdom web of things (W2T). J Supercomput 64(3):862–882

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the National Science Foundation of China (61420106005, 61272345), International Science & Technology Cooperation Program of China (2013DFA32180), and the Beijing Natural Science Foundation (4132023).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ning Zhong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Huang, J., Zhou, E. et al. Cognition-inspired route evaluation using mobile phone data. Nat Comput 14, 637–648 (2015). https://doi.org/10.1007/s11047-014-9479-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-014-9479-9

Keywords

Navigation