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Context-Based Traffic Recommendation System

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 165)

Abstract

In this paper, we propose a new traffic system recommendation based on support real-time flows in highly unpredictable sensor network environments. The approach system is real-time recommendation system which meet various demands of users. The proposed algorithm include two phases. First phase is proposed to deal with the real-time problem. By this way, the drivers are able to transfer on the way with the shortest-time. For second phase, a research algorithm based on Depth First Search (DFS) algorithm will recommend the paths which meet demands of drivers based their context such as the paths with include the famous landscapes or the paths where they can find out good restaurants for their break while driving.

Keywords

  • Recommendation system
  • Sensor network
  • DFS algorithm

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  • DOI: 10.1007/978-3-319-29236-6_13
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References

  1. Daponte, P., De Vito, L., Picariello, F., Rapuano, S., Tudosa, I.: Wireless sensor network for traffic safety. In: 2012 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), pp. 42–49. IEEE, Perugia (2012)

    Google Scholar 

  2. Liang, B.J.: Traffic flow detection based on wireless sensor network. J. Netw. 8(8), 1859–1865 (2013)

    Google Scholar 

  3. Li, X., Shu, W., Li, M.L., Huang, H.Y., Luo, P.E., Wu, M.Y.: Performance evaluation of vehicle-based mobile sensor networks for traffic monitoring. In: IEEE Transactions on Environmental Energy and Structural Monitoring Systems (EESMS), 2012 IEEE Workshop, vol. 58, no. 4, pp. 1647–1653. IEEE (2009)

    Google Scholar 

  4. Francesco, R., Lior, R., Bracha, S.: Introduction to recommender systems handbook. In: Francesco, R., Lior, R., Bracha, S., Paul, B.K. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, New York (2011)

    Google Scholar 

  5. Phanich, M., Pholkul, P., Phimoltares, S.: Food recommendation system using clustering analysis for diabetic patients. In: 2010 International Conference on Information Science and Applications (ICISA), pp. 1–8. IEEE, Seoul, April 2010

    Google Scholar 

  6. Soo-Hyun, C., Young-Hak, K., Jae-Bum, P.: Music recommendation system for public places based on sensor network. IJCSNS Int. J. Comput. Sci. Netw. Secur. 7(8), 172–180 (2007)

    Google Scholar 

  7. Wang, H., Li, G.L., Hu, H.Q., Chen, S., Shen, B.W., Wu, H., Li, W.S., Tan, K.L.: R3: a real-time route recommendation system. In: 40th International Conference on Very Large Data Bases, pp. 1549–1552. IEEE, Hangzhou (2014)

    Google Scholar 

  8. Liu, L., Xu, J., Liao, S.S., Chen, H.: A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication. J. Expert Syst. Appl. 41(7), 3409–3417 (2014)

    CrossRef  Google Scholar 

  9. Meehan, K., Lunney, T., Curran, K., McCaughey, A.: Context-aware intelligent recommendation system for tourism. In: Pervasive Computing and Communications Workshops, pp. 328–331. IEEE, San Diego (2013)

    Google Scholar 

  10. Patcharee, S., Anongnart, S.: Personalized Trip Information for E-Tourism Recommendation System Based on Bayes Theorem. Research and Practical Issues of Enterprise Information Systems II, vol. 255, pp. 1271–1275. Springer, New York (2008)

    CrossRef  Google Scholar 

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154). Also, this research was supported by the MSIP Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2015-H8501-15-1018) supervised by the IITP(Institute for Information and communications Technology Promotion).

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Correspondence to Xuan Hau Pham or Jason J. Jung .

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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Bui, KH.N., Pham, X.H., Jung, J.J., Lee, OJ., Hong, MS. (2016). Context-Based Traffic Recommendation System. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-29236-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29235-9

  • Online ISBN: 978-3-319-29236-6

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