Data Mining Method of Sequential Patterns for Vehicle Trajectory Prediction in VANET


In order to provide the future direction of vehicle flow for travelers, it is necessary to predict the driving trajectory of vehicles running on the road in a certain segments. According to the characteristics of vehicle sequential pattern, the topology of vehicular ad hoc network (VANET) is linked to the real road vehicle movement trajectory. Some new definitions related to sequential patterns in VANET environment are proposed. Based on RSU and V2V schemes, a vehicle movement database is established, and sequential pattern data mining is carried out. Afterward, their communication overhead is evaluated. The support and confidence of the movement rules generated by vehicle routing patterns are calculated to extract the probability of frequent driving trajectories.

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  1. 1.

    Eze, E. C., Zhang, S., & Liu, E. (2014). Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward. In 2014 20th International conference on automation and computing (ICAC) (pp. 176–181). IEEE.

  2. 2.

    Zhao, J., & Cao, G. (2008). VADD: Vehicle-assisted data delivery in vehicular ad hoc networks. IEEE Transactions on Vehicular Technology, 57(3), 1910–1922.

    Article  Google Scholar 

  3. 3.

    Turcanu, I., Salvo, P., Baiocchi, A., et al. (2016). An integrated VANET-based data dissemination and collection protocol for complex urban scenarios. Ad Hoc Networks, 52, 28–38.

    Article  Google Scholar 

  4. 4.

    Lytrivis, P., Thomaidis, G., & Amditis, A. (2008). Cooperative path prediction in vehicular environments. In 11th International IEEE conference on intelligent transportation systems, 2008. ITSC 2008 (pp. 803–808). IEEE.

  5. 5.

    Anwar, F., Petrounias, I., Morris, T., et al. (2010). Discovery of events with negative behavior against given sequential patterns. In 2010 5th IEEE International conference on intelligent systems (IS) (pp. 373–378). IEEE.

  6. 6.

    Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., et al. (2014). A comprehensive survey on vehicular ad hoc network. Journal of Network and Computer Applications, 37, 380–392.

    Article  Google Scholar 

  7. 7.

    Merah, A. F., Samarah, S., Boukerche, A., et al. (2013). A sequential patterns data mining approach towards vehicular route prediction in VANETs. Mobile Networks and Applications, 18(6), 788–802.

    Article  Google Scholar 

  8. 8.

    Qi, W., Song, Q., Wang, X., et al. (2017). Trajectory data mining-based routing in DTN-enabled vehicular ad hoc networks. IEEE Access, 5, 24128–24138.

    Article  Google Scholar 

  9. 9.

    Ghebleh, R. (2017). A comparative classification of information dissemination approaches in vehicular ad hoc networks from distinctive viewpoints: A survey [J]. Computer Networks, 131, 15–37.

    Article  Google Scholar 

  10. 10.

    Zhang, H. (2019). Fault diagnosis method of vehicle driving data acquisition devices based on data mining. Journal of Automotive Safety and Energy, 10(1), 45–50.

    Google Scholar 

  11. 11.

    Li, H. X. (2018). Research on application of sequential pattern mining in traffic flow forecast. Journal of Xi’an University (Natural Science Edition), 21(2), 62–66.

    Google Scholar 

  12. 12.

    Tao, H., Feng, F. Q., Xiao, P., et al. (2016). Routing algorithm based on characteristics analysis of vehicle trace in vehicular ad hoc network. Journal on Communications, 37(6), 144–153.

    Google Scholar 

  13. 13.

    Zhang, F. S., Jin, B. H., Wang, Z. Y., et al. (2015). A routing mechanism over bus-based VANETs by mining trajectories. Chinese Journal of Computer, 38(3), 648–662.

    MathSciNet  Google Scholar 

  14. 14.

    Zhang, H., & Yao, Y. G. (2019). An integrative vulnerability evaluation model to urban road complex network. Wireless Personal Communications, 107(1), 193–204.

    Article  Google Scholar 

  15. 15.

    Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Proceedings of the eleventh international conference on data engineering (pp. 3–14). IEEE.

  16. 16.

    Merah, A. F., Samarah, S., & Boukerche, A. (2012). Vehicular movement patterns: A prediction-based route discovery technique for VANETs. In 2012 IEEE International conference on communications (ICC) (pp. 5291–5295). IEEE.

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The research is sponsored by the Natural Science Foundation of Inner Mongolia (2019MS07021), Transportation Department of Inner Mongolia Autonomous Region (NJ-2017-8), and State Scholarship Fund of the China Scholarship Council (201806815002). We are grateful to the anonymous reviewers for their insightful comments and recommendations.

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Correspondence to Hong Zhang.

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Zhang, H., He, L. Data Mining Method of Sequential Patterns for Vehicle Trajectory Prediction in VANET. Wireless Pers Commun (2020).

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  • Vehicle trajectory prediction
  • Vehicle routing sequence
  • Sequential patterns
  • Vehicular ad-hoc network
  • Data mining