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

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Abstract

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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Acknowledgements

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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Authors and Affiliations

  1. Transportation Institute of Inner Mongolia University, Hohhot, 010070, Inner Mongolia, China

    Hong Zhang

  2. Inner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications, Hohhot, 010070, Inner Mongolia, China

    Hong Zhang

  3. Guiyang Transport Development Research Center, Guiyang, 550000, China

    Li He

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  1. Hong Zhang
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  2. Li He
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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 117, 417–429 (2021). https://doi.org/10.1007/s11277-020-07876-0

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  • DOI: https://doi.org/10.1007/s11277-020-07876-0

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