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Wireless Networks

, Volume 25, Issue 4, pp 2143–2156 | Cite as

Vehicle trajectory prediction algorithm in vehicular network

  • Lei-lei Wang
  • Zhi-gang ChenEmail author
  • Jia WuEmail author
Article

Abstract

Vehicular ad hoc network has become an important component of the intelligent transportation system, what’s more, the vehicle trajectory prediction has gradually become one of the hotter issues in this research. Vehicle trajectory prediction cannot only provide accurate location services, but also can monitor traffic conditions in advance, and then recommend the best route for the vehicle. For this purpose, this research established a new method for vehicle trajectory prediction (TPVN), which is mainly applied to predict the vehicle trajectory in the short term. Based on the regularity of vehicle movement, the algorithm is helpful to predict the vehicle trajectory so as to estimate the position of the vehicle motion probability. To improve the prediction accuracy, the motion patterns are divided into two types: simple pattern and complex pattern. The advantage of the TPVN algorithm is that the calculation result not only predicts the movement behavior of vehicles in different motion patterns but also the probability distribution of all possible trajectories of the vehicle in the future. Simulation on a large number of true trajectory datasets shows that the performance of TPVN outperforms than other classical algorithms.

Keywords

VANET Vehicle trajectory prediction Regularity of motion Motion pattern 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 71633006, 616725407). This work is supported by the China Postdoctoral Science Foundation funded project (Grant No. 2017M612586). This work is supported by the Postdoctoral Science Foundation of Central South University (Grant No. 185684). Also, this work was supported partially by “Mobile Health” Ministry of Education-China Mobile Joint Laboratory.

Author contributions

L.W. and J.W. conceived and designed the experiments; L.W. performed the experiments; L.W analyzed the data; Z.C. and J.L contributed reagents/materials/analysis tools; L.W. wrote the paper.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of SoftwareCentral South UniversityChangsha CityChina
  2. 2.“Mobile Health” Ministry of Education-China Mobile Joint LaboratoryChangshaChina

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