Abstract
In contrast with the condition that the trajectory dataset of floating cars (taxis) can be easily obtained from the Internet, it is hard to get the trajectory data of social vehicles (private vehicles) because of personal privacy and government policies. This paper absorbs the idea of game theory, considers the influence of individuals in the group, and proposes a decision behavior based dataset generation (DBDG) model of vehicles to predict future inter-regional traffic. In addition, we adopt simulation tools and generative adversarial networks to train the trajectory prediction model so that the private vehicle trajectory dataset conforming to social rules (e.g., collisionless) is generated. Finally, we construct from macroscopic and microscopic perspectives to verify dataset generation methods proposed in this paper. The results show that the generated data not only has high accuracy and is valuable but can provide strong data support for the Internet of Vehicles and transportation research work.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (62072409), Zhejiang Provincial Natural Science Foundation (LR21F020003), and Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-B2020001).
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Chen, Q., Ma, K., Hou, M., Kong, X., Xia, F. (2021). Decision Behavior Based Private Vehicle Trajectory Generation Towards Smart Cities. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_10
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