- Title
- Decision behavior based private vehicle trajectory generation towards smart cities
- Creator
- Chen, Qiao; Ma, Kai; Hou, Mingliang; Kong, Xiangjie; Xia, Feng
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180274
- Identifier
- vital:15722
- Identifier
-
https://doi.org/10.1007/978-3-030-87571-8_10
- Identifier
- ISBN:0302-9743 (ISSN); 9783030875701 (ISBN)
- 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. © 2021, Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 18th International Conference on Web Information Systems and Applications, WISA 2021 Vol. 12999 LNCS, p. 109-120
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © Springer Nature Switzerland AG 2021
- Rights
- Open Access
- Subject
- Dataset generation; Generative adversarial networks; Smart cities; Spatial-temporal interaction
- Full Text
- Reviewed
- Funder
- National Natural Science Foundation of China (62072409) Zhejiang Provincial Natural Science Foundation (LR21F020003) Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-B2020001)
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