Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87571-8_10 | WEB INFORMATION SYSTEMS AND APPLICATIONS (WISA 2021) |
Keywords | DocType | Volume |
Smart cities, Spatial-temporal interaction, Dataset generation, Generative adversarial networks | Conference | 12999 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiao Chen | 1 | 0 | 0.34 |
| Kai Ma | 2 | 67 | 13.44 |
| Mingliang Hou | 3 | 2 | 1.73 |
| Xiangjie Kong | 4 | 425 | 46.56 |
| Feng Xia | 5 | 0 | 1.35 |