Paper Info
Title
RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern
Abstract
As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks.
Year
DOI
Venue
2022
10.1109/TVT.2022.3176243
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
Dataset generation,mobility pattern,region division,spatial-temporal interaction,trajectory data
Journal
71
Issue
ISSN
Citations 
9
0018-9545
0
PageRank 
References 
Authors
0.34
19
6
Name
Order
Citations
PageRank
Xiangjie Kong142546.56
Qiao Chen210.70
Mingliang Hou300.68
Azizur Rahim400.34
Kai Ma500.34
Feng Xia62013153.69