RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern

• Published in: IEEE transactions on vehicular technology 2022-09, Vol.71 (9), p.9225-9238
• Main Authors: Kong, Xiangjie, Chen, Qiao, Hou, Mingliang, Rahim, Azizur, Ma, Kai, Xia, Feng
• Format: Article
• Language: English
• Subjects: Automobiles; Data models; Dataset generation; Datasets; Interaction models; Internet of Things; Internet of Vehicles; Microscopy; mobility pattern; Privacy; Public transportation; Qualitative analysis; region division; Roads; spatial-temporal interaction; Traffic flow; Trajectory; trajectory data; Urban areas
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3176243

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.