@Article{cmes.2023.030260,
AUTHOR = {Dun Cao, Jia Ru, Jian Qin, Amr Tolba, Jin Wang, Min Zhu},
TITLE = {3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {138},
YEAR = {2024},
NUMBER = {2},
PAGES = {1365--1384},
URL = {http://www.techscience.com/CMES/v138n2/54630},
ISSN = {1526-1506},
ABSTRACT = {Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles, people,
transportation infrastructure, and networks, thereby realizing a more intelligent and efficient transportation system.
The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topological
structure of IoV to have the high space and time complexity. Network modeling and structure recognition for
3D roads can benefit the description of topological changes for IoV. This paper proposes a 3D general road model
based on discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles
are analyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed and
acceleration are studied. Finally, a general 3D road network model based on road section features is established.
This paper also presents intersection and road section recognition methods based on the structural features of
the 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted to
create the simulation scenario, and the simulation results validate the general 3D road network model and the
recognition method. Therefore, this work makes contributions to the field of intelligent transportation by providing
a comprehensive approach to modeling the 3D road network and its topological changes in achieving efficient traffic
flow and improved road safety.},
DOI = {10.32604/cmes.2023.030260}
}