Visualization of IoV Warning Data Based on Warning Velocity Association Model

Due to the frequent occurrence of the traffic accidents, the passive safety devices fail to meet people´s needs. Growing attention has been paid to active safety strategies such as the collision warnings. This paper aims at discovering the related characteristics between the active safety strategy and velocity through the visual analysis of Internet of Vehicles (IoV) warning data. First, seven datasets are collected, in which five datasets are used to formulate hypotheses, and the other two datasets are used for visualization. Second, the relationship hypotheses found between warnings and velocity is that one or more warnings tend to occur when the velocity is at a certain level, and one or more levels of velocity tend to make occurrence of certain warning. Then, the warning velocity association model (WVAM) is designed to visually validate the relation between warnings and velocity. With the help of visual analysis tool Gephi, we get renderings of relationship embodied in WVAM.