Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration
Monday, April 22, 2019, 10:30am
Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantly, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. Compared with state-of-the-art benchmark mobility models, VeMo improves the performance by average 10%.
Speaker: Yu Yang
Location : CoRE 305 (B)
Prof. Desheng Zhang (Chair), Prof. Richard Martin, Prof. Yongfeng Zhang, Prof. Mubbasir Kapadia
Event Type: Qualifying Exam
Dept. of Computer Science