Paper Info
Title
VeMo: Enable Transparent Vehicular Mobility Modeling at Individual Levels With Full Penetration
Abstract
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. 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, the experimental results show that VeMo outperforms them by 10 percent on average.
Year
DOI
Venue
2022
10.1109/TMC.2020.3044244
IEEE Transactions on Mobile Computing
Keywords
DocType
Volume
Vehicular mobility modeling,real-time locations,stationary sensors,toll systems,destination,route,speed
Journal
21
Issue
ISSN
Citations 
7
1536-1233
1
PageRank 
References 
Authors
0.36
35
6
Name
Order
Citations
PageRank
Yang Yu115138.02
Xiaoyang Xie210.36
Zhihan Fang3517.77
Fan Zhang410110.18
Yang Wang518845.73
Desheng Zhang635645.96